diff --git a/.gitattributes b/.gitattributes index a1feb35394b9c3f475511c05d60d366ed68008cb..b5bbfd52647c7fe152c479dbb957719d0ac07dd0 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,4 +1,4 @@ -# Auto detect text files and perform LF normalization -* text=auto +# Auto detect text files and perform LF normalization +* text=auto HomeImage.png filter=lfs diff=lfs merge=lfs -text stable_fast-1.0.5+torch222cu121-cp310-cp310-manylinux2014_x86_64.whl filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/manual.yml b/.github/workflows/manual.yml new file mode 100644 index 0000000000000000000000000000000000000000..e0e0b252ad5dd030a339b74a6e29db7672578f74 --- /dev/null +++ b/.github/workflows/manual.yml @@ -0,0 +1,91 @@ +name: Manual workflow + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + test: + runs-on: self-hosted + + steps: + - uses: actions/checkout@v3 + + - name: Set up Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: '3.10' + + - name: Cache dependencies + uses: actions/cache@v3 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }} + restore-keys: | + ${{ runner.os }}-pip- + + - name: Create virtual environment + run: | + python -m venv .venv + if [ "$RUNNER_OS" == "Windows" ]; then + . .venv/Scripts/activate + else + . .venv/bin/activate + fi + shell: bash + + - name: Install dependencies + run: | + if [ "$RUNNER_OS" == "Windows" ]; then + . .venv/Scripts/activate + else + . .venv/bin/activate + fi + python -m pip install --upgrade pip + pip install uv + pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124 + pip install "numpy<2.0.0" + if [ -f requirements.txt ]; then + uv pip install -r requirements.txt + fi + shell: bash + + - name: Test pipeline variants + run: | + if [ "$RUNNER_OS" == "Windows" ]; then + . .venv/Scripts/activate + else + . .venv/bin/activate + fi + # Test basic pipeline + python modules/user/pipeline.py "1girl" 512 512 1 1 --hires-fix --adetailer --autohdr --prio-speed + # Test image to image + python modules/user/pipeline.py "./_internal/output/Adetailer/LD-head_00001_.png" 512 512 1 1 --img2img --prio-speed + shell: bash + + - name: Upload test artifacts + if: always() + uses: actions/upload-artifact@v4 + with: + name: test-outputs-${{ github.sha }} + path: | + _internal/output/**/*.png + _internal/output/Classic/*.png + _internal/output/Flux/*.png + _internal/output/HF/*.png + retention-days: 5 + compression-level: 6 + if-no-files-found: warn + + - name: Report status + if: always() + run: | + if [ ${{ job.status }} == 'success' ]; then + echo "All tests passed successfully!" + else + echo "Some tests failed. Check the logs above for details." + exit 1 + fi + shell: bash diff --git a/.gitignore b/.gitignore index 6c1250ebc40278eabf71dfb09b75a2bdc56396c6..419a72fd31fe0de04bbb4e363bf1cb0951dd715a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,15 +1,15 @@ - -*.pyc -*.pth -*.pt -*.safetensors -*.gguf -*.png -/.idea -/htmlcov -.coverage -.toml -__pycache__ -.venv -!HomeImage.png -*.txt + +*.pyc +*.pth +*.pt +*.safetensors +*.gguf +*.png +/.idea +/htmlcov +.coverage +.toml +__pycache__ +.venv +!HomeImage.png +*.txt diff --git a/.gradio/certificate.pem b/.gradio/certificate.pem index b85c8037f6b60976b2546fdbae88312c5246d9a3..30aa93639f903461a0f91745c832895eb0b370f8 100644 --- a/.gradio/certificate.pem +++ b/.gradio/certificate.pem @@ -1,31 +1,31 @@ ------BEGIN CERTIFICATE----- -MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw -TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh -cmNoIEdyb3VwMRUwEwYDVQQDEwxJU1JHIFJvb3QgWDEwHhcNMTUwNjA0MTEwNDM4 -WhcNMzUwNjA0MTEwNDM4WjBPMQswCQYDVQQGEwJVUzEpMCcGA1UEChMgSW50ZXJu -ZXQgU2VjdXJpdHkgUmVzZWFyY2ggR3JvdXAxFTATBgNVBAMTDElTUkcgUm9vdCBY -MTCCAiIwDQYJKoZIhvcNAQEBBQADggIPADCCAgoCggIBAK3oJHP0FDfzm54rVygc -h77ct984kIxuPOZXoHj3dcKi/vVqbvYATyjb3miGbESTtrFj/RQSa78f0uoxmyF+ -0TM8ukj13Xnfs7j/EvEhmkvBioZxaUpmZmyPfjxwv60pIgbz5MDmgK7iS4+3mX6U -A5/TR5d8mUgjU+g4rk8Kb4Mu0UlXjIB0ttov0DiNewNwIRt18jA8+o+u3dpjq+sW -T8KOEUt+zwvo/7V3LvSye0rgTBIlDHCNAymg4VMk7BPZ7hm/ELNKjD+Jo2FR3qyH -B5T0Y3HsLuJvW5iB4YlcNHlsdu87kGJ55tukmi8mxdAQ4Q7e2RCOFvu396j3x+UC -B5iPNgiV5+I3lg02dZ77DnKxHZu8A/lJBdiB3QW0KtZB6awBdpUKD9jf1b0SHzUv -KBds0pjBqAlkd25HN7rOrFleaJ1/ctaJxQZBKT5ZPt0m9STJEadao0xAH0ahmbWn -OlFuhjuefXKnEgV4We0+UXgVCwOPjdAvBbI+e0ocS3MFEvzG6uBQE3xDk3SzynTn -jh8BCNAw1FtxNrQHusEwMFxIt4I7mKZ9YIqioymCzLq9gwQbooMDQaHWBfEbwrbw -qHyGO0aoSCqI3Haadr8faqU9GY/rOPNk3sgrDQoo//fb4hVC1CLQJ13hef4Y53CI -rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV -HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq -hkiG9w0BAQsFAAOCAgEAVR9YqbyyqFDQDLHYGmkgJykIrGF1XIpu+ILlaS/V9lZL -ubhzEFnTIZd+50xx+7LSYK05qAvqFyFWhfFQDlnrzuBZ6brJFe+GnY+EgPbk6ZGQ -3BebYhtF8GaV0nxvwuo77x/Py9auJ/GpsMiu/X1+mvoiBOv/2X/qkSsisRcOj/KK -NFtY2PwByVS5uCbMiogziUwthDyC3+6WVwW6LLv3xLfHTjuCvjHIInNzktHCgKQ5 -ORAzI4JMPJ+GslWYHb4phowim57iaztXOoJwTdwJx4nLCgdNbOhdjsnvzqvHu7Ur -TkXWStAmzOVyyghqpZXjFaH3pO3JLF+l+/+sKAIuvtd7u+Nxe5AW0wdeRlN8NwdC -jNPElpzVmbUq4JUagEiuTDkHzsxHpFKVK7q4+63SM1N95R1NbdWhscdCb+ZAJzVc -oyi3B43njTOQ5yOf+1CceWxG1bQVs5ZufpsMljq4Ui0/1lvh+wjChP4kqKOJ2qxq -4RgqsahDYVvTH9w7jXbyLeiNdd8XM2w9U/t7y0Ff/9yi0GE44Za4rF2LN9d11TPA -mRGunUHBcnWEvgJBQl9nJEiU0Zsnvgc/ubhPgXRR4Xq37Z0j4r7g1SgEEzwxA57d -emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc= ------END CERTIFICATE----- +-----BEGIN CERTIFICATE----- +MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw +TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh +cmNoIEdyb3VwMRUwEwYDVQQDEwxJU1JHIFJvb3QgWDEwHhcNMTUwNjA0MTEwNDM4 +WhcNMzUwNjA0MTEwNDM4WjBPMQswCQYDVQQGEwJVUzEpMCcGA1UEChMgSW50ZXJu +ZXQgU2VjdXJpdHkgUmVzZWFyY2ggR3JvdXAxFTATBgNVBAMTDElTUkcgUm9vdCBY +MTCCAiIwDQYJKoZIhvcNAQEBBQADggIPADCCAgoCggIBAK3oJHP0FDfzm54rVygc +h77ct984kIxuPOZXoHj3dcKi/vVqbvYATyjb3miGbESTtrFj/RQSa78f0uoxmyF+ +0TM8ukj13Xnfs7j/EvEhmkvBioZxaUpmZmyPfjxwv60pIgbz5MDmgK7iS4+3mX6U +A5/TR5d8mUgjU+g4rk8Kb4Mu0UlXjIB0ttov0DiNewNwIRt18jA8+o+u3dpjq+sW +T8KOEUt+zwvo/7V3LvSye0rgTBIlDHCNAymg4VMk7BPZ7hm/ELNKjD+Jo2FR3qyH +B5T0Y3HsLuJvW5iB4YlcNHlsdu87kGJ55tukmi8mxdAQ4Q7e2RCOFvu396j3x+UC +B5iPNgiV5+I3lg02dZ77DnKxHZu8A/lJBdiB3QW0KtZB6awBdpUKD9jf1b0SHzUv +KBds0pjBqAlkd25HN7rOrFleaJ1/ctaJxQZBKT5ZPt0m9STJEadao0xAH0ahmbWn +OlFuhjuefXKnEgV4We0+UXgVCwOPjdAvBbI+e0ocS3MFEvzG6uBQE3xDk3SzynTn +jh8BCNAw1FtxNrQHusEwMFxIt4I7mKZ9YIqioymCzLq9gwQbooMDQaHWBfEbwrbw +qHyGO0aoSCqI3Haadr8faqU9GY/rOPNk3sgrDQoo//fb4hVC1CLQJ13hef4Y53CI +rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV +HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq +hkiG9w0BAQsFAAOCAgEAVR9YqbyyqFDQDLHYGmkgJykIrGF1XIpu+ILlaS/V9lZL +ubhzEFnTIZd+50xx+7LSYK05qAvqFyFWhfFQDlnrzuBZ6brJFe+GnY+EgPbk6ZGQ +3BebYhtF8GaV0nxvwuo77x/Py9auJ/GpsMiu/X1+mvoiBOv/2X/qkSsisRcOj/KK +NFtY2PwByVS5uCbMiogziUwthDyC3+6WVwW6LLv3xLfHTjuCvjHIInNzktHCgKQ5 +ORAzI4JMPJ+GslWYHb4phowim57iaztXOoJwTdwJx4nLCgdNbOhdjsnvzqvHu7Ur +TkXWStAmzOVyyghqpZXjFaH3pO3JLF+l+/+sKAIuvtd7u+Nxe5AW0wdeRlN8NwdC +jNPElpzVmbUq4JUagEiuTDkHzsxHpFKVK7q4+63SM1N95R1NbdWhscdCb+ZAJzVc +oyi3B43njTOQ5yOf+1CceWxG1bQVs5ZufpsMljq4Ui0/1lvh+wjChP4kqKOJ2qxq +4RgqsahDYVvTH9w7jXbyLeiNdd8XM2w9U/t7y0Ff/9yi0GE44Za4rF2LN9d11TPA +mRGunUHBcnWEvgJBQl9nJEiU0Zsnvgc/ubhPgXRR4Xq37Z0j4r7g1SgEEzwxA57d +emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc= +-----END CERTIFICATE----- diff --git a/.vscode/settings.json b/.vscode/settings.json index 83375e1ec5cdb8bb6f9c828567a8bc7c26b8964e..45332a2bc5d3b53cf26ccf8114090efb714684c7 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -1,13 +1,13 @@ -{ - "python.testing.unittestArgs": [ - "-v", - "-s", - ".", - "-p", - "*test.py" - ], - "python.testing.pytestEnabled": false, - "python.testing.unittestEnabled": true, - "python.analysis.autoImportCompletions": true, - "python.analysis.typeCheckingMode": "off" +{ + "python.testing.unittestArgs": [ + "-v", + "-s", + ".", + "-p", + "*test.py" + ], + "python.testing.pytestEnabled": false, + "python.testing.unittestEnabled": true, + "python.analysis.autoImportCompletions": true, + "python.analysis.typeCheckingMode": "off" } \ No newline at end of file diff --git a/Compiler.py b/Compiler.py index dc0f687378d0b89e55ed93678c858c3c98cd8f5e..27edfadce72d45b7d1449df5258d445933628a61 100644 --- a/Compiler.py +++ b/Compiler.py @@ -1,106 +1,106 @@ -import os -import re - -files_ordered = [ - "./modules/Utilities/util.py", - "./modules/sample/sampling_util.py", - "./modules/Device/Device.py", - "./modules/cond/cond_util.py", - "./modules/cond/cond.py", - "./modules/sample/ksampler_util.py", - "./modules/cond/cast.py", - "./modules/Attention/AttentionMethods.py", - "./modules/AutoEncoders/taesd.py", - "./modules/cond/cond.py", - "./modules/cond/Activation.py", - "./modules/Attention/Attention.py", - "./modules/sample/samplers.py", - "./modules/sample/CFG.py", - "./modules/NeuralNetwork/transformer.py", - "./modules/sample/sampling.py", - "./modules/clip/CLIPTextModel.py", - "./modules/AutoEncoders/ResBlock.py", - "./modules/AutoDetailer/mask_util.py", - "./modules/NeuralNetwork/unet.py", - "./modules/SD15/SDClip.py", - "./modules/SD15/SDToken.py", - "./modules/UltimateSDUpscale/USDU_util.py", - "./modules/StableFast/SF_util.py", - "./modules/Utilities/Latent.py", - "./modules/AutoDetailer/SEGS.py", - "./modules/AutoDetailer/tensor_util.py", - "./modules/AutoDetailer/AD_util.py", - "./modules/clip/FluxClip.py", - "./modules/Model/ModelPatcher.py", - "./modules/Model/ModelBase.py", - "./modules/UltimateSDUpscale/image_util.py", - "./modules/UltimateSDUpscale/RDRB.py", - "./modules/StableFast/ModuleFactory.py", - "./modules/AutoDetailer/bbox.py", - "./modules/AutoEncoders/VariationalAE.py", - "./modules/clip/Clip.py", - "./modules/Model/LoRas.py", - "./modules/BlackForest/Flux.py", - "./modules/UltimateSDUpscale/USDU_upscaler.py", - "./modules/StableFast/ModuleTracing.py", - "./modules/hidiffusion/utils.py", - "./modules/FileManaging/Downloader.py", - "./modules/AutoDetailer/SAM.py", - "./modules/AutoDetailer/ADetailer.py", - "./modules/Quantize/Quantizer.py", - "./modules/FileManaging/Loader.py", - "./modules/SD15/SD15.py", - "./modules/UltimateSDUpscale/UltimateSDUpscale.py", - "./modules/StableFast/StableFast.py", - "./modules/hidiffusion/msw_msa_attention.py", - "./modules/FileManaging/ImageSaver.py", - "./modules/Utilities/Enhancer.py", - "./modules/Utilities/upscale.py", - "./modules/user/pipeline.py", -] - -def get_file_patterns(): - patterns = [] - seen = set() - for path in files_ordered: - filename = os.path.basename(path) - name = os.path.splitext(filename)[0] - if name not in seen: - # Pattern 1: matches module name when not in brackets or after a dot - pattern1 = rf'(? - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index 4567604918090bbebe5717f507cc556f5d50704e..1012cdba1f5b18cdc878d2bd0bf5b037075cbb83 100644 --- a/README.md +++ b/README.md @@ -1,144 +1,144 @@ ---- -title: LightDiffusion-Next -app_file: app.py -sdk: gradio -sdk_version: 5.14.0 ---- -
- -# Say hi to LightDiffusion-Next 👋 - -[![demo platform](https://img.shields.io/badge/Play%20with%20LightDiffusion%21-LightDiffusion%20demo%20platform-lightblue)](https://huggingface.co/spaces/Aatricks/LightDiffusion-Next)  - -**LightDiffusion-Next** is the fastest AI-powered image generation GUI/CLI, combining speed, precision, and flexibility in one cohesive tool. -
-
- - Logo - - -
-
- -As a refactored and improved version of the original [LightDiffusion repository](https://github.com/Aatrick/LightDiffusion), this project enhances usability, maintainability, and functionality while introducing a host of new features to streamline your creative workflows. - -## Motivation: - -**LightDiffusion** was originally meant to be made in Rust, but due to the lack of support for the Rust language in the AI community, it was made in Python with the goal of being the simplest and fastest AI image generation tool. - -That's when the first version of LightDiffusion was born which only counted [3000 lines of code](https://github.com/LightDiffusion/LightDiffusion-original), only using Pytorch. With time, the [project](https://github.com/Aatrick/LightDiffusion) grew and became more complex, and the need for a refactor was evident. This is where **LightDiffusion-Next** comes in, with a more modular and maintainable codebase, and a plethora of new features and optimizations. - -📚 Learn more in the [official documentation](https://aatrick.github.io/LightDiffusion/). - ---- - -## 🌟 Highlights - -![image](https://github.com/user-attachments/assets/b994fe0d-3a2e-44ff-93a4-46919cf865e3) - -**LightDiffusion-Next** offers a powerful suite of tools to cater to creators at every level. At its core, it supports **Text-to-Image** (Txt2Img) and **Image-to-Image** (Img2Img) generation, offering a variety of upscale methods and samplers, to make it easier to create stunning images with minimal effort. - -Advanced users can take advantage of features like **attention syntax**, **Hires-Fix** or **ADetailer**. These tools provide better quality and flexibility for generating complex and high-resolution outputs. - -**LightDiffusion-Next** is fine-tuned for **performance**. Features such as **Xformers** acceleration, **BFloat16** precision support, **WaveSpeed** dynamic caching, and **Stable-Fast** model compilation (which offers up to a 70% speed boost) ensure smooth and efficient operation, even on demanding workloads. - ---- - -## ✨ Feature Showcase - -Here’s what makes LightDiffusion-Next stand out: - -- **Speed and Efficiency**: - Enjoy industry-leading performance with built-in Xformers, Pytorch, Wavespeed and Stable-Fast optimizations, achieving up to 30% faster speeds compared to the rest of the AI image generation backends in SD1.5 and up to 2x for Flux. - -- **Automatic Detailing**: - Effortlessly enhance faces and body details with AI-driven tools based on the [Impact Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack). - -- **State Preservation**: - Save and resume your progress with saved states, ensuring seamless transitions between sessions. - -- **Advanced GUI and CLI**: - Work through a user-friendly graphical interface or leverage the streamlined pipeline for CLI-based workflows. - -- **Integration-Ready**: - Collaborate and create directly in Discord with [Boubou](https://github.com/Aatrick/Boubou), or preview images dynamically with the optional **TAESD preview mode**. - -- **Image Previewing**: - Get a real-time preview of your generated images with TAESD, allowing for user-friendly and interactive workflows. - -- **Image Upscaling**: - Enhance your images with advanced upscaling options like UltimateSDUpscaling, ensuring high-quality results every time. - -- **Prompt Refinement**: - Use the Ollama-powered automatic prompt enhancer to refine your prompts and generate more accurate and detailed outputs. - -- **LoRa and Textual Inversion Embeddings**: - Leverage LoRa and textual inversion embeddings for highly customized and nuanced results, adding a new dimension to your creative process. - -- **Low-End Device Support**: - Run LightDiffusion-Next on low-end devices with as little as 2GB of VRAM or even no GPU, ensuring accessibility for all users. - ---- - -## ⚡ Performance Benchmarks - -**LightDiffusion-Next** dominates in performance: - -| **Tool** | **Speed (it/s)** | -|------------------------------------|------------------| -| **LightDiffusion with Stable-Fast** | 2.8 | -| **LightDiffusion** | 1.8 | -| **ComfyUI** | 1.4 | -| **SDForge** | 1.3 | -| **SDWebUI** | 0.9 | - -(All benchmarks are based on a 1024x1024 resolution with a batch size of 1 using BFloat16 precision without tweaking installations. Made with a 3060 mobile GPU using SD1.5.) - -With its unmatched speed and efficiency, LightDiffusion-Next sets the benchmark for AI image generation tools. - ---- - -## 🛠 Installation - -### Quick Start - -1. Download a release or clone this repository. -2. Run `run.bat` in a terminal. -3. Start creating! - -### Command-Line Pipeline - -For a GUI-free experience, use the pipeline: -```bash -pipeline.bat -``` -Use `pipeline.bat -h` for more options. - ---- - -### Advanced Setup - -- **Install from Source**: - Install dependencies via: - ```bash - pip install -r requirements.txt - ``` - Add your SD1/1.5 safetensors model to the `checkpoints` directory, then launch the application. - -- **⚡Stable-Fast Optimization**: - Follow [this guide](https://github.com/chengzeyi/stable-fast?tab=readme-ov-file#installation) to enable Stable-Fast mode for optimal performance. - -- **🦙 Prompt Enhancer**: - Refine your prompts with Ollama: - ```bash - pip install ollama - ollama run deepseek-r1 - ``` - See the [Ollama guide](https://github.com/ollama/ollama?tab=readme-ov-file) for details. - -- **🤖 Discord Integration**: - Set up the Discord bot by following the [Boubou installation guide](https://github.com/Aatrick/Boubou). - ---- - -🎨 Enjoy exploring the powerful features of LightDiffusion-Next! +--- +title: LightDiffusion-Next +app_file: app.py +sdk: gradio +sdk_version: 5.20.0 +--- +
+ +# Say hi to LightDiffusion-Next 👋 + +[![demo platform](https://img.shields.io/badge/Play%20with%20LightDiffusion%21-LightDiffusion%20demo%20platform-lightblue)](https://huggingface.co/spaces/Aatricks/LightDiffusion-Next)  + +**LightDiffusion-Next** is the fastest AI-powered image generation GUI/CLI, combining speed, precision, and flexibility in one cohesive tool. +
+
+ + Logo + + +
+
+ +As a refactored and improved version of the original [LightDiffusion repository](https://github.com/Aatrick/LightDiffusion), this project enhances usability, maintainability, and functionality while introducing a host of new features to streamline your creative workflows. + +## Motivation: + +**LightDiffusion** was originally meant to be made in Rust, but due to the lack of support for the Rust language in the AI community, it was made in Python with the goal of being the simplest and fastest AI image generation tool. + +That's when the first version of LightDiffusion was born which only counted [3000 lines of code](https://github.com/LightDiffusion/LightDiffusion-original), only using Pytorch. With time, the [project](https://github.com/Aatrick/LightDiffusion) grew and became more complex, and the need for a refactor was evident. This is where **LightDiffusion-Next** comes in, with a more modular and maintainable codebase, and a plethora of new features and optimizations. + +📚 Learn more in the [official documentation](https://aatrick.github.io/LightDiffusion/). + +--- + +## 🌟 Highlights + +![image](https://github.com/user-attachments/assets/b994fe0d-3a2e-44ff-93a4-46919cf865e3) + +**LightDiffusion-Next** offers a powerful suite of tools to cater to creators at every level. At its core, it supports **Text-to-Image** (Txt2Img) and **Image-to-Image** (Img2Img) generation, offering a variety of upscale methods and samplers, to make it easier to create stunning images with minimal effort. + +Advanced users can take advantage of features like **attention syntax**, **Hires-Fix** or **ADetailer**. These tools provide better quality and flexibility for generating complex and high-resolution outputs. + +**LightDiffusion-Next** is fine-tuned for **performance**. Features such as **Xformers** acceleration, **BFloat16** precision support, **WaveSpeed** dynamic caching, and **Stable-Fast** model compilation (which offers up to a 70% speed boost) ensure smooth and efficient operation, even on demanding workloads. + +--- + +## ✨ Feature Showcase + +Here’s what makes LightDiffusion-Next stand out: + +- **Speed and Efficiency**: + Enjoy industry-leading performance with built-in Xformers, Pytorch, Wavespeed and Stable-Fast optimizations, achieving up to 30% faster speeds compared to the rest of the AI image generation backends in SD1.5 and up to 2x for Flux. + +- **Automatic Detailing**: + Effortlessly enhance faces and body details with AI-driven tools based on the [Impact Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack). + +- **State Preservation**: + Save and resume your progress with saved states, ensuring seamless transitions between sessions. + +- **Advanced GUI, WebUI and CLI**: + Work through a user-friendly graphical interface as GUI or in the browser using Gradio or leverage the streamlined pipeline for CLI-based workflows. + +- **Integration-Ready**: + Collaborate and create directly in Discord with [Boubou](https://github.com/Aatrick/Boubou), or preview images dynamically with the optional **TAESD preview mode**. + +- **Image Previewing**: + Get a real-time preview of your generated images with TAESD, allowing for user-friendly and interactive workflows. + +- **Image Upscaling**: + Enhance your images with advanced upscaling options like UltimateSDUpscaling, ensuring high-quality results every time. + +- **Prompt Refinement**: + Use the Ollama-powered automatic prompt enhancer to refine your prompts and generate more accurate and detailed outputs. + +- **LoRa and Textual Inversion Embeddings**: + Leverage LoRa and textual inversion embeddings for highly customized and nuanced results, adding a new dimension to your creative process. + +- **Low-End Device Support**: + Run LightDiffusion-Next on low-end devices with as little as 2GB of VRAM or even no GPU, ensuring accessibility for all users. + +--- + +## ⚡ Performance Benchmarks + +**LightDiffusion-Next** dominates in performance: + +| **Tool** | **Speed (it/s)** | +|------------------------------------|------------------| +| **LightDiffusion with Stable-Fast** | 2.8 | +| **LightDiffusion** | 1.8 | +| **ComfyUI** | 1.4 | +| **SDForge** | 1.3 | +| **SDWebUI** | 0.9 | + +(All benchmarks are based on a 1024x1024 resolution with a batch size of 1 using BFloat16 precision without tweaking installations. Made with a 3060 mobile GPU using SD1.5.) + +With its unmatched speed and efficiency, LightDiffusion-Next sets the benchmark for AI image generation tools. + +--- + +## 🛠 Installation + +### Quick Start + +1. Download a release or clone this repository. +2. Run `run.bat` in a terminal. +3. Start creating! + +### Command-Line Pipeline + +For a GUI-free experience, use the pipeline: +```bash +pipeline.bat +``` +Use `pipeline.bat -h` for more options. + +--- + +### Advanced Setup + +- **Install from Source**: + Install dependencies via: + ```bash + pip install -r requirements.txt + ``` + Add your SD1/1.5 safetensors model to the `checkpoints` directory, then launch the application. + +- **⚡Stable-Fast Optimization**: + Follow [this guide](https://github.com/chengzeyi/stable-fast?tab=readme-ov-file#installation) to enable Stable-Fast mode for optimal performance. + +- **🦙 Prompt Enhancer**: + Refine your prompts with Ollama: + ```bash + pip install ollama + ollama run deepseek-r1 + ``` + See the [Ollama guide](https://github.com/ollama/ollama?tab=readme-ov-file) for details. + +- **🤖 Discord Integration**: + Set up the Discord bot by following the [Boubou installation guide](https://github.com/Aatrick/Boubou). + +--- + +🎨 Enjoy exploring the powerful features of LightDiffusion-Next! diff --git a/_internal/clip/sd1_clip_config.json b/_internal/clip/sd1_clip_config.json index 0158a1fd52727adf22359238285afafb150f66f2..6ba60d6d6cc77bc279927c54b43d5d5d658c9a11 100644 --- a/_internal/clip/sd1_clip_config.json +++ b/_internal/clip/sd1_clip_config.json @@ -1,25 +1,25 @@ -{ - "_name_or_path": "openai/clip-vit-large-patch14", - "architectures": [ - "CLIPTextModel" - ], - "attention_dropout": 0.0, - "bos_token_id": 0, - "dropout": 0.0, - "eos_token_id": 2, - "hidden_act": "quick_gelu", - "hidden_size": 768, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 77, - "model_type": "clip_text_model", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "pad_token_id": 1, - "projection_dim": 768, - "torch_dtype": "float32", - "transformers_version": "4.24.0", - "vocab_size": 49408 -} +{ + "_name_or_path": "openai/clip-vit-large-patch14", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "projection_dim": 768, + "torch_dtype": "float32", + "transformers_version": "4.24.0", + "vocab_size": 49408 +} diff --git a/_internal/output/Adetailer/Adetailer_images_end_up_here b/_internal/output/Adetailer/Adetailer_images_end_up_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/_internal/output/Flux/Flux_images_end_up_here b/_internal/output/Flux/Flux_images_end_up_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/_internal/output/HiresFix/HiresFixed_images_end_up_here b/_internal/output/HiresFix/HiresFixed_images_end_up_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/_internal/output/Img2Img/Upscaled_images_end_up_here b/_internal/output/Img2Img/Upscaled_images_end_up_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/_internal/output/classic/normal_images_end_up_here b/_internal/output/classic/normal_images_end_up_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/_internal/sd1_tokenizer/special_tokens_map.json b/_internal/sd1_tokenizer/special_tokens_map.json index 2c2130b544c0c5a72d5d00da071ba130a9800fb2..ad919e089d4d032a933a2a9087de2857d55af3bc 100644 --- a/_internal/sd1_tokenizer/special_tokens_map.json +++ b/_internal/sd1_tokenizer/special_tokens_map.json @@ -1,24 +1,24 @@ -{ - "bos_token": { - "content": "<|startoftext|>", - "lstrip": false, - "normalized": true, - "rstrip": false, - "single_word": false - }, - "eos_token": { - "content": "<|endoftext|>", - "lstrip": false, - "normalized": true, - "rstrip": false, - "single_word": false - }, - "pad_token": "<|endoftext|>", - "unk_token": { - "content": "<|endoftext|>", - "lstrip": false, - "normalized": true, - "rstrip": false, - "single_word": false - } -} +{ + "bos_token": { + "content": "<|startoftext|>", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "pad_token": "<|endoftext|>", + "unk_token": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/_internal/sd1_tokenizer/tokenizer_config.json 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false, - "single_word": false - } -} +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "<|startoftext|>", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "do_lower_case": true, + "eos_token": { + "__type": "AddedToken", + "content": "<|endoftext|>", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "model_max_length": 77, + "name_or_path": "openai/clip-vit-large-patch14", + "pad_token": "<|endoftext|>", + "special_tokens_map_file": "./special_tokens_map.json", + "tokenizer_class": "CLIPTokenizer", + "unk_token": { + "__type": "AddedToken", + "content": "<|endoftext|>", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/_internal/sd1_tokenizer/vocab.json b/_internal/sd1_tokenizer/vocab.json index 469be27c5c010538f845f518c4f5e8574c78f7c8..521b2c5def638d17b1e463ce25597140a1720005 100644 --- 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255, + "Ń": 511 +} diff --git a/app.py b/app.py index 1ac23ff7b58471ee8b792793ae2097d668df79c8..ba6ecd3ef3a894a2309329268164ff58802d0c0b 100644 --- a/app.py +++ b/app.py @@ -1,195 +1,211 @@ -import glob -import cv2 -import gradio as gr -import sys -import os -from PIL import Image -import numpy as np -import spaces -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) - -from modules.user.pipeline import pipeline -import torch - -def load_generated_images(): - """Load generated images with given prefix from disk""" - image_files = glob.glob("./_internal/output/*") - - # If there are no image files, return - if not image_files: - return [] - - # Sort files by modification time in descending order - image_files.sort(key=os.path.getmtime, reverse=True) - - # Get most recent timestamp - latest_time = os.path.getmtime(image_files[0]) - - # Get all images from same batch (within 1 second of most recent) - batch_images = [] - for file in image_files: - if abs(os.path.getmtime(file) - latest_time) < 1.0: - try: - img = Image.open(file) - batch_images.append(img) - except: - continue - - if not batch_images: - return [] - return batch_images - -@spaces.GPU -def generate_images( - prompt: str, - width: int = 512, - height: int = 512, - num_images: int = 1, - batch_size: int = 1, - hires_fix: bool = False, - adetailer: bool = False, - enhance_prompt: bool = False, - img2img_enabled: bool = False, - img2img_image: str = None, - stable_fast: bool = False, - reuse_seed: bool = False, - flux_enabled: bool = False, - prio_speed: bool = False, - progress=gr.Progress() -): - """Generate images using the LightDiffusion pipeline""" - try: - if img2img_enabled and img2img_image is not None: - # Convert numpy array to PIL Image - if isinstance(img2img_image, np.ndarray): - img_pil = Image.fromarray(img2img_image) - img_pil.save("temp_img2img.png") - prompt = "temp_img2img.png" - - # Run pipeline and capture saved images - with torch.inference_mode(): - pipeline( - prompt=prompt, - w=width, - h=height, - number=num_images, - batch=batch_size, - hires_fix=hires_fix, - adetailer=adetailer, - enhance_prompt=enhance_prompt, - img2img=img2img_enabled, - stable_fast=stable_fast, - reuse_seed=reuse_seed, - flux_enabled=flux_enabled, - prio_speed=prio_speed - ) - - # Clean up temporary file if it exists - if os.path.exists("temp_img2img.png"): - os.remove("temp_img2img.png") - - return load_generated_images() - - except Exception as e: - import traceback - print(traceback.format_exc()) - # Clean up temporary file if it exists - if os.path.exists("temp_img2img.png"): - os.remove("temp_img2img.png") - return [Image.new('RGB', (512, 512), color='black')] - -# Create Gradio interface -with gr.Blocks(title="LightDiffusion Web UI") as demo: - gr.Markdown("# LightDiffusion Web UI") - gr.Markdown("Generate AI images using LightDiffusion") - gr.Markdown("This is the demo for LightDiffusion, the fastest diffusion backend for generating images. https://github.com/LightDiffusion/LightDiffusion-Next") - - with gr.Row(): - with gr.Column(): - # Input components - prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...") - - with gr.Row(): - width = gr.Slider(minimum=64, maximum=2048, value=512, step=64, label="Width") - height = gr.Slider(minimum=64, maximum=2048, value=512, step=64, label="Height") - - with gr.Row(): - num_images = gr.Slider(minimum=1, maximum=10, value=1, step=1, label="Number of Images") - batch_size = gr.Slider(minimum=1, maximum=4, value=1, step=1, label="Batch Size") - - with gr.Row(): - hires_fix = gr.Checkbox(label="HiRes Fix") - adetailer = gr.Checkbox(label="Auto Face/Body Enhancement") - enhance_prompt = gr.Checkbox(label="Enhance Prompt") - stable_fast = gr.Checkbox(label="Stable Fast Mode") - - with gr.Row(): - reuse_seed = gr.Checkbox(label="Reuse Seed") - flux_enabled = gr.Checkbox(label="Flux Mode") - prio_speed = gr.Checkbox(label="Prioritize Speed") - - with gr.Row(): - img2img_enabled = gr.Checkbox(label="Image to Image Mode") - img2img_image = gr.Image(label="Input Image for img2img", visible=False) - - # Make input image visible only when img2img is enabled - img2img_enabled.change( - fn=lambda x: gr.update(visible=x), - inputs=[img2img_enabled], - outputs=[img2img_image] - ) - - generate_btn = gr.Button("Generate") - - # Output gallery - gallery = gr.Gallery( - label="Generated Images", - show_label=True, - elem_id="gallery", - columns=[2], - rows=[2], - object_fit="contain", - height="auto" - ) - - # Connect generate button to pipeline - generate_btn.click( - fn=generate_images, - inputs=[ - prompt, - width, - height, - num_images, - batch_size, - hires_fix, - adetailer, - enhance_prompt, - img2img_enabled, - img2img_image, - stable_fast, - reuse_seed, - flux_enabled, - prio_speed - ], - outputs=gallery - ) - -def is_huggingface_space(): - return "SPACE_ID" in os.environ - -# For local testing -if __name__ == "__main__": - if is_huggingface_space(): - demo.launch( - debug=False, - server_name="0.0.0.0", - server_port=7860 # Standard HF Spaces port - ) - else: - demo.launch( - server_name="0.0.0.0", - server_port=8000, - auth=None, - share=True, # Only enable sharing locally - debug=True - ) \ No newline at end of file +import glob +import gradio as gr +import sys +import os +from PIL import Image +import numpy as np +import spaces + +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) + +from modules.user.pipeline import pipeline +import torch + + +def load_generated_images(): + """Load generated images with given prefix from disk""" + image_files = glob.glob("./_internal/output/**/*.png") + + # If there are no image files, return + if not image_files: + return [] + + # Sort files by modification time in descending order + image_files.sort(key=os.path.getmtime, reverse=True) + + # Get most recent timestamp + latest_time = os.path.getmtime(image_files[0]) + + # Get all images from same batch (within 1 second of most recent) + batch_images = [] + for file in image_files: + if abs(os.path.getmtime(file) - latest_time) < 1.0: + try: + img = Image.open(file) + batch_images.append(img) + except: + continue + + if not batch_images: + return [] + return batch_images + + +@spaces.GPU +def generate_images( + prompt: str, + width: int = 512, + height: int = 512, + num_images: int = 1, + batch_size: int = 1, + hires_fix: bool = False, + adetailer: bool = False, + enhance_prompt: bool = False, + img2img_enabled: bool = False, + img2img_image: str = None, + stable_fast: bool = False, + reuse_seed: bool = False, + flux_enabled: bool = False, + prio_speed: bool = False, + progress=gr.Progress(), +): + """Generate images using the LightDiffusion pipeline""" + try: + if img2img_enabled and img2img_image is not None: + # Convert numpy array to PIL Image + if isinstance(img2img_image, np.ndarray): + img_pil = Image.fromarray(img2img_image) + img_pil.save("temp_img2img.png") + prompt = "temp_img2img.png" + + # Run pipeline and capture saved images + with torch.inference_mode(): + pipeline( + prompt=prompt, + w=width, + h=height, + number=num_images, + batch=batch_size, + hires_fix=hires_fix, + adetailer=adetailer, + enhance_prompt=enhance_prompt, + img2img=img2img_enabled, + stable_fast=stable_fast, + reuse_seed=reuse_seed, + flux_enabled=flux_enabled, + prio_speed=prio_speed, + ) + + # Clean up temporary file if it exists + if os.path.exists("temp_img2img.png"): + os.remove("temp_img2img.png") + + return load_generated_images() + + except Exception: + import traceback + + print(traceback.format_exc()) + # Clean up temporary file if it exists + if os.path.exists("temp_img2img.png"): + os.remove("temp_img2img.png") + return [Image.new("RGB", (512, 512), color="black")] + + +# Create Gradio interface +with gr.Blocks(title="LightDiffusion Web UI") as demo: + gr.Markdown("# LightDiffusion Web UI") + gr.Markdown("Generate AI images using LightDiffusion") + gr.Markdown( + "This is the demo for LightDiffusion, the fastest diffusion backend for generating images. https://github.com/LightDiffusion/LightDiffusion-Next" + ) + + with gr.Row(): + with gr.Column(): + # Input components + prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...") + + with gr.Row(): + width = gr.Slider( + minimum=64, maximum=2048, value=512, step=64, label="Width" + ) + height = gr.Slider( + minimum=64, maximum=2048, value=512, step=64, label="Height" + ) + + with gr.Row(): + num_images = gr.Slider( + minimum=1, maximum=10, value=1, step=1, label="Number of Images" + ) + batch_size = gr.Slider( + minimum=1, maximum=4, value=1, step=1, label="Batch Size" + ) + + with gr.Row(): + hires_fix = gr.Checkbox(label="HiRes Fix") + adetailer = gr.Checkbox(label="Auto Face/Body Enhancement") + enhance_prompt = gr.Checkbox(label="Enhance Prompt") + stable_fast = gr.Checkbox(label="Stable Fast Mode") + + with gr.Row(): + reuse_seed = gr.Checkbox(label="Reuse Seed") + flux_enabled = gr.Checkbox(label="Flux Mode") + prio_speed = gr.Checkbox(label="Prioritize Speed") + + with gr.Row(): + img2img_enabled = gr.Checkbox(label="Image to Image Mode") + img2img_image = gr.Image(label="Input Image for img2img", visible=False) + + # Make input image visible only when img2img is enabled + img2img_enabled.change( + fn=lambda x: gr.update(visible=x), + inputs=[img2img_enabled], + outputs=[img2img_image], + ) + + generate_btn = gr.Button("Generate") + + # Output gallery + gallery = gr.Gallery( + label="Generated Images", + show_label=True, + elem_id="gallery", + columns=[2], + rows=[2], + object_fit="contain", + height="auto", + ) + + # Connect generate button to pipeline + generate_btn.click( + fn=generate_images, + inputs=[ + prompt, + width, + height, + num_images, + batch_size, + hires_fix, + adetailer, + enhance_prompt, + img2img_enabled, + img2img_image, + stable_fast, + reuse_seed, + flux_enabled, + prio_speed, + ], + outputs=gallery, + ) + + +def is_huggingface_space(): + return "SPACE_ID" in os.environ + + +# For local testing +if __name__ == "__main__": + if is_huggingface_space(): + demo.launch( + debug=False, + server_name="0.0.0.0", + server_port=7860, # Standard HF Spaces port + ) + else: + demo.launch( + server_name="0.0.0.0", + server_port=8000, + auth=None, + share=True, # Only enable sharing locally + debug=True, + ) diff --git a/modules/Attention/Attention.py b/modules/Attention/Attention.py index e06a142cd154b4de2b2f1ae72928c4cf0577c95b..c22d33bb6a86dee5338e5a072daffa8f3428b73b 100644 --- a/modules/Attention/Attention.py +++ b/modules/Attention/Attention.py @@ -1,191 +1,191 @@ -import torch -import torch.nn as nn -import logging - -from modules.Utilities import util -from modules.Attention import AttentionMethods -from modules.Device import Device -from modules.cond import cast - - -def Normalize( - in_channels: int, dtype: torch.dtype = None, device: torch.device = None -) -> torch.nn.GroupNorm: - """#### Normalize the input channels. - - #### Args: - - `in_channels` (int): The input channels. - - `dtype` (torch.dtype, optional): The data type. Defaults to `None`. - - `device` (torch.device, optional): The device. Defaults to `None`. - - #### Returns: - - `torch.nn.GroupNorm`: The normalized input channels - """ - return torch.nn.GroupNorm( - num_groups=32, - num_channels=in_channels, - eps=1e-6, - affine=True, - dtype=dtype, - device=device, - ) - - -if Device.xformers_enabled(): - logging.info("Using xformers cross attention") - optimized_attention = AttentionMethods.attention_xformers -else: - logging.info("Using pytorch cross attention") - optimized_attention = AttentionMethods.attention_pytorch - -optimized_attention_masked = optimized_attention - - -def optimized_attention_for_device() -> AttentionMethods.attention_pytorch: - """#### Get the optimized attention for a device. - - #### Returns: - - `function`: The optimized attention function. - """ - return AttentionMethods.attention_pytorch - - -class CrossAttention(nn.Module): - """#### Cross attention module, which applies attention across the query and context. - - #### Args: - - `query_dim` (int): The query dimension. - - `context_dim` (int, optional): The context dimension. Defaults to `None`. - - `heads` (int, optional): The number of heads. Defaults to `8`. - - `dim_head` (int, optional): The head dimension. Defaults to `64`. - - `dropout` (float, optional): The dropout rate. Defaults to `0.0`. - - `dtype` (torch.dtype, optional): The data type. Defaults to `None`. - - `device` (torch.device, optional): The device. Defaults to `None`. - - `operations` (cast.disable_weight_init, optional): The operations. Defaults to `cast.disable_weight_init`. - """ - - def __init__( - self, - query_dim: int, - context_dim: int = None, - heads: int = 8, - dim_head: int = 64, - dropout: float = 0.0, - dtype: torch.dtype = None, - device: torch.device = None, - operations: cast.disable_weight_init = cast.disable_weight_init, - ): - super().__init__() - inner_dim = dim_head * heads - context_dim = util.default(context_dim, query_dim) - - self.heads = heads - self.dim_head = dim_head - - self.to_q = operations.Linear( - query_dim, inner_dim, bias=False, dtype=dtype, device=device - ) - self.to_k = operations.Linear( - context_dim, inner_dim, bias=False, dtype=dtype, device=device - ) - self.to_v = operations.Linear( - context_dim, inner_dim, bias=False, dtype=dtype, device=device - ) - - self.to_out = nn.Sequential( - operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), - nn.Dropout(dropout), - ) - - def forward( - self, - x: torch.Tensor, - context: torch.Tensor = None, - value: torch.Tensor = None, - mask: torch.Tensor = None, - ) -> torch.Tensor: - """#### Forward pass of the cross attention module. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `context` (torch.Tensor, optional): The context tensor. Defaults to `None`. - - `value` (torch.Tensor, optional): The value tensor. Defaults to `None`. - - `mask` (torch.Tensor, optional): The mask tensor. Defaults to `None`. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - q = self.to_q(x) - context = util.default(context, x) - k = self.to_k(context) - v = self.to_v(context) - - out = optimized_attention(q, k, v, self.heads) - return self.to_out(out) - - -class AttnBlock(nn.Module): - """#### Attention block, which applies attention to the input tensor. - - #### Args: - - `in_channels` (int): The input channels. - """ - - def __init__(self, in_channels: int): - super().__init__() - self.in_channels = in_channels - - self.norm = Normalize(in_channels) - self.q = cast.disable_weight_init.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.k = cast.disable_weight_init.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.v = cast.disable_weight_init.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.proj_out = cast.disable_weight_init.Conv2d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - - if Device.xformers_enabled_vae(): - logging.info("Using xformers attention in VAE") - self.optimized_attention = AttentionMethods.xformers_attention - else: - logging.info("Using pytorch attention in VAE") - self.optimized_attention = AttentionMethods.pytorch_attention - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass of the attention block. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - h_ = self.optimized_attention(q, k, v) - - h_ = self.proj_out(h_) - - return x + h_ - - -def make_attn(in_channels: int, attn_type: str = "vanilla") -> AttnBlock: - """#### Make an attention block. - - #### Args: - - `in_channels` (int): The input channels. - - `attn_type` (str, optional): The attention type. Defaults to "vanilla". - - #### Returns: - - `AttnBlock`: A class instance of the attention block. - """ - return AttnBlock(in_channels) +import torch +import torch.nn as nn +import logging + +from modules.Utilities import util +from modules.Attention import AttentionMethods +from modules.Device import Device +from modules.cond import cast + + +def Normalize( + in_channels: int, dtype: torch.dtype = None, device: torch.device = None +) -> torch.nn.GroupNorm: + """#### Normalize the input channels. + + #### Args: + - `in_channels` (int): The input channels. + - `dtype` (torch.dtype, optional): The data type. Defaults to `None`. + - `device` (torch.device, optional): The device. Defaults to `None`. + + #### Returns: + - `torch.nn.GroupNorm`: The normalized input channels + """ + return torch.nn.GroupNorm( + num_groups=32, + num_channels=in_channels, + eps=1e-6, + affine=True, + dtype=dtype, + device=device, + ) + + +if Device.xformers_enabled(): + logging.info("Using xformers cross attention") + optimized_attention = AttentionMethods.attention_xformers +else: + logging.info("Using pytorch cross attention") + optimized_attention = AttentionMethods.attention_pytorch + +optimized_attention_masked = optimized_attention + + +def optimized_attention_for_device() -> AttentionMethods.attention_pytorch: + """#### Get the optimized attention for a device. + + #### Returns: + - `function`: The optimized attention function. + """ + return AttentionMethods.attention_pytorch + + +class CrossAttention(nn.Module): + """#### Cross attention module, which applies attention across the query and context. + + #### Args: + - `query_dim` (int): The query dimension. + - `context_dim` (int, optional): The context dimension. Defaults to `None`. + - `heads` (int, optional): The number of heads. Defaults to `8`. + - `dim_head` (int, optional): The head dimension. Defaults to `64`. + - `dropout` (float, optional): The dropout rate. Defaults to `0.0`. + - `dtype` (torch.dtype, optional): The data type. Defaults to `None`. + - `device` (torch.device, optional): The device. Defaults to `None`. + - `operations` (cast.disable_weight_init, optional): The operations. Defaults to `cast.disable_weight_init`. + """ + + def __init__( + self, + query_dim: int, + context_dim: int = None, + heads: int = 8, + dim_head: int = 64, + dropout: float = 0.0, + dtype: torch.dtype = None, + device: torch.device = None, + operations: cast.disable_weight_init = cast.disable_weight_init, + ): + super().__init__() + inner_dim = dim_head * heads + context_dim = util.default(context_dim, query_dim) + + self.heads = heads + self.dim_head = dim_head + + self.to_q = operations.Linear( + query_dim, inner_dim, bias=False, dtype=dtype, device=device + ) + self.to_k = operations.Linear( + context_dim, inner_dim, bias=False, dtype=dtype, device=device + ) + self.to_v = operations.Linear( + context_dim, inner_dim, bias=False, dtype=dtype, device=device + ) + + self.to_out = nn.Sequential( + operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), + nn.Dropout(dropout), + ) + + def forward( + self, + x: torch.Tensor, + context: torch.Tensor = None, + value: torch.Tensor = None, + mask: torch.Tensor = None, + ) -> torch.Tensor: + """#### Forward pass of the cross attention module. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `context` (torch.Tensor, optional): The context tensor. Defaults to `None`. + - `value` (torch.Tensor, optional): The value tensor. Defaults to `None`. + - `mask` (torch.Tensor, optional): The mask tensor. Defaults to `None`. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + q = self.to_q(x) + context = util.default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + out = optimized_attention(q, k, v, self.heads) + return self.to_out(out) + + +class AttnBlock(nn.Module): + """#### Attention block, which applies attention to the input tensor. + + #### Args: + - `in_channels` (int): The input channels. + """ + + def __init__(self, in_channels: int): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = cast.disable_weight_init.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = cast.disable_weight_init.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = cast.disable_weight_init.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = cast.disable_weight_init.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + if Device.xformers_enabled_vae(): + logging.info("Using xformers attention in VAE") + self.optimized_attention = AttentionMethods.xformers_attention + else: + logging.info("Using pytorch attention in VAE") + self.optimized_attention = AttentionMethods.pytorch_attention + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass of the attention block. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + h_ = self.optimized_attention(q, k, v) + + h_ = self.proj_out(h_) + + return x + h_ + + +def make_attn(in_channels: int, attn_type: str = "vanilla") -> AttnBlock: + """#### Make an attention block. + + #### Args: + - `in_channels` (int): The input channels. + - `attn_type` (str, optional): The attention type. Defaults to "vanilla". + + #### Returns: + - `AttnBlock`: A class instance of the attention block. + """ + return AttnBlock(in_channels) diff --git a/modules/Attention/AttentionMethods.py b/modules/Attention/AttentionMethods.py index e803075c664aa255a464650b1c63e18c1292778b..6c37cd76297d71879b41d934851749673f67d743 100644 --- a/modules/Attention/AttentionMethods.py +++ b/modules/Attention/AttentionMethods.py @@ -1,197 +1,197 @@ -try : - import xformers -except ImportError: - pass -import torch - -BROKEN_XFORMERS = False -try: - x_vers = xformers.__version__ - # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error) - BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20") -except: - pass - - -def attention_xformers( - q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask=None, skip_reshape=False, flux=False -) -> torch.Tensor: - """#### Make an attention call using xformers. Fastest attention implementation. - - #### Args: - - `q` (torch.Tensor): The query tensor. - - `k` (torch.Tensor): The key tensor, must have the same shape as `q`. - - `v` (torch.Tensor): The value tensor, must have the same shape as `q`. - - `heads` (int): The number of heads, must be a divisor of the hidden dimension. - - `mask` (torch.Tensor, optional): The mask tensor. Defaults to `None`. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if not flux: - b, _, dim_head = q.shape - dim_head //= heads - - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(b, -1, heads, dim_head) - .permute(0, 2, 1, 3) - .reshape(b * heads, -1, dim_head) - .contiguous(), - (q, k, v), - ) - - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) - - out = ( - out.unsqueeze(0) - .reshape(b, heads, -1, dim_head) - .permute(0, 2, 1, 3) - .reshape(b, -1, heads * dim_head) - ) - return out - else: - if skip_reshape: - b, _, _, dim_head = q.shape - else: - b, _, dim_head = q.shape - dim_head //= heads - - disabled_xformers = False - - if BROKEN_XFORMERS: - if b * heads > 65535: - disabled_xformers = True - - if not disabled_xformers: - if torch.jit.is_tracing() or torch.jit.is_scripting(): - disabled_xformers = True - - if disabled_xformers: - return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape) - - if skip_reshape: - q, k, v = map( - lambda t: t.reshape(b * heads, -1, dim_head), - (q, k, v), - ) - else: - q, k, v = map( - lambda t: t.reshape(b, -1, heads, dim_head), - (q, k, v), - ) - - if mask is not None: - pad = 8 - q.shape[1] % 8 - mask_out = torch.empty( - [q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device - ) - mask_out[:, :, : mask.shape[-1]] = mask - mask = mask_out[:, :, : mask.shape[-1]] - - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) - - if skip_reshape: - out = ( - out.unsqueeze(0) - .reshape(b, heads, -1, dim_head) - .permute(0, 2, 1, 3) - .reshape(b, -1, heads * dim_head) - ) - else: - out = out.reshape(b, -1, heads * dim_head) - - return out - - -def attention_pytorch( - q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask=None, skip_reshape=False, flux=False -) -> torch.Tensor: - """#### Make an attention call using PyTorch. - - #### Args: - - `q` (torch.Tensor): The query tensor. - - `k` (torch.Tensor): The key tensor, must have the same shape as `q. - - `v` (torch.Tensor): The value tensor, must have the same shape as `q. - - `heads` (int): The number of heads, must be a divisor of the hidden dimension. - - `mask` (torch.Tensor, optional): The mask tensor. Defaults to `None`. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if not flux: - b, _, dim_head = q.shape - dim_head //= heads - q, k, v = map( - lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), - (q, k, v), - ) - - out = torch.nn.functional.scaled_dot_product_attention( - q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False - ) - out = out.transpose(1, 2).reshape(b, -1, heads * dim_head) - return out - else: - if skip_reshape: - b, _, _, dim_head = q.shape - else: - b, _, dim_head = q.shape - dim_head //= heads - q, k, v = map( - lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), - (q, k, v), - ) - - out = torch.nn.functional.scaled_dot_product_attention( - q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False - ) - out = out.transpose(1, 2).reshape(b, -1, heads * dim_head) - return out - -def xformers_attention( - q: torch.Tensor, k: torch.Tensor, v: torch.Tensor -) -> torch.Tensor: - """#### Compute attention using xformers. - - #### Args: - - `q` (torch.Tensor): The query tensor. - - `k` (torch.Tensor): The key tensor, must have the same shape as `q`. - - `v` (torch.Tensor): The value tensor, must have the same shape as `q`. - - Returns: - - `torch.Tensor`: The output tensor. - """ - B, C, H, W = q.shape - q, k, v = map( - lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), - (q, k, v), - ) - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) - out = out.transpose(1, 2).reshape(B, C, H, W) - return out - - -def pytorch_attention( - q: torch.Tensor, k: torch.Tensor, v: torch.Tensor -) -> torch.Tensor: - """#### Compute attention using PyTorch. - - #### Args: - - `q` (torch.Tensor): The query tensor. - - `k` (torch.Tensor): The key tensor, must have the same shape as `q. - - `v` (torch.Tensor): The value tensor, must have the same shape as `q. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - B, C, H, W = q.shape - q, k, v = map( - lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(), - (q, k, v), - ) - out = torch.nn.functional.scaled_dot_product_attention( - q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False - ) - out = out.transpose(2, 3).reshape(B, C, H, W) - return out +try : + import xformers +except ImportError: + pass +import torch + +BROKEN_XFORMERS = False +try: + x_vers = xformers.__version__ + # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error) + BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20") +except: + pass + + +def attention_xformers( + q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask=None, skip_reshape=False, flux=False +) -> torch.Tensor: + """#### Make an attention call using xformers. Fastest attention implementation. + + #### Args: + - `q` (torch.Tensor): The query tensor. + - `k` (torch.Tensor): The key tensor, must have the same shape as `q`. + - `v` (torch.Tensor): The value tensor, must have the same shape as `q`. + - `heads` (int): The number of heads, must be a divisor of the hidden dimension. + - `mask` (torch.Tensor, optional): The mask tensor. Defaults to `None`. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if not flux: + b, _, dim_head = q.shape + dim_head //= heads + + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) + + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return out + else: + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + + disabled_xformers = False + + if BROKEN_XFORMERS: + if b * heads > 65535: + disabled_xformers = True + + if not disabled_xformers: + if torch.jit.is_tracing() or torch.jit.is_scripting(): + disabled_xformers = True + + if disabled_xformers: + return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape) + + if skip_reshape: + q, k, v = map( + lambda t: t.reshape(b * heads, -1, dim_head), + (q, k, v), + ) + else: + q, k, v = map( + lambda t: t.reshape(b, -1, heads, dim_head), + (q, k, v), + ) + + if mask is not None: + pad = 8 - q.shape[1] % 8 + mask_out = torch.empty( + [q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device + ) + mask_out[:, :, : mask.shape[-1]] = mask + mask = mask_out[:, :, : mask.shape[-1]] + + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) + + if skip_reshape: + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + else: + out = out.reshape(b, -1, heads * dim_head) + + return out + + +def attention_pytorch( + q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask=None, skip_reshape=False, flux=False +) -> torch.Tensor: + """#### Make an attention call using PyTorch. + + #### Args: + - `q` (torch.Tensor): The query tensor. + - `k` (torch.Tensor): The key tensor, must have the same shape as `q. + - `v` (torch.Tensor): The value tensor, must have the same shape as `q. + - `heads` (int): The number of heads, must be a divisor of the hidden dimension. + - `mask` (torch.Tensor, optional): The mask tensor. Defaults to `None`. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if not flux: + b, _, dim_head = q.shape + dim_head //= heads + q, k, v = map( + lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), + (q, k, v), + ) + + out = torch.nn.functional.scaled_dot_product_attention( + q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False + ) + out = out.transpose(1, 2).reshape(b, -1, heads * dim_head) + return out + else: + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + q, k, v = map( + lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), + (q, k, v), + ) + + out = torch.nn.functional.scaled_dot_product_attention( + q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False + ) + out = out.transpose(1, 2).reshape(b, -1, heads * dim_head) + return out + +def xformers_attention( + q: torch.Tensor, k: torch.Tensor, v: torch.Tensor +) -> torch.Tensor: + """#### Compute attention using xformers. + + #### Args: + - `q` (torch.Tensor): The query tensor. + - `k` (torch.Tensor): The key tensor, must have the same shape as `q`. + - `v` (torch.Tensor): The value tensor, must have the same shape as `q`. + + Returns: + - `torch.Tensor`: The output tensor. + """ + B, C, H, W = q.shape + q, k, v = map( + lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), + (q, k, v), + ) + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) + out = out.transpose(1, 2).reshape(B, C, H, W) + return out + + +def pytorch_attention( + q: torch.Tensor, k: torch.Tensor, v: torch.Tensor +) -> torch.Tensor: + """#### Compute attention using PyTorch. + + #### Args: + - `q` (torch.Tensor): The query tensor. + - `k` (torch.Tensor): The key tensor, must have the same shape as `q. + - `v` (torch.Tensor): The value tensor, must have the same shape as `q. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + B, C, H, W = q.shape + q, k, v = map( + lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(), + (q, k, v), + ) + out = torch.nn.functional.scaled_dot_product_attention( + q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False + ) + out = out.transpose(2, 3).reshape(B, C, H, W) + return out diff --git a/modules/AutoDetailer/AD_util.py b/modules/AutoDetailer/AD_util.py index 90f99167cacc2153d767be9f2054c2624bac9bd8..e2b529f1d5132570c61dacefbe181a9062a1d845 100644 --- a/modules/AutoDetailer/AD_util.py +++ b/modules/AutoDetailer/AD_util.py @@ -1,245 +1,245 @@ -from typing import List -import cv2 -import numpy as np -import torch -from ultralytics import YOLO -from PIL import Image - -orig_torch_load = torch.load - -# importing YOLO breaking original torch.load capabilities -torch.load = orig_torch_load - - -def load_yolo(model_path: str) -> YOLO: - """#### Load YOLO model. - - #### Args: - - `model_path` (str): The path to the YOLO model. - - #### Returns: - - `YOLO`: The YOLO model initialized with the specified model path. - """ - try: - return YOLO(model_path) - except ModuleNotFoundError: - print("please download yolo model") - - -def inference_bbox( - model: YOLO, - image: Image.Image, - confidence: float = 0.3, - device: str = "", -) -> List: - """#### Perform inference on an image and return bounding boxes. - - #### Args: - - `model` (YOLO): The YOLO model. - - `image` (Image.Image): The image to perform inference on. - - `confidence` (float): The confidence threshold for the bounding boxes. - - `device` (str): The device to run the model on. - - #### Returns: - - `List[List[str, List[int], np.ndarray, float]]`: The list of bounding boxes. - """ - pred = model(image, conf=confidence, device=device) - - bboxes = pred[0].boxes.xyxy.cpu().numpy() - cv2_image = np.array(image) - cv2_image = cv2_image[:, :, ::-1].copy() # Convert RGB to BGR for cv2 processing - cv2_gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY) - - segms = [] - for x0, y0, x1, y1 in bboxes: - cv2_mask = np.zeros(cv2_gray.shape, np.uint8) - cv2.rectangle(cv2_mask, (int(x0), int(y0)), (int(x1), int(y1)), 255, -1) - cv2_mask_bool = cv2_mask.astype(bool) - segms.append(cv2_mask_bool) - - results = [[], [], [], []] - for i in range(len(bboxes)): - results[0].append(pred[0].names[int(pred[0].boxes[i].cls.item())]) - results[1].append(bboxes[i]) - results[2].append(segms[i]) - results[3].append(pred[0].boxes[i].conf.cpu().numpy()) - - return results - - -def create_segmasks(results: List) -> List: - """#### Create segmentation masks from the results of the inference. - - #### Args: - - `results` (List[List[str, List[int], np.ndarray, float]]): The results of the inference. - - #### Returns: - - `List[List[int], np.ndarray, float]`: The list of segmentation masks. - """ - bboxs = results[1] - segms = results[2] - confidence = results[3] - - results = [] - for i in range(len(segms)): - item = (bboxs[i], segms[i].astype(np.float32), confidence[i]) - results.append(item) - return results - - -def dilate_masks(segmasks: List, dilation_factor: int, iter: int = 1) -> List: - """#### Dilate the segmentation masks. - - #### Args: - - `segmasks` (List[List[int], np.ndarray, float]): The segmentation masks. - - `dilation_factor` (int): The dilation factor. - - `iter` (int): The number of iterations. - - #### Returns: - - `List[List[int], np.ndarray, float]`: The dilated segmentation masks. - """ - dilated_masks = [] - kernel = np.ones((abs(dilation_factor), abs(dilation_factor)), np.uint8) - - for i in range(len(segmasks)): - cv2_mask = segmasks[i][1] - - dilated_mask = cv2.dilate(cv2_mask, kernel, iter) - - item = (segmasks[i][0], dilated_mask, segmasks[i][2]) - dilated_masks.append(item) - - return dilated_masks - - -def normalize_region(limit: int, startp: int, size: int) -> List: - """#### Normalize the region. - - #### Args: - - `limit` (int): The limit. - - `startp` (int): The start point. - - `size` (int): The size. - - #### Returns: - - `List[int]`: The normalized start and end points. - """ - if startp < 0: - new_endp = min(limit, size) - new_startp = 0 - elif startp + size > limit: - new_startp = max(0, limit - size) - new_endp = limit - else: - new_startp = startp - new_endp = min(limit, startp + size) - - return int(new_startp), int(new_endp) - - -def make_crop_region(w: int, h: int, bbox: List, crop_factor: float) -> List: - """#### Make the crop region. - - #### Args: - - `w` (int): The width. - - `h` (int): The height. - - `bbox` (List[int]): The bounding box. - - `crop_factor` (float): The crop factor. - - #### Returns: - - `List[x1: int, y1: int, x2: int, y2: int]`: The crop region. - """ - x1 = bbox[0] - y1 = bbox[1] - x2 = bbox[2] - y2 = bbox[3] - - bbox_w = x2 - x1 - bbox_h = y2 - y1 - - crop_w = bbox_w * crop_factor - crop_h = bbox_h * crop_factor - - kernel_x = x1 + bbox_w / 2 - kernel_y = y1 + bbox_h / 2 - - new_x1 = int(kernel_x - crop_w / 2) - new_y1 = int(kernel_y - crop_h / 2) - - # make sure position in (w,h) - new_x1, new_x2 = normalize_region(w, new_x1, crop_w) - new_y1, new_y2 = normalize_region(h, new_y1, crop_h) - - return [new_x1, new_y1, new_x2, new_y2] - - -def crop_ndarray2(npimg: np.ndarray, crop_region: List) -> np.ndarray: - """#### Crop the ndarray in 2 dimensions. - - #### Args: - - `npimg` (np.ndarray): The ndarray to crop. - - `crop_region` (List[int]): The crop region. - - #### Returns: - - `np.ndarray`: The cropped ndarray. - """ - x1 = crop_region[0] - y1 = crop_region[1] - x2 = crop_region[2] - y2 = crop_region[3] - - cropped = npimg[y1:y2, x1:x2] - - return cropped - - -def crop_ndarray4(npimg: np.ndarray, crop_region: List) -> np.ndarray: - """#### Crop the ndarray in 4 dimensions. - - #### Args: - - `npimg` (np.ndarray): The ndarray to crop. - - `crop_region` (List[int]): The crop region. - - #### Returns: - - `np.ndarray`: The cropped ndarray. - """ - x1 = crop_region[0] - y1 = crop_region[1] - x2 = crop_region[2] - y2 = crop_region[3] - - cropped = npimg[:, y1:y2, x1:x2, :] - - return cropped - - -def crop_image(image: Image.Image, crop_region: List) -> Image.Image: - """#### Crop the image. - - #### Args: - - `image` (Image.Image): The image to crop. - - `crop_region` (List[int]): The crop region. - - #### Returns: - - `Image.Image`: The cropped image. - """ - return crop_ndarray4(image, crop_region) - - -def segs_scale_match(segs: List[np.ndarray], target_shape: List) -> List: - """#### Match the scale of the segmentation masks. - - #### Args: - - `segs` (List[np.ndarray]): The segmentation masks. - - `target_shape` (List[int]): The target shape. - - #### Returns: - - `List[np.ndarray]`: The matched segmentation masks. - """ - h = segs[0][0] - w = segs[0][1] - - th = target_shape[1] - tw = target_shape[2] - - if (h == th and w == tw) or h == 0 or w == 0: - return segs +from typing import List +import cv2 +import numpy as np +import torch +from ultralytics import YOLO +from PIL import Image + +orig_torch_load = torch.load + +# importing YOLO breaking original torch.load capabilities +torch.load = orig_torch_load + + +def load_yolo(model_path: str) -> YOLO: + """#### Load YOLO model. + + #### Args: + - `model_path` (str): The path to the YOLO model. + + #### Returns: + - `YOLO`: The YOLO model initialized with the specified model path. + """ + try: + return YOLO(model_path) + except ModuleNotFoundError: + print("please download yolo model") + + +def inference_bbox( + model: YOLO, + image: Image.Image, + confidence: float = 0.3, + device: str = "", +) -> List: + """#### Perform inference on an image and return bounding boxes. + + #### Args: + - `model` (YOLO): The YOLO model. + - `image` (Image.Image): The image to perform inference on. + - `confidence` (float): The confidence threshold for the bounding boxes. + - `device` (str): The device to run the model on. + + #### Returns: + - `List[List[str, List[int], np.ndarray, float]]`: The list of bounding boxes. + """ + pred = model(image, conf=confidence, device=device) + + bboxes = pred[0].boxes.xyxy.cpu().numpy() + cv2_image = np.array(image) + cv2_image = cv2_image[:, :, ::-1].copy() # Convert RGB to BGR for cv2 processing + cv2_gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY) + + segms = [] + for x0, y0, x1, y1 in bboxes: + cv2_mask = np.zeros(cv2_gray.shape, np.uint8) + cv2.rectangle(cv2_mask, (int(x0), int(y0)), (int(x1), int(y1)), 255, -1) + cv2_mask_bool = cv2_mask.astype(bool) + segms.append(cv2_mask_bool) + + results = [[], [], [], []] + for i in range(len(bboxes)): + results[0].append(pred[0].names[int(pred[0].boxes[i].cls.item())]) + results[1].append(bboxes[i]) + results[2].append(segms[i]) + results[3].append(pred[0].boxes[i].conf.cpu().numpy()) + + return results + + +def create_segmasks(results: List) -> List: + """#### Create segmentation masks from the results of the inference. + + #### Args: + - `results` (List[List[str, List[int], np.ndarray, float]]): The results of the inference. + + #### Returns: + - `List[List[int], np.ndarray, float]`: The list of segmentation masks. + """ + bboxs = results[1] + segms = results[2] + confidence = results[3] + + results = [] + for i in range(len(segms)): + item = (bboxs[i], segms[i].astype(np.float32), confidence[i]) + results.append(item) + return results + + +def dilate_masks(segmasks: List, dilation_factor: int, iter: int = 1) -> List: + """#### Dilate the segmentation masks. + + #### Args: + - `segmasks` (List[List[int], np.ndarray, float]): The segmentation masks. + - `dilation_factor` (int): The dilation factor. + - `iter` (int): The number of iterations. + + #### Returns: + - `List[List[int], np.ndarray, float]`: The dilated segmentation masks. + """ + dilated_masks = [] + kernel = np.ones((abs(dilation_factor), abs(dilation_factor)), np.uint8) + + for i in range(len(segmasks)): + cv2_mask = segmasks[i][1] + + dilated_mask = cv2.dilate(cv2_mask, kernel, iter) + + item = (segmasks[i][0], dilated_mask, segmasks[i][2]) + dilated_masks.append(item) + + return dilated_masks + + +def normalize_region(limit: int, startp: int, size: int) -> List: + """#### Normalize the region. + + #### Args: + - `limit` (int): The limit. + - `startp` (int): The start point. + - `size` (int): The size. + + #### Returns: + - `List[int]`: The normalized start and end points. + """ + if startp < 0: + new_endp = min(limit, size) + new_startp = 0 + elif startp + size > limit: + new_startp = max(0, limit - size) + new_endp = limit + else: + new_startp = startp + new_endp = min(limit, startp + size) + + return int(new_startp), int(new_endp) + + +def make_crop_region(w: int, h: int, bbox: List, crop_factor: float) -> List: + """#### Make the crop region. + + #### Args: + - `w` (int): The width. + - `h` (int): The height. + - `bbox` (List[int]): The bounding box. + - `crop_factor` (float): The crop factor. + + #### Returns: + - `List[x1: int, y1: int, x2: int, y2: int]`: The crop region. + """ + x1 = bbox[0] + y1 = bbox[1] + x2 = bbox[2] + y2 = bbox[3] + + bbox_w = x2 - x1 + bbox_h = y2 - y1 + + crop_w = bbox_w * crop_factor + crop_h = bbox_h * crop_factor + + kernel_x = x1 + bbox_w / 2 + kernel_y = y1 + bbox_h / 2 + + new_x1 = int(kernel_x - crop_w / 2) + new_y1 = int(kernel_y - crop_h / 2) + + # make sure position in (w,h) + new_x1, new_x2 = normalize_region(w, new_x1, crop_w) + new_y1, new_y2 = normalize_region(h, new_y1, crop_h) + + return [new_x1, new_y1, new_x2, new_y2] + + +def crop_ndarray2(npimg: np.ndarray, crop_region: List) -> np.ndarray: + """#### Crop the ndarray in 2 dimensions. + + #### Args: + - `npimg` (np.ndarray): The ndarray to crop. + - `crop_region` (List[int]): The crop region. + + #### Returns: + - `np.ndarray`: The cropped ndarray. + """ + x1 = crop_region[0] + y1 = crop_region[1] + x2 = crop_region[2] + y2 = crop_region[3] + + cropped = npimg[y1:y2, x1:x2] + + return cropped + + +def crop_ndarray4(npimg: np.ndarray, crop_region: List) -> np.ndarray: + """#### Crop the ndarray in 4 dimensions. + + #### Args: + - `npimg` (np.ndarray): The ndarray to crop. + - `crop_region` (List[int]): The crop region. + + #### Returns: + - `np.ndarray`: The cropped ndarray. + """ + x1 = crop_region[0] + y1 = crop_region[1] + x2 = crop_region[2] + y2 = crop_region[3] + + cropped = npimg[:, y1:y2, x1:x2, :] + + return cropped + + +def crop_image(image: Image.Image, crop_region: List) -> Image.Image: + """#### Crop the image. + + #### Args: + - `image` (Image.Image): The image to crop. + - `crop_region` (List[int]): The crop region. + + #### Returns: + - `Image.Image`: The cropped image. + """ + return crop_ndarray4(image, crop_region) + + +def segs_scale_match(segs: List[np.ndarray], target_shape: List) -> List: + """#### Match the scale of the segmentation masks. + + #### Args: + - `segs` (List[np.ndarray]): The segmentation masks. + - `target_shape` (List[int]): The target shape. + + #### Returns: + - `List[np.ndarray]`: The matched segmentation masks. + """ + h = segs[0][0] + w = segs[0][1] + + th = target_shape[1] + tw = target_shape[2] + + if (h == th and w == tw) or h == 0 or w == 0: + return segs diff --git a/modules/AutoDetailer/ADetailer.py b/modules/AutoDetailer/ADetailer.py index bc1f6a52d71679560e9c22ec3b2a5c8845bd86d0..35784f295277320c06274151098a29b67c516759 100644 --- a/modules/AutoDetailer/ADetailer.py +++ b/modules/AutoDetailer/ADetailer.py @@ -1,952 +1,952 @@ -import math -import torch -from typing import Any, Dict, Optional, Tuple - -from modules.AutoDetailer import AD_util, bbox, tensor_util -from modules.AutoDetailer import SEGS -from modules.Utilities import util -from modules.AutoEncoders import VariationalAE -from modules.Device import Device -from modules.sample import ksampler_util, samplers, sampling, sampling_util - -# FIXME: Improve slow inference times - - -class DifferentialDiffusion: - """#### Class for applying differential diffusion to a model.""" - - def apply(self, model: torch.nn.Module) -> Tuple[torch.nn.Module]: - """#### Apply differential diffusion to a model. - - #### Args: - - `model` (torch.nn.Module): The input model. - - #### Returns: - - `Tuple[torch.nn.Module]`: The modified model. - """ - model = model.clone() - model.set_model_denoise_mask_function(self.forward) - return (model,) - - def forward( - self, - sigma: torch.Tensor, - denoise_mask: torch.Tensor, - extra_options: Dict[str, Any], - ) -> torch.Tensor: - """#### Forward function for differential diffusion. - - #### Args: - - `sigma` (torch.Tensor): The sigma tensor. - - `denoise_mask` (torch.Tensor): The denoise mask tensor. - - `extra_options` (Dict[str, Any]): Additional options. - - #### Returns: - - `torch.Tensor`: The processed denoise mask tensor. - """ - model = extra_options["model"] - step_sigmas = extra_options["sigmas"] - sigma_to = model.inner_model.model_sampling.sigma_min - sigma_from = step_sigmas[0] - - ts_from = model.inner_model.model_sampling.timestep(sigma_from) - ts_to = model.inner_model.model_sampling.timestep(sigma_to) - current_ts = model.inner_model.model_sampling.timestep(sigma[0]) - - threshold = (current_ts - ts_to) / (ts_from - ts_to) - - return (denoise_mask >= threshold).to(denoise_mask.dtype) - - -def to_latent_image(pixels: torch.Tensor, vae: VariationalAE.VAE) -> torch.Tensor: - """#### Convert pixels to a latent image using a VAE. - - #### Args: - - `pixels` (torch.Tensor): The input pixel tensor. - - `vae` (VariationalAE.VAE): The VAE model. - - #### Returns: - - `torch.Tensor`: The latent image tensor. - """ - pixels.shape[1] - pixels.shape[2] - return VariationalAE.VAEEncode().encode(vae, pixels)[0] - - -def calculate_sigmas2( - model: torch.nn.Module, sampler: str, scheduler: str, steps: int -) -> torch.Tensor: - """#### Calculate sigmas for a model. - - #### Args: - - `model` (torch.nn.Module): The input model. - - `sampler` (str): The sampler name. - - `scheduler` (str): The scheduler name. - - `steps` (int): The number of steps. - - #### Returns: - - `torch.Tensor`: The calculated sigmas. - """ - return ksampler_util.calculate_sigmas( - model.get_model_object("model_sampling"), scheduler, steps - ) - - -def get_noise_sampler( - x: torch.Tensor, cpu: bool, total_sigmas: torch.Tensor, **kwargs -) -> Optional[sampling_util.BrownianTreeNoiseSampler]: - """#### Get a noise sampler. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `cpu` (bool): Whether to use CPU. - - `total_sigmas` (torch.Tensor): The total sigmas tensor. - - `kwargs` (dict): Additional arguments. - - #### Returns: - - `Optional[sampling_util.BrownianTreeNoiseSampler]`: The noise sampler. - """ - if "extra_args" in kwargs and "seed" in kwargs["extra_args"]: - sigma_min, sigma_max = total_sigmas[total_sigmas > 0].min(), total_sigmas.max() - seed = kwargs["extra_args"].get("seed", None) - return sampling_util.BrownianTreeNoiseSampler( - x, sigma_min, sigma_max, seed=seed, cpu=cpu - ) - return None - - -def ksampler2( - sampler_name: str, - total_sigmas: torch.Tensor, - extra_options: Dict[str, Any] = {}, - inpaint_options: Dict[str, Any] = {}, - pipeline: bool = False, -) -> sampling.KSAMPLER: - """#### Get a ksampler. - - #### Args: - - `sampler_name` (str): The sampler name. - - `total_sigmas` (torch.Tensor): The total sigmas tensor. - - `extra_options` (Dict[str, Any], optional): Additional options. Defaults to {}. - - `inpaint_options` (Dict[str, Any], optional): Inpaint options. Defaults to {}. - - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. - - #### Returns: - - `sampling.KSAMPLER`: The ksampler. - """ - if sampler_name == "dpmpp_2m_sde": - - def sample_dpmpp_sde(model, x, sigmas, pipeline, **kwargs): - noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs) - if noise_sampler is not None: - kwargs["noise_sampler"] = noise_sampler - - return samplers.sample_dpmpp_2m_sde( - model, x, sigmas, pipeline=pipeline, **kwargs - ) - - sampler_function = sample_dpmpp_sde - - else: - return sampling.sampler_object(sampler_name, pipeline=pipeline) - - return sampling.KSAMPLER(sampler_function, extra_options, inpaint_options) - - -class Noise_RandomNoise: - """#### Class for generating random noise.""" - - def __init__(self, seed: int): - """#### Initialize the Noise_RandomNoise class. - - #### Args: - - `seed` (int): The seed for random noise. - """ - self.seed = seed - - def generate_noise(self, input_latent: Dict[str, torch.Tensor]) -> torch.Tensor: - """#### Generate random noise. - - #### Args: - - `input_latent` (Dict[str, torch.Tensor]): The input latent tensor. - - #### Returns: - - `torch.Tensor`: The generated noise tensor. - """ - latent_image = input_latent["samples"] - batch_inds = ( - input_latent["batch_index"] if "batch_index" in input_latent else None - ) - return ksampler_util.prepare_noise(latent_image, self.seed, batch_inds) - - -def sample_with_custom_noise( - model: torch.nn.Module, - add_noise: bool, - noise_seed: int, - cfg: int, - positive: Any, - negative: Any, - sampler: Any, - sigmas: torch.Tensor, - latent_image: Dict[str, torch.Tensor], - noise: Optional[torch.Tensor] = None, - callback: Optional[callable] = None, - pipeline: bool = False, -) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: - """#### Sample with custom noise. - - #### Args: - - `model` (torch.nn.Module): The input model. - - `add_noise` (bool): Whether to add noise. - - `noise_seed` (int): The noise seed. - - `cfg` (int): Classifier-Free Guidance Scale - - `positive` (Any): The positive prompt. - - `negative` (Any): The negative prompt. - - `sampler` (Any): The sampler. - - `sigmas` (torch.Tensor): The sigmas tensor. - - `latent_image` (Dict[str, torch.Tensor]): The latent image tensor. - - `noise` (Optional[torch.Tensor], optional): The noise tensor. Defaults to None. - - `callback` (Optional[callable], optional): The callback function. Defaults to None. - - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. - - #### Returns: - - `Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]`: The sampled and denoised tensors. - """ - latent = latent_image - latent_image = latent["samples"] - - out = latent.copy() - out["samples"] = latent_image - - if noise is None: - noise = Noise_RandomNoise(noise_seed).generate_noise(out) - - noise_mask = None - if "noise_mask" in latent: - noise_mask = latent["noise_mask"] - - disable_pbar = not util.PROGRESS_BAR_ENABLED - - device = Device.get_torch_device() - - noise = noise.to(device) - latent_image = latent_image.to(device) - if noise_mask is not None: - noise_mask = noise_mask.to(device) - - samples = sampling.sample_custom( - model, - noise, - cfg, - sampler, - sigmas, - positive, - negative, - latent_image, - noise_mask=noise_mask, - disable_pbar=disable_pbar, - seed=noise_seed, - pipeline=pipeline, - ) - - samples = samples.to(Device.intermediate_device()) - - out["samples"] = samples - out_denoised = out - return out, out_denoised - - -def separated_sample( - model: torch.nn.Module, - add_noise: bool, - seed: int, - steps: int, - cfg: int, - sampler_name: str, - scheduler: str, - positive: Any, - negative: Any, - latent_image: Dict[str, torch.Tensor], - start_at_step: Optional[int], - end_at_step: Optional[int], - return_with_leftover_noise: bool, - sigma_ratio: float = 1.0, - sampler_opt: Optional[Dict[str, Any]] = None, - noise: Optional[torch.Tensor] = None, - callback: Optional[callable] = None, - scheduler_func: Optional[callable] = None, - pipeline: bool = False, -) -> Dict[str, torch.Tensor]: - """#### Perform separated sampling. - - #### Args: - - `model` (torch.nn.Module): The input model. - - `add_noise` (bool): Whether to add noise. - - `seed` (int): The seed for random noise. - - `steps` (int): The number of steps. - - `cfg` (int): Classifier-Free Guidance Scale - - `sampler_name` (str): The sampler name. - - `scheduler` (str): The scheduler name. - - `positive` (Any): The positive prompt. - - `negative` (Any): The negative prompt. - - `latent_image` (Dict[str, torch.Tensor]): The latent image tensor. - - `start_at_step` (Optional[int]): The step to start at. - - `end_at_step` (Optional[int]): The step to end at. - - `return_with_leftover_noise` (bool): Whether to return with leftover noise. - - `sigma_ratio` (float, optional): The sigma ratio. Defaults to 1.0. - - `sampler_opt` (Optional[Dict[str, Any]], optional): The sampler options. Defaults to None. - - `noise` (Optional[torch.Tensor], optional): The noise tensor. Defaults to None. - - `callback` (Optional[callable], optional): The callback function. Defaults to None. - - `scheduler_func` (Optional[callable], optional): The scheduler function. Defaults to None. - - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. - - #### Returns: - - `Dict[str, torch.Tensor]`: The sampled tensor. - """ - total_sigmas = calculate_sigmas2(model, sampler_name, scheduler, steps) - - sigmas = total_sigmas - - if start_at_step is not None: - sigmas = sigmas[start_at_step:] * sigma_ratio - - impact_sampler = ksampler2(sampler_name, total_sigmas, pipeline=pipeline) - - res = sample_with_custom_noise( - model, - add_noise, - seed, - cfg, - positive, - negative, - impact_sampler, - sigmas, - latent_image, - noise=noise, - callback=callback, - pipeline=pipeline, - ) - - return res[1] - - -def ksampler_wrapper( - model: torch.nn.Module, - seed: int, - steps: int, - cfg: int, - sampler_name: str, - scheduler: str, - positive: Any, - negative: Any, - latent_image: Dict[str, torch.Tensor], - denoise: float, - refiner_ratio: Optional[float] = None, - refiner_model: Optional[torch.nn.Module] = None, - refiner_clip: Optional[Any] = None, - refiner_positive: Optional[Any] = None, - refiner_negative: Optional[Any] = None, - sigma_factor: float = 1.0, - noise: Optional[torch.Tensor] = None, - scheduler_func: Optional[callable] = None, - pipeline: bool = False, -) -> Dict[str, torch.Tensor]: - """#### Wrapper for ksampler. - - #### Args: - - `model` (torch.nn.Module): The input model. - - `seed` (int): The seed for random noise. - - `steps` (int): The number of steps. - - `cfg` (int): Classifier-Free Guidance Scale - - `sampler_name` (str): The sampler name. - - `scheduler` (str): The scheduler name. - - `positive` (Any): The positive prompt. - - `negative` (Any): The negative prompt. - - `latent_image` (Dict[str, torch.Tensor]): The latent image tensor. - - `denoise` (float): The denoise factor. - - `refiner_ratio` (Optional[float], optional): The refiner ratio. Defaults to None. - - `refiner_model` (Optional[torch.nn.Module], optional): The refiner model. Defaults to None. - - `refiner_clip` (Optional[Any], optional): The refiner clip. Defaults to None. - - `refiner_positive` (Optional[Any], optional): The refiner positive prompt. Defaults to None. - - `refiner_negative` (Optional[Any], optional): The refiner negative prompt. Defaults to None. - - `sigma_factor` (float, optional): The sigma factor. Defaults to 1.0. - - `noise` (Optional[torch.Tensor], optional): The noise tensor. Defaults to None. - - `scheduler_func` (Optional[callable], optional): The scheduler function. Defaults to None. - - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. - - #### Returns: - - `Dict[str, torch.Tensor]`: The refined latent tensor. - """ - advanced_steps = math.floor(steps / denoise) - start_at_step = advanced_steps - steps - end_at_step = start_at_step + steps - refined_latent = separated_sample( - model, - True, - seed, - advanced_steps, - cfg, - sampler_name, - scheduler, - positive, - negative, - latent_image, - start_at_step, - end_at_step, - False, - sigma_ratio=sigma_factor, - noise=noise, - scheduler_func=scheduler_func, - pipeline=pipeline, - ) - - return refined_latent - - -def enhance_detail( - image: torch.Tensor, - model: torch.nn.Module, - clip: Any, - vae: VariationalAE.VAE, - guide_size: int, - guide_size_for_bbox: bool, - max_size: int, - bbox: Tuple[int, int, int, int], - seed: int, - steps: int, - cfg: int, - sampler_name: str, - scheduler: str, - positive: Any, - negative: Any, - denoise: float, - noise_mask: Optional[torch.Tensor], - force_inpaint: bool, - wildcard_opt: Optional[Any] = None, - wildcard_opt_concat_mode: Optional[Any] = None, - detailer_hook: Optional[callable] = None, - refiner_ratio: Optional[float] = None, - refiner_model: Optional[torch.nn.Module] = None, - refiner_clip: Optional[Any] = None, - refiner_positive: Optional[Any] = None, - refiner_negative: Optional[Any] = None, - control_net_wrapper: Optional[Any] = None, - cycle: int = 1, - inpaint_model: bool = False, - noise_mask_feather: int = 0, - scheduler_func: Optional[callable] = None, - pipeline: bool = False, -) -> Tuple[torch.Tensor, Optional[Any]]: - """#### Enhance detail of an image. - - #### Args: - - `image` (torch.Tensor): The input image tensor. - - `model` (torch.nn.Module): The model. - - `clip` (Any): The clip model. - - `vae` (VariationalAE.VAE): The VAE model. - - `guide_size` (int): The guide size. - - `guide_size_for_bbox` (bool): Whether to use guide size for bbox. - - `max_size` (int): The maximum size. - - `bbox` (Tuple[int, int, int, int]): The bounding box. - - `seed` (int): The seed for random noise. - - `steps` (int): The number of steps. - - `cfg` (int): Classifier-Free Guidance Scale - - `sampler_name` (str): The sampler name. - - `scheduler` (str): The scheduler name. - - `positive` (Any): The positive prompt. - - `negative` (Any): The negative prompt. - - `denoise` (float): The denoise factor. - - `noise_mask` (Optional[torch.Tensor]): The noise mask tensor. - - `force_inpaint` (bool): Whether to force inpaint. - - `wildcard_opt` (Optional[Any], optional): The wildcard options. Defaults to None. - - `wildcard_opt_concat_mode` (Optional[Any], optional): The wildcard concat mode. Defaults to None. - - `detailer_hook` (Optional[callable], optional): The detailer hook. Defaults to None. - - `refiner_ratio` (Optional[float], optional): The refiner ratio. Defaults to None. - - `refiner_model` (Optional[torch.nn.Module], optional): The refiner model. Defaults to None. - - `refiner_clip` (Optional[Any], optional): The refiner clip. Defaults to None. - - `refiner_positive` (Optional[Any], optional): The refiner positive prompt. Defaults to None. - - `refiner_negative` (Optional[Any], optional): The refiner negative prompt. Defaults to None. - - `control_net_wrapper` (Optional[Any], optional): The control net wrapper. Defaults to None. - - `cycle` (int, optional): The number of cycles. Defaults to 1. - - `inpaint_model` (bool, optional): Whether to use inpaint model. Defaults to False. - - `noise_mask_feather` (int, optional): The noise mask feather. Defaults to 0. - - `scheduler_func` (Optional[callable], optional): The scheduler function. Defaults to None. - - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. - - #### Returns: - - `Tuple[torch.Tensor, Optional[Any]]`: The refined image tensor and optional cnet_pils. - """ - if noise_mask is not None: - noise_mask = tensor_util.tensor_gaussian_blur_mask( - noise_mask, noise_mask_feather - ) - noise_mask = noise_mask.squeeze(3) - - h = image.shape[1] - w = image.shape[2] - - bbox_h = bbox[3] - bbox[1] - bbox_w = bbox[2] - bbox[0] - - # for cropped_size - upscale = guide_size / min(w, h) - - new_w = int(w * upscale) - new_h = int(h * upscale) - - if new_w > max_size or new_h > max_size: - upscale *= max_size / max(new_w, new_h) - new_w = int(w * upscale) - new_h = int(h * upscale) - - if upscale <= 1.0 or new_w == 0 or new_h == 0: - print("Detailer: force inpaint") - upscale = 1.0 - new_w = w - new_h = h - - print( - f"Detailer: segment upscale for ({bbox_w, bbox_h}) | crop region {w, h} x {upscale} -> {new_w, new_h}" - ) - - # upscale - upscaled_image = tensor_util.tensor_resize(image, new_w, new_h) - - cnet_pils = None - - # prepare mask - latent_image = to_latent_image(upscaled_image, vae) - if noise_mask is not None: - latent_image["noise_mask"] = noise_mask - - refined_latent = latent_image - - # ksampler - for i in range(0, cycle): - ( - model2, - seed2, - steps2, - cfg2, - sampler_name2, - scheduler2, - positive2, - negative2, - _upscaled_latent2, - denoise2, - ) = ( - model, - seed + i, - steps, - cfg, - sampler_name, - scheduler, - positive, - negative, - latent_image, - denoise, - ) - noise = None - - refined_latent = ksampler_wrapper( - model2, - seed2, - steps2, - cfg2, - sampler_name2, - scheduler2, - positive2, - negative2, - refined_latent, - denoise2, - refiner_ratio, - refiner_model, - refiner_clip, - refiner_positive, - refiner_negative, - noise=noise, - scheduler_func=scheduler_func, - pipeline=pipeline, - ) - - # non-latent downscale - latent downscale cause bad quality - try: - # try to decode image normally - refined_image = vae.decode(refined_latent["samples"]) - except Exception: - # usually an out-of-memory exception from the decode, so try a tiled approach - refined_image = vae.decode_tiled( - refined_latent["samples"], - tile_x=64, - tile_y=64, - ) - - # downscale - refined_image = tensor_util.tensor_resize(refined_image, w, h) - - # prevent mixing of device - refined_image = refined_image.cpu() - - # don't convert to latent - latent break image - # preserving pil is much better - return refined_image, cnet_pils - - -class DetailerForEach: - """#### Class for detailing each segment of an image.""" - - @staticmethod - def do_detail( - image: torch.Tensor, - segs: Tuple[torch.Tensor, Any], - model: torch.nn.Module, - clip: Any, - vae: VariationalAE.VAE, - guide_size: int, - guide_size_for_bbox: bool, - max_size: int, - seed: int, - steps: int, - cfg: int, - sampler_name: str, - scheduler: str, - positive: Any, - negative: Any, - denoise: float, - feather: int, - noise_mask: Optional[torch.Tensor], - force_inpaint: bool, - wildcard_opt: Optional[Any] = None, - detailer_hook: Optional[callable] = None, - refiner_ratio: Optional[float] = None, - refiner_model: Optional[torch.nn.Module] = None, - refiner_clip: Optional[Any] = None, - refiner_positive: Optional[Any] = None, - refiner_negative: Optional[Any] = None, - cycle: int = 1, - inpaint_model: bool = False, - noise_mask_feather: int = 0, - scheduler_func_opt: Optional[callable] = None, - pipeline: bool = False, - ) -> Tuple[torch.Tensor, list, list, list, list, Tuple[torch.Tensor, list]]: - """#### Perform detailing on each segment of an image. - - #### Args: - - `image` (torch.Tensor): The input image tensor. - - `segs` (Tuple[torch.Tensor, Any]): The segments. - - `model` (torch.nn.Module): The model. - - `clip` (Any): The clip model. - - `vae` (VariationalAE.VAE): The VAE model. - - `guide_size` (int): The guide size. - - `guide_size_for_bbox` (bool): Whether to use guide size for bbox. - - `max_size` (int): The maximum size. - - `seed` (int): The seed for random noise. - - `steps` (int): The number of steps. - - `cfg` (int): Classifier-Free Guidance Scale. - - `sampler_name` (str): The sampler name. - - `scheduler` (str): The scheduler name. - - `positive` (Any): The positive prompt. - - `negative` (Any): The negative prompt. - - `denoise` (float): The denoise factor. - - `feather` (int): The feather value. - - `noise_mask` (Optional[torch.Tensor]): The noise mask tensor. - - `force_inpaint` (bool): Whether to force inpaint. - - `wildcard_opt` (Optional[Any], optional): The wildcard options. Defaults to None. - - `detailer_hook` (Optional[callable], optional): The detailer hook. Defaults to None. - - `refiner_ratio` (Optional[float], optional): The refiner ratio. Defaults to None. - - `refiner_model` (Optional[torch.nn.Module], optional): The refiner model. Defaults to None. - - `refiner_clip` (Optional[Any], optional): The refiner clip. Defaults to None. - - `refiner_positive` (Optional[Any], optional): The refiner positive prompt. Defaults to None. - - `refiner_negative` (Optional[Any], optional): The refiner negative prompt. Defaults to None. - - `cycle` (int, optional): The number of cycles. Defaults to 1. - - `inpaint_model` (bool, optional): Whether to use inpaint model. Defaults to False. - - `noise_mask_feather` (int, optional): The noise mask feather. Defaults to 0. - - `scheduler_func_opt` (Optional[callable], optional): The scheduler function. Defaults to None. - - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. - - #### Returns: - - `Tuple[torch.Tensor, list, list, list, list, Tuple[torch.Tensor, list]]`: The detailed image tensor, cropped list, enhanced list, enhanced alpha list, cnet PIL list, and new segments. - """ - image = image.clone() - enhanced_alpha_list = [] - enhanced_list = [] - cropped_list = [] - cnet_pil_list = [] - - segs = AD_util.segs_scale_match(segs, image.shape) - new_segs = [] - - wildcard_concat_mode = None - wmode, wildcard_chooser = bbox.process_wildcard_for_segs(wildcard_opt) - - ordered_segs = segs[1] - - if ( - noise_mask_feather > 0 - and "denoise_mask_function" not in model.model_options - ): - model = DifferentialDiffusion().apply(model)[0] - - for i, seg in enumerate(ordered_segs): - cropped_image = AD_util.crop_ndarray4( - image.cpu().numpy(), seg.crop_region - ) # Never use seg.cropped_image to handle overlapping area - cropped_image = tensor_util.to_tensor(cropped_image) - mask = tensor_util.to_tensor(seg.cropped_mask) - mask = tensor_util.tensor_gaussian_blur_mask(mask, feather) - - is_mask_all_zeros = (seg.cropped_mask == 0).all().item() - if is_mask_all_zeros: - print("Detailer: segment skip [empty mask]") - continue - - cropped_mask = seg.cropped_mask - - seg_seed, wildcard_item = wildcard_chooser.get(seg) - - seg_seed = seed + i if seg_seed is None else seg_seed - - cropped_positive = [ - [ - condition, - { - k: ( - crop_condition_mask(v, image, seg.crop_region) - if k == "mask" - else v - ) - for k, v in details.items() - }, - ] - for condition, details in positive - ] - - cropped_negative = [ - [ - condition, - { - k: ( - crop_condition_mask(v, image, seg.crop_region) - if k == "mask" - else v - ) - for k, v in details.items() - }, - ] - for condition, details in negative - ] - - orig_cropped_image = cropped_image.clone() - enhanced_image, cnet_pils = enhance_detail( - cropped_image, - model, - clip, - vae, - guide_size, - guide_size_for_bbox, - max_size, - seg.bbox, - seg_seed, - steps, - cfg, - sampler_name, - scheduler, - cropped_positive, - cropped_negative, - denoise, - cropped_mask, - force_inpaint, - wildcard_opt=wildcard_item, - wildcard_opt_concat_mode=wildcard_concat_mode, - detailer_hook=detailer_hook, - refiner_ratio=refiner_ratio, - refiner_model=refiner_model, - refiner_clip=refiner_clip, - refiner_positive=refiner_positive, - refiner_negative=refiner_negative, - control_net_wrapper=seg.control_net_wrapper, - cycle=cycle, - inpaint_model=inpaint_model, - noise_mask_feather=noise_mask_feather, - scheduler_func=scheduler_func_opt, - pipeline=pipeline, - ) - - if enhanced_image is not None: - # don't latent composite-> converting to latent caused poor quality - # use image paste - image = image.cpu() - enhanced_image = enhanced_image.cpu() - tensor_util.tensor_paste( - image, - enhanced_image, - (seg.crop_region[0], seg.crop_region[1]), - mask, - ) # this code affecting to `cropped_image`. - enhanced_list.append(enhanced_image) - - # Convert enhanced_pil_alpha to RGBA mode - enhanced_image_alpha = tensor_util.tensor_convert_rgba(enhanced_image) - new_seg_image = ( - enhanced_image.numpy() - ) # alpha should not be applied to seg_image - # Apply the mask - mask = tensor_util.tensor_resize( - mask, *tensor_util.tensor_get_size(enhanced_image) - ) - tensor_util.tensor_putalpha(enhanced_image_alpha, mask) - enhanced_alpha_list.append(enhanced_image_alpha) - - cropped_list.append(orig_cropped_image) # NOTE: Don't use `cropped_image` - - new_seg = SEGS.SEG( - new_seg_image, - seg.cropped_mask, - seg.confidence, - seg.crop_region, - seg.bbox, - seg.label, - seg.control_net_wrapper, - ) - new_segs.append(new_seg) - - image_tensor = tensor_util.tensor_convert_rgb(image) - - cropped_list.sort(key=lambda x: x.shape, reverse=True) - enhanced_list.sort(key=lambda x: x.shape, reverse=True) - enhanced_alpha_list.sort(key=lambda x: x.shape, reverse=True) - - return ( - image_tensor, - cropped_list, - enhanced_list, - enhanced_alpha_list, - cnet_pil_list, - (segs[0], new_segs), - ) - - -def empty_pil_tensor(w: int = 64, h: int = 64) -> torch.Tensor: - """#### Create an empty PIL tensor. - - #### Args: - - `w` (int, optional): The width of the tensor. Defaults to 64. - - `h` (int, optional): The height of the tensor. Defaults to 64. - - #### Returns: - - `torch.Tensor`: The empty tensor. - """ - return torch.zeros((1, h, w, 3), dtype=torch.float32) - - -class DetailerForEachTest(DetailerForEach): - """#### Test class for DetailerForEach.""" - - def doit( - self, - image: torch.Tensor, - segs: Any, - model: torch.nn.Module, - clip: Any, - vae: VariationalAE.VAE, - guide_size: int, - guide_size_for: bool, - max_size: int, - seed: int, - steps: int, - cfg: Any, - sampler_name: str, - scheduler: str, - positive: Any, - negative: Any, - denoise: float, - feather: int, - noise_mask: Optional[torch.Tensor], - force_inpaint: bool, - wildcard: Optional[Any], - detailer_hook: Optional[callable] = None, - cycle: int = 1, - inpaint_model: bool = False, - noise_mask_feather: int = 0, - scheduler_func_opt: Optional[callable] = None, - pipeline: bool = False, - ) -> Tuple[torch.Tensor, list, list, list, list]: - """#### Perform detail enhancement for testing. - - #### Args: - - `image` (torch.Tensor): The input image tensor. - - `segs` (Any): The segments. - - `model` (torch.nn.Module): The model. - - `clip` (Any): The clip model. - - `vae` (VariationalAE.VAE): The VAE model. - - `guide_size` (int): The guide size. - - `guide_size_for` (bool): Whether to use guide size for. - - `max_size` (int): The maximum size. - - `seed` (int): The seed for random noise. - - `steps` (int): The number of steps. - - `cfg` (Any): The configuration. - - `sampler_name` (str): The sampler name. - - `scheduler` (str): The scheduler name. - - `positive` (Any): The positive prompt. - - `negative` (Any): The negative prompt. - - `denoise` (float): The denoise factor. - - `feather` (int): The feather value. - - `noise_mask` (Optional[torch.Tensor]): The noise mask tensor. - - `force_inpaint` (bool): Whether to force inpaint. - - `wildcard` (Optional[Any]): The wildcard options. - - `detailer_hook` (Optional[callable], optional): The detailer hook. Defaults to None. - - `cycle` (int, optional): The number of cycles. Defaults to 1. - - `inpaint_model` (bool, optional): Whether to use inpaint model. Defaults to False. - - `noise_mask_feather` (int, optional): The noise mask feather. Defaults to 0. - - `scheduler_func_opt` (Optional[callable], optional): The scheduler function. Defaults to None. - - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. - - #### Returns: - - `Tuple[torch.Tensor, list, list, list, list]`: The enhanced image tensor, cropped list, cropped enhanced list, cropped enhanced alpha list, and cnet PIL list. - """ - ( - enhanced_img, - cropped, - cropped_enhanced, - cropped_enhanced_alpha, - cnet_pil_list, - new_segs, - ) = DetailerForEach.do_detail( - image, - segs, - model, - clip, - vae, - guide_size, - guide_size_for, - max_size, - seed, - steps, - cfg, - sampler_name, - scheduler, - positive, - negative, - denoise, - feather, - noise_mask, - force_inpaint, - wildcard, - detailer_hook, - cycle=cycle, - inpaint_model=inpaint_model, - noise_mask_feather=noise_mask_feather, - scheduler_func_opt=scheduler_func_opt, - pipeline=pipeline, - ) - - cnet_pil_list = [empty_pil_tensor()] - - return ( - enhanced_img, - cropped, - cropped_enhanced, - cropped_enhanced_alpha, - cnet_pil_list, - ) +import math +import torch +from typing import Any, Dict, Optional, Tuple + +from modules.AutoDetailer import AD_util, bbox, tensor_util +from modules.AutoDetailer import SEGS +from modules.Utilities import util +from modules.AutoEncoders import VariationalAE +from modules.Device import Device +from modules.sample import ksampler_util, samplers, sampling, sampling_util + +# FIXME: Improve slow inference times + + +class DifferentialDiffusion: + """#### Class for applying differential diffusion to a model.""" + + def apply(self, model: torch.nn.Module) -> Tuple[torch.nn.Module]: + """#### Apply differential diffusion to a model. + + #### Args: + - `model` (torch.nn.Module): The input model. + + #### Returns: + - `Tuple[torch.nn.Module]`: The modified model. + """ + model = model.clone() + model.set_model_denoise_mask_function(self.forward) + return (model,) + + def forward( + self, + sigma: torch.Tensor, + denoise_mask: torch.Tensor, + extra_options: Dict[str, Any], + ) -> torch.Tensor: + """#### Forward function for differential diffusion. + + #### Args: + - `sigma` (torch.Tensor): The sigma tensor. + - `denoise_mask` (torch.Tensor): The denoise mask tensor. + - `extra_options` (Dict[str, Any]): Additional options. + + #### Returns: + - `torch.Tensor`: The processed denoise mask tensor. + """ + model = extra_options["model"] + step_sigmas = extra_options["sigmas"] + sigma_to = model.inner_model.model_sampling.sigma_min + sigma_from = step_sigmas[0] + + ts_from = model.inner_model.model_sampling.timestep(sigma_from) + ts_to = model.inner_model.model_sampling.timestep(sigma_to) + current_ts = model.inner_model.model_sampling.timestep(sigma[0]) + + threshold = (current_ts - ts_to) / (ts_from - ts_to) + + return (denoise_mask >= threshold).to(denoise_mask.dtype) + + +def to_latent_image(pixels: torch.Tensor, vae: VariationalAE.VAE) -> torch.Tensor: + """#### Convert pixels to a latent image using a VAE. + + #### Args: + - `pixels` (torch.Tensor): The input pixel tensor. + - `vae` (VariationalAE.VAE): The VAE model. + + #### Returns: + - `torch.Tensor`: The latent image tensor. + """ + pixels.shape[1] + pixels.shape[2] + return VariationalAE.VAEEncode().encode(vae, pixels)[0] + + +def calculate_sigmas2( + model: torch.nn.Module, sampler: str, scheduler: str, steps: int +) -> torch.Tensor: + """#### Calculate sigmas for a model. + + #### Args: + - `model` (torch.nn.Module): The input model. + - `sampler` (str): The sampler name. + - `scheduler` (str): The scheduler name. + - `steps` (int): The number of steps. + + #### Returns: + - `torch.Tensor`: The calculated sigmas. + """ + return ksampler_util.calculate_sigmas( + model.get_model_object("model_sampling"), scheduler, steps + ) + + +def get_noise_sampler( + x: torch.Tensor, cpu: bool, total_sigmas: torch.Tensor, **kwargs +) -> Optional[sampling_util.BrownianTreeNoiseSampler]: + """#### Get a noise sampler. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `cpu` (bool): Whether to use CPU. + - `total_sigmas` (torch.Tensor): The total sigmas tensor. + - `kwargs` (dict): Additional arguments. + + #### Returns: + - `Optional[sampling_util.BrownianTreeNoiseSampler]`: The noise sampler. + """ + if "extra_args" in kwargs and "seed" in kwargs["extra_args"]: + sigma_min, sigma_max = total_sigmas[total_sigmas > 0].min(), total_sigmas.max() + seed = kwargs["extra_args"].get("seed", None) + return sampling_util.BrownianTreeNoiseSampler( + x, sigma_min, sigma_max, seed=seed, cpu=cpu + ) + return None + + +def ksampler2( + sampler_name: str, + total_sigmas: torch.Tensor, + extra_options: Dict[str, Any] = {}, + inpaint_options: Dict[str, Any] = {}, + pipeline: bool = False, +) -> sampling.KSAMPLER: + """#### Get a ksampler. + + #### Args: + - `sampler_name` (str): The sampler name. + - `total_sigmas` (torch.Tensor): The total sigmas tensor. + - `extra_options` (Dict[str, Any], optional): Additional options. Defaults to {}. + - `inpaint_options` (Dict[str, Any], optional): Inpaint options. Defaults to {}. + - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. + + #### Returns: + - `sampling.KSAMPLER`: The ksampler. + """ + if sampler_name == "dpmpp_2m_sde": + + def sample_dpmpp_sde(model, x, sigmas, pipeline, **kwargs): + noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs) + if noise_sampler is not None: + kwargs["noise_sampler"] = noise_sampler + + return samplers.sample_dpmpp_2m_sde( + model, x, sigmas, pipeline=pipeline, **kwargs + ) + + sampler_function = sample_dpmpp_sde + + else: + return sampling.sampler_object(sampler_name, pipeline=pipeline) + + return sampling.KSAMPLER(sampler_function, extra_options, inpaint_options) + + +class Noise_RandomNoise: + """#### Class for generating random noise.""" + + def __init__(self, seed: int): + """#### Initialize the Noise_RandomNoise class. + + #### Args: + - `seed` (int): The seed for random noise. + """ + self.seed = seed + + def generate_noise(self, input_latent: Dict[str, torch.Tensor]) -> torch.Tensor: + """#### Generate random noise. + + #### Args: + - `input_latent` (Dict[str, torch.Tensor]): The input latent tensor. + + #### Returns: + - `torch.Tensor`: The generated noise tensor. + """ + latent_image = input_latent["samples"] + batch_inds = ( + input_latent["batch_index"] if "batch_index" in input_latent else None + ) + return ksampler_util.prepare_noise(latent_image, self.seed, batch_inds) + + +def sample_with_custom_noise( + model: torch.nn.Module, + add_noise: bool, + noise_seed: int, + cfg: int, + positive: Any, + negative: Any, + sampler: Any, + sigmas: torch.Tensor, + latent_image: Dict[str, torch.Tensor], + noise: Optional[torch.Tensor] = None, + callback: Optional[callable] = None, + pipeline: bool = False, +) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]: + """#### Sample with custom noise. + + #### Args: + - `model` (torch.nn.Module): The input model. + - `add_noise` (bool): Whether to add noise. + - `noise_seed` (int): The noise seed. + - `cfg` (int): Classifier-Free Guidance Scale + - `positive` (Any): The positive prompt. + - `negative` (Any): The negative prompt. + - `sampler` (Any): The sampler. + - `sigmas` (torch.Tensor): The sigmas tensor. + - `latent_image` (Dict[str, torch.Tensor]): The latent image tensor. + - `noise` (Optional[torch.Tensor], optional): The noise tensor. Defaults to None. + - `callback` (Optional[callable], optional): The callback function. Defaults to None. + - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. + + #### Returns: + - `Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]`: The sampled and denoised tensors. + """ + latent = latent_image + latent_image = latent["samples"] + + out = latent.copy() + out["samples"] = latent_image + + if noise is None: + noise = Noise_RandomNoise(noise_seed).generate_noise(out) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + disable_pbar = not util.PROGRESS_BAR_ENABLED + + device = Device.get_torch_device() + + noise = noise.to(device) + latent_image = latent_image.to(device) + if noise_mask is not None: + noise_mask = noise_mask.to(device) + + samples = sampling.sample_custom( + model, + noise, + cfg, + sampler, + sigmas, + positive, + negative, + latent_image, + noise_mask=noise_mask, + disable_pbar=disable_pbar, + seed=noise_seed, + pipeline=pipeline, + ) + + samples = samples.to(Device.intermediate_device()) + + out["samples"] = samples + out_denoised = out + return out, out_denoised + + +def separated_sample( + model: torch.nn.Module, + add_noise: bool, + seed: int, + steps: int, + cfg: int, + sampler_name: str, + scheduler: str, + positive: Any, + negative: Any, + latent_image: Dict[str, torch.Tensor], + start_at_step: Optional[int], + end_at_step: Optional[int], + return_with_leftover_noise: bool, + sigma_ratio: float = 1.0, + sampler_opt: Optional[Dict[str, Any]] = None, + noise: Optional[torch.Tensor] = None, + callback: Optional[callable] = None, + scheduler_func: Optional[callable] = None, + pipeline: bool = False, +) -> Dict[str, torch.Tensor]: + """#### Perform separated sampling. + + #### Args: + - `model` (torch.nn.Module): The input model. + - `add_noise` (bool): Whether to add noise. + - `seed` (int): The seed for random noise. + - `steps` (int): The number of steps. + - `cfg` (int): Classifier-Free Guidance Scale + - `sampler_name` (str): The sampler name. + - `scheduler` (str): The scheduler name. + - `positive` (Any): The positive prompt. + - `negative` (Any): The negative prompt. + - `latent_image` (Dict[str, torch.Tensor]): The latent image tensor. + - `start_at_step` (Optional[int]): The step to start at. + - `end_at_step` (Optional[int]): The step to end at. + - `return_with_leftover_noise` (bool): Whether to return with leftover noise. + - `sigma_ratio` (float, optional): The sigma ratio. Defaults to 1.0. + - `sampler_opt` (Optional[Dict[str, Any]], optional): The sampler options. Defaults to None. + - `noise` (Optional[torch.Tensor], optional): The noise tensor. Defaults to None. + - `callback` (Optional[callable], optional): The callback function. Defaults to None. + - `scheduler_func` (Optional[callable], optional): The scheduler function. Defaults to None. + - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. + + #### Returns: + - `Dict[str, torch.Tensor]`: The sampled tensor. + """ + total_sigmas = calculate_sigmas2(model, sampler_name, scheduler, steps) + + sigmas = total_sigmas + + if start_at_step is not None: + sigmas = sigmas[start_at_step:] * sigma_ratio + + impact_sampler = ksampler2(sampler_name, total_sigmas, pipeline=pipeline) + + res = sample_with_custom_noise( + model, + add_noise, + seed, + cfg, + positive, + negative, + impact_sampler, + sigmas, + latent_image, + noise=noise, + callback=callback, + pipeline=pipeline, + ) + + return res[1] + + +def ksampler_wrapper( + model: torch.nn.Module, + seed: int, + steps: int, + cfg: int, + sampler_name: str, + scheduler: str, + positive: Any, + negative: Any, + latent_image: Dict[str, torch.Tensor], + denoise: float, + refiner_ratio: Optional[float] = None, + refiner_model: Optional[torch.nn.Module] = None, + refiner_clip: Optional[Any] = None, + refiner_positive: Optional[Any] = None, + refiner_negative: Optional[Any] = None, + sigma_factor: float = 1.0, + noise: Optional[torch.Tensor] = None, + scheduler_func: Optional[callable] = None, + pipeline: bool = False, +) -> Dict[str, torch.Tensor]: + """#### Wrapper for ksampler. + + #### Args: + - `model` (torch.nn.Module): The input model. + - `seed` (int): The seed for random noise. + - `steps` (int): The number of steps. + - `cfg` (int): Classifier-Free Guidance Scale + - `sampler_name` (str): The sampler name. + - `scheduler` (str): The scheduler name. + - `positive` (Any): The positive prompt. + - `negative` (Any): The negative prompt. + - `latent_image` (Dict[str, torch.Tensor]): The latent image tensor. + - `denoise` (float): The denoise factor. + - `refiner_ratio` (Optional[float], optional): The refiner ratio. Defaults to None. + - `refiner_model` (Optional[torch.nn.Module], optional): The refiner model. Defaults to None. + - `refiner_clip` (Optional[Any], optional): The refiner clip. Defaults to None. + - `refiner_positive` (Optional[Any], optional): The refiner positive prompt. Defaults to None. + - `refiner_negative` (Optional[Any], optional): The refiner negative prompt. Defaults to None. + - `sigma_factor` (float, optional): The sigma factor. Defaults to 1.0. + - `noise` (Optional[torch.Tensor], optional): The noise tensor. Defaults to None. + - `scheduler_func` (Optional[callable], optional): The scheduler function. Defaults to None. + - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. + + #### Returns: + - `Dict[str, torch.Tensor]`: The refined latent tensor. + """ + advanced_steps = math.floor(steps / denoise) + start_at_step = advanced_steps - steps + end_at_step = start_at_step + steps + refined_latent = separated_sample( + model, + True, + seed, + advanced_steps, + cfg, + sampler_name, + scheduler, + positive, + negative, + latent_image, + start_at_step, + end_at_step, + False, + sigma_ratio=sigma_factor, + noise=noise, + scheduler_func=scheduler_func, + pipeline=pipeline, + ) + + return refined_latent + + +def enhance_detail( + image: torch.Tensor, + model: torch.nn.Module, + clip: Any, + vae: VariationalAE.VAE, + guide_size: int, + guide_size_for_bbox: bool, + max_size: int, + bbox: Tuple[int, int, int, int], + seed: int, + steps: int, + cfg: int, + sampler_name: str, + scheduler: str, + positive: Any, + negative: Any, + denoise: float, + noise_mask: Optional[torch.Tensor], + force_inpaint: bool, + wildcard_opt: Optional[Any] = None, + wildcard_opt_concat_mode: Optional[Any] = None, + detailer_hook: Optional[callable] = None, + refiner_ratio: Optional[float] = None, + refiner_model: Optional[torch.nn.Module] = None, + refiner_clip: Optional[Any] = None, + refiner_positive: Optional[Any] = None, + refiner_negative: Optional[Any] = None, + control_net_wrapper: Optional[Any] = None, + cycle: int = 1, + inpaint_model: bool = False, + noise_mask_feather: int = 0, + scheduler_func: Optional[callable] = None, + pipeline: bool = False, +) -> Tuple[torch.Tensor, Optional[Any]]: + """#### Enhance detail of an image. + + #### Args: + - `image` (torch.Tensor): The input image tensor. + - `model` (torch.nn.Module): The model. + - `clip` (Any): The clip model. + - `vae` (VariationalAE.VAE): The VAE model. + - `guide_size` (int): The guide size. + - `guide_size_for_bbox` (bool): Whether to use guide size for bbox. + - `max_size` (int): The maximum size. + - `bbox` (Tuple[int, int, int, int]): The bounding box. + - `seed` (int): The seed for random noise. + - `steps` (int): The number of steps. + - `cfg` (int): Classifier-Free Guidance Scale + - `sampler_name` (str): The sampler name. + - `scheduler` (str): The scheduler name. + - `positive` (Any): The positive prompt. + - `negative` (Any): The negative prompt. + - `denoise` (float): The denoise factor. + - `noise_mask` (Optional[torch.Tensor]): The noise mask tensor. + - `force_inpaint` (bool): Whether to force inpaint. + - `wildcard_opt` (Optional[Any], optional): The wildcard options. Defaults to None. + - `wildcard_opt_concat_mode` (Optional[Any], optional): The wildcard concat mode. Defaults to None. + - `detailer_hook` (Optional[callable], optional): The detailer hook. Defaults to None. + - `refiner_ratio` (Optional[float], optional): The refiner ratio. Defaults to None. + - `refiner_model` (Optional[torch.nn.Module], optional): The refiner model. Defaults to None. + - `refiner_clip` (Optional[Any], optional): The refiner clip. Defaults to None. + - `refiner_positive` (Optional[Any], optional): The refiner positive prompt. Defaults to None. + - `refiner_negative` (Optional[Any], optional): The refiner negative prompt. Defaults to None. + - `control_net_wrapper` (Optional[Any], optional): The control net wrapper. Defaults to None. + - `cycle` (int, optional): The number of cycles. Defaults to 1. + - `inpaint_model` (bool, optional): Whether to use inpaint model. Defaults to False. + - `noise_mask_feather` (int, optional): The noise mask feather. Defaults to 0. + - `scheduler_func` (Optional[callable], optional): The scheduler function. Defaults to None. + - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. + + #### Returns: + - `Tuple[torch.Tensor, Optional[Any]]`: The refined image tensor and optional cnet_pils. + """ + if noise_mask is not None: + noise_mask = tensor_util.tensor_gaussian_blur_mask( + noise_mask, noise_mask_feather + ) + noise_mask = noise_mask.squeeze(3) + + h = image.shape[1] + w = image.shape[2] + + bbox_h = bbox[3] - bbox[1] + bbox_w = bbox[2] - bbox[0] + + # for cropped_size + upscale = guide_size / min(w, h) + + new_w = int(w * upscale) + new_h = int(h * upscale) + + if new_w > max_size or new_h > max_size: + upscale *= max_size / max(new_w, new_h) + new_w = int(w * upscale) + new_h = int(h * upscale) + + if upscale <= 1.0 or new_w == 0 or new_h == 0: + print("Detailer: force inpaint") + upscale = 1.0 + new_w = w + new_h = h + + print( + f"Detailer: segment upscale for ({bbox_w, bbox_h}) | crop region {w, h} x {upscale} -> {new_w, new_h}" + ) + + # upscale + upscaled_image = tensor_util.tensor_resize(image, new_w, new_h) + + cnet_pils = None + + # prepare mask + latent_image = to_latent_image(upscaled_image, vae) + if noise_mask is not None: + latent_image["noise_mask"] = noise_mask + + refined_latent = latent_image + + # ksampler + for i in range(0, cycle): + ( + model2, + seed2, + steps2, + cfg2, + sampler_name2, + scheduler2, + positive2, + negative2, + _upscaled_latent2, + denoise2, + ) = ( + model, + seed + i, + steps, + cfg, + sampler_name, + scheduler, + positive, + negative, + latent_image, + denoise, + ) + noise = None + + refined_latent = ksampler_wrapper( + model2, + seed2, + steps2, + cfg2, + sampler_name2, + scheduler2, + positive2, + negative2, + refined_latent, + denoise2, + refiner_ratio, + refiner_model, + refiner_clip, + refiner_positive, + refiner_negative, + noise=noise, + scheduler_func=scheduler_func, + pipeline=pipeline, + ) + + # non-latent downscale - latent downscale cause bad quality + try: + # try to decode image normally + refined_image = vae.decode(refined_latent["samples"]) + except Exception: + # usually an out-of-memory exception from the decode, so try a tiled approach + refined_image = vae.decode_tiled( + refined_latent["samples"], + tile_x=64, + tile_y=64, + ) + + # downscale + refined_image = tensor_util.tensor_resize(refined_image, w, h) + + # prevent mixing of device + refined_image = refined_image.cpu() + + # don't convert to latent - latent break image + # preserving pil is much better + return refined_image, cnet_pils + + +class DetailerForEach: + """#### Class for detailing each segment of an image.""" + + @staticmethod + def do_detail( + image: torch.Tensor, + segs: Tuple[torch.Tensor, Any], + model: torch.nn.Module, + clip: Any, + vae: VariationalAE.VAE, + guide_size: int, + guide_size_for_bbox: bool, + max_size: int, + seed: int, + steps: int, + cfg: int, + sampler_name: str, + scheduler: str, + positive: Any, + negative: Any, + denoise: float, + feather: int, + noise_mask: Optional[torch.Tensor], + force_inpaint: bool, + wildcard_opt: Optional[Any] = None, + detailer_hook: Optional[callable] = None, + refiner_ratio: Optional[float] = None, + refiner_model: Optional[torch.nn.Module] = None, + refiner_clip: Optional[Any] = None, + refiner_positive: Optional[Any] = None, + refiner_negative: Optional[Any] = None, + cycle: int = 1, + inpaint_model: bool = False, + noise_mask_feather: int = 0, + scheduler_func_opt: Optional[callable] = None, + pipeline: bool = False, + ) -> Tuple[torch.Tensor, list, list, list, list, Tuple[torch.Tensor, list]]: + """#### Perform detailing on each segment of an image. + + #### Args: + - `image` (torch.Tensor): The input image tensor. + - `segs` (Tuple[torch.Tensor, Any]): The segments. + - `model` (torch.nn.Module): The model. + - `clip` (Any): The clip model. + - `vae` (VariationalAE.VAE): The VAE model. + - `guide_size` (int): The guide size. + - `guide_size_for_bbox` (bool): Whether to use guide size for bbox. + - `max_size` (int): The maximum size. + - `seed` (int): The seed for random noise. + - `steps` (int): The number of steps. + - `cfg` (int): Classifier-Free Guidance Scale. + - `sampler_name` (str): The sampler name. + - `scheduler` (str): The scheduler name. + - `positive` (Any): The positive prompt. + - `negative` (Any): The negative prompt. + - `denoise` (float): The denoise factor. + - `feather` (int): The feather value. + - `noise_mask` (Optional[torch.Tensor]): The noise mask tensor. + - `force_inpaint` (bool): Whether to force inpaint. + - `wildcard_opt` (Optional[Any], optional): The wildcard options. Defaults to None. + - `detailer_hook` (Optional[callable], optional): The detailer hook. Defaults to None. + - `refiner_ratio` (Optional[float], optional): The refiner ratio. Defaults to None. + - `refiner_model` (Optional[torch.nn.Module], optional): The refiner model. Defaults to None. + - `refiner_clip` (Optional[Any], optional): The refiner clip. Defaults to None. + - `refiner_positive` (Optional[Any], optional): The refiner positive prompt. Defaults to None. + - `refiner_negative` (Optional[Any], optional): The refiner negative prompt. Defaults to None. + - `cycle` (int, optional): The number of cycles. Defaults to 1. + - `inpaint_model` (bool, optional): Whether to use inpaint model. Defaults to False. + - `noise_mask_feather` (int, optional): The noise mask feather. Defaults to 0. + - `scheduler_func_opt` (Optional[callable], optional): The scheduler function. Defaults to None. + - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. + + #### Returns: + - `Tuple[torch.Tensor, list, list, list, list, Tuple[torch.Tensor, list]]`: The detailed image tensor, cropped list, enhanced list, enhanced alpha list, cnet PIL list, and new segments. + """ + image = image.clone() + enhanced_alpha_list = [] + enhanced_list = [] + cropped_list = [] + cnet_pil_list = [] + + segs = AD_util.segs_scale_match(segs, image.shape) + new_segs = [] + + wildcard_concat_mode = None + wmode, wildcard_chooser = bbox.process_wildcard_for_segs(wildcard_opt) + + ordered_segs = segs[1] + + if ( + noise_mask_feather > 0 + and "denoise_mask_function" not in model.model_options + ): + model = DifferentialDiffusion().apply(model)[0] + + for i, seg in enumerate(ordered_segs): + cropped_image = AD_util.crop_ndarray4( + image.cpu().numpy(), seg.crop_region + ) # Never use seg.cropped_image to handle overlapping area + cropped_image = tensor_util.to_tensor(cropped_image) + mask = tensor_util.to_tensor(seg.cropped_mask) + mask = tensor_util.tensor_gaussian_blur_mask(mask, feather) + + is_mask_all_zeros = (seg.cropped_mask == 0).all().item() + if is_mask_all_zeros: + print("Detailer: segment skip [empty mask]") + continue + + cropped_mask = seg.cropped_mask + + seg_seed, wildcard_item = wildcard_chooser.get(seg) + + seg_seed = seed + i if seg_seed is None else seg_seed + + cropped_positive = [ + [ + condition, + { + k: ( + crop_condition_mask(v, image, seg.crop_region) + if k == "mask" + else v + ) + for k, v in details.items() + }, + ] + for condition, details in positive + ] + + cropped_negative = [ + [ + condition, + { + k: ( + crop_condition_mask(v, image, seg.crop_region) + if k == "mask" + else v + ) + for k, v in details.items() + }, + ] + for condition, details in negative + ] + + orig_cropped_image = cropped_image.clone() + enhanced_image, cnet_pils = enhance_detail( + cropped_image, + model, + clip, + vae, + guide_size, + guide_size_for_bbox, + max_size, + seg.bbox, + seg_seed, + steps, + cfg, + sampler_name, + scheduler, + cropped_positive, + cropped_negative, + denoise, + cropped_mask, + force_inpaint, + wildcard_opt=wildcard_item, + wildcard_opt_concat_mode=wildcard_concat_mode, + detailer_hook=detailer_hook, + refiner_ratio=refiner_ratio, + refiner_model=refiner_model, + refiner_clip=refiner_clip, + refiner_positive=refiner_positive, + refiner_negative=refiner_negative, + control_net_wrapper=seg.control_net_wrapper, + cycle=cycle, + inpaint_model=inpaint_model, + noise_mask_feather=noise_mask_feather, + scheduler_func=scheduler_func_opt, + pipeline=pipeline, + ) + + if enhanced_image is not None: + # don't latent composite-> converting to latent caused poor quality + # use image paste + image = image.cpu() + enhanced_image = enhanced_image.cpu() + tensor_util.tensor_paste( + image, + enhanced_image, + (seg.crop_region[0], seg.crop_region[1]), + mask, + ) # this code affecting to `cropped_image`. + enhanced_list.append(enhanced_image) + + # Convert enhanced_pil_alpha to RGBA mode + enhanced_image_alpha = tensor_util.tensor_convert_rgba(enhanced_image) + new_seg_image = ( + enhanced_image.numpy() + ) # alpha should not be applied to seg_image + # Apply the mask + mask = tensor_util.tensor_resize( + mask, *tensor_util.tensor_get_size(enhanced_image) + ) + tensor_util.tensor_putalpha(enhanced_image_alpha, mask) + enhanced_alpha_list.append(enhanced_image_alpha) + + cropped_list.append(orig_cropped_image) # NOTE: Don't use `cropped_image` + + new_seg = SEGS.SEG( + new_seg_image, + seg.cropped_mask, + seg.confidence, + seg.crop_region, + seg.bbox, + seg.label, + seg.control_net_wrapper, + ) + new_segs.append(new_seg) + + image_tensor = tensor_util.tensor_convert_rgb(image) + + cropped_list.sort(key=lambda x: x.shape, reverse=True) + enhanced_list.sort(key=lambda x: x.shape, reverse=True) + enhanced_alpha_list.sort(key=lambda x: x.shape, reverse=True) + + return ( + image_tensor, + cropped_list, + enhanced_list, + enhanced_alpha_list, + cnet_pil_list, + (segs[0], new_segs), + ) + + +def empty_pil_tensor(w: int = 64, h: int = 64) -> torch.Tensor: + """#### Create an empty PIL tensor. + + #### Args: + - `w` (int, optional): The width of the tensor. Defaults to 64. + - `h` (int, optional): The height of the tensor. Defaults to 64. + + #### Returns: + - `torch.Tensor`: The empty tensor. + """ + return torch.zeros((1, h, w, 3), dtype=torch.float32) + + +class DetailerForEachTest(DetailerForEach): + """#### Test class for DetailerForEach.""" + + def doit( + self, + image: torch.Tensor, + segs: Any, + model: torch.nn.Module, + clip: Any, + vae: VariationalAE.VAE, + guide_size: int, + guide_size_for: bool, + max_size: int, + seed: int, + steps: int, + cfg: Any, + sampler_name: str, + scheduler: str, + positive: Any, + negative: Any, + denoise: float, + feather: int, + noise_mask: Optional[torch.Tensor], + force_inpaint: bool, + wildcard: Optional[Any], + detailer_hook: Optional[callable] = None, + cycle: int = 1, + inpaint_model: bool = False, + noise_mask_feather: int = 0, + scheduler_func_opt: Optional[callable] = None, + pipeline: bool = False, + ) -> Tuple[torch.Tensor, list, list, list, list]: + """#### Perform detail enhancement for testing. + + #### Args: + - `image` (torch.Tensor): The input image tensor. + - `segs` (Any): The segments. + - `model` (torch.nn.Module): The model. + - `clip` (Any): The clip model. + - `vae` (VariationalAE.VAE): The VAE model. + - `guide_size` (int): The guide size. + - `guide_size_for` (bool): Whether to use guide size for. + - `max_size` (int): The maximum size. + - `seed` (int): The seed for random noise. + - `steps` (int): The number of steps. + - `cfg` (Any): The configuration. + - `sampler_name` (str): The sampler name. + - `scheduler` (str): The scheduler name. + - `positive` (Any): The positive prompt. + - `negative` (Any): The negative prompt. + - `denoise` (float): The denoise factor. + - `feather` (int): The feather value. + - `noise_mask` (Optional[torch.Tensor]): The noise mask tensor. + - `force_inpaint` (bool): Whether to force inpaint. + - `wildcard` (Optional[Any]): The wildcard options. + - `detailer_hook` (Optional[callable], optional): The detailer hook. Defaults to None. + - `cycle` (int, optional): The number of cycles. Defaults to 1. + - `inpaint_model` (bool, optional): Whether to use inpaint model. Defaults to False. + - `noise_mask_feather` (int, optional): The noise mask feather. Defaults to 0. + - `scheduler_func_opt` (Optional[callable], optional): The scheduler function. Defaults to None. + - `pipeline` (bool, optional): Whether to use pipeline. Defaults to False. + + #### Returns: + - `Tuple[torch.Tensor, list, list, list, list]`: The enhanced image tensor, cropped list, cropped enhanced list, cropped enhanced alpha list, and cnet PIL list. + """ + ( + enhanced_img, + cropped, + cropped_enhanced, + cropped_enhanced_alpha, + cnet_pil_list, + new_segs, + ) = DetailerForEach.do_detail( + image, + segs, + model, + clip, + vae, + guide_size, + guide_size_for, + max_size, + seed, + steps, + cfg, + sampler_name, + scheduler, + positive, + negative, + denoise, + feather, + noise_mask, + force_inpaint, + wildcard, + detailer_hook, + cycle=cycle, + inpaint_model=inpaint_model, + noise_mask_feather=noise_mask_feather, + scheduler_func_opt=scheduler_func_opt, + pipeline=pipeline, + ) + + cnet_pil_list = [empty_pil_tensor()] + + return ( + enhanced_img, + cropped, + cropped_enhanced, + cropped_enhanced_alpha, + cnet_pil_list, + ) diff --git a/modules/AutoDetailer/SAM.py b/modules/AutoDetailer/SAM.py index 50e271302ad47ff183a9013b26f987ee9c70a2fa..d22eec83bc9980d578550b1e0f4812c5ec9581e5 100644 --- a/modules/AutoDetailer/SAM.py +++ b/modules/AutoDetailer/SAM.py @@ -1,300 +1,300 @@ -import os -import numpy as np -from segment_anything import SamPredictor, sam_model_registry -import torch - -from modules.AutoDetailer import mask_util -from modules.Device import Device - - -def sam_predict( - predictor: SamPredictor, points: list, plabs: list, bbox: list, threshold: float -) -> list: - """#### Predict masks using SAM. - - #### Args: - - `predictor` (SamPredictor): The SAM predictor. - - `points` (list): List of points. - - `plabs` (list): List of point labels. - - `bbox` (list): Bounding box. - - `threshold` (float): Threshold for mask selection. - - #### Returns: - - `list`: List of predicted masks. - """ - point_coords = None if not points else np.array(points) - point_labels = None if not plabs else np.array(plabs) - - box = np.array([bbox]) if bbox is not None else None - - cur_masks, scores, _ = predictor.predict( - point_coords=point_coords, point_labels=point_labels, box=box - ) - - total_masks = [] - - selected = False - max_score = 0 - max_mask = None - for idx in range(len(scores)): - if scores[idx] > max_score: - max_score = scores[idx] - max_mask = cur_masks[idx] - - if scores[idx] >= threshold: - selected = True - total_masks.append(cur_masks[idx]) - else: - pass - - if not selected and max_mask is not None: - total_masks.append(max_mask) - - return total_masks - - -def is_same_device(a: torch.device, b: torch.device) -> bool: - """#### Check if two devices are the same. - - #### Args: - - `a` (torch.device): The first device. - - `b` (torch.device): The second device. - - #### Returns: - - `bool`: Whether the devices are the same. - """ - a_device = torch.device(a) if isinstance(a, str) else a - b_device = torch.device(b) if isinstance(b, str) else b - return a_device.type == b_device.type and a_device.index == b_device.index - - -class SafeToGPU: - """#### Class to safely move objects to GPU.""" - - def __init__(self, size: int): - self.size = size - - def to_device(self, obj: torch.nn.Module, device: torch.device) -> None: - """#### Move an object to a device. - - #### Args: - - `obj` (torch.nn.Module): The object to move. - - `device` (torch.device): The target device. - """ - if is_same_device(device, "cpu"): - obj.to(device) - else: - if is_same_device(obj.device, "cpu"): # cpu to gpu - Device.free_memory(self.size * 1.3, device) - if Device.get_free_memory(device) > self.size * 1.3: - try: - obj.to(device) - except: - print( - f"WARN: The model is not moved to the '{device}' due to insufficient memory. [1]" - ) - else: - print( - f"WARN: The model is not moved to the '{device}' due to insufficient memory. [2]" - ) - - -class SAMWrapper: - """#### Wrapper class for SAM model.""" - - def __init__( - self, model: torch.nn.Module, is_auto_mode: bool, safe_to_gpu: SafeToGPU = None - ): - self.model = model - self.safe_to_gpu = safe_to_gpu if safe_to_gpu is not None else SafeToGPU() - self.is_auto_mode = is_auto_mode - - def prepare_device(self) -> None: - """#### Prepare the device for the model.""" - if self.is_auto_mode: - device = Device.get_torch_device() - self.safe_to_gpu.to_device(self.model, device=device) - - def release_device(self) -> None: - """#### Release the device from the model.""" - if self.is_auto_mode: - self.model.to(device="cpu") - - def predict( - self, image: np.ndarray, points: list, plabs: list, bbox: list, threshold: float - ) -> list: - """#### Predict masks using the SAM model. - - #### Args: - - `image` (np.ndarray): The input image. - - `points` (list): List of points. - - `plabs` (list): List of point labels. - - `bbox` (list): Bounding box. - - `threshold` (float): Threshold for mask selection. - - #### Returns: - - `list`: List of predicted masks. - """ - predictor = SamPredictor(self.model) - predictor.set_image(image, "RGB") - - return sam_predict(predictor, points, plabs, bbox, threshold) - - -class SAMLoader: - """#### Class to load SAM models.""" - - def load_model(self, model_name: str, device_mode: str = "auto") -> tuple: - """#### Load a SAM model. - - #### Args: - - `model_name` (str): The name of the model. - - `device_mode` (str, optional): The device mode. Defaults to "auto". - - #### Returns: - - `tuple`: The loaded SAM model. - """ - modelname = "./_internal/yolos/" + model_name - - if "vit_h" in model_name: - model_kind = "vit_h" - elif "vit_l" in model_name: - model_kind = "vit_l" - else: - model_kind = "vit_b" - - sam = sam_model_registry[model_kind](checkpoint=modelname) - size = os.path.getsize(modelname) - safe_to = SafeToGPU(size) - - # Unless user explicitly wants to use CPU, we use GPU - device = Device.get_torch_device() if device_mode == "Prefer GPU" else "CPU" - - if device_mode == "Prefer GPU": - safe_to.to_device(sam, device) - - is_auto_mode = device_mode == "AUTO" - - sam_obj = SAMWrapper(sam, is_auto_mode=is_auto_mode, safe_to_gpu=safe_to) - sam.sam_wrapper = sam_obj - - print(f"Loads SAM model: {modelname} (device:{device_mode})") - return (sam,) - - -def make_sam_mask( - sam: SAMWrapper, - segs: tuple, - image: torch.Tensor, - detection_hint: bool, - dilation: int, - threshold: float, - bbox_expansion: int, - mask_hint_threshold: float, - mask_hint_use_negative: bool, -) -> torch.Tensor: - """#### Create a SAM mask. - - #### Args: - - `sam` (SAMWrapper): The SAM wrapper. - - `segs` (tuple): Segmentation information. - - `image` (torch.Tensor): The input image. - - `detection_hint` (bool): Whether to use detection hint. - - `dilation` (int): Dilation value. - - `threshold` (float): Threshold for mask selection. - - `bbox_expansion` (int): Bounding box expansion value. - - `mask_hint_threshold` (float): Mask hint threshold. - - `mask_hint_use_negative` (bool): Whether to use negative mask hint. - - #### Returns: - - `torch.Tensor`: The created SAM mask. - """ - sam_obj = sam.sam_wrapper - sam_obj.prepare_device() - - try: - image = np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) - - total_masks = [] - # seg_shape = segs[0] - segs = segs[1] - for i in range(len(segs)): - bbox = segs[i].bbox - center = mask_util.center_of_bbox(bbox) - x1 = max(bbox[0] - bbox_expansion, 0) - y1 = max(bbox[1] - bbox_expansion, 0) - x2 = min(bbox[2] + bbox_expansion, image.shape[1]) - y2 = min(bbox[3] + bbox_expansion, image.shape[0]) - dilated_bbox = [x1, y1, x2, y2] - points = [] - plabs = [] - points.append(center) - plabs = [1] # 1 = foreground point, 0 = background point - detected_masks = sam_obj.predict( - image, points, plabs, dilated_bbox, threshold - ) - total_masks += detected_masks - - # merge every collected masks - mask = mask_util.combine_masks2(total_masks) - - finally: - sam_obj.release_device() - - if mask is not None: - mask = mask.float() - mask = mask_util.dilate_mask(mask.cpu().numpy(), dilation) - mask = torch.from_numpy(mask) - - mask = mask_util.make_3d_mask(mask) - return mask - else: - return None - - -class SAMDetectorCombined: - """#### Class to combine SAM detection.""" - - def doit( - self, - sam_model: SAMWrapper, - segs: tuple, - image: torch.Tensor, - detection_hint: bool, - dilation: int, - threshold: float, - bbox_expansion: int, - mask_hint_threshold: float, - mask_hint_use_negative: bool, - ) -> tuple: - """#### Combine SAM detection. - - #### Args: - - `sam_model` (SAMWrapper): The SAM wrapper. - - `segs` (tuple): Segmentation information. - - `image` (torch.Tensor): The input image. - - `detection_hint` (bool): Whether to use detection hint. - - `dilation` (int): Dilation value. - - `threshold` (float): Threshold for mask selection. - - `bbox_expansion` (int): Bounding box expansion value. - - `mask_hint_threshold` (float): Mask hint threshold. - - `mask_hint_use_negative` (bool): Whether to use negative mask hint. - - #### Returns: - - `tuple`: The combined SAM detection result. - """ - sam = make_sam_mask( - sam_model, - segs, - image, - detection_hint, - dilation, - threshold, - bbox_expansion, - mask_hint_threshold, - mask_hint_use_negative, - ) - if sam is not None: - return (sam,) - else: - return None +import os +import numpy as np +from segment_anything import SamPredictor, sam_model_registry +import torch + +from modules.AutoDetailer import mask_util +from modules.Device import Device + + +def sam_predict( + predictor: SamPredictor, points: list, plabs: list, bbox: list, threshold: float +) -> list: + """#### Predict masks using SAM. + + #### Args: + - `predictor` (SamPredictor): The SAM predictor. + - `points` (list): List of points. + - `plabs` (list): List of point labels. + - `bbox` (list): Bounding box. + - `threshold` (float): Threshold for mask selection. + + #### Returns: + - `list`: List of predicted masks. + """ + point_coords = None if not points else np.array(points) + point_labels = None if not plabs else np.array(plabs) + + box = np.array([bbox]) if bbox is not None else None + + cur_masks, scores, _ = predictor.predict( + point_coords=point_coords, point_labels=point_labels, box=box + ) + + total_masks = [] + + selected = False + max_score = 0 + max_mask = None + for idx in range(len(scores)): + if scores[idx] > max_score: + max_score = scores[idx] + max_mask = cur_masks[idx] + + if scores[idx] >= threshold: + selected = True + total_masks.append(cur_masks[idx]) + else: + pass + + if not selected and max_mask is not None: + total_masks.append(max_mask) + + return total_masks + + +def is_same_device(a: torch.device, b: torch.device) -> bool: + """#### Check if two devices are the same. + + #### Args: + - `a` (torch.device): The first device. + - `b` (torch.device): The second device. + + #### Returns: + - `bool`: Whether the devices are the same. + """ + a_device = torch.device(a) if isinstance(a, str) else a + b_device = torch.device(b) if isinstance(b, str) else b + return a_device.type == b_device.type and a_device.index == b_device.index + + +class SafeToGPU: + """#### Class to safely move objects to GPU.""" + + def __init__(self, size: int): + self.size = size + + def to_device(self, obj: torch.nn.Module, device: torch.device) -> None: + """#### Move an object to a device. + + #### Args: + - `obj` (torch.nn.Module): The object to move. + - `device` (torch.device): The target device. + """ + if is_same_device(device, "cpu"): + obj.to(device) + else: + if is_same_device(obj.device, "cpu"): # cpu to gpu + Device.free_memory(self.size * 1.3, device) + if Device.get_free_memory(device) > self.size * 1.3: + try: + obj.to(device) + except: + print( + f"WARN: The model is not moved to the '{device}' due to insufficient memory. [1]" + ) + else: + print( + f"WARN: The model is not moved to the '{device}' due to insufficient memory. [2]" + ) + + +class SAMWrapper: + """#### Wrapper class for SAM model.""" + + def __init__( + self, model: torch.nn.Module, is_auto_mode: bool, safe_to_gpu: SafeToGPU = None + ): + self.model = model + self.safe_to_gpu = safe_to_gpu if safe_to_gpu is not None else SafeToGPU() + self.is_auto_mode = is_auto_mode + + def prepare_device(self) -> None: + """#### Prepare the device for the model.""" + if self.is_auto_mode: + device = Device.get_torch_device() + self.safe_to_gpu.to_device(self.model, device=device) + + def release_device(self) -> None: + """#### Release the device from the model.""" + if self.is_auto_mode: + self.model.to(device="cpu") + + def predict( + self, image: np.ndarray, points: list, plabs: list, bbox: list, threshold: float + ) -> list: + """#### Predict masks using the SAM model. + + #### Args: + - `image` (np.ndarray): The input image. + - `points` (list): List of points. + - `plabs` (list): List of point labels. + - `bbox` (list): Bounding box. + - `threshold` (float): Threshold for mask selection. + + #### Returns: + - `list`: List of predicted masks. + """ + predictor = SamPredictor(self.model) + predictor.set_image(image, "RGB") + + return sam_predict(predictor, points, plabs, bbox, threshold) + + +class SAMLoader: + """#### Class to load SAM models.""" + + def load_model(self, model_name: str, device_mode: str = "auto") -> tuple: + """#### Load a SAM model. + + #### Args: + - `model_name` (str): The name of the model. + - `device_mode` (str, optional): The device mode. Defaults to "auto". + + #### Returns: + - `tuple`: The loaded SAM model. + """ + modelname = "./_internal/yolos/" + model_name + + if "vit_h" in model_name: + model_kind = "vit_h" + elif "vit_l" in model_name: + model_kind = "vit_l" + else: + model_kind = "vit_b" + + sam = sam_model_registry[model_kind](checkpoint=modelname) + size = os.path.getsize(modelname) + safe_to = SafeToGPU(size) + + # Unless user explicitly wants to use CPU, we use GPU + device = Device.get_torch_device() if device_mode == "Prefer GPU" else "CPU" + + if device_mode == "Prefer GPU": + safe_to.to_device(sam, device) + + is_auto_mode = device_mode == "AUTO" + + sam_obj = SAMWrapper(sam, is_auto_mode=is_auto_mode, safe_to_gpu=safe_to) + sam.sam_wrapper = sam_obj + + print(f"Loads SAM model: {modelname} (device:{device_mode})") + return (sam,) + + +def make_sam_mask( + sam: SAMWrapper, + segs: tuple, + image: torch.Tensor, + detection_hint: bool, + dilation: int, + threshold: float, + bbox_expansion: int, + mask_hint_threshold: float, + mask_hint_use_negative: bool, +) -> torch.Tensor: + """#### Create a SAM mask. + + #### Args: + - `sam` (SAMWrapper): The SAM wrapper. + - `segs` (tuple): Segmentation information. + - `image` (torch.Tensor): The input image. + - `detection_hint` (bool): Whether to use detection hint. + - `dilation` (int): Dilation value. + - `threshold` (float): Threshold for mask selection. + - `bbox_expansion` (int): Bounding box expansion value. + - `mask_hint_threshold` (float): Mask hint threshold. + - `mask_hint_use_negative` (bool): Whether to use negative mask hint. + + #### Returns: + - `torch.Tensor`: The created SAM mask. + """ + sam_obj = sam.sam_wrapper + sam_obj.prepare_device() + + try: + image = np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + + total_masks = [] + # seg_shape = segs[0] + segs = segs[1] + for i in range(len(segs)): + bbox = segs[i].bbox + center = mask_util.center_of_bbox(bbox) + x1 = max(bbox[0] - bbox_expansion, 0) + y1 = max(bbox[1] - bbox_expansion, 0) + x2 = min(bbox[2] + bbox_expansion, image.shape[1]) + y2 = min(bbox[3] + bbox_expansion, image.shape[0]) + dilated_bbox = [x1, y1, x2, y2] + points = [] + plabs = [] + points.append(center) + plabs = [1] # 1 = foreground point, 0 = background point + detected_masks = sam_obj.predict( + image, points, plabs, dilated_bbox, threshold + ) + total_masks += detected_masks + + # merge every collected masks + mask = mask_util.combine_masks2(total_masks) + + finally: + sam_obj.release_device() + + if mask is not None: + mask = mask.float() + mask = mask_util.dilate_mask(mask.cpu().numpy(), dilation) + mask = torch.from_numpy(mask) + + mask = mask_util.make_3d_mask(mask) + return mask + else: + return None + + +class SAMDetectorCombined: + """#### Class to combine SAM detection.""" + + def doit( + self, + sam_model: SAMWrapper, + segs: tuple, + image: torch.Tensor, + detection_hint: bool, + dilation: int, + threshold: float, + bbox_expansion: int, + mask_hint_threshold: float, + mask_hint_use_negative: bool, + ) -> tuple: + """#### Combine SAM detection. + + #### Args: + - `sam_model` (SAMWrapper): The SAM wrapper. + - `segs` (tuple): Segmentation information. + - `image` (torch.Tensor): The input image. + - `detection_hint` (bool): Whether to use detection hint. + - `dilation` (int): Dilation value. + - `threshold` (float): Threshold for mask selection. + - `bbox_expansion` (int): Bounding box expansion value. + - `mask_hint_threshold` (float): Mask hint threshold. + - `mask_hint_use_negative` (bool): Whether to use negative mask hint. + + #### Returns: + - `tuple`: The combined SAM detection result. + """ + sam = make_sam_mask( + sam_model, + segs, + image, + detection_hint, + dilation, + threshold, + bbox_expansion, + mask_hint_threshold, + mask_hint_use_negative, + ) + if sam is not None: + return (sam,) + else: + return None diff --git a/modules/AutoDetailer/SEGS.py b/modules/AutoDetailer/SEGS.py index b9f6a823edf095f9736880d80093c10b05809e2d..13d3330befc71b55288fa6e318c7aa977d6b9c09 100644 --- a/modules/AutoDetailer/SEGS.py +++ b/modules/AutoDetailer/SEGS.py @@ -1,95 +1,95 @@ -from collections import namedtuple -import numpy as np -import torch -from modules.AutoDetailer import mask_util - -SEG = namedtuple( - "SEG", - [ - "cropped_image", - "cropped_mask", - "confidence", - "crop_region", - "bbox", - "label", - "control_net_wrapper", - ], - defaults=[None], -) - - -def segs_bitwise_and_mask(segs: tuple, mask: torch.Tensor) -> tuple: - """#### Apply bitwise AND operation between segmentation masks and a given mask. - - #### Args: - - `segs` (tuple): A tuple containing segmentation information. - - `mask` (torch.Tensor): The mask tensor. - - #### Returns: - - `tuple`: A tuple containing the original segmentation and the updated items. - """ - mask = mask_util.make_2d_mask(mask) - items = [] - - mask = (mask.cpu().numpy() * 255).astype(np.uint8) - - for seg in segs[1]: - cropped_mask = (seg.cropped_mask * 255).astype(np.uint8) - crop_region = seg.crop_region - - cropped_mask2 = mask[ - crop_region[1] : crop_region[3], crop_region[0] : crop_region[2] - ] - - new_mask = np.bitwise_and(cropped_mask.astype(np.uint8), cropped_mask2) - new_mask = new_mask.astype(np.float32) / 255.0 - - item = SEG( - seg.cropped_image, - new_mask, - seg.confidence, - seg.crop_region, - seg.bbox, - seg.label, - None, - ) - items.append(item) - - return segs[0], items - - -class SegsBitwiseAndMask: - """#### Class to apply bitwise AND operation between segmentation masks and a given mask.""" - - def doit(self, segs: tuple, mask: torch.Tensor) -> tuple: - """#### Apply bitwise AND operation between segmentation masks and a given mask. - - #### Args: - - `segs` (tuple): A tuple containing segmentation information. - - `mask` (torch.Tensor): The mask tensor. - - #### Returns: - - `tuple`: A tuple containing the original segmentation and the updated items. - """ - return (segs_bitwise_and_mask(segs, mask),) - - -class SEGSLabelFilter: - """#### Class to filter segmentation labels.""" - - @staticmethod - def filter(segs: tuple, labels: list) -> tuple: - """#### Filter segmentation labels. - - #### Args: - - `segs` (tuple): A tuple containing segmentation information. - - `labels` (list): A list of labels to filter. - - #### Returns: - - `tuple`: A tuple containing the original segmentation and an empty list. - """ - labels = set([label.strip() for label in labels]) - return ( - segs, - (segs[0], []), - ) +from collections import namedtuple +import numpy as np +import torch +from modules.AutoDetailer import mask_util + +SEG = namedtuple( + "SEG", + [ + "cropped_image", + "cropped_mask", + "confidence", + "crop_region", + "bbox", + "label", + "control_net_wrapper", + ], + defaults=[None], +) + + +def segs_bitwise_and_mask(segs: tuple, mask: torch.Tensor) -> tuple: + """#### Apply bitwise AND operation between segmentation masks and a given mask. + + #### Args: + - `segs` (tuple): A tuple containing segmentation information. + - `mask` (torch.Tensor): The mask tensor. + + #### Returns: + - `tuple`: A tuple containing the original segmentation and the updated items. + """ + mask = mask_util.make_2d_mask(mask) + items = [] + + mask = (mask.cpu().numpy() * 255).astype(np.uint8) + + for seg in segs[1]: + cropped_mask = (seg.cropped_mask * 255).astype(np.uint8) + crop_region = seg.crop_region + + cropped_mask2 = mask[ + crop_region[1] : crop_region[3], crop_region[0] : crop_region[2] + ] + + new_mask = np.bitwise_and(cropped_mask.astype(np.uint8), cropped_mask2) + new_mask = new_mask.astype(np.float32) / 255.0 + + item = SEG( + seg.cropped_image, + new_mask, + seg.confidence, + seg.crop_region, + seg.bbox, + seg.label, + None, + ) + items.append(item) + + return segs[0], items + + +class SegsBitwiseAndMask: + """#### Class to apply bitwise AND operation between segmentation masks and a given mask.""" + + def doit(self, segs: tuple, mask: torch.Tensor) -> tuple: + """#### Apply bitwise AND operation between segmentation masks and a given mask. + + #### Args: + - `segs` (tuple): A tuple containing segmentation information. + - `mask` (torch.Tensor): The mask tensor. + + #### Returns: + - `tuple`: A tuple containing the original segmentation and the updated items. + """ + return (segs_bitwise_and_mask(segs, mask),) + + +class SEGSLabelFilter: + """#### Class to filter segmentation labels.""" + + @staticmethod + def filter(segs: tuple, labels: list) -> tuple: + """#### Filter segmentation labels. + + #### Args: + - `segs` (tuple): A tuple containing segmentation information. + - `labels` (list): A list of labels to filter. + + #### Returns: + - `tuple`: A tuple containing the original segmentation and an empty list. + """ + labels = set([label.strip() for label in labels]) + return ( + segs, + (segs[0], []), + ) diff --git a/modules/AutoDetailer/bbox.py b/modules/AutoDetailer/bbox.py index 6b50e639aafe719acdeec942ca925bfc42394341..2164cde95a7fbca00b965d1d1e547a5e01dc8bdd 100644 --- a/modules/AutoDetailer/bbox.py +++ b/modules/AutoDetailer/bbox.py @@ -1,203 +1,203 @@ -import torch -from ultralytics import YOLO -from modules.AutoDetailer import SEGS, AD_util, tensor_util -from typing import List, Tuple, Optional - - -class UltraBBoxDetector: - """#### Class to detect bounding boxes using a YOLO model.""" - - bbox_model: Optional[YOLO] = None - - def __init__(self, bbox_model: YOLO): - """#### Initialize the UltraBBoxDetector with a YOLO model. - - #### Args: - - `bbox_model` (YOLO): The YOLO model to use for detection. - """ - self.bbox_model = bbox_model - - def detect( - self, - image: torch.Tensor, - threshold: float, - dilation: int, - crop_factor: float, - drop_size: int = 1, - detailer_hook: Optional[callable] = None, - ) -> Tuple[Tuple[int, int], List[SEGS.SEG]]: - """#### Detect bounding boxes in an image. - - #### Args: - - `image` (torch.Tensor): The input image tensor. - - `threshold` (float): The detection threshold. - - `dilation` (int): The dilation factor for masks. - - `crop_factor` (float): The crop factor for bounding boxes. - - `drop_size` (int, optional): The minimum size of bounding boxes to keep. Defaults to 1. - - `detailer_hook` (callable, optional): A hook function for additional processing. Defaults to None. - - #### Returns: - - `Tuple[Tuple[int, int], List[SEGS.SEG]]`: The shape of the image and a list of detected segments. - """ - drop_size = max(drop_size, 1) - detected_results = AD_util.inference_bbox( - self.bbox_model, tensor_util.tensor2pil(image), threshold - ) - segmasks = AD_util.create_segmasks(detected_results) - - if dilation > 0: - segmasks = AD_util.dilate_masks(segmasks, dilation) - - items = [] - h = image.shape[1] - w = image.shape[2] - - for x, label in zip(segmasks, detected_results[0]): - item_bbox = x[0] - item_mask = x[1] - - y1, x1, y2, x2 = item_bbox - - if ( - x2 - x1 > drop_size and y2 - y1 > drop_size - ): # minimum dimension must be (2,2) to avoid squeeze issue - crop_region = AD_util.make_crop_region(w, h, item_bbox, crop_factor) - - cropped_image = AD_util.crop_image(image, crop_region) - cropped_mask = AD_util.crop_ndarray2(item_mask, crop_region) - confidence = x[2] - - item = SEGS.SEG( - cropped_image, - cropped_mask, - confidence, - crop_region, - item_bbox, - label, - None, - ) - - items.append(item) - - shape = image.shape[1], image.shape[2] - segs = shape, items - - return segs - - -class UltraSegmDetector: - """#### Class to detect segments using a YOLO model.""" - - bbox_model: Optional[YOLO] = None - - def __init__(self, bbox_model: YOLO): - """#### Initialize the UltraSegmDetector with a YOLO model. - - #### Args: - - `bbox_model` (YOLO): The YOLO model to use for detection. - """ - self.bbox_model = bbox_model - - -class NO_SEGM_DETECTOR: - """#### Placeholder class for no segment detector.""" - - pass - - -class UltralyticsDetectorProvider: - """#### Class to provide YOLO models for detection.""" - - def doit(self, model_name: str) -> Tuple[UltraBBoxDetector, UltraSegmDetector]: - """#### Load a YOLO model and return detectors. - - #### Args: - - `model_name` (str): The name of the YOLO model to load. - - #### Returns: - - `Tuple[UltraBBoxDetector, UltraSegmDetector]`: The bounding box and segment detectors. - """ - model = AD_util.load_yolo("./_internal/yolos/" + model_name) - return UltraBBoxDetector(model), UltraSegmDetector(model) - - -class BboxDetectorForEach: - """#### Class to detect bounding boxes for each segment.""" - - def doit( - self, - bbox_detector: UltraBBoxDetector, - image: torch.Tensor, - threshold: float, - dilation: int, - crop_factor: float, - drop_size: int, - labels: Optional[str] = None, - detailer_hook: Optional[callable] = None, - ) -> Tuple[Tuple[int, int], List[SEGS.SEG]]: - """#### Detect bounding boxes for each segment in an image. - - #### Args: - - `bbox_detector` (UltraBBoxDetector): The bounding box detector. - - `image` (torch.Tensor): The input image tensor. - - `threshold` (float): The detection threshold. - - `dilation` (int): The dilation factor for masks. - - `crop_factor` (float): The crop factor for bounding boxes. - - `drop_size` (int): The minimum size of bounding boxes to keep. - - `labels` (str, optional): The labels to filter. Defaults to None. - - `detailer_hook` (callable, optional): A hook function for additional processing. Defaults to None. - - #### Returns: - - `Tuple[Tuple[int, int], List[SEGS.SEG]]`: The shape of the image and a list of detected segments. - """ - segs = bbox_detector.detect( - image, threshold, dilation, crop_factor, drop_size, detailer_hook - ) - - if labels is not None and labels != "": - labels = labels.split(",") - if len(labels) > 0: - segs, _ = SEGS.SEGSLabelFilter.filter(segs, labels) - - return segs - - -class WildcardChooser: - """#### Class to choose wildcards for segments.""" - - def __init__(self, items: List[Tuple[None, str]], randomize_when_exhaust: bool): - """#### Initialize the WildcardChooser. - - #### Args: - - `items` (List[Tuple[None, str]]): The list of items to choose from. - - `randomize_when_exhaust` (bool): Whether to randomize when the list is exhausted. - """ - self.i = 0 - self.items = items - self.randomize_when_exhaust = randomize_when_exhaust - - def get(self, seg: SEGS.SEG) -> Tuple[None, str]: - """#### Get the next item from the list. - - #### Args: - - `seg` (SEGS.SEG): The segment. - - #### Returns: - - `Tuple[None, str]`: The next item from the list. - """ - item = self.items[self.i] - self.i += 1 - - return item - - -def process_wildcard_for_segs(wildcard: str) -> Tuple[None, WildcardChooser]: - """#### Process a wildcard for segments. - - #### Args: - - `wildcard` (str): The wildcard. - - #### Returns: - - `Tuple[None, WildcardChooser]`: The processed wildcard and a WildcardChooser. - """ - return None, WildcardChooser([(None, wildcard)], False) +import torch +from ultralytics import YOLO +from modules.AutoDetailer import SEGS, AD_util, tensor_util +from typing import List, Tuple, Optional + + +class UltraBBoxDetector: + """#### Class to detect bounding boxes using a YOLO model.""" + + bbox_model: Optional[YOLO] = None + + def __init__(self, bbox_model: YOLO): + """#### Initialize the UltraBBoxDetector with a YOLO model. + + #### Args: + - `bbox_model` (YOLO): The YOLO model to use for detection. + """ + self.bbox_model = bbox_model + + def detect( + self, + image: torch.Tensor, + threshold: float, + dilation: int, + crop_factor: float, + drop_size: int = 1, + detailer_hook: Optional[callable] = None, + ) -> Tuple[Tuple[int, int], List[SEGS.SEG]]: + """#### Detect bounding boxes in an image. + + #### Args: + - `image` (torch.Tensor): The input image tensor. + - `threshold` (float): The detection threshold. + - `dilation` (int): The dilation factor for masks. + - `crop_factor` (float): The crop factor for bounding boxes. + - `drop_size` (int, optional): The minimum size of bounding boxes to keep. Defaults to 1. + - `detailer_hook` (callable, optional): A hook function for additional processing. Defaults to None. + + #### Returns: + - `Tuple[Tuple[int, int], List[SEGS.SEG]]`: The shape of the image and a list of detected segments. + """ + drop_size = max(drop_size, 1) + detected_results = AD_util.inference_bbox( + self.bbox_model, tensor_util.tensor2pil(image), threshold + ) + segmasks = AD_util.create_segmasks(detected_results) + + if dilation > 0: + segmasks = AD_util.dilate_masks(segmasks, dilation) + + items = [] + h = image.shape[1] + w = image.shape[2] + + for x, label in zip(segmasks, detected_results[0]): + item_bbox = x[0] + item_mask = x[1] + + y1, x1, y2, x2 = item_bbox + + if ( + x2 - x1 > drop_size and y2 - y1 > drop_size + ): # minimum dimension must be (2,2) to avoid squeeze issue + crop_region = AD_util.make_crop_region(w, h, item_bbox, crop_factor) + + cropped_image = AD_util.crop_image(image, crop_region) + cropped_mask = AD_util.crop_ndarray2(item_mask, crop_region) + confidence = x[2] + + item = SEGS.SEG( + cropped_image, + cropped_mask, + confidence, + crop_region, + item_bbox, + label, + None, + ) + + items.append(item) + + shape = image.shape[1], image.shape[2] + segs = shape, items + + return segs + + +class UltraSegmDetector: + """#### Class to detect segments using a YOLO model.""" + + bbox_model: Optional[YOLO] = None + + def __init__(self, bbox_model: YOLO): + """#### Initialize the UltraSegmDetector with a YOLO model. + + #### Args: + - `bbox_model` (YOLO): The YOLO model to use for detection. + """ + self.bbox_model = bbox_model + + +class NO_SEGM_DETECTOR: + """#### Placeholder class for no segment detector.""" + + pass + + +class UltralyticsDetectorProvider: + """#### Class to provide YOLO models for detection.""" + + def doit(self, model_name: str) -> Tuple[UltraBBoxDetector, UltraSegmDetector]: + """#### Load a YOLO model and return detectors. + + #### Args: + - `model_name` (str): The name of the YOLO model to load. + + #### Returns: + - `Tuple[UltraBBoxDetector, UltraSegmDetector]`: The bounding box and segment detectors. + """ + model = AD_util.load_yolo("./_internal/yolos/" + model_name) + return UltraBBoxDetector(model), UltraSegmDetector(model) + + +class BboxDetectorForEach: + """#### Class to detect bounding boxes for each segment.""" + + def doit( + self, + bbox_detector: UltraBBoxDetector, + image: torch.Tensor, + threshold: float, + dilation: int, + crop_factor: float, + drop_size: int, + labels: Optional[str] = None, + detailer_hook: Optional[callable] = None, + ) -> Tuple[Tuple[int, int], List[SEGS.SEG]]: + """#### Detect bounding boxes for each segment in an image. + + #### Args: + - `bbox_detector` (UltraBBoxDetector): The bounding box detector. + - `image` (torch.Tensor): The input image tensor. + - `threshold` (float): The detection threshold. + - `dilation` (int): The dilation factor for masks. + - `crop_factor` (float): The crop factor for bounding boxes. + - `drop_size` (int): The minimum size of bounding boxes to keep. + - `labels` (str, optional): The labels to filter. Defaults to None. + - `detailer_hook` (callable, optional): A hook function for additional processing. Defaults to None. + + #### Returns: + - `Tuple[Tuple[int, int], List[SEGS.SEG]]`: The shape of the image and a list of detected segments. + """ + segs = bbox_detector.detect( + image, threshold, dilation, crop_factor, drop_size, detailer_hook + ) + + if labels is not None and labels != "": + labels = labels.split(",") + if len(labels) > 0: + segs, _ = SEGS.SEGSLabelFilter.filter(segs, labels) + + return segs + + +class WildcardChooser: + """#### Class to choose wildcards for segments.""" + + def __init__(self, items: List[Tuple[None, str]], randomize_when_exhaust: bool): + """#### Initialize the WildcardChooser. + + #### Args: + - `items` (List[Tuple[None, str]]): The list of items to choose from. + - `randomize_when_exhaust` (bool): Whether to randomize when the list is exhausted. + """ + self.i = 0 + self.items = items + self.randomize_when_exhaust = randomize_when_exhaust + + def get(self, seg: SEGS.SEG) -> Tuple[None, str]: + """#### Get the next item from the list. + + #### Args: + - `seg` (SEGS.SEG): The segment. + + #### Returns: + - `Tuple[None, str]`: The next item from the list. + """ + item = self.items[self.i] + self.i += 1 + + return item + + +def process_wildcard_for_segs(wildcard: str) -> Tuple[None, WildcardChooser]: + """#### Process a wildcard for segments. + + #### Args: + - `wildcard` (str): The wildcard. + + #### Returns: + - `Tuple[None, WildcardChooser]`: The processed wildcard and a WildcardChooser. + """ + return None, WildcardChooser([(None, wildcard)], False) diff --git a/modules/AutoDetailer/mask_util.py b/modules/AutoDetailer/mask_util.py index 90113debefc5f2a54bf8c502512c3ecd7b9678e4..eb145bdab7f39cec61e837d643a04905359e3b9a 100644 --- a/modules/AutoDetailer/mask_util.py +++ b/modules/AutoDetailer/mask_util.py @@ -1,80 +1,80 @@ -import numpy as np -import torch - - -def center_of_bbox(bbox: list) -> tuple[float, float]: - """#### Calculate the center of a bounding box. - - #### Args: - - `bbox` (list): The bounding box coordinates [x1, y1, x2, y2]. - - #### Returns: - - `tuple[float, float]`: The center coordinates (x, y). - """ - w, h = bbox[2] - bbox[0], bbox[3] - bbox[1] - return bbox[0] + w / 2, bbox[1] + h / 2 - - -def make_2d_mask(mask: torch.Tensor) -> torch.Tensor: - """#### Convert a mask to 2D. - - #### Args: - - `mask` (torch.Tensor): The input mask tensor. - - #### Returns: - - `torch.Tensor`: The 2D mask tensor. - """ - if len(mask.shape) == 4: - return mask.squeeze(0).squeeze(0) - elif len(mask.shape) == 3: - return mask.squeeze(0) - return mask - - -def combine_masks2(masks: list) -> torch.Tensor | None: - """#### Combine multiple masks into one. - - #### Args: - - `masks` (list): A list of mask tensors. - - #### Returns: - - `torch.Tensor | None`: The combined mask tensor or None if no masks are provided. - """ - try: - mask = torch.from_numpy(np.array(masks[0]).astype(np.uint8)) - except: - print("No Human Detected") - return None - return mask - - -def dilate_mask( - mask: torch.Tensor, dilation_factor: int, iter: int = 1 -) -> torch.Tensor: - """#### Dilate a mask. - - #### Args: - - `mask` (torch.Tensor): The input mask tensor. - - `dilation_factor` (int): The dilation factor. - - `iter` (int, optional): The number of iterations. Defaults to 1. - - #### Returns: - - `torch.Tensor`: The dilated mask tensor. - """ - return make_2d_mask(mask) - - -def make_3d_mask(mask: torch.Tensor) -> torch.Tensor: - """#### Convert a mask to 3D. - - #### Args: - - `mask` (torch.Tensor): The input mask tensor. - - #### Returns: - - `torch.Tensor`: The 3D mask tensor. - """ - if len(mask.shape) == 4: - return mask.squeeze(0) - elif len(mask.shape) == 2: - return mask.unsqueeze(0) - return mask +import numpy as np +import torch + + +def center_of_bbox(bbox: list) -> tuple[float, float]: + """#### Calculate the center of a bounding box. + + #### Args: + - `bbox` (list): The bounding box coordinates [x1, y1, x2, y2]. + + #### Returns: + - `tuple[float, float]`: The center coordinates (x, y). + """ + w, h = bbox[2] - bbox[0], bbox[3] - bbox[1] + return bbox[0] + w / 2, bbox[1] + h / 2 + + +def make_2d_mask(mask: torch.Tensor) -> torch.Tensor: + """#### Convert a mask to 2D. + + #### Args: + - `mask` (torch.Tensor): The input mask tensor. + + #### Returns: + - `torch.Tensor`: The 2D mask tensor. + """ + if len(mask.shape) == 4: + return mask.squeeze(0).squeeze(0) + elif len(mask.shape) == 3: + return mask.squeeze(0) + return mask + + +def combine_masks2(masks: list) -> torch.Tensor | None: + """#### Combine multiple masks into one. + + #### Args: + - `masks` (list): A list of mask tensors. + + #### Returns: + - `torch.Tensor | None`: The combined mask tensor or None if no masks are provided. + """ + try: + mask = torch.from_numpy(np.array(masks[0]).astype(np.uint8)) + except: + print("No Human Detected") + return None + return mask + + +def dilate_mask( + mask: torch.Tensor, dilation_factor: int, iter: int = 1 +) -> torch.Tensor: + """#### Dilate a mask. + + #### Args: + - `mask` (torch.Tensor): The input mask tensor. + - `dilation_factor` (int): The dilation factor. + - `iter` (int, optional): The number of iterations. Defaults to 1. + + #### Returns: + - `torch.Tensor`: The dilated mask tensor. + """ + return make_2d_mask(mask) + + +def make_3d_mask(mask: torch.Tensor) -> torch.Tensor: + """#### Convert a mask to 3D. + + #### Args: + - `mask` (torch.Tensor): The input mask tensor. + + #### Returns: + - `torch.Tensor`: The 3D mask tensor. + """ + if len(mask.shape) == 4: + return mask.squeeze(0) + elif len(mask.shape) == 2: + return mask.unsqueeze(0) + return mask diff --git a/modules/AutoDetailer/tensor_util.py b/modules/AutoDetailer/tensor_util.py index 8f7282dc83c9875ce401edb3fcd8c95332fccac7..f4b0d4c1f875451c50d814eec5489ea4b4a2bd3f 100644 --- a/modules/AutoDetailer/tensor_util.py +++ b/modules/AutoDetailer/tensor_util.py @@ -1,253 +1,253 @@ -import numpy as np -import torch -from PIL import Image -import torchvision - -from modules.Device import Device - - -def _tensor_check_image(image: torch.Tensor) -> None: - """#### Check if the input is a valid tensor image. - - #### Args: - - `image` (torch.Tensor): The input tensor image. - """ - return - - -def tensor2pil(image: torch.Tensor) -> Image.Image: - """#### Convert a tensor to a PIL image. - - #### Args: - - `image` (torch.Tensor): The input tensor. - - #### Returns: - - `Image.Image`: The converted PIL image. - """ - _tensor_check_image(image) - return Image.fromarray( - np.clip(255.0 * image.cpu().numpy().squeeze(0), 0, 255).astype(np.uint8) - ) - - -def general_tensor_resize(image: torch.Tensor, w: int, h: int) -> torch.Tensor: - """#### Resize a tensor image using bilinear interpolation. - - #### Args: - - `image` (torch.Tensor): The input tensor image. - - `w` (int): The target width. - - `h` (int): The target height. - - #### Returns: - - `torch.Tensor`: The resized tensor image. - """ - _tensor_check_image(image) - image = image.permute(0, 3, 1, 2) - image = torch.nn.functional.interpolate(image, size=(h, w), mode="bilinear") - image = image.permute(0, 2, 3, 1) - return image - - -def pil2tensor(image: Image.Image) -> torch.Tensor: - """#### Convert a PIL image to a tensor. - - #### Args: - - `image` (Image.Image): The input PIL image. - - #### Returns: - - `torch.Tensor`: The converted tensor. - """ - return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) - - -class TensorBatchBuilder: - """#### Class for building a batch of tensors.""" - - def __init__(self): - self.tensor: torch.Tensor | None = None - - def concat(self, new_tensor: torch.Tensor) -> None: - """#### Concatenate a new tensor to the batch. - - #### Args: - - `new_tensor` (torch.Tensor): The new tensor to concatenate. - """ - self.tensor = new_tensor - - -LANCZOS = Image.Resampling.LANCZOS if hasattr(Image, "Resampling") else Image.LANCZOS - - -def tensor_resize(image: torch.Tensor, w: int, h: int) -> torch.Tensor: - """#### Resize a tensor image. - - #### Args: - - `image` (torch.Tensor): The input tensor image. - - `w` (int): The target width. - - `h` (int): The target height. - - #### Returns: - - `torch.Tensor`: The resized tensor image. - """ - _tensor_check_image(image) - if image.shape[3] >= 3: - scaled_images = TensorBatchBuilder() - for single_image in image: - single_image = single_image.unsqueeze(0) - single_pil = tensor2pil(single_image) - scaled_pil = single_pil.resize((w, h), resample=LANCZOS) - - single_image = pil2tensor(scaled_pil) - scaled_images.concat(single_image) - - return scaled_images.tensor - else: - return general_tensor_resize(image, w, h) - - -def tensor_paste( - image1: torch.Tensor, - image2: torch.Tensor, - left_top: tuple[int, int], - mask: torch.Tensor, -) -> None: - """#### Paste one tensor image onto another using a mask. - - #### Args: - - `image1` (torch.Tensor): The base tensor image. - - `image2` (torch.Tensor): The tensor image to paste. - - `left_top` (tuple[int, int]): The top-left corner where the image2 will be pasted. - - `mask` (torch.Tensor): The mask tensor. - """ - _tensor_check_image(image1) - _tensor_check_image(image2) - _tensor_check_mask(mask) - - x, y = left_top - _, h1, w1, _ = image1.shape - _, h2, w2, _ = image2.shape - - # calculate image patch size - w = min(w1, x + w2) - x - h = min(h1, y + h2) - y - - mask = mask[:, :h, :w, :] - image1[:, y : y + h, x : x + w, :] = (1 - mask) * image1[ - :, y : y + h, x : x + w, : - ] + mask * image2[:, :h, :w, :] - return - - -def tensor_convert_rgba(image: torch.Tensor, prefer_copy: bool = True) -> torch.Tensor: - """#### Convert a tensor image to RGBA format. - - #### Args: - - `image` (torch.Tensor): The input tensor image. - - `prefer_copy` (bool, optional): Whether to prefer copying the tensor. Defaults to True. - - #### Returns: - - `torch.Tensor`: The converted RGBA tensor image. - """ - _tensor_check_image(image) - alpha = torch.ones((*image.shape[:-1], 1)) - return torch.cat((image, alpha), axis=-1) - - -def tensor_convert_rgb(image: torch.Tensor, prefer_copy: bool = True) -> torch.Tensor: - """#### Convert a tensor image to RGB format. - - #### Args: - - `image` (torch.Tensor): The input tensor image. - - `prefer_copy` (bool, optional): Whether to prefer copying the tensor. Defaults to True. - - #### Returns: - - `torch.Tensor`: The converted RGB tensor image. - """ - _tensor_check_image(image) - return image - - -def tensor_get_size(image: torch.Tensor) -> tuple[int, int]: - """#### Get the size of a tensor image. - - #### Args: - - `image` (torch.Tensor): The input tensor image. - - #### Returns: - - `tuple[int, int]`: The width and height of the tensor image. - """ - _tensor_check_image(image) - _, h, w, _ = image.shape - return (w, h) - - -def tensor_putalpha(image: torch.Tensor, mask: torch.Tensor) -> None: - """#### Add an alpha channel to a tensor image using a mask. - - #### Args: - - `image` (torch.Tensor): The input tensor image. - - `mask` (torch.Tensor): The mask tensor. - """ - _tensor_check_image(image) - _tensor_check_mask(mask) - image[..., -1] = mask[..., 0] - - -def _tensor_check_mask(mask: torch.Tensor) -> None: - """#### Check if the input is a valid tensor mask. - - #### Args: - - `mask` (torch.Tensor): The input tensor mask. - """ - return - - -def tensor_gaussian_blur_mask( - mask: torch.Tensor | np.ndarray, kernel_size: int, sigma: float = 10.0 -) -> torch.Tensor: - """#### Apply Gaussian blur to a tensor mask. - - #### Args: - - `mask` (torch.Tensor | np.ndarray): The input tensor mask. - - `kernel_size` (int): The size of the Gaussian kernel. - - `sigma` (float, optional): The standard deviation of the Gaussian kernel. Defaults to 10.0. - - #### Returns: - - `torch.Tensor`: The blurred tensor mask. - """ - if isinstance(mask, np.ndarray): - mask = torch.from_numpy(mask) - - if mask.ndim == 2: - mask = mask[None, ..., None] - - _tensor_check_mask(mask) - - kernel_size = kernel_size * 2 + 1 - - prev_device = mask.device - device = Device.get_torch_device() - mask.to(device) - - # apply gaussian blur - mask = mask[:, None, ..., 0] - blurred_mask = torchvision.transforms.GaussianBlur( - kernel_size=kernel_size, sigma=sigma - )(mask) - blurred_mask = blurred_mask[:, 0, ..., None] - - blurred_mask.to(prev_device) - - return blurred_mask - - -def to_tensor(image: np.ndarray) -> torch.Tensor: - """#### Convert a numpy array to a tensor. - - #### Args: - - `image` (np.ndarray): The input numpy array. - - #### Returns: - - `torch.Tensor`: The converted tensor. - """ - return torch.from_numpy(image) +import numpy as np +import torch +from PIL import Image +import torchvision + +from modules.Device import Device + + +def _tensor_check_image(image: torch.Tensor) -> None: + """#### Check if the input is a valid tensor image. + + #### Args: + - `image` (torch.Tensor): The input tensor image. + """ + return + + +def tensor2pil(image: torch.Tensor) -> Image.Image: + """#### Convert a tensor to a PIL image. + + #### Args: + - `image` (torch.Tensor): The input tensor. + + #### Returns: + - `Image.Image`: The converted PIL image. + """ + _tensor_check_image(image) + return Image.fromarray( + np.clip(255.0 * image.cpu().numpy().squeeze(0), 0, 255).astype(np.uint8) + ) + + +def general_tensor_resize(image: torch.Tensor, w: int, h: int) -> torch.Tensor: + """#### Resize a tensor image using bilinear interpolation. + + #### Args: + - `image` (torch.Tensor): The input tensor image. + - `w` (int): The target width. + - `h` (int): The target height. + + #### Returns: + - `torch.Tensor`: The resized tensor image. + """ + _tensor_check_image(image) + image = image.permute(0, 3, 1, 2) + image = torch.nn.functional.interpolate(image, size=(h, w), mode="bilinear") + image = image.permute(0, 2, 3, 1) + return image + + +def pil2tensor(image: Image.Image) -> torch.Tensor: + """#### Convert a PIL image to a tensor. + + #### Args: + - `image` (Image.Image): The input PIL image. + + #### Returns: + - `torch.Tensor`: The converted tensor. + """ + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + + +class TensorBatchBuilder: + """#### Class for building a batch of tensors.""" + + def __init__(self): + self.tensor: torch.Tensor | None = None + + def concat(self, new_tensor: torch.Tensor) -> None: + """#### Concatenate a new tensor to the batch. + + #### Args: + - `new_tensor` (torch.Tensor): The new tensor to concatenate. + """ + self.tensor = new_tensor + + +LANCZOS = Image.Resampling.LANCZOS if hasattr(Image, "Resampling") else Image.LANCZOS + + +def tensor_resize(image: torch.Tensor, w: int, h: int) -> torch.Tensor: + """#### Resize a tensor image. + + #### Args: + - `image` (torch.Tensor): The input tensor image. + - `w` (int): The target width. + - `h` (int): The target height. + + #### Returns: + - `torch.Tensor`: The resized tensor image. + """ + _tensor_check_image(image) + if image.shape[3] >= 3: + scaled_images = TensorBatchBuilder() + for single_image in image: + single_image = single_image.unsqueeze(0) + single_pil = tensor2pil(single_image) + scaled_pil = single_pil.resize((w, h), resample=LANCZOS) + + single_image = pil2tensor(scaled_pil) + scaled_images.concat(single_image) + + return scaled_images.tensor + else: + return general_tensor_resize(image, w, h) + + +def tensor_paste( + image1: torch.Tensor, + image2: torch.Tensor, + left_top: tuple[int, int], + mask: torch.Tensor, +) -> None: + """#### Paste one tensor image onto another using a mask. + + #### Args: + - `image1` (torch.Tensor): The base tensor image. + - `image2` (torch.Tensor): The tensor image to paste. + - `left_top` (tuple[int, int]): The top-left corner where the image2 will be pasted. + - `mask` (torch.Tensor): The mask tensor. + """ + _tensor_check_image(image1) + _tensor_check_image(image2) + _tensor_check_mask(mask) + + x, y = left_top + _, h1, w1, _ = image1.shape + _, h2, w2, _ = image2.shape + + # calculate image patch size + w = min(w1, x + w2) - x + h = min(h1, y + h2) - y + + mask = mask[:, :h, :w, :] + image1[:, y : y + h, x : x + w, :] = (1 - mask) * image1[ + :, y : y + h, x : x + w, : + ] + mask * image2[:, :h, :w, :] + return + + +def tensor_convert_rgba(image: torch.Tensor, prefer_copy: bool = True) -> torch.Tensor: + """#### Convert a tensor image to RGBA format. + + #### Args: + - `image` (torch.Tensor): The input tensor image. + - `prefer_copy` (bool, optional): Whether to prefer copying the tensor. Defaults to True. + + #### Returns: + - `torch.Tensor`: The converted RGBA tensor image. + """ + _tensor_check_image(image) + alpha = torch.ones((*image.shape[:-1], 1)) + return torch.cat((image, alpha), axis=-1) + + +def tensor_convert_rgb(image: torch.Tensor, prefer_copy: bool = True) -> torch.Tensor: + """#### Convert a tensor image to RGB format. + + #### Args: + - `image` (torch.Tensor): The input tensor image. + - `prefer_copy` (bool, optional): Whether to prefer copying the tensor. Defaults to True. + + #### Returns: + - `torch.Tensor`: The converted RGB tensor image. + """ + _tensor_check_image(image) + return image + + +def tensor_get_size(image: torch.Tensor) -> tuple[int, int]: + """#### Get the size of a tensor image. + + #### Args: + - `image` (torch.Tensor): The input tensor image. + + #### Returns: + - `tuple[int, int]`: The width and height of the tensor image. + """ + _tensor_check_image(image) + _, h, w, _ = image.shape + return (w, h) + + +def tensor_putalpha(image: torch.Tensor, mask: torch.Tensor) -> None: + """#### Add an alpha channel to a tensor image using a mask. + + #### Args: + - `image` (torch.Tensor): The input tensor image. + - `mask` (torch.Tensor): The mask tensor. + """ + _tensor_check_image(image) + _tensor_check_mask(mask) + image[..., -1] = mask[..., 0] + + +def _tensor_check_mask(mask: torch.Tensor) -> None: + """#### Check if the input is a valid tensor mask. + + #### Args: + - `mask` (torch.Tensor): The input tensor mask. + """ + return + + +def tensor_gaussian_blur_mask( + mask: torch.Tensor | np.ndarray, kernel_size: int, sigma: float = 10.0 +) -> torch.Tensor: + """#### Apply Gaussian blur to a tensor mask. + + #### Args: + - `mask` (torch.Tensor | np.ndarray): The input tensor mask. + - `kernel_size` (int): The size of the Gaussian kernel. + - `sigma` (float, optional): The standard deviation of the Gaussian kernel. Defaults to 10.0. + + #### Returns: + - `torch.Tensor`: The blurred tensor mask. + """ + if isinstance(mask, np.ndarray): + mask = torch.from_numpy(mask) + + if mask.ndim == 2: + mask = mask[None, ..., None] + + _tensor_check_mask(mask) + + kernel_size = kernel_size * 2 + 1 + + prev_device = mask.device + device = Device.get_torch_device() + mask.to(device) + + # apply gaussian blur + mask = mask[:, None, ..., 0] + blurred_mask = torchvision.transforms.GaussianBlur( + kernel_size=kernel_size, sigma=sigma + )(mask) + blurred_mask = blurred_mask[:, 0, ..., None] + + blurred_mask.to(prev_device) + + return blurred_mask + + +def to_tensor(image: np.ndarray) -> torch.Tensor: + """#### Convert a numpy array to a tensor. + + #### Args: + - `image` (np.ndarray): The input numpy array. + + #### Returns: + - `torch.Tensor`: The converted tensor. + """ + return torch.from_numpy(image) diff --git a/modules/AutoEncoders/ResBlock.py b/modules/AutoEncoders/ResBlock.py index 0c1c2957ae4266797e824535fed944a2deb54aac..cc1323f696bd1bb519733138c22dd3ef93e63b53 100644 --- a/modules/AutoEncoders/ResBlock.py +++ b/modules/AutoEncoders/ResBlock.py @@ -1,406 +1,406 @@ -from abc import abstractmethod -from typing import Optional, Any, Dict - -import torch -from modules.NeuralNetwork import transformer -import torch.nn as nn -import torch.nn.functional as F - -from modules.Attention import Attention -from modules.cond import cast -from modules.sample import sampling_util - - -oai_ops = cast.disable_weight_init - - -class TimestepBlock1(nn.Module): - """#### Abstract class representing a timestep block.""" - - @abstractmethod - def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the timestep block. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `emb` (torch.Tensor): The embedding tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - pass - - -def forward_timestep_embed1( - ts: nn.ModuleList, - x: torch.Tensor, - emb: torch.Tensor, - context: Optional[torch.Tensor] = None, - transformer_options: Optional[Dict[str, Any]] = {}, - output_shape: Optional[torch.Size] = None, - time_context: Optional[torch.Tensor] = None, - num_video_frames: Optional[int] = None, - image_only_indicator: Optional[bool] = None, -) -> torch.Tensor: - """#### Forward pass for timestep embedding. - - #### Args: - - `ts` (nn.ModuleList): The list of timestep blocks. - - `x` (torch.Tensor): The input tensor. - - `emb` (torch.Tensor): The embedding tensor. - - `context` (torch.Tensor, optional): The context tensor. Defaults to None. - - `transformer_options` (dict, optional): The transformer options. Defaults to {}. - - `output_shape` (torch.Size, optional): The output shape. Defaults to None. - - `time_context` (torch.Tensor, optional): The time context tensor. Defaults to None. - - `num_video_frames` (int, optional): The number of video frames. Defaults to None. - - `image_only_indicator` (bool, optional): The image only indicator. Defaults to None. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - for layer in ts: - if isinstance(layer, TimestepBlock1): - x = layer(x, emb) - elif isinstance(layer, transformer.SpatialTransformer): - x = layer(x, context, transformer_options) - if "transformer_index" in transformer_options: - transformer_options["transformer_index"] += 1 - elif isinstance(layer, Upsample1): - x = layer(x, output_shape=output_shape) - else: - x = layer(x) - return x - - -class Upsample1(nn.Module): - """#### Class representing an upsample layer.""" - - def __init__( - self, - channels: int, - use_conv: bool, - dims: int = 2, - out_channels: Optional[int] = None, - padding: int = 1, - dtype: Optional[torch.dtype] = None, - device: Optional[torch.device] = None, - operations: Any = oai_ops, - ): - """#### Initialize the upsample layer. - - #### Args: - - `channels` (int): The number of input channels. - - `use_conv` (bool): Whether to use convolution. - - `dims` (int, optional): The number of dimensions. Defaults to 2. - - `out_channels` (int, optional): The number of output channels. Defaults to None. - - `padding` (int, optional): The padding size. Defaults to 1. - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - - `device` (torch.device, optional): The device. Defaults to None. - - `operations` (any, optional): The operations. Defaults to oai_ops. - """ - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - if use_conv: - self.conv = operations.conv_nd( - dims, - self.channels, - self.out_channels, - 3, - padding=padding, - dtype=dtype, - device=device, - ) - - def forward( - self, x: torch.Tensor, output_shape: Optional[torch.Size] = None - ) -> torch.Tensor: - """#### Forward pass for the upsample layer. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `output_shape` (torch.Size, optional): The output shape. Defaults to None. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - assert x.shape[1] == self.channels - shape = [x.shape[2] * 2, x.shape[3] * 2] - if output_shape is not None: - shape[0] = output_shape[2] - shape[1] = output_shape[3] - - x = F.interpolate(x, size=shape, mode="nearest") - if self.use_conv: - x = self.conv(x) - return x - - -class Downsample1(nn.Module): - """#### Class representing a downsample layer.""" - - def __init__( - self, - channels: int, - use_conv: bool, - dims: int = 2, - out_channels: Optional[int] = None, - padding: int = 1, - dtype: Optional[torch.dtype] = None, - device: Optional[torch.device] = None, - operations: Any = oai_ops, - ): - """#### Initialize the downsample layer. - - #### Args: - - `channels` (int): The number of input channels. - - `use_conv` (bool): Whether to use convolution. - - `dims` (int, optional): The number of dimensions. Defaults to 2. - - `out_channels` (int, optional): The number of output channels. Defaults to None. - - `padding` (int, optional): The padding size. Defaults to 1. - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - - `device` (torch.device, optional): The device. Defaults to None. - - `operations` (any, optional): The operations. Defaults to oai_ops. - """ - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - stride = 2 if dims != 3 else (1, 2, 2) - self.op = operations.conv_nd( - dims, - self.channels, - self.out_channels, - 3, - stride=stride, - padding=padding, - dtype=dtype, - device=device, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the downsample layer. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - assert x.shape[1] == self.channels - return self.op(x) - - -class ResBlock1(TimestepBlock1): - """#### Class representing a residual block layer.""" - - def __init__( - self, - channels: int, - emb_channels: int, - dropout: float, - out_channels: Optional[int] = None, - use_conv: bool = False, - use_scale_shift_norm: bool = False, - dims: int = 2, - use_checkpoint: bool = False, - up: bool = False, - down: bool = False, - kernel_size: int = 3, - exchange_temb_dims: bool = False, - skip_t_emb: bool = False, - dtype: Optional[torch.dtype] = None, - device: Optional[torch.device] = None, - operations: Any = oai_ops, - ): - """#### Initialize the residual block layer. - - #### Args: - - `channels` (int): The number of input channels. - - `emb_channels` (int): The number of embedding channels. - - `dropout` (float): The dropout rate. - - `out_channels` (int, optional): The number of output channels. Defaults to None. - - `use_conv` (bool, optional): Whether to use convolution. Defaults to False. - - `use_scale_shift_norm` (bool, optional): Whether to use scale shift normalization. Defaults to False. - - `dims` (int, optional): The number of dimensions. Defaults to 2. - - `use_checkpoint` (bool, optional): Whether to use checkpointing. Defaults to False. - - `up` (bool, optional): Whether to use upsampling. Defaults to False. - - `down` (bool, optional): Whether to use downsampling. Defaults to False. - - `kernel_size` (int, optional): The kernel size. Defaults to 3. - - `exchange_temb_dims` (bool, optional): Whether to exchange embedding dimensions. Defaults to False. - - `skip_t_emb` (bool, optional): Whether to skip embedding. Defaults to False. - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - - `device` (torch.device, optional): The device. Defaults to None. - - `operations` (any, optional): The operations. Defaults to oai_ops. - """ - super().__init__() - self.channels = channels - self.emb_channels = emb_channels - self.dropout = dropout - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.use_checkpoint = use_checkpoint - self.use_scale_shift_norm = use_scale_shift_norm - self.exchange_temb_dims = exchange_temb_dims - - padding = kernel_size // 2 - - self.in_layers = nn.Sequential( - operations.GroupNorm(32, channels, dtype=dtype, device=device), - nn.SiLU(), - operations.conv_nd( - dims, - channels, - self.out_channels, - kernel_size, - padding=padding, - dtype=dtype, - device=device, - ), - ) - - self.updown = up or down - - self.h_upd = self.x_upd = nn.Identity() - - self.skip_t_emb = skip_t_emb - self.emb_layers = nn.Sequential( - nn.SiLU(), - operations.Linear( - emb_channels, - (2 * self.out_channels if use_scale_shift_norm else self.out_channels), - dtype=dtype, - device=device, - ), - ) - self.out_layers = nn.Sequential( - operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device), - nn.SiLU(), - nn.Dropout(p=dropout), - operations.conv_nd( - dims, - self.out_channels, - self.out_channels, - kernel_size, - padding=padding, - dtype=dtype, - device=device, - ), - ) - - if self.out_channels == channels: - self.skip_connection = nn.Identity() - else: - self.skip_connection = operations.conv_nd( - dims, channels, self.out_channels, 1, dtype=dtype, device=device - ) - - def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the residual block layer. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `emb` (torch.Tensor): The embedding tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - return sampling_util.checkpoint( - self._forward, (x, emb), self.parameters(), self.use_checkpoint - ) - - def _forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: - """#### Internal forward pass for the residual block layer. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `emb` (torch.Tensor): The embedding tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - h = self.in_layers(x) - - emb_out = None - if not self.skip_t_emb: - emb_out = self.emb_layers(emb).type(h.dtype) - while len(emb_out.shape) < len(h.shape): - emb_out = emb_out[..., None] - if emb_out is not None: - h = h + emb_out - h = self.out_layers(h) - return self.skip_connection(x) + h - - -ops = cast.disable_weight_init - - -class ResnetBlock(nn.Module): - """#### Class representing a ResNet block layer.""" - - def __init__( - self, - *, - in_channels: int, - out_channels: Optional[int] = None, - conv_shortcut: bool = False, - dropout: float, - temb_channels: int = 512, - ): - """#### Initialize the ResNet block layer. - - #### Args: - - `in_channels` (int): The number of input channels. - - `out_channels` (int, optional): The number of output channels. Defaults to None. - - `conv_shortcut` (bool, optional): Whether to use convolution shortcut. Defaults to False. - - `dropout` (float): The dropout rate. - - `temb_channels` (int, optional): The number of embedding channels. Defaults to 512. - """ - super().__init__() - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - self.out_channels = out_channels - self.use_conv_shortcut = conv_shortcut - - self.swish = torch.nn.SiLU(inplace=True) - self.norm1 = Attention.Normalize(in_channels) - self.conv1 = ops.Conv2d( - in_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - self.norm2 = Attention.Normalize(out_channels) - self.dropout = torch.nn.Dropout(dropout, inplace=True) - self.conv2 = ops.Conv2d( - out_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - if self.in_channels != self.out_channels: - self.nin_shortcut = ops.Conv2d( - in_channels, out_channels, kernel_size=1, stride=1, padding=0 - ) - - def forward(self, x: torch.Tensor, temb: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the ResNet block layer. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `temb` (torch.Tensor): The embedding tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - h = x - h = self.norm1(h) - h = self.swish(h) - h = self.conv1(h) - - h = self.norm2(h) - h = self.swish(h) - h = self.dropout(h) - h = self.conv2(h) - - if self.in_channels != self.out_channels: - x = self.nin_shortcut(x) - - return x + h +from abc import abstractmethod +from typing import Optional, Any, Dict + +import torch +from modules.NeuralNetwork import transformer +import torch.nn as nn +import torch.nn.functional as F + +from modules.Attention import Attention +from modules.cond import cast +from modules.sample import sampling_util + + +oai_ops = cast.disable_weight_init + + +class TimestepBlock1(nn.Module): + """#### Abstract class representing a timestep block.""" + + @abstractmethod + def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the timestep block. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `emb` (torch.Tensor): The embedding tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + pass + + +def forward_timestep_embed1( + ts: nn.ModuleList, + x: torch.Tensor, + emb: torch.Tensor, + context: Optional[torch.Tensor] = None, + transformer_options: Optional[Dict[str, Any]] = {}, + output_shape: Optional[torch.Size] = None, + time_context: Optional[torch.Tensor] = None, + num_video_frames: Optional[int] = None, + image_only_indicator: Optional[bool] = None, +) -> torch.Tensor: + """#### Forward pass for timestep embedding. + + #### Args: + - `ts` (nn.ModuleList): The list of timestep blocks. + - `x` (torch.Tensor): The input tensor. + - `emb` (torch.Tensor): The embedding tensor. + - `context` (torch.Tensor, optional): The context tensor. Defaults to None. + - `transformer_options` (dict, optional): The transformer options. Defaults to {}. + - `output_shape` (torch.Size, optional): The output shape. Defaults to None. + - `time_context` (torch.Tensor, optional): The time context tensor. Defaults to None. + - `num_video_frames` (int, optional): The number of video frames. Defaults to None. + - `image_only_indicator` (bool, optional): The image only indicator. Defaults to None. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + for layer in ts: + if isinstance(layer, TimestepBlock1): + x = layer(x, emb) + elif isinstance(layer, transformer.SpatialTransformer): + x = layer(x, context, transformer_options) + if "transformer_index" in transformer_options: + transformer_options["transformer_index"] += 1 + elif isinstance(layer, Upsample1): + x = layer(x, output_shape=output_shape) + else: + x = layer(x) + return x + + +class Upsample1(nn.Module): + """#### Class representing an upsample layer.""" + + def __init__( + self, + channels: int, + use_conv: bool, + dims: int = 2, + out_channels: Optional[int] = None, + padding: int = 1, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + operations: Any = oai_ops, + ): + """#### Initialize the upsample layer. + + #### Args: + - `channels` (int): The number of input channels. + - `use_conv` (bool): Whether to use convolution. + - `dims` (int, optional): The number of dimensions. Defaults to 2. + - `out_channels` (int, optional): The number of output channels. Defaults to None. + - `padding` (int, optional): The padding size. Defaults to 1. + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + - `device` (torch.device, optional): The device. Defaults to None. + - `operations` (any, optional): The operations. Defaults to oai_ops. + """ + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = operations.conv_nd( + dims, + self.channels, + self.out_channels, + 3, + padding=padding, + dtype=dtype, + device=device, + ) + + def forward( + self, x: torch.Tensor, output_shape: Optional[torch.Size] = None + ) -> torch.Tensor: + """#### Forward pass for the upsample layer. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `output_shape` (torch.Size, optional): The output shape. Defaults to None. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + assert x.shape[1] == self.channels + shape = [x.shape[2] * 2, x.shape[3] * 2] + if output_shape is not None: + shape[0] = output_shape[2] + shape[1] = output_shape[3] + + x = F.interpolate(x, size=shape, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + + +class Downsample1(nn.Module): + """#### Class representing a downsample layer.""" + + def __init__( + self, + channels: int, + use_conv: bool, + dims: int = 2, + out_channels: Optional[int] = None, + padding: int = 1, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + operations: Any = oai_ops, + ): + """#### Initialize the downsample layer. + + #### Args: + - `channels` (int): The number of input channels. + - `use_conv` (bool): Whether to use convolution. + - `dims` (int, optional): The number of dimensions. Defaults to 2. + - `out_channels` (int, optional): The number of output channels. Defaults to None. + - `padding` (int, optional): The padding size. Defaults to 1. + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + - `device` (torch.device, optional): The device. Defaults to None. + - `operations` (any, optional): The operations. Defaults to oai_ops. + """ + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + self.op = operations.conv_nd( + dims, + self.channels, + self.out_channels, + 3, + stride=stride, + padding=padding, + dtype=dtype, + device=device, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the downsample layer. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock1(TimestepBlock1): + """#### Class representing a residual block layer.""" + + def __init__( + self, + channels: int, + emb_channels: int, + dropout: float, + out_channels: Optional[int] = None, + use_conv: bool = False, + use_scale_shift_norm: bool = False, + dims: int = 2, + use_checkpoint: bool = False, + up: bool = False, + down: bool = False, + kernel_size: int = 3, + exchange_temb_dims: bool = False, + skip_t_emb: bool = False, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + operations: Any = oai_ops, + ): + """#### Initialize the residual block layer. + + #### Args: + - `channels` (int): The number of input channels. + - `emb_channels` (int): The number of embedding channels. + - `dropout` (float): The dropout rate. + - `out_channels` (int, optional): The number of output channels. Defaults to None. + - `use_conv` (bool, optional): Whether to use convolution. Defaults to False. + - `use_scale_shift_norm` (bool, optional): Whether to use scale shift normalization. Defaults to False. + - `dims` (int, optional): The number of dimensions. Defaults to 2. + - `use_checkpoint` (bool, optional): Whether to use checkpointing. Defaults to False. + - `up` (bool, optional): Whether to use upsampling. Defaults to False. + - `down` (bool, optional): Whether to use downsampling. Defaults to False. + - `kernel_size` (int, optional): The kernel size. Defaults to 3. + - `exchange_temb_dims` (bool, optional): Whether to exchange embedding dimensions. Defaults to False. + - `skip_t_emb` (bool, optional): Whether to skip embedding. Defaults to False. + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + - `device` (torch.device, optional): The device. Defaults to None. + - `operations` (any, optional): The operations. Defaults to oai_ops. + """ + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + self.exchange_temb_dims = exchange_temb_dims + + padding = kernel_size // 2 + + self.in_layers = nn.Sequential( + operations.GroupNorm(32, channels, dtype=dtype, device=device), + nn.SiLU(), + operations.conv_nd( + dims, + channels, + self.out_channels, + kernel_size, + padding=padding, + dtype=dtype, + device=device, + ), + ) + + self.updown = up or down + + self.h_upd = self.x_upd = nn.Identity() + + self.skip_t_emb = skip_t_emb + self.emb_layers = nn.Sequential( + nn.SiLU(), + operations.Linear( + emb_channels, + (2 * self.out_channels if use_scale_shift_norm else self.out_channels), + dtype=dtype, + device=device, + ), + ) + self.out_layers = nn.Sequential( + operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device), + nn.SiLU(), + nn.Dropout(p=dropout), + operations.conv_nd( + dims, + self.out_channels, + self.out_channels, + kernel_size, + padding=padding, + dtype=dtype, + device=device, + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + else: + self.skip_connection = operations.conv_nd( + dims, channels, self.out_channels, 1, dtype=dtype, device=device + ) + + def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the residual block layer. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `emb` (torch.Tensor): The embedding tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + return sampling_util.checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + def _forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: + """#### Internal forward pass for the residual block layer. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `emb` (torch.Tensor): The embedding tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + h = self.in_layers(x) + + emb_out = None + if not self.skip_t_emb: + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if emb_out is not None: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +ops = cast.disable_weight_init + + +class ResnetBlock(nn.Module): + """#### Class representing a ResNet block layer.""" + + def __init__( + self, + *, + in_channels: int, + out_channels: Optional[int] = None, + conv_shortcut: bool = False, + dropout: float, + temb_channels: int = 512, + ): + """#### Initialize the ResNet block layer. + + #### Args: + - `in_channels` (int): The number of input channels. + - `out_channels` (int, optional): The number of output channels. Defaults to None. + - `conv_shortcut` (bool, optional): Whether to use convolution shortcut. Defaults to False. + - `dropout` (float): The dropout rate. + - `temb_channels` (int, optional): The number of embedding channels. Defaults to 512. + """ + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.swish = torch.nn.SiLU(inplace=True) + self.norm1 = Attention.Normalize(in_channels) + self.conv1 = ops.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + self.norm2 = Attention.Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout, inplace=True) + self.conv2 = ops.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + self.nin_shortcut = ops.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x: torch.Tensor, temb: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the ResNet block layer. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `temb` (torch.Tensor): The embedding tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + h = x + h = self.norm1(h) + h = self.swish(h) + h = self.conv1(h) + + h = self.norm2(h) + h = self.swish(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + x = self.nin_shortcut(x) + + return x + h diff --git a/modules/AutoEncoders/VariationalAE.py b/modules/AutoEncoders/VariationalAE.py index f04ace918e25df71c0274d9b3d58e6a526c6f09f..076876b7d58ac6988d737827f9ed0b777ed71ad9 100644 --- a/modules/AutoEncoders/VariationalAE.py +++ b/modules/AutoEncoders/VariationalAE.py @@ -1,824 +1,824 @@ -import logging -from typing import Dict, Optional, Tuple, Union -import numpy as np -import torch -from modules.Model import ModelPatcher -import torch.nn as nn - -from modules.Attention import Attention -from modules.AutoEncoders import ResBlock -from modules.Device import Device -from modules.Utilities import util -from modules.cond import cast - - -class DiagonalGaussianDistribution(object): - """#### Represents a diagonal Gaussian distribution parameterized by mean and log-variance. - - #### Attributes: - - `parameters` (torch.Tensor): The concatenated mean and log-variance of the distribution. - - `mean` (torch.Tensor): The mean of the distribution. - - `logvar` (torch.Tensor): The log-variance of the distribution, clamped between -30.0 and 20.0. - - `std` (torch.Tensor): The standard deviation of the distribution, computed as exp(0.5 * logvar). - - `var` (torch.Tensor): The variance of the distribution, computed as exp(logvar). - - `deterministic` (bool): If True, the distribution is deterministic. - - #### Methods: - - `sample() -> torch.Tensor`: - Samples from the distribution using the reparameterization trick. - - `kl(other: DiagonalGaussianDistribution = None) -> torch.Tensor`: - Computes the Kullback-Leibler divergence between this distribution and a standard normal distribution. - If `other` is provided, computes the KL divergence between this distribution and `other`. - """ - - def __init__(self, parameters: torch.Tensor, deterministic: bool = False): - self.parameters = parameters - self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) - self.logvar = torch.clamp(self.logvar, -30.0, 20.0) - self.deterministic = deterministic - self.std = torch.exp(0.5 * self.logvar) - self.var = torch.exp(self.logvar) - - def sample(self) -> torch.Tensor: - """#### Samples from the distribution using the reparameterization trick. - - #### Returns: - - `torch.Tensor`: A sample from the distribution. - """ - x = self.mean + self.std * torch.randn(self.mean.shape).to( - device=self.parameters.device - ) - return x - - def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor: - """#### Computes the Kullback-Leibler divergence between this distribution and a standard normal distribution. - - If `other` is provided, computes the KL divergence between this distribution and `other`. - - #### Args: - - `other` (DiagonalGaussianDistribution, optional): Another distribution to compute the KL divergence with. - - #### Returns: - - `torch.Tensor`: The KL divergence. - """ - return 0.5 * torch.sum( - torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, - dim=[1, 2, 3], - ) - - -class DiagonalGaussianRegularizer(torch.nn.Module): - """#### Regularizer for diagonal Gaussian distributions.""" - - def __init__(self, sample: bool = True): - """#### Initialize the regularizer. - - #### Args: - - `sample` (bool, optional): Whether to sample from the distribution. Defaults to True. - """ - super().__init__() - self.sample = sample - - def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: - """#### Forward pass for the regularizer. - - #### Args: - - `z` (torch.Tensor): The input tensor. - - #### Returns: - - `Tuple[torch.Tensor, dict]`: The regularized tensor and a log dictionary. - """ - log = dict() - posterior = DiagonalGaussianDistribution(z) - if self.sample: - z = posterior.sample() - else: - z = posterior.mode() - kl_loss = posterior.kl() - kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] - log["kl_loss"] = kl_loss - return z, log - - -class AutoencodingEngine(nn.Module): - """#### Class representing an autoencoding engine.""" - - def __init__(self, encoder: nn.Module, decoder: nn.Module, regularizer: nn.Module, flux: bool = False): - """#### Initialize the autoencoding engine. - - #### Args: - - `encoder` (nn.Module): The encoder module. - - `decoder` (nn.Module): The decoder module. - - `regularizer` (nn.Module): The regularizer module. - """ - super().__init__() - self.encoder = encoder - self.decoder = decoder - self.regularization = regularizer - if not flux: - self.post_quant_conv = cast.disable_weight_init.Conv2d(4, 4, 1) - self.quant_conv = cast.disable_weight_init.Conv2d(8, 8, 1) - - def get_last_layer(self): - """#### Get the last layer of the decoder. - - Returns: - - `nn.Module`: The last layer of the decoder. - """ - return self.decoder.get_last_layer() - - def decode(self, z: torch.Tensor, flux:bool = False, **kwargs) -> torch.Tensor: - """#### Decode the latent tensor. - - #### Args: - - `z` (torch.Tensor): The latent tensor. - - `decoder_kwargs` (dict): Additional arguments for the decoder. - - #### Returns: - - `torch.Tensor`: The decoded tensor. - """ - if flux: - x = self.decoder(z, **kwargs) - return x - dec = self.post_quant_conv(z) - dec = self.decoder(dec, **kwargs) - return dec - - - def encode( - self, - x: torch.Tensor, - return_reg_log: bool = False, - unregularized: bool = False, - flux: bool = False, - ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: - """#### Encode the input tensor. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `return_reg_log` (bool, optional): Whether to return the regularization log. Defaults to False. - - #### Returns: - - `Union[torch.Tensor, Tuple[torch.Tensor, dict]]`: The encoded tensor and optionally the regularization log. - """ - z = self.encoder(x) - if not flux: - z = self.quant_conv(z) - if unregularized: - return z, dict() - z, reg_log = self.regularization(z) - if return_reg_log: - return z, reg_log - return z - -ops = cast.disable_weight_init - -if Device.xformers_enabled_vae(): - pass - - -def nonlinearity(x: torch.Tensor) -> torch.Tensor: - """#### Apply the swish nonlinearity. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - return x * torch.sigmoid(x) - - -class Upsample(nn.Module): - """#### Class representing an upsample layer.""" - - def __init__(self, in_channels: int, with_conv: bool): - """#### Initialize the upsample layer. - - #### Args: - - `in_channels` (int): The number of input channels. - - `with_conv` (bool): Whether to use convolution. - """ - super().__init__() - self.with_conv = with_conv - if self.with_conv: - self.conv = ops.Conv2d( - in_channels, in_channels, kernel_size=3, stride=1, padding=1 - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the upsample layer. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") - if self.with_conv: - x = self.conv(x) - return x - - -class Downsample(nn.Module): - """#### Class representing a downsample layer.""" - - def __init__(self, in_channels: int, with_conv: bool): - """#### Initialize the downsample layer. - - #### Args: - - `in_channels` (int): The number of input channels. - - `with_conv` (bool): Whether to use convolution. - """ - super().__init__() - self.with_conv = with_conv - if self.with_conv: - # no asymmetric padding in torch conv, must do it ourselves - self.conv = ops.Conv2d( - in_channels, in_channels, kernel_size=3, stride=2, padding=0 - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the downsample layer. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - pad = (0, 1, 0, 1) - x = torch.nn.functional.pad(x, pad, mode="constant", value=0) - x = self.conv(x) - return x - - -class Encoder(nn.Module): - """#### Class representing an encoder.""" - - def __init__( - self, - *, - ch: int, - out_ch: int, - ch_mult: Tuple[int, ...] = (1, 2, 4, 8), - num_res_blocks: int, - attn_resolutions: Tuple[int, ...], - dropout: float = 0.0, - resamp_with_conv: bool = True, - in_channels: int, - resolution: int, - z_channels: int, - double_z: bool = True, - use_linear_attn: bool = False, - attn_type: str = "vanilla", - **ignore_kwargs, - ): - """#### Initialize the encoder. - - #### Args: - - `ch` (int): The base number of channels. - - `out_ch` (int): The number of output channels. - - `ch_mult` (Tuple[int, ...], optional): Channel multiplier at each resolution. Defaults to (1, 2, 4, 8). - - `num_res_blocks` (int): The number of residual blocks. - - `attn_resolutions` (Tuple[int, ...]): The resolutions at which to apply attention. - - `dropout` (float, optional): The dropout rate. Defaults to 0.0. - - `resamp_with_conv` (bool, optional): Whether to use convolution for resampling. Defaults to True. - - `in_channels` (int): The number of input channels. - - `resolution` (int): The resolution of the input. - - `z_channels` (int): The number of latent channels. - - `double_z` (bool, optional): Whether to double the latent channels. Defaults to True. - - `use_linear_attn` (bool, optional): Whether to use linear attention. Defaults to False. - - `attn_type` (str, optional): The type of attention. Defaults to "vanilla". - """ - super().__init__() - if use_linear_attn: - attn_type = "linear" - self.ch = ch - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - - # downsampling - self.conv_in = ops.Conv2d( - in_channels, self.ch, kernel_size=3, stride=1, padding=1 - ) - - curr_res = resolution - in_ch_mult = (1,) + tuple(ch_mult) - self.in_ch_mult = in_ch_mult - self.down = nn.ModuleList() - for i_level in range(self.num_resolutions): - block = nn.ModuleList() - attn = nn.ModuleList() - block_in = ch * in_ch_mult[i_level] - block_out = ch * ch_mult[i_level] - for i_block in range(self.num_res_blocks): - block.append( - ResBlock.ResnetBlock( - in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout, - ) - ) - block_in = block_out - down = nn.Module() - down.block = block - down.attn = attn - if i_level != self.num_resolutions - 1: - down.downsample = Downsample(block_in, resamp_with_conv) - curr_res = curr_res // 2 - self.down.append(down) - - # middle - self.mid = nn.Module() - self.mid.block_1 = ResBlock.ResnetBlock( - in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - ) - self.mid.attn_1 = Attention.make_attn(block_in, attn_type=attn_type) - self.mid.block_2 = ResBlock.ResnetBlock( - in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - ) - - # end - self.norm_out = Attention.Normalize(block_in) - self.conv_out = ops.Conv2d( - block_in, - 2 * z_channels if double_z else z_channels, - kernel_size=3, - stride=1, - padding=1, - ) - self._device = torch.device("cpu") - self._dtype = torch.float32 - - def to(self, device=None, dtype=None): - """#### Move the encoder to a device and data type. - - #### Args: - - `device` (torch.device, optional): The device to move to. Defaults to None. - - `dtype` (torch.dtype, optional): The data type to move to. Defaults to None. - - #### Returns: - - `nn.Module`: The encoder. - """ - if device is not None: - self._device = device - if dtype is not None: - self._dtype = dtype - return super().to(device=device, dtype=dtype) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the encoder. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The encoded tensor. - """ - if x.device != self._device or x.dtype != self._dtype: - self.to(device=x.device, dtype=x.dtype) - # timestep embedding - temb = None - # downsampling - h = self.conv_in(x) - for i_level in range(self.num_resolutions): - for i_block in range(self.num_res_blocks): - h = self.down[i_level].block[i_block](h, temb) - if len(self.down[i_level].attn) > 0: - h = self.down[i_level].attn[i_block](h) - if i_level != self.num_resolutions - 1: - h = self.down[i_level].downsample(h) - - # middle - h = self.mid.block_1(h, temb) - h = self.mid.attn_1(h) - h = self.mid.block_2(h, temb) - - # end - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - return h - - -class Decoder(nn.Module): - """#### Class representing a decoder.""" - - def __init__( - self, - *, - ch: int, - out_ch: int, - ch_mult: Tuple[int, ...] = (1, 2, 4, 8), - num_res_blocks: int, - attn_resolutions: Tuple[int, ...], - dropout: float = 0.0, - resamp_with_conv: bool = True, - in_channels: int, - resolution: int, - z_channels: int, - give_pre_end: bool = False, - tanh_out: bool = False, - use_linear_attn: bool = False, - conv_out_op: nn.Module = ops.Conv2d, - resnet_op: nn.Module = ResBlock.ResnetBlock, - attn_op: nn.Module = Attention.AttnBlock, - **ignorekwargs, - ): - """#### Initialize the decoder. - - #### Args: - - `ch` (int): The base number of channels. - - `out_ch` (int): The number of output channels. - - `ch_mult` (Tuple[int, ...], optional): Channel multiplier at each resolution. Defaults to (1, 2, 4, 8). - - `num_res_blocks` (int): The number of residual blocks. - - `attn_resolutions` (Tuple[int, ...]): The resolutions at which to apply attention. - - `dropout` (float, optional): The dropout rate. Defaults to 0.0. - - `resamp_with_conv` (bool, optional): Whether to use convolution for resampling. Defaults to True. - - `in_channels` (int): The number of input channels. - - `resolution` (int): The resolution of the input. - - `z_channels` (int): The number of latent channels. - - `give_pre_end` (bool, optional): Whether to give pre-end. Defaults to False. - - `tanh_out` (bool, optional): Whether to use tanh activation at the output. Defaults to False. - - `use_linear_attn` (bool, optional): Whether to use linear attention. Defaults to False. - - `conv_out_op` (nn.Module, optional): The convolution output operation. Defaults to ops.Conv2d. - - `resnet_op` (nn.Module, optional): The residual block operation. Defaults to ResBlock.ResnetBlock. - - `attn_op` (nn.Module, optional): The attention block operation. Defaults to Attention.AttnBlock. - """ - super().__init__() - self.ch = ch - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - self.give_pre_end = give_pre_end - self.tanh_out = tanh_out - - # compute in_ch_mult, block_in and curr_res at lowest res - (1,) + tuple(ch_mult) - block_in = ch * ch_mult[self.num_resolutions - 1] - curr_res = resolution // 2 ** (self.num_resolutions - 1) - self.z_shape = (1, z_channels, curr_res, curr_res) - logging.debug( - "Working with z of shape {} = {} dimensions.".format( - self.z_shape, np.prod(self.z_shape) - ) - ) - - # z to block_in - self.conv_in = ops.Conv2d( - z_channels, block_in, kernel_size=3, stride=1, padding=1 - ) - - # middle - self.mid = nn.Module() - self.mid.block_1 = resnet_op( - in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - ) - self.mid.attn_1 = attn_op(block_in) - self.mid.block_2 = resnet_op( - in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - ) - - # upsampling - self.up = nn.ModuleList() - for i_level in reversed(range(self.num_resolutions)): - block = nn.ModuleList() - attn = nn.ModuleList() - block_out = ch * ch_mult[i_level] - for i_block in range(self.num_res_blocks + 1): - block.append( - resnet_op( - in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout, - ) - ) - block_in = block_out - up = nn.Module() - up.block = block - up.attn = attn - if i_level != 0: - up.upsample = Upsample(block_in, resamp_with_conv) - curr_res = curr_res * 2 - self.up.insert(0, up) # prepend to get consistent order - - # end - self.norm_out = Attention.Normalize(block_in) - self.conv_out = conv_out_op( - block_in, out_ch, kernel_size=3, stride=1, padding=1 - ) - - def forward(self, z: torch.Tensor, **kwargs) -> torch.Tensor: - """#### Forward pass for the decoder. - - #### Args: - - `z` (torch.Tensor): The input tensor. - - `**kwargs`: Additional arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - - """ - # assert z.shape[1:] == self.z_shape[1:] - self.last_z_shape = z.shape - - # timestep embedding - temb = None - - # z to block_in - h = self.conv_in(z) - - # middle - h = self.mid.block_1(h, temb, **kwargs) - h = self.mid.attn_1(h, **kwargs) - h = self.mid.block_2(h, temb, **kwargs) - - # upsampling - for i_level in reversed(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks + 1): - h = self.up[i_level].block[i_block](h, temb, **kwargs) - if i_level != 0: - h = self.up[i_level].upsample(h) - - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h, **kwargs) - return h - - -class VAE: - """#### Class representing a Variational Autoencoder (VAE).""" - - def __init__( - self, - sd: Optional[dict] = None, - device: Optional[torch.device] = None, - config: Optional[dict] = None, - dtype: Optional[torch.dtype] = None, - flux: Optional[bool] = False, - ): - """#### Initialize the VAE. - - #### Args: - - `sd` (dict, optional): The state dictionary. Defaults to None. - - `device` (torch.device, optional): The device to use. Defaults to None. - - `config` (dict, optional): The configuration dictionary. Defaults to None. - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - """ - self.memory_used_encode = lambda shape, dtype: ( - 1767 * shape[2] * shape[3] - ) * Device.dtype_size( - dtype - ) # These are for AutoencoderKL and need tweaking (should be lower) - self.memory_used_decode = lambda shape, dtype: ( - 2178 * shape[2] * shape[3] * 64 - ) * Device.dtype_size(dtype) - self.downscale_ratio = 8 - self.upscale_ratio = 8 - self.latent_channels = 4 - self.output_channels = 3 - self.process_input = lambda image: image * 2.0 - 1.0 - self.process_output = lambda image: torch.clamp( - (image + 1.0) / 2.0, min=0.0, max=1.0 - ) - self.working_dtypes = [torch.bfloat16, torch.float32] - - if config is None: - if "decoder.conv_in.weight" in sd: - # default SD1.x/SD2.x VAE parameters - ddconfig = { - "double_z": True, - "z_channels": 4, - "resolution": 256, - "in_channels": 3, - "out_ch": 3, - "ch": 128, - "ch_mult": [1, 2, 4, 4], - "num_res_blocks": 2, - "attn_resolutions": [], - "dropout": 0.0, - } - - if ( - "encoder.down.2.downsample.conv.weight" not in sd - and "decoder.up.3.upsample.conv.weight" not in sd - ): # Stable diffusion x4 upscaler VAE - ddconfig["ch_mult"] = [1, 2, 4] - self.downscale_ratio = 4 - self.upscale_ratio = 4 - - self.latent_channels = ddconfig["z_channels"] = sd[ - "decoder.conv_in.weight" - ].shape[1] - # Initialize model - self.first_stage_model = AutoencodingEngine( - Encoder(**ddconfig), - Decoder(**ddconfig), - DiagonalGaussianRegularizer(), - flux=flux - ) - else: - logging.warning("WARNING: No VAE weights detected, VAE not initalized.") - self.first_stage_model = None - return - - self.first_stage_model = self.first_stage_model.eval() - - m, u = self.first_stage_model.load_state_dict(sd, strict=False) - if len(m) > 0: - logging.warning("Missing VAE keys {}".format(m)) - - if len(u) > 0: - logging.debug("Leftover VAE keys {}".format(u)) - - if device is None: - device = Device.vae_device() - self.device = device - offload_device = Device.vae_offload_device() - if dtype is None: - dtype = Device.vae_dtype() - self.vae_dtype = dtype - self.first_stage_model.to(self.vae_dtype) - self.output_device = Device.intermediate_device() - - self.patcher = ModelPatcher.ModelPatcher( - self.first_stage_model, - load_device=self.device, - offload_device=offload_device, - ) - logging.debug( - "VAE load device: {}, offload device: {}, dtype: {}".format( - self.device, offload_device, self.vae_dtype - ) - ) - - - def vae_encode_crop_pixels(self, pixels: torch.Tensor) -> torch.Tensor: - """#### Crop the input pixels to be compatible with the VAE. - - #### Args: - - `pixels` (torch.Tensor): The input pixel tensor. - - #### Returns: - - `torch.Tensor`: The cropped pixel tensor. - """ - (pixels.shape[1] // self.downscale_ratio) * self.downscale_ratio - (pixels.shape[2] // self.downscale_ratio) * self.downscale_ratio - return pixels - - def decode(self, samples_in: torch.Tensor, flux:bool = False) -> torch.Tensor: - """#### Decode the latent samples to pixel samples. - - #### Args: - - `samples_in` (torch.Tensor): The input latent samples. - - #### Returns: - - `torch.Tensor`: The decoded pixel samples. - """ - memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) - Device.load_models_gpu([self.patcher], memory_required=memory_used) - free_memory = Device.get_free_memory(self.device) - batch_number = int(free_memory / memory_used) - batch_number = max(1, batch_number) - - pixel_samples = torch.empty( - ( - samples_in.shape[0], - 3, - round(samples_in.shape[2] * self.upscale_ratio), - round(samples_in.shape[3] * self.upscale_ratio), - ), - device=self.output_device, - ) - for x in range(0, samples_in.shape[0], batch_number): - samples = ( - samples_in[x : x + batch_number].to(self.vae_dtype).to(self.device) - ) - pixel_samples[x : x + batch_number] = self.process_output( - self.first_stage_model.decode(samples, flux=flux).to(self.output_device).float() - ) - pixel_samples = pixel_samples.to(self.output_device).movedim(1, -1) - return pixel_samples - - - def encode(self, pixel_samples: torch.Tensor, flux:bool = False) -> torch.Tensor: - """#### Encode the pixel samples to latent samples. - - #### Args: - - `pixel_samples` (torch.Tensor): The input pixel samples. - - #### Returns: - - `torch.Tensor`: The encoded latent samples. - """ - pixel_samples = self.vae_encode_crop_pixels(pixel_samples) - pixel_samples = pixel_samples.movedim(-1, 1) - memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) - Device.load_models_gpu([self.patcher], memory_required=memory_used) - free_memory = Device.get_free_memory(self.device) - batch_number = int(free_memory / memory_used) - batch_number = max(1, batch_number) - samples = torch.empty( - ( - pixel_samples.shape[0], - self.latent_channels, - round(pixel_samples.shape[2] // self.downscale_ratio), - round(pixel_samples.shape[3] // self.downscale_ratio), - ), - device=self.output_device, - ) - for x in range(0, pixel_samples.shape[0], batch_number): - pixels_in = ( - self.process_input(pixel_samples[x : x + batch_number]) - .to(self.vae_dtype) - .to(self.device) - ) - samples[x : x + batch_number] = ( - self.first_stage_model.encode(pixels_in, flux=flux).to(self.output_device).float() - ) - - return samples - - def get_sd(self): - """#### Get the state dictionary. - - #### Returns: - - `dict`: The state dictionary. - """ - return self.first_stage_model.state_dict() - - -class VAEDecode: - """#### Class for decoding VAE samples.""" - - def decode(self, vae: VAE, samples: dict, flux:bool = False) -> Tuple[torch.Tensor]: - """#### Decode the VAE samples. - - #### Args: - - `vae` (VAE): The VAE instance. - - `samples` (dict): The samples dictionary. - - #### Returns: - - `Tuple[torch.Tensor]`: The decoded samples. - """ - return (vae.decode(samples["samples"], flux=flux),) - - -class VAEEncode: - """#### Class for encoding VAE samples.""" - - def encode(self, vae: VAE, pixels: torch.Tensor, flux:bool = False) -> Tuple[dict]: - """#### Encode the VAE samples. - - #### Args: - - `vae` (VAE): The VAE instance. - - `pixels` (torch.Tensor): The input pixel tensor. - - #### Returns: - - `Tuple[dict]`: The encoded samples dictionary. - """ - t = vae.encode(pixels[:, :, :, :3], flux=flux) - return ({"samples": t},) - - -class VAELoader: - """#### Class for loading VAEs.""" - # TODO: scale factor? - def load_vae(self, vae_name): - """#### Load the VAE. - - #### Args: - - `vae_name`: The name of the VAE. - - #### Returns: - - `Tuple[VAE]`: The VAE instance. - """ - if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]: - sd = self.load_taesd(vae_name) - else: - vae_path = "./_internal/vae/" + vae_name - sd = util.load_torch_file(vae_path) - vae = VAE(sd=sd) - return (vae,) - - +import logging +from typing import Dict, Optional, Tuple, Union +import numpy as np +import torch +from modules.Model import ModelPatcher +import torch.nn as nn + +from modules.Attention import Attention +from modules.AutoEncoders import ResBlock +from modules.Device import Device +from modules.Utilities import util +from modules.cond import cast + + +class DiagonalGaussianDistribution(object): + """#### Represents a diagonal Gaussian distribution parameterized by mean and log-variance. + + #### Attributes: + - `parameters` (torch.Tensor): The concatenated mean and log-variance of the distribution. + - `mean` (torch.Tensor): The mean of the distribution. + - `logvar` (torch.Tensor): The log-variance of the distribution, clamped between -30.0 and 20.0. + - `std` (torch.Tensor): The standard deviation of the distribution, computed as exp(0.5 * logvar). + - `var` (torch.Tensor): The variance of the distribution, computed as exp(logvar). + - `deterministic` (bool): If True, the distribution is deterministic. + + #### Methods: + - `sample() -> torch.Tensor`: + Samples from the distribution using the reparameterization trick. + - `kl(other: DiagonalGaussianDistribution = None) -> torch.Tensor`: + Computes the Kullback-Leibler divergence between this distribution and a standard normal distribution. + If `other` is provided, computes the KL divergence between this distribution and `other`. + """ + + def __init__(self, parameters: torch.Tensor, deterministic: bool = False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + + def sample(self) -> torch.Tensor: + """#### Samples from the distribution using the reparameterization trick. + + #### Returns: + - `torch.Tensor`: A sample from the distribution. + """ + x = self.mean + self.std * torch.randn(self.mean.shape).to( + device=self.parameters.device + ) + return x + + def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor: + """#### Computes the Kullback-Leibler divergence between this distribution and a standard normal distribution. + + If `other` is provided, computes the KL divergence between this distribution and `other`. + + #### Args: + - `other` (DiagonalGaussianDistribution, optional): Another distribution to compute the KL divergence with. + + #### Returns: + - `torch.Tensor`: The KL divergence. + """ + return 0.5 * torch.sum( + torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, + dim=[1, 2, 3], + ) + + +class DiagonalGaussianRegularizer(torch.nn.Module): + """#### Regularizer for diagonal Gaussian distributions.""" + + def __init__(self, sample: bool = True): + """#### Initialize the regularizer. + + #### Args: + - `sample` (bool, optional): Whether to sample from the distribution. Defaults to True. + """ + super().__init__() + self.sample = sample + + def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: + """#### Forward pass for the regularizer. + + #### Args: + - `z` (torch.Tensor): The input tensor. + + #### Returns: + - `Tuple[torch.Tensor, dict]`: The regularized tensor and a log dictionary. + """ + log = dict() + posterior = DiagonalGaussianDistribution(z) + if self.sample: + z = posterior.sample() + else: + z = posterior.mode() + kl_loss = posterior.kl() + kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] + log["kl_loss"] = kl_loss + return z, log + + +class AutoencodingEngine(nn.Module): + """#### Class representing an autoencoding engine.""" + + def __init__(self, encoder: nn.Module, decoder: nn.Module, regularizer: nn.Module, flux: bool = False): + """#### Initialize the autoencoding engine. + + #### Args: + - `encoder` (nn.Module): The encoder module. + - `decoder` (nn.Module): The decoder module. + - `regularizer` (nn.Module): The regularizer module. + """ + super().__init__() + self.encoder = encoder + self.decoder = decoder + self.regularization = regularizer + if not flux: + self.post_quant_conv = cast.disable_weight_init.Conv2d(4, 4, 1) + self.quant_conv = cast.disable_weight_init.Conv2d(8, 8, 1) + + def get_last_layer(self): + """#### Get the last layer of the decoder. + + Returns: + - `nn.Module`: The last layer of the decoder. + """ + return self.decoder.get_last_layer() + + def decode(self, z: torch.Tensor, flux:bool = False, **kwargs) -> torch.Tensor: + """#### Decode the latent tensor. + + #### Args: + - `z` (torch.Tensor): The latent tensor. + - `decoder_kwargs` (dict): Additional arguments for the decoder. + + #### Returns: + - `torch.Tensor`: The decoded tensor. + """ + if flux: + x = self.decoder(z, **kwargs) + return x + dec = self.post_quant_conv(z) + dec = self.decoder(dec, **kwargs) + return dec + + + def encode( + self, + x: torch.Tensor, + return_reg_log: bool = False, + unregularized: bool = False, + flux: bool = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: + """#### Encode the input tensor. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `return_reg_log` (bool, optional): Whether to return the regularization log. Defaults to False. + + #### Returns: + - `Union[torch.Tensor, Tuple[torch.Tensor, dict]]`: The encoded tensor and optionally the regularization log. + """ + z = self.encoder(x) + if not flux: + z = self.quant_conv(z) + if unregularized: + return z, dict() + z, reg_log = self.regularization(z) + if return_reg_log: + return z, reg_log + return z + +ops = cast.disable_weight_init + +if Device.xformers_enabled_vae(): + pass + + +def nonlinearity(x: torch.Tensor) -> torch.Tensor: + """#### Apply the swish nonlinearity. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + return x * torch.sigmoid(x) + + +class Upsample(nn.Module): + """#### Class representing an upsample layer.""" + + def __init__(self, in_channels: int, with_conv: bool): + """#### Initialize the upsample layer. + + #### Args: + - `in_channels` (int): The number of input channels. + - `with_conv` (bool): Whether to use convolution. + """ + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = ops.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the upsample layer. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + """#### Class representing a downsample layer.""" + + def __init__(self, in_channels: int, with_conv: bool): + """#### Initialize the downsample layer. + + #### Args: + - `in_channels` (int): The number of input channels. + - `with_conv` (bool): Whether to use convolution. + """ + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = ops.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the downsample layer. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x + + +class Encoder(nn.Module): + """#### Class representing an encoder.""" + + def __init__( + self, + *, + ch: int, + out_ch: int, + ch_mult: Tuple[int, ...] = (1, 2, 4, 8), + num_res_blocks: int, + attn_resolutions: Tuple[int, ...], + dropout: float = 0.0, + resamp_with_conv: bool = True, + in_channels: int, + resolution: int, + z_channels: int, + double_z: bool = True, + use_linear_attn: bool = False, + attn_type: str = "vanilla", + **ignore_kwargs, + ): + """#### Initialize the encoder. + + #### Args: + - `ch` (int): The base number of channels. + - `out_ch` (int): The number of output channels. + - `ch_mult` (Tuple[int, ...], optional): Channel multiplier at each resolution. Defaults to (1, 2, 4, 8). + - `num_res_blocks` (int): The number of residual blocks. + - `attn_resolutions` (Tuple[int, ...]): The resolutions at which to apply attention. + - `dropout` (float, optional): The dropout rate. Defaults to 0.0. + - `resamp_with_conv` (bool, optional): Whether to use convolution for resampling. Defaults to True. + - `in_channels` (int): The number of input channels. + - `resolution` (int): The resolution of the input. + - `z_channels` (int): The number of latent channels. + - `double_z` (bool, optional): Whether to double the latent channels. Defaults to True. + - `use_linear_attn` (bool, optional): Whether to use linear attention. Defaults to False. + - `attn_type` (str, optional): The type of attention. Defaults to "vanilla". + """ + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = ops.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResBlock.ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResBlock.ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = Attention.make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResBlock.ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # end + self.norm_out = Attention.Normalize(block_in) + self.conv_out = ops.Conv2d( + block_in, + 2 * z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1, + ) + self._device = torch.device("cpu") + self._dtype = torch.float32 + + def to(self, device=None, dtype=None): + """#### Move the encoder to a device and data type. + + #### Args: + - `device` (torch.device, optional): The device to move to. Defaults to None. + - `dtype` (torch.dtype, optional): The data type to move to. Defaults to None. + + #### Returns: + - `nn.Module`: The encoder. + """ + if device is not None: + self._device = device + if dtype is not None: + self._dtype = dtype + return super().to(device=device, dtype=dtype) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the encoder. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The encoded tensor. + """ + if x.device != self._device or x.dtype != self._dtype: + self.to(device=x.device, dtype=x.dtype) + # timestep embedding + temb = None + # downsampling + h = self.conv_in(x) + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + if i_level != self.num_resolutions - 1: + h = self.down[i_level].downsample(h) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + """#### Class representing a decoder.""" + + def __init__( + self, + *, + ch: int, + out_ch: int, + ch_mult: Tuple[int, ...] = (1, 2, 4, 8), + num_res_blocks: int, + attn_resolutions: Tuple[int, ...], + dropout: float = 0.0, + resamp_with_conv: bool = True, + in_channels: int, + resolution: int, + z_channels: int, + give_pre_end: bool = False, + tanh_out: bool = False, + use_linear_attn: bool = False, + conv_out_op: nn.Module = ops.Conv2d, + resnet_op: nn.Module = ResBlock.ResnetBlock, + attn_op: nn.Module = Attention.AttnBlock, + **ignorekwargs, + ): + """#### Initialize the decoder. + + #### Args: + - `ch` (int): The base number of channels. + - `out_ch` (int): The number of output channels. + - `ch_mult` (Tuple[int, ...], optional): Channel multiplier at each resolution. Defaults to (1, 2, 4, 8). + - `num_res_blocks` (int): The number of residual blocks. + - `attn_resolutions` (Tuple[int, ...]): The resolutions at which to apply attention. + - `dropout` (float, optional): The dropout rate. Defaults to 0.0. + - `resamp_with_conv` (bool, optional): Whether to use convolution for resampling. Defaults to True. + - `in_channels` (int): The number of input channels. + - `resolution` (int): The resolution of the input. + - `z_channels` (int): The number of latent channels. + - `give_pre_end` (bool, optional): Whether to give pre-end. Defaults to False. + - `tanh_out` (bool, optional): Whether to use tanh activation at the output. Defaults to False. + - `use_linear_attn` (bool, optional): Whether to use linear attention. Defaults to False. + - `conv_out_op` (nn.Module, optional): The convolution output operation. Defaults to ops.Conv2d. + - `resnet_op` (nn.Module, optional): The residual block operation. Defaults to ResBlock.ResnetBlock. + - `attn_op` (nn.Module, optional): The attention block operation. Defaults to Attention.AttnBlock. + """ + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + (1,) + tuple(ch_mult) + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + logging.debug( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = ops.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + self.mid = nn.Module() + self.mid.block_1 = resnet_op( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = attn_op(block_in) + self.mid.block_2 = resnet_op( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + resnet_op( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Attention.Normalize(block_in) + self.conv_out = conv_out_op( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z: torch.Tensor, **kwargs) -> torch.Tensor: + """#### Forward pass for the decoder. + + #### Args: + - `z` (torch.Tensor): The input tensor. + - `**kwargs`: Additional arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + + """ + # assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb, **kwargs) + h = self.mid.attn_1(h, **kwargs) + h = self.mid.block_2(h, temb, **kwargs) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb, **kwargs) + if i_level != 0: + h = self.up[i_level].upsample(h) + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h, **kwargs) + return h + + +class VAE: + """#### Class representing a Variational Autoencoder (VAE).""" + + def __init__( + self, + sd: Optional[dict] = None, + device: Optional[torch.device] = None, + config: Optional[dict] = None, + dtype: Optional[torch.dtype] = None, + flux: Optional[bool] = False, + ): + """#### Initialize the VAE. + + #### Args: + - `sd` (dict, optional): The state dictionary. Defaults to None. + - `device` (torch.device, optional): The device to use. Defaults to None. + - `config` (dict, optional): The configuration dictionary. Defaults to None. + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + """ + self.memory_used_encode = lambda shape, dtype: ( + 1767 * shape[2] * shape[3] + ) * Device.dtype_size( + dtype + ) # These are for AutoencoderKL and need tweaking (should be lower) + self.memory_used_decode = lambda shape, dtype: ( + 2178 * shape[2] * shape[3] * 64 + ) * Device.dtype_size(dtype) + self.downscale_ratio = 8 + self.upscale_ratio = 8 + self.latent_channels = 4 + self.output_channels = 3 + self.process_input = lambda image: image * 2.0 - 1.0 + self.process_output = lambda image: torch.clamp( + (image + 1.0) / 2.0, min=0.0, max=1.0 + ) + self.working_dtypes = [torch.bfloat16, torch.float32] + + if config is None: + if "decoder.conv_in.weight" in sd: + # default SD1.x/SD2.x VAE parameters + ddconfig = { + "double_z": True, + "z_channels": 4, + "resolution": 256, + "in_channels": 3, + "out_ch": 3, + "ch": 128, + "ch_mult": [1, 2, 4, 4], + "num_res_blocks": 2, + "attn_resolutions": [], + "dropout": 0.0, + } + + if ( + "encoder.down.2.downsample.conv.weight" not in sd + and "decoder.up.3.upsample.conv.weight" not in sd + ): # Stable diffusion x4 upscaler VAE + ddconfig["ch_mult"] = [1, 2, 4] + self.downscale_ratio = 4 + self.upscale_ratio = 4 + + self.latent_channels = ddconfig["z_channels"] = sd[ + "decoder.conv_in.weight" + ].shape[1] + # Initialize model + self.first_stage_model = AutoencodingEngine( + Encoder(**ddconfig), + Decoder(**ddconfig), + DiagonalGaussianRegularizer(), + flux=flux + ) + else: + logging.warning("WARNING: No VAE weights detected, VAE not initalized.") + self.first_stage_model = None + return + + self.first_stage_model = self.first_stage_model.eval() + + m, u = self.first_stage_model.load_state_dict(sd, strict=False) + if len(m) > 0: + logging.warning("Missing VAE keys {}".format(m)) + + if len(u) > 0: + logging.debug("Leftover VAE keys {}".format(u)) + + if device is None: + device = Device.vae_device() + self.device = device + offload_device = Device.vae_offload_device() + if dtype is None: + dtype = Device.vae_dtype() + self.vae_dtype = dtype + self.first_stage_model.to(self.vae_dtype) + self.output_device = Device.intermediate_device() + + self.patcher = ModelPatcher.ModelPatcher( + self.first_stage_model, + load_device=self.device, + offload_device=offload_device, + ) + logging.debug( + "VAE load device: {}, offload device: {}, dtype: {}".format( + self.device, offload_device, self.vae_dtype + ) + ) + + + def vae_encode_crop_pixels(self, pixels: torch.Tensor) -> torch.Tensor: + """#### Crop the input pixels to be compatible with the VAE. + + #### Args: + - `pixels` (torch.Tensor): The input pixel tensor. + + #### Returns: + - `torch.Tensor`: The cropped pixel tensor. + """ + (pixels.shape[1] // self.downscale_ratio) * self.downscale_ratio + (pixels.shape[2] // self.downscale_ratio) * self.downscale_ratio + return pixels + + def decode(self, samples_in: torch.Tensor, flux:bool = False) -> torch.Tensor: + """#### Decode the latent samples to pixel samples. + + #### Args: + - `samples_in` (torch.Tensor): The input latent samples. + + #### Returns: + - `torch.Tensor`: The decoded pixel samples. + """ + memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) + Device.load_models_gpu([self.patcher], memory_required=memory_used) + free_memory = Device.get_free_memory(self.device) + batch_number = int(free_memory / memory_used) + batch_number = max(1, batch_number) + + pixel_samples = torch.empty( + ( + samples_in.shape[0], + 3, + round(samples_in.shape[2] * self.upscale_ratio), + round(samples_in.shape[3] * self.upscale_ratio), + ), + device=self.output_device, + ) + for x in range(0, samples_in.shape[0], batch_number): + samples = ( + samples_in[x : x + batch_number].to(self.vae_dtype).to(self.device) + ) + pixel_samples[x : x + batch_number] = self.process_output( + self.first_stage_model.decode(samples, flux=flux).to(self.output_device).float() + ) + pixel_samples = pixel_samples.to(self.output_device).movedim(1, -1) + return pixel_samples + + + def encode(self, pixel_samples: torch.Tensor, flux:bool = False) -> torch.Tensor: + """#### Encode the pixel samples to latent samples. + + #### Args: + - `pixel_samples` (torch.Tensor): The input pixel samples. + + #### Returns: + - `torch.Tensor`: The encoded latent samples. + """ + pixel_samples = self.vae_encode_crop_pixels(pixel_samples) + pixel_samples = pixel_samples.movedim(-1, 1) + memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) + Device.load_models_gpu([self.patcher], memory_required=memory_used) + free_memory = Device.get_free_memory(self.device) + batch_number = int(free_memory / memory_used) + batch_number = max(1, batch_number) + samples = torch.empty( + ( + pixel_samples.shape[0], + self.latent_channels, + round(pixel_samples.shape[2] // self.downscale_ratio), + round(pixel_samples.shape[3] // self.downscale_ratio), + ), + device=self.output_device, + ) + for x in range(0, pixel_samples.shape[0], batch_number): + pixels_in = ( + self.process_input(pixel_samples[x : x + batch_number]) + .to(self.vae_dtype) + .to(self.device) + ) + samples[x : x + batch_number] = ( + self.first_stage_model.encode(pixels_in, flux=flux).to(self.output_device).float() + ) + + return samples + + def get_sd(self): + """#### Get the state dictionary. + + #### Returns: + - `dict`: The state dictionary. + """ + return self.first_stage_model.state_dict() + + +class VAEDecode: + """#### Class for decoding VAE samples.""" + + def decode(self, vae: VAE, samples: dict, flux:bool = False) -> Tuple[torch.Tensor]: + """#### Decode the VAE samples. + + #### Args: + - `vae` (VAE): The VAE instance. + - `samples` (dict): The samples dictionary. + + #### Returns: + - `Tuple[torch.Tensor]`: The decoded samples. + """ + return (vae.decode(samples["samples"], flux=flux),) + + +class VAEEncode: + """#### Class for encoding VAE samples.""" + + def encode(self, vae: VAE, pixels: torch.Tensor, flux:bool = False) -> Tuple[dict]: + """#### Encode the VAE samples. + + #### Args: + - `vae` (VAE): The VAE instance. + - `pixels` (torch.Tensor): The input pixel tensor. + + #### Returns: + - `Tuple[dict]`: The encoded samples dictionary. + """ + t = vae.encode(pixels[:, :, :, :3], flux=flux) + return ({"samples": t},) + + +class VAELoader: + """#### Class for loading VAEs.""" + # TODO: scale factor? + def load_vae(self, vae_name): + """#### Load the VAE. + + #### Args: + - `vae_name`: The name of the VAE. + + #### Returns: + - `Tuple[VAE]`: The VAE instance. + """ + if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]: + sd = self.load_taesd(vae_name) + else: + vae_path = "./_internal/vae/" + vae_name + sd = util.load_torch_file(vae_path) + vae = VAE(sd=sd) + return (vae,) + + diff --git a/modules/AutoEncoders/taesd.py b/modules/AutoEncoders/taesd.py index ddd7018d896f83331d5262a468145372603434c2..d9373f767b677c4f8e659c1082f3122f7be54fc2 100644 --- a/modules/AutoEncoders/taesd.py +++ b/modules/AutoEncoders/taesd.py @@ -1,310 +1,310 @@ -""" -Tiny AutoEncoder for Stable Diffusion -(DNN for encoding / decoding SD's latent space) -""" - -# TODO: Check if multiprocessing is possible for this module -from PIL import Image -import numpy as np -from sympy import im -import torch -from modules.Utilities import util -import torch.nn as nn - -from modules.cond import cast -from modules.user import app_instance - - -def conv(n_in: int, n_out: int, **kwargs) -> cast.disable_weight_init.Conv2d: - """#### Create a convolutional layer. - - #### Args: - - `n_in` (int): The number of input channels. - - `n_out` (int): The number of output channels. - - #### Returns: - - `torch.nn.Module`: The convolutional layer. - """ - return cast.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) - - -class Clamp(nn.Module): - """#### Class representing a clamping layer.""" - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass of the clamping layer. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The clamped tensor. - """ - return torch.tanh(x / 3) * 3 - - -class Block(nn.Module): - """#### Class representing a block layer.""" - - def __init__(self, n_in: int, n_out: int): - """#### Initialize the block layer. - - #### Args: - - `n_in` (int): The number of input channels. - - `n_out` (int): The number of output channels. - - #### Returns: - - `Block`: The block layer. - """ - super().__init__() - self.conv = nn.Sequential( - conv(n_in, n_out), - nn.ReLU(), - conv(n_out, n_out), - nn.ReLU(), - conv(n_out, n_out), - ) - self.skip = ( - cast.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) - if n_in != n_out - else nn.Identity() - ) - self.fuse = nn.ReLU() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.fuse(self.conv(x) + self.skip(x)) - - -def Encoder2(latent_channels: int = 4) -> nn.Sequential: - """#### Create an encoder. - - #### Args: - - `latent_channels` (int, optional): The number of latent channels. Defaults to 4. - - #### Returns: - - `torch.nn.Module`: The encoder. - """ - return nn.Sequential( - conv(3, 64), - Block(64, 64), - conv(64, 64, stride=2, bias=False), - Block(64, 64), - Block(64, 64), - Block(64, 64), - conv(64, 64, stride=2, bias=False), - Block(64, 64), - Block(64, 64), - Block(64, 64), - conv(64, 64, stride=2, bias=False), - Block(64, 64), - Block(64, 64), - Block(64, 64), - conv(64, latent_channels), - ) - - -def Decoder2(latent_channels: int = 4) -> nn.Sequential: - """#### Create a decoder. - - #### Args: - - `latent_channels` (int, optional): The number of latent channels. Defaults to 4. - - #### Returns: - - `torch.nn.Module`: The decoder. - """ - return nn.Sequential( - Clamp(), - conv(latent_channels, 64), - nn.ReLU(), - Block(64, 64), - Block(64, 64), - Block(64, 64), - nn.Upsample(scale_factor=2), - conv(64, 64, bias=False), - Block(64, 64), - Block(64, 64), - Block(64, 64), - nn.Upsample(scale_factor=2), - conv(64, 64, bias=False), - Block(64, 64), - Block(64, 64), - Block(64, 64), - nn.Upsample(scale_factor=2), - conv(64, 64, bias=False), - Block(64, 64), - conv(64, 3), - ) - - -class TAESD(nn.Module): - """#### Class representing a Tiny AutoEncoder for Stable Diffusion. - - #### Attributes: - - `latent_magnitude` (float): Magnitude of the latent space. - - `latent_shift` (float): Shift value for the latent space. - - `vae_shift` (torch.nn.Parameter): Shift parameter for the VAE. - - `vae_scale` (torch.nn.Parameter): Scale parameter for the VAE. - - `taesd_encoder` (Encoder2): Encoder network for the TAESD. - - `taesd_decoder` (Decoder2): Decoder network for the TAESD. - - #### Args: - - `encoder_path` (str, optional): Path to the encoder model file. Defaults to None. - - `decoder_path` (str, optional): Path to the decoder model file. Defaults to "./_internal/vae_approx/taesd_decoder.safetensors". - - `latent_channels` (int, optional): Number of channels in the latent space. Defaults to 4. - - #### Methods: - - `scale_latents(x)`: - Scales raw latents to the range [0, 1]. - - `unscale_latents(x)`: - Unscales latents from the range [0, 1] to raw latents. - - `decode(x)`: - Decodes the given latent representation to the original space. - - `encode(x)`: - Encodes the given input to the latent space. - """ - - latent_magnitude = 3 - latent_shift = 0.5 - - def __init__( - self, - encoder_path: str = None, - decoder_path: str = None, - latent_channels: int = 4, - ): - """#### Initialize the TAESD model. - - #### Args: - - `encoder_path` (str, optional): Path to the encoder model file. Defaults to None. - - `decoder_path` (str, optional): Path to the decoder model file. Defaults to "./_internal/vae_approx/taesd_decoder.safetensors". - - `latent_channels` (int, optional): Number of channels in the latent space. Defaults to 4. - """ - super().__init__() - self.vae_shift = torch.nn.Parameter(torch.tensor(0.0)) - self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) - self.taesd_encoder = Encoder2(latent_channels) - self.taesd_decoder = Decoder2(latent_channels) - decoder_path = ( - "./_internal/vae_approx/taesd_decoder.safetensors" - if decoder_path is None - else decoder_path - ) - if encoder_path is not None: - self.taesd_encoder.load_state_dict( - util.load_torch_file(encoder_path, safe_load=True) - ) - if decoder_path is not None: - self.taesd_decoder.load_state_dict( - util.load_torch_file(decoder_path, safe_load=True) - ) - - @staticmethod - def scale_latents(x: torch.Tensor) -> torch.Tensor: - """#### Scales raw latents to the range [0, 1]. - - #### Args: - - `x` (torch.Tensor): The raw latents. - - #### Returns: - - `torch.Tensor`: The scaled latents. - """ - return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) - - @staticmethod - def unscale_latents(x: torch.Tensor) -> torch.Tensor: - """#### Unscales latents from the range [0, 1] to raw latents. - - #### Args: - - `x` (torch.Tensor): The scaled latents. - - #### Returns: - - `torch.Tensor`: The raw latents. - """ - return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) - - def decode(self, x: torch.Tensor) -> torch.Tensor: - """#### Decodes the given latent representation to the original space. - - #### Args: - - `x` (torch.Tensor): The latent representation. - - #### Returns: - - `torch.Tensor`: The decoded representation. - """ - device = next(self.taesd_decoder.parameters()).device - x = x.to(device) - x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale) - x_sample = x_sample.sub(0.5).mul(2) - return x_sample - - def encode(self, x: torch.Tensor) -> torch.Tensor: - """#### Encodes the given input to the latent space. - - #### Args: - - `x` (torch.Tensor): The input. - - #### Returns: - - `torch.Tensor`: The latent representation. - """ - device = next(self.taesd_encoder.parameters()).device - x = x.to(device) - return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift - - -def taesd_preview(x: torch.Tensor, flux: bool = False): - """#### Preview the batched latent tensors as images. - - #### Args: - - `x` (torch.Tensor): Input latent tensor with shape [B,C,H,W] - - `flux` (bool, optional): Whether using flux model (for channel ordering). Defaults to False. - """ - if app_instance.app.previewer_var.get() is True: - taesd_instance = TAESD() - - # Handle channel dimension - if x.shape[1] != 4: - desired_channels = 4 - current_channels = x.shape[1] - - if current_channels > desired_channels: - x = x[:, :desired_channels, :, :] - else: - padding = torch.zeros(x.shape[0], desired_channels - current_channels, - x.shape[2], x.shape[3], device=x.device) - x = torch.cat([x, padding], dim=1) - - # Process entire batch at once - decoded_batch = taesd_instance.decode(x) - - images = [] - - # Convert each image in batch - for decoded in decoded_batch: - # Handle channel dimension - if decoded.shape[0] == 1: - decoded = decoded.repeat(3, 1, 1) - - # Apply different normalization for flux vs standard mode - if flux: - # For flux: Assume BGR ordering and different normalization - decoded = decoded[[2,1,0], :, :] # BGR -> RGB - # Adjust normalization for flux model range - decoded = decoded.clamp(-1, 1) - decoded = (decoded + 1.0) * 0.5 # Scale from [-1,1] to [0,1] - else: - # Standard normalization - decoded = (decoded + 1.0) / 2.0 - - # Convert to numpy and uint8 - image_np = (decoded.cpu().detach().numpy() * 255.0) - image_np = np.transpose(image_np, (1, 2, 0)) - image_np = np.clip(image_np, 0, 255).astype(np.uint8) - - # Create PIL Image - img = Image.fromarray(image_np, mode='RGB') - images.append(img) - - # Update display with all images - app_instance.app.update_image(images) - else: - pass +""" +Tiny AutoEncoder for Stable Diffusion +(DNN for encoding / decoding SD's latent space) +""" + +# TODO: Check if multiprocessing is possible for this module +from PIL import Image +import numpy as np +from sympy import im +import torch +from modules.Utilities import util +import torch.nn as nn + +from modules.cond import cast +from modules.user import app_instance + + +def conv(n_in: int, n_out: int, **kwargs) -> cast.disable_weight_init.Conv2d: + """#### Create a convolutional layer. + + #### Args: + - `n_in` (int): The number of input channels. + - `n_out` (int): The number of output channels. + + #### Returns: + - `torch.nn.Module`: The convolutional layer. + """ + return cast.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) + + +class Clamp(nn.Module): + """#### Class representing a clamping layer.""" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass of the clamping layer. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The clamped tensor. + """ + return torch.tanh(x / 3) * 3 + + +class Block(nn.Module): + """#### Class representing a block layer.""" + + def __init__(self, n_in: int, n_out: int): + """#### Initialize the block layer. + + #### Args: + - `n_in` (int): The number of input channels. + - `n_out` (int): The number of output channels. + + #### Returns: + - `Block`: The block layer. + """ + super().__init__() + self.conv = nn.Sequential( + conv(n_in, n_out), + nn.ReLU(), + conv(n_out, n_out), + nn.ReLU(), + conv(n_out, n_out), + ) + self.skip = ( + cast.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) + if n_in != n_out + else nn.Identity() + ) + self.fuse = nn.ReLU() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.fuse(self.conv(x) + self.skip(x)) + + +def Encoder2(latent_channels: int = 4) -> nn.Sequential: + """#### Create an encoder. + + #### Args: + - `latent_channels` (int, optional): The number of latent channels. Defaults to 4. + + #### Returns: + - `torch.nn.Module`: The encoder. + """ + return nn.Sequential( + conv(3, 64), + Block(64, 64), + conv(64, 64, stride=2, bias=False), + Block(64, 64), + Block(64, 64), + Block(64, 64), + conv(64, 64, stride=2, bias=False), + Block(64, 64), + Block(64, 64), + Block(64, 64), + conv(64, 64, stride=2, bias=False), + Block(64, 64), + Block(64, 64), + Block(64, 64), + conv(64, latent_channels), + ) + + +def Decoder2(latent_channels: int = 4) -> nn.Sequential: + """#### Create a decoder. + + #### Args: + - `latent_channels` (int, optional): The number of latent channels. Defaults to 4. + + #### Returns: + - `torch.nn.Module`: The decoder. + """ + return nn.Sequential( + Clamp(), + conv(latent_channels, 64), + nn.ReLU(), + Block(64, 64), + Block(64, 64), + Block(64, 64), + nn.Upsample(scale_factor=2), + conv(64, 64, bias=False), + Block(64, 64), + Block(64, 64), + Block(64, 64), + nn.Upsample(scale_factor=2), + conv(64, 64, bias=False), + Block(64, 64), + Block(64, 64), + Block(64, 64), + nn.Upsample(scale_factor=2), + conv(64, 64, bias=False), + Block(64, 64), + conv(64, 3), + ) + + +class TAESD(nn.Module): + """#### Class representing a Tiny AutoEncoder for Stable Diffusion. + + #### Attributes: + - `latent_magnitude` (float): Magnitude of the latent space. + - `latent_shift` (float): Shift value for the latent space. + - `vae_shift` (torch.nn.Parameter): Shift parameter for the VAE. + - `vae_scale` (torch.nn.Parameter): Scale parameter for the VAE. + - `taesd_encoder` (Encoder2): Encoder network for the TAESD. + - `taesd_decoder` (Decoder2): Decoder network for the TAESD. + + #### Args: + - `encoder_path` (str, optional): Path to the encoder model file. Defaults to None. + - `decoder_path` (str, optional): Path to the decoder model file. Defaults to "./_internal/vae_approx/taesd_decoder.safetensors". + - `latent_channels` (int, optional): Number of channels in the latent space. Defaults to 4. + + #### Methods: + - `scale_latents(x)`: + Scales raw latents to the range [0, 1]. + - `unscale_latents(x)`: + Unscales latents from the range [0, 1] to raw latents. + - `decode(x)`: + Decodes the given latent representation to the original space. + - `encode(x)`: + Encodes the given input to the latent space. + """ + + latent_magnitude = 3 + latent_shift = 0.5 + + def __init__( + self, + encoder_path: str = None, + decoder_path: str = None, + latent_channels: int = 4, + ): + """#### Initialize the TAESD model. + + #### Args: + - `encoder_path` (str, optional): Path to the encoder model file. Defaults to None. + - `decoder_path` (str, optional): Path to the decoder model file. Defaults to "./_internal/vae_approx/taesd_decoder.safetensors". + - `latent_channels` (int, optional): Number of channels in the latent space. Defaults to 4. + """ + super().__init__() + self.vae_shift = torch.nn.Parameter(torch.tensor(0.0)) + self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) + self.taesd_encoder = Encoder2(latent_channels) + self.taesd_decoder = Decoder2(latent_channels) + decoder_path = ( + "./_internal/vae_approx/taesd_decoder.safetensors" + if decoder_path is None + else decoder_path + ) + if encoder_path is not None: + self.taesd_encoder.load_state_dict( + util.load_torch_file(encoder_path, safe_load=True) + ) + if decoder_path is not None: + self.taesd_decoder.load_state_dict( + util.load_torch_file(decoder_path, safe_load=True) + ) + + @staticmethod + def scale_latents(x: torch.Tensor) -> torch.Tensor: + """#### Scales raw latents to the range [0, 1]. + + #### Args: + - `x` (torch.Tensor): The raw latents. + + #### Returns: + - `torch.Tensor`: The scaled latents. + """ + return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) + + @staticmethod + def unscale_latents(x: torch.Tensor) -> torch.Tensor: + """#### Unscales latents from the range [0, 1] to raw latents. + + #### Args: + - `x` (torch.Tensor): The scaled latents. + + #### Returns: + - `torch.Tensor`: The raw latents. + """ + return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) + + def decode(self, x: torch.Tensor) -> torch.Tensor: + """#### Decodes the given latent representation to the original space. + + #### Args: + - `x` (torch.Tensor): The latent representation. + + #### Returns: + - `torch.Tensor`: The decoded representation. + """ + device = next(self.taesd_decoder.parameters()).device + x = x.to(device) + x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale) + x_sample = x_sample.sub(0.5).mul(2) + return x_sample + + def encode(self, x: torch.Tensor) -> torch.Tensor: + """#### Encodes the given input to the latent space. + + #### Args: + - `x` (torch.Tensor): The input. + + #### Returns: + - `torch.Tensor`: The latent representation. + """ + device = next(self.taesd_encoder.parameters()).device + x = x.to(device) + return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift + + +def taesd_preview(x: torch.Tensor, flux: bool = False): + """#### Preview the batched latent tensors as images. + + #### Args: + - `x` (torch.Tensor): Input latent tensor with shape [B,C,H,W] + - `flux` (bool, optional): Whether using flux model (for channel ordering). Defaults to False. + """ + if app_instance.app.previewer_var.get() is True: + taesd_instance = TAESD() + + # Handle channel dimension + if x.shape[1] != 4: + desired_channels = 4 + current_channels = x.shape[1] + + if current_channels > desired_channels: + x = x[:, :desired_channels, :, :] + else: + padding = torch.zeros(x.shape[0], desired_channels - current_channels, + x.shape[2], x.shape[3], device=x.device) + x = torch.cat([x, padding], dim=1) + + # Process entire batch at once + decoded_batch = taesd_instance.decode(x) + + images = [] + + # Convert each image in batch + for decoded in decoded_batch: + # Handle channel dimension + if decoded.shape[0] == 1: + decoded = decoded.repeat(3, 1, 1) + + # Apply different normalization for flux vs standard mode + if flux: + # For flux: Assume BGR ordering and different normalization + decoded = decoded[[2,1,0], :, :] # BGR -> RGB + # Adjust normalization for flux model range + decoded = decoded.clamp(-1, 1) + decoded = (decoded + 1.0) * 0.5 # Scale from [-1,1] to [0,1] + else: + # Standard normalization + decoded = (decoded + 1.0) / 2.0 + + # Convert to numpy and uint8 + image_np = (decoded.cpu().detach().numpy() * 255.0) + image_np = np.transpose(image_np, (1, 2, 0)) + image_np = np.clip(image_np, 0, 255).astype(np.uint8) + + # Create PIL Image + img = Image.fromarray(image_np, mode='RGB') + images.append(img) + + # Update display with all images + app_instance.app.update_image(images) + else: + pass diff --git a/modules/AutoHDR/ahdr.py b/modules/AutoHDR/ahdr.py new file mode 100644 index 0000000000000000000000000000000000000000..92b36fbda20e0d4ef81560ddcb4bc28724a52d2a --- /dev/null +++ b/modules/AutoHDR/ahdr.py @@ -0,0 +1,127 @@ +# Taken and adapted from https://github.com/SuperBeastsAI/ComfyUI-SuperBeasts + +import numpy as np +from PIL import Image, ImageOps, ImageDraw, ImageFilter, ImageEnhance, ImageCms +from PIL.PngImagePlugin import PngInfo +import torch +import torch.nn.functional as F +import json +import random + + +sRGB_profile = ImageCms.createProfile("sRGB") +Lab_profile = ImageCms.createProfile("LAB") + +# Tensor to PIL +def tensor2pil(image): + return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + +# PIL to Tensor +def pil2tensor(image): + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + +def adjust_shadows_non_linear(luminance, shadow_intensity, max_shadow_adjustment=1.5): + lum_array = np.array(luminance, dtype=np.float32) / 255.0 # Normalize + # Apply a non-linear darkening effect based on shadow_intensity + shadows = lum_array ** (1 / (1 + shadow_intensity * max_shadow_adjustment)) + return np.clip(shadows * 255, 0, 255).astype(np.uint8) # Re-scale to [0, 255] + +def adjust_highlights_non_linear(luminance, highlight_intensity, max_highlight_adjustment=1.5): + lum_array = np.array(luminance, dtype=np.float32) / 255.0 # Normalize + # Brighten highlights more aggressively based on highlight_intensity + highlights = 1 - (1 - lum_array) ** (1 + highlight_intensity * max_highlight_adjustment) + return np.clip(highlights * 255, 0, 255).astype(np.uint8) # Re-scale to [0, 255] + +def merge_adjustments_with_blend_modes(luminance, shadows, highlights, hdr_intensity, shadow_intensity, highlight_intensity): + # Ensure the data is in the correct format for processing + base = np.array(luminance, dtype=np.float32) + + # Scale the adjustments based on hdr_intensity + scaled_shadow_intensity = shadow_intensity ** 2 * hdr_intensity + scaled_highlight_intensity = highlight_intensity ** 2 * hdr_intensity + + # Create luminance-based masks for shadows and highlights + shadow_mask = np.clip((1 - (base / 255)) ** 2, 0, 1) + highlight_mask = np.clip((base / 255) ** 2, 0, 1) + + # Apply the adjustments using the masks + adjusted_shadows = np.clip(base * (1 - shadow_mask * scaled_shadow_intensity), 0, 255) + adjusted_highlights = np.clip(base + (255 - base) * highlight_mask * scaled_highlight_intensity, 0, 255) + + # Combine the adjusted shadows and highlights + adjusted_luminance = np.clip(adjusted_shadows + adjusted_highlights - base, 0, 255) + + # Blend the adjusted luminance with the original luminance based on hdr_intensity + final_luminance = np.clip(base * (1 - hdr_intensity) + adjusted_luminance * hdr_intensity, 0, 255).astype(np.uint8) + + return Image.fromarray(final_luminance) + +def apply_gamma_correction(lum_array, gamma): + """ + Apply gamma correction to the luminance array. + :param lum_array: Luminance channel as a NumPy array. + :param gamma: Gamma value for correction. + """ + if gamma == 0: + return np.clip(lum_array, 0, 255).astype(np.uint8) + + epsilon = 1e-7 # Small value to avoid dividing by zero + gamma_corrected = 1 / (1.1 - gamma) + adjusted = 255 * ((lum_array / 255) ** gamma_corrected) + return np.clip(adjusted, 0, 255).astype(np.uint8) + +# create a wrapper function that can apply a function to multiple images in a batch while passing all other arguments to the function +def apply_to_batch(func): + def wrapper(self, image, *args, **kwargs): + images = [] + for img in image: + images.append(func(self, img, *args, **kwargs)) + batch_tensor = torch.cat(images, dim=0) + return (batch_tensor, ) + return wrapper + +class HDREffects: + @apply_to_batch + def apply_hdr2(self, image, hdr_intensity=0.75, shadow_intensity=0.25, highlight_intensity=0.5, gamma_intensity=0.25, contrast=0.1, enhance_color=0.25): + # Load the image + img = tensor2pil(image) + + # Step 1: Convert RGB to LAB for better color preservation + img_lab = ImageCms.profileToProfile(img, sRGB_profile, Lab_profile, outputMode='LAB') + + # Extract L, A, and B channels + luminance, a, b = img_lab.split() + + # Convert luminance to a NumPy array for processing + lum_array = np.array(luminance, dtype=np.float32) + + # Preparing adjustment layers (shadows, midtones, highlights) + # This example assumes you have methods to extract or calculate these adjustments + shadows_adjusted = adjust_shadows_non_linear(luminance, shadow_intensity) + highlights_adjusted = adjust_highlights_non_linear(luminance, highlight_intensity) + + + merged_adjustments = merge_adjustments_with_blend_modes(lum_array, shadows_adjusted, highlights_adjusted, hdr_intensity, shadow_intensity, highlight_intensity) + + # Apply gamma correction with a base_gamma value (define based on desired effect) + gamma_corrected = apply_gamma_correction(np.array(merged_adjustments), gamma_intensity) + gamma_corrected = Image.fromarray(gamma_corrected).resize(a.size) + + + # Merge L channel back with original A and B channels + adjusted_lab = Image.merge('LAB', (gamma_corrected, a, b)) + + # Step 3: Convert LAB back to RGB + img_adjusted = ImageCms.profileToProfile(adjusted_lab, Lab_profile, sRGB_profile, outputMode='RGB') + + + # Enhance contrast + enhancer = ImageEnhance.Contrast(img_adjusted) + contrast_adjusted = enhancer.enhance(1 + contrast) + + + # Enhance color saturation + enhancer = ImageEnhance.Color(contrast_adjusted) + color_adjusted = enhancer.enhance(1 + enhance_color * 0.2) + + return pil2tensor(color_adjusted) \ No newline at end of file diff --git a/modules/BlackForest/Flux.py b/modules/BlackForest/Flux.py index 53e43fdf1a8420809ee5f7233c3f036bdf71bdc4..12d7cb24f7d52e3a85376f8f6758aba24b4c22ce 100644 --- a/modules/BlackForest/Flux.py +++ b/modules/BlackForest/Flux.py @@ -1,853 +1,853 @@ -# Original code can be found on: https://github.com/black-forest-labs/flux - - -from dataclasses import dataclass -from einops import rearrange, repeat -import torch -import torch.nn as nn - -from modules.Attention import Attention -from modules.Device import Device -from modules.Model import ModelBase -from modules.Utilities import Latent -from modules.cond import cast, cond -from modules.sample import sampling, sampling_util - - -# Define the attention mechanism -def attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, pe: torch.Tensor) -> torch.Tensor: - """#### Compute the attention mechanism. - - #### Args: - - `q` (Tensor): The query tensor. - - `k` (Tensor): The key tensor. - - `v` (Tensor): The value tensor. - - `pe` (Tensor): The positional encoding tensor. - - #### Returns: - - `Tensor`: The attention tensor. - """ - q, k = apply_rope(q, k, pe) - heads = q.shape[1] - x = Attention.optimized_attention(q, k, v, heads, skip_reshape=True, flux=True) - return x - -# Define the rotary positional encoding (RoPE) -def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: - """#### Compute the rotary positional encoding. - - #### Args: - - `pos` (Tensor): The position tensor. - - `dim` (int): The dimension of the tensor. - - `theta` (int): The theta value for scaling. - - #### Returns: - - `Tensor`: The rotary positional encoding tensor. - """ - assert dim % 2 == 0 - if Device.is_device_mps(pos.device) or Device.is_intel_xpu(): - device = torch.device("cpu") - else: - device = pos.device - - scale = torch.linspace( - 0, (dim - 2) / dim, steps=dim // 2, dtype=torch.float64, device=device - ) - omega = 1.0 / (theta**scale) - out = torch.einsum( - "...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega - ) - out = torch.stack( - [torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1 - ) - out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) - return out.to(dtype=torch.float32, device=pos.device) - -# Apply the rotary positional encoding to the query and key tensors -def apply_rope(xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor) -> tuple: - """#### Apply the rotary positional encoding to the query and key tensors. - - #### Args: - - `xq` (Tensor): The query tensor. - - `xk` (Tensor): The key tensor. - - `freqs_cis` (Tensor): The frequency tensor. - - #### Returns: - - `tuple`: The modified query and key tensors. - """ - xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) - xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) - xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] - xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] - return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) - -# Define the embedding class -class EmbedND(nn.Module): - def __init__(self, dim: int, theta: int, axes_dim: list): - """#### Initialize the EmbedND class. - - #### Args: - - `dim` (int): The dimension of the tensor. - - `theta` (int): The theta value for scaling. - - `axes_dim` (list): The list of axis dimensions. - """ - super().__init__() - self.dim = dim - self.theta = theta - self.axes_dim = axes_dim - - def forward(self, ids: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the EmbedND class. - - #### Args: - - `ids` (Tensor): The input tensor. - - #### Returns: - - `Tensor`: The embedded tensor. - """ - n_axes = ids.shape[-1] - emb = torch.cat( - [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], - dim=-3, - ) - return emb.unsqueeze(1) - -# Define the MLP embedder class -class MLPEmbedder(nn.Module): - def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None): - """#### Initialize the MLPEmbedder class. - - #### Args: - - `in_dim` (int): The input dimension. - - `hidden_dim` (int): The hidden dimension. - - `dtype` (optional): The data type. - - `device` (optional): The device. - - `operations` (optional): The operations module. - """ - super().__init__() - self.in_layer = operations.Linear( - in_dim, hidden_dim, bias=True, dtype=dtype, device=device - ) - self.silu = nn.SiLU() - self.out_layer = operations.Linear( - hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the MLPEmbedder class. - - #### Args: - - `x` (Tensor): The input tensor. - - #### Returns: - - `Tensor`: The output tensor. - """ - return self.out_layer(self.silu(self.in_layer(x))) - -# Define the RMS normalization class -class RMSNorm(nn.Module): - def __init__(self, dim: int, dtype=None, device=None, operations=None): - """#### Initialize the RMSNorm class. - - #### Args: - - `dim` (int): The dimension of the tensor. - - `dtype` (optional): The data type. - - `device` (optional): The device. - - `operations` (optional): The operations module. - """ - super().__init__() - self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the RMSNorm class. - - #### Args: - - `x` (Tensor): The input tensor. - - #### Returns: - - `Tensor`: The normalized tensor. - """ - return rms_norm(x, self.scale, 1e-6) - -# Define the query-key normalization class -class QKNorm(nn.Module): - def __init__(self, dim: int, dtype=None, device=None, operations=None): - """#### Initialize the QKNorm class. - - #### Args: - - `dim` (int): The dimension of the tensor. - - `dtype` (optional): The data type. - - `device` (optional): The device. - - `operations` (optional): The operations module. - """ - super().__init__() - self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations) - self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations) - - def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> tuple: - """#### Forward pass for the QKNorm class. - - #### Args: - - `q` (Tensor): The query tensor. - - `k` (Tensor): The key tensor. - - `v` (Tensor): The value tensor. - - #### Returns: - - `tuple`: The normalized query and key tensors. - """ - q = self.query_norm(q) - k = self.key_norm(k) - return q.to(v), k.to(v) - -# Define the self-attention class -class SelfAttention(nn.Module): - def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None): - """#### Initialize the SelfAttention class. - - #### Args: - - `dim` (int): The dimension of the tensor. - - `num_heads` (int, optional): The number of attention heads. Defaults to 8. - - `qkv_bias` (bool, optional): Whether to use bias in QKV projection. Defaults to False. - - `dtype` (optional): The data type. - - `device` (optional): The device. - - `operations` (optional): The operations module. - """ - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - - self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) - self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) - self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) - -# Define the modulation output dataclass -@dataclass -class ModulationOut: - shift: torch.Tensor - scale: torch.Tensor - gate: torch.Tensor - -# Define the modulation class -class Modulation(nn.Module): - def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None): - """#### Initialize the Modulation class. - - #### Args: - - `dim` (int): The dimension of the tensor. - - `double` (bool): Whether to use double modulation. - - `dtype` (optional): The data type. - - `device` (optional): The device. - - `operations` (optional): The operations module. - """ - super().__init__() - self.is_double = double - self.multiplier = 6 if double else 3 - self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device) - - def forward(self, vec: torch.Tensor) -> tuple: - """#### Forward pass for the Modulation class. - - #### Args: - - `vec` (Tensor): The input tensor. - - #### Returns: - - `tuple`: The modulation output. - """ - out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1) - return (ModulationOut(*out[:3]), ModulationOut(*out[3:]) if self.is_double else None) - -# Define the double stream block class -class DoubleStreamBlock(nn.Module): - def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, dtype=None, device=None, operations=None): - """#### Initialize the DoubleStreamBlock class. - - #### Args: - - `hidden_size` (int): The hidden size. - - `num_heads` (int): The number of attention heads. - - `mlp_ratio` (float): The MLP ratio. - - `qkv_bias` (bool, optional): Whether to use bias in QKV projection. Defaults to False. - - `dtype` (optional): The data type. - - `device` (optional): The device. - - `operations` (optional): The operations module. - """ - super().__init__() - - mlp_hidden_dim = int(hidden_size * mlp_ratio) - self.num_heads = num_heads - self.hidden_size = hidden_size - self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) - self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) - self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.img_mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.GELU(approximate="tanh"), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) - - self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) - self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) - self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.txt_mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.GELU(approximate="tanh"), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) - - def forward(self, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, pe: torch.Tensor) -> tuple: - """#### Forward pass for the DoubleStreamBlock class. - - #### Args: - - `img` (Tensor): The image tensor. - - `txt` (Tensor): The text tensor. - - `vec` (Tensor): The vector tensor. - - `pe` (Tensor): The positional encoding tensor. - - #### Returns: - - `tuple`: The modified image and text tensors. - """ - img_mod1, img_mod2 = self.img_mod(vec) - txt_mod1, txt_mod2 = self.txt_mod(vec) - - # prepare image for attention - img_modulated = self.img_norm1(img) - img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift - img_qkv = self.img_attn.qkv(img_modulated) - img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) - - # prepare txt for attention - txt_modulated = self.txt_norm1(txt) - txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift - txt_qkv = self.txt_attn.qkv(txt_modulated) - txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) - - # run actual attention - attn = attention( - torch.cat((txt_q, img_q), dim=2), - torch.cat((txt_k, img_k), dim=2), - torch.cat((txt_v, img_v), dim=2), - pe=pe, - ) - - txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] - - # calculate the img bloks - img = img + img_mod1.gate * self.img_attn.proj(img_attn) - img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) - - # calculate the txt bloks - txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) - txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) - - if txt.dtype == torch.float16: - txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) - - return img, txt - -# Define the single stream block class -class SingleStreamBlock(nn.Module): - """ - A DiT block with parallel linear layers as described in - https://arxiv.org/abs/2302.05442 and adapted modulation interface. - """ - - def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0, qk_scale: float = None, dtype=None, device=None, operations=None): - """#### Initialize the SingleStreamBlock class. - - #### Args: - - `hidden_size` (int): The hidden size. - - `num_heads` (int): The number of attention heads. - - `mlp_ratio` (float, optional): The MLP ratio. Defaults to 4.0. - - `qk_scale` (float, optional): The QK scale. Defaults to None. - - `dtype` (optional): The data type. - - `device` (optional): The device. - - `operations` (optional): The operations module. - """ - super().__init__() - self.hidden_dim = hidden_size - self.num_heads = num_heads - head_dim = hidden_size // num_heads - self.scale = qk_scale or head_dim**-0.5 - - self.mlp_hidden_dim = int(hidden_size * mlp_ratio) - # qkv and mlp_in - self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) - # proj and mlp_out - self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) - - self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) - - self.hidden_size = hidden_size - self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - - self.mlp_act = nn.GELU(approximate="tanh") - self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) - - def forward(self, x: torch.Tensor, vec: torch.Tensor, pe: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the SingleStreamBlock class. - - #### Args: - - `x` (Tensor): The input tensor. - - `vec` (Tensor): The vector tensor. - - `pe` (Tensor): The positional encoding tensor. - - #### Returns: - - `Tensor`: The modified tensor. - """ - mod, _ = self.modulation(vec) - x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift - qkv, mlp = torch.split( - self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1 - ) - - q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute( - 2, 0, 3, 1, 4 - ) - q, k = self.norm(q, k, v) - - # compute attention - attn = attention(q, k, v, pe=pe) - # compute activation in mlp stream, cat again and run second linear layer - output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) - x += mod.gate * output - if x.dtype == torch.float16: - x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) - return x - -class LastLayer(nn.Module): - def __init__( - self, - hidden_size: int, - patch_size: int, - out_channels: int, - dtype=None, - device=None, - operations=None, - ): - """#### Initialize the LastLayer class. - - #### Args: - - `hidden_size` (int): The hidden size. - - `patch_size` (int): The patch size. - - `out_channels` (int): The number of output channels. - - `dtype` (optional): The data type. - - `device` (optional): The device. - - `operations` (optional): The operations module. - """ - super().__init__() - self.norm_final = operations.LayerNorm( - hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device - ) - self.linear = operations.Linear( - hidden_size, - patch_size * patch_size * out_channels, - bias=True, - dtype=dtype, - device=device, - ) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear( - hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device - ), - ) - - def forward(self, x: torch.Tensor, vec: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the LastLayer class. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `vec` (torch.Tensor): The vector tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1) - x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] - x = self.linear(x) - return x - - -def pad_to_patch_size(img: torch.Tensor, patch_size: tuple = (2, 2), padding_mode: str = "circular") -> torch.Tensor: - """#### Pad the image to the specified patch size. - - #### Args: - - `img` (torch.Tensor): The input image tensor. - - `patch_size` (tuple, optional): The patch size. Defaults to (2, 2). - - `padding_mode` (str, optional): The padding mode. Defaults to "circular". - - #### Returns: - - `torch.Tensor`: The padded image tensor. - """ - if ( - padding_mode == "circular" - and torch.jit.is_tracing() - or torch.jit.is_scripting() - ): - padding_mode = "reflect" - pad_h = (patch_size[0] - img.shape[-2] % patch_size[0]) % patch_size[0] - pad_w = (patch_size[1] - img.shape[-1] % patch_size[1]) % patch_size[1] - return torch.nn.functional.pad(img, (0, pad_w, 0, pad_h), mode=padding_mode) - - -try: - rms_norm_torch = torch.nn.functional.rms_norm -except Exception: - rms_norm_torch = None - - -def rms_norm(x: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6) -> torch.Tensor: - """#### Apply RMS normalization to the input tensor. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `weight` (torch.Tensor): The weight tensor. - - `eps` (float, optional): The epsilon value for numerical stability. Defaults to 1e-6. - - #### Returns: - - `torch.Tensor`: The normalized tensor. - """ - if rms_norm_torch is not None and not ( - torch.jit.is_tracing() or torch.jit.is_scripting() - ): - return rms_norm_torch( - x, - weight.shape, - weight=cast.cast_to(weight, dtype=x.dtype, device=x.device), - eps=eps, - ) - else: - rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps) - return (x * rrms) * cast.cast_to(weight, dtype=x.dtype, device=x.device) - - -@dataclass -class FluxParams: - in_channels: int - vec_in_dim: int - context_in_dim: int - hidden_size: int - mlp_ratio: float - num_heads: int - depth: int - depth_single_blocks: int - axes_dim: list - theta: int - qkv_bias: bool - guidance_embed: bool - - -class Flux3(nn.Module): - """ - Transformer model for flow matching on sequences. - """ - - def __init__( - self, - image_model=None, - final_layer: bool = True, - dtype=None, - device=None, - operations=None, - **kwargs, - ): - """#### Initialize the Flux3 class. - - #### Args: - - `image_model` (optional): The image model. - - `final_layer` (bool, optional): Whether to include the final layer. Defaults to True. - - `dtype` (optional): The data type. - - `device` (optional): The device. - - `operations` (optional): The operations module. - - `**kwargs`: Additional keyword arguments. - """ - super().__init__() - self.dtype = dtype - params = FluxParams(**kwargs) - self.params = params - self.in_channels = params.in_channels * 2 * 2 - self.out_channels = self.in_channels - if params.hidden_size % params.num_heads != 0: - raise ValueError( - f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" - ) - pe_dim = params.hidden_size // params.num_heads - if sum(params.axes_dim) != pe_dim: - raise ValueError( - f"Got {params.axes_dim} but expected positional dim {pe_dim}" - ) - self.hidden_size = params.hidden_size - self.num_heads = params.num_heads - self.pe_embedder = EmbedND( - dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim - ) - self.img_in = operations.Linear( - self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device - ) - self.time_in = MLPEmbedder( - in_dim=256, - hidden_dim=self.hidden_size, - dtype=dtype, - device=device, - operations=operations, - ) - self.vector_in = MLPEmbedder( - params.vec_in_dim, - self.hidden_size, - dtype=dtype, - device=device, - operations=operations, - ) - self.guidance_in = ( - MLPEmbedder( - in_dim=256, - hidden_dim=self.hidden_size, - dtype=dtype, - device=device, - operations=operations, - ) - if params.guidance_embed - else nn.Identity() - ) - self.txt_in = operations.Linear( - params.context_in_dim, self.hidden_size, dtype=dtype, device=device - ) - - self.double_blocks = nn.ModuleList( - [ - DoubleStreamBlock( - self.hidden_size, - self.num_heads, - mlp_ratio=params.mlp_ratio, - qkv_bias=params.qkv_bias, - dtype=dtype, - device=device, - operations=operations, - ) - for _ in range(params.depth) - ] - ) - - self.single_blocks = nn.ModuleList( - [ - SingleStreamBlock( - self.hidden_size, - self.num_heads, - mlp_ratio=params.mlp_ratio, - dtype=dtype, - device=device, - operations=operations, - ) - for _ in range(params.depth_single_blocks) - ] - ) - - if final_layer: - self.final_layer = LastLayer( - self.hidden_size, - 1, - self.out_channels, - dtype=dtype, - device=device, - operations=operations, - ) - - def forward_orig( - self, - img: torch.Tensor, - img_ids: torch.Tensor, - txt: torch.Tensor, - txt_ids: torch.Tensor, - timesteps: torch.Tensor, - y: torch.Tensor, - guidance: torch.Tensor = None, - control=None, - ) -> torch.Tensor: - """#### Original forward pass for the Flux3 class. - - #### Args: - - `img` (torch.Tensor): The image tensor. - - `img_ids` (torch.Tensor): The image IDs tensor. - - `txt` (torch.Tensor): The text tensor. - - `txt_ids` (torch.Tensor): The text IDs tensor. - - `timesteps` (torch.Tensor): The timesteps tensor. - - `y` (torch.Tensor): The vector tensor. - - `guidance` (torch.Tensor, optional): The guidance tensor. Defaults to None. - - `control` (optional): The control tensor. Defaults to None. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if img.ndim != 3 or txt.ndim != 3: - raise ValueError("Input img and txt tensors must have 3 dimensions.") - - # running on sequences img - img = self.img_in(img) - vec = self.time_in(sampling_util.timestep_embedding_flux(timesteps, 256).to(img.dtype)) - if self.params.guidance_embed: - if guidance is None: - raise ValueError( - "Didn't get guidance strength for guidance distilled model." - ) - vec = vec + self.guidance_in( - sampling_util.timestep_embedding_flux(guidance, 256).to(img.dtype) - ) - - vec = vec + self.vector_in(y) - txt = self.txt_in(txt) - - ids = torch.cat((txt_ids, img_ids), dim=1) - pe = self.pe_embedder(ids) - - for i, block in enumerate(self.double_blocks): - img, txt = block(img=img, txt=txt, vec=vec, pe=pe) - - if control is not None: # Controlnet - control_i = control.get("input") - if i < len(control_i): - add = control_i[i] - if add is not None: - img += add - - img = torch.cat((txt, img), 1) - - for i, block in enumerate(self.single_blocks): - img = block(img, vec=vec, pe=pe) - - if control is not None: # Controlnet - control_o = control.get("output") - if i < len(control_o): - add = control_o[i] - if add is not None: - img[:, txt.shape[1] :, ...] += add - - img = img[:, txt.shape[1] :, ...] - - img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) - return img - - def forward(self, x: torch.Tensor, timestep: torch.Tensor, context: torch.Tensor, y: torch.Tensor, guidance: torch.Tensor, control=None, **kwargs) -> torch.Tensor: - """#### Forward pass for the Flux3 class. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `timestep` (torch.Tensor): The timestep tensor. - - `context` (torch.Tensor): The context tensor. - - `y` (torch.Tensor): The vector tensor. - - `guidance` (torch.Tensor): The guidance tensor. - - `control` (optional): The control tensor. Defaults to None. - - `**kwargs`: Additional keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - bs, c, h, w = x.shape - patch_size = 2 - x = pad_to_patch_size(x, (patch_size, patch_size)) - - img = rearrange( - x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size - ) - - h_len = (h + (patch_size // 2)) // patch_size - w_len = (w + (patch_size // 2)) // patch_size - img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) - img_ids[..., 1] = ( - img_ids[..., 1] - + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[ - :, None - ] - ) - img_ids[..., 2] = ( - img_ids[..., 2] - + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[ - None, : - ] - ) - img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) - - txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) - out = self.forward_orig( - img, img_ids, context, txt_ids, timestep, y, guidance, control - ) - return rearrange( - out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2 - )[:, :, :h, :w] - - -class Flux2(ModelBase.BaseModel): - def __init__(self, model_config: dict, model_type=sampling.ModelType.FLUX, device=None): - """#### Initialize the Flux2 class. - - #### Args: - - `model_config` (dict): The model configuration. - - `model_type` (sampling.ModelType, optional): The model type. Defaults to sampling.ModelType.FLUX. - - `device` (optional): The device. - """ - super().__init__(model_config, model_type, device=device, unet_model=Flux3, flux=True) - - def encode_adm(self, **kwargs) -> torch.Tensor: - """#### Encode the ADM. - - #### Args: - - `**kwargs`: Additional keyword arguments. - - #### Returns: - - `torch.Tensor`: The encoded ADM tensor. - """ - return kwargs["pooled_output"] - - def extra_conds(self, **kwargs) -> dict: - """#### Get extra conditions. - - #### Args: - - `**kwargs`: Additional keyword arguments. - - #### Returns: - - `dict`: The extra conditions. - """ - out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out["c_crossattn"] = cond.CONDRegular(cross_attn) - out["guidance"] = cond.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)])) - return out - - -class Flux(ModelBase.BASE): - unet_config = { - "image_model": "flux", - "guidance_embed": True, - } - - sampling_settings = {} - - unet_extra_config = {} - latent_format = Latent.Flux1 - - memory_usage_factor = 2.8 - - supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict: dict, prefix: str = "", device=None) -> Flux2: - """#### Get the model. - - #### Args: - - `state_dict` (dict): The state dictionary. - - `prefix` (str, optional): The prefix. Defaults to "". - - `device` (optional): The device. - - #### Returns: - - `Flux2`: The Flux2 model. - """ - out = Flux2(self, device=device) - return out - - +# Original code can be found on: https://github.com/black-forest-labs/flux + + +from dataclasses import dataclass +from einops import rearrange, repeat +import torch +import torch.nn as nn + +from modules.Attention import Attention +from modules.Device import Device +from modules.Model import ModelBase +from modules.Utilities import Latent +from modules.cond import cast, cond +from modules.sample import sampling, sampling_util + + +# Define the attention mechanism +def attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, pe: torch.Tensor) -> torch.Tensor: + """#### Compute the attention mechanism. + + #### Args: + - `q` (Tensor): The query tensor. + - `k` (Tensor): The key tensor. + - `v` (Tensor): The value tensor. + - `pe` (Tensor): The positional encoding tensor. + + #### Returns: + - `Tensor`: The attention tensor. + """ + q, k = apply_rope(q, k, pe) + heads = q.shape[1] + x = Attention.optimized_attention(q, k, v, heads, skip_reshape=True, flux=True) + return x + +# Define the rotary positional encoding (RoPE) +def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: + """#### Compute the rotary positional encoding. + + #### Args: + - `pos` (Tensor): The position tensor. + - `dim` (int): The dimension of the tensor. + - `theta` (int): The theta value for scaling. + + #### Returns: + - `Tensor`: The rotary positional encoding tensor. + """ + assert dim % 2 == 0 + if Device.is_device_mps(pos.device) or Device.is_intel_xpu(): + device = torch.device("cpu") + else: + device = pos.device + + scale = torch.linspace( + 0, (dim - 2) / dim, steps=dim // 2, dtype=torch.float64, device=device + ) + omega = 1.0 / (theta**scale) + out = torch.einsum( + "...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega + ) + out = torch.stack( + [torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1 + ) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.to(dtype=torch.float32, device=pos.device) + +# Apply the rotary positional encoding to the query and key tensors +def apply_rope(xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor) -> tuple: + """#### Apply the rotary positional encoding to the query and key tensors. + + #### Args: + - `xq` (Tensor): The query tensor. + - `xk` (Tensor): The key tensor. + - `freqs_cis` (Tensor): The frequency tensor. + + #### Returns: + - `tuple`: The modified query and key tensors. + """ + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) + +# Define the embedding class +class EmbedND(nn.Module): + def __init__(self, dim: int, theta: int, axes_dim: list): + """#### Initialize the EmbedND class. + + #### Args: + - `dim` (int): The dimension of the tensor. + - `theta` (int): The theta value for scaling. + - `axes_dim` (list): The list of axis dimensions. + """ + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + + def forward(self, ids: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the EmbedND class. + + #### Args: + - `ids` (Tensor): The input tensor. + + #### Returns: + - `Tensor`: The embedded tensor. + """ + n_axes = ids.shape[-1] + emb = torch.cat( + [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], + dim=-3, + ) + return emb.unsqueeze(1) + +# Define the MLP embedder class +class MLPEmbedder(nn.Module): + def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None): + """#### Initialize the MLPEmbedder class. + + #### Args: + - `in_dim` (int): The input dimension. + - `hidden_dim` (int): The hidden dimension. + - `dtype` (optional): The data type. + - `device` (optional): The device. + - `operations` (optional): The operations module. + """ + super().__init__() + self.in_layer = operations.Linear( + in_dim, hidden_dim, bias=True, dtype=dtype, device=device + ) + self.silu = nn.SiLU() + self.out_layer = operations.Linear( + hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the MLPEmbedder class. + + #### Args: + - `x` (Tensor): The input tensor. + + #### Returns: + - `Tensor`: The output tensor. + """ + return self.out_layer(self.silu(self.in_layer(x))) + +# Define the RMS normalization class +class RMSNorm(nn.Module): + def __init__(self, dim: int, dtype=None, device=None, operations=None): + """#### Initialize the RMSNorm class. + + #### Args: + - `dim` (int): The dimension of the tensor. + - `dtype` (optional): The data type. + - `device` (optional): The device. + - `operations` (optional): The operations module. + """ + super().__init__() + self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the RMSNorm class. + + #### Args: + - `x` (Tensor): The input tensor. + + #### Returns: + - `Tensor`: The normalized tensor. + """ + return rms_norm(x, self.scale, 1e-6) + +# Define the query-key normalization class +class QKNorm(nn.Module): + def __init__(self, dim: int, dtype=None, device=None, operations=None): + """#### Initialize the QKNorm class. + + #### Args: + - `dim` (int): The dimension of the tensor. + - `dtype` (optional): The data type. + - `device` (optional): The device. + - `operations` (optional): The operations module. + """ + super().__init__() + self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations) + self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations) + + def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> tuple: + """#### Forward pass for the QKNorm class. + + #### Args: + - `q` (Tensor): The query tensor. + - `k` (Tensor): The key tensor. + - `v` (Tensor): The value tensor. + + #### Returns: + - `tuple`: The normalized query and key tensors. + """ + q = self.query_norm(q) + k = self.key_norm(k) + return q.to(v), k.to(v) + +# Define the self-attention class +class SelfAttention(nn.Module): + def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None): + """#### Initialize the SelfAttention class. + + #### Args: + - `dim` (int): The dimension of the tensor. + - `num_heads` (int, optional): The number of attention heads. Defaults to 8. + - `qkv_bias` (bool, optional): Whether to use bias in QKV projection. Defaults to False. + - `dtype` (optional): The data type. + - `device` (optional): The device. + - `operations` (optional): The operations module. + """ + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) + self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) + +# Define the modulation output dataclass +@dataclass +class ModulationOut: + shift: torch.Tensor + scale: torch.Tensor + gate: torch.Tensor + +# Define the modulation class +class Modulation(nn.Module): + def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None): + """#### Initialize the Modulation class. + + #### Args: + - `dim` (int): The dimension of the tensor. + - `double` (bool): Whether to use double modulation. + - `dtype` (optional): The data type. + - `device` (optional): The device. + - `operations` (optional): The operations module. + """ + super().__init__() + self.is_double = double + self.multiplier = 6 if double else 3 + self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device) + + def forward(self, vec: torch.Tensor) -> tuple: + """#### Forward pass for the Modulation class. + + #### Args: + - `vec` (Tensor): The input tensor. + + #### Returns: + - `tuple`: The modulation output. + """ + out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1) + return (ModulationOut(*out[:3]), ModulationOut(*out[3:]) if self.is_double else None) + +# Define the double stream block class +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, dtype=None, device=None, operations=None): + """#### Initialize the DoubleStreamBlock class. + + #### Args: + - `hidden_size` (int): The hidden size. + - `num_heads` (int): The number of attention heads. + - `mlp_ratio` (float): The MLP ratio. + - `qkv_bias` (bool, optional): Whether to use bias in QKV projection. Defaults to False. + - `dtype` (optional): The data type. + - `device` (optional): The device. + - `operations` (optional): The operations module. + """ + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) + self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) + self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + def forward(self, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, pe: torch.Tensor) -> tuple: + """#### Forward pass for the DoubleStreamBlock class. + + #### Args: + - `img` (Tensor): The image tensor. + - `txt` (Tensor): The text tensor. + - `vec` (Tensor): The vector tensor. + - `pe` (Tensor): The positional encoding tensor. + + #### Returns: + - `tuple`: The modified image and text tensors. + """ + img_mod1, img_mod2 = self.img_mod(vec) + txt_mod1, txt_mod2 = self.txt_mod(vec) + + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + attn = attention( + torch.cat((txt_q, img_q), dim=2), + torch.cat((txt_k, img_k), dim=2), + torch.cat((txt_v, img_v), dim=2), + pe=pe, + ) + + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] + + # calculate the img bloks + img = img + img_mod1.gate * self.img_attn.proj(img_attn) + img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) + txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + + if txt.dtype == torch.float16: + txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) + + return img, txt + +# Define the single stream block class +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0, qk_scale: float = None, dtype=None, device=None, operations=None): + """#### Initialize the SingleStreamBlock class. + + #### Args: + - `hidden_size` (int): The hidden size. + - `num_heads` (int): The number of attention heads. + - `mlp_ratio` (float, optional): The MLP ratio. Defaults to 4.0. + - `qk_scale` (float, optional): The QK scale. Defaults to None. + - `dtype` (optional): The data type. + - `device` (optional): The device. + - `operations` (optional): The operations module. + """ + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) + # proj and mlp_out + self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) + + self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) + + self.hidden_size = hidden_size + self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.mlp_act = nn.GELU(approximate="tanh") + self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) + + def forward(self, x: torch.Tensor, vec: torch.Tensor, pe: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the SingleStreamBlock class. + + #### Args: + - `x` (Tensor): The input tensor. + - `vec` (Tensor): The vector tensor. + - `pe` (Tensor): The positional encoding tensor. + + #### Returns: + - `Tensor`: The modified tensor. + """ + mod, _ = self.modulation(vec) + x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + qkv, mlp = torch.split( + self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1 + ) + + q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute( + 2, 0, 3, 1, 4 + ) + q, k = self.norm(q, k, v) + + # compute attention + attn = attention(q, k, v, pe=pe) + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + x += mod.gate * output + if x.dtype == torch.float16: + x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) + return x + +class LastLayer(nn.Module): + def __init__( + self, + hidden_size: int, + patch_size: int, + out_channels: int, + dtype=None, + device=None, + operations=None, + ): + """#### Initialize the LastLayer class. + + #### Args: + - `hidden_size` (int): The hidden size. + - `patch_size` (int): The patch size. + - `out_channels` (int): The number of output channels. + - `dtype` (optional): The data type. + - `device` (optional): The device. + - `operations` (optional): The operations module. + """ + super().__init__() + self.norm_final = operations.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device + ) + self.linear = operations.Linear( + hidden_size, + patch_size * patch_size * out_channels, + bias=True, + dtype=dtype, + device=device, + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear( + hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device + ), + ) + + def forward(self, x: torch.Tensor, vec: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the LastLayer class. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `vec` (torch.Tensor): The vector tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1) + x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = self.linear(x) + return x + + +def pad_to_patch_size(img: torch.Tensor, patch_size: tuple = (2, 2), padding_mode: str = "circular") -> torch.Tensor: + """#### Pad the image to the specified patch size. + + #### Args: + - `img` (torch.Tensor): The input image tensor. + - `patch_size` (tuple, optional): The patch size. Defaults to (2, 2). + - `padding_mode` (str, optional): The padding mode. Defaults to "circular". + + #### Returns: + - `torch.Tensor`: The padded image tensor. + """ + if ( + padding_mode == "circular" + and torch.jit.is_tracing() + or torch.jit.is_scripting() + ): + padding_mode = "reflect" + pad_h = (patch_size[0] - img.shape[-2] % patch_size[0]) % patch_size[0] + pad_w = (patch_size[1] - img.shape[-1] % patch_size[1]) % patch_size[1] + return torch.nn.functional.pad(img, (0, pad_w, 0, pad_h), mode=padding_mode) + + +try: + rms_norm_torch = torch.nn.functional.rms_norm +except Exception: + rms_norm_torch = None + + +def rms_norm(x: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6) -> torch.Tensor: + """#### Apply RMS normalization to the input tensor. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `weight` (torch.Tensor): The weight tensor. + - `eps` (float, optional): The epsilon value for numerical stability. Defaults to 1e-6. + + #### Returns: + - `torch.Tensor`: The normalized tensor. + """ + if rms_norm_torch is not None and not ( + torch.jit.is_tracing() or torch.jit.is_scripting() + ): + return rms_norm_torch( + x, + weight.shape, + weight=cast.cast_to(weight, dtype=x.dtype, device=x.device), + eps=eps, + ) + else: + rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps) + return (x * rrms) * cast.cast_to(weight, dtype=x.dtype, device=x.device) + + +@dataclass +class FluxParams: + in_channels: int + vec_in_dim: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list + theta: int + qkv_bias: bool + guidance_embed: bool + + +class Flux3(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__( + self, + image_model=None, + final_layer: bool = True, + dtype=None, + device=None, + operations=None, + **kwargs, + ): + """#### Initialize the Flux3 class. + + #### Args: + - `image_model` (optional): The image model. + - `final_layer` (bool, optional): Whether to include the final layer. Defaults to True. + - `dtype` (optional): The data type. + - `device` (optional): The device. + - `operations` (optional): The operations module. + - `**kwargs`: Additional keyword arguments. + """ + super().__init__() + self.dtype = dtype + params = FluxParams(**kwargs) + self.params = params + self.in_channels = params.in_channels * 2 * 2 + self.out_channels = self.in_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError( + f"Got {params.axes_dim} but expected positional dim {pe_dim}" + ) + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND( + dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim + ) + self.img_in = operations.Linear( + self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device + ) + self.time_in = MLPEmbedder( + in_dim=256, + hidden_dim=self.hidden_size, + dtype=dtype, + device=device, + operations=operations, + ) + self.vector_in = MLPEmbedder( + params.vec_in_dim, + self.hidden_size, + dtype=dtype, + device=device, + operations=operations, + ) + self.guidance_in = ( + MLPEmbedder( + in_dim=256, + hidden_dim=self.hidden_size, + dtype=dtype, + device=device, + operations=operations, + ) + if params.guidance_embed + else nn.Identity() + ) + self.txt_in = operations.Linear( + params.context_in_dim, self.hidden_size, dtype=dtype, device=device + ) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + dtype=dtype, + device=device, + operations=operations, + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + dtype=dtype, + device=device, + operations=operations, + ) + for _ in range(params.depth_single_blocks) + ] + ) + + if final_layer: + self.final_layer = LastLayer( + self.hidden_size, + 1, + self.out_channels, + dtype=dtype, + device=device, + operations=operations, + ) + + def forward_orig( + self, + img: torch.Tensor, + img_ids: torch.Tensor, + txt: torch.Tensor, + txt_ids: torch.Tensor, + timesteps: torch.Tensor, + y: torch.Tensor, + guidance: torch.Tensor = None, + control=None, + ) -> torch.Tensor: + """#### Original forward pass for the Flux3 class. + + #### Args: + - `img` (torch.Tensor): The image tensor. + - `img_ids` (torch.Tensor): The image IDs tensor. + - `txt` (torch.Tensor): The text tensor. + - `txt_ids` (torch.Tensor): The text IDs tensor. + - `timesteps` (torch.Tensor): The timesteps tensor. + - `y` (torch.Tensor): The vector tensor. + - `guidance` (torch.Tensor, optional): The guidance tensor. Defaults to None. + - `control` (optional): The control tensor. Defaults to None. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(sampling_util.timestep_embedding_flux(timesteps, 256).to(img.dtype)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError( + "Didn't get guidance strength for guidance distilled model." + ) + vec = vec + self.guidance_in( + sampling_util.timestep_embedding_flux(guidance, 256).to(img.dtype) + ) + + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + for i, block in enumerate(self.double_blocks): + img, txt = block(img=img, txt=txt, vec=vec, pe=pe) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + img = block(img, vec=vec, pe=pe) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1] :, ...] += add + + img = img[:, txt.shape[1] :, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + return img + + def forward(self, x: torch.Tensor, timestep: torch.Tensor, context: torch.Tensor, y: torch.Tensor, guidance: torch.Tensor, control=None, **kwargs) -> torch.Tensor: + """#### Forward pass for the Flux3 class. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `timestep` (torch.Tensor): The timestep tensor. + - `context` (torch.Tensor): The context tensor. + - `y` (torch.Tensor): The vector tensor. + - `guidance` (torch.Tensor): The guidance tensor. + - `control` (optional): The control tensor. Defaults to None. + - `**kwargs`: Additional keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + bs, c, h, w = x.shape + patch_size = 2 + x = pad_to_patch_size(x, (patch_size, patch_size)) + + img = rearrange( + x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size + ) + + h_len = (h + (patch_size // 2)) // patch_size + w_len = (w + (patch_size // 2)) // patch_size + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[..., 1] = ( + img_ids[..., 1] + + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[ + :, None + ] + ) + img_ids[..., 2] = ( + img_ids[..., 2] + + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[ + None, : + ] + ) + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + out = self.forward_orig( + img, img_ids, context, txt_ids, timestep, y, guidance, control + ) + return rearrange( + out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2 + )[:, :, :h, :w] + + +class Flux2(ModelBase.BaseModel): + def __init__(self, model_config: dict, model_type=sampling.ModelType.FLUX, device=None): + """#### Initialize the Flux2 class. + + #### Args: + - `model_config` (dict): The model configuration. + - `model_type` (sampling.ModelType, optional): The model type. Defaults to sampling.ModelType.FLUX. + - `device` (optional): The device. + """ + super().__init__(model_config, model_type, device=device, unet_model=Flux3, flux=True) + + def encode_adm(self, **kwargs) -> torch.Tensor: + """#### Encode the ADM. + + #### Args: + - `**kwargs`: Additional keyword arguments. + + #### Returns: + - `torch.Tensor`: The encoded ADM tensor. + """ + return kwargs["pooled_output"] + + def extra_conds(self, **kwargs) -> dict: + """#### Get extra conditions. + + #### Args: + - `**kwargs`: Additional keyword arguments. + + #### Returns: + - `dict`: The extra conditions. + """ + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out["c_crossattn"] = cond.CONDRegular(cross_attn) + out["guidance"] = cond.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)])) + return out + + +class Flux(ModelBase.BASE): + unet_config = { + "image_model": "flux", + "guidance_embed": True, + } + + sampling_settings = {} + + unet_extra_config = {} + latent_format = Latent.Flux1 + + memory_usage_factor = 2.8 + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict: dict, prefix: str = "", device=None) -> Flux2: + """#### Get the model. + + #### Args: + - `state_dict` (dict): The state dictionary. + - `prefix` (str, optional): The prefix. Defaults to "". + - `device` (optional): The device. + + #### Returns: + - `Flux2`: The Flux2 model. + """ + out = Flux2(self, device=device) + return out + + models = [Flux] \ No newline at end of file diff --git a/modules/Device/Device.py b/modules/Device/Device.py index ee59dd7018bb06e3016d77ac3ea03fb2ae511099..8ee97ea0897cad074c498b0c44d994a2faffb66f 100644 --- a/modules/Device/Device.py +++ b/modules/Device/Device.py @@ -1,1608 +1,1608 @@ -import logging -import platform -import sys -from enum import Enum -from typing import Tuple, Union -import packaging.version - -import psutil -import torch - -if packaging.version.parse(torch.__version__) >= packaging.version.parse("1.12.0"): - torch.backends.cuda.matmul.allow_tf32 = True - - -class VRAMState(Enum): - """#### Enum for VRAM states. - """ - DISABLED = 0 # No vram present: no need to move _internal to vram - NO_VRAM = 1 # Very low vram: enable all the options to save vram - LOW_VRAM = 2 - NORMAL_VRAM = 3 - HIGH_VRAM = 4 - SHARED = 5 # No dedicated vram: memory shared between CPU and GPU but _internal still need to be moved between both. - - -class CPUState(Enum): - """#### Enum for CPU states. - """ - GPU = 0 - CPU = 1 - MPS = 2 - - -# Determine VRAM State -vram_state = VRAMState.NORMAL_VRAM -set_vram_to = VRAMState.NORMAL_VRAM -cpu_state = CPUState.GPU - -total_vram = 0 - -lowvram_available = True -xpu_available = False - -directml_enabled = False -try: - if torch.xpu.is_available(): - xpu_available = True -except: - pass - -try: - if torch.backends.mps.is_available(): - cpu_state = CPUState.MPS - import torch.mps -except: - pass - - -def is_intel_xpu() -> bool: - """#### Check if Intel XPU is available. - - #### Returns: - - `bool`: Whether Intel XPU is available. - """ - global cpu_state - global xpu_available - if cpu_state == CPUState.GPU: - if xpu_available: - return True - return False - - -def get_torch_device() -> torch.device: - """#### Get the torch device. - - #### Returns: - - `torch.device`: The torch device. - """ - global directml_enabled - global cpu_state - if directml_enabled: - global directml_device - return directml_device - if cpu_state == CPUState.MPS: - return torch.device("mps") - if cpu_state == CPUState.CPU: - return torch.device("cpu") - else: - if is_intel_xpu(): - return torch.device("xpu", torch.xpu.current_device()) - else: - if torch.cuda.is_available(): - return torch.device(torch.cuda.current_device()) - else: - return torch.device("cpu") - - -def get_total_memory(dev: torch.device = None, torch_total_too: bool = False) -> int: - """#### Get the total memory. - - #### Args: - - `dev` (torch.device, optional): The device. Defaults to None. - - `torch_total_too` (bool, optional): Whether to get the total memory in PyTorch. Defaults to False. - - #### Returns: - - `int`: The total memory. - """ - global directml_enabled - if dev is None: - dev = get_torch_device() - - if hasattr(dev, "type") and (dev.type == "cpu" or dev.type == "mps"): - mem_total = psutil.virtual_memory().total - mem_total_torch = mem_total - else: - if directml_enabled: - mem_total = 1024 * 1024 * 1024 - mem_total_torch = mem_total - elif is_intel_xpu(): - stats = torch.xpu.memory_stats(dev) - mem_reserved = stats["reserved_bytes.all.current"] - mem_total_torch = mem_reserved - mem_total = torch.xpu.get_device_properties(dev).total_memory - else: - stats = torch.cuda.memory_stats(dev) - mem_reserved = stats["reserved_bytes.all.current"] - _, mem_total_cuda = torch.cuda.mem_get_info(dev) - mem_total_torch = mem_reserved - mem_total = mem_total_cuda - - if torch_total_too: - return (mem_total, mem_total_torch) - else: - return mem_total - - -total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) -total_ram = psutil.virtual_memory().total / (1024 * 1024) -logging.info( - "Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram) -) -try: - OOM_EXCEPTION = torch.cuda.OutOfMemoryError -except: - OOM_EXCEPTION = Exception - -XFORMERS_VERSION = "" -XFORMERS_ENABLED_VAE = True -try: - import xformers - import xformers.ops - - XFORMERS_IS_AVAILABLE = True - try: - XFORMERS_IS_AVAILABLE = xformers._has_cpp_library - except: - pass - try: - XFORMERS_VERSION = xformers.version.__version__ - logging.info("xformers version: {}".format(XFORMERS_VERSION)) - if XFORMERS_VERSION.startswith("0.0.18"): - logging.warning( - "\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images." - ) - logging.warning( - "Please downgrade or upgrade xformers to a different version.\n" - ) - XFORMERS_ENABLED_VAE = False - except: - pass -except: - XFORMERS_IS_AVAILABLE = False - - -def is_nvidia() -> bool: - """#### Checks if user has an Nvidia GPU - - #### Returns - - `bool`: Whether the GPU is Nvidia - """ - global cpu_state - if cpu_state == CPUState.GPU: - if torch.version.cuda: - return True - return False - - -ENABLE_PYTORCH_ATTENTION = False - -VAE_DTYPE = torch.float32 - -try: - if is_nvidia(): - torch_version = torch.version.__version__ - if int(torch_version[0]) >= 2: - if ENABLE_PYTORCH_ATTENTION is False: - ENABLE_PYTORCH_ATTENTION = True - if ( - torch.cuda.is_bf16_supported() - and torch.cuda.get_device_properties(torch.cuda.current_device()).major - >= 8 - ): - VAE_DTYPE = torch.bfloat16 -except: - pass - -if is_intel_xpu(): - VAE_DTYPE = torch.bfloat16 - -if ENABLE_PYTORCH_ATTENTION: - torch.backends.cuda.enable_math_sdp(True) - torch.backends.cuda.enable_flash_sdp(True) - torch.backends.cuda.enable_mem_efficient_sdp(True) - - -FORCE_FP32 = False -FORCE_FP16 = False - -if lowvram_available: - if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): - vram_state = set_vram_to - -if cpu_state != CPUState.GPU: - vram_state = VRAMState.DISABLED - -if cpu_state == CPUState.MPS: - vram_state = VRAMState.SHARED - -logging.info(f"Set vram state to: {vram_state.name}") - -DISABLE_SMART_MEMORY = False - -if DISABLE_SMART_MEMORY: - logging.info("Disabling smart memory management") - - -def get_torch_device_name(device: torch.device) -> str: - """#### Get the name of the torch compatible device - - #### Args: - - `device` (torch.device): the device - - #### Returns: - - `str`: the name of the device - """ - if hasattr(device, "type"): - if device.type == "cuda": - try: - allocator_backend = torch.cuda.get_allocator_backend() - except: - allocator_backend = "" - return "{} {} : {}".format( - device, torch.cuda.get_device_name(device), allocator_backend - ) - else: - return "{}".format(device.type) - elif is_intel_xpu(): - return "{} {}".format(device, torch.xpu.get_device_name(device)) - else: - return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) - - -try: - logging.info("Device: {}".format(get_torch_device_name(get_torch_device()))) -except: - logging.warning("Could not pick default device.") - -logging.info("VAE dtype: {}".format(VAE_DTYPE)) - -current_loaded_models = [] - - -def module_size(module: torch.nn.Module) -> int: - """#### Get the size of a module - - #### Args: - - `module` (torch.nn.Module): The module - - #### Returns: - - `int`: The size of the module - """ - module_mem = 0 - sd = module.state_dict() - for k in sd: - t = sd[k] - module_mem += t.nelement() * t.element_size() - return module_mem - - -class LoadedModel: - """#### Class to load a model - """ - def __init__(self, model: torch.nn.Module): - """#### Initialize the class - - #### Args: - - `model`: The model - """ - self.model = model - self.device = model.load_device - self.weights_loaded = False - self.real_model = None - - def model_memory(self): - """#### Get the model memory - - #### Returns: - - `int`: The model memory - """ - return self.model.model_size() - - - def model_offloaded_memory(self): - """#### Get the offloaded model memory - - #### Returns: - - `int`: The offloaded model memory - """ - return self.model.model_size() - self.model.loaded_size() - - def model_memory_required(self, device: torch.device) -> int: - """#### Get the required model memory - - #### Args: - - `device`: The device - - #### Returns: - - `int`: The required model memory - """ - if hasattr(self.model, 'current_loaded_device') and device == self.model.current_loaded_device(): - return self.model_offloaded_memory() - else: - return self.model_memory() - - def model_load(self, lowvram_model_memory: int = 0, force_patch_weights: bool = False) -> torch.nn.Module: - """#### Load the model - - #### Args: - - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. - - `force_patch_weights` (bool, optional): Whether to force patch the weights. Defaults to False. - - #### Returns: - - `torch.nn.Module`: The real model - """ - patch_model_to = self.device - - self.model.model_patches_to(self.device) - self.model.model_patches_to(self.model.model_dtype()) - - load_weights = not self.weights_loaded - - try: - if hasattr(self.model, "patch_model_lowvram") and lowvram_model_memory > 0 and load_weights: - self.real_model = self.model.patch_model_lowvram( - device_to=patch_model_to, - lowvram_model_memory=lowvram_model_memory, - force_patch_weights=force_patch_weights, - ) - else: - self.real_model = self.model.patch_model( - device_to=patch_model_to, patch_weights=load_weights - ) - except Exception as e: - self.model.unpatch_model(self.model.offload_device) - self.model_unload() - raise e - self.weights_loaded = True - return self.real_model - - def model_load_flux(self, lowvram_model_memory: int = 0, force_patch_weights: bool = False) -> torch.nn.Module: - """#### Load the model - - #### Args: - - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. - - `force_patch_weights` (bool, optional): Whether to force patch the weights. Defaults to False. - - #### Returns: - - `torch.nn.Module`: The real model - """ - patch_model_to = self.device - - self.model.model_patches_to(self.device) - self.model.model_patches_to(self.model.model_dtype()) - - load_weights = not self.weights_loaded - - if self.model.loaded_size() > 0: - use_more_vram = lowvram_model_memory - if use_more_vram == 0: - use_more_vram = 1e32 - self.model_use_more_vram(use_more_vram) - else: - try: - self.real_model = self.model.patch_model_flux( - device_to=patch_model_to, - lowvram_model_memory=lowvram_model_memory, - load_weights=load_weights, - force_patch_weights=force_patch_weights, - ) - except Exception as e: - self.model.unpatch_model(self.model.offload_device) - self.model_unload() - raise e - - if ( - is_intel_xpu() - and "ipex" in globals() - and self.real_model is not None - ): - import ipex - with torch.no_grad(): - self.real_model = ipex.optimize( - self.real_model.eval(), - inplace=True, - graph_mode=True, - concat_linear=True, - ) - - self.weights_loaded = True - return self.real_model - - def should_reload_model(self, force_patch_weights: bool = False) -> bool: - """#### Checks if the model should be reloaded - - #### Args: - - `force_patch_weights` (bool, optional): If model reloading should be enforced. Defaults to False. - - #### Returns: - - `bool`: Whether the model should be reloaded - """ - if force_patch_weights and self.model.lowvram_patch_counter > 0: - return True - return False - - def model_unload(self, unpatch_weights: bool = True) -> None: - """#### Unloads the patched model - - #### Args: - - `unpatch_weights` (bool, optional): Whether the weights should be unpatched. Defaults to True. - """ - self.model.unpatch_model( - self.model.offload_device, unpatch_weights=unpatch_weights - ) - self.model.model_patches_to(self.model.offload_device) - self.weights_loaded = self.weights_loaded and not unpatch_weights - self.real_model = None - - def model_use_more_vram(self, extra_memory: int) -> int: - """#### Use more VRAM - - #### Args: - - `extra_memory`: The extra memory - """ - return self.model.partially_load(self.device, extra_memory) - - def __eq__(self, other: torch.nn.Module) -> bool: - """#### Verify if the model is equal to another - - #### Args: - - `other` (torch.nn.Module): the other model - - #### Returns: - - `bool`: Whether the two models are equal - """ - return self.model is other.model - - -def minimum_inference_memory() -> int: - """#### The minimum memory requirement for inference, equals to 1024^3 - - #### Returns: - - `int`: the memory requirement - """ - return 1024 * 1024 * 1024 - - -def unload_model_clones(model: torch.nn.Module, unload_weights_only:bool = True, force_unload: bool = True) -> bool: - """#### Unloads the model clones - - #### Args: - - `model` (torch.nn.Module): The model - - `unload_weights_only` (bool, optional): Whether to unload only the weights. Defaults to True. - - `force_unload` (bool, optional): Whether to force the unload. Defaults to True. - - #### Returns: - - `bool`: Whether the model was unloaded - """ - to_unload = [] - for i in range(len(current_loaded_models)): - if model.is_clone(current_loaded_models[i].model): - to_unload = [i] + to_unload - - if len(to_unload) == 0: - return True - - same_weights = 0 - - if same_weights == len(to_unload): - unload_weight = False - else: - unload_weight = True - - if not force_unload: - if unload_weights_only and unload_weight is False: - return None - - for i in to_unload: - logging.debug("unload clone {} {}".format(i, unload_weight)) - current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight) - - return unload_weight - - -def free_memory(memory_required: int, device: torch.device, keep_loaded: list = []) -> None: - """#### Free memory - - #### Args: - - `memory_required` (int): The required memory - - `device` (torch.device): The device - - `keep_loaded` (list, optional): The list of loaded models to keep. Defaults to []. - """ - unloaded_model = [] - can_unload = [] - - for i in range(len(current_loaded_models) - 1, -1, -1): - shift_model = current_loaded_models[i] - if shift_model.device == device: - if shift_model not in keep_loaded: - can_unload.append( - (sys.getrefcount(shift_model.model), shift_model.model_memory(), i) - ) - - for x in sorted(can_unload): - i = x[-1] - if not DISABLE_SMART_MEMORY: - if get_free_memory(device) > memory_required: - break - current_loaded_models[i].model_unload() - unloaded_model.append(i) - - for i in sorted(unloaded_model, reverse=True): - current_loaded_models.pop(i) - - if len(unloaded_model) > 0: - soft_empty_cache() - else: - if vram_state != VRAMState.HIGH_VRAM: - mem_free_total, mem_free_torch = get_free_memory( - device, torch_free_too=True - ) - if mem_free_torch > mem_free_total * 0.25: - soft_empty_cache() - -def use_more_memory(extra_memory: int, loaded_models: list, device: torch.device) -> None: - """#### Use more memory - - #### Args: - - `extra_memory` (int): The extra memory - - `loaded_models` (list): The loaded models - - `device` (torch.device): The device - """ - for m in loaded_models: - if m.device == device: - extra_memory -= m.model_use_more_vram(extra_memory) - if extra_memory <= 0: - break - -WINDOWS = any(platform.win32_ver()) - -EXTRA_RESERVED_VRAM = 400 * 1024 * 1024 -if WINDOWS: - EXTRA_RESERVED_VRAM = ( - 600 * 1024 * 1024 - ) # Windows is higher because of the shared vram issue - -def extra_reserved_memory() -> int: - """#### Extra reserved memory - - #### Returns: - - `int`: The extra reserved memory - """ - return EXTRA_RESERVED_VRAM - -def offloaded_memory(loaded_models: list, device: torch.device) -> int: - """#### Offloaded memory - - #### Args: - - `loaded_models` (list): The loaded models - - `device` (torch.device): The device - - #### Returns: - - `int`: The offloaded memory - """ - offloaded_mem = 0 - for m in loaded_models: - if m.device == device: - offloaded_mem += m.model_offloaded_memory() - return offloaded_mem - -def load_models_gpu(models: list, memory_required: int = 0, force_patch_weights: bool = False, minimum_memory_required=None, force_full_load=False, flux_enabled: bool = False) -> None: - """#### Load models on the GPU - - #### Args: - - `models`(list): The models - - `memory_required` (int, optional): The required memory. Defaults to 0. - - `force_patch_weights` (bool, optional): Whether to force patch the weights. Defaults to False. - - `minimum_memory_required` (int, optional): The minimum memory required. Defaults to None. - - `force_full_load` (bool, optional - - `flux_enabled` (bool, optional): Whether flux is enabled. Defaults to False. - """ - global vram_state - if not flux_enabled: - - inference_memory = minimum_inference_memory() - extra_mem = max(inference_memory, memory_required) - - models = set(models) - - models_to_load = [] - models_already_loaded = [] - for x in models: - loaded_model = LoadedModel(x) - loaded = None - - try: - loaded_model_index = current_loaded_models.index(loaded_model) - except: - loaded_model_index = None - - if loaded_model_index is not None: - loaded = current_loaded_models[loaded_model_index] - if loaded.should_reload_model(force_patch_weights=force_patch_weights): - current_loaded_models.pop(loaded_model_index).model_unload( - unpatch_weights=True - ) - loaded = None - else: - models_already_loaded.append(loaded) - - if loaded is None: - if hasattr(x, "model"): - logging.info(f"Requested to load {x.model.__class__.__name__}") - models_to_load.append(loaded_model) - - if len(models_to_load) == 0: - devs = set(map(lambda a: a.device, models_already_loaded)) - for d in devs: - if d != torch.device("cpu"): - free_memory(extra_mem, d, models_already_loaded) - return - - logging.info( - f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}" - ) - - total_memory_required = {} - for loaded_model in models_to_load: - if ( - unload_model_clones( - loaded_model.model, unload_weights_only=True, force_unload=False - ) - is True - ): # unload clones where the weights are different - total_memory_required[loaded_model.device] = total_memory_required.get( - loaded_model.device, 0 - ) + loaded_model.model_memory_required(loaded_model.device) - - for device in total_memory_required: - if device != torch.device("cpu"): - free_memory( - total_memory_required[device] * 1.3 + extra_mem, - device, - models_already_loaded, - ) - - for loaded_model in models_to_load: - weights_unloaded = unload_model_clones( - loaded_model.model, unload_weights_only=False, force_unload=False - ) # unload the rest of the clones where the weights can stay loaded - if weights_unloaded is not None: - loaded_model.weights_loaded = not weights_unloaded - - for loaded_model in models_to_load: - model = loaded_model.model - torch_dev = model.load_device - if is_device_cpu(torch_dev): - vram_set_state = VRAMState.DISABLED - else: - vram_set_state = vram_state - lowvram_model_memory = 0 - if lowvram_available and ( - vram_set_state == VRAMState.LOW_VRAM - or vram_set_state == VRAMState.NORMAL_VRAM - ): - model_size = loaded_model.model_memory_required(torch_dev) - current_free_mem = get_free_memory(torch_dev) - lowvram_model_memory = int( - max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3) - ) - if model_size > ( - current_free_mem - inference_memory - ): # only switch to lowvram if really necessary - vram_set_state = VRAMState.LOW_VRAM - else: - lowvram_model_memory = 0 - - if vram_set_state == VRAMState.NO_VRAM: - lowvram_model_memory = 64 * 1024 * 1024 - - loaded_model.model_load( - lowvram_model_memory, force_patch_weights=force_patch_weights - ) - current_loaded_models.insert(0, loaded_model) - return - else: - inference_memory = minimum_inference_memory() - extra_mem = max(inference_memory, memory_required + extra_reserved_memory()) - if minimum_memory_required is None: - minimum_memory_required = extra_mem - else: - minimum_memory_required = max( - inference_memory, minimum_memory_required + extra_reserved_memory() - ) - - models = set(models) - - models_to_load = [] - models_already_loaded = [] - for x in models: - loaded_model = LoadedModel(x) - loaded = None - - try: - loaded_model_index = current_loaded_models.index(loaded_model) - except: - loaded_model_index = None - - if loaded_model_index is not None: - loaded = current_loaded_models[loaded_model_index] - if loaded.should_reload_model( - force_patch_weights=force_patch_weights - ): # TODO: cleanup this model reload logic - current_loaded_models.pop(loaded_model_index).model_unload( - unpatch_weights=True - ) - loaded = None - else: - loaded.currently_used = True - models_already_loaded.append(loaded) - - if loaded is None: - if hasattr(x, "model"): - logging.info(f"Requested to load {x.model.__class__.__name__}") - models_to_load.append(loaded_model) - - if len(models_to_load) == 0: - devs = set(map(lambda a: a.device, models_already_loaded)) - for d in devs: - if d != torch.device("cpu"): - free_memory( - extra_mem + offloaded_memory(models_already_loaded, d), - d, - models_already_loaded, - ) - free_mem = get_free_memory(d) - if free_mem < minimum_memory_required: - logging.info( - "Unloading models for lowram load." - ) # TODO: partial model unloading when this case happens, also handle the opposite case where models can be unlowvramed. - models_to_load = free_memory(minimum_memory_required, d) - logging.info("{} models unloaded.".format(len(models_to_load))) - else: - use_more_memory( - free_mem - minimum_memory_required, models_already_loaded, d - ) - if len(models_to_load) == 0: - return - - logging.info( - f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}" - ) - - total_memory_required = {} - for loaded_model in models_to_load: - unload_model_clones( - loaded_model.model, unload_weights_only=True, force_unload=False - ) # unload clones where the weights are different - total_memory_required[loaded_model.device] = total_memory_required.get( - loaded_model.device, 0 - ) + loaded_model.model_memory_required(loaded_model.device) - - for loaded_model in models_already_loaded: - total_memory_required[loaded_model.device] = total_memory_required.get( - loaded_model.device, 0 - ) + loaded_model.model_memory_required(loaded_model.device) - - for loaded_model in models_to_load: - weights_unloaded = unload_model_clones( - loaded_model.model, unload_weights_only=False, force_unload=False - ) # unload the rest of the clones where the weights can stay loaded - if weights_unloaded is not None: - loaded_model.weights_loaded = not weights_unloaded - - for device in total_memory_required: - if device != torch.device("cpu"): - free_memory( - total_memory_required[device] * 1.1 + extra_mem, - device, - models_already_loaded, - ) - - for loaded_model in models_to_load: - model = loaded_model.model - torch_dev = model.load_device - if is_device_cpu(torch_dev): - vram_set_state = VRAMState.DISABLED - else: - vram_set_state = vram_state - lowvram_model_memory = 0 - if ( - lowvram_available - and ( - vram_set_state == VRAMState.LOW_VRAM - or vram_set_state == VRAMState.NORMAL_VRAM - ) - and not force_full_load - ): - model_size = loaded_model.model_memory_required(torch_dev) - current_free_mem = get_free_memory(torch_dev) - lowvram_model_memory = max( - 64 * (1024 * 1024), - (current_free_mem - minimum_memory_required), - min( - current_free_mem * 0.4, - current_free_mem - minimum_inference_memory(), - ), - ) - if ( - model_size <= lowvram_model_memory - ): # only switch to lowvram if really necessary - lowvram_model_memory = 0 - - if vram_set_state == VRAMState.NO_VRAM: - lowvram_model_memory = 64 * 1024 * 1024 - - loaded_model.model_load_flux( - lowvram_model_memory, force_patch_weights=force_patch_weights - ) - current_loaded_models.insert(0, loaded_model) - - devs = set(map(lambda a: a.device, models_already_loaded)) - for d in devs: - if d != torch.device("cpu"): - free_mem = get_free_memory(d) - if free_mem > minimum_memory_required: - use_more_memory( - free_mem - minimum_memory_required, models_already_loaded, d - ) - return - -def load_model_gpu(model: torch.nn.Module, flux_enabled:bool = False) -> None: - """#### Load a model on the GPU - - #### Args: - - `model` (torch.nn.Module): The model - - `flux_enable` (bool, optional): Whether flux is enabled. Defaults to False. - """ - return load_models_gpu([model], flux_enabled=flux_enabled) - - -def cleanup_models(keep_clone_weights_loaded:bool = False): - """#### Cleanup the models - - #### Args: - - `keep_clone_weights_loaded` (bool, optional): Whether to keep the clone weights loaded. Defaults to False. - """ - to_delete = [] - for i in range(len(current_loaded_models)): - if sys.getrefcount(current_loaded_models[i].model) <= 2: - if not keep_clone_weights_loaded: - to_delete = [i] + to_delete - elif ( - sys.getrefcount(current_loaded_models[i].real_model) <= 3 - ): # references from .real_model + the .model - to_delete = [i] + to_delete - - for i in to_delete: - x = current_loaded_models.pop(i) - x.model_unload() - del x - - -def dtype_size(dtype: torch.dtype) -> int: - """#### Get the size of a dtype - - #### Args: - - `dtype` (torch.dtype): The dtype - - #### Returns: - - `int`: The size of the dtype - """ - dtype_size = 4 - if dtype == torch.float16 or dtype == torch.bfloat16: - dtype_size = 2 - elif dtype == torch.float32: - dtype_size = 4 - else: - try: - dtype_size = dtype.itemsize - except: # Old pytorch doesn't have .itemsize - pass - return dtype_size - - -def unet_offload_device() -> torch.device: - """#### Get the offload device for UNet - - #### Returns: - - `torch.device`: The offload device - """ - if vram_state == VRAMState.HIGH_VRAM: - return get_torch_device() - else: - return torch.device("cpu") - - -def unet_inital_load_device(parameters, dtype) -> torch.device: - """#### Get the initial load device for UNet - - #### Args: - - `parameters` (int): The parameters - - `dtype` (torch.dtype): The dtype - - #### Returns: - - `torch.device`: The initial load device - """ - torch_dev = get_torch_device() - if vram_state == VRAMState.HIGH_VRAM: - return torch_dev - - cpu_dev = torch.device("cpu") - if DISABLE_SMART_MEMORY: - return cpu_dev - - model_size = dtype_size(dtype) * parameters - - mem_dev = get_free_memory(torch_dev) - mem_cpu = get_free_memory(cpu_dev) - if mem_dev > mem_cpu and model_size < mem_dev: - return torch_dev - else: - return cpu_dev - - -def unet_dtype( - device: torch.dtype = None, - model_params: int = 0, - supported_dtypes: list = [torch.float16, torch.bfloat16, torch.float32], -) -> torch.dtype: - """#### Get the dtype for UNet - - #### Args: - - `device` (torch.dtype, optional): The device. Defaults to None. - - `model_params` (int, optional): The model parameters. Defaults to 0. - - `supported_dtypes` (list, optional): The supported dtypes. Defaults to [torch.float16, torch.bfloat16, torch.float32]. - - #### Returns: - - `torch.dtype`: The dtype - """ - if should_use_fp16(device=device, model_params=model_params, manual_cast=True): - if torch.float16 in supported_dtypes: - return torch.float16 - if should_use_bf16(device, model_params=model_params, manual_cast=True): - if torch.bfloat16 in supported_dtypes: - return torch.bfloat16 - return torch.float32 - - -# None means no manual cast -def unet_manual_cast( - weight_dtype: torch.dtype, - inference_device: torch.device, - supported_dtypes: list = [torch.float16, torch.bfloat16, torch.float32], -) -> torch.dtype: - """#### Manual cast for UNet - - #### Args: - - `weight_dtype` (torch.dtype): The dtype of the weights - - `inference_device` (torch.device): The device used for inference - - `supported_dtypes` (list, optional): The supported dtypes. Defaults to [torch.float16, torch.bfloat16, torch.float32]. - - #### Returns: - - `torch.dtype`: The dtype - """ - if weight_dtype == torch.float32: - return None - - fp16_supported = should_use_fp16(inference_device, prioritize_performance=False) - if fp16_supported and weight_dtype == torch.float16: - return None - - bf16_supported = should_use_bf16(inference_device) - if bf16_supported and weight_dtype == torch.bfloat16: - return None - - if fp16_supported and torch.float16 in supported_dtypes: - return torch.float16 - - elif bf16_supported and torch.bfloat16 in supported_dtypes: - return torch.bfloat16 - else: - return torch.float32 - - -def text_encoder_offload_device() -> torch.device: - """#### Get the offload device for the text encoder - - #### Returns: - - `torch.device`: The offload device - """ - return torch.device("cpu") - - -def text_encoder_device() -> torch.device: - """#### Get the device for the text encoder - - #### Returns: - - `torch.device`: The device - """ - if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: - if should_use_fp16(prioritize_performance=False): - return get_torch_device() - else: - return torch.device("cpu") - else: - return torch.device("cpu") - -def text_encoder_initial_device(load_device: torch.device, offload_device: torch.device, model_size: int = 0) -> torch.device: - """#### Get the initial device for the text encoder - - #### Args: - - `load_device` (torch.device): The load device - - `offload_device` (torch.device): The offload device - - `model_size` (int, optional): The model size. Defaults to 0. - - #### Returns: - - `torch.device`: The initial device - """ - if load_device == offload_device or model_size <= 1024 * 1024 * 1024: - return offload_device - - if is_device_mps(load_device): - return offload_device - - mem_l = get_free_memory(load_device) - mem_o = get_free_memory(offload_device) - if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l: - return load_device - else: - return offload_device - - -def text_encoder_dtype(device: torch.device = None) -> torch.dtype: - """#### Get the dtype for the text encoder - - #### Args: - - `device` (torch.device, optional): The device used by the text encoder. Defaults to None. - - Returns: - torch.dtype: The dtype - """ - if is_device_cpu(device): - return torch.float16 - - return torch.float16 - - -def intermediate_device() -> torch.device: - """#### Get the intermediate device - - #### Returns: - - `torch.device`: The intermediate device - """ - return torch.device("cpu") - - -def vae_device() -> torch.device: - """#### Get the VAE device - - #### Returns: - - `torch.device`: The VAE device - """ - return get_torch_device() - - -def vae_offload_device() -> torch.device: - """#### Get the offload device for VAE - - #### Returns: - - `torch.device`: The offload device - """ - return torch.device("cpu") - - -def vae_dtype(): - """#### Get the dtype for VAE - - #### Returns: - - `torch.dtype`: The dtype - """ - global VAE_DTYPE - return VAE_DTYPE - - -def get_autocast_device(dev: torch.device) -> str: - """#### Get the autocast device - - #### Args: - - `dev` (torch.device): The device - - #### Returns: - - `str`: The autocast device type - """ - if hasattr(dev, "type"): - return dev.type - return "cuda" - - -def supports_dtype(device: torch.device, dtype: torch.dtype) -> bool: - """#### Check if the device supports the dtype - - #### Args: - - `device` (torch.device): The device to check - - `dtype` (torch.dtype): The dtype to check support - - #### Returns: - - `bool`: Whether the dtype is supported by the device - """ - if dtype == torch.float32: - return True - if is_device_cpu(device): - return False - if dtype == torch.float16: - return True - if dtype == torch.bfloat16: - return True - return False - - -def device_supports_non_blocking(device: torch.device) -> bool: - """#### Check if the device supports non-blocking - - #### Args: - - `device` (torch.device): The device to check - - #### Returns: - - `bool`: Whether the device supports non-blocking - """ - if is_device_mps(device): - return False # pytorch bug? mps doesn't support non blocking - return True - -def supports_cast(device: torch.device, dtype: torch.dtype): # TODO - """#### Check if the device supports casting - - #### Args: - - `device`: The device - - `dtype`: The dtype - - #### Returns: - - `bool`: Whether the device supports casting - """ - if dtype == torch.float32: - return True - if dtype == torch.float16: - return True - if directml_enabled: - return False - if dtype == torch.bfloat16: - return True - if is_device_mps(device): - return False - if dtype == torch.float8_e4m3fn: - return True - if dtype == torch.float8_e5m2: - return True - return False - -def cast_to_device(tensor: torch.Tensor, device: torch.device, dtype: torch.dtype, copy: bool = False) -> torch.Tensor: - """#### Cast a tensor to a device - - #### Args: - - `tensor` (torch.Tensor): The tensor to cast - - `device` (torch.device): The device to cast the tensor to - - `dtype` (torch.dtype): The dtype precision to cast to - - `copy` (bool, optional): Whether to copy the tensor. Defaults to False. - - #### Returns: - - `torch.Tensor`: The tensor cast to the device - """ - device_supports_cast = False - if tensor.dtype == torch.float32 or tensor.dtype == torch.float16: - device_supports_cast = True - elif tensor.dtype == torch.bfloat16: - if hasattr(device, "type") and device.type.startswith("cuda"): - device_supports_cast = True - elif is_intel_xpu(): - device_supports_cast = True - - non_blocking = device_supports_non_blocking(device) - - if device_supports_cast: - if copy: - if tensor.device == device: - return tensor.to(dtype, copy=copy, non_blocking=non_blocking) - return tensor.to(device, copy=copy, non_blocking=non_blocking).to( - dtype, non_blocking=non_blocking - ) - else: - return tensor.to(device, non_blocking=non_blocking).to( - dtype, non_blocking=non_blocking - ) - else: - return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking) - -def pick_weight_dtype(dtype: torch.dtype, fallback_dtype: torch.dtype, device: torch.device) -> torch.dtype: - """#### Pick the weight dtype - - #### Args: - - `dtype`: The dtype - - `fallback_dtype`: The fallback dtype - - `device`: The device - - #### Returns: - - `torch.dtype`: The weight dtype - """ - if dtype is None: - dtype = fallback_dtype - elif dtype_size(dtype) > dtype_size(fallback_dtype): - dtype = fallback_dtype - - if not supports_cast(device, dtype): - dtype = fallback_dtype - - return dtype - -def xformers_enabled() -> bool: - """#### Check if xformers is enabled - - #### Returns: - - `bool`: Whether xformers is enabled - """ - global directml_enabled - global cpu_state - if cpu_state != CPUState.GPU: - return False - if is_intel_xpu(): - return False - if directml_enabled: - return False - return XFORMERS_IS_AVAILABLE - - -def xformers_enabled_vae() -> bool: - """#### Check if xformers is enabled for VAE - - #### Returns: - - `bool`: Whether xformers is enabled for VAE - """ - enabled = xformers_enabled() - if not enabled: - return False - - return XFORMERS_ENABLED_VAE - - -def pytorch_attention_enabled() -> bool: - """#### Check if PyTorch attention is enabled - - #### Returns: - - `bool`: Whether PyTorch attention is enabled - """ - global ENABLE_PYTORCH_ATTENTION - return ENABLE_PYTORCH_ATTENTION - -def pytorch_attention_flash_attention() -> bool: - """#### Check if PyTorch flash attention is enabled and supported. - - #### Returns: - - `bool`: True if PyTorch flash attention is enabled and supported, False otherwise. - """ - global ENABLE_PYTORCH_ATTENTION - if ENABLE_PYTORCH_ATTENTION: - if is_nvidia(): # pytorch flash attention only works on Nvidia - return True - return False - - -def get_free_memory(dev: torch.device = None, torch_free_too: bool = False) -> Union[int, Tuple[int, int]]: - """#### Get the free memory available on the device. - - #### Args: - - `dev` (torch.device, optional): The device to check memory for. Defaults to None. - - `torch_free_too` (bool, optional): Whether to return both total and torch free memory. Defaults to False. - - #### Returns: - - `int` or `Tuple[int, int]`: The free memory available. If `torch_free_too` is True, returns a tuple of total and torch free memory. - """ - global directml_enabled - if dev is None: - dev = get_torch_device() - - if hasattr(dev, "type") and (dev.type == "cpu" or dev.type == "mps"): - mem_free_total = psutil.virtual_memory().available - mem_free_torch = mem_free_total - else: - if directml_enabled: - mem_free_total = 1024 * 1024 * 1024 - mem_free_torch = mem_free_total - elif is_intel_xpu(): - stats = torch.xpu.memory_stats(dev) - mem_active = stats["active_bytes.all.current"] - mem_reserved = stats["reserved_bytes.all.current"] - mem_free_torch = mem_reserved - mem_active - mem_free_xpu = ( - torch.xpu.get_device_properties(dev).total_memory - mem_reserved - ) - mem_free_total = mem_free_xpu + mem_free_torch - else: - stats = torch.cuda.memory_stats(dev) - mem_active = stats["active_bytes.all.current"] - mem_reserved = stats["reserved_bytes.all.current"] - mem_free_cuda, _ = torch.cuda.mem_get_info(dev) - mem_free_torch = mem_reserved - mem_active - mem_free_total = mem_free_cuda + mem_free_torch - - if torch_free_too: - return (mem_free_total, mem_free_torch) - else: - return mem_free_total - - -def cpu_mode() -> bool: - """#### Check if the current mode is CPU. - - #### Returns: - - `bool`: True if the current mode is CPU, False otherwise. - """ - global cpu_state - return cpu_state == CPUState.CPU - - -def mps_mode() -> bool: - """#### Check if the current mode is MPS. - - #### Returns: - - `bool`: True if the current mode is MPS, False otherwise. - """ - global cpu_state - return cpu_state == CPUState.MPS - - -def is_device_type(device: torch.device, type: str) -> bool: - """#### Check if the device is of a specific type. - - #### Args: - - `device` (torch.device): The device to check. - - `type` (str): The type to check for. - - #### Returns: - - `bool`: True if the device is of the specified type, False otherwise. - """ - if hasattr(device, "type"): - if device.type == type: - return True - return False - - -def is_device_cpu(device: torch.device) -> bool: - """#### Check if the device is a CPU. - - #### Args: - - `device` (torch.device): The device to check. - - #### Returns: - - `bool`: True if the device is a CPU, False otherwise. - """ - return is_device_type(device, "cpu") - - -def is_device_mps(device: torch.device) -> bool: - """#### Check if the device is an MPS. - - #### Args: - - `device` (torch.device): The device to check. - - #### Returns: - - `bool`: True if the device is an MPS, False otherwise. - """ - return is_device_type(device, "mps") - - -def is_device_cuda(device: torch.device) -> bool: - """#### Check if the device is a CUDA device. - - #### Args: - - `device` (torch.device): The device to check. - - #### Returns: - - `bool`: True if the device is a CUDA device, False otherwise. - """ - return is_device_type(device, "cuda") - - -def should_use_fp16( - device: torch.device = None, model_params: int = 0, prioritize_performance: bool = True, manual_cast: bool = False -) -> bool: - """#### Determine if FP16 should be used. - - #### Args: - - `device` (torch.device, optional): The device to check. Defaults to None. - - `model_params` (int, optional): The number of model parameters. Defaults to 0. - - `prioritize_performance` (bool, optional): Whether to prioritize performance. Defaults to True. - - `manual_cast` (bool, optional): Whether to manually cast. Defaults to False. - - #### Returns: - - `bool`: True if FP16 should be used, False otherwise. - """ - global directml_enabled - - if device is not None: - if is_device_cpu(device): - return False - - if FORCE_FP16: - return True - - if device is not None: - if is_device_mps(device): - return True - - if FORCE_FP32: - return False - - if directml_enabled: - return False - - if mps_mode(): - return True - - if cpu_mode(): - return False - - if is_intel_xpu(): - return True - - if torch.version.hip: - return True - - if torch.cuda.is_available(): - props = torch.cuda.get_device_properties("cuda") - else: - return False - if props.major >= 8: - return True - - if props.major < 6: - return False - - fp16_works = False - nvidia_10_series = [ - "1080", - "1070", - "titan x", - "p3000", - "p3200", - "p4000", - "p4200", - "p5000", - "p5200", - "p6000", - "1060", - "1050", - "p40", - "p100", - "p6", - "p4", - ] - for x in nvidia_10_series: - if x in props.name.lower(): - fp16_works = True - - if fp16_works or manual_cast: - free_model_memory = get_free_memory() * 0.9 - minimum_inference_memory() - if (not prioritize_performance) or model_params * 4 > free_model_memory: - return True - - if props.major < 7: - return False - - nvidia_16_series = [ - "1660", - "1650", - "1630", - "T500", - "T550", - "T600", - "MX550", - "MX450", - "CMP 30HX", - "T2000", - "T1000", - "T1200", - ] - for x in nvidia_16_series: - if x in props.name: - return False - - return True - - -def should_use_bf16( - device: torch.device = None, model_params: int = 0, prioritize_performance: bool = True, manual_cast: bool = False -) -> bool: - """#### Determine if BF16 should be used. - - #### Args: - - `device` (torch.device, optional): The device to check. Defaults to None. - - `model_params` (int, optional): The number of model parameters. Defaults to 0. - - `prioritize_performance` (bool, optional): Whether to prioritize performance. Defaults to True. - - `manual_cast` (bool, optional): Whether to manually cast. Defaults to False. - - #### Returns: - - `bool`: True if BF16 should be used, False otherwise. - """ - if device is not None: - if is_device_cpu(device): - return False - - if device is not None: - if is_device_mps(device): - return False - - if FORCE_FP32: - return False - - if directml_enabled: - return False - - if cpu_mode() or mps_mode(): - return False - - if is_intel_xpu(): - return True - - if device is None: - device = torch.device("cuda") - - props = torch.cuda.get_device_properties(device) - if props.major >= 8: - return True - - bf16_works = torch.cuda.is_bf16_supported() - - if bf16_works or manual_cast: - free_model_memory = get_free_memory() * 0.9 - minimum_inference_memory() - if (not prioritize_performance) or model_params * 4 > free_model_memory: - return True - - return False - - -def soft_empty_cache(force: bool = False) -> None: - """#### Softly empty the cache. - - #### Args: - - `force` (bool, optional): Whether to force emptying the cache. Defaults to False. - """ - global cpu_state - if cpu_state == CPUState.MPS: - torch.mps.empty_cache() - elif is_intel_xpu(): - torch.xpu.empty_cache() - elif torch.cuda.is_available(): - if ( - force or is_nvidia() - ): # This seems to make things worse on ROCm so I only do it for cuda - torch.cuda.empty_cache() - torch.cuda.ipc_collect() - - -def unload_all_models() -> None: - """#### Unload all models.""" - free_memory(1e30, get_torch_device()) - - -def resolve_lowvram_weight(weight: torch.Tensor, model: torch.nn.Module, key: str) -> torch.Tensor: - """#### Resolve low VRAM weight. - - #### Args: - - `weight` (torch.Tensor): The weight tensor. - - `model` (torch.nn.Module): The model. - - `key` (str): The key. - - #### Returns: - - `torch.Tensor`: The resolved weight tensor. - """ +import logging +import platform +import sys +from enum import Enum +from typing import Tuple, Union +import packaging.version + +import psutil +import torch + +if packaging.version.parse(torch.__version__) >= packaging.version.parse("1.12.0"): + torch.backends.cuda.matmul.allow_tf32 = True + + +class VRAMState(Enum): + """#### Enum for VRAM states. + """ + DISABLED = 0 # No vram present: no need to move _internal to vram + NO_VRAM = 1 # Very low vram: enable all the options to save vram + LOW_VRAM = 2 + NORMAL_VRAM = 3 + HIGH_VRAM = 4 + SHARED = 5 # No dedicated vram: memory shared between CPU and GPU but _internal still need to be moved between both. + + +class CPUState(Enum): + """#### Enum for CPU states. + """ + GPU = 0 + CPU = 1 + MPS = 2 + + +# Determine VRAM State +vram_state = VRAMState.NORMAL_VRAM +set_vram_to = VRAMState.NORMAL_VRAM +cpu_state = CPUState.GPU + +total_vram = 0 + +lowvram_available = True +xpu_available = False + +directml_enabled = False +try: + if torch.xpu.is_available(): + xpu_available = True +except: + pass + +try: + if torch.backends.mps.is_available(): + cpu_state = CPUState.MPS + import torch.mps +except: + pass + + +def is_intel_xpu() -> bool: + """#### Check if Intel XPU is available. + + #### Returns: + - `bool`: Whether Intel XPU is available. + """ + global cpu_state + global xpu_available + if cpu_state == CPUState.GPU: + if xpu_available: + return True + return False + + +def get_torch_device() -> torch.device: + """#### Get the torch device. + + #### Returns: + - `torch.device`: The torch device. + """ + global directml_enabled + global cpu_state + if directml_enabled: + global directml_device + return directml_device + if cpu_state == CPUState.MPS: + return torch.device("mps") + if cpu_state == CPUState.CPU: + return torch.device("cpu") + else: + if is_intel_xpu(): + return torch.device("xpu", torch.xpu.current_device()) + else: + if torch.cuda.is_available(): + return torch.device(torch.cuda.current_device()) + else: + return torch.device("cpu") + + +def get_total_memory(dev: torch.device = None, torch_total_too: bool = False) -> int: + """#### Get the total memory. + + #### Args: + - `dev` (torch.device, optional): The device. Defaults to None. + - `torch_total_too` (bool, optional): Whether to get the total memory in PyTorch. Defaults to False. + + #### Returns: + - `int`: The total memory. + """ + global directml_enabled + if dev is None: + dev = get_torch_device() + + if hasattr(dev, "type") and (dev.type == "cpu" or dev.type == "mps"): + mem_total = psutil.virtual_memory().total + mem_total_torch = mem_total + else: + if directml_enabled: + mem_total = 1024 * 1024 * 1024 + mem_total_torch = mem_total + elif is_intel_xpu(): + stats = torch.xpu.memory_stats(dev) + mem_reserved = stats["reserved_bytes.all.current"] + mem_total_torch = mem_reserved + mem_total = torch.xpu.get_device_properties(dev).total_memory + else: + stats = torch.cuda.memory_stats(dev) + mem_reserved = stats["reserved_bytes.all.current"] + _, mem_total_cuda = torch.cuda.mem_get_info(dev) + mem_total_torch = mem_reserved + mem_total = mem_total_cuda + + if torch_total_too: + return (mem_total, mem_total_torch) + else: + return mem_total + + +total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) +total_ram = psutil.virtual_memory().total / (1024 * 1024) +logging.info( + "Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram) +) +try: + OOM_EXCEPTION = torch.cuda.OutOfMemoryError +except: + OOM_EXCEPTION = Exception + +XFORMERS_VERSION = "" +XFORMERS_ENABLED_VAE = True +try: + import xformers + import xformers.ops + + XFORMERS_IS_AVAILABLE = True + try: + XFORMERS_IS_AVAILABLE = xformers._has_cpp_library + except: + pass + try: + XFORMERS_VERSION = xformers.version.__version__ + logging.info("xformers version: {}".format(XFORMERS_VERSION)) + if XFORMERS_VERSION.startswith("0.0.18"): + logging.warning( + "\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images." + ) + logging.warning( + "Please downgrade or upgrade xformers to a different version.\n" + ) + XFORMERS_ENABLED_VAE = False + except: + pass +except: + XFORMERS_IS_AVAILABLE = False + + +def is_nvidia() -> bool: + """#### Checks if user has an Nvidia GPU + + #### Returns + - `bool`: Whether the GPU is Nvidia + """ + global cpu_state + if cpu_state == CPUState.GPU: + if torch.version.cuda: + return True + return False + + +ENABLE_PYTORCH_ATTENTION = False + +VAE_DTYPE = torch.float32 + +try: + if is_nvidia(): + torch_version = torch.version.__version__ + if int(torch_version[0]) >= 2: + if ENABLE_PYTORCH_ATTENTION is False: + ENABLE_PYTORCH_ATTENTION = True + if ( + torch.cuda.is_bf16_supported() + and torch.cuda.get_device_properties(torch.cuda.current_device()).major + >= 8 + ): + VAE_DTYPE = torch.bfloat16 +except: + pass + +if is_intel_xpu(): + VAE_DTYPE = torch.bfloat16 + +if ENABLE_PYTORCH_ATTENTION: + torch.backends.cuda.enable_math_sdp(True) + torch.backends.cuda.enable_flash_sdp(True) + torch.backends.cuda.enable_mem_efficient_sdp(True) + + +FORCE_FP32 = False +FORCE_FP16 = False + +if lowvram_available: + if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): + vram_state = set_vram_to + +if cpu_state != CPUState.GPU: + vram_state = VRAMState.DISABLED + +if cpu_state == CPUState.MPS: + vram_state = VRAMState.SHARED + +logging.info(f"Set vram state to: {vram_state.name}") + +DISABLE_SMART_MEMORY = False + +if DISABLE_SMART_MEMORY: + logging.info("Disabling smart memory management") + + +def get_torch_device_name(device: torch.device) -> str: + """#### Get the name of the torch compatible device + + #### Args: + - `device` (torch.device): the device + + #### Returns: + - `str`: the name of the device + """ + if hasattr(device, "type"): + if device.type == "cuda": + try: + allocator_backend = torch.cuda.get_allocator_backend() + except: + allocator_backend = "" + return "{} {} : {}".format( + device, torch.cuda.get_device_name(device), allocator_backend + ) + else: + return "{}".format(device.type) + elif is_intel_xpu(): + return "{} {}".format(device, torch.xpu.get_device_name(device)) + else: + return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) + + +try: + logging.info("Device: {}".format(get_torch_device_name(get_torch_device()))) +except: + logging.warning("Could not pick default device.") + +logging.info("VAE dtype: {}".format(VAE_DTYPE)) + +current_loaded_models = [] + + +def module_size(module: torch.nn.Module) -> int: + """#### Get the size of a module + + #### Args: + - `module` (torch.nn.Module): The module + + #### Returns: + - `int`: The size of the module + """ + module_mem = 0 + sd = module.state_dict() + for k in sd: + t = sd[k] + module_mem += t.nelement() * t.element_size() + return module_mem + + +class LoadedModel: + """#### Class to load a model + """ + def __init__(self, model: torch.nn.Module): + """#### Initialize the class + + #### Args: + - `model`: The model + """ + self.model = model + self.device = model.load_device + self.weights_loaded = False + self.real_model = None + + def model_memory(self): + """#### Get the model memory + + #### Returns: + - `int`: The model memory + """ + return self.model.model_size() + + + def model_offloaded_memory(self): + """#### Get the offloaded model memory + + #### Returns: + - `int`: The offloaded model memory + """ + return self.model.model_size() - self.model.loaded_size() + + def model_memory_required(self, device: torch.device) -> int: + """#### Get the required model memory + + #### Args: + - `device`: The device + + #### Returns: + - `int`: The required model memory + """ + if hasattr(self.model, 'current_loaded_device') and device == self.model.current_loaded_device(): + return self.model_offloaded_memory() + else: + return self.model_memory() + + def model_load(self, lowvram_model_memory: int = 0, force_patch_weights: bool = False) -> torch.nn.Module: + """#### Load the model + + #### Args: + - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. + - `force_patch_weights` (bool, optional): Whether to force patch the weights. Defaults to False. + + #### Returns: + - `torch.nn.Module`: The real model + """ + patch_model_to = self.device + + self.model.model_patches_to(self.device) + self.model.model_patches_to(self.model.model_dtype()) + + load_weights = not self.weights_loaded + + try: + if hasattr(self.model, "patch_model_lowvram") and lowvram_model_memory > 0 and load_weights: + self.real_model = self.model.patch_model_lowvram( + device_to=patch_model_to, + lowvram_model_memory=lowvram_model_memory, + force_patch_weights=force_patch_weights, + ) + else: + self.real_model = self.model.patch_model( + device_to=patch_model_to, patch_weights=load_weights + ) + except Exception as e: + self.model.unpatch_model(self.model.offload_device) + self.model_unload() + raise e + self.weights_loaded = True + return self.real_model + + def model_load_flux(self, lowvram_model_memory: int = 0, force_patch_weights: bool = False) -> torch.nn.Module: + """#### Load the model + + #### Args: + - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. + - `force_patch_weights` (bool, optional): Whether to force patch the weights. Defaults to False. + + #### Returns: + - `torch.nn.Module`: The real model + """ + patch_model_to = self.device + + self.model.model_patches_to(self.device) + self.model.model_patches_to(self.model.model_dtype()) + + load_weights = not self.weights_loaded + + if self.model.loaded_size() > 0: + use_more_vram = lowvram_model_memory + if use_more_vram == 0: + use_more_vram = 1e32 + self.model_use_more_vram(use_more_vram) + else: + try: + self.real_model = self.model.patch_model_flux( + device_to=patch_model_to, + lowvram_model_memory=lowvram_model_memory, + load_weights=load_weights, + force_patch_weights=force_patch_weights, + ) + except Exception as e: + self.model.unpatch_model(self.model.offload_device) + self.model_unload() + raise e + + if ( + is_intel_xpu() + and "ipex" in globals() + and self.real_model is not None + ): + import ipex + with torch.no_grad(): + self.real_model = ipex.optimize( + self.real_model.eval(), + inplace=True, + graph_mode=True, + concat_linear=True, + ) + + self.weights_loaded = True + return self.real_model + + def should_reload_model(self, force_patch_weights: bool = False) -> bool: + """#### Checks if the model should be reloaded + + #### Args: + - `force_patch_weights` (bool, optional): If model reloading should be enforced. Defaults to False. + + #### Returns: + - `bool`: Whether the model should be reloaded + """ + if force_patch_weights and self.model.lowvram_patch_counter > 0: + return True + return False + + def model_unload(self, unpatch_weights: bool = True) -> None: + """#### Unloads the patched model + + #### Args: + - `unpatch_weights` (bool, optional): Whether the weights should be unpatched. Defaults to True. + """ + self.model.unpatch_model( + self.model.offload_device, unpatch_weights=unpatch_weights + ) + self.model.model_patches_to(self.model.offload_device) + self.weights_loaded = self.weights_loaded and not unpatch_weights + self.real_model = None + + def model_use_more_vram(self, extra_memory: int) -> int: + """#### Use more VRAM + + #### Args: + - `extra_memory`: The extra memory + """ + return self.model.partially_load(self.device, extra_memory) + + def __eq__(self, other: torch.nn.Module) -> bool: + """#### Verify if the model is equal to another + + #### Args: + - `other` (torch.nn.Module): the other model + + #### Returns: + - `bool`: Whether the two models are equal + """ + return self.model is other.model + + +def minimum_inference_memory() -> int: + """#### The minimum memory requirement for inference, equals to 1024^3 + + #### Returns: + - `int`: the memory requirement + """ + return 1024 * 1024 * 1024 + + +def unload_model_clones(model: torch.nn.Module, unload_weights_only:bool = True, force_unload: bool = True) -> bool: + """#### Unloads the model clones + + #### Args: + - `model` (torch.nn.Module): The model + - `unload_weights_only` (bool, optional): Whether to unload only the weights. Defaults to True. + - `force_unload` (bool, optional): Whether to force the unload. Defaults to True. + + #### Returns: + - `bool`: Whether the model was unloaded + """ + to_unload = [] + for i in range(len(current_loaded_models)): + if model.is_clone(current_loaded_models[i].model): + to_unload = [i] + to_unload + + if len(to_unload) == 0: + return True + + same_weights = 0 + + if same_weights == len(to_unload): + unload_weight = False + else: + unload_weight = True + + if not force_unload: + if unload_weights_only and unload_weight is False: + return None + + for i in to_unload: + logging.debug("unload clone {} {}".format(i, unload_weight)) + current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight) + + return unload_weight + + +def free_memory(memory_required: int, device: torch.device, keep_loaded: list = []) -> None: + """#### Free memory + + #### Args: + - `memory_required` (int): The required memory + - `device` (torch.device): The device + - `keep_loaded` (list, optional): The list of loaded models to keep. Defaults to []. + """ + unloaded_model = [] + can_unload = [] + + for i in range(len(current_loaded_models) - 1, -1, -1): + shift_model = current_loaded_models[i] + if shift_model.device == device: + if shift_model not in keep_loaded: + can_unload.append( + (sys.getrefcount(shift_model.model), shift_model.model_memory(), i) + ) + + for x in sorted(can_unload): + i = x[-1] + if not DISABLE_SMART_MEMORY: + if get_free_memory(device) > memory_required: + break + current_loaded_models[i].model_unload() + unloaded_model.append(i) + + for i in sorted(unloaded_model, reverse=True): + current_loaded_models.pop(i) + + if len(unloaded_model) > 0: + soft_empty_cache() + else: + if vram_state != VRAMState.HIGH_VRAM: + mem_free_total, mem_free_torch = get_free_memory( + device, torch_free_too=True + ) + if mem_free_torch > mem_free_total * 0.25: + soft_empty_cache() + +def use_more_memory(extra_memory: int, loaded_models: list, device: torch.device) -> None: + """#### Use more memory + + #### Args: + - `extra_memory` (int): The extra memory + - `loaded_models` (list): The loaded models + - `device` (torch.device): The device + """ + for m in loaded_models: + if m.device == device: + extra_memory -= m.model_use_more_vram(extra_memory) + if extra_memory <= 0: + break + +WINDOWS = any(platform.win32_ver()) + +EXTRA_RESERVED_VRAM = 400 * 1024 * 1024 +if WINDOWS: + EXTRA_RESERVED_VRAM = ( + 600 * 1024 * 1024 + ) # Windows is higher because of the shared vram issue + +def extra_reserved_memory() -> int: + """#### Extra reserved memory + + #### Returns: + - `int`: The extra reserved memory + """ + return EXTRA_RESERVED_VRAM + +def offloaded_memory(loaded_models: list, device: torch.device) -> int: + """#### Offloaded memory + + #### Args: + - `loaded_models` (list): The loaded models + - `device` (torch.device): The device + + #### Returns: + - `int`: The offloaded memory + """ + offloaded_mem = 0 + for m in loaded_models: + if m.device == device: + offloaded_mem += m.model_offloaded_memory() + return offloaded_mem + +def load_models_gpu(models: list, memory_required: int = 0, force_patch_weights: bool = False, minimum_memory_required=None, force_full_load=False, flux_enabled: bool = False) -> None: + """#### Load models on the GPU + + #### Args: + - `models`(list): The models + - `memory_required` (int, optional): The required memory. Defaults to 0. + - `force_patch_weights` (bool, optional): Whether to force patch the weights. Defaults to False. + - `minimum_memory_required` (int, optional): The minimum memory required. Defaults to None. + - `force_full_load` (bool, optional + - `flux_enabled` (bool, optional): Whether flux is enabled. Defaults to False. + """ + global vram_state + if not flux_enabled: + + inference_memory = minimum_inference_memory() + extra_mem = max(inference_memory, memory_required) + + models = set(models) + + models_to_load = [] + models_already_loaded = [] + for x in models: + loaded_model = LoadedModel(x) + loaded = None + + try: + loaded_model_index = current_loaded_models.index(loaded_model) + except: + loaded_model_index = None + + if loaded_model_index is not None: + loaded = current_loaded_models[loaded_model_index] + if loaded.should_reload_model(force_patch_weights=force_patch_weights): + current_loaded_models.pop(loaded_model_index).model_unload( + unpatch_weights=True + ) + loaded = None + else: + models_already_loaded.append(loaded) + + if loaded is None: + if hasattr(x, "model"): + logging.info(f"Requested to load {x.model.__class__.__name__}") + models_to_load.append(loaded_model) + + if len(models_to_load) == 0: + devs = set(map(lambda a: a.device, models_already_loaded)) + for d in devs: + if d != torch.device("cpu"): + free_memory(extra_mem, d, models_already_loaded) + return + + logging.info( + f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}" + ) + + total_memory_required = {} + for loaded_model in models_to_load: + if ( + unload_model_clones( + loaded_model.model, unload_weights_only=True, force_unload=False + ) + is True + ): # unload clones where the weights are different + total_memory_required[loaded_model.device] = total_memory_required.get( + loaded_model.device, 0 + ) + loaded_model.model_memory_required(loaded_model.device) + + for device in total_memory_required: + if device != torch.device("cpu"): + free_memory( + total_memory_required[device] * 1.3 + extra_mem, + device, + models_already_loaded, + ) + + for loaded_model in models_to_load: + weights_unloaded = unload_model_clones( + loaded_model.model, unload_weights_only=False, force_unload=False + ) # unload the rest of the clones where the weights can stay loaded + if weights_unloaded is not None: + loaded_model.weights_loaded = not weights_unloaded + + for loaded_model in models_to_load: + model = loaded_model.model + torch_dev = model.load_device + if is_device_cpu(torch_dev): + vram_set_state = VRAMState.DISABLED + else: + vram_set_state = vram_state + lowvram_model_memory = 0 + if lowvram_available and ( + vram_set_state == VRAMState.LOW_VRAM + or vram_set_state == VRAMState.NORMAL_VRAM + ): + model_size = loaded_model.model_memory_required(torch_dev) + current_free_mem = get_free_memory(torch_dev) + lowvram_model_memory = int( + max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3) + ) + if model_size > ( + current_free_mem - inference_memory + ): # only switch to lowvram if really necessary + vram_set_state = VRAMState.LOW_VRAM + else: + lowvram_model_memory = 0 + + if vram_set_state == VRAMState.NO_VRAM: + lowvram_model_memory = 64 * 1024 * 1024 + + loaded_model.model_load( + lowvram_model_memory, force_patch_weights=force_patch_weights + ) + current_loaded_models.insert(0, loaded_model) + return + else: + inference_memory = minimum_inference_memory() + extra_mem = max(inference_memory, memory_required + extra_reserved_memory()) + if minimum_memory_required is None: + minimum_memory_required = extra_mem + else: + minimum_memory_required = max( + inference_memory, minimum_memory_required + extra_reserved_memory() + ) + + models = set(models) + + models_to_load = [] + models_already_loaded = [] + for x in models: + loaded_model = LoadedModel(x) + loaded = None + + try: + loaded_model_index = current_loaded_models.index(loaded_model) + except: + loaded_model_index = None + + if loaded_model_index is not None: + loaded = current_loaded_models[loaded_model_index] + if loaded.should_reload_model( + force_patch_weights=force_patch_weights + ): # TODO: cleanup this model reload logic + current_loaded_models.pop(loaded_model_index).model_unload( + unpatch_weights=True + ) + loaded = None + else: + loaded.currently_used = True + models_already_loaded.append(loaded) + + if loaded is None: + if hasattr(x, "model"): + logging.info(f"Requested to load {x.model.__class__.__name__}") + models_to_load.append(loaded_model) + + if len(models_to_load) == 0: + devs = set(map(lambda a: a.device, models_already_loaded)) + for d in devs: + if d != torch.device("cpu"): + free_memory( + extra_mem + offloaded_memory(models_already_loaded, d), + d, + models_already_loaded, + ) + free_mem = get_free_memory(d) + if free_mem < minimum_memory_required: + logging.info( + "Unloading models for lowram load." + ) # TODO: partial model unloading when this case happens, also handle the opposite case where models can be unlowvramed. + models_to_load = free_memory(minimum_memory_required, d) + logging.info("{} models unloaded.".format(len(models_to_load))) + else: + use_more_memory( + free_mem - minimum_memory_required, models_already_loaded, d + ) + if len(models_to_load) == 0: + return + + logging.info( + f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}" + ) + + total_memory_required = {} + for loaded_model in models_to_load: + unload_model_clones( + loaded_model.model, unload_weights_only=True, force_unload=False + ) # unload clones where the weights are different + total_memory_required[loaded_model.device] = total_memory_required.get( + loaded_model.device, 0 + ) + loaded_model.model_memory_required(loaded_model.device) + + for loaded_model in models_already_loaded: + total_memory_required[loaded_model.device] = total_memory_required.get( + loaded_model.device, 0 + ) + loaded_model.model_memory_required(loaded_model.device) + + for loaded_model in models_to_load: + weights_unloaded = unload_model_clones( + loaded_model.model, unload_weights_only=False, force_unload=False + ) # unload the rest of the clones where the weights can stay loaded + if weights_unloaded is not None: + loaded_model.weights_loaded = not weights_unloaded + + for device in total_memory_required: + if device != torch.device("cpu"): + free_memory( + total_memory_required[device] * 1.1 + extra_mem, + device, + models_already_loaded, + ) + + for loaded_model in models_to_load: + model = loaded_model.model + torch_dev = model.load_device + if is_device_cpu(torch_dev): + vram_set_state = VRAMState.DISABLED + else: + vram_set_state = vram_state + lowvram_model_memory = 0 + if ( + lowvram_available + and ( + vram_set_state == VRAMState.LOW_VRAM + or vram_set_state == VRAMState.NORMAL_VRAM + ) + and not force_full_load + ): + model_size = loaded_model.model_memory_required(torch_dev) + current_free_mem = get_free_memory(torch_dev) + lowvram_model_memory = max( + 64 * (1024 * 1024), + (current_free_mem - minimum_memory_required), + min( + current_free_mem * 0.4, + current_free_mem - minimum_inference_memory(), + ), + ) + if ( + model_size <= lowvram_model_memory + ): # only switch to lowvram if really necessary + lowvram_model_memory = 0 + + if vram_set_state == VRAMState.NO_VRAM: + lowvram_model_memory = 64 * 1024 * 1024 + + loaded_model.model_load_flux( + lowvram_model_memory, force_patch_weights=force_patch_weights + ) + current_loaded_models.insert(0, loaded_model) + + devs = set(map(lambda a: a.device, models_already_loaded)) + for d in devs: + if d != torch.device("cpu"): + free_mem = get_free_memory(d) + if free_mem > minimum_memory_required: + use_more_memory( + free_mem - minimum_memory_required, models_already_loaded, d + ) + return + +def load_model_gpu(model: torch.nn.Module, flux_enabled:bool = False) -> None: + """#### Load a model on the GPU + + #### Args: + - `model` (torch.nn.Module): The model + - `flux_enable` (bool, optional): Whether flux is enabled. Defaults to False. + """ + return load_models_gpu([model], flux_enabled=flux_enabled) + + +def cleanup_models(keep_clone_weights_loaded:bool = False): + """#### Cleanup the models + + #### Args: + - `keep_clone_weights_loaded` (bool, optional): Whether to keep the clone weights loaded. Defaults to False. + """ + to_delete = [] + for i in range(len(current_loaded_models)): + if sys.getrefcount(current_loaded_models[i].model) <= 2: + if not keep_clone_weights_loaded: + to_delete = [i] + to_delete + elif ( + sys.getrefcount(current_loaded_models[i].real_model) <= 3 + ): # references from .real_model + the .model + to_delete = [i] + to_delete + + for i in to_delete: + x = current_loaded_models.pop(i) + x.model_unload() + del x + + +def dtype_size(dtype: torch.dtype) -> int: + """#### Get the size of a dtype + + #### Args: + - `dtype` (torch.dtype): The dtype + + #### Returns: + - `int`: The size of the dtype + """ + dtype_size = 4 + if dtype == torch.float16 or dtype == torch.bfloat16: + dtype_size = 2 + elif dtype == torch.float32: + dtype_size = 4 + else: + try: + dtype_size = dtype.itemsize + except: # Old pytorch doesn't have .itemsize + pass + return dtype_size + + +def unet_offload_device() -> torch.device: + """#### Get the offload device for UNet + + #### Returns: + - `torch.device`: The offload device + """ + if vram_state == VRAMState.HIGH_VRAM: + return get_torch_device() + else: + return torch.device("cpu") + + +def unet_inital_load_device(parameters, dtype) -> torch.device: + """#### Get the initial load device for UNet + + #### Args: + - `parameters` (int): The parameters + - `dtype` (torch.dtype): The dtype + + #### Returns: + - `torch.device`: The initial load device + """ + torch_dev = get_torch_device() + if vram_state == VRAMState.HIGH_VRAM: + return torch_dev + + cpu_dev = torch.device("cpu") + if DISABLE_SMART_MEMORY: + return cpu_dev + + model_size = dtype_size(dtype) * parameters + + mem_dev = get_free_memory(torch_dev) + mem_cpu = get_free_memory(cpu_dev) + if mem_dev > mem_cpu and model_size < mem_dev: + return torch_dev + else: + return cpu_dev + + +def unet_dtype( + device: torch.dtype = None, + model_params: int = 0, + supported_dtypes: list = [torch.float16, torch.bfloat16, torch.float32], +) -> torch.dtype: + """#### Get the dtype for UNet + + #### Args: + - `device` (torch.dtype, optional): The device. Defaults to None. + - `model_params` (int, optional): The model parameters. Defaults to 0. + - `supported_dtypes` (list, optional): The supported dtypes. Defaults to [torch.float16, torch.bfloat16, torch.float32]. + + #### Returns: + - `torch.dtype`: The dtype + """ + if should_use_fp16(device=device, model_params=model_params, manual_cast=True): + if torch.float16 in supported_dtypes: + return torch.float16 + if should_use_bf16(device, model_params=model_params, manual_cast=True): + if torch.bfloat16 in supported_dtypes: + return torch.bfloat16 + return torch.float32 + + +# None means no manual cast +def unet_manual_cast( + weight_dtype: torch.dtype, + inference_device: torch.device, + supported_dtypes: list = [torch.float16, torch.bfloat16, torch.float32], +) -> torch.dtype: + """#### Manual cast for UNet + + #### Args: + - `weight_dtype` (torch.dtype): The dtype of the weights + - `inference_device` (torch.device): The device used for inference + - `supported_dtypes` (list, optional): The supported dtypes. Defaults to [torch.float16, torch.bfloat16, torch.float32]. + + #### Returns: + - `torch.dtype`: The dtype + """ + if weight_dtype == torch.float32: + return None + + fp16_supported = should_use_fp16(inference_device, prioritize_performance=False) + if fp16_supported and weight_dtype == torch.float16: + return None + + bf16_supported = should_use_bf16(inference_device) + if bf16_supported and weight_dtype == torch.bfloat16: + return None + + if fp16_supported and torch.float16 in supported_dtypes: + return torch.float16 + + elif bf16_supported and torch.bfloat16 in supported_dtypes: + return torch.bfloat16 + else: + return torch.float32 + + +def text_encoder_offload_device() -> torch.device: + """#### Get the offload device for the text encoder + + #### Returns: + - `torch.device`: The offload device + """ + return torch.device("cpu") + + +def text_encoder_device() -> torch.device: + """#### Get the device for the text encoder + + #### Returns: + - `torch.device`: The device + """ + if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: + if should_use_fp16(prioritize_performance=False): + return get_torch_device() + else: + return torch.device("cpu") + else: + return torch.device("cpu") + +def text_encoder_initial_device(load_device: torch.device, offload_device: torch.device, model_size: int = 0) -> torch.device: + """#### Get the initial device for the text encoder + + #### Args: + - `load_device` (torch.device): The load device + - `offload_device` (torch.device): The offload device + - `model_size` (int, optional): The model size. Defaults to 0. + + #### Returns: + - `torch.device`: The initial device + """ + if load_device == offload_device or model_size <= 1024 * 1024 * 1024: + return offload_device + + if is_device_mps(load_device): + return offload_device + + mem_l = get_free_memory(load_device) + mem_o = get_free_memory(offload_device) + if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l: + return load_device + else: + return offload_device + + +def text_encoder_dtype(device: torch.device = None) -> torch.dtype: + """#### Get the dtype for the text encoder + + #### Args: + - `device` (torch.device, optional): The device used by the text encoder. Defaults to None. + + Returns: + torch.dtype: The dtype + """ + if is_device_cpu(device): + return torch.float16 + + return torch.float16 + + +def intermediate_device() -> torch.device: + """#### Get the intermediate device + + #### Returns: + - `torch.device`: The intermediate device + """ + return torch.device("cpu") + + +def vae_device() -> torch.device: + """#### Get the VAE device + + #### Returns: + - `torch.device`: The VAE device + """ + return get_torch_device() + + +def vae_offload_device() -> torch.device: + """#### Get the offload device for VAE + + #### Returns: + - `torch.device`: The offload device + """ + return torch.device("cpu") + + +def vae_dtype(): + """#### Get the dtype for VAE + + #### Returns: + - `torch.dtype`: The dtype + """ + global VAE_DTYPE + return VAE_DTYPE + + +def get_autocast_device(dev: torch.device) -> str: + """#### Get the autocast device + + #### Args: + - `dev` (torch.device): The device + + #### Returns: + - `str`: The autocast device type + """ + if hasattr(dev, "type"): + return dev.type + return "cuda" + + +def supports_dtype(device: torch.device, dtype: torch.dtype) -> bool: + """#### Check if the device supports the dtype + + #### Args: + - `device` (torch.device): The device to check + - `dtype` (torch.dtype): The dtype to check support + + #### Returns: + - `bool`: Whether the dtype is supported by the device + """ + if dtype == torch.float32: + return True + if is_device_cpu(device): + return False + if dtype == torch.float16: + return True + if dtype == torch.bfloat16: + return True + return False + + +def device_supports_non_blocking(device: torch.device) -> bool: + """#### Check if the device supports non-blocking + + #### Args: + - `device` (torch.device): The device to check + + #### Returns: + - `bool`: Whether the device supports non-blocking + """ + if is_device_mps(device): + return False # pytorch bug? mps doesn't support non blocking + return True + +def supports_cast(device: torch.device, dtype: torch.dtype): # TODO + """#### Check if the device supports casting + + #### Args: + - `device`: The device + - `dtype`: The dtype + + #### Returns: + - `bool`: Whether the device supports casting + """ + if dtype == torch.float32: + return True + if dtype == torch.float16: + return True + if directml_enabled: + return False + if dtype == torch.bfloat16: + return True + if is_device_mps(device): + return False + if dtype == torch.float8_e4m3fn: + return True + if dtype == torch.float8_e5m2: + return True + return False + +def cast_to_device(tensor: torch.Tensor, device: torch.device, dtype: torch.dtype, copy: bool = False) -> torch.Tensor: + """#### Cast a tensor to a device + + #### Args: + - `tensor` (torch.Tensor): The tensor to cast + - `device` (torch.device): The device to cast the tensor to + - `dtype` (torch.dtype): The dtype precision to cast to + - `copy` (bool, optional): Whether to copy the tensor. Defaults to False. + + #### Returns: + - `torch.Tensor`: The tensor cast to the device + """ + device_supports_cast = False + if tensor.dtype == torch.float32 or tensor.dtype == torch.float16: + device_supports_cast = True + elif tensor.dtype == torch.bfloat16: + if hasattr(device, "type") and device.type.startswith("cuda"): + device_supports_cast = True + elif is_intel_xpu(): + device_supports_cast = True + + non_blocking = device_supports_non_blocking(device) + + if device_supports_cast: + if copy: + if tensor.device == device: + return tensor.to(dtype, copy=copy, non_blocking=non_blocking) + return tensor.to(device, copy=copy, non_blocking=non_blocking).to( + dtype, non_blocking=non_blocking + ) + else: + return tensor.to(device, non_blocking=non_blocking).to( + dtype, non_blocking=non_blocking + ) + else: + return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking) + +def pick_weight_dtype(dtype: torch.dtype, fallback_dtype: torch.dtype, device: torch.device) -> torch.dtype: + """#### Pick the weight dtype + + #### Args: + - `dtype`: The dtype + - `fallback_dtype`: The fallback dtype + - `device`: The device + + #### Returns: + - `torch.dtype`: The weight dtype + """ + if dtype is None: + dtype = fallback_dtype + elif dtype_size(dtype) > dtype_size(fallback_dtype): + dtype = fallback_dtype + + if not supports_cast(device, dtype): + dtype = fallback_dtype + + return dtype + +def xformers_enabled() -> bool: + """#### Check if xformers is enabled + + #### Returns: + - `bool`: Whether xformers is enabled + """ + global directml_enabled + global cpu_state + if cpu_state != CPUState.GPU: + return False + if is_intel_xpu(): + return False + if directml_enabled: + return False + return XFORMERS_IS_AVAILABLE + + +def xformers_enabled_vae() -> bool: + """#### Check if xformers is enabled for VAE + + #### Returns: + - `bool`: Whether xformers is enabled for VAE + """ + enabled = xformers_enabled() + if not enabled: + return False + + return XFORMERS_ENABLED_VAE + + +def pytorch_attention_enabled() -> bool: + """#### Check if PyTorch attention is enabled + + #### Returns: + - `bool`: Whether PyTorch attention is enabled + """ + global ENABLE_PYTORCH_ATTENTION + return ENABLE_PYTORCH_ATTENTION + +def pytorch_attention_flash_attention() -> bool: + """#### Check if PyTorch flash attention is enabled and supported. + + #### Returns: + - `bool`: True if PyTorch flash attention is enabled and supported, False otherwise. + """ + global ENABLE_PYTORCH_ATTENTION + if ENABLE_PYTORCH_ATTENTION: + if is_nvidia(): # pytorch flash attention only works on Nvidia + return True + return False + + +def get_free_memory(dev: torch.device = None, torch_free_too: bool = False) -> Union[int, Tuple[int, int]]: + """#### Get the free memory available on the device. + + #### Args: + - `dev` (torch.device, optional): The device to check memory for. Defaults to None. + - `torch_free_too` (bool, optional): Whether to return both total and torch free memory. Defaults to False. + + #### Returns: + - `int` or `Tuple[int, int]`: The free memory available. If `torch_free_too` is True, returns a tuple of total and torch free memory. + """ + global directml_enabled + if dev is None: + dev = get_torch_device() + + if hasattr(dev, "type") and (dev.type == "cpu" or dev.type == "mps"): + mem_free_total = psutil.virtual_memory().available + mem_free_torch = mem_free_total + else: + if directml_enabled: + mem_free_total = 1024 * 1024 * 1024 + mem_free_torch = mem_free_total + elif is_intel_xpu(): + stats = torch.xpu.memory_stats(dev) + mem_active = stats["active_bytes.all.current"] + mem_reserved = stats["reserved_bytes.all.current"] + mem_free_torch = mem_reserved - mem_active + mem_free_xpu = ( + torch.xpu.get_device_properties(dev).total_memory - mem_reserved + ) + mem_free_total = mem_free_xpu + mem_free_torch + else: + stats = torch.cuda.memory_stats(dev) + mem_active = stats["active_bytes.all.current"] + mem_reserved = stats["reserved_bytes.all.current"] + mem_free_cuda, _ = torch.cuda.mem_get_info(dev) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_cuda + mem_free_torch + + if torch_free_too: + return (mem_free_total, mem_free_torch) + else: + return mem_free_total + + +def cpu_mode() -> bool: + """#### Check if the current mode is CPU. + + #### Returns: + - `bool`: True if the current mode is CPU, False otherwise. + """ + global cpu_state + return cpu_state == CPUState.CPU + + +def mps_mode() -> bool: + """#### Check if the current mode is MPS. + + #### Returns: + - `bool`: True if the current mode is MPS, False otherwise. + """ + global cpu_state + return cpu_state == CPUState.MPS + + +def is_device_type(device: torch.device, type: str) -> bool: + """#### Check if the device is of a specific type. + + #### Args: + - `device` (torch.device): The device to check. + - `type` (str): The type to check for. + + #### Returns: + - `bool`: True if the device is of the specified type, False otherwise. + """ + if hasattr(device, "type"): + if device.type == type: + return True + return False + + +def is_device_cpu(device: torch.device) -> bool: + """#### Check if the device is a CPU. + + #### Args: + - `device` (torch.device): The device to check. + + #### Returns: + - `bool`: True if the device is a CPU, False otherwise. + """ + return is_device_type(device, "cpu") + + +def is_device_mps(device: torch.device) -> bool: + """#### Check if the device is an MPS. + + #### Args: + - `device` (torch.device): The device to check. + + #### Returns: + - `bool`: True if the device is an MPS, False otherwise. + """ + return is_device_type(device, "mps") + + +def is_device_cuda(device: torch.device) -> bool: + """#### Check if the device is a CUDA device. + + #### Args: + - `device` (torch.device): The device to check. + + #### Returns: + - `bool`: True if the device is a CUDA device, False otherwise. + """ + return is_device_type(device, "cuda") + + +def should_use_fp16( + device: torch.device = None, model_params: int = 0, prioritize_performance: bool = True, manual_cast: bool = False +) -> bool: + """#### Determine if FP16 should be used. + + #### Args: + - `device` (torch.device, optional): The device to check. Defaults to None. + - `model_params` (int, optional): The number of model parameters. Defaults to 0. + - `prioritize_performance` (bool, optional): Whether to prioritize performance. Defaults to True. + - `manual_cast` (bool, optional): Whether to manually cast. Defaults to False. + + #### Returns: + - `bool`: True if FP16 should be used, False otherwise. + """ + global directml_enabled + + if device is not None: + if is_device_cpu(device): + return False + + if FORCE_FP16: + return True + + if device is not None: + if is_device_mps(device): + return True + + if FORCE_FP32: + return False + + if directml_enabled: + return False + + if mps_mode(): + return True + + if cpu_mode(): + return False + + if is_intel_xpu(): + return True + + if torch.version.hip: + return True + + if torch.cuda.is_available(): + props = torch.cuda.get_device_properties("cuda") + else: + return False + if props.major >= 8: + return True + + if props.major < 6: + return False + + fp16_works = False + nvidia_10_series = [ + "1080", + "1070", + "titan x", + "p3000", + "p3200", + "p4000", + "p4200", + "p5000", + "p5200", + "p6000", + "1060", + "1050", + "p40", + "p100", + "p6", + "p4", + ] + for x in nvidia_10_series: + if x in props.name.lower(): + fp16_works = True + + if fp16_works or manual_cast: + free_model_memory = get_free_memory() * 0.9 - minimum_inference_memory() + if (not prioritize_performance) or model_params * 4 > free_model_memory: + return True + + if props.major < 7: + return False + + nvidia_16_series = [ + "1660", + "1650", + "1630", + "T500", + "T550", + "T600", + "MX550", + "MX450", + "CMP 30HX", + "T2000", + "T1000", + "T1200", + ] + for x in nvidia_16_series: + if x in props.name: + return False + + return True + + +def should_use_bf16( + device: torch.device = None, model_params: int = 0, prioritize_performance: bool = True, manual_cast: bool = False +) -> bool: + """#### Determine if BF16 should be used. + + #### Args: + - `device` (torch.device, optional): The device to check. Defaults to None. + - `model_params` (int, optional): The number of model parameters. Defaults to 0. + - `prioritize_performance` (bool, optional): Whether to prioritize performance. Defaults to True. + - `manual_cast` (bool, optional): Whether to manually cast. Defaults to False. + + #### Returns: + - `bool`: True if BF16 should be used, False otherwise. + """ + if device is not None: + if is_device_cpu(device): + return False + + if device is not None: + if is_device_mps(device): + return False + + if FORCE_FP32: + return False + + if directml_enabled: + return False + + if cpu_mode() or mps_mode(): + return False + + if is_intel_xpu(): + return True + + if device is None: + device = torch.device("cuda") + + props = torch.cuda.get_device_properties(device) + if props.major >= 8: + return True + + bf16_works = torch.cuda.is_bf16_supported() + + if bf16_works or manual_cast: + free_model_memory = get_free_memory() * 0.9 - minimum_inference_memory() + if (not prioritize_performance) or model_params * 4 > free_model_memory: + return True + + return False + + +def soft_empty_cache(force: bool = False) -> None: + """#### Softly empty the cache. + + #### Args: + - `force` (bool, optional): Whether to force emptying the cache. Defaults to False. + """ + global cpu_state + if cpu_state == CPUState.MPS: + torch.mps.empty_cache() + elif is_intel_xpu(): + torch.xpu.empty_cache() + elif torch.cuda.is_available(): + if ( + force or is_nvidia() + ): # This seems to make things worse on ROCm so I only do it for cuda + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + + +def unload_all_models() -> None: + """#### Unload all models.""" + free_memory(1e30, get_torch_device()) + + +def resolve_lowvram_weight(weight: torch.Tensor, model: torch.nn.Module, key: str) -> torch.Tensor: + """#### Resolve low VRAM weight. + + #### Args: + - `weight` (torch.Tensor): The weight tensor. + - `model` (torch.nn.Module): The model. + - `key` (str): The key. + + #### Returns: + - `torch.Tensor`: The resolved weight tensor. + """ return weight \ No newline at end of file diff --git a/modules/FileManaging/Downloader.py b/modules/FileManaging/Downloader.py index 836bb74a97d76d55b229ba301a65d133416363ee..fd46d79a511842d0de92bdf42de011e9ff82fcb6 100644 --- a/modules/FileManaging/Downloader.py +++ b/modules/FileManaging/Downloader.py @@ -1,116 +1,116 @@ -import glob -from huggingface_hub import hf_hub_download - - -def CheckAndDownload(): - """#### Check and download all the necessary safetensors and checkpoints models""" - if glob.glob("./_internal/checkpoints/*.safetensors") == []: - - hf_hub_download( - repo_id="Meina/MeinaMix", - filename="Meina V10 - baked VAE.safetensors", - local_dir="./_internal/checkpoints/", - ) - hf_hub_download( - repo_id="Lykon/DreamShaper", - filename="DreamShaper_8_pruned.safetensors", - local_dir="./_internal/checkpoints/", - ) - if glob.glob("./_internal/yolos/*.pt") == []: - - hf_hub_download( - repo_id="Bingsu/adetailer", - filename="hand_yolov9c.pt", - local_dir="./_internal/yolos/", - ) - hf_hub_download( - repo_id="Bingsu/adetailer", - filename="face_yolov9c.pt", - local_dir="./_internal/yolos/", - ) - hf_hub_download( - repo_id="Bingsu/adetailer", - filename="person_yolov8m-seg.pt", - local_dir="./_internal/yolos/", - ) - hf_hub_download( - repo_id="segments-arnaud/sam_vit_b", - filename="sam_vit_b_01ec64.pth", - local_dir="./_internal/yolos/", - ) - if glob.glob("./_internal/ESRGAN/*.pth") == []: - - hf_hub_download( - repo_id="lllyasviel/Annotators", - filename="RealESRGAN_x4plus.pth", - local_dir="./_internal/ESRGAN/", - ) - if glob.glob("./_internal/loras/*.safetensors") == []: - - hf_hub_download( - repo_id="EvilEngine/add_detail", - filename="add_detail.safetensors", - local_dir="./_internal/loras/", - ) - if glob.glob("./_internal/embeddings/*.pt") == []: - - hf_hub_download( - repo_id="EvilEngine/badhandv4", - filename="badhandv4.pt", - local_dir="./_internal/embeddings/", - ) - # hf_hub_download( - # repo_id="segments-arnaud/sam_vit_b", - # filename="EasyNegative.safetensors", - # local_dir="./_internal/embeddings/", - # ) - if glob.glob("./_internal/vae_approx/*.pth") == []: - - hf_hub_download( - repo_id="madebyollin/taesd", - filename="taesd_decoder.safetensors", - local_dir="./_internal/vae_approx/", - ) - -def CheckAndDownloadFlux(): - """#### Check and download all the necessary safetensors and checkpoints models for FLUX""" - if glob.glob("./_internal/embeddings/*.pt") == []: - hf_hub_download( - repo_id="EvilEngine/badhandv4", - filename="badhandv4.pt", - local_dir="./_internal/embeddings", - ) - if glob.glob("./_internal/unet/*.gguf") == []: - - hf_hub_download( - repo_id="city96/FLUX.1-dev-gguf", - filename="flux1-dev-Q8_0.gguf", - local_dir="./_internal/unet", - ) - if glob.glob("./_internal/clip/*.gguf") == []: - - hf_hub_download( - repo_id="city96/t5-v1_1-xxl-encoder-gguf", - filename="t5-v1_1-xxl-encoder-Q8_0.gguf", - local_dir="./_internal/clip", - ) - hf_hub_download( - repo_id="comfyanonymous/flux_text_encoders", - filename="clip_l.safetensors", - local_dir="./_internal/clip", - ) - if glob.glob("./_internal/vae/*.safetensors") == []: - - hf_hub_download( - repo_id="black-forest-labs/FLUX.1-schnell", - filename="ae.safetensors", - local_dir="./_internal/vae", - ) - - if glob.glob("./_internal/vae_approx/*.pth") == []: - - hf_hub_download( - repo_id="madebyollin/taef1", - filename="diffusion_pytorch_model.safetensors", - local_dir="./_internal/vae_approx/", - ) +import glob +from huggingface_hub import hf_hub_download + + +def CheckAndDownload(): + """#### Check and download all the necessary safetensors and checkpoints models""" + if glob.glob("./_internal/checkpoints/*.safetensors") == []: + + hf_hub_download( + repo_id="Meina/MeinaMix", + filename="Meina V10 - baked VAE.safetensors", + local_dir="./_internal/checkpoints/", + ) + hf_hub_download( + repo_id="Lykon/DreamShaper", + filename="DreamShaper_8_pruned.safetensors", + local_dir="./_internal/checkpoints/", + ) + if glob.glob("./_internal/yolos/*.pt") == []: + + hf_hub_download( + repo_id="Bingsu/adetailer", + filename="hand_yolov9c.pt", + local_dir="./_internal/yolos/", + ) + hf_hub_download( + repo_id="Bingsu/adetailer", + filename="face_yolov9c.pt", + local_dir="./_internal/yolos/", + ) + hf_hub_download( + repo_id="Bingsu/adetailer", + filename="person_yolov8m-seg.pt", + local_dir="./_internal/yolos/", + ) + hf_hub_download( + repo_id="segments-arnaud/sam_vit_b", + filename="sam_vit_b_01ec64.pth", + local_dir="./_internal/yolos/", + ) + if glob.glob("./_internal/ESRGAN/*.pth") == []: + + hf_hub_download( + repo_id="lllyasviel/Annotators", + filename="RealESRGAN_x4plus.pth", + local_dir="./_internal/ESRGAN/", + ) + if glob.glob("./_internal/loras/*.safetensors") == []: + + hf_hub_download( + repo_id="EvilEngine/add_detail", + filename="add_detail.safetensors", + local_dir="./_internal/loras/", + ) + if glob.glob("./_internal/embeddings/*.pt") == []: + + hf_hub_download( + repo_id="EvilEngine/badhandv4", + filename="badhandv4.pt", + local_dir="./_internal/embeddings/", + ) + # hf_hub_download( + # repo_id="segments-arnaud/sam_vit_b", + # filename="EasyNegative.safetensors", + # local_dir="./_internal/embeddings/", + # ) + if glob.glob("./_internal/vae_approx/*.pth") == []: + + hf_hub_download( + repo_id="madebyollin/taesd", + filename="taesd_decoder.safetensors", + local_dir="./_internal/vae_approx/", + ) + +def CheckAndDownloadFlux(): + """#### Check and download all the necessary safetensors and checkpoints models for FLUX""" + if glob.glob("./_internal/embeddings/*.pt") == []: + hf_hub_download( + repo_id="EvilEngine/badhandv4", + filename="badhandv4.pt", + local_dir="./_internal/embeddings", + ) + if glob.glob("./_internal/unet/*.gguf") == []: + + hf_hub_download( + repo_id="city96/FLUX.1-dev-gguf", + filename="flux1-dev-Q8_0.gguf", + local_dir="./_internal/unet", + ) + if glob.glob("./_internal/clip/*.gguf") == []: + + hf_hub_download( + repo_id="city96/t5-v1_1-xxl-encoder-gguf", + filename="t5-v1_1-xxl-encoder-Q8_0.gguf", + local_dir="./_internal/clip", + ) + hf_hub_download( + repo_id="comfyanonymous/flux_text_encoders", + filename="clip_l.safetensors", + local_dir="./_internal/clip", + ) + if glob.glob("./_internal/vae/*.safetensors") == []: + + hf_hub_download( + repo_id="black-forest-labs/FLUX.1-schnell", + filename="ae.safetensors", + local_dir="./_internal/vae", + ) + + if glob.glob("./_internal/vae_approx/*.pth") == []: + + hf_hub_download( + repo_id="madebyollin/taef1", + filename="diffusion_pytorch_model.safetensors", + local_dir="./_internal/vae_approx/", + ) diff --git a/modules/FileManaging/ImageSaver.py b/modules/FileManaging/ImageSaver.py index da267d4fe7335594eedde0a6a6883569532dcdfe..90e8c7c79392699c9fc9c86bc063696aeb455cb2 100644 --- a/modules/FileManaging/ImageSaver.py +++ b/modules/FileManaging/ImageSaver.py @@ -1,126 +1,148 @@ -import os -import numpy as np -from PIL import Image - -output_directory = "./_internal/output" - - -def get_output_directory() -> str: - """#### Get the output directory. - - #### Returns: - - `str`: The output directory. - """ - global output_directory - return output_directory - - -def get_save_image_path( - filename_prefix: str, output_dir: str, image_width: int = 0, image_height: int = 0 -) -> tuple: - """#### Get the save image path. - - #### Args: - - `filename_prefix` (str): The filename prefix. - - `output_dir` (str): The output directory. - - `image_width` (int, optional): The image width. Defaults to 0. - - `image_height` (int, optional): The image height. Defaults to 0. - - #### Returns: - - `tuple`: The full output folder, filename, counter, subfolder, and filename prefix. - """ - - def map_filename(filename: str) -> tuple: - prefix_len = len(os.path.basename(filename_prefix)) - prefix = filename[: prefix_len + 1] - try: - digits = int(filename[prefix_len + 1 :].split("_")[0]) - except: - digits = 0 - return (digits, prefix) - - def compute_vars(input: str, image_width: int, image_height: int) -> str: - input = input.replace("%width%", str(image_width)) - input = input.replace("%height%", str(image_height)) - return input - - filename_prefix = compute_vars(filename_prefix, image_width, image_height) - - subfolder = os.path.dirname(os.path.normpath(filename_prefix)) - filename = os.path.basename(os.path.normpath(filename_prefix)) - - full_output_folder = os.path.join(output_dir, subfolder) - try: - counter = ( - max( - filter( - lambda a: a[1][:-1] == filename and a[1][-1] == "_", - map(map_filename, os.listdir(full_output_folder)), - ) - )[0] - + 1 - ) - except ValueError: - counter = 1 - except FileNotFoundError: - os.makedirs(full_output_folder, exist_ok=True) - counter = 1 - return full_output_folder, filename, counter, subfolder, filename_prefix - - -MAX_RESOLUTION = 16384 - - -class SaveImage: - """#### Class for saving images.""" - - def __init__(self): - """#### Initialize the SaveImage class.""" - self.output_dir = get_output_directory() - self.type = "output" - self.prefix_append = "" - self.compress_level = 4 - - def save_images( - self, - images: list, - filename_prefix: str = "LD", - prompt: str = None, - extra_pnginfo: dict = None, - ) -> dict: - """#### Save images to the output directory. - - #### Args: - - `images` (list): The list of images. - - `filename_prefix` (str, optional): The filename prefix. Defaults to "LD". - - `prompt` (str, optional): The prompt. Defaults to None. - - `extra_pnginfo` (dict, optional): Additional PNG info. Defaults to None. - - #### Returns: - - `dict`: The saved images information. - """ - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = ( - get_save_image_path( - filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0] - ) - ) - results = list() - for batch_number, image in enumerate(images): - i = 255.0 * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - metadata = None - - filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - file = f"{filename_with_batch_num}_{counter:05}_.png" - img.save( - os.path.join(full_output_folder, file), - pnginfo=metadata, - compress_level=self.compress_level, - ) - results.append( - {"filename": file, "subfolder": subfolder, "type": self.type} - ) - counter += 1 - - return {"ui": {"images": results}} +import os +import numpy as np +from PIL import Image + +output_directory = "./_internal/output" + + +def get_output_directory() -> str: + """#### Get the output directory. + + #### Returns: + - `str`: The output directory. + """ + global output_directory + return output_directory + + +def get_save_image_path( + filename_prefix: str, output_dir: str, image_width: int = 0, image_height: int = 0 +) -> tuple: + """#### Get the save image path. + + #### Args: + - `filename_prefix` (str): The filename prefix. + - `output_dir` (str): The output directory. + - `image_width` (int, optional): The image width. Defaults to 0. + - `image_height` (int, optional): The image height. Defaults to 0. + + #### Returns: + - `tuple`: The full output folder, filename, counter, subfolder, and filename prefix. + """ + + def map_filename(filename: str) -> tuple: + prefix_len = len(os.path.basename(filename_prefix)) + prefix = filename[: prefix_len + 1] + try: + digits = int(filename[prefix_len + 1 :].split("_")[0]) + except: + digits = 0 + return (digits, prefix) + + def compute_vars(input: str, image_width: int, image_height: int) -> str: + input = input.replace("%width%", str(image_width)) + input = input.replace("%height%", str(image_height)) + return input + + filename_prefix = compute_vars(filename_prefix, image_width, image_height) + + subfolder = os.path.dirname(os.path.normpath(filename_prefix)) + filename = os.path.basename(os.path.normpath(filename_prefix)) + + full_output_folder = os.path.join(output_dir, subfolder) + subfolder_paths = [ + os.path.join(full_output_folder, x) + for x in ["Classic", "HiresFix", "Img2Img", "Flux", "Adetailer"] + ] + for path in subfolder_paths: + os.makedirs(path, exist_ok=True) + # Find highest counter across all subfolders + counter = 1 + for path in subfolder_paths: + if os.path.exists(path): + files = os.listdir(path) + if files: + numbers = [ + map_filename(f)[0] + for f in files + if f.startswith(filename) and f.endswith(".png") + ] + if numbers: + counter = max(max(numbers) + 1, counter) + + return full_output_folder, filename, counter, subfolder, filename_prefix + + +MAX_RESOLUTION = 16384 + + +class SaveImage: + """#### Class for saving images.""" + + def __init__(self): + """#### Initialize the SaveImage class.""" + self.output_dir = get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + def save_images( + self, + images: list, + filename_prefix: str = "LD", + prompt: str = None, + extra_pnginfo: dict = None, + ) -> dict: + """#### Save images to the output directory. + + #### Args: + - `images` (list): The list of images. + - `filename_prefix` (str, optional): The filename prefix. Defaults to "LD". + - `prompt` (str, optional): The prompt. Defaults to None. + - `extra_pnginfo` (dict, optional): Additional PNG info. Defaults to None. + + #### Returns: + - `dict`: The saved images information. + """ + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = ( + get_save_image_path( + filename_prefix, self.output_dir, images[0].shape[-2], images[0].shape[-1] + ) + ) + results = list() + for batch_number, image in enumerate(images): + # Ensure correct shape by squeezing extra dimensions + i = 255.0 * image.cpu().numpy() + i = np.squeeze(i) # Remove extra dimensions + + # Ensure we have a valid 3D array (height, width, channels) + if i.ndim == 4: + i = i.reshape(-1, i.shape[-2], i.shape[-1]) + + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + metadata = None + + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.png" + if filename_prefix == "LD-HF": + full_output_folder = os.path.join(full_output_folder, "HiresFix") + elif filename_prefix == "LD-I2I": + full_output_folder = os.path.join(full_output_folder, "Img2Img") + elif filename_prefix == "LD-Flux": + full_output_folder = os.path.join(full_output_folder, "Flux") + elif filename_prefix == "LD-head" or filename_prefix == "LD-body": + full_output_folder = os.path.join(full_output_folder, "Adetailer") + else: + full_output_folder = os.path.join(full_output_folder, "Classic") + img.save( + os.path.join(full_output_folder, file), + pnginfo=metadata, + compress_level=self.compress_level, + ) + results.append( + {"filename": file, "subfolder": subfolder, "type": self.type} + ) + counter += 1 + + return {"ui": {"images": results}} \ No newline at end of file diff --git a/modules/FileManaging/Loader.py b/modules/FileManaging/Loader.py index 0ec27711e1e657f7ace8c6eb7702194baca572fb..8c490e4b4c4a3c66ce167234b4f6aea29773e0b8 100644 --- a/modules/FileManaging/Loader.py +++ b/modules/FileManaging/Loader.py @@ -1,138 +1,138 @@ -import logging -import torch -from modules.Utilities import util -from modules.AutoEncoders import VariationalAE -from modules.Device import Device -from modules.Model import ModelPatcher -from modules.NeuralNetwork import unet -from modules.clip import Clip - - -def load_checkpoint_guess_config( - ckpt_path: str, - output_vae: bool = True, - output_clip: bool = True, - output_clipvision: bool = False, - embedding_directory: str = None, - output_model: bool = True, -) -> tuple: - """#### Load a checkpoint and guess the configuration. - - #### Args: - - `ckpt_path` (str): The path to the checkpoint file. - - `output_vae` (bool, optional): Whether to output the VAE. Defaults to True. - - `output_clip` (bool, optional): Whether to output the CLIP. Defaults to True. - - `output_clipvision` (bool, optional): Whether to output the CLIP vision. Defaults to False. - - `embedding_directory` (str, optional): The embedding directory. Defaults to None. - - `output_model` (bool, optional): Whether to output the model. Defaults to True. - - #### Returns: - - `tuple`: The model patcher, CLIP, VAE, and CLIP vision. - """ - sd = util.load_torch_file(ckpt_path) - sd.keys() - clip = None - clipvision = None - vae = None - model = None - model_patcher = None - clip_target = None - - parameters = util.calculate_parameters(sd, "model.diffusion_model.") - load_device = Device.get_torch_device() - - model_config = unet.model_config_from_unet(sd, "model.diffusion_model.") - unet_dtype = unet.unet_dtype1( - model_params=parameters, - supported_dtypes=model_config.supported_inference_dtypes, - ) - manual_cast_dtype = Device.unet_manual_cast( - unet_dtype, load_device, model_config.supported_inference_dtypes - ) - model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) - - if output_model: - inital_load_device = Device.unet_inital_load_device(parameters, unet_dtype) - Device.unet_offload_device() - model = model_config.get_model( - sd, "model.diffusion_model.", device=inital_load_device - ) - model.load_model_weights(sd, "model.diffusion_model.") - - if output_vae: - vae_sd = util.state_dict_prefix_replace( - sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True - ) - vae_sd = model_config.process_vae_state_dict(vae_sd) - vae = VariationalAE.VAE(sd=vae_sd) - - if output_clip: - clip_target = model_config.clip_target() - if clip_target is not None: - clip_sd = model_config.process_clip_state_dict(sd) - if len(clip_sd) > 0: - clip = Clip.CLIP(clip_target, embedding_directory=embedding_directory) - m, u = clip.load_sd(clip_sd, full_model=True) - if len(m) > 0: - m_filter = list( - filter( - lambda a: ".logit_scale" not in a - and ".transformer.text_projection.weight" not in a, - m, - ) - ) - if len(m_filter) > 0: - logging.warning("clip missing: {}".format(m)) - else: - logging.debug("clip missing: {}".format(m)) - - if len(u) > 0: - logging.debug("clip unexpected {}:".format(u)) - else: - logging.warning( - "no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded." - ) - - left_over = sd.keys() - if len(left_over) > 0: - logging.debug("left over keys: {}".format(left_over)) - - if output_model: - model_patcher = ModelPatcher.ModelPatcher( - model, - load_device=load_device, - offload_device=Device.unet_offload_device(), - current_device=inital_load_device, - ) - if inital_load_device != torch.device("cpu"): - logging.info("loaded straight to GPU") - Device.load_model_gpu(model_patcher) - - return (model_patcher, clip, vae, clipvision) - - -class CheckpointLoaderSimple: - """#### Class for loading checkpoints.""" - - def load_checkpoint( - self, ckpt_name: str, output_vae: bool = True, output_clip: bool = True - ) -> tuple: - """#### Load a checkpoint. - - #### Args: - - `ckpt_name` (str): The name of the checkpoint. - - `output_vae` (bool, optional): Whether to output the VAE. Defaults to True. - - `output_clip` (bool, optional): Whether to output the CLIP. Defaults to True. - - #### Returns: - - `tuple`: The model patcher, CLIP, and VAE. - """ - ckpt_path = f"{ckpt_name}" - out = load_checkpoint_guess_config( - ckpt_path, - output_vae=output_vae, - output_clip=output_clip, - embedding_directory="./_internal/embeddings/", - ) - print("loading", ckpt_path) - return out[:3] +import logging +import torch +from modules.Utilities import util +from modules.AutoEncoders import VariationalAE +from modules.Device import Device +from modules.Model import ModelPatcher +from modules.NeuralNetwork import unet +from modules.clip import Clip + + +def load_checkpoint_guess_config( + ckpt_path: str, + output_vae: bool = True, + output_clip: bool = True, + output_clipvision: bool = False, + embedding_directory: str = None, + output_model: bool = True, +) -> tuple: + """#### Load a checkpoint and guess the configuration. + + #### Args: + - `ckpt_path` (str): The path to the checkpoint file. + - `output_vae` (bool, optional): Whether to output the VAE. Defaults to True. + - `output_clip` (bool, optional): Whether to output the CLIP. Defaults to True. + - `output_clipvision` (bool, optional): Whether to output the CLIP vision. Defaults to False. + - `embedding_directory` (str, optional): The embedding directory. Defaults to None. + - `output_model` (bool, optional): Whether to output the model. Defaults to True. + + #### Returns: + - `tuple`: The model patcher, CLIP, VAE, and CLIP vision. + """ + sd = util.load_torch_file(ckpt_path) + sd.keys() + clip = None + clipvision = None + vae = None + model = None + model_patcher = None + clip_target = None + + parameters = util.calculate_parameters(sd, "model.diffusion_model.") + load_device = Device.get_torch_device() + + model_config = unet.model_config_from_unet(sd, "model.diffusion_model.") + unet_dtype = unet.unet_dtype1( + model_params=parameters, + supported_dtypes=model_config.supported_inference_dtypes, + ) + manual_cast_dtype = Device.unet_manual_cast( + unet_dtype, load_device, model_config.supported_inference_dtypes + ) + model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) + + if output_model: + inital_load_device = Device.unet_inital_load_device(parameters, unet_dtype) + Device.unet_offload_device() + model = model_config.get_model( + sd, "model.diffusion_model.", device=inital_load_device + ) + model.load_model_weights(sd, "model.diffusion_model.") + + if output_vae: + vae_sd = util.state_dict_prefix_replace( + sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True + ) + vae_sd = model_config.process_vae_state_dict(vae_sd) + vae = VariationalAE.VAE(sd=vae_sd) + + if output_clip: + clip_target = model_config.clip_target() + if clip_target is not None: + clip_sd = model_config.process_clip_state_dict(sd) + if len(clip_sd) > 0: + clip = Clip.CLIP(clip_target, embedding_directory=embedding_directory) + m, u = clip.load_sd(clip_sd, full_model=True) + if len(m) > 0: + m_filter = list( + filter( + lambda a: ".logit_scale" not in a + and ".transformer.text_projection.weight" not in a, + m, + ) + ) + if len(m_filter) > 0: + logging.warning("clip missing: {}".format(m)) + else: + logging.debug("clip missing: {}".format(m)) + + if len(u) > 0: + logging.debug("clip unexpected {}:".format(u)) + else: + logging.warning( + "no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded." + ) + + left_over = sd.keys() + if len(left_over) > 0: + logging.debug("left over keys: {}".format(left_over)) + + if output_model: + model_patcher = ModelPatcher.ModelPatcher( + model, + load_device=load_device, + offload_device=Device.unet_offload_device(), + current_device=inital_load_device, + ) + if inital_load_device != torch.device("cpu"): + logging.info("loaded straight to GPU") + Device.load_model_gpu(model_patcher) + + return (model_patcher, clip, vae, clipvision) + + +class CheckpointLoaderSimple: + """#### Class for loading checkpoints.""" + + def load_checkpoint( + self, ckpt_name: str, output_vae: bool = True, output_clip: bool = True + ) -> tuple: + """#### Load a checkpoint. + + #### Args: + - `ckpt_name` (str): The name of the checkpoint. + - `output_vae` (bool, optional): Whether to output the VAE. Defaults to True. + - `output_clip` (bool, optional): Whether to output the CLIP. Defaults to True. + + #### Returns: + - `tuple`: The model patcher, CLIP, and VAE. + """ + ckpt_path = f"{ckpt_name}" + out = load_checkpoint_guess_config( + ckpt_path, + output_vae=output_vae, + output_clip=output_clip, + embedding_directory="./_internal/embeddings/", + ) + print("loading", ckpt_path) + return out[:3] diff --git a/modules/Model/LoRas.py b/modules/Model/LoRas.py index 63a33d0a7bc17693d2aa1e253324c7b71104e39f..b10fc97c039fb715db59d78727d641aca3872688 100644 --- a/modules/Model/LoRas.py +++ b/modules/Model/LoRas.py @@ -1,193 +1,193 @@ -import torch -from modules.Utilities import util -from modules.NeuralNetwork import unet - -LORA_CLIP_MAP = { - "mlp.fc1": "mlp_fc1", - "mlp.fc2": "mlp_fc2", - "self_attn.k_proj": "self_attn_k_proj", - "self_attn.q_proj": "self_attn_q_proj", - "self_attn.v_proj": "self_attn_v_proj", - "self_attn.out_proj": "self_attn_out_proj", -} - - -def load_lora(lora: dict, to_load: dict) -> dict: - """#### Load a LoRA model. - - #### Args: - - `lora` (dict): The LoRA model state dictionary. - - `to_load` (dict): The keys to load from the LoRA model. - - #### Returns: - - `dict`: The loaded LoRA model. - """ - patch_dict = {} - loaded_keys = set() - for x in to_load: - alpha_name = "{}.alpha".format(x) - alpha = None - if alpha_name in lora.keys(): - alpha = lora[alpha_name].item() - loaded_keys.add(alpha_name) - - "{}.dora_scale".format(x) - dora_scale = None - - regular_lora = "{}.lora_up.weight".format(x) - "{}_lora.up.weight".format(x) - "{}.lora_linear_layer.up.weight".format(x) - A_name = None - - if regular_lora in lora.keys(): - A_name = regular_lora - B_name = "{}.lora_down.weight".format(x) - "{}.lora_mid.weight".format(x) - - if A_name is not None: - mid = None - patch_dict[to_load[x]] = ( - "lora", - (lora[A_name], lora[B_name], alpha, mid, dora_scale), - ) - loaded_keys.add(A_name) - loaded_keys.add(B_name) - return patch_dict - - -def model_lora_keys_clip(model: torch.nn.Module, key_map: dict = {}) -> dict: - """#### Get the keys for a LoRA model's CLIP component. - - #### Args: - - `model` (torch.nn.Module): The LoRA model. - - `key_map` (dict, optional): The key map. Defaults to {}. - - #### Returns: - - `dict`: The keys for the CLIP component. - """ - sdk = model.state_dict().keys() - - text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" - for b in range(32): - for c in LORA_CLIP_MAP: - k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) - if k in sdk: - lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) - key_map[lora_key] = k - lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format( - b, LORA_CLIP_MAP[c] - ) # SDXL base - key_map[lora_key] = k - lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format( - b, c - ) # diffusers lora - key_map[lora_key] = k - return key_map - - -def model_lora_keys_unet(model: torch.nn.Module, key_map: dict = {}) -> dict: - """#### Get the keys for a LoRA model's UNet component. - - #### Args: - - `model` (torch.nn.Module): The LoRA model. - - `key_map` (dict, optional): The key map. Defaults to {}. - - #### Returns: - - `dict`: The keys for the UNet component. - """ - sdk = model.state_dict().keys() - - for k in sdk: - if k.startswith("diffusion_model.") and k.endswith(".weight"): - key_lora = k[len("diffusion_model.") : -len(".weight")].replace(".", "_") - key_map["lora_unet_{}".format(key_lora)] = k - key_map["lora_prior_unet_{}".format(key_lora)] = k # cascade lora: - - diffusers_keys = unet.unet_to_diffusers(model.model_config.unet_config) - for k in diffusers_keys: - if k.endswith(".weight"): - unet_key = "diffusion_model.{}".format(diffusers_keys[k]) - key_lora = k[: -len(".weight")].replace(".", "_") - key_map["lora_unet_{}".format(key_lora)] = unet_key - - diffusers_lora_prefix = ["", "unet."] - for p in diffusers_lora_prefix: - diffusers_lora_key = "{}{}".format( - p, k[: -len(".weight")].replace(".to_", ".processor.to_") - ) - if diffusers_lora_key.endswith(".to_out.0"): - diffusers_lora_key = diffusers_lora_key[:-2] - key_map[diffusers_lora_key] = unet_key - return key_map - - -def load_lora_for_models( - model: object, clip: object, lora: dict, strength_model: float, strength_clip: float -) -> tuple: - """#### Load a LoRA model for the given models. - - #### Args: - - `model` (object): The model. - - `clip` (object): The CLIP model. - - `lora` (dict): The LoRA model state dictionary. - - `strength_model` (float): The strength of the model. - - `strength_clip` (float): The strength of the CLIP model. - - #### Returns: - - `tuple`: The new model patcher and CLIP model. - """ - key_map = {} - if model is not None: - key_map = model_lora_keys_unet(model.model, key_map) - if clip is not None: - key_map = model_lora_keys_clip(clip.cond_stage_model, key_map) - - loaded = load_lora(lora, key_map) - new_modelpatcher = model.clone() - k = new_modelpatcher.add_patches(loaded, strength_model) - - new_clip = clip.clone() - k1 = new_clip.add_patches(loaded, strength_clip) - k = set(k) - k1 = set(k1) - - return (new_modelpatcher, new_clip) - - -class LoraLoader: - """#### Class for loading LoRA models.""" - - def __init__(self): - """#### Initialize the LoraLoader class.""" - self.loaded_lora = None - - def load_lora( - self, - model: object, - clip: object, - lora_name: str, - strength_model: float, - strength_clip: float, - ) -> tuple: - """#### Load a LoRA model. - - #### Args: - - `model` (object): The model. - - `clip` (object): The CLIP model. - - `lora_name` (str): The name of the LoRA model. - - `strength_model` (float): The strength of the model. - - `strength_clip` (float): The strength of the CLIP model. - - #### Returns: - - `tuple`: The new model patcher and CLIP model. - """ - lora_path = util.get_full_path("loras", lora_name) - lora = None - if lora is None: - lora = util.load_torch_file(lora_path, safe_load=True) - self.loaded_lora = (lora_path, lora) - - model_lora, clip_lora = load_lora_for_models( - model, clip, lora, strength_model, strength_clip - ) - return (model_lora, clip_lora) +import torch +from modules.Utilities import util +from modules.NeuralNetwork import unet + +LORA_CLIP_MAP = { + "mlp.fc1": "mlp_fc1", + "mlp.fc2": "mlp_fc2", + "self_attn.k_proj": "self_attn_k_proj", + "self_attn.q_proj": "self_attn_q_proj", + "self_attn.v_proj": "self_attn_v_proj", + "self_attn.out_proj": "self_attn_out_proj", +} + + +def load_lora(lora: dict, to_load: dict) -> dict: + """#### Load a LoRA model. + + #### Args: + - `lora` (dict): The LoRA model state dictionary. + - `to_load` (dict): The keys to load from the LoRA model. + + #### Returns: + - `dict`: The loaded LoRA model. + """ + patch_dict = {} + loaded_keys = set() + for x in to_load: + alpha_name = "{}.alpha".format(x) + alpha = None + if alpha_name in lora.keys(): + alpha = lora[alpha_name].item() + loaded_keys.add(alpha_name) + + "{}.dora_scale".format(x) + dora_scale = None + + regular_lora = "{}.lora_up.weight".format(x) + "{}_lora.up.weight".format(x) + "{}.lora_linear_layer.up.weight".format(x) + A_name = None + + if regular_lora in lora.keys(): + A_name = regular_lora + B_name = "{}.lora_down.weight".format(x) + "{}.lora_mid.weight".format(x) + + if A_name is not None: + mid = None + patch_dict[to_load[x]] = ( + "lora", + (lora[A_name], lora[B_name], alpha, mid, dora_scale), + ) + loaded_keys.add(A_name) + loaded_keys.add(B_name) + return patch_dict + + +def model_lora_keys_clip(model: torch.nn.Module, key_map: dict = {}) -> dict: + """#### Get the keys for a LoRA model's CLIP component. + + #### Args: + - `model` (torch.nn.Module): The LoRA model. + - `key_map` (dict, optional): The key map. Defaults to {}. + + #### Returns: + - `dict`: The keys for the CLIP component. + """ + sdk = model.state_dict().keys() + + text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" + for b in range(32): + for c in LORA_CLIP_MAP: + k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format( + b, LORA_CLIP_MAP[c] + ) # SDXL base + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format( + b, c + ) # diffusers lora + key_map[lora_key] = k + return key_map + + +def model_lora_keys_unet(model: torch.nn.Module, key_map: dict = {}) -> dict: + """#### Get the keys for a LoRA model's UNet component. + + #### Args: + - `model` (torch.nn.Module): The LoRA model. + - `key_map` (dict, optional): The key map. Defaults to {}. + + #### Returns: + - `dict`: The keys for the UNet component. + """ + sdk = model.state_dict().keys() + + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + key_lora = k[len("diffusion_model.") : -len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = k + key_map["lora_prior_unet_{}".format(key_lora)] = k # cascade lora: + + diffusers_keys = unet.unet_to_diffusers(model.model_config.unet_config) + for k in diffusers_keys: + if k.endswith(".weight"): + unet_key = "diffusion_model.{}".format(diffusers_keys[k]) + key_lora = k[: -len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = unet_key + + diffusers_lora_prefix = ["", "unet."] + for p in diffusers_lora_prefix: + diffusers_lora_key = "{}{}".format( + p, k[: -len(".weight")].replace(".to_", ".processor.to_") + ) + if diffusers_lora_key.endswith(".to_out.0"): + diffusers_lora_key = diffusers_lora_key[:-2] + key_map[diffusers_lora_key] = unet_key + return key_map + + +def load_lora_for_models( + model: object, clip: object, lora: dict, strength_model: float, strength_clip: float +) -> tuple: + """#### Load a LoRA model for the given models. + + #### Args: + - `model` (object): The model. + - `clip` (object): The CLIP model. + - `lora` (dict): The LoRA model state dictionary. + - `strength_model` (float): The strength of the model. + - `strength_clip` (float): The strength of the CLIP model. + + #### Returns: + - `tuple`: The new model patcher and CLIP model. + """ + key_map = {} + if model is not None: + key_map = model_lora_keys_unet(model.model, key_map) + if clip is not None: + key_map = model_lora_keys_clip(clip.cond_stage_model, key_map) + + loaded = load_lora(lora, key_map) + new_modelpatcher = model.clone() + k = new_modelpatcher.add_patches(loaded, strength_model) + + new_clip = clip.clone() + k1 = new_clip.add_patches(loaded, strength_clip) + k = set(k) + k1 = set(k1) + + return (new_modelpatcher, new_clip) + + +class LoraLoader: + """#### Class for loading LoRA models.""" + + def __init__(self): + """#### Initialize the LoraLoader class.""" + self.loaded_lora = None + + def load_lora( + self, + model: object, + clip: object, + lora_name: str, + strength_model: float, + strength_clip: float, + ) -> tuple: + """#### Load a LoRA model. + + #### Args: + - `model` (object): The model. + - `clip` (object): The CLIP model. + - `lora_name` (str): The name of the LoRA model. + - `strength_model` (float): The strength of the model. + - `strength_clip` (float): The strength of the CLIP model. + + #### Returns: + - `tuple`: The new model patcher and CLIP model. + """ + lora_path = util.get_full_path("loras", lora_name) + lora = None + if lora is None: + lora = util.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + model_lora, clip_lora = load_lora_for_models( + model, clip, lora, strength_model, strength_clip + ) + return (model_lora, clip_lora) diff --git a/modules/Model/ModelBase.py b/modules/Model/ModelBase.py index 7b7a412c94ff57f25371b0b4b964fe3a3aa8eef5..00a6e3c9c6be0a7ab47008900ee2a66792ba576d 100644 --- a/modules/Model/ModelBase.py +++ b/modules/Model/ModelBase.py @@ -1,363 +1,363 @@ -import logging -import math -import torch - -from modules.Utilities import Latent -from modules.Device import Device -from modules.NeuralNetwork import unet -from modules.cond import cast, cond -from modules.sample import sampling - - -class BaseModel(torch.nn.Module): - """#### Base class for models.""" - - def __init__( - self, - model_config: object, - model_type: sampling.ModelType = sampling.ModelType.EPS, - device: torch.device = None, - unet_model: object = unet.UNetModel1, - flux: bool = False, - ): - """#### Initialize the BaseModel class. - - #### Args: - - `model_config` (object): The model configuration. - - `model_type` (sampling.ModelType, optional): The model type. Defaults to sampling.ModelType.EPS. - - `device` (torch.device, optional): The device to use. Defaults to None. - - `unet_model` (object, optional): The UNet model. Defaults to unet.UNetModel1. - """ - super().__init__() - - unet_config = model_config.unet_config - self.latent_format = model_config.latent_format - self.model_config = model_config - self.manual_cast_dtype = model_config.manual_cast_dtype - self.device = device - if flux: - if not unet_config.get("disable_unet_model_creation", False): - operations = model_config.custom_operations - self.diffusion_model = unet_model( - **unet_config, device=device, operations=operations - ) - logging.info( - "model weight dtype {}, manual cast: {}".format( - self.get_dtype(), self.manual_cast_dtype - ) - ) - else: - if not unet_config.get("disable_unet_model_creation", False): - if self.manual_cast_dtype is not None: - operations = cast.manual_cast - else: - operations = cast.disable_weight_init - self.diffusion_model = unet_model( - **unet_config, device=device, operations=operations - ) - self.model_type = model_type - self.model_sampling = sampling.model_sampling(model_config, model_type, flux=flux) - - self.adm_channels = unet_config.get("adm_in_channels", None) - if self.adm_channels is None: - self.adm_channels = 0 - - self.concat_keys = () - logging.info("model_type {}".format(model_type.name)) - logging.debug("adm {}".format(self.adm_channels)) - self.memory_usage_factor = model_config.memory_usage_factor if flux else 2.0 - - def apply_model( - self, - x: torch.Tensor, - t: torch.Tensor, - c_concat: torch.Tensor = None, - c_crossattn: torch.Tensor = None, - control: torch.Tensor = None, - transformer_options: dict = {}, - **kwargs, - ) -> torch.Tensor: - """#### Apply the model to the input tensor. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `t` (torch.Tensor): The timestep tensor. - - `c_concat` (torch.Tensor, optional): The concatenated condition tensor. Defaults to None. - - `c_crossattn` (torch.Tensor, optional): The cross-attention condition tensor. Defaults to None. - - `control` (torch.Tensor, optional): The control tensor. Defaults to None. - - `transformer_options` (dict, optional): The transformer options. Defaults to {}. - - `**kwargs`: Additional keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - sigma = t - xc = self.model_sampling.calculate_input(sigma, x) - if c_concat is not None: - xc = torch.cat([xc] + [c_concat], dim=1) - - context = c_crossattn - dtype = self.get_dtype() - - if self.manual_cast_dtype is not None: - dtype = self.manual_cast_dtype - - xc = xc.to(dtype) - t = self.model_sampling.timestep(t).float() - context = context.to(dtype) - extra_conds = {} - for o in kwargs: - extra = kwargs[o] - if hasattr(extra, "dtype"): - if extra.dtype != torch.int and extra.dtype != torch.long: - extra = extra.to(dtype) - extra_conds[o] = extra - - model_output = self.diffusion_model( - xc, - t, - context=context, - control=control, - transformer_options=transformer_options, - **extra_conds, - ).float() - return self.model_sampling.calculate_denoised(sigma, model_output, x) - - def get_dtype(self) -> torch.dtype: - """#### Get the data type of the model. - - #### Returns: - - `torch.dtype`: The data type. - """ - return self.diffusion_model.dtype - - def encode_adm(self, **kwargs) -> None: - """#### Encode the ADM. - - #### Args: - - `**kwargs`: Additional keyword arguments. - - #### Returns: - - `None`: The encoded ADM. - """ - return None - - def extra_conds(self, **kwargs) -> dict: - """#### Get the extra conditions. - - #### Args: - - `**kwargs`: Additional keyword arguments. - - #### Returns: - - `dict`: The extra conditions. - """ - out = {} - adm = self.encode_adm(**kwargs) - if adm is not None: - out["y"] = cond.CONDRegular(adm) - - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out["c_crossattn"] = cond.CONDCrossAttn(cross_attn) - - cross_attn_cnet = kwargs.get("cross_attn_controlnet", None) - if cross_attn_cnet is not None: - out["crossattn_controlnet"] = cond.CONDCrossAttn(cross_attn_cnet) - - return out - - def load_model_weights(self, sd: dict, unet_prefix: str = "") -> "BaseModel": - """#### Load the model weights. - - #### Args: - - `sd` (dict): The state dictionary. - - `unet_prefix` (str, optional): The UNet prefix. Defaults to "". - - #### Returns: - - `BaseModel`: The model with loaded weights. - """ - to_load = {} - keys = list(sd.keys()) - for k in keys: - if k.startswith(unet_prefix): - to_load[k[len(unet_prefix) :]] = sd.pop(k) - - to_load = self.model_config.process_unet_state_dict(to_load) - m, u = self.diffusion_model.load_state_dict(to_load, strict=False) - if len(m) > 0: - logging.warning("unet missing: {}".format(m)) - - if len(u) > 0: - logging.warning("unet unexpected: {}".format(u)) - del to_load - return self - - def process_latent_in(self, latent: torch.Tensor) -> torch.Tensor: - """#### Process the latent input. - - #### Args: - - `latent` (torch.Tensor): The latent tensor. - - #### Returns: - - `torch.Tensor`: The processed latent tensor. - """ - return self.latent_format.process_in(latent) - - def process_latent_out(self, latent: torch.Tensor) -> torch.Tensor: - """#### Process the latent output. - - #### Args: - - `latent` (torch.Tensor): The latent tensor. - - #### Returns: - - `torch.Tensor`: The processed latent tensor. - """ - return self.latent_format.process_out(latent) - - def memory_required(self, input_shape: tuple) -> float: - """#### Calculate the memory required for the model. - - #### Args: - - `input_shape` (tuple): The input shape. - - #### Returns: - - `float`: The memory required. - """ - dtype = self.get_dtype() - if self.manual_cast_dtype is not None: - dtype = self.manual_cast_dtype - # TODO: this needs to be tweaked - area = input_shape[0] * math.prod(input_shape[2:]) - return (area * Device.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * ( - 1024 * 1024 - ) - - -class BASE: - """#### Base class for model configurations.""" - - unet_config = {} - unet_extra_config = { - "num_heads": -1, - "num_head_channels": 64, - } - - required_keys = {} - - clip_prefix = [] - clip_vision_prefix = None - noise_aug_config = None - sampling_settings = {} - latent_format = Latent.LatentFormat - vae_key_prefix = ["first_stage_model."] - text_encoder_key_prefix = ["cond_stage_model."] - supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] - - memory_usage_factor = 2.0 - - manual_cast_dtype = None - custom_operations = None - - @classmethod - def matches(cls, unet_config: dict, state_dict: dict = None) -> bool: - """#### Check if the UNet configuration matches. - - #### Args: - - `unet_config` (dict): The UNet configuration. - - `state_dict` (dict, optional): The state dictionary. Defaults to None. - - #### Returns: - - `bool`: Whether the configuration matches. - """ - for k in cls.unet_config: - if k not in unet_config or cls.unet_config[k] != unet_config[k]: - return False - if state_dict is not None: - for k in cls.required_keys: - if k not in state_dict: - return False - return True - - def model_type(self, state_dict: dict, prefix: str = "") -> sampling.ModelType: - """#### Get the model type. - - #### Args: - - `state_dict` (dict): The state dictionary. - - `prefix` (str, optional): The prefix. Defaults to "". - - #### Returns: - - `sampling.ModelType`: The model type. - """ - return sampling.ModelType.EPS - - def inpaint_model(self) -> bool: - """#### Check if the model is an inpaint model. - - #### Returns: - - `bool`: Whether the model is an inpaint model. - """ - return self.unet_config["in_channels"] > 4 - - def __init__(self, unet_config: dict): - """#### Initialize the BASE class. - - #### Args: - - `unet_config` (dict): The UNet configuration. - """ - self.unet_config = unet_config.copy() - self.sampling_settings = self.sampling_settings.copy() - self.latent_format = self.latent_format() - for x in self.unet_extra_config: - self.unet_config[x] = self.unet_extra_config[x] - - def get_model( - self, state_dict: dict, prefix: str = "", device: torch.device = None - ) -> BaseModel: - """#### Get the model. - - #### Args: - - `state_dict` (dict): The state dictionary. - - `prefix` (str, optional): The prefix. Defaults to "". - - `device` (torch.device, optional): The device to use. Defaults to None. - - #### Returns: - - `BaseModel`: The model. - """ - out = BaseModel( - self, model_type=self.model_type(state_dict, prefix), device=device - ) - return out - - def process_unet_state_dict(self, state_dict: dict) -> dict: - """#### Process the UNet state dictionary. - - #### Args: - - `state_dict` (dict): The state dictionary. - - #### Returns: - - `dict`: The processed state dictionary. - """ - return state_dict - - def process_vae_state_dict(self, state_dict: dict) -> dict: - """#### Process the VAE state dictionary. - - #### Args: - - `state_dict` (dict): The state dictionary. - - #### Returns: - - `dict`: The processed state dictionary. - """ - return state_dict - - def set_inference_dtype( - self, dtype: torch.dtype, manual_cast_dtype: torch.dtype - ) -> None: - """#### Set the inference data type. - - #### Args: - - `dtype` (torch.dtype): The data type. - - `manual_cast_dtype` (torch.dtype): The manual cast data type. - """ - self.unet_config["dtype"] = dtype - self.manual_cast_dtype = manual_cast_dtype +import logging +import math +import torch + +from modules.Utilities import Latent +from modules.Device import Device +from modules.NeuralNetwork import unet +from modules.cond import cast, cond +from modules.sample import sampling + + +class BaseModel(torch.nn.Module): + """#### Base class for models.""" + + def __init__( + self, + model_config: object, + model_type: sampling.ModelType = sampling.ModelType.EPS, + device: torch.device = None, + unet_model: object = unet.UNetModel1, + flux: bool = False, + ): + """#### Initialize the BaseModel class. + + #### Args: + - `model_config` (object): The model configuration. + - `model_type` (sampling.ModelType, optional): The model type. Defaults to sampling.ModelType.EPS. + - `device` (torch.device, optional): The device to use. Defaults to None. + - `unet_model` (object, optional): The UNet model. Defaults to unet.UNetModel1. + """ + super().__init__() + + unet_config = model_config.unet_config + self.latent_format = model_config.latent_format + self.model_config = model_config + self.manual_cast_dtype = model_config.manual_cast_dtype + self.device = device + if flux: + if not unet_config.get("disable_unet_model_creation", False): + operations = model_config.custom_operations + self.diffusion_model = unet_model( + **unet_config, device=device, operations=operations + ) + logging.info( + "model weight dtype {}, manual cast: {}".format( + self.get_dtype(), self.manual_cast_dtype + ) + ) + else: + if not unet_config.get("disable_unet_model_creation", False): + if self.manual_cast_dtype is not None: + operations = cast.manual_cast + else: + operations = cast.disable_weight_init + self.diffusion_model = unet_model( + **unet_config, device=device, operations=operations + ) + self.model_type = model_type + self.model_sampling = sampling.model_sampling(model_config, model_type, flux=flux) + + self.adm_channels = unet_config.get("adm_in_channels", None) + if self.adm_channels is None: + self.adm_channels = 0 + + self.concat_keys = () + logging.info("model_type {}".format(model_type.name)) + logging.debug("adm {}".format(self.adm_channels)) + self.memory_usage_factor = model_config.memory_usage_factor if flux else 2.0 + + def apply_model( + self, + x: torch.Tensor, + t: torch.Tensor, + c_concat: torch.Tensor = None, + c_crossattn: torch.Tensor = None, + control: torch.Tensor = None, + transformer_options: dict = {}, + **kwargs, + ) -> torch.Tensor: + """#### Apply the model to the input tensor. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `t` (torch.Tensor): The timestep tensor. + - `c_concat` (torch.Tensor, optional): The concatenated condition tensor. Defaults to None. + - `c_crossattn` (torch.Tensor, optional): The cross-attention condition tensor. Defaults to None. + - `control` (torch.Tensor, optional): The control tensor. Defaults to None. + - `transformer_options` (dict, optional): The transformer options. Defaults to {}. + - `**kwargs`: Additional keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + sigma = t + xc = self.model_sampling.calculate_input(sigma, x) + if c_concat is not None: + xc = torch.cat([xc] + [c_concat], dim=1) + + context = c_crossattn + dtype = self.get_dtype() + + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + + xc = xc.to(dtype) + t = self.model_sampling.timestep(t).float() + context = context.to(dtype) + extra_conds = {} + for o in kwargs: + extra = kwargs[o] + if hasattr(extra, "dtype"): + if extra.dtype != torch.int and extra.dtype != torch.long: + extra = extra.to(dtype) + extra_conds[o] = extra + + model_output = self.diffusion_model( + xc, + t, + context=context, + control=control, + transformer_options=transformer_options, + **extra_conds, + ).float() + return self.model_sampling.calculate_denoised(sigma, model_output, x) + + def get_dtype(self) -> torch.dtype: + """#### Get the data type of the model. + + #### Returns: + - `torch.dtype`: The data type. + """ + return self.diffusion_model.dtype + + def encode_adm(self, **kwargs) -> None: + """#### Encode the ADM. + + #### Args: + - `**kwargs`: Additional keyword arguments. + + #### Returns: + - `None`: The encoded ADM. + """ + return None + + def extra_conds(self, **kwargs) -> dict: + """#### Get the extra conditions. + + #### Args: + - `**kwargs`: Additional keyword arguments. + + #### Returns: + - `dict`: The extra conditions. + """ + out = {} + adm = self.encode_adm(**kwargs) + if adm is not None: + out["y"] = cond.CONDRegular(adm) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out["c_crossattn"] = cond.CONDCrossAttn(cross_attn) + + cross_attn_cnet = kwargs.get("cross_attn_controlnet", None) + if cross_attn_cnet is not None: + out["crossattn_controlnet"] = cond.CONDCrossAttn(cross_attn_cnet) + + return out + + def load_model_weights(self, sd: dict, unet_prefix: str = "") -> "BaseModel": + """#### Load the model weights. + + #### Args: + - `sd` (dict): The state dictionary. + - `unet_prefix` (str, optional): The UNet prefix. Defaults to "". + + #### Returns: + - `BaseModel`: The model with loaded weights. + """ + to_load = {} + keys = list(sd.keys()) + for k in keys: + if k.startswith(unet_prefix): + to_load[k[len(unet_prefix) :]] = sd.pop(k) + + to_load = self.model_config.process_unet_state_dict(to_load) + m, u = self.diffusion_model.load_state_dict(to_load, strict=False) + if len(m) > 0: + logging.warning("unet missing: {}".format(m)) + + if len(u) > 0: + logging.warning("unet unexpected: {}".format(u)) + del to_load + return self + + def process_latent_in(self, latent: torch.Tensor) -> torch.Tensor: + """#### Process the latent input. + + #### Args: + - `latent` (torch.Tensor): The latent tensor. + + #### Returns: + - `torch.Tensor`: The processed latent tensor. + """ + return self.latent_format.process_in(latent) + + def process_latent_out(self, latent: torch.Tensor) -> torch.Tensor: + """#### Process the latent output. + + #### Args: + - `latent` (torch.Tensor): The latent tensor. + + #### Returns: + - `torch.Tensor`: The processed latent tensor. + """ + return self.latent_format.process_out(latent) + + def memory_required(self, input_shape: tuple) -> float: + """#### Calculate the memory required for the model. + + #### Args: + - `input_shape` (tuple): The input shape. + + #### Returns: + - `float`: The memory required. + """ + dtype = self.get_dtype() + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + # TODO: this needs to be tweaked + area = input_shape[0] * math.prod(input_shape[2:]) + return (area * Device.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * ( + 1024 * 1024 + ) + + +class BASE: + """#### Base class for model configurations.""" + + unet_config = {} + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + } + + required_keys = {} + + clip_prefix = [] + clip_vision_prefix = None + noise_aug_config = None + sampling_settings = {} + latent_format = Latent.LatentFormat + vae_key_prefix = ["first_stage_model."] + text_encoder_key_prefix = ["cond_stage_model."] + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + memory_usage_factor = 2.0 + + manual_cast_dtype = None + custom_operations = None + + @classmethod + def matches(cls, unet_config: dict, state_dict: dict = None) -> bool: + """#### Check if the UNet configuration matches. + + #### Args: + - `unet_config` (dict): The UNet configuration. + - `state_dict` (dict, optional): The state dictionary. Defaults to None. + + #### Returns: + - `bool`: Whether the configuration matches. + """ + for k in cls.unet_config: + if k not in unet_config or cls.unet_config[k] != unet_config[k]: + return False + if state_dict is not None: + for k in cls.required_keys: + if k not in state_dict: + return False + return True + + def model_type(self, state_dict: dict, prefix: str = "") -> sampling.ModelType: + """#### Get the model type. + + #### Args: + - `state_dict` (dict): The state dictionary. + - `prefix` (str, optional): The prefix. Defaults to "". + + #### Returns: + - `sampling.ModelType`: The model type. + """ + return sampling.ModelType.EPS + + def inpaint_model(self) -> bool: + """#### Check if the model is an inpaint model. + + #### Returns: + - `bool`: Whether the model is an inpaint model. + """ + return self.unet_config["in_channels"] > 4 + + def __init__(self, unet_config: dict): + """#### Initialize the BASE class. + + #### Args: + - `unet_config` (dict): The UNet configuration. + """ + self.unet_config = unet_config.copy() + self.sampling_settings = self.sampling_settings.copy() + self.latent_format = self.latent_format() + for x in self.unet_extra_config: + self.unet_config[x] = self.unet_extra_config[x] + + def get_model( + self, state_dict: dict, prefix: str = "", device: torch.device = None + ) -> BaseModel: + """#### Get the model. + + #### Args: + - `state_dict` (dict): The state dictionary. + - `prefix` (str, optional): The prefix. Defaults to "". + - `device` (torch.device, optional): The device to use. Defaults to None. + + #### Returns: + - `BaseModel`: The model. + """ + out = BaseModel( + self, model_type=self.model_type(state_dict, prefix), device=device + ) + return out + + def process_unet_state_dict(self, state_dict: dict) -> dict: + """#### Process the UNet state dictionary. + + #### Args: + - `state_dict` (dict): The state dictionary. + + #### Returns: + - `dict`: The processed state dictionary. + """ + return state_dict + + def process_vae_state_dict(self, state_dict: dict) -> dict: + """#### Process the VAE state dictionary. + + #### Args: + - `state_dict` (dict): The state dictionary. + + #### Returns: + - `dict`: The processed state dictionary. + """ + return state_dict + + def set_inference_dtype( + self, dtype: torch.dtype, manual_cast_dtype: torch.dtype + ) -> None: + """#### Set the inference data type. + + #### Args: + - `dtype` (torch.dtype): The data type. + - `manual_cast_dtype` (torch.dtype): The manual cast data type. + """ + self.unet_config["dtype"] = dtype + self.manual_cast_dtype = manual_cast_dtype diff --git a/modules/Model/ModelPatcher.py b/modules/Model/ModelPatcher.py index 818e951d55706753040f443694f2e082e3d8279d..1e76e0eb286d786d856b80ac173624f30a00bd5e 100644 --- a/modules/Model/ModelPatcher.py +++ b/modules/Model/ModelPatcher.py @@ -1,779 +1,779 @@ -import copy -import logging -import uuid - -import torch - -from modules.NeuralNetwork import unet -from modules.Utilities import util -from modules.Device import Device - -def wipe_lowvram_weight(m): - if hasattr(m, "prev_comfy_cast_weights"): - m.comfy_cast_weights = m.prev_comfy_cast_weights - del m.prev_comfy_cast_weights - m.weight_function = None - m.bias_function = None - -class ModelPatcher: - def __init__( - self, - model: torch.nn.Module, - load_device: torch.device, - offload_device: torch.device, - size: int = 0, - current_device: torch.device = None, - weight_inplace_update: bool = False, - ): - """#### Initialize the ModelPatcher class. - - #### Args: - - `model` (torch.nn.Module): The model. - - `load_device` (torch.device): The device to load the model on. - - `offload_device` (torch.device): The device to offload the model to. - - `size` (int, optional): The size of the model. Defaults to 0. - - `current_device` (torch.device, optional): The current device. Defaults to None. - - `weight_inplace_update` (bool, optional): Whether to update weights in place. Defaults to False. - """ - self.size = size - self.model = model - self.patches = {} - self.backup = {} - self.object_patches = {} - self.object_patches_backup = {} - self.model_options = {"transformer_options": {}} - self.model_size() - self.load_device = load_device - self.offload_device = offload_device - if current_device is None: - self.current_device = self.offload_device - else: - self.current_device = current_device - - self.weight_inplace_update = weight_inplace_update - self.model_lowvram = False - self.lowvram_patch_counter = 0 - self.patches_uuid = uuid.uuid4() - - if not hasattr(self.model, "model_loaded_weight_memory"): - self.model.model_loaded_weight_memory = 0 - - if not hasattr(self.model, "model_lowvram"): - self.model.model_lowvram = False - - if not hasattr(self.model, "lowvram_patch_counter"): - self.model.lowvram_patch_counter = 0 - - def loaded_size(self) -> int: - """#### Get the loaded size - - #### Returns: - - `int`: The loaded size - """ - return self.model.model_loaded_weight_memory - - def model_size(self) -> int: - """#### Get the size of the model. - - #### Returns: - - `int`: The size of the model. - """ - if self.size > 0: - return self.size - model_sd = self.model.state_dict() - self.size = Device.module_size(self.model) - self.model_keys = set(model_sd.keys()) - return self.size - - def clone(self) -> "ModelPatcher": - """#### Clone the ModelPatcher object. - - #### Returns: - - `ModelPatcher`: The cloned ModelPatcher object. - """ - n = ModelPatcher( - self.model, - self.load_device, - self.offload_device, - self.size, - self.current_device, - weight_inplace_update=self.weight_inplace_update, - ) - n.patches = {} - for k in self.patches: - n.patches[k] = self.patches[k][:] - n.patches_uuid = self.patches_uuid - - n.object_patches = self.object_patches.copy() - n.model_options = copy.deepcopy(self.model_options) - n.model_keys = self.model_keys - n.backup = self.backup - n.object_patches_backup = self.object_patches_backup - return n - - def is_clone(self, other: object) -> bool: - """#### Check if the object is a clone. - - #### Args: - - `other` (object): The other object. - - #### Returns: - - `bool`: Whether the object is a clone. - """ - if hasattr(other, "model") and self.model is other.model: - return True - return False - - def memory_required(self, input_shape: tuple) -> float: - """#### Calculate the memory required for the model. - - #### Args: - - `input_shape` (tuple): The input shape. - - #### Returns: - - `float`: The memory required. - """ - return self.model.memory_required(input_shape=input_shape) - - def set_model_unet_function_wrapper(self, unet_wrapper_function: callable) -> None: - """#### Set the UNet function wrapper for the model. - - #### Args: - - `unet_wrapper_function` (callable): The UNet function wrapper. - """ - self.model_options["model_function_wrapper"] = unet_wrapper_function - - def set_model_denoise_mask_function(self, denoise_mask_function: callable) -> None: - """#### Set the denoise mask function for the model. - - #### Args: - - `denoise_mask_function` (callable): The denoise mask function. - """ - self.model_options["denoise_mask_function"] = denoise_mask_function - - def get_model_object(self, name: str) -> object: - """#### Get an object from the model. - - #### Args: - - `name` (str): The name of the object. - - #### Returns: - - `object`: The object. - """ - return util.get_attr(self.model, name) - - def model_patches_to(self, device: torch.device) -> None: - """#### Move model patches to a device. - - #### Args: - - `device` (torch.device): The device. - """ - self.model_options["transformer_options"] - if "model_function_wrapper" in self.model_options: - wrap_func = self.model_options["model_function_wrapper"] - if hasattr(wrap_func, "to"): - self.model_options["model_function_wrapper"] = wrap_func.to(device) - - def model_dtype(self) -> torch.dtype: - """#### Get the data type of the model. - - #### Returns: - - `torch.dtype`: The data type. - """ - if hasattr(self.model, "get_dtype"): - return self.model.get_dtype() - - def add_patches( - self, patches: dict, strength_patch: float = 1.0, strength_model: float = 1.0 - ) -> list: - """#### Add patches to the model. - - #### Args: - - `patches` (dict): The patches to add. - - `strength_patch` (float, optional): The strength of the patches. Defaults to 1.0. - - `strength_model` (float, optional): The strength of the model. Defaults to 1.0. - - #### Returns: - - `list`: The list of patched keys. - """ - p = set() - for k in patches: - if k in self.model_keys: - p.add(k) - current_patches = self.patches.get(k, []) - current_patches.append((strength_patch, patches[k], strength_model)) - self.patches[k] = current_patches - - self.patches_uuid = uuid.uuid4() - return list(p) - - def set_model_patch(self, patch: list, name: str): - """#### Set a patch for the model. - - #### Args: - - `patch` (list): The patch. - - `name` (str): The name of the patch. - """ - to = self.model_options["transformer_options"] - if "patches" not in to: - to["patches"] = {} - to["patches"][name] = to["patches"].get(name, []) + [patch] - - def set_model_attn1_patch(self, patch: list): - """#### Set the attention 1 patch for the model. - - #### Args: - - `patch` (list): The patch. - """ - self.set_model_patch(patch, "attn1_patch") - - def set_model_attn2_patch(self, patch: list): - """#### Set the attention 2 patch for the model. - - #### Args: - - `patch` (list): The patch. - """ - self.set_model_patch(patch, "attn2_patch") - - def set_model_attn1_output_patch(self, patch: list): - """#### Set the attention 1 output patch for the model. - - #### Args: - - `patch` (list): The patch. - """ - self.set_model_patch(patch, "attn1_output_patch") - - def set_model_attn2_output_patch(self, patch: list): - """#### Set the attention 2 output patch for the model. - - #### Args: - - `patch` (list): The patch. - """ - self.set_model_patch(patch, "attn2_output_patch") - - def model_state_dict(self, filter_prefix: str = None) -> dict: - """#### Get the state dictionary of the model. - - #### Args: - - `filter_prefix` (str, optional): The prefix to filter. Defaults to None. - - #### Returns: - - `dict`: The state dictionary. - """ - sd = self.model.state_dict() - list(sd.keys()) - return sd - - def patch_weight_to_device(self, key: str, device_to: torch.device = None) -> None: - """#### Patch the weight of a key to a device. - - #### Args: - - `key` (str): The key. - - `device_to` (torch.device, optional): The device to patch to. Defaults to None. - """ - if key not in self.patches: - return - - weight = util.get_attr(self.model, key) - - inplace_update = self.weight_inplace_update - - if key not in self.backup: - self.backup[key] = weight.to( - device=self.offload_device, copy=inplace_update - ) - - if device_to is not None: - temp_weight = Device.cast_to_device( - weight, device_to, torch.float32, copy=True - ) - else: - temp_weight = weight.to(torch.float32, copy=True) - out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to( - weight.dtype - ) - if inplace_update: - util.copy_to_param(self.model, key, out_weight) - else: - util.set_attr_param(self.model, key, out_weight) - - def load( - self, - device_to: torch.device = None, - lowvram_model_memory: int = 0, - force_patch_weights: bool = False, - full_load: bool = False, - ): - """#### Load the model. - - #### Args: - - `device_to` (torch.device, optional): The device to load to. Defaults to None. - - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. - - `force_patch_weights` (bool, optional): Whether to force patch weights. Defaults to False. - - `full_load` (bool, optional): Whether to fully load the model. Defaults to False. - """ - mem_counter = 0 - patch_counter = 0 - lowvram_counter = 0 - loading = [] - for n, m in self.model.named_modules(): - if hasattr(m, "comfy_cast_weights") or hasattr(m, "weight"): - loading.append((Device.module_size(m), n, m)) - - load_completely = [] - loading.sort(reverse=True) - for x in loading: - n = x[1] - m = x[2] - module_mem = x[0] - - lowvram_weight = False - - if not full_load and hasattr(m, "comfy_cast_weights"): - if mem_counter + module_mem >= lowvram_model_memory: - lowvram_weight = True - lowvram_counter += 1 - if hasattr(m, "prev_comfy_cast_weights"): # Already lowvramed - continue - - weight_key = "{}.weight".format(n) - bias_key = "{}.bias".format(n) - - if lowvram_weight: - if weight_key in self.patches: - if force_patch_weights: - self.patch_weight_to_device(weight_key) - if bias_key in self.patches: - if force_patch_weights: - self.patch_weight_to_device(bias_key) - - m.prev_comfy_cast_weights = m.comfy_cast_weights - m.comfy_cast_weights = True - else: - if hasattr(m, "comfy_cast_weights"): - if m.comfy_cast_weights: - wipe_lowvram_weight(m) - - if hasattr(m, "weight"): - mem_counter += module_mem - load_completely.append((module_mem, n, m)) - - load_completely.sort(reverse=True) - for x in load_completely: - n = x[1] - m = x[2] - weight_key = "{}.weight".format(n) - bias_key = "{}.bias".format(n) - if hasattr(m, "comfy_patched_weights"): - if m.comfy_patched_weights is True: - continue - - self.patch_weight_to_device(weight_key, device_to=device_to) - self.patch_weight_to_device(bias_key, device_to=device_to) - logging.debug("lowvram: loaded module regularly {} {}".format(n, m)) - m.comfy_patched_weights = True - - for x in load_completely: - x[2].to(device_to) - - if lowvram_counter > 0: - logging.info( - "loaded partially {} {} {}".format( - lowvram_model_memory / (1024 * 1024), - mem_counter / (1024 * 1024), - patch_counter, - ) - ) - self.model.model_lowvram = True - else: - logging.info( - "loaded completely {} {} {}".format( - lowvram_model_memory / (1024 * 1024), - mem_counter / (1024 * 1024), - full_load, - ) - ) - self.model.model_lowvram = False - if full_load: - self.model.to(device_to) - mem_counter = self.model_size() - - - self.model.lowvram_patch_counter += patch_counter - self.model.device = device_to - self.model.model_loaded_weight_memory = mem_counter - - def patch_model_flux( - self, - device_to: torch.device = None, - lowvram_model_memory: int =0, - load_weights: bool = True, - force_patch_weights: bool = False, - ): - """#### Patch the model. - - #### Args: - - `device_to` (torch.device, optional): The device to patch to. Defaults to None. - - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. - - `load_weights` (bool, optional): Whether to load weights. Defaults to True. - - `force_patch_weights` (bool, optional): Whether to force patch weights. Defaults to False. - - #### Returns: - - `torch.nn.Module`: The patched model. - """ - for k in self.object_patches: - old = util.set_attr(self.model, k, self.object_patches[k]) - if k not in self.object_patches_backup: - self.object_patches_backup[k] = old - - if lowvram_model_memory == 0: - full_load = True - else: - full_load = False - - if load_weights: - self.load( - device_to, - lowvram_model_memory=lowvram_model_memory, - force_patch_weights=force_patch_weights, - full_load=full_load, - ) - return self.model - - def patch_model_lowvram_flux( - self, - device_to: torch.device = None, - lowvram_model_memory: int = 0, - force_patch_weights: bool = False, - ) -> torch.nn.Module: - """#### Patch the model for low VRAM. - - #### Args: - - `device_to` (torch.device, optional): The device to patch to. Defaults to None. - - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. - - `force_patch_weights` (bool, optional): Whether to force patch weights. Defaults to False. - - #### Returns: - - `torch.nn.Module`: The patched model. - """ - self.patch_model(device_to) - - logging.info( - "loading in lowvram mode {}".format(lowvram_model_memory / (1024 * 1024)) - ) - - class LowVramPatch: - def __init__(self, key: str, model_patcher: "ModelPatcher"): - self.key = key - self.model_patcher = model_patcher - - def __call__(self, weight: torch.Tensor) -> torch.Tensor: - return self.model_patcher.calculate_weight( - self.model_patcher.patches[self.key], weight, self.key - ) - - mem_counter = 0 - patch_counter = 0 - for n, m in self.model.named_modules(): - lowvram_weight = False - if hasattr(m, "comfy_cast_weights"): - module_mem = Device.module_size(m) - if mem_counter + module_mem >= lowvram_model_memory: - lowvram_weight = True - - weight_key = "{}.weight".format(n) - bias_key = "{}.bias".format(n) - - if lowvram_weight: - if weight_key in self.patches: - if force_patch_weights: - self.patch_weight_to_device(weight_key) - else: - m.weight_function = LowVramPatch(weight_key, self) - patch_counter += 1 - if bias_key in self.patches: - if force_patch_weights: - self.patch_weight_to_device(bias_key) - else: - m.bias_function = LowVramPatch(bias_key, self) - patch_counter += 1 - - m.prev_comfy_cast_weights = m.comfy_cast_weights - m.comfy_cast_weights = True - else: - if hasattr(m, "weight"): - self.patch_weight_to_device(weight_key, device_to) - self.patch_weight_to_device(bias_key, device_to) - m.to(device_to) - mem_counter += Device.module_size(m) - logging.debug("lowvram: loaded module regularly {}".format(m)) - - self.model_lowvram = True - self.lowvram_patch_counter = patch_counter - return self.model - - def patch_model( - self, device_to: torch.device = None, patch_weights: bool = True - ) -> torch.nn.Module: - """#### Patch the model. - - #### Args: - - `device_to` (torch.device, optional): The device to patch to. Defaults to None. - - `patch_weights` (bool, optional): Whether to patch weights. Defaults to True. - - #### Returns: - - `torch.nn.Module`: The patched model. - """ - for k in self.object_patches: - old = util.set_attr(self.model, k, self.object_patches[k]) - if k not in self.object_patches_backup: - self.object_patches_backup[k] = old - - if patch_weights: - model_sd = self.model_state_dict() - for key in self.patches: - if key not in model_sd: - logging.warning( - "could not patch. key doesn't exist in model: {}".format(key) - ) - continue - - self.patch_weight_to_device(key, device_to) - - if device_to is not None: - self.model.to(device_to) - self.current_device = device_to - - return self.model - - def patch_model_lowvram( - self, - device_to: torch.device = None, - lowvram_model_memory: int = 0, - force_patch_weights: bool = False, - ) -> torch.nn.Module: - """#### Patch the model for low VRAM. - - #### Args: - - `device_to` (torch.device, optional): The device to patch to. Defaults to None. - - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. - - `force_patch_weights` (bool, optional): Whether to force patch weights. Defaults to False. - - #### Returns: - - `torch.nn.Module`: The patched model. - """ - self.patch_model(device_to, patch_weights=False) - - logging.info( - "loading in lowvram mode {}".format(lowvram_model_memory / (1024 * 1024)) - ) - - class LowVramPatch: - def __init__(self, key: str, model_patcher: "ModelPatcher"): - self.key = key - self.model_patcher = model_patcher - - def __call__(self, weight: torch.Tensor) -> torch.Tensor: - return self.model_patcher.calculate_weight( - self.model_patcher.patches[self.key], weight, self.key - ) - - mem_counter = 0 - patch_counter = 0 - for n, m in self.model.named_modules(): - lowvram_weight = False - if hasattr(m, "comfy_cast_weights"): - module_mem = Device.module_size(m) - if mem_counter + module_mem >= lowvram_model_memory: - lowvram_weight = True - - weight_key = "{}.weight".format(n) - bias_key = "{}.bias".format(n) - - if lowvram_weight: - if weight_key in self.patches: - if force_patch_weights: - self.patch_weight_to_device(weight_key) - else: - m.weight_function = LowVramPatch(weight_key, self) - patch_counter += 1 - if bias_key in self.patches: - if force_patch_weights: - self.patch_weight_to_device(bias_key) - else: - m.bias_function = LowVramPatch(bias_key, self) - patch_counter += 1 - - m.prev_comfy_cast_weights = m.comfy_cast_weights - m.comfy_cast_weights = True - else: - if hasattr(m, "weight"): - self.patch_weight_to_device(weight_key, device_to) - self.patch_weight_to_device(bias_key, device_to) - m.to(device_to) - mem_counter += Device.module_size(m) - logging.debug("lowvram: loaded module regularly {}".format(m)) - - self.model_lowvram = True - self.lowvram_patch_counter = patch_counter - return self.model - - def calculate_weight( - self, patches: list, weight: torch.Tensor, key: str - ) -> torch.Tensor: - """#### Calculate the weight of a key. - - #### Args: - - `patches` (list): The list of patches. - - `weight` (torch.Tensor): The weight tensor. - - `key` (str): The key. - - #### Returns: - - `torch.Tensor`: The calculated weight. - """ - for p in patches: - alpha = p[0] - v = p[1] - p[2] - v[0] - v = v[1] - mat1 = Device.cast_to_device(v[0], weight.device, torch.float32) - mat2 = Device.cast_to_device(v[1], weight.device, torch.float32) - v[4] - if v[2] is not None: - alpha *= v[2] / mat2.shape[0] - weight += ( - (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))) - .reshape(weight.shape) - .type(weight.dtype) - ) - return weight - - def unpatch_model( - self, device_to: torch.device = None, unpatch_weights: bool = True - ) -> None: - """#### Unpatch the model. - - #### Args: - - `device_to` (torch.device, optional): The device to unpatch to. Defaults to None. - - `unpatch_weights` (bool, optional): Whether to unpatch weights. Defaults to True. - """ - if unpatch_weights: - keys = list(self.backup.keys()) - for k in keys: - util.set_attr_param(self.model, k, self.backup[k]) - self.backup.clear() - if device_to is not None: - self.model.to(device_to) - self.current_device = device_to - - keys = list(self.object_patches_backup.keys()) - self.object_patches_backup.clear() - - def partially_load(self, device_to: torch.device, extra_memory: int = 0) -> int: - """#### Partially load the model. - - #### Args: - - `device_to` (torch.device): The device to load to. - - `extra_memory` (int, optional): The extra memory. Defaults to 0. - - #### Returns: - - `int`: The memory loaded. - """ - self.unpatch_model(unpatch_weights=False) - self.patch_model(patch_weights=False) - full_load = False - if self.model.model_lowvram is False: - return 0 - if self.model.model_loaded_weight_memory + extra_memory > self.model_size(): - full_load = True - current_used = self.model.model_loaded_weight_memory - self.load( - device_to, - lowvram_model_memory=current_used + extra_memory, - full_load=full_load, - ) - return self.model.model_loaded_weight_memory - current_used - - def add_object_patch(self, name, obj): - self.object_patches[name] = obj - -def unet_prefix_from_state_dict(state_dict: dict) -> str: - """#### Get the UNet prefix from the state dictionary. - - #### Args: - - `state_dict` (dict): The state dictionary. - - #### Returns: - - `str`: The UNet prefix. - """ - candidates = [ - "model.diffusion_model.", # ldm/sgm models - "model.model.", # audio models - ] - counts = {k: 0 for k in candidates} - for k in state_dict: - for c in candidates: - if k.startswith(c): - counts[c] += 1 - break - - top = max(counts, key=counts.get) - if counts[top] > 5: - return top - else: - return "model." # aura flow and others - -def load_diffusion_model_state_dict( - sd, model_options={} -) -> ModelPatcher: - """#### Load the diffusion model state dictionary. - - #### Args: - - `sd`: The state dictionary. - - `model_options` (dict, optional): The model options. Defaults to {}. - - #### Returns: - - `ModelPatcher`: The model patcher. - """ - # load unet in diffusers or regular format - dtype = model_options.get("dtype", None) - - # Allow loading unets from checkpoint files - diffusion_model_prefix = unet_prefix_from_state_dict(sd) - temp_sd = util.state_dict_prefix_replace( - sd, {diffusion_model_prefix: ""}, filter_keys=True - ) - if len(temp_sd) > 0: - sd = temp_sd - - parameters = util.calculate_parameters(sd) - load_device = Device.get_torch_device() - model_config = unet.model_config_from_unet(sd, "") - - if model_config is not None: - new_sd = sd - - offload_device = Device.unet_offload_device() - if dtype is None: - unet_dtype2 = Device.unet_dtype( - model_params=parameters, - supported_dtypes=model_config.supported_inference_dtypes, - ) - else: - unet_dtype2 = dtype - - manual_cast_dtype = Device.unet_manual_cast( - unet_dtype2, load_device, model_config.supported_inference_dtypes - ) - model_config.set_inference_dtype(unet_dtype2, manual_cast_dtype) - model_config.custom_operations = model_options.get( - "custom_operations", model_config.custom_operations - ) - model = model_config.get_model(new_sd, "") - model = model.to(offload_device) - model.load_model_weights(new_sd, "") - left_over = sd.keys() - if len(left_over) > 0: - logging.info("left over keys in unet: {}".format(left_over)) - return ModelPatcher(model, load_device=load_device, offload_device=offload_device) +import copy +import logging +import uuid + +import torch + +from modules.NeuralNetwork import unet +from modules.Utilities import util +from modules.Device import Device + +def wipe_lowvram_weight(m): + if hasattr(m, "prev_comfy_cast_weights"): + m.comfy_cast_weights = m.prev_comfy_cast_weights + del m.prev_comfy_cast_weights + m.weight_function = None + m.bias_function = None + +class ModelPatcher: + def __init__( + self, + model: torch.nn.Module, + load_device: torch.device, + offload_device: torch.device, + size: int = 0, + current_device: torch.device = None, + weight_inplace_update: bool = False, + ): + """#### Initialize the ModelPatcher class. + + #### Args: + - `model` (torch.nn.Module): The model. + - `load_device` (torch.device): The device to load the model on. + - `offload_device` (torch.device): The device to offload the model to. + - `size` (int, optional): The size of the model. Defaults to 0. + - `current_device` (torch.device, optional): The current device. Defaults to None. + - `weight_inplace_update` (bool, optional): Whether to update weights in place. Defaults to False. + """ + self.size = size + self.model = model + self.patches = {} + self.backup = {} + self.object_patches = {} + self.object_patches_backup = {} + self.model_options = {"transformer_options": {}} + self.model_size() + self.load_device = load_device + self.offload_device = offload_device + if current_device is None: + self.current_device = self.offload_device + else: + self.current_device = current_device + + self.weight_inplace_update = weight_inplace_update + self.model_lowvram = False + self.lowvram_patch_counter = 0 + self.patches_uuid = uuid.uuid4() + + if not hasattr(self.model, "model_loaded_weight_memory"): + self.model.model_loaded_weight_memory = 0 + + if not hasattr(self.model, "model_lowvram"): + self.model.model_lowvram = False + + if not hasattr(self.model, "lowvram_patch_counter"): + self.model.lowvram_patch_counter = 0 + + def loaded_size(self) -> int: + """#### Get the loaded size + + #### Returns: + - `int`: The loaded size + """ + return self.model.model_loaded_weight_memory + + def model_size(self) -> int: + """#### Get the size of the model. + + #### Returns: + - `int`: The size of the model. + """ + if self.size > 0: + return self.size + model_sd = self.model.state_dict() + self.size = Device.module_size(self.model) + self.model_keys = set(model_sd.keys()) + return self.size + + def clone(self) -> "ModelPatcher": + """#### Clone the ModelPatcher object. + + #### Returns: + - `ModelPatcher`: The cloned ModelPatcher object. + """ + n = ModelPatcher( + self.model, + self.load_device, + self.offload_device, + self.size, + self.current_device, + weight_inplace_update=self.weight_inplace_update, + ) + n.patches = {} + for k in self.patches: + n.patches[k] = self.patches[k][:] + n.patches_uuid = self.patches_uuid + + n.object_patches = self.object_patches.copy() + n.model_options = copy.deepcopy(self.model_options) + n.model_keys = self.model_keys + n.backup = self.backup + n.object_patches_backup = self.object_patches_backup + return n + + def is_clone(self, other: object) -> bool: + """#### Check if the object is a clone. + + #### Args: + - `other` (object): The other object. + + #### Returns: + - `bool`: Whether the object is a clone. + """ + if hasattr(other, "model") and self.model is other.model: + return True + return False + + def memory_required(self, input_shape: tuple) -> float: + """#### Calculate the memory required for the model. + + #### Args: + - `input_shape` (tuple): The input shape. + + #### Returns: + - `float`: The memory required. + """ + return self.model.memory_required(input_shape=input_shape) + + def set_model_unet_function_wrapper(self, unet_wrapper_function: callable) -> None: + """#### Set the UNet function wrapper for the model. + + #### Args: + - `unet_wrapper_function` (callable): The UNet function wrapper. + """ + self.model_options["model_function_wrapper"] = unet_wrapper_function + + def set_model_denoise_mask_function(self, denoise_mask_function: callable) -> None: + """#### Set the denoise mask function for the model. + + #### Args: + - `denoise_mask_function` (callable): The denoise mask function. + """ + self.model_options["denoise_mask_function"] = denoise_mask_function + + def get_model_object(self, name: str) -> object: + """#### Get an object from the model. + + #### Args: + - `name` (str): The name of the object. + + #### Returns: + - `object`: The object. + """ + return util.get_attr(self.model, name) + + def model_patches_to(self, device: torch.device) -> None: + """#### Move model patches to a device. + + #### Args: + - `device` (torch.device): The device. + """ + self.model_options["transformer_options"] + if "model_function_wrapper" in self.model_options: + wrap_func = self.model_options["model_function_wrapper"] + if hasattr(wrap_func, "to"): + self.model_options["model_function_wrapper"] = wrap_func.to(device) + + def model_dtype(self) -> torch.dtype: + """#### Get the data type of the model. + + #### Returns: + - `torch.dtype`: The data type. + """ + if hasattr(self.model, "get_dtype"): + return self.model.get_dtype() + + def add_patches( + self, patches: dict, strength_patch: float = 1.0, strength_model: float = 1.0 + ) -> list: + """#### Add patches to the model. + + #### Args: + - `patches` (dict): The patches to add. + - `strength_patch` (float, optional): The strength of the patches. Defaults to 1.0. + - `strength_model` (float, optional): The strength of the model. Defaults to 1.0. + + #### Returns: + - `list`: The list of patched keys. + """ + p = set() + for k in patches: + if k in self.model_keys: + p.add(k) + current_patches = self.patches.get(k, []) + current_patches.append((strength_patch, patches[k], strength_model)) + self.patches[k] = current_patches + + self.patches_uuid = uuid.uuid4() + return list(p) + + def set_model_patch(self, patch: list, name: str): + """#### Set a patch for the model. + + #### Args: + - `patch` (list): The patch. + - `name` (str): The name of the patch. + """ + to = self.model_options["transformer_options"] + if "patches" not in to: + to["patches"] = {} + to["patches"][name] = to["patches"].get(name, []) + [patch] + + def set_model_attn1_patch(self, patch: list): + """#### Set the attention 1 patch for the model. + + #### Args: + - `patch` (list): The patch. + """ + self.set_model_patch(patch, "attn1_patch") + + def set_model_attn2_patch(self, patch: list): + """#### Set the attention 2 patch for the model. + + #### Args: + - `patch` (list): The patch. + """ + self.set_model_patch(patch, "attn2_patch") + + def set_model_attn1_output_patch(self, patch: list): + """#### Set the attention 1 output patch for the model. + + #### Args: + - `patch` (list): The patch. + """ + self.set_model_patch(patch, "attn1_output_patch") + + def set_model_attn2_output_patch(self, patch: list): + """#### Set the attention 2 output patch for the model. + + #### Args: + - `patch` (list): The patch. + """ + self.set_model_patch(patch, "attn2_output_patch") + + def model_state_dict(self, filter_prefix: str = None) -> dict: + """#### Get the state dictionary of the model. + + #### Args: + - `filter_prefix` (str, optional): The prefix to filter. Defaults to None. + + #### Returns: + - `dict`: The state dictionary. + """ + sd = self.model.state_dict() + list(sd.keys()) + return sd + + def patch_weight_to_device(self, key: str, device_to: torch.device = None) -> None: + """#### Patch the weight of a key to a device. + + #### Args: + - `key` (str): The key. + - `device_to` (torch.device, optional): The device to patch to. Defaults to None. + """ + if key not in self.patches: + return + + weight = util.get_attr(self.model, key) + + inplace_update = self.weight_inplace_update + + if key not in self.backup: + self.backup[key] = weight.to( + device=self.offload_device, copy=inplace_update + ) + + if device_to is not None: + temp_weight = Device.cast_to_device( + weight, device_to, torch.float32, copy=True + ) + else: + temp_weight = weight.to(torch.float32, copy=True) + out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to( + weight.dtype + ) + if inplace_update: + util.copy_to_param(self.model, key, out_weight) + else: + util.set_attr_param(self.model, key, out_weight) + + def load( + self, + device_to: torch.device = None, + lowvram_model_memory: int = 0, + force_patch_weights: bool = False, + full_load: bool = False, + ): + """#### Load the model. + + #### Args: + - `device_to` (torch.device, optional): The device to load to. Defaults to None. + - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. + - `force_patch_weights` (bool, optional): Whether to force patch weights. Defaults to False. + - `full_load` (bool, optional): Whether to fully load the model. Defaults to False. + """ + mem_counter = 0 + patch_counter = 0 + lowvram_counter = 0 + loading = [] + for n, m in self.model.named_modules(): + if hasattr(m, "comfy_cast_weights") or hasattr(m, "weight"): + loading.append((Device.module_size(m), n, m)) + + load_completely = [] + loading.sort(reverse=True) + for x in loading: + n = x[1] + m = x[2] + module_mem = x[0] + + lowvram_weight = False + + if not full_load and hasattr(m, "comfy_cast_weights"): + if mem_counter + module_mem >= lowvram_model_memory: + lowvram_weight = True + lowvram_counter += 1 + if hasattr(m, "prev_comfy_cast_weights"): # Already lowvramed + continue + + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + + if lowvram_weight: + if weight_key in self.patches: + if force_patch_weights: + self.patch_weight_to_device(weight_key) + if bias_key in self.patches: + if force_patch_weights: + self.patch_weight_to_device(bias_key) + + m.prev_comfy_cast_weights = m.comfy_cast_weights + m.comfy_cast_weights = True + else: + if hasattr(m, "comfy_cast_weights"): + if m.comfy_cast_weights: + wipe_lowvram_weight(m) + + if hasattr(m, "weight"): + mem_counter += module_mem + load_completely.append((module_mem, n, m)) + + load_completely.sort(reverse=True) + for x in load_completely: + n = x[1] + m = x[2] + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + if hasattr(m, "comfy_patched_weights"): + if m.comfy_patched_weights is True: + continue + + self.patch_weight_to_device(weight_key, device_to=device_to) + self.patch_weight_to_device(bias_key, device_to=device_to) + logging.debug("lowvram: loaded module regularly {} {}".format(n, m)) + m.comfy_patched_weights = True + + for x in load_completely: + x[2].to(device_to) + + if lowvram_counter > 0: + logging.info( + "loaded partially {} {} {}".format( + lowvram_model_memory / (1024 * 1024), + mem_counter / (1024 * 1024), + patch_counter, + ) + ) + self.model.model_lowvram = True + else: + logging.info( + "loaded completely {} {} {}".format( + lowvram_model_memory / (1024 * 1024), + mem_counter / (1024 * 1024), + full_load, + ) + ) + self.model.model_lowvram = False + if full_load: + self.model.to(device_to) + mem_counter = self.model_size() + + + self.model.lowvram_patch_counter += patch_counter + self.model.device = device_to + self.model.model_loaded_weight_memory = mem_counter + + def patch_model_flux( + self, + device_to: torch.device = None, + lowvram_model_memory: int =0, + load_weights: bool = True, + force_patch_weights: bool = False, + ): + """#### Patch the model. + + #### Args: + - `device_to` (torch.device, optional): The device to patch to. Defaults to None. + - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. + - `load_weights` (bool, optional): Whether to load weights. Defaults to True. + - `force_patch_weights` (bool, optional): Whether to force patch weights. Defaults to False. + + #### Returns: + - `torch.nn.Module`: The patched model. + """ + for k in self.object_patches: + old = util.set_attr(self.model, k, self.object_patches[k]) + if k not in self.object_patches_backup: + self.object_patches_backup[k] = old + + if lowvram_model_memory == 0: + full_load = True + else: + full_load = False + + if load_weights: + self.load( + device_to, + lowvram_model_memory=lowvram_model_memory, + force_patch_weights=force_patch_weights, + full_load=full_load, + ) + return self.model + + def patch_model_lowvram_flux( + self, + device_to: torch.device = None, + lowvram_model_memory: int = 0, + force_patch_weights: bool = False, + ) -> torch.nn.Module: + """#### Patch the model for low VRAM. + + #### Args: + - `device_to` (torch.device, optional): The device to patch to. Defaults to None. + - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. + - `force_patch_weights` (bool, optional): Whether to force patch weights. Defaults to False. + + #### Returns: + - `torch.nn.Module`: The patched model. + """ + self.patch_model(device_to) + + logging.info( + "loading in lowvram mode {}".format(lowvram_model_memory / (1024 * 1024)) + ) + + class LowVramPatch: + def __init__(self, key: str, model_patcher: "ModelPatcher"): + self.key = key + self.model_patcher = model_patcher + + def __call__(self, weight: torch.Tensor) -> torch.Tensor: + return self.model_patcher.calculate_weight( + self.model_patcher.patches[self.key], weight, self.key + ) + + mem_counter = 0 + patch_counter = 0 + for n, m in self.model.named_modules(): + lowvram_weight = False + if hasattr(m, "comfy_cast_weights"): + module_mem = Device.module_size(m) + if mem_counter + module_mem >= lowvram_model_memory: + lowvram_weight = True + + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + + if lowvram_weight: + if weight_key in self.patches: + if force_patch_weights: + self.patch_weight_to_device(weight_key) + else: + m.weight_function = LowVramPatch(weight_key, self) + patch_counter += 1 + if bias_key in self.patches: + if force_patch_weights: + self.patch_weight_to_device(bias_key) + else: + m.bias_function = LowVramPatch(bias_key, self) + patch_counter += 1 + + m.prev_comfy_cast_weights = m.comfy_cast_weights + m.comfy_cast_weights = True + else: + if hasattr(m, "weight"): + self.patch_weight_to_device(weight_key, device_to) + self.patch_weight_to_device(bias_key, device_to) + m.to(device_to) + mem_counter += Device.module_size(m) + logging.debug("lowvram: loaded module regularly {}".format(m)) + + self.model_lowvram = True + self.lowvram_patch_counter = patch_counter + return self.model + + def patch_model( + self, device_to: torch.device = None, patch_weights: bool = True + ) -> torch.nn.Module: + """#### Patch the model. + + #### Args: + - `device_to` (torch.device, optional): The device to patch to. Defaults to None. + - `patch_weights` (bool, optional): Whether to patch weights. Defaults to True. + + #### Returns: + - `torch.nn.Module`: The patched model. + """ + for k in self.object_patches: + old = util.set_attr(self.model, k, self.object_patches[k]) + if k not in self.object_patches_backup: + self.object_patches_backup[k] = old + + if patch_weights: + model_sd = self.model_state_dict() + for key in self.patches: + if key not in model_sd: + logging.warning( + "could not patch. key doesn't exist in model: {}".format(key) + ) + continue + + self.patch_weight_to_device(key, device_to) + + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to + + return self.model + + def patch_model_lowvram( + self, + device_to: torch.device = None, + lowvram_model_memory: int = 0, + force_patch_weights: bool = False, + ) -> torch.nn.Module: + """#### Patch the model for low VRAM. + + #### Args: + - `device_to` (torch.device, optional): The device to patch to. Defaults to None. + - `lowvram_model_memory` (int, optional): The low VRAM model memory. Defaults to 0. + - `force_patch_weights` (bool, optional): Whether to force patch weights. Defaults to False. + + #### Returns: + - `torch.nn.Module`: The patched model. + """ + self.patch_model(device_to, patch_weights=False) + + logging.info( + "loading in lowvram mode {}".format(lowvram_model_memory / (1024 * 1024)) + ) + + class LowVramPatch: + def __init__(self, key: str, model_patcher: "ModelPatcher"): + self.key = key + self.model_patcher = model_patcher + + def __call__(self, weight: torch.Tensor) -> torch.Tensor: + return self.model_patcher.calculate_weight( + self.model_patcher.patches[self.key], weight, self.key + ) + + mem_counter = 0 + patch_counter = 0 + for n, m in self.model.named_modules(): + lowvram_weight = False + if hasattr(m, "comfy_cast_weights"): + module_mem = Device.module_size(m) + if mem_counter + module_mem >= lowvram_model_memory: + lowvram_weight = True + + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + + if lowvram_weight: + if weight_key in self.patches: + if force_patch_weights: + self.patch_weight_to_device(weight_key) + else: + m.weight_function = LowVramPatch(weight_key, self) + patch_counter += 1 + if bias_key in self.patches: + if force_patch_weights: + self.patch_weight_to_device(bias_key) + else: + m.bias_function = LowVramPatch(bias_key, self) + patch_counter += 1 + + m.prev_comfy_cast_weights = m.comfy_cast_weights + m.comfy_cast_weights = True + else: + if hasattr(m, "weight"): + self.patch_weight_to_device(weight_key, device_to) + self.patch_weight_to_device(bias_key, device_to) + m.to(device_to) + mem_counter += Device.module_size(m) + logging.debug("lowvram: loaded module regularly {}".format(m)) + + self.model_lowvram = True + self.lowvram_patch_counter = patch_counter + return self.model + + def calculate_weight( + self, patches: list, weight: torch.Tensor, key: str + ) -> torch.Tensor: + """#### Calculate the weight of a key. + + #### Args: + - `patches` (list): The list of patches. + - `weight` (torch.Tensor): The weight tensor. + - `key` (str): The key. + + #### Returns: + - `torch.Tensor`: The calculated weight. + """ + for p in patches: + alpha = p[0] + v = p[1] + p[2] + v[0] + v = v[1] + mat1 = Device.cast_to_device(v[0], weight.device, torch.float32) + mat2 = Device.cast_to_device(v[1], weight.device, torch.float32) + v[4] + if v[2] is not None: + alpha *= v[2] / mat2.shape[0] + weight += ( + (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))) + .reshape(weight.shape) + .type(weight.dtype) + ) + return weight + + def unpatch_model( + self, device_to: torch.device = None, unpatch_weights: bool = True + ) -> None: + """#### Unpatch the model. + + #### Args: + - `device_to` (torch.device, optional): The device to unpatch to. Defaults to None. + - `unpatch_weights` (bool, optional): Whether to unpatch weights. Defaults to True. + """ + if unpatch_weights: + keys = list(self.backup.keys()) + for k in keys: + util.set_attr_param(self.model, k, self.backup[k]) + self.backup.clear() + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to + + keys = list(self.object_patches_backup.keys()) + self.object_patches_backup.clear() + + def partially_load(self, device_to: torch.device, extra_memory: int = 0) -> int: + """#### Partially load the model. + + #### Args: + - `device_to` (torch.device): The device to load to. + - `extra_memory` (int, optional): The extra memory. Defaults to 0. + + #### Returns: + - `int`: The memory loaded. + """ + self.unpatch_model(unpatch_weights=False) + self.patch_model(patch_weights=False) + full_load = False + if self.model.model_lowvram is False: + return 0 + if self.model.model_loaded_weight_memory + extra_memory > self.model_size(): + full_load = True + current_used = self.model.model_loaded_weight_memory + self.load( + device_to, + lowvram_model_memory=current_used + extra_memory, + full_load=full_load, + ) + return self.model.model_loaded_weight_memory - current_used + + def add_object_patch(self, name, obj): + self.object_patches[name] = obj + +def unet_prefix_from_state_dict(state_dict: dict) -> str: + """#### Get the UNet prefix from the state dictionary. + + #### Args: + - `state_dict` (dict): The state dictionary. + + #### Returns: + - `str`: The UNet prefix. + """ + candidates = [ + "model.diffusion_model.", # ldm/sgm models + "model.model.", # audio models + ] + counts = {k: 0 for k in candidates} + for k in state_dict: + for c in candidates: + if k.startswith(c): + counts[c] += 1 + break + + top = max(counts, key=counts.get) + if counts[top] > 5: + return top + else: + return "model." # aura flow and others + +def load_diffusion_model_state_dict( + sd, model_options={} +) -> ModelPatcher: + """#### Load the diffusion model state dictionary. + + #### Args: + - `sd`: The state dictionary. + - `model_options` (dict, optional): The model options. Defaults to {}. + + #### Returns: + - `ModelPatcher`: The model patcher. + """ + # load unet in diffusers or regular format + dtype = model_options.get("dtype", None) + + # Allow loading unets from checkpoint files + diffusion_model_prefix = unet_prefix_from_state_dict(sd) + temp_sd = util.state_dict_prefix_replace( + sd, {diffusion_model_prefix: ""}, filter_keys=True + ) + if len(temp_sd) > 0: + sd = temp_sd + + parameters = util.calculate_parameters(sd) + load_device = Device.get_torch_device() + model_config = unet.model_config_from_unet(sd, "") + + if model_config is not None: + new_sd = sd + + offload_device = Device.unet_offload_device() + if dtype is None: + unet_dtype2 = Device.unet_dtype( + model_params=parameters, + supported_dtypes=model_config.supported_inference_dtypes, + ) + else: + unet_dtype2 = dtype + + manual_cast_dtype = Device.unet_manual_cast( + unet_dtype2, load_device, model_config.supported_inference_dtypes + ) + model_config.set_inference_dtype(unet_dtype2, manual_cast_dtype) + model_config.custom_operations = model_options.get( + "custom_operations", model_config.custom_operations + ) + model = model_config.get_model(new_sd, "") + model = model.to(offload_device) + model.load_model_weights(new_sd, "") + left_over = sd.keys() + if len(left_over) > 0: + logging.info("left over keys in unet: {}".format(left_over)) + return ModelPatcher(model, load_device=load_device, offload_device=offload_device) diff --git a/modules/NeuralNetwork/transformer.py b/modules/NeuralNetwork/transformer.py index ecb0392a27d89203145d3cef679468b92ee552b3..aaf55f8e9e21afcff5788f26ec56949757fb9df1 100644 --- a/modules/NeuralNetwork/transformer.py +++ b/modules/NeuralNetwork/transformer.py @@ -1,443 +1,443 @@ -from einops import rearrange -import torch -from modules.Utilities import util -import torch.nn as nn -from modules.Attention import Attention -from modules.Device import Device -from modules.cond import Activation -from modules.cond import cast -from modules.sample import sampling_util - -if Device.xformers_enabled(): - pass - -ops = cast.disable_weight_init - -_ATTN_PRECISION = "fp32" - - -class FeedForward(nn.Module): - """#### FeedForward neural network module. - - #### Args: - - `dim` (int): The input dimension. - - `dim_out` (int, optional): The output dimension. Defaults to None. - - `mult` (int, optional): The multiplier for the inner dimension. Defaults to 4. - - `glu` (bool, optional): Whether to use Gated Linear Units. Defaults to False. - - `dropout` (float, optional): The dropout rate. Defaults to 0.0. - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - - `device` (torch.device, optional): The device. Defaults to None. - - `operations` (object, optional): The operations module. Defaults to `ops`. - """ - - def __init__( - self, - dim: int, - dim_out: int = None, - mult: int = 4, - glu: bool = False, - dropout: float = 0.0, - dtype: torch.dtype = None, - device: torch.device = None, - operations: object = ops, - ): - super().__init__() - inner_dim = int(dim * mult) - dim_out = util.default(dim_out, dim) - project_in = ( - nn.Sequential( - operations.Linear(dim, inner_dim, dtype=dtype, device=device), nn.GELU() - ) - if not glu - else Activation.GEGLU(dim, inner_dim) - ) - - self.net = nn.Sequential( - project_in, - nn.Dropout(dropout), - operations.Linear(inner_dim, dim_out, dtype=dtype, device=device), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass of the FeedForward network. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - return self.net(x) - - -class BasicTransformerBlock(nn.Module): - """#### Basic Transformer block. - - #### Args: - - `dim` (int): The input dimension. - - `n_heads` (int): The number of attention heads. - - `d_head` (int): The dimension of each attention head. - - `dropout` (float, optional): The dropout rate. Defaults to 0.0. - - `context_dim` (int, optional): The context dimension. Defaults to None. - - `gated_ff` (bool, optional): Whether to use Gated FeedForward. Defaults to True. - - `checkpoint` (bool, optional): Whether to use checkpointing. Defaults to True. - - `ff_in` (bool, optional): Whether to use FeedForward input. Defaults to False. - - `inner_dim` (int, optional): The inner dimension. Defaults to None. - - `disable_self_attn` (bool, optional): Whether to disable self-attention. Defaults to False. - - `disable_temporal_crossattention` (bool, optional): Whether to disable temporal cross-attention. Defaults to False. - - `switch_temporal_ca_to_sa` (bool, optional): Whether to switch temporal cross-attention to self-attention. Defaults to False. - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - - `device` (torch.device, optional): The device. Defaults to None. - - `operations` (object, optional): The operations module. Defaults to `ops`. - """ - - def __init__( - self, - dim: int, - n_heads: int, - d_head: int, - dropout: float = 0.0, - context_dim: int = None, - gated_ff: bool = True, - checkpoint: bool = True, - ff_in: bool = False, - inner_dim: int = None, - disable_self_attn: bool = False, - disable_temporal_crossattention: bool = False, - switch_temporal_ca_to_sa: bool = False, - dtype: torch.dtype = None, - device: torch.device = None, - operations: object = ops, - ): - super().__init__() - - self.ff_in = ff_in or inner_dim is not None - if inner_dim is None: - inner_dim = dim - - self.is_res = inner_dim == dim - self.disable_self_attn = disable_self_attn - self.attn1 = Attention.CrossAttention( - query_dim=inner_dim, - heads=n_heads, - dim_head=d_head, - dropout=dropout, - context_dim=context_dim if self.disable_self_attn else None, - dtype=dtype, - device=device, - operations=operations, - ) # is a self-attention if not self.disable_self_attn - self.ff = FeedForward( - inner_dim, - dim_out=dim, - dropout=dropout, - glu=gated_ff, - dtype=dtype, - device=device, - operations=operations, - ) - - context_dim_attn2 = None - if not switch_temporal_ca_to_sa: - context_dim_attn2 = context_dim - - self.attn2 = Attention.CrossAttention( - query_dim=inner_dim, - context_dim=context_dim_attn2, - heads=n_heads, - dim_head=d_head, - dropout=dropout, - dtype=dtype, - device=device, - operations=operations, - ) # is self-attn if context is none - self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) - - self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) - self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) - self.checkpoint = checkpoint - self.n_heads = n_heads - self.d_head = d_head - self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa - - def forward( - self, - x: torch.Tensor, - context: torch.Tensor = None, - transformer_options: dict = {}, - ) -> torch.Tensor: - """#### Forward pass of the Basic Transformer block. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `context` (torch.Tensor, optional): The context tensor. Defaults to None. - - `transformer_options` (dict, optional): Additional transformer options. Defaults to {}. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - return sampling_util.checkpoint( - self._forward, - (x, context, transformer_options), - self.parameters(), - self.checkpoint, - ) - - def _forward( - self, - x: torch.Tensor, - context: torch.Tensor = None, - transformer_options: dict = {}, - ) -> torch.Tensor: - """#### Internal forward pass of the Basic Transformer block. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `context` (torch.Tensor, optional): The context tensor. Defaults to None. - - `transformer_options` (dict, optional): Additional transformer options. Defaults to {}. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - extra_options = {} - block = transformer_options.get("block", None) - block_index = transformer_options.get("block_index", 0) - transformer_patches_replace = {} - - for k in transformer_options: - extra_options[k] = transformer_options[k] - - extra_options["n_heads"] = self.n_heads - extra_options["dim_head"] = self.d_head - - n = self.norm1(x) - context_attn1 = None - value_attn1 = None - - transformer_block = (block[0], block[1], block_index) - attn1_replace_patch = transformer_patches_replace.get("attn1", {}) - block_attn1 = transformer_block - if block_attn1 not in attn1_replace_patch: - block_attn1 = block - - n = self.attn1(n, context=context_attn1, value=value_attn1) - - x += n - - if self.attn2 is not None: - n = self.norm2(x) - context_attn2 = context - value_attn2 = None - - attn2_replace_patch = transformer_patches_replace.get("attn2", {}) - block_attn2 = transformer_block - if block_attn2 not in attn2_replace_patch: - block_attn2 = block - n = self.attn2(n, context=context_attn2, value=value_attn2) - - x += n - if self.is_res: - x_skip = x - x = self.ff(self.norm3(x)) - if self.is_res: - x += x_skip - - return x - - -class SpatialTransformer(nn.Module): - """#### Spatial Transformer module. - - #### Args: - - `in_channels` (int): The number of input channels. - - `n_heads` (int): The number of attention heads. - - `d_head` (int): The dimension of each attention head. - - `depth` (int, optional): The depth of the transformer. Defaults to 1. - - `dropout` (float, optional): The dropout rate. Defaults to 0.0. - - `context_dim` (int, optional): The context dimension. Defaults to None. - - `disable_self_attn` (bool, optional): Whether to disable self-attention. Defaults to False. - - `use_linear` (bool, optional): Whether to use linear projections. Defaults to False. - - `use_checkpoint` (bool, optional): Whether to use checkpointing. Defaults to True. - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - - `device` (torch.device, optional): The device. Defaults to None. - - `operations` (object, optional): The operations module. Defaults to `ops`. - """ - - def __init__( - self, - in_channels: int, - n_heads: int, - d_head: int, - depth: int = 1, - dropout: float = 0.0, - context_dim: int = None, - disable_self_attn: bool = False, - use_linear: bool = False, - use_checkpoint: bool = True, - dtype: torch.dtype = None, - device: torch.device = None, - operations: object = ops, - ): - super().__init__() - if util.exists(context_dim) and not isinstance(context_dim, list): - context_dim = [context_dim] * depth - self.in_channels = in_channels - inner_dim = n_heads * d_head - self.norm = operations.GroupNorm( - num_groups=32, - num_channels=in_channels, - eps=1e-6, - affine=True, - dtype=dtype, - device=device, - ) - if not use_linear: - self.proj_in = operations.Conv2d( - in_channels, - inner_dim, - kernel_size=1, - stride=1, - padding=0, - dtype=dtype, - device=device, - ) - else: - self.proj_in = operations.Linear( - in_channels, inner_dim, dtype=dtype, device=device - ) - - self.transformer_blocks = nn.ModuleList( - [ - BasicTransformerBlock( - inner_dim, - n_heads, - d_head, - dropout=dropout, - context_dim=context_dim[d], - disable_self_attn=disable_self_attn, - checkpoint=use_checkpoint, - dtype=dtype, - device=device, - operations=operations, - ) - for d in range(depth) - ] - ) - if not use_linear: - self.proj_out = operations.Conv2d( - inner_dim, - in_channels, - kernel_size=1, - stride=1, - padding=0, - dtype=dtype, - device=device, - ) - else: - self.proj_out = operations.Linear( - in_channels, inner_dim, dtype=dtype, device=device - ) - self.use_linear = use_linear - - def forward( - self, - x: torch.Tensor, - context: torch.Tensor = None, - transformer_options: dict = {}, - ) -> torch.Tensor: - """#### Forward pass of the Spatial Transformer. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `context` (torch.Tensor, optional): The context tensor. Defaults to None. - - `transformer_options` (dict, optional): Additional transformer options. Defaults to {}. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - # note: if no context is given, cross-attention defaults to self-attention - if not isinstance(context, list): - context = [context] * len(self.transformer_blocks) - b, c, h, w = x.shape - x_in = x - x = self.norm(x) - if not self.use_linear: - x = self.proj_in(x) - x = rearrange(x, "b c h w -> b (h w) c").contiguous() - if self.use_linear: - x = self.proj_in(x) - for i, block in enumerate(self.transformer_blocks): - transformer_options["block_index"] = i - x = block(x, context=context[i], transformer_options=transformer_options) - if self.use_linear: - x = self.proj_out(x) - x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous() - if not self.use_linear: - x = self.proj_out(x) - return x + x_in - - -def count_blocks(state_dict_keys: list, prefix_string: str) -> int: - """#### Count the number of blocks in a state dictionary. - - #### Args: - - `state_dict_keys` (list): The list of state dictionary keys. - - `prefix_string` (str): The prefix string to match. - - #### Returns: - - `int`: The number of blocks. - """ - count = 0 - while True: - c = False - for k in state_dict_keys: - if k.startswith(prefix_string.format(count)): - c = True - break - if c is False: - break - count += 1 - return count - - -def calculate_transformer_depth( - prefix: str, state_dict_keys: list, state_dict: dict -) -> tuple: - """#### Calculate the depth of a transformer. - - #### Args: - - `prefix` (str): The prefix string. - - `state_dict_keys` (list): The list of state dictionary keys. - - `state_dict` (dict): The state dictionary. - - #### Returns: - - `tuple`: The transformer depth, context dimension, use of linear in transformer, and time stack. - """ - context_dim = None - use_linear_in_transformer = False - - transformer_prefix = prefix + "1.transformer_blocks." - transformer_keys = sorted( - list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys)) - ) - if len(transformer_keys) > 0: - last_transformer_depth = count_blocks( - state_dict_keys, transformer_prefix + "{}" - ) - context_dim = state_dict[ - "{}0.attn2.to_k.weight".format(transformer_prefix) - ].shape[1] - use_linear_in_transformer = ( - len(state_dict["{}1.proj_in.weight".format(prefix)].shape) == 2 - ) - time_stack = ( - "{}1.time_stack.0.attn1.to_q.weight".format(prefix) in state_dict - or "{}1.time_mix_blocks.0.attn1.to_q.weight".format(prefix) in state_dict - ) - return ( - last_transformer_depth, - context_dim, - use_linear_in_transformer, - time_stack, - ) - return None +from einops import rearrange +import torch +from modules.Utilities import util +import torch.nn as nn +from modules.Attention import Attention +from modules.Device import Device +from modules.cond import Activation +from modules.cond import cast +from modules.sample import sampling_util + +if Device.xformers_enabled(): + pass + +ops = cast.disable_weight_init + +_ATTN_PRECISION = "fp32" + + +class FeedForward(nn.Module): + """#### FeedForward neural network module. + + #### Args: + - `dim` (int): The input dimension. + - `dim_out` (int, optional): The output dimension. Defaults to None. + - `mult` (int, optional): The multiplier for the inner dimension. Defaults to 4. + - `glu` (bool, optional): Whether to use Gated Linear Units. Defaults to False. + - `dropout` (float, optional): The dropout rate. Defaults to 0.0. + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + - `device` (torch.device, optional): The device. Defaults to None. + - `operations` (object, optional): The operations module. Defaults to `ops`. + """ + + def __init__( + self, + dim: int, + dim_out: int = None, + mult: int = 4, + glu: bool = False, + dropout: float = 0.0, + dtype: torch.dtype = None, + device: torch.device = None, + operations: object = ops, + ): + super().__init__() + inner_dim = int(dim * mult) + dim_out = util.default(dim_out, dim) + project_in = ( + nn.Sequential( + operations.Linear(dim, inner_dim, dtype=dtype, device=device), nn.GELU() + ) + if not glu + else Activation.GEGLU(dim, inner_dim) + ) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + operations.Linear(inner_dim, dim_out, dtype=dtype, device=device), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass of the FeedForward network. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + return self.net(x) + + +class BasicTransformerBlock(nn.Module): + """#### Basic Transformer block. + + #### Args: + - `dim` (int): The input dimension. + - `n_heads` (int): The number of attention heads. + - `d_head` (int): The dimension of each attention head. + - `dropout` (float, optional): The dropout rate. Defaults to 0.0. + - `context_dim` (int, optional): The context dimension. Defaults to None. + - `gated_ff` (bool, optional): Whether to use Gated FeedForward. Defaults to True. + - `checkpoint` (bool, optional): Whether to use checkpointing. Defaults to True. + - `ff_in` (bool, optional): Whether to use FeedForward input. Defaults to False. + - `inner_dim` (int, optional): The inner dimension. Defaults to None. + - `disable_self_attn` (bool, optional): Whether to disable self-attention. Defaults to False. + - `disable_temporal_crossattention` (bool, optional): Whether to disable temporal cross-attention. Defaults to False. + - `switch_temporal_ca_to_sa` (bool, optional): Whether to switch temporal cross-attention to self-attention. Defaults to False. + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + - `device` (torch.device, optional): The device. Defaults to None. + - `operations` (object, optional): The operations module. Defaults to `ops`. + """ + + def __init__( + self, + dim: int, + n_heads: int, + d_head: int, + dropout: float = 0.0, + context_dim: int = None, + gated_ff: bool = True, + checkpoint: bool = True, + ff_in: bool = False, + inner_dim: int = None, + disable_self_attn: bool = False, + disable_temporal_crossattention: bool = False, + switch_temporal_ca_to_sa: bool = False, + dtype: torch.dtype = None, + device: torch.device = None, + operations: object = ops, + ): + super().__init__() + + self.ff_in = ff_in or inner_dim is not None + if inner_dim is None: + inner_dim = dim + + self.is_res = inner_dim == dim + self.disable_self_attn = disable_self_attn + self.attn1 = Attention.CrossAttention( + query_dim=inner_dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None, + dtype=dtype, + device=device, + operations=operations, + ) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward( + inner_dim, + dim_out=dim, + dropout=dropout, + glu=gated_ff, + dtype=dtype, + device=device, + operations=operations, + ) + + context_dim_attn2 = None + if not switch_temporal_ca_to_sa: + context_dim_attn2 = context_dim + + self.attn2 = Attention.CrossAttention( + query_dim=inner_dim, + context_dim=context_dim_attn2, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + dtype=dtype, + device=device, + operations=operations, + ) # is self-attn if context is none + self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + + self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + self.checkpoint = checkpoint + self.n_heads = n_heads + self.d_head = d_head + self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa + + def forward( + self, + x: torch.Tensor, + context: torch.Tensor = None, + transformer_options: dict = {}, + ) -> torch.Tensor: + """#### Forward pass of the Basic Transformer block. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `context` (torch.Tensor, optional): The context tensor. Defaults to None. + - `transformer_options` (dict, optional): Additional transformer options. Defaults to {}. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + return sampling_util.checkpoint( + self._forward, + (x, context, transformer_options), + self.parameters(), + self.checkpoint, + ) + + def _forward( + self, + x: torch.Tensor, + context: torch.Tensor = None, + transformer_options: dict = {}, + ) -> torch.Tensor: + """#### Internal forward pass of the Basic Transformer block. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `context` (torch.Tensor, optional): The context tensor. Defaults to None. + - `transformer_options` (dict, optional): Additional transformer options. Defaults to {}. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + extra_options = {} + block = transformer_options.get("block", None) + block_index = transformer_options.get("block_index", 0) + transformer_patches_replace = {} + + for k in transformer_options: + extra_options[k] = transformer_options[k] + + extra_options["n_heads"] = self.n_heads + extra_options["dim_head"] = self.d_head + + n = self.norm1(x) + context_attn1 = None + value_attn1 = None + + transformer_block = (block[0], block[1], block_index) + attn1_replace_patch = transformer_patches_replace.get("attn1", {}) + block_attn1 = transformer_block + if block_attn1 not in attn1_replace_patch: + block_attn1 = block + + n = self.attn1(n, context=context_attn1, value=value_attn1) + + x += n + + if self.attn2 is not None: + n = self.norm2(x) + context_attn2 = context + value_attn2 = None + + attn2_replace_patch = transformer_patches_replace.get("attn2", {}) + block_attn2 = transformer_block + if block_attn2 not in attn2_replace_patch: + block_attn2 = block + n = self.attn2(n, context=context_attn2, value=value_attn2) + + x += n + if self.is_res: + x_skip = x + x = self.ff(self.norm3(x)) + if self.is_res: + x += x_skip + + return x + + +class SpatialTransformer(nn.Module): + """#### Spatial Transformer module. + + #### Args: + - `in_channels` (int): The number of input channels. + - `n_heads` (int): The number of attention heads. + - `d_head` (int): The dimension of each attention head. + - `depth` (int, optional): The depth of the transformer. Defaults to 1. + - `dropout` (float, optional): The dropout rate. Defaults to 0.0. + - `context_dim` (int, optional): The context dimension. Defaults to None. + - `disable_self_attn` (bool, optional): Whether to disable self-attention. Defaults to False. + - `use_linear` (bool, optional): Whether to use linear projections. Defaults to False. + - `use_checkpoint` (bool, optional): Whether to use checkpointing. Defaults to True. + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + - `device` (torch.device, optional): The device. Defaults to None. + - `operations` (object, optional): The operations module. Defaults to `ops`. + """ + + def __init__( + self, + in_channels: int, + n_heads: int, + d_head: int, + depth: int = 1, + dropout: float = 0.0, + context_dim: int = None, + disable_self_attn: bool = False, + use_linear: bool = False, + use_checkpoint: bool = True, + dtype: torch.dtype = None, + device: torch.device = None, + operations: object = ops, + ): + super().__init__() + if util.exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] * depth + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = operations.GroupNorm( + num_groups=32, + num_channels=in_channels, + eps=1e-6, + affine=True, + dtype=dtype, + device=device, + ) + if not use_linear: + self.proj_in = operations.Conv2d( + in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0, + dtype=dtype, + device=device, + ) + else: + self.proj_in = operations.Linear( + in_channels, inner_dim, dtype=dtype, device=device + ) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim[d], + disable_self_attn=disable_self_attn, + checkpoint=use_checkpoint, + dtype=dtype, + device=device, + operations=operations, + ) + for d in range(depth) + ] + ) + if not use_linear: + self.proj_out = operations.Conv2d( + inner_dim, + in_channels, + kernel_size=1, + stride=1, + padding=0, + dtype=dtype, + device=device, + ) + else: + self.proj_out = operations.Linear( + in_channels, inner_dim, dtype=dtype, device=device + ) + self.use_linear = use_linear + + def forward( + self, + x: torch.Tensor, + context: torch.Tensor = None, + transformer_options: dict = {}, + ) -> torch.Tensor: + """#### Forward pass of the Spatial Transformer. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `context` (torch.Tensor, optional): The context tensor. Defaults to None. + - `transformer_options` (dict, optional): Additional transformer options. Defaults to {}. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] * len(self.transformer_blocks) + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c").contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + transformer_options["block_index"] = i + x = block(x, context=context[i], transformer_options=transformer_options) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + + +def count_blocks(state_dict_keys: list, prefix_string: str) -> int: + """#### Count the number of blocks in a state dictionary. + + #### Args: + - `state_dict_keys` (list): The list of state dictionary keys. + - `prefix_string` (str): The prefix string to match. + + #### Returns: + - `int`: The number of blocks. + """ + count = 0 + while True: + c = False + for k in state_dict_keys: + if k.startswith(prefix_string.format(count)): + c = True + break + if c is False: + break + count += 1 + return count + + +def calculate_transformer_depth( + prefix: str, state_dict_keys: list, state_dict: dict +) -> tuple: + """#### Calculate the depth of a transformer. + + #### Args: + - `prefix` (str): The prefix string. + - `state_dict_keys` (list): The list of state dictionary keys. + - `state_dict` (dict): The state dictionary. + + #### Returns: + - `tuple`: The transformer depth, context dimension, use of linear in transformer, and time stack. + """ + context_dim = None + use_linear_in_transformer = False + + transformer_prefix = prefix + "1.transformer_blocks." + transformer_keys = sorted( + list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys)) + ) + if len(transformer_keys) > 0: + last_transformer_depth = count_blocks( + state_dict_keys, transformer_prefix + "{}" + ) + context_dim = state_dict[ + "{}0.attn2.to_k.weight".format(transformer_prefix) + ].shape[1] + use_linear_in_transformer = ( + len(state_dict["{}1.proj_in.weight".format(prefix)].shape) == 2 + ) + time_stack = ( + "{}1.time_stack.0.attn1.to_q.weight".format(prefix) in state_dict + or "{}1.time_mix_blocks.0.attn1.to_q.weight".format(prefix) in state_dict + ) + return ( + last_transformer_depth, + context_dim, + use_linear_in_transformer, + time_stack, + ) + return None diff --git a/modules/NeuralNetwork/unet.py b/modules/NeuralNetwork/unet.py index 637e5584059cc76f1a5c02cec64300a24d29548c..78ac3773237dcc335a3d595f80b0a76c5f0c7a51 100644 --- a/modules/NeuralNetwork/unet.py +++ b/modules/NeuralNetwork/unet.py @@ -1,1132 +1,1132 @@ -import logging -import math -from typing import Any, Dict, List, Optional -import torch.nn as nn -import torch as th -import torch - -from modules.Utilities import util -from modules.AutoEncoders import ResBlock -from modules.NeuralNetwork import transformer -from modules.cond import cast -from modules.sample import sampling, sampling_util - -UNET_MAP_ATTENTIONS = { - "proj_in.weight", - "proj_in.bias", - "proj_out.weight", - "proj_out.bias", - "norm.weight", - "norm.bias", -} - -TRANSFORMER_BLOCKS = { - "norm1.weight", - "norm1.bias", - "norm2.weight", - "norm2.bias", - "norm3.weight", - "norm3.bias", - "attn1.to_q.weight", - "attn1.to_k.weight", - "attn1.to_v.weight", - "attn1.to_out.0.weight", - "attn1.to_out.0.bias", - "attn2.to_q.weight", - "attn2.to_k.weight", - "attn2.to_v.weight", - "attn2.to_out.0.weight", - "attn2.to_out.0.bias", - "ff.net.0.proj.weight", - "ff.net.0.proj.bias", - "ff.net.2.weight", - "ff.net.2.bias", -} - -UNET_MAP_RESNET = { - "in_layers.2.weight": "conv1.weight", - "in_layers.2.bias": "conv1.bias", - "emb_layers.1.weight": "time_emb_proj.weight", - "emb_layers.1.bias": "time_emb_proj.bias", - "out_layers.3.weight": "conv2.weight", - "out_layers.3.bias": "conv2.bias", - "skip_connection.weight": "conv_shortcut.weight", - "skip_connection.bias": "conv_shortcut.bias", - "in_layers.0.weight": "norm1.weight", - "in_layers.0.bias": "norm1.bias", - "out_layers.0.weight": "norm2.weight", - "out_layers.0.bias": "norm2.bias", -} - -UNET_MAP_BASIC = { - ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), - ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), - ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), - ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), - ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), - ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), - ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), - ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), - ("input_blocks.0.0.weight", "conv_in.weight"), - ("input_blocks.0.0.bias", "conv_in.bias"), - ("out.0.weight", "conv_norm_out.weight"), - ("out.0.bias", "conv_norm_out.bias"), - ("out.2.weight", "conv_out.weight"), - ("out.2.bias", "conv_out.bias"), - ("time_embed.0.weight", "time_embedding.linear_1.weight"), - ("time_embed.0.bias", "time_embedding.linear_1.bias"), - ("time_embed.2.weight", "time_embedding.linear_2.weight"), - ("time_embed.2.bias", "time_embedding.linear_2.bias"), -} - -# taken from https://github.com/TencentARC/T2I-Adapter - - -def unet_to_diffusers(unet_config: dict) -> dict: - """#### Convert a UNet configuration to a diffusers configuration. - - #### Args: - - `unet_config` (dict): The UNet configuration. - - #### Returns: - - `dict`: The diffusers configuration. - """ - if "num_res_blocks" not in unet_config: - return {} - num_res_blocks = unet_config["num_res_blocks"] - channel_mult = unet_config["channel_mult"] - transformer_depth = unet_config["transformer_depth"][:] - transformer_depth_output = unet_config["transformer_depth_output"][:] - num_blocks = len(channel_mult) - - transformers_mid = unet_config.get("transformer_depth_middle", None) - - diffusers_unet_map = {} - for x in range(num_blocks): - n = 1 + (num_res_blocks[x] + 1) * x - for i in range(num_res_blocks[x]): - for b in UNET_MAP_RESNET: - diffusers_unet_map[ - "down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b]) - ] = "input_blocks.{}.0.{}".format(n, b) - num_transformers = transformer_depth.pop(0) - if num_transformers > 0: - for b in UNET_MAP_ATTENTIONS: - diffusers_unet_map[ - "down_blocks.{}.attentions.{}.{}".format(x, i, b) - ] = "input_blocks.{}.1.{}".format(n, b) - for t in range(num_transformers): - for b in TRANSFORMER_BLOCKS: - diffusers_unet_map[ - "down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format( - x, i, t, b - ) - ] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) - n += 1 - for k in ["weight", "bias"]: - diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = ( - "input_blocks.{}.0.op.{}".format(n, k) - ) - - i = 0 - for b in UNET_MAP_ATTENTIONS: - diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = ( - "middle_block.1.{}".format(b) - ) - for t in range(transformers_mid): - for b in TRANSFORMER_BLOCKS: - diffusers_unet_map[ - "mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b) - ] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) - - for i, n in enumerate([0, 2]): - for b in UNET_MAP_RESNET: - diffusers_unet_map[ - "mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b]) - ] = "middle_block.{}.{}".format(n, b) - - num_res_blocks = list(reversed(num_res_blocks)) - for x in range(num_blocks): - n = (num_res_blocks[x] + 1) * x - length = num_res_blocks[x] + 1 - for i in range(length): - c = 0 - for b in UNET_MAP_RESNET: - diffusers_unet_map[ - "up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b]) - ] = "output_blocks.{}.0.{}".format(n, b) - c += 1 - num_transformers = transformer_depth_output.pop() - if num_transformers > 0: - c += 1 - for b in UNET_MAP_ATTENTIONS: - diffusers_unet_map[ - "up_blocks.{}.attentions.{}.{}".format(x, i, b) - ] = "output_blocks.{}.1.{}".format(n, b) - for t in range(num_transformers): - for b in TRANSFORMER_BLOCKS: - diffusers_unet_map[ - "up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format( - x, i, t, b - ) - ] = "output_blocks.{}.1.transformer_blocks.{}.{}".format( - n, t, b - ) - if i == length - 1: - for k in ["weight", "bias"]: - diffusers_unet_map[ - "up_blocks.{}.upsamplers.0.conv.{}".format(x, k) - ] = "output_blocks.{}.{}.conv.{}".format(n, c, k) - n += 1 - - for k in UNET_MAP_BASIC: - diffusers_unet_map[k[1]] = k[0] - - return diffusers_unet_map - - -def apply_control1(h: th.Tensor, control: any, name: str) -> th.Tensor: - """#### Apply control to a tensor. - - #### Args: - - `h` (torch.Tensor): The input tensor. - - `control` (any): The control to apply. - - `name` (str): The name of the control. - - #### Returns: - - `torch.Tensor`: The controlled tensor. - """ - return h - - -oai_ops = cast.disable_weight_init - - -class UNetModel1(nn.Module): - """#### UNet Model class.""" - - def __init__( - self, - image_size: int, - in_channels: int, - model_channels: int, - out_channels: int, - num_res_blocks: list, - dropout: float = 0, - channel_mult: tuple = (1, 2, 4, 8), - conv_resample: bool = True, - dims: int = 2, - num_classes: int = None, - use_checkpoint: bool = False, - dtype: th.dtype = th.float32, - num_heads: int = -1, - num_head_channels: int = -1, - num_heads_upsample: int = -1, - use_scale_shift_norm: bool = False, - resblock_updown: bool = False, - use_new_attention_order: bool = False, - use_spatial_transformer: bool = False, # custom transformer support - transformer_depth: int = 1, # custom transformer support - context_dim: int = None, # custom transformer support - n_embed: int = None, # custom support for prediction of discrete ids into codebook of first stage vq model - legacy: bool = True, - disable_self_attentions: list = None, - num_attention_blocks: list = None, - disable_middle_self_attn: bool = False, - use_linear_in_transformer: bool = False, - adm_in_channels: int = None, - transformer_depth_middle: int = None, - transformer_depth_output: list = None, - use_temporal_resblock: bool = False, - use_temporal_attention: bool = False, - time_context_dim: int = None, - extra_ff_mix_layer: bool = False, - use_spatial_context: bool = False, - merge_strategy: any = None, - merge_factor: float = 0.0, - video_kernel_size: int = None, - disable_temporal_crossattention: bool = False, - max_ddpm_temb_period: int = 10000, - device: th.device = None, - operations: any = oai_ops, - ): - """#### Initialize the UNetModel1 class. - - #### Args: - - `image_size` (int): The size of the input image. - - `in_channels` (int): The number of input channels. - - `model_channels` (int): The number of model channels. - - `out_channels` (int): The number of output channels. - - `num_res_blocks` (list): The number of residual blocks. - - `dropout` (float, optional): The dropout rate. Defaults to 0. - - `channel_mult` (tuple, optional): The channel multiplier. Defaults to (1, 2, 4, 8). - - `conv_resample` (bool, optional): Whether to use convolutional resampling. Defaults to True. - - `dims` (int, optional): The number of dimensions. Defaults to 2. - - `num_classes` (int, optional): The number of classes. Defaults to None. - - `use_checkpoint` (bool, optional): Whether to use checkpointing. Defaults to False. - - `dtype` (torch.dtype, optional): The data type. Defaults to torch.float32. - - `num_heads` (int, optional): The number of heads. Defaults to -1. - - `num_head_channels` (int, optional): The number of head channels. Defaults to -1. - - `num_heads_upsample` (int, optional): The number of heads for upsampling. Defaults to -1. - - `use_scale_shift_norm` (bool, optional): Whether to use scale-shift normalization. Defaults to False. - - `resblock_updown` (bool, optional): Whether to use residual blocks for up/down sampling. Defaults to False. - - `use_new_attention_order` (bool, optional): Whether to use a new attention order. Defaults to False. - - `use_spatial_transformer` (bool, optional): Whether to use a spatial transformer. Defaults to False. - - `transformer_depth` (int, optional): The depth of the transformer. Defaults to 1. - - `context_dim` (int, optional): The context dimension. Defaults to None. - - `n_embed` (int, optional): The number of embeddings. Defaults to None. - - `legacy` (bool, optional): Whether to use legacy mode. Defaults to True. - - `disable_self_attentions` (list, optional): The list of self-attentions to disable. Defaults to None. - - `num_attention_blocks` (list, optional): The number of attention blocks. Defaults to None. - - `disable_middle_self_attn` (bool, optional): Whether to disable middle self-attention. Defaults to False. - - `use_linear_in_transformer` (bool, optional): Whether to use linear in transformer. Defaults to False. - - `adm_in_channels` (int, optional): The number of ADM input channels. Defaults to None. - - `transformer_depth_middle` (int, optional): The depth of the middle transformer. Defaults to None. - - `transformer_depth_output` (list, optional): The depth of the output transformer. Defaults to None. - - `use_temporal_resblock` (bool, optional): Whether to use temporal residual blocks. Defaults to False. - - `use_temporal_attention` (bool, optional): Whether to use temporal attention. Defaults to False. - - `time_context_dim` (int, optional): The time context dimension. Defaults to None. - - `extra_ff_mix_layer` (bool, optional): Whether to use an extra feed-forward mix layer. Defaults to False. - - `use_spatial_context` (bool, optional): Whether to use spatial context. Defaults to False. - - `merge_strategy` (any, optional): The merge strategy. Defaults to None. - - `merge_factor` (float, optional): The merge factor. Defaults to 0.0. - - `video_kernel_size` (int, optional): The video kernel size. Defaults to None. - - `disable_temporal_crossattention` (bool, optional): Whether to disable temporal cross-attention. Defaults to False. - - `max_ddpm_temb_period` (int, optional): The maximum DDPM temporal embedding period. Defaults to 10000. - - `device` (torch.device, optional): The device to use. Defaults to None. - - `operations` (any, optional): The operations to use. Defaults to oai_ops. - """ - super().__init__() - - if context_dim is not None: - self.context_dim = context_dim - - if num_heads_upsample == -1: - num_heads_upsample = num_heads - if num_head_channels == -1: - assert num_heads != -1, "Either num_heads or num_head_channels has to be set" - - self.in_channels = in_channels - self.model_channels = model_channels - self.out_channels = out_channels - self.num_res_blocks = num_res_blocks - - transformer_depth = transformer_depth[:] - transformer_depth_output = transformer_depth_output[:] - - self.dropout = dropout - self.channel_mult = channel_mult - self.conv_resample = conv_resample - self.num_classes = num_classes - self.use_checkpoint = use_checkpoint - self.dtype = dtype - self.num_heads = num_heads - self.num_head_channels = num_head_channels - self.num_heads_upsample = num_heads_upsample - self.use_temporal_resblocks = use_temporal_resblock - self.predict_codebook_ids = n_embed is not None - - self.default_num_video_frames = None - - time_embed_dim = model_channels * 4 - self.time_embed = nn.Sequential( - operations.Linear( - model_channels, time_embed_dim, dtype=self.dtype, device=device - ), - nn.SiLU(), - operations.Linear( - time_embed_dim, time_embed_dim, dtype=self.dtype, device=device - ), - ) - - self.input_blocks = nn.ModuleList( - [ - sampling.TimestepEmbedSequential1( - operations.conv_nd( - dims, - in_channels, - model_channels, - 3, - padding=1, - dtype=self.dtype, - device=device, - ) - ) - ] - ) - self._feature_size = model_channels - input_block_chans = [model_channels] - ch = model_channels - ds = 1 - - def get_attention_layer( - ch: int, - num_heads: int, - dim_head: int, - depth: int = 1, - context_dim: int = None, - use_checkpoint: bool = False, - disable_self_attn: bool = False, - ) -> transformer.SpatialTransformer: - """#### Get an attention layer. - - #### Args: - - `ch` (int): The number of channels. - - `num_heads` (int): The number of heads. - - `dim_head` (int): The dimension of each head. - - `depth` (int, optional): The depth of the transformer. Defaults to 1. - - `context_dim` (int, optional): The context dimension. Defaults to None. - - `use_checkpoint` (bool, optional): Whether to use checkpointing. Defaults to False. - - `disable_self_attn` (bool, optional): Whether to disable self-attention. Defaults to False. - - #### Returns: - - `transformer.SpatialTransformer`: The attention layer. - """ - return transformer.SpatialTransformer( - ch, - num_heads, - dim_head, - depth=depth, - context_dim=context_dim, - disable_self_attn=disable_self_attn, - use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, - dtype=self.dtype, - device=device, - operations=operations, - ) - - def get_resblock( - merge_factor: float, - merge_strategy: any, - video_kernel_size: int, - ch: int, - time_embed_dim: int, - dropout: float, - out_channels: int, - dims: int, - use_checkpoint: bool, - use_scale_shift_norm: bool, - down: bool = False, - up: bool = False, - dtype: th.dtype = None, - device: th.device = None, - operations: any = oai_ops, - ) -> ResBlock.ResBlock1: - """#### Get a residual block. - - #### Args: - - `merge_factor` (float): The merge factor. - - `merge_strategy` (any): The merge strategy. - - `video_kernel_size` (int): The video kernel size. - - `ch` (int): The number of channels. - - `time_embed_dim` (int): The time embedding dimension. - - `dropout` (float): The dropout rate. - - `out_channels` (int): The number of output channels. - - `dims` (int): The number of dimensions. - - `use_checkpoint` (bool): Whether to use checkpointing. - - `use_scale_shift_norm` (bool): Whether to use scale-shift normalization. - - `down` (bool, optional): Whether to use downsampling. Defaults to False. - - `up` (bool, optional): Whether to use upsampling. Defaults to False. - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - - `device` (torch.device, optional): The device. Defaults to None. - - `operations` (any, optional): The operations to use. Defaults to oai_ops. - - #### Returns: - - `ResBlock.ResBlock1`: The residual block. - """ - return ResBlock.ResBlock1( - channels=ch, - emb_channels=time_embed_dim, - dropout=dropout, - out_channels=out_channels, - use_checkpoint=use_checkpoint, - dims=dims, - use_scale_shift_norm=use_scale_shift_norm, - down=down, - up=up, - dtype=dtype, - device=device, - operations=operations, - ) - - self.double_blocks = nn.ModuleList() - for level, mult in enumerate(channel_mult): - for nr in range(self.num_res_blocks[level]): - layers = [ - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=mult * model_channels, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations, - ) - ] - ch = mult * model_channels - num_transformers = transformer_depth.pop(0) - if num_transformers > 0: - dim_head = ch // num_heads - disabled_sa = False - - if ( - not util.exists(num_attention_blocks) - or nr < num_attention_blocks[level] - ): - layers.append( - get_attention_layer( - ch, - num_heads, - dim_head, - depth=num_transformers, - context_dim=context_dim, - disable_self_attn=disabled_sa, - use_checkpoint=use_checkpoint, - ) - ) - self.input_blocks.append(sampling.TimestepEmbedSequential1(*layers)) - self._feature_size += ch - input_block_chans.append(ch) - if level != len(channel_mult) - 1: - out_ch = ch - self.input_blocks.append( - sampling.TimestepEmbedSequential1( - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - down=True, - dtype=self.dtype, - device=device, - operations=operations, - ) - if resblock_updown - else ResBlock.Downsample1( - ch, - conv_resample, - dims=dims, - out_channels=out_ch, - dtype=self.dtype, - device=device, - operations=operations, - ) - ) - ) - ch = out_ch - input_block_chans.append(ch) - ds *= 2 - self._feature_size += ch - - dim_head = ch // num_heads - mid_block = [ - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=None, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations, - ) - ] - - self.middle_block = None - if transformer_depth_middle >= -1: - if transformer_depth_middle >= 0: - mid_block += [ - get_attention_layer( # always uses a self-attn - ch, - num_heads, - dim_head, - depth=transformer_depth_middle, - context_dim=context_dim, - disable_self_attn=disable_middle_self_attn, - use_checkpoint=use_checkpoint, - ), - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=None, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations, - ), - ] - self.middle_block = sampling.TimestepEmbedSequential1(*mid_block) - self._feature_size += ch - - self.output_blocks = nn.ModuleList([]) - for level, mult in list(enumerate(channel_mult))[::-1]: - for i in range(self.num_res_blocks[level] + 1): - ich = input_block_chans.pop() - layers = [ - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch + ich, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=model_channels * mult, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations, - ) - ] - ch = model_channels * mult - num_transformers = transformer_depth_output.pop() - if num_transformers > 0: - dim_head = ch // num_heads - disabled_sa = False - - if ( - not util.exists(num_attention_blocks) - or i < num_attention_blocks[level] - ): - layers.append( - get_attention_layer( - ch, - num_heads, - dim_head, - depth=num_transformers, - context_dim=context_dim, - disable_self_attn=disabled_sa, - use_checkpoint=use_checkpoint, - ) - ) - if level and i == self.num_res_blocks[level]: - out_ch = ch - layers.append( - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - up=True, - dtype=self.dtype, - device=device, - operations=operations, - ) - if resblock_updown - else ResBlock.Upsample1( - ch, - conv_resample, - dims=dims, - out_channels=out_ch, - dtype=self.dtype, - device=device, - operations=operations, - ) - ) - ds //= 2 - self.output_blocks.append(sampling.TimestepEmbedSequential1(*layers)) - self._feature_size += ch - - self.out = nn.Sequential( - operations.GroupNorm(32, ch, dtype=self.dtype, device=device), - nn.SiLU(), - util.zero_module( - operations.conv_nd( - dims, - model_channels, - out_channels, - 3, - padding=1, - dtype=self.dtype, - device=device, - ) - ), - ) - - def forward( - self, - x: torch.Tensor, - timesteps: Optional[torch.Tensor] = None, - context: Optional[torch.Tensor] = None, - y: Optional[torch.Tensor] = None, - control: Optional[torch.Tensor] = None, - transformer_options: Dict[str, Any] = {}, - **kwargs: Any, - ) -> torch.Tensor: - """#### Forward pass of the UNet model. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `timesteps` (Optional[torch.Tensor], optional): The timesteps tensor. Defaults to None. - - `context` (Optional[torch.Tensor], optional): The context tensor. Defaults to None. - - `y` (Optional[torch.Tensor], optional): The class labels tensor. Defaults to None. - - `control` (Optional[torch.Tensor], optional): The control tensor. Defaults to None. - - `transformer_options` (Dict[str, Any], optional): Options for the transformer. Defaults to {}. - - `**kwargs` (Any): Additional keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - transformer_options["original_shape"] = list(x.shape) - transformer_options["transformer_index"] = 0 - transformer_patches = transformer_options.get("patches", {}) - - num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) - image_only_indicator = kwargs.get("image_only_indicator", None) - time_context = kwargs.get("time_context", None) - - assert (y is not None) == ( - self.num_classes is not None - ), "must specify y if and only if the model is class-conditional" - hs = [] - t_emb = sampling_util.timestep_embedding( - timesteps, self.model_channels - ).to(x.dtype) - emb = self.time_embed(t_emb) - h = x - for id, module in enumerate(self.input_blocks): - transformer_options["block"] = ("input", id) - h = ResBlock.forward_timestep_embed1( - module, - h, - emb, - context, - transformer_options, - time_context=time_context, - num_video_frames=num_video_frames, - image_only_indicator=image_only_indicator, - ) - h = apply_control1(h, control, "input") - hs.append(h) - - transformer_options["block"] = ("middle", 0) - if self.middle_block is not None: - h = ResBlock.forward_timestep_embed1( - self.middle_block, - h, - emb, - context, - transformer_options, - time_context=time_context, - num_video_frames=num_video_frames, - image_only_indicator=image_only_indicator, - ) - h = apply_control1(h, control, "middle") - - for id, module in enumerate(self.output_blocks): - transformer_options["block"] = ("output", id) - hsp = hs.pop() - hsp = apply_control1(hsp, control, "output") - - h = torch.cat([h, hsp], dim=1) - del hsp - if len(hs) > 0: - output_shape = hs[-1].shape - else: - output_shape = None - h = ResBlock.forward_timestep_embed1( - module, - h, - emb, - context, - transformer_options, - output_shape, - time_context=time_context, - num_video_frames=num_video_frames, - image_only_indicator=image_only_indicator, - ) - h = h.type(x.dtype) - return self.out(h) - - -def detect_unet_config(state_dict: Dict[str, torch.Tensor], key_prefix: str) -> Dict[str, Any]: - """#### Detect the UNet configuration from a state dictionary. - - #### Args: - - `state_dict` (Dict[str, torch.Tensor]): The state dictionary. - - `key_prefix` (str): The key prefix. - - #### Returns: - - `Dict[str, Any]`: The detected UNet configuration. - """ - state_dict_keys = list(state_dict.keys()) - - if ( - "{}joint_blocks.0.context_block.attn.qkv.weight".format(key_prefix) - in state_dict_keys - ): # mmdit model - unet_config = {} - unet_config["in_channels"] = state_dict[ - "{}x_embedder.proj.weight".format(key_prefix) - ].shape[1] - patch_size = state_dict["{}x_embedder.proj.weight".format(key_prefix)].shape[2] - unet_config["patch_size"] = patch_size - final_layer = "{}final_layer.linear.weight".format(key_prefix) - if final_layer in state_dict: - unet_config["out_channels"] = state_dict[final_layer].shape[0] // ( - patch_size * patch_size - ) - - unet_config["depth"] = ( - state_dict["{}x_embedder.proj.weight".format(key_prefix)].shape[0] // 64 - ) - unet_config["input_size"] = None - y_key = "{}y_embedder.mlp.0.weight".format(key_prefix) - if y_key in state_dict_keys: - unet_config["adm_in_channels"] = state_dict[y_key].shape[1] - - context_key = "{}context_embedder.weight".format(key_prefix) - if context_key in state_dict_keys: - in_features = state_dict[context_key].shape[1] - out_features = state_dict[context_key].shape[0] - unet_config["context_embedder_config"] = { - "target": "torch.nn.Linear", - "params": {"in_features": in_features, "out_features": out_features}, - } - num_patches_key = "{}pos_embed".format(key_prefix) - if num_patches_key in state_dict_keys: - num_patches = state_dict[num_patches_key].shape[1] - unet_config["num_patches"] = num_patches - unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches)) - - rms_qk = "{}joint_blocks.0.context_block.attn.ln_q.weight".format(key_prefix) - if rms_qk in state_dict_keys: - unet_config["qk_norm"] = "rms" - - unet_config["pos_embed_scaling_factor"] = None # unused for inference - context_processor = "{}context_processor.layers.0.attn.qkv.weight".format( - key_prefix - ) - if context_processor in state_dict_keys: - unet_config["context_processor_layers"] = transformer.count_blocks( - state_dict_keys, - "{}context_processor.layers.".format(key_prefix) + "{}.", - ) - return unet_config - - if "{}clf.1.weight".format(key_prefix) in state_dict_keys: # stable cascade - unet_config = {} - text_mapper_name = "{}clip_txt_mapper.weight".format(key_prefix) - if text_mapper_name in state_dict_keys: - unet_config["stable_cascade_stage"] = "c" - w = state_dict[text_mapper_name] - if w.shape[0] == 1536: # stage c lite - unet_config["c_cond"] = 1536 - unet_config["c_hidden"] = [1536, 1536] - unet_config["nhead"] = [24, 24] - unet_config["blocks"] = [[4, 12], [12, 4]] - elif w.shape[0] == 2048: # stage c full - unet_config["c_cond"] = 2048 - elif "{}clip_mapper.weight".format(key_prefix) in state_dict_keys: - unet_config["stable_cascade_stage"] = "b" - w = state_dict["{}down_blocks.1.0.channelwise.0.weight".format(key_prefix)] - if w.shape[-1] == 640: - unet_config["c_hidden"] = [320, 640, 1280, 1280] - unet_config["nhead"] = [-1, -1, 20, 20] - unet_config["blocks"] = [[2, 6, 28, 6], [6, 28, 6, 2]] - unet_config["block_repeat"] = [[1, 1, 1, 1], [3, 3, 2, 2]] - elif w.shape[-1] == 576: # stage b lite - unet_config["c_hidden"] = [320, 576, 1152, 1152] - unet_config["nhead"] = [-1, 9, 18, 18] - unet_config["blocks"] = [[2, 4, 14, 4], [4, 14, 4, 2]] - unet_config["block_repeat"] = [[1, 1, 1, 1], [2, 2, 2, 2]] - return unet_config - - if ( - "{}transformer.rotary_pos_emb.inv_freq".format(key_prefix) in state_dict_keys - ): # stable audio dit - unet_config = {} - unet_config["audio_model"] = "dit1.0" - return unet_config - - if ( - "{}double_layers.0.attn.w1q.weight".format(key_prefix) in state_dict_keys - ): # aura flow dit - unet_config = {} - unet_config["max_seq"] = state_dict[ - "{}positional_encoding".format(key_prefix) - ].shape[1] - unet_config["cond_seq_dim"] = state_dict[ - "{}cond_seq_linear.weight".format(key_prefix) - ].shape[1] - double_layers = transformer.count_blocks( - state_dict_keys, "{}double_layers.".format(key_prefix) + "{}." - ) - single_layers = transformer.count_blocks( - state_dict_keys, "{}single_layers.".format(key_prefix) + "{}." - ) - unet_config["n_double_layers"] = double_layers - unet_config["n_layers"] = double_layers + single_layers - return unet_config - - if "{}mlp_t5.0.weight".format(key_prefix) in state_dict_keys: # Hunyuan DiT - unet_config = {} - unet_config["image_model"] = "hydit" - unet_config["depth"] = transformer.count_blocks( - state_dict_keys, "{}blocks.".format(key_prefix) + "{}." - ) - unet_config["hidden_size"] = state_dict[ - "{}x_embedder.proj.weight".format(key_prefix) - ].shape[0] - if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: # DiT-g/2 - unet_config["mlp_ratio"] = 4.3637 - if state_dict["{}extra_embedder.0.weight".format(key_prefix)].shape[1] == 3968: - unet_config["size_cond"] = True - unet_config["use_style_cond"] = True - unet_config["image_model"] = "hydit1" - return unet_config - - if ( - "{}double_blocks.0.img_attn.norm.key_norm.scale".format(key_prefix) - in state_dict_keys - ): # Flux - dit_config = {} - dit_config["image_model"] = "flux" - dit_config["in_channels"] = 16 - dit_config["vec_in_dim"] = 768 - dit_config["context_in_dim"] = 4096 - dit_config["hidden_size"] = 3072 - dit_config["mlp_ratio"] = 4.0 - dit_config["num_heads"] = 24 - dit_config["depth"] = transformer.count_blocks( - state_dict_keys, "{}double_blocks.".format(key_prefix) + "{}." - ) - dit_config["depth_single_blocks"] = transformer.count_blocks( - state_dict_keys, "{}single_blocks.".format(key_prefix) + "{}." - ) - dit_config["axes_dim"] = [16, 56, 56] - dit_config["theta"] = 10000 - dit_config["qkv_bias"] = True - dit_config["guidance_embed"] = ( - "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys - ) - return dit_config - - if "{}input_blocks.0.0.weight".format(key_prefix) not in state_dict_keys: - return None - - unet_config = { - "use_checkpoint": False, - "image_size": 32, - "use_spatial_transformer": True, - "legacy": False, - } - - y_input = "{}label_emb.0.0.weight".format(key_prefix) - if y_input in state_dict_keys: - unet_config["num_classes"] = "sequential" - unet_config["adm_in_channels"] = state_dict[y_input].shape[1] - else: - unet_config["adm_in_channels"] = None - - model_channels = state_dict["{}input_blocks.0.0.weight".format(key_prefix)].shape[0] - in_channels = state_dict["{}input_blocks.0.0.weight".format(key_prefix)].shape[1] - - out_key = "{}out.2.weight".format(key_prefix) - if out_key in state_dict: - out_channels = state_dict[out_key].shape[0] - else: - out_channels = 4 - - num_res_blocks = [] - channel_mult = [] - transformer_depth = [] - transformer_depth_output = [] - context_dim = None - use_linear_in_transformer = False - - video_model = False - video_model_cross = False - - current_res = 1 - count = 0 - - last_res_blocks = 0 - last_channel_mult = 0 - - input_block_count = transformer.count_blocks( - state_dict_keys, "{}input_blocks".format(key_prefix) + ".{}." - ) - for count in range(input_block_count): - prefix = "{}input_blocks.{}.".format(key_prefix, count) - prefix_output = "{}output_blocks.{}.".format( - key_prefix, input_block_count - count - 1 - ) - - block_keys = sorted( - list(filter(lambda a: a.startswith(prefix), state_dict_keys)) - ) - if len(block_keys) == 0: - break - - block_keys_output = sorted( - list(filter(lambda a: a.startswith(prefix_output), state_dict_keys)) - ) - - if "{}0.op.weight".format(prefix) in block_keys: # new layer - num_res_blocks.append(last_res_blocks) - channel_mult.append(last_channel_mult) - - current_res *= 2 - last_res_blocks = 0 - last_channel_mult = 0 - out = transformer.calculate_transformer_depth( - prefix_output, state_dict_keys, state_dict - ) - if out is not None: - transformer_depth_output.append(out[0]) - else: - transformer_depth_output.append(0) - else: - res_block_prefix = "{}0.in_layers.0.weight".format(prefix) - if res_block_prefix in block_keys: - last_res_blocks += 1 - last_channel_mult = ( - state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] - // model_channels - ) - - out = transformer.calculate_transformer_depth(prefix, state_dict_keys, state_dict) - if out is not None: - transformer_depth.append(out[0]) - if context_dim is None: - context_dim = out[1] - use_linear_in_transformer = out[2] - out[3] - else: - transformer_depth.append(0) - - res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output) - if res_block_prefix in block_keys_output: - out = transformer.calculate_transformer_depth( - prefix_output, state_dict_keys, state_dict - ) - if out is not None: - transformer_depth_output.append(out[0]) - else: - transformer_depth_output.append(0) - - num_res_blocks.append(last_res_blocks) - channel_mult.append(last_channel_mult) - if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys: - transformer_depth_middle = transformer.count_blocks( - state_dict_keys, - "{}middle_block.1.transformer_blocks.".format(key_prefix) + "{}", - ) - elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys: - transformer_depth_middle = -1 - else: - transformer_depth_middle = -2 - - unet_config["in_channels"] = in_channels - unet_config["out_channels"] = out_channels - unet_config["model_channels"] = model_channels - unet_config["num_res_blocks"] = num_res_blocks - unet_config["transformer_depth"] = transformer_depth - unet_config["transformer_depth_output"] = transformer_depth_output - unet_config["channel_mult"] = channel_mult - unet_config["transformer_depth_middle"] = transformer_depth_middle - unet_config["use_linear_in_transformer"] = use_linear_in_transformer - unet_config["context_dim"] = context_dim - - if video_model: - unet_config["extra_ff_mix_layer"] = True - unet_config["use_spatial_context"] = True - unet_config["merge_strategy"] = "learned_with_images" - unet_config["merge_factor"] = 0.0 - unet_config["video_kernel_size"] = [3, 1, 1] - unet_config["use_temporal_resblock"] = True - unet_config["use_temporal_attention"] = True - unet_config["disable_temporal_crossattention"] = not video_model_cross - else: - unet_config["use_temporal_resblock"] = False - unet_config["use_temporal_attention"] = False - - return unet_config - - -def model_config_from_unet_config(unet_config: Dict[str, Any], state_dict: Optional[Dict[str, torch.Tensor]] = None) -> Any: - """#### Get the model configuration from a UNet configuration. - - #### Args: - - `unet_config` (Dict[str, Any]): The UNet configuration. - - `state_dict` (Optional[Dict[str, torch.Tensor]], optional): The state dictionary. Defaults to None. - - #### Returns: - - `Any`: The model configuration. - """ - from modules.SD15 import SD15 - - for model_config in SD15.models: - if model_config.matches(unet_config, state_dict): - return model_config(unet_config) - - logging.error("no match {}".format(unet_config)) - return None - - -def model_config_from_unet(state_dict: Dict[str, torch.Tensor], unet_key_prefix: str, use_base_if_no_match: bool = False) -> Any: - """#### Get the model configuration from a UNet state dictionary. - - #### Args: - - `state_dict` (Dict[str, torch.Tensor]): The state dictionary. - - `unet_key_prefix` (str): The UNet key prefix. - - `use_base_if_no_match` (bool, optional): Whether to use the base configuration if no match is found. Defaults to False. - - #### Returns: - - `Any`: The model configuration. - """ - unet_config = detect_unet_config(state_dict, unet_key_prefix) - if unet_config is None: - return None - model_config = model_config_from_unet_config(unet_config, state_dict) - return model_config - - -def unet_dtype1( - device: Optional[torch.device] = None, - model_params: int = 0, - supported_dtypes: List[torch.dtype] = [torch.float16, torch.bfloat16, torch.float32], -) -> torch.dtype: - """#### Get the dtype for the UNet model. - - #### Args: - - `device` (Optional[torch.device], optional): The device. Defaults to None. - - `model_params` (int, optional): The model parameters. Defaults to 0. - - `supported_dtypes` (List[torch.dtype], optional): The supported dtypes. Defaults to [torch.float16, torch.bfloat16, torch.float32]. - - #### Returns: - - `torch.dtype`: The dtype for the UNet model. - """ +import logging +import math +from typing import Any, Dict, List, Optional +import torch.nn as nn +import torch as th +import torch + +from modules.Utilities import util +from modules.AutoEncoders import ResBlock +from modules.NeuralNetwork import transformer +from modules.cond import cast +from modules.sample import sampling, sampling_util + +UNET_MAP_ATTENTIONS = { + "proj_in.weight", + "proj_in.bias", + "proj_out.weight", + "proj_out.bias", + "norm.weight", + "norm.bias", +} + +TRANSFORMER_BLOCKS = { + "norm1.weight", + "norm1.bias", + "norm2.weight", + "norm2.bias", + "norm3.weight", + "norm3.bias", + "attn1.to_q.weight", + "attn1.to_k.weight", + "attn1.to_v.weight", + "attn1.to_out.0.weight", + "attn1.to_out.0.bias", + "attn2.to_q.weight", + "attn2.to_k.weight", + "attn2.to_v.weight", + "attn2.to_out.0.weight", + "attn2.to_out.0.bias", + "ff.net.0.proj.weight", + "ff.net.0.proj.bias", + "ff.net.2.weight", + "ff.net.2.bias", +} + +UNET_MAP_RESNET = { + "in_layers.2.weight": "conv1.weight", + "in_layers.2.bias": "conv1.bias", + "emb_layers.1.weight": "time_emb_proj.weight", + "emb_layers.1.bias": "time_emb_proj.bias", + "out_layers.3.weight": "conv2.weight", + "out_layers.3.bias": "conv2.bias", + "skip_connection.weight": "conv_shortcut.weight", + "skip_connection.bias": "conv_shortcut.bias", + "in_layers.0.weight": "norm1.weight", + "in_layers.0.bias": "norm1.bias", + "out_layers.0.weight": "norm2.weight", + "out_layers.0.bias": "norm2.bias", +} + +UNET_MAP_BASIC = { + ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), + ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias"), +} + +# taken from https://github.com/TencentARC/T2I-Adapter + + +def unet_to_diffusers(unet_config: dict) -> dict: + """#### Convert a UNet configuration to a diffusers configuration. + + #### Args: + - `unet_config` (dict): The UNet configuration. + + #### Returns: + - `dict`: The diffusers configuration. + """ + if "num_res_blocks" not in unet_config: + return {} + num_res_blocks = unet_config["num_res_blocks"] + channel_mult = unet_config["channel_mult"] + transformer_depth = unet_config["transformer_depth"][:] + transformer_depth_output = unet_config["transformer_depth_output"][:] + num_blocks = len(channel_mult) + + transformers_mid = unet_config.get("transformer_depth_middle", None) + + diffusers_unet_map = {} + for x in range(num_blocks): + n = 1 + (num_res_blocks[x] + 1) * x + for i in range(num_res_blocks[x]): + for b in UNET_MAP_RESNET: + diffusers_unet_map[ + "down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b]) + ] = "input_blocks.{}.0.{}".format(n, b) + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map[ + "down_blocks.{}.attentions.{}.{}".format(x, i, b) + ] = "input_blocks.{}.1.{}".format(n, b) + for t in range(num_transformers): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map[ + "down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format( + x, i, t, b + ) + ] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) + n += 1 + for k in ["weight", "bias"]: + diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = ( + "input_blocks.{}.0.op.{}".format(n, k) + ) + + i = 0 + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = ( + "middle_block.1.{}".format(b) + ) + for t in range(transformers_mid): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map[ + "mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b) + ] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) + + for i, n in enumerate([0, 2]): + for b in UNET_MAP_RESNET: + diffusers_unet_map[ + "mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b]) + ] = "middle_block.{}.{}".format(n, b) + + num_res_blocks = list(reversed(num_res_blocks)) + for x in range(num_blocks): + n = (num_res_blocks[x] + 1) * x + length = num_res_blocks[x] + 1 + for i in range(length): + c = 0 + for b in UNET_MAP_RESNET: + diffusers_unet_map[ + "up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b]) + ] = "output_blocks.{}.0.{}".format(n, b) + c += 1 + num_transformers = transformer_depth_output.pop() + if num_transformers > 0: + c += 1 + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map[ + "up_blocks.{}.attentions.{}.{}".format(x, i, b) + ] = "output_blocks.{}.1.{}".format(n, b) + for t in range(num_transformers): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map[ + "up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format( + x, i, t, b + ) + ] = "output_blocks.{}.1.transformer_blocks.{}.{}".format( + n, t, b + ) + if i == length - 1: + for k in ["weight", "bias"]: + diffusers_unet_map[ + "up_blocks.{}.upsamplers.0.conv.{}".format(x, k) + ] = "output_blocks.{}.{}.conv.{}".format(n, c, k) + n += 1 + + for k in UNET_MAP_BASIC: + diffusers_unet_map[k[1]] = k[0] + + return diffusers_unet_map + + +def apply_control1(h: th.Tensor, control: any, name: str) -> th.Tensor: + """#### Apply control to a tensor. + + #### Args: + - `h` (torch.Tensor): The input tensor. + - `control` (any): The control to apply. + - `name` (str): The name of the control. + + #### Returns: + - `torch.Tensor`: The controlled tensor. + """ + return h + + +oai_ops = cast.disable_weight_init + + +class UNetModel1(nn.Module): + """#### UNet Model class.""" + + def __init__( + self, + image_size: int, + in_channels: int, + model_channels: int, + out_channels: int, + num_res_blocks: list, + dropout: float = 0, + channel_mult: tuple = (1, 2, 4, 8), + conv_resample: bool = True, + dims: int = 2, + num_classes: int = None, + use_checkpoint: bool = False, + dtype: th.dtype = th.float32, + num_heads: int = -1, + num_head_channels: int = -1, + num_heads_upsample: int = -1, + use_scale_shift_norm: bool = False, + resblock_updown: bool = False, + use_new_attention_order: bool = False, + use_spatial_transformer: bool = False, # custom transformer support + transformer_depth: int = 1, # custom transformer support + context_dim: int = None, # custom transformer support + n_embed: int = None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy: bool = True, + disable_self_attentions: list = None, + num_attention_blocks: list = None, + disable_middle_self_attn: bool = False, + use_linear_in_transformer: bool = False, + adm_in_channels: int = None, + transformer_depth_middle: int = None, + transformer_depth_output: list = None, + use_temporal_resblock: bool = False, + use_temporal_attention: bool = False, + time_context_dim: int = None, + extra_ff_mix_layer: bool = False, + use_spatial_context: bool = False, + merge_strategy: any = None, + merge_factor: float = 0.0, + video_kernel_size: int = None, + disable_temporal_crossattention: bool = False, + max_ddpm_temb_period: int = 10000, + device: th.device = None, + operations: any = oai_ops, + ): + """#### Initialize the UNetModel1 class. + + #### Args: + - `image_size` (int): The size of the input image. + - `in_channels` (int): The number of input channels. + - `model_channels` (int): The number of model channels. + - `out_channels` (int): The number of output channels. + - `num_res_blocks` (list): The number of residual blocks. + - `dropout` (float, optional): The dropout rate. Defaults to 0. + - `channel_mult` (tuple, optional): The channel multiplier. Defaults to (1, 2, 4, 8). + - `conv_resample` (bool, optional): Whether to use convolutional resampling. Defaults to True. + - `dims` (int, optional): The number of dimensions. Defaults to 2. + - `num_classes` (int, optional): The number of classes. Defaults to None. + - `use_checkpoint` (bool, optional): Whether to use checkpointing. Defaults to False. + - `dtype` (torch.dtype, optional): The data type. Defaults to torch.float32. + - `num_heads` (int, optional): The number of heads. Defaults to -1. + - `num_head_channels` (int, optional): The number of head channels. Defaults to -1. + - `num_heads_upsample` (int, optional): The number of heads for upsampling. Defaults to -1. + - `use_scale_shift_norm` (bool, optional): Whether to use scale-shift normalization. Defaults to False. + - `resblock_updown` (bool, optional): Whether to use residual blocks for up/down sampling. Defaults to False. + - `use_new_attention_order` (bool, optional): Whether to use a new attention order. Defaults to False. + - `use_spatial_transformer` (bool, optional): Whether to use a spatial transformer. Defaults to False. + - `transformer_depth` (int, optional): The depth of the transformer. Defaults to 1. + - `context_dim` (int, optional): The context dimension. Defaults to None. + - `n_embed` (int, optional): The number of embeddings. Defaults to None. + - `legacy` (bool, optional): Whether to use legacy mode. Defaults to True. + - `disable_self_attentions` (list, optional): The list of self-attentions to disable. Defaults to None. + - `num_attention_blocks` (list, optional): The number of attention blocks. Defaults to None. + - `disable_middle_self_attn` (bool, optional): Whether to disable middle self-attention. Defaults to False. + - `use_linear_in_transformer` (bool, optional): Whether to use linear in transformer. Defaults to False. + - `adm_in_channels` (int, optional): The number of ADM input channels. Defaults to None. + - `transformer_depth_middle` (int, optional): The depth of the middle transformer. Defaults to None. + - `transformer_depth_output` (list, optional): The depth of the output transformer. Defaults to None. + - `use_temporal_resblock` (bool, optional): Whether to use temporal residual blocks. Defaults to False. + - `use_temporal_attention` (bool, optional): Whether to use temporal attention. Defaults to False. + - `time_context_dim` (int, optional): The time context dimension. Defaults to None. + - `extra_ff_mix_layer` (bool, optional): Whether to use an extra feed-forward mix layer. Defaults to False. + - `use_spatial_context` (bool, optional): Whether to use spatial context. Defaults to False. + - `merge_strategy` (any, optional): The merge strategy. Defaults to None. + - `merge_factor` (float, optional): The merge factor. Defaults to 0.0. + - `video_kernel_size` (int, optional): The video kernel size. Defaults to None. + - `disable_temporal_crossattention` (bool, optional): Whether to disable temporal cross-attention. Defaults to False. + - `max_ddpm_temb_period` (int, optional): The maximum DDPM temporal embedding period. Defaults to 10000. + - `device` (torch.device, optional): The device to use. Defaults to None. + - `operations` (any, optional): The operations to use. Defaults to oai_ops. + """ + super().__init__() + + if context_dim is not None: + self.context_dim = context_dim + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + if num_head_channels == -1: + assert num_heads != -1, "Either num_heads or num_head_channels has to be set" + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + + transformer_depth = transformer_depth[:] + transformer_depth_output = transformer_depth_output[:] + + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = dtype + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.use_temporal_resblocks = use_temporal_resblock + self.predict_codebook_ids = n_embed is not None + + self.default_num_video_frames = None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + operations.Linear( + model_channels, time_embed_dim, dtype=self.dtype, device=device + ), + nn.SiLU(), + operations.Linear( + time_embed_dim, time_embed_dim, dtype=self.dtype, device=device + ), + ) + + self.input_blocks = nn.ModuleList( + [ + sampling.TimestepEmbedSequential1( + operations.conv_nd( + dims, + in_channels, + model_channels, + 3, + padding=1, + dtype=self.dtype, + device=device, + ) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + + def get_attention_layer( + ch: int, + num_heads: int, + dim_head: int, + depth: int = 1, + context_dim: int = None, + use_checkpoint: bool = False, + disable_self_attn: bool = False, + ) -> transformer.SpatialTransformer: + """#### Get an attention layer. + + #### Args: + - `ch` (int): The number of channels. + - `num_heads` (int): The number of heads. + - `dim_head` (int): The dimension of each head. + - `depth` (int, optional): The depth of the transformer. Defaults to 1. + - `context_dim` (int, optional): The context dimension. Defaults to None. + - `use_checkpoint` (bool, optional): Whether to use checkpointing. Defaults to False. + - `disable_self_attn` (bool, optional): Whether to disable self-attention. Defaults to False. + + #### Returns: + - `transformer.SpatialTransformer`: The attention layer. + """ + return transformer.SpatialTransformer( + ch, + num_heads, + dim_head, + depth=depth, + context_dim=context_dim, + disable_self_attn=disable_self_attn, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + dtype=self.dtype, + device=device, + operations=operations, + ) + + def get_resblock( + merge_factor: float, + merge_strategy: any, + video_kernel_size: int, + ch: int, + time_embed_dim: int, + dropout: float, + out_channels: int, + dims: int, + use_checkpoint: bool, + use_scale_shift_norm: bool, + down: bool = False, + up: bool = False, + dtype: th.dtype = None, + device: th.device = None, + operations: any = oai_ops, + ) -> ResBlock.ResBlock1: + """#### Get a residual block. + + #### Args: + - `merge_factor` (float): The merge factor. + - `merge_strategy` (any): The merge strategy. + - `video_kernel_size` (int): The video kernel size. + - `ch` (int): The number of channels. + - `time_embed_dim` (int): The time embedding dimension. + - `dropout` (float): The dropout rate. + - `out_channels` (int): The number of output channels. + - `dims` (int): The number of dimensions. + - `use_checkpoint` (bool): Whether to use checkpointing. + - `use_scale_shift_norm` (bool): Whether to use scale-shift normalization. + - `down` (bool, optional): Whether to use downsampling. Defaults to False. + - `up` (bool, optional): Whether to use upsampling. Defaults to False. + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + - `device` (torch.device, optional): The device. Defaults to None. + - `operations` (any, optional): The operations to use. Defaults to oai_ops. + + #### Returns: + - `ResBlock.ResBlock1`: The residual block. + """ + return ResBlock.ResBlock1( + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + use_checkpoint=use_checkpoint, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + down=down, + up=up, + dtype=dtype, + device=device, + operations=operations, + ) + + self.double_blocks = nn.ModuleList() + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ) + ] + ch = mult * model_channels + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + dim_head = ch // num_heads + disabled_sa = False + + if ( + not util.exists(num_attention_blocks) + or nr < num_attention_blocks[level] + ): + layers.append( + get_attention_layer( + ch, + num_heads, + dim_head, + depth=num_transformers, + context_dim=context_dim, + disable_self_attn=disabled_sa, + use_checkpoint=use_checkpoint, + ) + ) + self.input_blocks.append(sampling.TimestepEmbedSequential1(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + sampling.TimestepEmbedSequential1( + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + dtype=self.dtype, + device=device, + operations=operations, + ) + if resblock_updown + else ResBlock.Downsample1( + ch, + conv_resample, + dims=dims, + out_channels=out_ch, + dtype=self.dtype, + device=device, + operations=operations, + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + dim_head = ch // num_heads + mid_block = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ) + ] + + self.middle_block = None + if transformer_depth_middle >= -1: + if transformer_depth_middle >= 0: + mid_block += [ + get_attention_layer( # always uses a self-attn + ch, + num_heads, + dim_head, + depth=transformer_depth_middle, + context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, + use_checkpoint=use_checkpoint, + ), + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ), + ] + self.middle_block = sampling.TimestepEmbedSequential1(*mid_block) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch + ich, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ) + ] + ch = model_channels * mult + num_transformers = transformer_depth_output.pop() + if num_transformers > 0: + dim_head = ch // num_heads + disabled_sa = False + + if ( + not util.exists(num_attention_blocks) + or i < num_attention_blocks[level] + ): + layers.append( + get_attention_layer( + ch, + num_heads, + dim_head, + depth=num_transformers, + context_dim=context_dim, + disable_self_attn=disabled_sa, + use_checkpoint=use_checkpoint, + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + dtype=self.dtype, + device=device, + operations=operations, + ) + if resblock_updown + else ResBlock.Upsample1( + ch, + conv_resample, + dims=dims, + out_channels=out_ch, + dtype=self.dtype, + device=device, + operations=operations, + ) + ) + ds //= 2 + self.output_blocks.append(sampling.TimestepEmbedSequential1(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + operations.GroupNorm(32, ch, dtype=self.dtype, device=device), + nn.SiLU(), + util.zero_module( + operations.conv_nd( + dims, + model_channels, + out_channels, + 3, + padding=1, + dtype=self.dtype, + device=device, + ) + ), + ) + + def forward( + self, + x: torch.Tensor, + timesteps: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + control: Optional[torch.Tensor] = None, + transformer_options: Dict[str, Any] = {}, + **kwargs: Any, + ) -> torch.Tensor: + """#### Forward pass of the UNet model. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `timesteps` (Optional[torch.Tensor], optional): The timesteps tensor. Defaults to None. + - `context` (Optional[torch.Tensor], optional): The context tensor. Defaults to None. + - `y` (Optional[torch.Tensor], optional): The class labels tensor. Defaults to None. + - `control` (Optional[torch.Tensor], optional): The control tensor. Defaults to None. + - `transformer_options` (Dict[str, Any], optional): Options for the transformer. Defaults to {}. + - `**kwargs` (Any): Additional keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + transformer_options["original_shape"] = list(x.shape) + transformer_options["transformer_index"] = 0 + transformer_patches = transformer_options.get("patches", {}) + + num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) + image_only_indicator = kwargs.get("image_only_indicator", None) + time_context = kwargs.get("time_context", None) + + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = sampling_util.timestep_embedding( + timesteps, self.model_channels + ).to(x.dtype) + emb = self.time_embed(t_emb) + h = x + for id, module in enumerate(self.input_blocks): + transformer_options["block"] = ("input", id) + h = ResBlock.forward_timestep_embed1( + module, + h, + emb, + context, + transformer_options, + time_context=time_context, + num_video_frames=num_video_frames, + image_only_indicator=image_only_indicator, + ) + h = apply_control1(h, control, "input") + hs.append(h) + + transformer_options["block"] = ("middle", 0) + if self.middle_block is not None: + h = ResBlock.forward_timestep_embed1( + self.middle_block, + h, + emb, + context, + transformer_options, + time_context=time_context, + num_video_frames=num_video_frames, + image_only_indicator=image_only_indicator, + ) + h = apply_control1(h, control, "middle") + + for id, module in enumerate(self.output_blocks): + transformer_options["block"] = ("output", id) + hsp = hs.pop() + hsp = apply_control1(hsp, control, "output") + + h = torch.cat([h, hsp], dim=1) + del hsp + if len(hs) > 0: + output_shape = hs[-1].shape + else: + output_shape = None + h = ResBlock.forward_timestep_embed1( + module, + h, + emb, + context, + transformer_options, + output_shape, + time_context=time_context, + num_video_frames=num_video_frames, + image_only_indicator=image_only_indicator, + ) + h = h.type(x.dtype) + return self.out(h) + + +def detect_unet_config(state_dict: Dict[str, torch.Tensor], key_prefix: str) -> Dict[str, Any]: + """#### Detect the UNet configuration from a state dictionary. + + #### Args: + - `state_dict` (Dict[str, torch.Tensor]): The state dictionary. + - `key_prefix` (str): The key prefix. + + #### Returns: + - `Dict[str, Any]`: The detected UNet configuration. + """ + state_dict_keys = list(state_dict.keys()) + + if ( + "{}joint_blocks.0.context_block.attn.qkv.weight".format(key_prefix) + in state_dict_keys + ): # mmdit model + unet_config = {} + unet_config["in_channels"] = state_dict[ + "{}x_embedder.proj.weight".format(key_prefix) + ].shape[1] + patch_size = state_dict["{}x_embedder.proj.weight".format(key_prefix)].shape[2] + unet_config["patch_size"] = patch_size + final_layer = "{}final_layer.linear.weight".format(key_prefix) + if final_layer in state_dict: + unet_config["out_channels"] = state_dict[final_layer].shape[0] // ( + patch_size * patch_size + ) + + unet_config["depth"] = ( + state_dict["{}x_embedder.proj.weight".format(key_prefix)].shape[0] // 64 + ) + unet_config["input_size"] = None + y_key = "{}y_embedder.mlp.0.weight".format(key_prefix) + if y_key in state_dict_keys: + unet_config["adm_in_channels"] = state_dict[y_key].shape[1] + + context_key = "{}context_embedder.weight".format(key_prefix) + if context_key in state_dict_keys: + in_features = state_dict[context_key].shape[1] + out_features = state_dict[context_key].shape[0] + unet_config["context_embedder_config"] = { + "target": "torch.nn.Linear", + "params": {"in_features": in_features, "out_features": out_features}, + } + num_patches_key = "{}pos_embed".format(key_prefix) + if num_patches_key in state_dict_keys: + num_patches = state_dict[num_patches_key].shape[1] + unet_config["num_patches"] = num_patches + unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches)) + + rms_qk = "{}joint_blocks.0.context_block.attn.ln_q.weight".format(key_prefix) + if rms_qk in state_dict_keys: + unet_config["qk_norm"] = "rms" + + unet_config["pos_embed_scaling_factor"] = None # unused for inference + context_processor = "{}context_processor.layers.0.attn.qkv.weight".format( + key_prefix + ) + if context_processor in state_dict_keys: + unet_config["context_processor_layers"] = transformer.count_blocks( + state_dict_keys, + "{}context_processor.layers.".format(key_prefix) + "{}.", + ) + return unet_config + + if "{}clf.1.weight".format(key_prefix) in state_dict_keys: # stable cascade + unet_config = {} + text_mapper_name = "{}clip_txt_mapper.weight".format(key_prefix) + if text_mapper_name in state_dict_keys: + unet_config["stable_cascade_stage"] = "c" + w = state_dict[text_mapper_name] + if w.shape[0] == 1536: # stage c lite + unet_config["c_cond"] = 1536 + unet_config["c_hidden"] = [1536, 1536] + unet_config["nhead"] = [24, 24] + unet_config["blocks"] = [[4, 12], [12, 4]] + elif w.shape[0] == 2048: # stage c full + unet_config["c_cond"] = 2048 + elif "{}clip_mapper.weight".format(key_prefix) in state_dict_keys: + unet_config["stable_cascade_stage"] = "b" + w = state_dict["{}down_blocks.1.0.channelwise.0.weight".format(key_prefix)] + if w.shape[-1] == 640: + unet_config["c_hidden"] = [320, 640, 1280, 1280] + unet_config["nhead"] = [-1, -1, 20, 20] + unet_config["blocks"] = [[2, 6, 28, 6], [6, 28, 6, 2]] + unet_config["block_repeat"] = [[1, 1, 1, 1], [3, 3, 2, 2]] + elif w.shape[-1] == 576: # stage b lite + unet_config["c_hidden"] = [320, 576, 1152, 1152] + unet_config["nhead"] = [-1, 9, 18, 18] + unet_config["blocks"] = [[2, 4, 14, 4], [4, 14, 4, 2]] + unet_config["block_repeat"] = [[1, 1, 1, 1], [2, 2, 2, 2]] + return unet_config + + if ( + "{}transformer.rotary_pos_emb.inv_freq".format(key_prefix) in state_dict_keys + ): # stable audio dit + unet_config = {} + unet_config["audio_model"] = "dit1.0" + return unet_config + + if ( + "{}double_layers.0.attn.w1q.weight".format(key_prefix) in state_dict_keys + ): # aura flow dit + unet_config = {} + unet_config["max_seq"] = state_dict[ + "{}positional_encoding".format(key_prefix) + ].shape[1] + unet_config["cond_seq_dim"] = state_dict[ + "{}cond_seq_linear.weight".format(key_prefix) + ].shape[1] + double_layers = transformer.count_blocks( + state_dict_keys, "{}double_layers.".format(key_prefix) + "{}." + ) + single_layers = transformer.count_blocks( + state_dict_keys, "{}single_layers.".format(key_prefix) + "{}." + ) + unet_config["n_double_layers"] = double_layers + unet_config["n_layers"] = double_layers + single_layers + return unet_config + + if "{}mlp_t5.0.weight".format(key_prefix) in state_dict_keys: # Hunyuan DiT + unet_config = {} + unet_config["image_model"] = "hydit" + unet_config["depth"] = transformer.count_blocks( + state_dict_keys, "{}blocks.".format(key_prefix) + "{}." + ) + unet_config["hidden_size"] = state_dict[ + "{}x_embedder.proj.weight".format(key_prefix) + ].shape[0] + if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: # DiT-g/2 + unet_config["mlp_ratio"] = 4.3637 + if state_dict["{}extra_embedder.0.weight".format(key_prefix)].shape[1] == 3968: + unet_config["size_cond"] = True + unet_config["use_style_cond"] = True + unet_config["image_model"] = "hydit1" + return unet_config + + if ( + "{}double_blocks.0.img_attn.norm.key_norm.scale".format(key_prefix) + in state_dict_keys + ): # Flux + dit_config = {} + dit_config["image_model"] = "flux" + dit_config["in_channels"] = 16 + dit_config["vec_in_dim"] = 768 + dit_config["context_in_dim"] = 4096 + dit_config["hidden_size"] = 3072 + dit_config["mlp_ratio"] = 4.0 + dit_config["num_heads"] = 24 + dit_config["depth"] = transformer.count_blocks( + state_dict_keys, "{}double_blocks.".format(key_prefix) + "{}." + ) + dit_config["depth_single_blocks"] = transformer.count_blocks( + state_dict_keys, "{}single_blocks.".format(key_prefix) + "{}." + ) + dit_config["axes_dim"] = [16, 56, 56] + dit_config["theta"] = 10000 + dit_config["qkv_bias"] = True + dit_config["guidance_embed"] = ( + "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys + ) + return dit_config + + if "{}input_blocks.0.0.weight".format(key_prefix) not in state_dict_keys: + return None + + unet_config = { + "use_checkpoint": False, + "image_size": 32, + "use_spatial_transformer": True, + "legacy": False, + } + + y_input = "{}label_emb.0.0.weight".format(key_prefix) + if y_input in state_dict_keys: + unet_config["num_classes"] = "sequential" + unet_config["adm_in_channels"] = state_dict[y_input].shape[1] + else: + unet_config["adm_in_channels"] = None + + model_channels = state_dict["{}input_blocks.0.0.weight".format(key_prefix)].shape[0] + in_channels = state_dict["{}input_blocks.0.0.weight".format(key_prefix)].shape[1] + + out_key = "{}out.2.weight".format(key_prefix) + if out_key in state_dict: + out_channels = state_dict[out_key].shape[0] + else: + out_channels = 4 + + num_res_blocks = [] + channel_mult = [] + transformer_depth = [] + transformer_depth_output = [] + context_dim = None + use_linear_in_transformer = False + + video_model = False + video_model_cross = False + + current_res = 1 + count = 0 + + last_res_blocks = 0 + last_channel_mult = 0 + + input_block_count = transformer.count_blocks( + state_dict_keys, "{}input_blocks".format(key_prefix) + ".{}." + ) + for count in range(input_block_count): + prefix = "{}input_blocks.{}.".format(key_prefix, count) + prefix_output = "{}output_blocks.{}.".format( + key_prefix, input_block_count - count - 1 + ) + + block_keys = sorted( + list(filter(lambda a: a.startswith(prefix), state_dict_keys)) + ) + if len(block_keys) == 0: + break + + block_keys_output = sorted( + list(filter(lambda a: a.startswith(prefix_output), state_dict_keys)) + ) + + if "{}0.op.weight".format(prefix) in block_keys: # new layer + num_res_blocks.append(last_res_blocks) + channel_mult.append(last_channel_mult) + + current_res *= 2 + last_res_blocks = 0 + last_channel_mult = 0 + out = transformer.calculate_transformer_depth( + prefix_output, state_dict_keys, state_dict + ) + if out is not None: + transformer_depth_output.append(out[0]) + else: + transformer_depth_output.append(0) + else: + res_block_prefix = "{}0.in_layers.0.weight".format(prefix) + if res_block_prefix in block_keys: + last_res_blocks += 1 + last_channel_mult = ( + state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] + // model_channels + ) + + out = transformer.calculate_transformer_depth(prefix, state_dict_keys, state_dict) + if out is not None: + transformer_depth.append(out[0]) + if context_dim is None: + context_dim = out[1] + use_linear_in_transformer = out[2] + out[3] + else: + transformer_depth.append(0) + + res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output) + if res_block_prefix in block_keys_output: + out = transformer.calculate_transformer_depth( + prefix_output, state_dict_keys, state_dict + ) + if out is not None: + transformer_depth_output.append(out[0]) + else: + transformer_depth_output.append(0) + + num_res_blocks.append(last_res_blocks) + channel_mult.append(last_channel_mult) + if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys: + transformer_depth_middle = transformer.count_blocks( + state_dict_keys, + "{}middle_block.1.transformer_blocks.".format(key_prefix) + "{}", + ) + elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys: + transformer_depth_middle = -1 + else: + transformer_depth_middle = -2 + + unet_config["in_channels"] = in_channels + unet_config["out_channels"] = out_channels + unet_config["model_channels"] = model_channels + unet_config["num_res_blocks"] = num_res_blocks + unet_config["transformer_depth"] = transformer_depth + unet_config["transformer_depth_output"] = transformer_depth_output + unet_config["channel_mult"] = channel_mult + unet_config["transformer_depth_middle"] = transformer_depth_middle + unet_config["use_linear_in_transformer"] = use_linear_in_transformer + unet_config["context_dim"] = context_dim + + if video_model: + unet_config["extra_ff_mix_layer"] = True + unet_config["use_spatial_context"] = True + unet_config["merge_strategy"] = "learned_with_images" + unet_config["merge_factor"] = 0.0 + unet_config["video_kernel_size"] = [3, 1, 1] + unet_config["use_temporal_resblock"] = True + unet_config["use_temporal_attention"] = True + unet_config["disable_temporal_crossattention"] = not video_model_cross + else: + unet_config["use_temporal_resblock"] = False + unet_config["use_temporal_attention"] = False + + return unet_config + + +def model_config_from_unet_config(unet_config: Dict[str, Any], state_dict: Optional[Dict[str, torch.Tensor]] = None) -> Any: + """#### Get the model configuration from a UNet configuration. + + #### Args: + - `unet_config` (Dict[str, Any]): The UNet configuration. + - `state_dict` (Optional[Dict[str, torch.Tensor]], optional): The state dictionary. Defaults to None. + + #### Returns: + - `Any`: The model configuration. + """ + from modules.SD15 import SD15 + + for model_config in SD15.models: + if model_config.matches(unet_config, state_dict): + return model_config(unet_config) + + logging.error("no match {}".format(unet_config)) + return None + + +def model_config_from_unet(state_dict: Dict[str, torch.Tensor], unet_key_prefix: str, use_base_if_no_match: bool = False) -> Any: + """#### Get the model configuration from a UNet state dictionary. + + #### Args: + - `state_dict` (Dict[str, torch.Tensor]): The state dictionary. + - `unet_key_prefix` (str): The UNet key prefix. + - `use_base_if_no_match` (bool, optional): Whether to use the base configuration if no match is found. Defaults to False. + + #### Returns: + - `Any`: The model configuration. + """ + unet_config = detect_unet_config(state_dict, unet_key_prefix) + if unet_config is None: + return None + model_config = model_config_from_unet_config(unet_config, state_dict) + return model_config + + +def unet_dtype1( + device: Optional[torch.device] = None, + model_params: int = 0, + supported_dtypes: List[torch.dtype] = [torch.float16, torch.bfloat16, torch.float32], +) -> torch.dtype: + """#### Get the dtype for the UNet model. + + #### Args: + - `device` (Optional[torch.device], optional): The device. Defaults to None. + - `model_params` (int, optional): The model parameters. Defaults to 0. + - `supported_dtypes` (List[torch.dtype], optional): The supported dtypes. Defaults to [torch.float16, torch.bfloat16, torch.float32]. + + #### Returns: + - `torch.dtype`: The dtype for the UNet model. + """ return torch.float16 \ No newline at end of file diff --git a/modules/Quantize/Quantizer.py b/modules/Quantize/Quantizer.py index 2bb3f9255a0d10b50f19ba6635b5bb6fdb8cbeb2..c83a03805468c644fa6543f4f92a6f5573ba55e7 100644 --- a/modules/Quantize/Quantizer.py +++ b/modules/Quantize/Quantizer.py @@ -1,1012 +1,1012 @@ -import copy -import logging -import gguf -import torch - -from modules.Device import Device -from modules.Model import ModelPatcher -from modules.Utilities import util -from modules.clip import Clip -from modules.cond import cast - -# Constants for torch-compatible quantization types -TORCH_COMPATIBLE_QTYPES = { - None, - gguf.GGMLQuantizationType.F32, - gguf.GGMLQuantizationType.F16, -} - - -def is_torch_compatible(tensor: torch.Tensor) -> bool: - """#### Check if a tensor is compatible with PyTorch operations. - - #### Args: - - `tensor` (torch.Tensor): The tensor to check. - - #### Returns: - - `bool`: Whether the tensor is torch-compatible. - """ - return ( - tensor is None - or getattr(tensor, "tensor_type", None) in TORCH_COMPATIBLE_QTYPES - ) - - -def is_quantized(tensor: torch.Tensor) -> bool: - """#### Check if a tensor is quantized. - - #### Args: - - `tensor` (torch.Tensor): The tensor to check. - - #### Returns: - - `bool`: Whether the tensor is quantized. - """ - return not is_torch_compatible(tensor) - - -def dequantize( - data: torch.Tensor, - qtype: gguf.GGMLQuantizationType, - oshape: tuple, - dtype: torch.dtype = None, -) -> torch.Tensor: - """#### Dequantize tensor back to usable shape/dtype. - - #### Args: - - `data` (torch.Tensor): The quantized data. - - `qtype` (gguf.GGMLQuantizationType): The quantization type. - - `oshape` (tuple): The output shape. - - `dtype` (torch.dtype, optional): The output dtype. Defaults to None. - - #### Returns: - - `torch.Tensor`: The dequantized tensor. - """ - # Get block size and type size for quantization format - block_size, type_size = gguf.GGML_QUANT_SIZES[qtype] - dequantize_blocks = dequantize_functions[qtype] - - # Reshape data into blocks - rows = data.reshape((-1, data.shape[-1])).view(torch.uint8) - n_blocks = rows.numel() // type_size - blocks = rows.reshape((n_blocks, type_size)) - - # Dequantize blocks and reshape to target shape - blocks = dequantize_blocks(blocks, block_size, type_size, dtype) - return blocks.reshape(oshape) - - -def split_block_dims(blocks: torch.Tensor, *args) -> list: - """#### Split blocks into dimensions. - - #### Args: - - `blocks` (torch.Tensor): The blocks to split. - - `*args`: The dimensions to split into. - - #### Returns: - - `list`: The split blocks. - """ - n_max = blocks.shape[1] - dims = list(args) + [n_max - sum(args)] - return torch.split(blocks, dims, dim=1) - - -# Legacy Quantization Functions -def dequantize_blocks_Q8_0( - blocks: torch.Tensor, block_size: int, type_size: int, dtype: torch.dtype = None -) -> torch.Tensor: - """#### Dequantize Q8_0 quantized blocks. - - #### Args: - - `blocks` (torch.Tensor): The quantized blocks. - - `block_size` (int): The block size. - - `type_size` (int): The type size. - - `dtype` (torch.dtype, optional): The output dtype. Defaults to None. - - #### Returns: - - `torch.Tensor`: The dequantized blocks. - """ - # Split blocks into scale and quantized values - d, x = split_block_dims(blocks, 2) - d = d.view(torch.float16).to(dtype) - x = x.view(torch.int8) - return d * x - - -# K Quants # -QK_K = 256 -K_SCALE_SIZE = 12 - -# Mapping of quantization types to dequantization functions -dequantize_functions = { - gguf.GGMLQuantizationType.Q8_0: dequantize_blocks_Q8_0, -} - - -def dequantize_tensor( - tensor: torch.Tensor, dtype: torch.dtype = None, dequant_dtype: torch.dtype = None -) -> torch.Tensor: - """#### Dequantize a potentially quantized tensor. - - #### Args: - - `tensor` (torch.Tensor): The tensor to dequantize. - - `dtype` (torch.dtype, optional): Target dtype. Defaults to None. - - `dequant_dtype` (torch.dtype, optional): Intermediate dequantization dtype. Defaults to None. - - #### Returns: - - `torch.Tensor`: The dequantized tensor. - """ - qtype = getattr(tensor, "tensor_type", None) - oshape = getattr(tensor, "tensor_shape", tensor.shape) - - if qtype in TORCH_COMPATIBLE_QTYPES: - return tensor.to(dtype) - elif qtype in dequantize_functions: - dequant_dtype = dtype if dequant_dtype == "target" else dequant_dtype - return dequantize(tensor.data, qtype, oshape, dtype=dequant_dtype).to(dtype) - - -class GGMLLayer(torch.nn.Module): - """#### Base class for GGML quantized layers. - - Handles dynamic dequantization of weights during forward pass. - """ - - comfy_cast_weights: bool = True - dequant_dtype: torch.dtype = None - patch_dtype: torch.dtype = None - torch_compatible_tensor_types: set = { - None, - gguf.GGMLQuantizationType.F32, - gguf.GGMLQuantizationType.F16, - } - - def is_ggml_quantized( - self, *, weight: torch.Tensor = None, bias: torch.Tensor = None - ) -> bool: - """#### Check if layer weights are GGML quantized. - - #### Args: - - `weight` (torch.Tensor, optional): Weight tensor to check. Defaults to self.weight. - - `bias` (torch.Tensor, optional): Bias tensor to check. Defaults to self.bias. - - #### Returns: - - `bool`: Whether weights are quantized. - """ - if weight is None: - weight = self.weight - if bias is None: - bias = self.bias - return is_quantized(weight) or is_quantized(bias) - - def _load_from_state_dict( - self, state_dict: dict, prefix: str, *args, **kwargs - ) -> None: - """#### Load quantized weights from state dict. - - #### Args: - - `state_dict` (dict): State dictionary. - - `prefix` (str): Key prefix. - - `*args`: Additional arguments. - - `**kwargs`: Additional keyword arguments. - """ - weight = state_dict.get(f"{prefix}weight") - bias = state_dict.get(f"{prefix}bias") - # Use modified loader for quantized or linear layers - if self.is_ggml_quantized(weight=weight, bias=bias) or isinstance( - self, torch.nn.Linear - ): - return self.ggml_load_from_state_dict(state_dict, prefix, *args, **kwargs) - return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) - - def ggml_load_from_state_dict( - self, - state_dict: dict, - prefix: str, - local_metadata: dict, - strict: bool, - missing_keys: list, - unexpected_keys: list, - error_msgs: list, - ) -> None: - """#### Load GGML quantized weights from state dict. - - #### Args: - - `state_dict` (dict): State dictionary. - - `prefix` (str): Key prefix. - - `local_metadata` (dict): Local metadata. - - `strict` (bool): Strict loading mode. - - `missing_keys` (list): Keys missing from state dict. - - `unexpected_keys` (list): Unexpected keys found. - - `error_msgs` (list): Error messages. - """ - prefix_len = len(prefix) - for k, v in state_dict.items(): - if k[prefix_len:] == "weight": - self.weight = torch.nn.Parameter(v, requires_grad=False) - elif k[prefix_len:] == "bias" and v is not None: - self.bias = torch.nn.Parameter(v, requires_grad=False) - else: - missing_keys.append(k) - - def _save_to_state_dict(self, *args, **kwargs) -> None: - """#### Save layer state to state dict. - - #### Args: - - `*args`: Additional arguments. - - `**kwargs`: Additional keyword arguments. - """ - if self.is_ggml_quantized(): - return self.ggml_save_to_state_dict(*args, **kwargs) - return super()._save_to_state_dict(*args, **kwargs) - - def ggml_save_to_state_dict( - self, destination: dict, prefix: str, keep_vars: bool - ) -> None: - """#### Save GGML layer state to state dict. - - #### Args: - - `destination` (dict): Destination dictionary. - - `prefix` (str): Key prefix. - - `keep_vars` (bool): Whether to keep variables. - """ - # Create fake tensors for VRAM estimation - weight = torch.zeros_like(self.weight, device=torch.device("meta")) - destination[prefix + "weight"] = weight - if self.bias is not None: - bias = torch.zeros_like(self.bias, device=torch.device("meta")) - destination[prefix + "bias"] = bias - return - - def get_weight(self, tensor: torch.Tensor, dtype: torch.dtype) -> torch.Tensor: - """#### Get dequantized weight tensor. - - #### Args: - - `tensor` (torch.Tensor): Input tensor. - - `dtype` (torch.dtype): Target dtype. - - #### Returns: - - `torch.Tensor`: Dequantized tensor. - """ - if tensor is None: - return - - # Consolidate and load patches to GPU asynchronously - patch_list = [] - device = tensor.device - for function, patches, key in getattr(tensor, "patches", []): - patch_list += move_patch_to_device(patches, device) - - # Dequantize tensor while patches load - weight = dequantize_tensor(tensor, dtype, self.dequant_dtype) - - # Apply patches - if patch_list: - if self.patch_dtype is None: - weight = function(patch_list, weight, key) - else: - # For testing, may degrade image quality - patch_dtype = ( - dtype if self.patch_dtype == "target" else self.patch_dtype - ) - weight = function(patch_list, weight, key, patch_dtype) - return weight - - def cast_bias_weight( - self, - input: torch.Tensor = None, - dtype: torch.dtype = None, - device: torch.device = None, - bias_dtype: torch.dtype = None, - ) -> tuple: - """#### Cast layer weights and bias to target dtype/device. - - #### Args: - - `input` (torch.Tensor, optional): Input tensor for type/device inference. - - `dtype` (torch.dtype, optional): Target dtype. - - `device` (torch.device, optional): Target device. - - `bias_dtype` (torch.dtype, optional): Target bias dtype. - - #### Returns: - - `tuple`: (cast_weight, cast_bias) - """ - if input is not None: - if dtype is None: - dtype = getattr(input, "dtype", torch.float32) - if bias_dtype is None: - bias_dtype = dtype - if device is None: - device = input.device - - bias = None - non_blocking = Device.device_supports_non_blocking(device) - if self.bias is not None: - bias = self.get_weight(self.bias.to(device), dtype) - bias = cast.cast_to( - bias, bias_dtype, device, non_blocking=non_blocking, copy=False - ) - - weight = self.get_weight(self.weight.to(device), dtype) - weight = cast.cast_to( - weight, dtype, device, non_blocking=non_blocking, copy=False - ) - return weight, bias - - def forward_comfy_cast_weights( - self, input: torch.Tensor, *args, **kwargs - ) -> torch.Tensor: - """#### Forward pass with weight casting. - - #### Args: - - `input` (torch.Tensor): Input tensor. - - `*args`: Additional arguments. - - `**kwargs`: Additional keyword arguments. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - if self.is_ggml_quantized(): - return self.forward_ggml_cast_weights(input, *args, **kwargs) - return super().forward_comfy_cast_weights(input, *args, **kwargs) - - -class GGMLOps(cast.manual_cast): - """ - Dequantize weights on the fly before doing the compute - """ - - class Linear(GGMLLayer, cast.manual_cast.Linear): - def __init__( - self, in_features, out_features, bias=True, device=None, dtype=None - ): - """ - Initialize the Linear layer. - - Args: - in_features (int): Number of input features. - out_features (int): Number of output features. - bias (bool, optional): If set to False, the layer will not learn an additive bias. Defaults to True. - device (torch.device, optional): The device to store the layer's parameters. Defaults to None. - dtype (torch.dtype, optional): The data type of the layer's parameters. Defaults to None. - """ - torch.nn.Module.__init__(self) - # TODO: better workaround for reserved memory spike on windows - # Issue is with `torch.empty` still reserving the full memory for the layer - # Windows doesn't over-commit memory so without this 24GB+ of pagefile is used - self.in_features = in_features - self.out_features = out_features - self.weight = None - self.bias = None - - def forward_ggml_cast_weights(self, input: torch.Tensor) -> torch.Tensor: - """ - Forward pass with GGML cast weights. - - Args: - input (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The output tensor. - """ - weight, bias = self.cast_bias_weight(input) - return torch.nn.functional.linear(input, weight, bias) - - class Embedding(GGMLLayer, cast.manual_cast.Embedding): - def forward_ggml_cast_weights( - self, input: torch.Tensor, out_dtype: torch.dtype = None - ) -> torch.Tensor: - """ - Forward pass with GGML cast weights for embedding. - - Args: - input (torch.Tensor): The input tensor. - out_dtype (torch.dtype, optional): The output data type. Defaults to None. - - Returns: - torch.Tensor: The output tensor. - """ - output_dtype = out_dtype - if ( - self.weight.dtype == torch.float16 - or self.weight.dtype == torch.bfloat16 - ): - out_dtype = None - weight, _bias = self.cast_bias_weight( - self, device=input.device, dtype=out_dtype - ) - return torch.nn.functional.embedding( - input, - weight, - self.padding_idx, - self.max_norm, - self.norm_type, - self.scale_grad_by_freq, - self.sparse, - ).to(dtype=output_dtype) - - -def gguf_sd_loader_get_orig_shape( - reader: gguf.GGUFReader, tensor_name: str -) -> torch.Size: - """#### Get the original shape of a tensor from a GGUF reader. - - #### Args: - - `reader` (gguf.GGUFReader): The GGUF reader. - - `tensor_name` (str): The name of the tensor. - - #### Returns: - - `torch.Size`: The original shape of the tensor. - """ - field_key = f"comfy.gguf.orig_shape.{tensor_name}" - field = reader.get_field(field_key) - if field is None: - return None - # Has original shape metadata, so we try to decode it. - if ( - len(field.types) != 2 - or field.types[0] != gguf.GGUFValueType.ARRAY - or field.types[1] != gguf.GGUFValueType.INT32 - ): - raise TypeError( - f"Bad original shape metadata for {field_key}: Expected ARRAY of INT32, got {field.types}" - ) - return torch.Size(tuple(int(field.parts[part_idx][0]) for part_idx in field.data)) - - -class GGMLTensor(torch.Tensor): - """ - Main tensor-like class for storing quantized weights - """ - - def __init__(self, *args, tensor_type, tensor_shape, patches=[], **kwargs): - """ - Initialize the GGMLTensor. - - Args: - *args: Variable length argument list. - tensor_type: The type of the tensor. - tensor_shape: The shape of the tensor. - patches (list, optional): List of patches. Defaults to []. - **kwargs: Arbitrary keyword arguments. - """ - super().__init__() - self.tensor_type = tensor_type - self.tensor_shape = tensor_shape - self.patches = patches - - def __new__(cls, *args, tensor_type, tensor_shape, patches=[], **kwargs): - """ - Create a new instance of GGMLTensor. - - Args: - *args: Variable length argument list. - tensor_type: The type of the tensor. - tensor_shape: The shape of the tensor. - patches (list, optional): List of patches. Defaults to []. - **kwargs: Arbitrary keyword arguments. - - Returns: - GGMLTensor: A new instance of GGMLTensor. - """ - return super().__new__(cls, *args, **kwargs) - - def to(self, *args, **kwargs): - """ - Convert the tensor to a specified device and/or dtype. - - Args: - *args: Variable length argument list. - **kwargs: Arbitrary keyword arguments. - - Returns: - GGMLTensor: The converted tensor. - """ - new = super().to(*args, **kwargs) - new.tensor_type = getattr(self, "tensor_type", None) - new.tensor_shape = getattr(self, "tensor_shape", new.data.shape) - new.patches = getattr(self, "patches", []).copy() - return new - - def clone(self, *args, **kwargs): - """ - Clone the tensor. - - Args: - *args: Variable length argument list. - **kwargs: Arbitrary keyword arguments. - - Returns: - GGMLTensor: The cloned tensor. - """ - return self - - def detach(self, *args, **kwargs): - """ - Detach the tensor from the computation graph. - - Args: - *args: Variable length argument list. - **kwargs: Arbitrary keyword arguments. - - Returns: - GGMLTensor: The detached tensor. - """ - return self - - def copy_(self, *args, **kwargs): - """ - Copy the values from another tensor into this tensor. - - Args: - *args: Variable length argument list. - **kwargs: Arbitrary keyword arguments. - - Returns: - GGMLTensor: The tensor with copied values. - """ - try: - return super().copy_(*args, **kwargs) - except Exception as e: - print(f"ignoring 'copy_' on tensor: {e}") - - def __deepcopy__(self, *args, **kwargs): - """ - Create a deep copy of the tensor. - - Args: - *args: Variable length argument list. - **kwargs: Arbitrary keyword arguments. - - Returns: - GGMLTensor: The deep copied tensor. - """ - new = super().__deepcopy__(*args, **kwargs) - new.tensor_type = getattr(self, "tensor_type", None) - new.tensor_shape = getattr(self, "tensor_shape", new.data.shape) - new.patches = getattr(self, "patches", []).copy() - return new - - @property - def shape(self): - """ - Get the shape of the tensor. - - Returns: - torch.Size: The shape of the tensor. - """ - if not hasattr(self, "tensor_shape"): - self.tensor_shape = self.size() - return self.tensor_shape - - -def gguf_sd_loader(path: str, handle_prefix: str = "model.diffusion_model."): - """#### Load a GGUF file into a state dict. - - #### Args: - - `path` (str): The path to the GGUF file. - - `handle_prefix` (str, optional): The prefix to handle. Defaults to "model.diffusion_model.". - - #### Returns: - - `dict`: The loaded state dict. - """ - reader = gguf.GGUFReader(path) - - # filter and strip prefix - has_prefix = False - if handle_prefix is not None: - prefix_len = len(handle_prefix) - tensor_names = set(tensor.name for tensor in reader.tensors) - has_prefix = any(s.startswith(handle_prefix) for s in tensor_names) - - tensors = [] - for tensor in reader.tensors: - sd_key = tensor_name = tensor.name - if has_prefix: - if not tensor_name.startswith(handle_prefix): - continue - sd_key = tensor_name[prefix_len:] - tensors.append((sd_key, tensor)) - - # detect and verify architecture - compat = None - arch_str = None - arch_field = reader.get_field("general.architecture") - if arch_field is not None: - if ( - len(arch_field.types) != 1 - or arch_field.types[0] != gguf.GGUFValueType.STRING - ): - raise TypeError( - f"Bad type for GGUF general.architecture key: expected string, got {arch_field.types!r}" - ) - arch_str = str(arch_field.parts[arch_field.data[-1]], encoding="utf-8") - if arch_str not in {"flux", "sd1", "sdxl", "t5", "t5encoder"}: - raise ValueError( - f"Unexpected architecture type in GGUF file, expected one of flux, sd1, sdxl, t5encoder but got {arch_str!r}" - ) - - # main loading loop - state_dict = {} - qtype_dict = {} - for sd_key, tensor in tensors: - tensor_name = tensor.name - tensor_type_str = str(tensor.tensor_type) - torch_tensor = torch.from_numpy(tensor.data) # mmap - - shape = gguf_sd_loader_get_orig_shape(reader, tensor_name) - if shape is None: - shape = torch.Size(tuple(int(v) for v in reversed(tensor.shape))) - # Workaround for stable-diffusion.cpp SDXL detection. - if compat == "sd.cpp" and arch_str == "sdxl": - if any( - [ - tensor_name.endswith(x) - for x in (".proj_in.weight", ".proj_out.weight") - ] - ): - while len(shape) > 2 and shape[-1] == 1: - shape = shape[:-1] - - # add to state dict - if tensor.tensor_type in { - gguf.GGMLQuantizationType.F32, - gguf.GGMLQuantizationType.F16, - }: - torch_tensor = torch_tensor.view(*shape) - state_dict[sd_key] = GGMLTensor( - torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape - ) - qtype_dict[tensor_type_str] = qtype_dict.get(tensor_type_str, 0) + 1 - - # sanity check debug print - print("\nggml_sd_loader:") - for k, v in qtype_dict.items(): - print(f" {k:30}{v:3}") - - return state_dict - - -class GGUFModelPatcher(ModelPatcher.ModelPatcher): - patch_on_device = False - - def unpatch_model(self, device_to=None, unpatch_weights=True): - """ - Unpatch the model. - - Args: - device_to (torch.device, optional): The device to move the model to. Defaults to None. - unpatch_weights (bool, optional): Whether to unpatch the weights. Defaults to True. - - Returns: - GGUFModelPatcher: The unpatched model. - """ - if unpatch_weights: - for p in self.model.parameters(): - if is_torch_compatible(p): - continue - patches = getattr(p, "patches", []) - if len(patches) > 0: - p.patches = [] - self.object_patches = {} - # TODO: Find another way to not unload after patches - return super().unpatch_model( - device_to=device_to, unpatch_weights=unpatch_weights - ) - - mmap_released = False - - def load(self, *args, force_patch_weights=False, **kwargs): - """ - Load the model. - - Args: - *args: Variable length argument list. - force_patch_weights (bool, optional): Whether to force patch weights. Defaults to False. - **kwargs: Arbitrary keyword arguments. - """ - super().load(*args, force_patch_weights=True, **kwargs) - - # make sure nothing stays linked to mmap after first load - if not self.mmap_released: - linked = [] - if kwargs.get("lowvram_model_memory", 0) > 0: - for n, m in self.model.named_modules(): - if hasattr(m, "weight"): - device = getattr(m.weight, "device", None) - if device == self.offload_device: - linked.append((n, m)) - continue - if hasattr(m, "bias"): - device = getattr(m.bias, "device", None) - if device == self.offload_device: - linked.append((n, m)) - continue - if linked: - print(f"Attempting to release mmap ({len(linked)})") - for n, m in linked: - # TODO: possible to OOM, find better way to detach - m.to(self.load_device).to(self.offload_device) - self.mmap_released = True - - def add_object_patch(self, name, obj): - self.object_patches[name] = obj - - def clone(self, *args, **kwargs): - """ - Clone the model patcher. - - Args: - *args: Variable length argument list. - **kwargs: Arbitrary keyword arguments. - - Returns: - GGUFModelPatcher: The cloned model patcher. - """ - n = GGUFModelPatcher( - self.model, - self.load_device, - self.offload_device, - self.size, - weight_inplace_update=self.weight_inplace_update, - ) - n.patches = {} - for k in self.patches: - n.patches[k] = self.patches[k][:] - n.patches_uuid = self.patches_uuid - - n.object_patches = self.object_patches.copy() - n.model_options = copy.deepcopy(self.model_options) - n.backup = self.backup - n.object_patches_backup = self.object_patches_backup - n.patch_on_device = getattr(self, "patch_on_device", False) - return n - - -class UnetLoaderGGUF: - def load_unet( - self, - unet_name: str, - dequant_dtype: str = None, - patch_dtype: str = None, - patch_on_device: bool = None, - ) -> tuple: - """ - Load the UNet model. - - Args: - unet_name (str): The name of the UNet model. - dequant_dtype (str, optional): The dequantization data type. Defaults to None. - patch_dtype (str, optional): The patch data type. Defaults to None. - patch_on_device (bool, optional): Whether to patch on device. Defaults to None. - - Returns: - tuple: The loaded model. - """ - ops = GGMLOps() - - if dequant_dtype in ("default", None): - ops.Linear.dequant_dtype = None - elif dequant_dtype in ["target"]: - ops.Linear.dequant_dtype = dequant_dtype - else: - ops.Linear.dequant_dtype = getattr(torch, dequant_dtype) - - if patch_dtype in ("default", None): - ops.Linear.patch_dtype = None - elif patch_dtype in ["target"]: - ops.Linear.patch_dtype = patch_dtype - else: - ops.Linear.patch_dtype = getattr(torch, patch_dtype) - - unet_path = "./_internal/unet/" + unet_name - sd = gguf_sd_loader(unet_path) - model = ModelPatcher.load_diffusion_model_state_dict( - sd, model_options={"custom_operations": ops} - ) - if model is None: - logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path)) - raise RuntimeError( - "ERROR: Could not detect model type of: {}".format(unet_path) - ) - model = GGUFModelPatcher.clone(model) - model.patch_on_device = patch_on_device - return (model,) - - -clip_sd_map = { - "enc.": "encoder.", - ".blk.": ".block.", - "token_embd": "shared", - "output_norm": "final_layer_norm", - "attn_q": "layer.0.SelfAttention.q", - "attn_k": "layer.0.SelfAttention.k", - "attn_v": "layer.0.SelfAttention.v", - "attn_o": "layer.0.SelfAttention.o", - "attn_norm": "layer.0.layer_norm", - "attn_rel_b": "layer.0.SelfAttention.relative_attention_bias", - "ffn_up": "layer.1.DenseReluDense.wi_1", - "ffn_down": "layer.1.DenseReluDense.wo", - "ffn_gate": "layer.1.DenseReluDense.wi_0", - "ffn_norm": "layer.1.layer_norm", -} - -clip_name_dict = { - "stable_diffusion": Clip.CLIPType.STABLE_DIFFUSION, - "sdxl": Clip.CLIPType.STABLE_DIFFUSION, - "sd3": Clip.CLIPType.SD3, - "flux": Clip.CLIPType.FLUX, -} - - -def gguf_clip_loader(path: str) -> dict: - """#### Load a CLIP model from a GGUF file. - - #### Args: - - `path` (str): The path to the GGUF file. - - #### Returns: - - `dict`: The loaded CLIP model. - """ - raw_sd = gguf_sd_loader(path) - assert "enc.blk.23.ffn_up.weight" in raw_sd, "Invalid Text Encoder!" - sd = {} - for k, v in raw_sd.items(): - for s, d in clip_sd_map.items(): - k = k.replace(s, d) - sd[k] = v - return sd - - -class CLIPLoaderGGUF: - def load_data(self, ckpt_paths: list) -> list: - """ - Load data from checkpoint paths. - - Args: - ckpt_paths (list): List of checkpoint paths. - - Returns: - list: List of loaded data. - """ - clip_data = [] - for p in ckpt_paths: - if p.endswith(".gguf"): - clip_data.append(gguf_clip_loader(p)) - else: - sd = util.load_torch_file(p, safe_load=True) - clip_data.append( - { - k: GGMLTensor( - v, - tensor_type=gguf.GGMLQuantizationType.F16, - tensor_shape=v.shape, - ) - for k, v in sd.items() - } - ) - return clip_data - - def load_patcher(self, clip_paths: list, clip_type: str, clip_data: list) -> Clip: - """ - Load the model patcher. - - Args: - clip_paths (list): List of clip paths. - clip_type (str): The type of the clip. - clip_data (list): List of clip data. - - Returns: - Clip: The loaded clip. - """ - clip = Clip.load_text_encoder_state_dicts( - clip_type=clip_type, - state_dicts=clip_data, - model_options={ - "custom_operations": GGMLOps, - "initial_device": Device.text_encoder_offload_device(), - }, - embedding_directory="models/embeddings", - ) - clip.patcher = GGUFModelPatcher.clone(clip.patcher) - - # for some reason this is just missing in some SAI checkpoints - if getattr(clip.cond_stage_model, "clip_l", None) is not None: - if ( - getattr( - clip.cond_stage_model.clip_l.transformer.text_projection.weight, - "tensor_shape", - None, - ) - is None - ): - clip.cond_stage_model.clip_l.transformer.text_projection = ( - cast.manual_cast.Linear(768, 768) - ) - if getattr(clip.cond_stage_model, "clip_g", None) is not None: - if ( - getattr( - clip.cond_stage_model.clip_g.transformer.text_projection.weight, - "tensor_shape", - None, - ) - is None - ): - clip.cond_stage_model.clip_g.transformer.text_projection = ( - cast.manual_cast.Linear(1280, 1280) - ) - - return clip - - -class DualCLIPLoaderGGUF(CLIPLoaderGGUF): - def load_clip(self, clip_name1: str, clip_name2: str, type: str) -> tuple: - """ - Load dual clips. - - Args: - clip_name1 (str): The name of the first clip. - clip_name2 (str): The name of the second clip. - type (str): The type of the clip. - - Returns: - tuple: The loaded clips. - """ - clip_path1 = "./_internal/clip/" + clip_name1 - clip_path2 = "./_internal/clip/" + clip_name2 - clip_paths = (clip_path1, clip_path2) - clip_type = clip_name_dict.get(type, Clip.CLIPType.STABLE_DIFFUSION) - return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),) - - -class CLIPTextEncodeFlux: - def encode( - self, - clip: Clip, - clip_l: str, - t5xxl: str, - guidance: str, - flux_enabled: bool = False, - ) -> tuple: - """ - Encode text using CLIP and T5XXL. - - Args: - clip (Clip): The clip object. - clip_l (str): The clip text. - t5xxl (str): The T5XXL text. - guidance (str): The guidance text. - flux_enabled (bool, optional): Whether flux is enabled. Defaults to False. - - Returns: - tuple: The encoded text. - """ - tokens = clip.tokenize(clip_l) - tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] - - output = clip.encode_from_tokens( - tokens, return_pooled=True, return_dict=True, flux_enabled=flux_enabled - ) - cond = output.pop("cond") - output["guidance"] = guidance - return ([[cond, output]],) - - -class ConditioningZeroOut: - def zero_out(self, conditioning: list) -> list: - """ - Zero out the conditioning. - - Args: - conditioning (list): The conditioning list. - - Returns: - list: The zeroed out conditioning. - """ - c = [] - for t in conditioning: - d = t[1].copy() - pooled_output = d.get("pooled_output", None) - if pooled_output is not None: - d["pooled_output"] = torch.zeros_like(pooled_output) - n = [torch.zeros_like(t[0]), d] - c.append(n) - return (c,) +import copy +import logging +import gguf +import torch + +from modules.Device import Device +from modules.Model import ModelPatcher +from modules.Utilities import util +from modules.clip import Clip +from modules.cond import cast + +# Constants for torch-compatible quantization types +TORCH_COMPATIBLE_QTYPES = { + None, + gguf.GGMLQuantizationType.F32, + gguf.GGMLQuantizationType.F16, +} + + +def is_torch_compatible(tensor: torch.Tensor) -> bool: + """#### Check if a tensor is compatible with PyTorch operations. + + #### Args: + - `tensor` (torch.Tensor): The tensor to check. + + #### Returns: + - `bool`: Whether the tensor is torch-compatible. + """ + return ( + tensor is None + or getattr(tensor, "tensor_type", None) in TORCH_COMPATIBLE_QTYPES + ) + + +def is_quantized(tensor: torch.Tensor) -> bool: + """#### Check if a tensor is quantized. + + #### Args: + - `tensor` (torch.Tensor): The tensor to check. + + #### Returns: + - `bool`: Whether the tensor is quantized. + """ + return not is_torch_compatible(tensor) + + +def dequantize( + data: torch.Tensor, + qtype: gguf.GGMLQuantizationType, + oshape: tuple, + dtype: torch.dtype = None, +) -> torch.Tensor: + """#### Dequantize tensor back to usable shape/dtype. + + #### Args: + - `data` (torch.Tensor): The quantized data. + - `qtype` (gguf.GGMLQuantizationType): The quantization type. + - `oshape` (tuple): The output shape. + - `dtype` (torch.dtype, optional): The output dtype. Defaults to None. + + #### Returns: + - `torch.Tensor`: The dequantized tensor. + """ + # Get block size and type size for quantization format + block_size, type_size = gguf.GGML_QUANT_SIZES[qtype] + dequantize_blocks = dequantize_functions[qtype] + + # Reshape data into blocks + rows = data.reshape((-1, data.shape[-1])).view(torch.uint8) + n_blocks = rows.numel() // type_size + blocks = rows.reshape((n_blocks, type_size)) + + # Dequantize blocks and reshape to target shape + blocks = dequantize_blocks(blocks, block_size, type_size, dtype) + return blocks.reshape(oshape) + + +def split_block_dims(blocks: torch.Tensor, *args) -> list: + """#### Split blocks into dimensions. + + #### Args: + - `blocks` (torch.Tensor): The blocks to split. + - `*args`: The dimensions to split into. + + #### Returns: + - `list`: The split blocks. + """ + n_max = blocks.shape[1] + dims = list(args) + [n_max - sum(args)] + return torch.split(blocks, dims, dim=1) + + +# Legacy Quantization Functions +def dequantize_blocks_Q8_0( + blocks: torch.Tensor, block_size: int, type_size: int, dtype: torch.dtype = None +) -> torch.Tensor: + """#### Dequantize Q8_0 quantized blocks. + + #### Args: + - `blocks` (torch.Tensor): The quantized blocks. + - `block_size` (int): The block size. + - `type_size` (int): The type size. + - `dtype` (torch.dtype, optional): The output dtype. Defaults to None. + + #### Returns: + - `torch.Tensor`: The dequantized blocks. + """ + # Split blocks into scale and quantized values + d, x = split_block_dims(blocks, 2) + d = d.view(torch.float16).to(dtype) + x = x.view(torch.int8) + return d * x + + +# K Quants # +QK_K = 256 +K_SCALE_SIZE = 12 + +# Mapping of quantization types to dequantization functions +dequantize_functions = { + gguf.GGMLQuantizationType.Q8_0: dequantize_blocks_Q8_0, +} + + +def dequantize_tensor( + tensor: torch.Tensor, dtype: torch.dtype = None, dequant_dtype: torch.dtype = None +) -> torch.Tensor: + """#### Dequantize a potentially quantized tensor. + + #### Args: + - `tensor` (torch.Tensor): The tensor to dequantize. + - `dtype` (torch.dtype, optional): Target dtype. Defaults to None. + - `dequant_dtype` (torch.dtype, optional): Intermediate dequantization dtype. Defaults to None. + + #### Returns: + - `torch.Tensor`: The dequantized tensor. + """ + qtype = getattr(tensor, "tensor_type", None) + oshape = getattr(tensor, "tensor_shape", tensor.shape) + + if qtype in TORCH_COMPATIBLE_QTYPES: + return tensor.to(dtype) + elif qtype in dequantize_functions: + dequant_dtype = dtype if dequant_dtype == "target" else dequant_dtype + return dequantize(tensor.data, qtype, oshape, dtype=dequant_dtype).to(dtype) + + +class GGMLLayer(torch.nn.Module): + """#### Base class for GGML quantized layers. + + Handles dynamic dequantization of weights during forward pass. + """ + + comfy_cast_weights: bool = True + dequant_dtype: torch.dtype = None + patch_dtype: torch.dtype = None + torch_compatible_tensor_types: set = { + None, + gguf.GGMLQuantizationType.F32, + gguf.GGMLQuantizationType.F16, + } + + def is_ggml_quantized( + self, *, weight: torch.Tensor = None, bias: torch.Tensor = None + ) -> bool: + """#### Check if layer weights are GGML quantized. + + #### Args: + - `weight` (torch.Tensor, optional): Weight tensor to check. Defaults to self.weight. + - `bias` (torch.Tensor, optional): Bias tensor to check. Defaults to self.bias. + + #### Returns: + - `bool`: Whether weights are quantized. + """ + if weight is None: + weight = self.weight + if bias is None: + bias = self.bias + return is_quantized(weight) or is_quantized(bias) + + def _load_from_state_dict( + self, state_dict: dict, prefix: str, *args, **kwargs + ) -> None: + """#### Load quantized weights from state dict. + + #### Args: + - `state_dict` (dict): State dictionary. + - `prefix` (str): Key prefix. + - `*args`: Additional arguments. + - `**kwargs`: Additional keyword arguments. + """ + weight = state_dict.get(f"{prefix}weight") + bias = state_dict.get(f"{prefix}bias") + # Use modified loader for quantized or linear layers + if self.is_ggml_quantized(weight=weight, bias=bias) or isinstance( + self, torch.nn.Linear + ): + return self.ggml_load_from_state_dict(state_dict, prefix, *args, **kwargs) + return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) + + def ggml_load_from_state_dict( + self, + state_dict: dict, + prefix: str, + local_metadata: dict, + strict: bool, + missing_keys: list, + unexpected_keys: list, + error_msgs: list, + ) -> None: + """#### Load GGML quantized weights from state dict. + + #### Args: + - `state_dict` (dict): State dictionary. + - `prefix` (str): Key prefix. + - `local_metadata` (dict): Local metadata. + - `strict` (bool): Strict loading mode. + - `missing_keys` (list): Keys missing from state dict. + - `unexpected_keys` (list): Unexpected keys found. + - `error_msgs` (list): Error messages. + """ + prefix_len = len(prefix) + for k, v in state_dict.items(): + if k[prefix_len:] == "weight": + self.weight = torch.nn.Parameter(v, requires_grad=False) + elif k[prefix_len:] == "bias" and v is not None: + self.bias = torch.nn.Parameter(v, requires_grad=False) + else: + missing_keys.append(k) + + def _save_to_state_dict(self, *args, **kwargs) -> None: + """#### Save layer state to state dict. + + #### Args: + - `*args`: Additional arguments. + - `**kwargs`: Additional keyword arguments. + """ + if self.is_ggml_quantized(): + return self.ggml_save_to_state_dict(*args, **kwargs) + return super()._save_to_state_dict(*args, **kwargs) + + def ggml_save_to_state_dict( + self, destination: dict, prefix: str, keep_vars: bool + ) -> None: + """#### Save GGML layer state to state dict. + + #### Args: + - `destination` (dict): Destination dictionary. + - `prefix` (str): Key prefix. + - `keep_vars` (bool): Whether to keep variables. + """ + # Create fake tensors for VRAM estimation + weight = torch.zeros_like(self.weight, device=torch.device("meta")) + destination[prefix + "weight"] = weight + if self.bias is not None: + bias = torch.zeros_like(self.bias, device=torch.device("meta")) + destination[prefix + "bias"] = bias + return + + def get_weight(self, tensor: torch.Tensor, dtype: torch.dtype) -> torch.Tensor: + """#### Get dequantized weight tensor. + + #### Args: + - `tensor` (torch.Tensor): Input tensor. + - `dtype` (torch.dtype): Target dtype. + + #### Returns: + - `torch.Tensor`: Dequantized tensor. + """ + if tensor is None: + return + + # Consolidate and load patches to GPU asynchronously + patch_list = [] + device = tensor.device + for function, patches, key in getattr(tensor, "patches", []): + patch_list += move_patch_to_device(patches, device) + + # Dequantize tensor while patches load + weight = dequantize_tensor(tensor, dtype, self.dequant_dtype) + + # Apply patches + if patch_list: + if self.patch_dtype is None: + weight = function(patch_list, weight, key) + else: + # For testing, may degrade image quality + patch_dtype = ( + dtype if self.patch_dtype == "target" else self.patch_dtype + ) + weight = function(patch_list, weight, key, patch_dtype) + return weight + + def cast_bias_weight( + self, + input: torch.Tensor = None, + dtype: torch.dtype = None, + device: torch.device = None, + bias_dtype: torch.dtype = None, + ) -> tuple: + """#### Cast layer weights and bias to target dtype/device. + + #### Args: + - `input` (torch.Tensor, optional): Input tensor for type/device inference. + - `dtype` (torch.dtype, optional): Target dtype. + - `device` (torch.device, optional): Target device. + - `bias_dtype` (torch.dtype, optional): Target bias dtype. + + #### Returns: + - `tuple`: (cast_weight, cast_bias) + """ + if input is not None: + if dtype is None: + dtype = getattr(input, "dtype", torch.float32) + if bias_dtype is None: + bias_dtype = dtype + if device is None: + device = input.device + + bias = None + non_blocking = Device.device_supports_non_blocking(device) + if self.bias is not None: + bias = self.get_weight(self.bias.to(device), dtype) + bias = cast.cast_to( + bias, bias_dtype, device, non_blocking=non_blocking, copy=False + ) + + weight = self.get_weight(self.weight.to(device), dtype) + weight = cast.cast_to( + weight, dtype, device, non_blocking=non_blocking, copy=False + ) + return weight, bias + + def forward_comfy_cast_weights( + self, input: torch.Tensor, *args, **kwargs + ) -> torch.Tensor: + """#### Forward pass with weight casting. + + #### Args: + - `input` (torch.Tensor): Input tensor. + - `*args`: Additional arguments. + - `**kwargs`: Additional keyword arguments. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + if self.is_ggml_quantized(): + return self.forward_ggml_cast_weights(input, *args, **kwargs) + return super().forward_comfy_cast_weights(input, *args, **kwargs) + + +class GGMLOps(cast.manual_cast): + """ + Dequantize weights on the fly before doing the compute + """ + + class Linear(GGMLLayer, cast.manual_cast.Linear): + def __init__( + self, in_features, out_features, bias=True, device=None, dtype=None + ): + """ + Initialize the Linear layer. + + Args: + in_features (int): Number of input features. + out_features (int): Number of output features. + bias (bool, optional): If set to False, the layer will not learn an additive bias. Defaults to True. + device (torch.device, optional): The device to store the layer's parameters. Defaults to None. + dtype (torch.dtype, optional): The data type of the layer's parameters. Defaults to None. + """ + torch.nn.Module.__init__(self) + # TODO: better workaround for reserved memory spike on windows + # Issue is with `torch.empty` still reserving the full memory for the layer + # Windows doesn't over-commit memory so without this 24GB+ of pagefile is used + self.in_features = in_features + self.out_features = out_features + self.weight = None + self.bias = None + + def forward_ggml_cast_weights(self, input: torch.Tensor) -> torch.Tensor: + """ + Forward pass with GGML cast weights. + + Args: + input (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor. + """ + weight, bias = self.cast_bias_weight(input) + return torch.nn.functional.linear(input, weight, bias) + + class Embedding(GGMLLayer, cast.manual_cast.Embedding): + def forward_ggml_cast_weights( + self, input: torch.Tensor, out_dtype: torch.dtype = None + ) -> torch.Tensor: + """ + Forward pass with GGML cast weights for embedding. + + Args: + input (torch.Tensor): The input tensor. + out_dtype (torch.dtype, optional): The output data type. Defaults to None. + + Returns: + torch.Tensor: The output tensor. + """ + output_dtype = out_dtype + if ( + self.weight.dtype == torch.float16 + or self.weight.dtype == torch.bfloat16 + ): + out_dtype = None + weight, _bias = self.cast_bias_weight( + self, device=input.device, dtype=out_dtype + ) + return torch.nn.functional.embedding( + input, + weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ).to(dtype=output_dtype) + + +def gguf_sd_loader_get_orig_shape( + reader: gguf.GGUFReader, tensor_name: str +) -> torch.Size: + """#### Get the original shape of a tensor from a GGUF reader. + + #### Args: + - `reader` (gguf.GGUFReader): The GGUF reader. + - `tensor_name` (str): The name of the tensor. + + #### Returns: + - `torch.Size`: The original shape of the tensor. + """ + field_key = f"comfy.gguf.orig_shape.{tensor_name}" + field = reader.get_field(field_key) + if field is None: + return None + # Has original shape metadata, so we try to decode it. + if ( + len(field.types) != 2 + or field.types[0] != gguf.GGUFValueType.ARRAY + or field.types[1] != gguf.GGUFValueType.INT32 + ): + raise TypeError( + f"Bad original shape metadata for {field_key}: Expected ARRAY of INT32, got {field.types}" + ) + return torch.Size(tuple(int(field.parts[part_idx][0]) for part_idx in field.data)) + + +class GGMLTensor(torch.Tensor): + """ + Main tensor-like class for storing quantized weights + """ + + def __init__(self, *args, tensor_type, tensor_shape, patches=[], **kwargs): + """ + Initialize the GGMLTensor. + + Args: + *args: Variable length argument list. + tensor_type: The type of the tensor. + tensor_shape: The shape of the tensor. + patches (list, optional): List of patches. Defaults to []. + **kwargs: Arbitrary keyword arguments. + """ + super().__init__() + self.tensor_type = tensor_type + self.tensor_shape = tensor_shape + self.patches = patches + + def __new__(cls, *args, tensor_type, tensor_shape, patches=[], **kwargs): + """ + Create a new instance of GGMLTensor. + + Args: + *args: Variable length argument list. + tensor_type: The type of the tensor. + tensor_shape: The shape of the tensor. + patches (list, optional): List of patches. Defaults to []. + **kwargs: Arbitrary keyword arguments. + + Returns: + GGMLTensor: A new instance of GGMLTensor. + """ + return super().__new__(cls, *args, **kwargs) + + def to(self, *args, **kwargs): + """ + Convert the tensor to a specified device and/or dtype. + + Args: + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Returns: + GGMLTensor: The converted tensor. + """ + new = super().to(*args, **kwargs) + new.tensor_type = getattr(self, "tensor_type", None) + new.tensor_shape = getattr(self, "tensor_shape", new.data.shape) + new.patches = getattr(self, "patches", []).copy() + return new + + def clone(self, *args, **kwargs): + """ + Clone the tensor. + + Args: + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Returns: + GGMLTensor: The cloned tensor. + """ + return self + + def detach(self, *args, **kwargs): + """ + Detach the tensor from the computation graph. + + Args: + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Returns: + GGMLTensor: The detached tensor. + """ + return self + + def copy_(self, *args, **kwargs): + """ + Copy the values from another tensor into this tensor. + + Args: + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Returns: + GGMLTensor: The tensor with copied values. + """ + try: + return super().copy_(*args, **kwargs) + except Exception as e: + print(f"ignoring 'copy_' on tensor: {e}") + + def __deepcopy__(self, *args, **kwargs): + """ + Create a deep copy of the tensor. + + Args: + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Returns: + GGMLTensor: The deep copied tensor. + """ + new = super().__deepcopy__(*args, **kwargs) + new.tensor_type = getattr(self, "tensor_type", None) + new.tensor_shape = getattr(self, "tensor_shape", new.data.shape) + new.patches = getattr(self, "patches", []).copy() + return new + + @property + def shape(self): + """ + Get the shape of the tensor. + + Returns: + torch.Size: The shape of the tensor. + """ + if not hasattr(self, "tensor_shape"): + self.tensor_shape = self.size() + return self.tensor_shape + + +def gguf_sd_loader(path: str, handle_prefix: str = "model.diffusion_model."): + """#### Load a GGUF file into a state dict. + + #### Args: + - `path` (str): The path to the GGUF file. + - `handle_prefix` (str, optional): The prefix to handle. Defaults to "model.diffusion_model.". + + #### Returns: + - `dict`: The loaded state dict. + """ + reader = gguf.GGUFReader(path) + + # filter and strip prefix + has_prefix = False + if handle_prefix is not None: + prefix_len = len(handle_prefix) + tensor_names = set(tensor.name for tensor in reader.tensors) + has_prefix = any(s.startswith(handle_prefix) for s in tensor_names) + + tensors = [] + for tensor in reader.tensors: + sd_key = tensor_name = tensor.name + if has_prefix: + if not tensor_name.startswith(handle_prefix): + continue + sd_key = tensor_name[prefix_len:] + tensors.append((sd_key, tensor)) + + # detect and verify architecture + compat = None + arch_str = None + arch_field = reader.get_field("general.architecture") + if arch_field is not None: + if ( + len(arch_field.types) != 1 + or arch_field.types[0] != gguf.GGUFValueType.STRING + ): + raise TypeError( + f"Bad type for GGUF general.architecture key: expected string, got {arch_field.types!r}" + ) + arch_str = str(arch_field.parts[arch_field.data[-1]], encoding="utf-8") + if arch_str not in {"flux", "sd1", "sdxl", "t5", "t5encoder"}: + raise ValueError( + f"Unexpected architecture type in GGUF file, expected one of flux, sd1, sdxl, t5encoder but got {arch_str!r}" + ) + + # main loading loop + state_dict = {} + qtype_dict = {} + for sd_key, tensor in tensors: + tensor_name = tensor.name + tensor_type_str = str(tensor.tensor_type) + torch_tensor = torch.from_numpy(tensor.data) # mmap + + shape = gguf_sd_loader_get_orig_shape(reader, tensor_name) + if shape is None: + shape = torch.Size(tuple(int(v) for v in reversed(tensor.shape))) + # Workaround for stable-diffusion.cpp SDXL detection. + if compat == "sd.cpp" and arch_str == "sdxl": + if any( + [ + tensor_name.endswith(x) + for x in (".proj_in.weight", ".proj_out.weight") + ] + ): + while len(shape) > 2 and shape[-1] == 1: + shape = shape[:-1] + + # add to state dict + if tensor.tensor_type in { + gguf.GGMLQuantizationType.F32, + gguf.GGMLQuantizationType.F16, + }: + torch_tensor = torch_tensor.view(*shape) + state_dict[sd_key] = GGMLTensor( + torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape + ) + qtype_dict[tensor_type_str] = qtype_dict.get(tensor_type_str, 0) + 1 + + # sanity check debug print + print("\nggml_sd_loader:") + for k, v in qtype_dict.items(): + print(f" {k:30}{v:3}") + + return state_dict + + +class GGUFModelPatcher(ModelPatcher.ModelPatcher): + patch_on_device = False + + def unpatch_model(self, device_to=None, unpatch_weights=True): + """ + Unpatch the model. + + Args: + device_to (torch.device, optional): The device to move the model to. Defaults to None. + unpatch_weights (bool, optional): Whether to unpatch the weights. Defaults to True. + + Returns: + GGUFModelPatcher: The unpatched model. + """ + if unpatch_weights: + for p in self.model.parameters(): + if is_torch_compatible(p): + continue + patches = getattr(p, "patches", []) + if len(patches) > 0: + p.patches = [] + self.object_patches = {} + # TODO: Find another way to not unload after patches + return super().unpatch_model( + device_to=device_to, unpatch_weights=unpatch_weights + ) + + mmap_released = False + + def load(self, *args, force_patch_weights=False, **kwargs): + """ + Load the model. + + Args: + *args: Variable length argument list. + force_patch_weights (bool, optional): Whether to force patch weights. Defaults to False. + **kwargs: Arbitrary keyword arguments. + """ + super().load(*args, force_patch_weights=True, **kwargs) + + # make sure nothing stays linked to mmap after first load + if not self.mmap_released: + linked = [] + if kwargs.get("lowvram_model_memory", 0) > 0: + for n, m in self.model.named_modules(): + if hasattr(m, "weight"): + device = getattr(m.weight, "device", None) + if device == self.offload_device: + linked.append((n, m)) + continue + if hasattr(m, "bias"): + device = getattr(m.bias, "device", None) + if device == self.offload_device: + linked.append((n, m)) + continue + if linked: + print(f"Attempting to release mmap ({len(linked)})") + for n, m in linked: + # TODO: possible to OOM, find better way to detach + m.to(self.load_device).to(self.offload_device) + self.mmap_released = True + + def add_object_patch(self, name, obj): + self.object_patches[name] = obj + + def clone(self, *args, **kwargs): + """ + Clone the model patcher. + + Args: + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Returns: + GGUFModelPatcher: The cloned model patcher. + """ + n = GGUFModelPatcher( + self.model, + self.load_device, + self.offload_device, + self.size, + weight_inplace_update=self.weight_inplace_update, + ) + n.patches = {} + for k in self.patches: + n.patches[k] = self.patches[k][:] + n.patches_uuid = self.patches_uuid + + n.object_patches = self.object_patches.copy() + n.model_options = copy.deepcopy(self.model_options) + n.backup = self.backup + n.object_patches_backup = self.object_patches_backup + n.patch_on_device = getattr(self, "patch_on_device", False) + return n + + +class UnetLoaderGGUF: + def load_unet( + self, + unet_name: str, + dequant_dtype: str = None, + patch_dtype: str = None, + patch_on_device: bool = None, + ) -> tuple: + """ + Load the UNet model. + + Args: + unet_name (str): The name of the UNet model. + dequant_dtype (str, optional): The dequantization data type. Defaults to None. + patch_dtype (str, optional): The patch data type. Defaults to None. + patch_on_device (bool, optional): Whether to patch on device. Defaults to None. + + Returns: + tuple: The loaded model. + """ + ops = GGMLOps() + + if dequant_dtype in ("default", None): + ops.Linear.dequant_dtype = None + elif dequant_dtype in ["target"]: + ops.Linear.dequant_dtype = dequant_dtype + else: + ops.Linear.dequant_dtype = getattr(torch, dequant_dtype) + + if patch_dtype in ("default", None): + ops.Linear.patch_dtype = None + elif patch_dtype in ["target"]: + ops.Linear.patch_dtype = patch_dtype + else: + ops.Linear.patch_dtype = getattr(torch, patch_dtype) + + unet_path = "./_internal/unet/" + unet_name + sd = gguf_sd_loader(unet_path) + model = ModelPatcher.load_diffusion_model_state_dict( + sd, model_options={"custom_operations": ops} + ) + if model is None: + logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path)) + raise RuntimeError( + "ERROR: Could not detect model type of: {}".format(unet_path) + ) + model = GGUFModelPatcher.clone(model) + model.patch_on_device = patch_on_device + return (model,) + + +clip_sd_map = { + "enc.": "encoder.", + ".blk.": ".block.", + "token_embd": "shared", + "output_norm": "final_layer_norm", + "attn_q": "layer.0.SelfAttention.q", + "attn_k": "layer.0.SelfAttention.k", + "attn_v": "layer.0.SelfAttention.v", + "attn_o": "layer.0.SelfAttention.o", + "attn_norm": "layer.0.layer_norm", + "attn_rel_b": "layer.0.SelfAttention.relative_attention_bias", + "ffn_up": "layer.1.DenseReluDense.wi_1", + "ffn_down": "layer.1.DenseReluDense.wo", + "ffn_gate": "layer.1.DenseReluDense.wi_0", + "ffn_norm": "layer.1.layer_norm", +} + +clip_name_dict = { + "stable_diffusion": Clip.CLIPType.STABLE_DIFFUSION, + "sdxl": Clip.CLIPType.STABLE_DIFFUSION, + "sd3": Clip.CLIPType.SD3, + "flux": Clip.CLIPType.FLUX, +} + + +def gguf_clip_loader(path: str) -> dict: + """#### Load a CLIP model from a GGUF file. + + #### Args: + - `path` (str): The path to the GGUF file. + + #### Returns: + - `dict`: The loaded CLIP model. + """ + raw_sd = gguf_sd_loader(path) + assert "enc.blk.23.ffn_up.weight" in raw_sd, "Invalid Text Encoder!" + sd = {} + for k, v in raw_sd.items(): + for s, d in clip_sd_map.items(): + k = k.replace(s, d) + sd[k] = v + return sd + + +class CLIPLoaderGGUF: + def load_data(self, ckpt_paths: list) -> list: + """ + Load data from checkpoint paths. + + Args: + ckpt_paths (list): List of checkpoint paths. + + Returns: + list: List of loaded data. + """ + clip_data = [] + for p in ckpt_paths: + if p.endswith(".gguf"): + clip_data.append(gguf_clip_loader(p)) + else: + sd = util.load_torch_file(p, safe_load=True) + clip_data.append( + { + k: GGMLTensor( + v, + tensor_type=gguf.GGMLQuantizationType.F16, + tensor_shape=v.shape, + ) + for k, v in sd.items() + } + ) + return clip_data + + def load_patcher(self, clip_paths: list, clip_type: str, clip_data: list) -> Clip: + """ + Load the model patcher. + + Args: + clip_paths (list): List of clip paths. + clip_type (str): The type of the clip. + clip_data (list): List of clip data. + + Returns: + Clip: The loaded clip. + """ + clip = Clip.load_text_encoder_state_dicts( + clip_type=clip_type, + state_dicts=clip_data, + model_options={ + "custom_operations": GGMLOps, + "initial_device": Device.text_encoder_offload_device(), + }, + embedding_directory="models/embeddings", + ) + clip.patcher = GGUFModelPatcher.clone(clip.patcher) + + # for some reason this is just missing in some SAI checkpoints + if getattr(clip.cond_stage_model, "clip_l", None) is not None: + if ( + getattr( + clip.cond_stage_model.clip_l.transformer.text_projection.weight, + "tensor_shape", + None, + ) + is None + ): + clip.cond_stage_model.clip_l.transformer.text_projection = ( + cast.manual_cast.Linear(768, 768) + ) + if getattr(clip.cond_stage_model, "clip_g", None) is not None: + if ( + getattr( + clip.cond_stage_model.clip_g.transformer.text_projection.weight, + "tensor_shape", + None, + ) + is None + ): + clip.cond_stage_model.clip_g.transformer.text_projection = ( + cast.manual_cast.Linear(1280, 1280) + ) + + return clip + + +class DualCLIPLoaderGGUF(CLIPLoaderGGUF): + def load_clip(self, clip_name1: str, clip_name2: str, type: str) -> tuple: + """ + Load dual clips. + + Args: + clip_name1 (str): The name of the first clip. + clip_name2 (str): The name of the second clip. + type (str): The type of the clip. + + Returns: + tuple: The loaded clips. + """ + clip_path1 = "./_internal/clip/" + clip_name1 + clip_path2 = "./_internal/clip/" + clip_name2 + clip_paths = (clip_path1, clip_path2) + clip_type = clip_name_dict.get(type, Clip.CLIPType.STABLE_DIFFUSION) + return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),) + + +class CLIPTextEncodeFlux: + def encode( + self, + clip: Clip, + clip_l: str, + t5xxl: str, + guidance: str, + flux_enabled: bool = False, + ) -> tuple: + """ + Encode text using CLIP and T5XXL. + + Args: + clip (Clip): The clip object. + clip_l (str): The clip text. + t5xxl (str): The T5XXL text. + guidance (str): The guidance text. + flux_enabled (bool, optional): Whether flux is enabled. Defaults to False. + + Returns: + tuple: The encoded text. + """ + tokens = clip.tokenize(clip_l) + tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] + + output = clip.encode_from_tokens( + tokens, return_pooled=True, return_dict=True, flux_enabled=flux_enabled + ) + cond = output.pop("cond") + output["guidance"] = guidance + return ([[cond, output]],) + + +class ConditioningZeroOut: + def zero_out(self, conditioning: list) -> list: + """ + Zero out the conditioning. + + Args: + conditioning (list): The conditioning list. + + Returns: + list: The zeroed out conditioning. + """ + c = [] + for t in conditioning: + d = t[1].copy() + pooled_output = d.get("pooled_output", None) + if pooled_output is not None: + d["pooled_output"] = torch.zeros_like(pooled_output) + n = [torch.zeros_like(t[0]), d] + c.append(n) + return (c,) diff --git a/modules/SD15/SD15.py b/modules/SD15/SD15.py index 5b213344e11540ce8dc284eb1d5a9d0309487649..f8c5fcabc3f76c33c07cd88f0b5d66f75512b3a9 100644 --- a/modules/SD15/SD15.py +++ b/modules/SD15/SD15.py @@ -1,81 +1,81 @@ -import torch -from modules.BlackForest import Flux -from modules.Utilities import util -from modules.Model import ModelBase -from modules.SD15 import SDClip, SDToken -from modules.Utilities import Latent -from modules.clip import Clip - - -class sm_SD15(ModelBase.BASE): - """#### Class representing the SD15 model. - - #### Args: - - `ModelBase.BASE` (ModelBase.BASE): The base model class. - """ - - unet_config: dict = { - "context_dim": 768, - "model_channels": 320, - "use_linear_in_transformer": False, - "adm_in_channels": None, - "use_temporal_attention": False, - } - - unet_extra_config: dict = { - "num_heads": 8, - "num_head_channels": -1, - } - - latent_format: Latent.SD15 = Latent.SD15 - - def process_clip_state_dict(self, state_dict: dict) -> dict: - """#### Process the state dictionary for the CLIP model. - - #### Args: - - `state_dict` (dict): The state dictionary. - - #### Returns: - - `dict`: The processed state dictionary. - """ - k = list(state_dict.keys()) - for x in k: - if x.startswith("cond_stage_model.transformer.") and not x.startswith( - "cond_stage_model.transformer.text_model." - ): - y = x.replace( - "cond_stage_model.transformer.", - "cond_stage_model.transformer.text_model.", - ) - state_dict[y] = state_dict.pop(x) - - if ( - "cond_stage_model.transformer.text_model.embeddings.position_ids" - in state_dict - ): - ids = state_dict[ - "cond_stage_model.transformer.text_model.embeddings.position_ids" - ] - if ids.dtype == torch.float32: - state_dict[ - "cond_stage_model.transformer.text_model.embeddings.position_ids" - ] = ids.round() - - replace_prefix = {} - replace_prefix["cond_stage_model."] = "clip_l." - state_dict = util.state_dict_prefix_replace( - state_dict, replace_prefix, filter_keys=True - ) - return state_dict - - def clip_target(self) -> Clip.ClipTarget: - """#### Get the target CLIP model. - - #### Returns: - - `Clip.ClipTarget`: The target CLIP model. - """ - return Clip.ClipTarget(SDToken.SD1Tokenizer, SDClip.SD1ClipModel) - -models = [ - sm_SD15, Flux.Flux +import torch +from modules.BlackForest import Flux +from modules.Utilities import util +from modules.Model import ModelBase +from modules.SD15 import SDClip, SDToken +from modules.Utilities import Latent +from modules.clip import Clip + + +class sm_SD15(ModelBase.BASE): + """#### Class representing the SD15 model. + + #### Args: + - `ModelBase.BASE` (ModelBase.BASE): The base model class. + """ + + unet_config: dict = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + } + + unet_extra_config: dict = { + "num_heads": 8, + "num_head_channels": -1, + } + + latent_format: Latent.SD15 = Latent.SD15 + + def process_clip_state_dict(self, state_dict: dict) -> dict: + """#### Process the state dictionary for the CLIP model. + + #### Args: + - `state_dict` (dict): The state dictionary. + + #### Returns: + - `dict`: The processed state dictionary. + """ + k = list(state_dict.keys()) + for x in k: + if x.startswith("cond_stage_model.transformer.") and not x.startswith( + "cond_stage_model.transformer.text_model." + ): + y = x.replace( + "cond_stage_model.transformer.", + "cond_stage_model.transformer.text_model.", + ) + state_dict[y] = state_dict.pop(x) + + if ( + "cond_stage_model.transformer.text_model.embeddings.position_ids" + in state_dict + ): + ids = state_dict[ + "cond_stage_model.transformer.text_model.embeddings.position_ids" + ] + if ids.dtype == torch.float32: + state_dict[ + "cond_stage_model.transformer.text_model.embeddings.position_ids" + ] = ids.round() + + replace_prefix = {} + replace_prefix["cond_stage_model."] = "clip_l." + state_dict = util.state_dict_prefix_replace( + state_dict, replace_prefix, filter_keys=True + ) + return state_dict + + def clip_target(self) -> Clip.ClipTarget: + """#### Get the target CLIP model. + + #### Returns: + - `Clip.ClipTarget`: The target CLIP model. + """ + return Clip.ClipTarget(SDToken.SD1Tokenizer, SDClip.SD1ClipModel) + +models = [ + sm_SD15, Flux.Flux ] \ No newline at end of file diff --git a/modules/SD15/SDClip.py b/modules/SD15/SDClip.py index 074f4cf0cfa0e8dc7632c80d5756bdc150dd9ff9..9193d48964d1f674f93b6e189ff46aa4df82e73c 100644 --- a/modules/SD15/SDClip.py +++ b/modules/SD15/SDClip.py @@ -1,403 +1,403 @@ -import json -import logging -import numbers -import torch -from modules.Device import Device -from modules.cond import cast -from modules.clip.CLIPTextModel import CLIPTextModel - - - -def gen_empty_tokens(special_tokens: dict, length: int) -> list: - """#### Generate a list of empty tokens. - - #### Args: - - `special_tokens` (dict): The special tokens. - - `length` (int): The length of the token list. - - #### Returns: - - `list`: The list of empty tokens. - """ - start_token = special_tokens.get("start", None) - end_token = special_tokens.get("end", None) - pad_token = special_tokens.get("pad") - output = [] - if start_token is not None: - output.append(start_token) - if end_token is not None: - output.append(end_token) - output += [pad_token] * (length - len(output)) - return output - - -class ClipTokenWeightEncoder: - """#### Class representing a CLIP token weight encoder.""" - - def encode_token_weights(self, token_weight_pairs: list) -> tuple: - """#### Encode token weights. - - #### Args: - - `token_weight_pairs` (list): The token weight pairs. - - #### Returns: - - `tuple`: The encoded tokens and the pooled output. - """ - to_encode = list() - max_token_len = 0 - has_weights = False - for x in token_weight_pairs: - tokens = list(map(lambda a: a[0], x)) - max_token_len = max(len(tokens), max_token_len) - has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) - to_encode.append(tokens) - - sections = len(to_encode) - if has_weights or sections == 0: - to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) - - o = self.encode(to_encode) - out, pooled = o[:2] - - if pooled is not None: - first_pooled = pooled[0:1].to(Device.intermediate_device()) - else: - first_pooled = pooled - - output = [] - for k in range(0, sections): - z = out[k : k + 1] - if has_weights: - z_empty = out[-1] - for i in range(len(z)): - for j in range(len(z[i])): - weight = token_weight_pairs[k][j][1] - if weight != 1.0: - z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] - output.append(z) - - if len(output) == 0: - r = (out[-1:].to(Device.intermediate_device()), first_pooled) - else: - r = (torch.cat(output, dim=-2).to(Device.intermediate_device()), first_pooled) - - if len(o) > 2: - extra = {} - for k in o[2]: - v = o[2][k] - if k == "attention_mask": - v = ( - v[:sections] - .flatten() - .unsqueeze(dim=0) - .to(Device.intermediate_device()) - ) - extra[k] = v - - r = r + (extra,) - return r - -class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): - """#### Uses the CLIP transformer encoder for text (from huggingface).""" - - LAYERS = ["last", "pooled", "hidden"] - - def __init__( - self, - version: str = "openai/clip-vit-large-patch14", - device: str = "cpu", - max_length: int = 77, - freeze: bool = True, - layer: str = "last", - layer_idx: int = None, - textmodel_json_config: str = None, - dtype: torch.dtype = None, - model_class: type = CLIPTextModel, - special_tokens: dict = {"start": 49406, "end": 49407, "pad": 49407}, - layer_norm_hidden_state: bool = True, - enable_attention_masks: bool = False, - zero_out_masked:bool = False, - return_projected_pooled: bool = True, - return_attention_masks: bool = False, - model_options={}, - ): - """#### Initialize the SDClipModel. - - #### Args: - - `version` (str, optional): The version of the model. Defaults to "openai/clip-vit-large-patch14". - - `device` (str, optional): The device to use. Defaults to "cpu". - - `max_length` (int, optional): The maximum length of the input. Defaults to 77. - - `freeze` (bool, optional): Whether to freeze the model parameters. Defaults to True. - - `layer` (str, optional): The layer to use. Defaults to "last". - - `layer_idx` (int, optional): The index of the layer. Defaults to None. - - `textmodel_json_config` (str, optional): The path to the JSON config file. Defaults to None. - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - - `model_class` (type, optional): The model class. Defaults to CLIPTextModel. - - `special_tokens` (dict, optional): The special tokens. Defaults to {"start": 49406, "end": 49407, "pad": 49407}. - - `layer_norm_hidden_state` (bool, optional): Whether to normalize the hidden state. Defaults to True. - - `enable_attention_masks` (bool, optional): Whether to enable attention masks. Defaults to False. - - `zero_out_masked` (bool, optional): Whether to zero out masked tokens. Defaults to False. - - `return_projected_pooled` (bool, optional): Whether to return the projected pooled output. Defaults to True. - - `return_attention_masks` (bool, optional): Whether to return the attention masks. Defaults to False. - - `model_options` (dict, optional): Additional model options. Defaults to {}. - """ - super().__init__() - assert layer in self.LAYERS - - if textmodel_json_config is None: - textmodel_json_config = "./_internal/clip/sd1_clip_config.json" - - with open(textmodel_json_config) as f: - config = json.load(f) - - operations = model_options.get("custom_operations", None) - if operations is None: - operations = cast.manual_cast - - self.operations = operations - self.transformer = model_class(config, dtype, device, self.operations) - self.num_layers = self.transformer.num_layers - - self.max_length = max_length - if freeze: - self.freeze() - self.layer = layer - self.layer_idx = None - self.special_tokens = special_tokens - - self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) - self.enable_attention_masks = enable_attention_masks - self.zero_out_masked = zero_out_masked - - self.layer_norm_hidden_state = layer_norm_hidden_state - self.return_projected_pooled = return_projected_pooled - self.return_attention_masks = return_attention_masks - - if layer == "hidden": - assert layer_idx is not None - assert abs(layer_idx) < self.num_layers - self.set_clip_options({"layer": layer_idx}) - self.options_default = ( - self.layer, - self.layer_idx, - self.return_projected_pooled, - ) - - def freeze(self) -> None: - """#### Freeze the model parameters.""" - self.transformer = self.transformer.eval() - for param in self.parameters(): - param.requires_grad = False - - def set_clip_options(self, options: dict) -> None: - """#### Set the CLIP options. - - #### Args: - - `options` (dict): The options to set. - """ - layer_idx = options.get("layer", self.layer_idx) - self.return_projected_pooled = options.get( - "projected_pooled", self.return_projected_pooled - ) - if layer_idx is None or abs(layer_idx) > self.num_layers: - self.layer = "last" - else: - self.layer = "hidden" - self.layer_idx = layer_idx - - def reset_clip_options(self) -> None: - """#### Reset the CLIP options to default.""" - self.layer = self.options_default[0] - self.layer_idx = self.options_default[1] - self.return_projected_pooled = self.options_default[2] - - def set_up_textual_embeddings(self, tokens: list, current_embeds: torch.nn.Embedding) -> list: - """#### Set up the textual embeddings. - - #### Args: - - `tokens` (list): The input tokens. - - `current_embeds` (torch.nn.Embedding): The current embeddings. - - #### Returns: - - `list`: The processed tokens. - """ - out_tokens = [] - next_new_token = token_dict_size = current_embeds.weight.shape[0] - embedding_weights = [] - - for x in tokens: - tokens_temp = [] - for y in x: - if isinstance(y, numbers.Integral): - tokens_temp += [int(y)] - else: - if y.shape[0] == current_embeds.weight.shape[1]: - embedding_weights += [y] - tokens_temp += [next_new_token] - next_new_token += 1 - else: - logging.warning( - "WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format( - y.shape[0], current_embeds.weight.shape[1] - ) - ) - while len(tokens_temp) < len(x): - tokens_temp += [self.special_tokens["pad"]] - out_tokens += [tokens_temp] - - n = token_dict_size - if len(embedding_weights) > 0: - new_embedding = self.operations.Embedding( - next_new_token + 1, - current_embeds.weight.shape[1], - device=current_embeds.weight.device, - dtype=current_embeds.weight.dtype, - ) - new_embedding.weight[:token_dict_size] = current_embeds.weight - for x in embedding_weights: - new_embedding.weight[n] = x - n += 1 - self.transformer.set_input_embeddings(new_embedding) - - processed_tokens = [] - for x in out_tokens: - processed_tokens += [ - list(map(lambda a: n if a == -1 else a, x)) - ] # The EOS token should always be the largest one - - return processed_tokens - - def forward(self, tokens: list) -> tuple: - """#### Forward pass of the model. - - #### Args: - - `tokens` (list): The input tokens. - - #### Returns: - - `tuple`: The output and the pooled output. - """ - backup_embeds = self.transformer.get_input_embeddings() - device = backup_embeds.weight.device - tokens = self.set_up_textual_embeddings(tokens, backup_embeds) - tokens = torch.LongTensor(tokens).to(device) - - attention_mask = None - if ( - self.enable_attention_masks - or self.zero_out_masked - or self.return_attention_masks - ): - attention_mask = torch.zeros_like(tokens) - end_token = self.special_tokens.get("end", -1) - for x in range(attention_mask.shape[0]): - for y in range(attention_mask.shape[1]): - attention_mask[x, y] = 1 - if tokens[x, y] == end_token: - break - - attention_mask_model = None - if self.enable_attention_masks: - attention_mask_model = attention_mask - - outputs = self.transformer( - tokens, - attention_mask_model, - intermediate_output=self.layer_idx, - final_layer_norm_intermediate=self.layer_norm_hidden_state, - dtype=torch.float32, - ) - self.transformer.set_input_embeddings(backup_embeds) - - if self.layer == "last": - z = outputs[0].float() - else: - z = outputs[1].float() - - if self.zero_out_masked: - z *= attention_mask.unsqueeze(-1).float() - - pooled_output = None - if len(outputs) >= 3: - if ( - not self.return_projected_pooled - and len(outputs) >= 4 - and outputs[3] is not None - ): - pooled_output = outputs[3].float() - elif outputs[2] is not None: - pooled_output = outputs[2].float() - - extra = {} - if self.return_attention_masks: - extra["attention_mask"] = attention_mask - - if len(extra) > 0: - return z, pooled_output, extra - - return z, pooled_output - - def encode(self, tokens: list) -> tuple: - """#### Encode the input tokens. - - #### Args: - - `tokens` (list): The input tokens. - - #### Returns: - - `tuple`: The encoded tokens and the pooled output. - """ - return self(tokens) - - def load_sd(self, sd: dict) -> None: - """#### Load the state dictionary. - - #### Args: - - `sd` (dict): The state dictionary. - """ - return self.transformer.load_state_dict(sd, strict=False) - - -class SD1ClipModel(torch.nn.Module): - """#### Class representing the SD1ClipModel.""" - - def __init__( - self, device: str = "cpu", dtype: torch.dtype = None, clip_name: str = "l", clip_model: type = SDClipModel, **kwargs - ): - """#### Initialize the SD1ClipModel. - - #### Args: - - `device` (str, optional): The device to use. Defaults to "cpu". - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - - `clip_name` (str, optional): The name of the CLIP model. Defaults to "l". - - `clip_model` (type, optional): The CLIP model class. Defaults to SDClipModel. - - `**kwargs`: Additional keyword arguments. - """ - super().__init__() - self.clip_name = clip_name - self.clip = "clip_{}".format(self.clip_name) - self.lowvram_patch_counter = 0 - self.model_loaded_weight_memory = 0 - setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs)) - - def set_clip_options(self, options: dict) -> None: - """#### Set the CLIP options. - - #### Args: - - `options` (dict): The options to set. - """ - getattr(self, self.clip).set_clip_options(options) - - def reset_clip_options(self) -> None: - """#### Reset the CLIP options to default.""" - getattr(self, self.clip).reset_clip_options() - - def encode_token_weights(self, token_weight_pairs: dict) -> tuple: - """#### Encode token weights. - - #### Args: - - `token_weight_pairs` (dict): The token weight pairs. - - #### Returns: - - `tuple`: The encoded tokens and the pooled output. - """ - token_weight_pairs = token_weight_pairs[self.clip_name] - out, pooled = getattr(self, self.clip).encode_token_weights(token_weight_pairs) +import json +import logging +import numbers +import torch +from modules.Device import Device +from modules.cond import cast +from modules.clip.CLIPTextModel import CLIPTextModel + + + +def gen_empty_tokens(special_tokens: dict, length: int) -> list: + """#### Generate a list of empty tokens. + + #### Args: + - `special_tokens` (dict): The special tokens. + - `length` (int): The length of the token list. + + #### Returns: + - `list`: The list of empty tokens. + """ + start_token = special_tokens.get("start", None) + end_token = special_tokens.get("end", None) + pad_token = special_tokens.get("pad") + output = [] + if start_token is not None: + output.append(start_token) + if end_token is not None: + output.append(end_token) + output += [pad_token] * (length - len(output)) + return output + + +class ClipTokenWeightEncoder: + """#### Class representing a CLIP token weight encoder.""" + + def encode_token_weights(self, token_weight_pairs: list) -> tuple: + """#### Encode token weights. + + #### Args: + - `token_weight_pairs` (list): The token weight pairs. + + #### Returns: + - `tuple`: The encoded tokens and the pooled output. + """ + to_encode = list() + max_token_len = 0 + has_weights = False + for x in token_weight_pairs: + tokens = list(map(lambda a: a[0], x)) + max_token_len = max(len(tokens), max_token_len) + has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) + to_encode.append(tokens) + + sections = len(to_encode) + if has_weights or sections == 0: + to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) + + o = self.encode(to_encode) + out, pooled = o[:2] + + if pooled is not None: + first_pooled = pooled[0:1].to(Device.intermediate_device()) + else: + first_pooled = pooled + + output = [] + for k in range(0, sections): + z = out[k : k + 1] + if has_weights: + z_empty = out[-1] + for i in range(len(z)): + for j in range(len(z[i])): + weight = token_weight_pairs[k][j][1] + if weight != 1.0: + z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] + output.append(z) + + if len(output) == 0: + r = (out[-1:].to(Device.intermediate_device()), first_pooled) + else: + r = (torch.cat(output, dim=-2).to(Device.intermediate_device()), first_pooled) + + if len(o) > 2: + extra = {} + for k in o[2]: + v = o[2][k] + if k == "attention_mask": + v = ( + v[:sections] + .flatten() + .unsqueeze(dim=0) + .to(Device.intermediate_device()) + ) + extra[k] = v + + r = r + (extra,) + return r + +class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): + """#### Uses the CLIP transformer encoder for text (from huggingface).""" + + LAYERS = ["last", "pooled", "hidden"] + + def __init__( + self, + version: str = "openai/clip-vit-large-patch14", + device: str = "cpu", + max_length: int = 77, + freeze: bool = True, + layer: str = "last", + layer_idx: int = None, + textmodel_json_config: str = None, + dtype: torch.dtype = None, + model_class: type = CLIPTextModel, + special_tokens: dict = {"start": 49406, "end": 49407, "pad": 49407}, + layer_norm_hidden_state: bool = True, + enable_attention_masks: bool = False, + zero_out_masked:bool = False, + return_projected_pooled: bool = True, + return_attention_masks: bool = False, + model_options={}, + ): + """#### Initialize the SDClipModel. + + #### Args: + - `version` (str, optional): The version of the model. Defaults to "openai/clip-vit-large-patch14". + - `device` (str, optional): The device to use. Defaults to "cpu". + - `max_length` (int, optional): The maximum length of the input. Defaults to 77. + - `freeze` (bool, optional): Whether to freeze the model parameters. Defaults to True. + - `layer` (str, optional): The layer to use. Defaults to "last". + - `layer_idx` (int, optional): The index of the layer. Defaults to None. + - `textmodel_json_config` (str, optional): The path to the JSON config file. Defaults to None. + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + - `model_class` (type, optional): The model class. Defaults to CLIPTextModel. + - `special_tokens` (dict, optional): The special tokens. Defaults to {"start": 49406, "end": 49407, "pad": 49407}. + - `layer_norm_hidden_state` (bool, optional): Whether to normalize the hidden state. Defaults to True. + - `enable_attention_masks` (bool, optional): Whether to enable attention masks. Defaults to False. + - `zero_out_masked` (bool, optional): Whether to zero out masked tokens. Defaults to False. + - `return_projected_pooled` (bool, optional): Whether to return the projected pooled output. Defaults to True. + - `return_attention_masks` (bool, optional): Whether to return the attention masks. Defaults to False. + - `model_options` (dict, optional): Additional model options. Defaults to {}. + """ + super().__init__() + assert layer in self.LAYERS + + if textmodel_json_config is None: + textmodel_json_config = "./_internal/clip/sd1_clip_config.json" + + with open(textmodel_json_config) as f: + config = json.load(f) + + operations = model_options.get("custom_operations", None) + if operations is None: + operations = cast.manual_cast + + self.operations = operations + self.transformer = model_class(config, dtype, device, self.operations) + self.num_layers = self.transformer.num_layers + + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = None + self.special_tokens = special_tokens + + self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) + self.enable_attention_masks = enable_attention_masks + self.zero_out_masked = zero_out_masked + + self.layer_norm_hidden_state = layer_norm_hidden_state + self.return_projected_pooled = return_projected_pooled + self.return_attention_masks = return_attention_masks + + if layer == "hidden": + assert layer_idx is not None + assert abs(layer_idx) < self.num_layers + self.set_clip_options({"layer": layer_idx}) + self.options_default = ( + self.layer, + self.layer_idx, + self.return_projected_pooled, + ) + + def freeze(self) -> None: + """#### Freeze the model parameters.""" + self.transformer = self.transformer.eval() + for param in self.parameters(): + param.requires_grad = False + + def set_clip_options(self, options: dict) -> None: + """#### Set the CLIP options. + + #### Args: + - `options` (dict): The options to set. + """ + layer_idx = options.get("layer", self.layer_idx) + self.return_projected_pooled = options.get( + "projected_pooled", self.return_projected_pooled + ) + if layer_idx is None or abs(layer_idx) > self.num_layers: + self.layer = "last" + else: + self.layer = "hidden" + self.layer_idx = layer_idx + + def reset_clip_options(self) -> None: + """#### Reset the CLIP options to default.""" + self.layer = self.options_default[0] + self.layer_idx = self.options_default[1] + self.return_projected_pooled = self.options_default[2] + + def set_up_textual_embeddings(self, tokens: list, current_embeds: torch.nn.Embedding) -> list: + """#### Set up the textual embeddings. + + #### Args: + - `tokens` (list): The input tokens. + - `current_embeds` (torch.nn.Embedding): The current embeddings. + + #### Returns: + - `list`: The processed tokens. + """ + out_tokens = [] + next_new_token = token_dict_size = current_embeds.weight.shape[0] + embedding_weights = [] + + for x in tokens: + tokens_temp = [] + for y in x: + if isinstance(y, numbers.Integral): + tokens_temp += [int(y)] + else: + if y.shape[0] == current_embeds.weight.shape[1]: + embedding_weights += [y] + tokens_temp += [next_new_token] + next_new_token += 1 + else: + logging.warning( + "WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format( + y.shape[0], current_embeds.weight.shape[1] + ) + ) + while len(tokens_temp) < len(x): + tokens_temp += [self.special_tokens["pad"]] + out_tokens += [tokens_temp] + + n = token_dict_size + if len(embedding_weights) > 0: + new_embedding = self.operations.Embedding( + next_new_token + 1, + current_embeds.weight.shape[1], + device=current_embeds.weight.device, + dtype=current_embeds.weight.dtype, + ) + new_embedding.weight[:token_dict_size] = current_embeds.weight + for x in embedding_weights: + new_embedding.weight[n] = x + n += 1 + self.transformer.set_input_embeddings(new_embedding) + + processed_tokens = [] + for x in out_tokens: + processed_tokens += [ + list(map(lambda a: n if a == -1 else a, x)) + ] # The EOS token should always be the largest one + + return processed_tokens + + def forward(self, tokens: list) -> tuple: + """#### Forward pass of the model. + + #### Args: + - `tokens` (list): The input tokens. + + #### Returns: + - `tuple`: The output and the pooled output. + """ + backup_embeds = self.transformer.get_input_embeddings() + device = backup_embeds.weight.device + tokens = self.set_up_textual_embeddings(tokens, backup_embeds) + tokens = torch.LongTensor(tokens).to(device) + + attention_mask = None + if ( + self.enable_attention_masks + or self.zero_out_masked + or self.return_attention_masks + ): + attention_mask = torch.zeros_like(tokens) + end_token = self.special_tokens.get("end", -1) + for x in range(attention_mask.shape[0]): + for y in range(attention_mask.shape[1]): + attention_mask[x, y] = 1 + if tokens[x, y] == end_token: + break + + attention_mask_model = None + if self.enable_attention_masks: + attention_mask_model = attention_mask + + outputs = self.transformer( + tokens, + attention_mask_model, + intermediate_output=self.layer_idx, + final_layer_norm_intermediate=self.layer_norm_hidden_state, + dtype=torch.float32, + ) + self.transformer.set_input_embeddings(backup_embeds) + + if self.layer == "last": + z = outputs[0].float() + else: + z = outputs[1].float() + + if self.zero_out_masked: + z *= attention_mask.unsqueeze(-1).float() + + pooled_output = None + if len(outputs) >= 3: + if ( + not self.return_projected_pooled + and len(outputs) >= 4 + and outputs[3] is not None + ): + pooled_output = outputs[3].float() + elif outputs[2] is not None: + pooled_output = outputs[2].float() + + extra = {} + if self.return_attention_masks: + extra["attention_mask"] = attention_mask + + if len(extra) > 0: + return z, pooled_output, extra + + return z, pooled_output + + def encode(self, tokens: list) -> tuple: + """#### Encode the input tokens. + + #### Args: + - `tokens` (list): The input tokens. + + #### Returns: + - `tuple`: The encoded tokens and the pooled output. + """ + return self(tokens) + + def load_sd(self, sd: dict) -> None: + """#### Load the state dictionary. + + #### Args: + - `sd` (dict): The state dictionary. + """ + return self.transformer.load_state_dict(sd, strict=False) + + +class SD1ClipModel(torch.nn.Module): + """#### Class representing the SD1ClipModel.""" + + def __init__( + self, device: str = "cpu", dtype: torch.dtype = None, clip_name: str = "l", clip_model: type = SDClipModel, **kwargs + ): + """#### Initialize the SD1ClipModel. + + #### Args: + - `device` (str, optional): The device to use. Defaults to "cpu". + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + - `clip_name` (str, optional): The name of the CLIP model. Defaults to "l". + - `clip_model` (type, optional): The CLIP model class. Defaults to SDClipModel. + - `**kwargs`: Additional keyword arguments. + """ + super().__init__() + self.clip_name = clip_name + self.clip = "clip_{}".format(self.clip_name) + self.lowvram_patch_counter = 0 + self.model_loaded_weight_memory = 0 + setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs)) + + def set_clip_options(self, options: dict) -> None: + """#### Set the CLIP options. + + #### Args: + - `options` (dict): The options to set. + """ + getattr(self, self.clip).set_clip_options(options) + + def reset_clip_options(self) -> None: + """#### Reset the CLIP options to default.""" + getattr(self, self.clip).reset_clip_options() + + def encode_token_weights(self, token_weight_pairs: dict) -> tuple: + """#### Encode token weights. + + #### Args: + - `token_weight_pairs` (dict): The token weight pairs. + + #### Returns: + - `tuple`: The encoded tokens and the pooled output. + """ + token_weight_pairs = token_weight_pairs[self.clip_name] + out, pooled = getattr(self, self.clip).encode_token_weights(token_weight_pairs) return out, pooled \ No newline at end of file diff --git a/modules/SD15/SDToken.py b/modules/SD15/SDToken.py index 94d58e5ca98e013fe335fb4d2facf5e82f963a99..231144edb1c3d173434a03dcdb68b69660533b7d 100644 --- a/modules/SD15/SDToken.py +++ b/modules/SD15/SDToken.py @@ -1,450 +1,450 @@ -import logging -import os -import traceback -import torch -from transformers import CLIPTokenizerFast - -def model_options_long_clip(sd, tokenizer_data, model_options): - w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None) - if w is None: - w = sd.get("text_model.embeddings.position_embedding.weight", None) - return tokenizer_data, model_options - -def parse_parentheses(string: str) -> list: - """#### Parse a string with nested parentheses. - - #### Args: - - `string` (str): The input string. - - #### Returns: - - `list`: The parsed list of strings. - """ - result = [] - current_item = "" - nesting_level = 0 - for char in string: - if char == "(": - if nesting_level == 0: - if current_item: - result.append(current_item) - current_item = "(" - else: - current_item = "(" - else: - current_item += char - nesting_level += 1 - elif char == ")": - nesting_level -= 1 - if nesting_level == 0: - result.append(current_item + ")") - current_item = "" - else: - current_item += char - else: - current_item += char - if current_item: - result.append(current_item) - return result - - -def token_weights(string: str, current_weight: float) -> list: - """#### Parse a string into tokens with weights. - - #### Args: - - `string` (str): The input string. - - `current_weight` (float): The current weight. - - #### Returns: - - `list`: The list of token-weight pairs. - """ - a = parse_parentheses(string) - out = [] - for x in a: - weight = current_weight - if len(x) >= 2 and x[-1] == ")" and x[0] == "(": - x = x[1:-1] - xx = x.rfind(":") - weight *= 1.1 - if xx > 0: - try: - weight = float(x[xx + 1 :]) - x = x[:xx] - except: - pass - out += token_weights(x, weight) - else: - out += [(x, current_weight)] - return out - - -def escape_important(text: str) -> str: - """#### Escape important characters in a string. - - #### Args: - - `text` (str): The input text. - - #### Returns: - - `str`: The escaped text. - """ - text = text.replace("\\)", "\0\1") - text = text.replace("\\(", "\0\2") - return text - - -def unescape_important(text: str) -> str: - """#### Unescape important characters in a string. - - #### Args: - - `text` (str): The input text. - - #### Returns: - - `str`: The unescaped text. - """ - text = text.replace("\0\1", ")") - text = text.replace("\0\2", "(") - return text - - -def expand_directory_list(directories: list) -> list: - """#### Expand a list of directories to include all subdirectories. - - #### Args: - - `directories` (list): The list of directories. - - #### Returns: - - `list`: The expanded list of directories. - """ - dirs = set() - for x in directories: - dirs.add(x) - for root, subdir, file in os.walk(x, followlinks=True): - dirs.add(root) - return list(dirs) - - -def load_embed(embedding_name: str, embedding_directory: list, embedding_size: int, embed_key: str = None) -> torch.Tensor: - """#### Load an embedding from a directory. - - #### Args: - - `embedding_name` (str): The name of the embedding. - - `embedding_directory` (list): The list of directories to search. - - `embedding_size` (int): The size of the embedding. - - `embed_key` (str, optional): The key for the embedding. Defaults to None. - - #### Returns: - - `torch.Tensor`: The loaded embedding. - """ - if isinstance(embedding_directory, str): - embedding_directory = [embedding_directory] - - embedding_directory = expand_directory_list(embedding_directory) - - valid_file = None - for embed_dir in embedding_directory: - embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name)) - embed_dir = os.path.abspath(embed_dir) - try: - if os.path.commonpath((embed_dir, embed_path)) != embed_dir: - continue - except: - continue - if not os.path.isfile(embed_path): - extensions = [".safetensors", ".pt", ".bin"] - for x in extensions: - t = embed_path + x - if os.path.isfile(t): - valid_file = t - break - else: - valid_file = embed_path - if valid_file is not None: - break - - if valid_file is None: - return None - - embed_path = valid_file - - embed_out = None - - try: - if embed_path.lower().endswith(".safetensors"): - import safetensors.torch - - embed = safetensors.torch.load_file(embed_path, device="cpu") - else: - if "weights_only" in torch.load.__code__.co_varnames: - embed = torch.load(embed_path, weights_only=True, map_location="cpu") - else: - embed = torch.load(embed_path, map_location="cpu") - except Exception: - logging.warning( - "{}\n\nerror loading embedding, skipping loading: {}".format( - traceback.format_exc(), embedding_name - ) - ) - return None - - if embed_out is None: - if "string_to_param" in embed: - values = embed["string_to_param"].values() - embed_out = next(iter(values)) - elif isinstance(embed, list): - out_list = [] - for x in range(len(embed)): - for k in embed[x]: - t = embed[x][k] - if t.shape[-1] != embedding_size: - continue - out_list.append(t.reshape(-1, t.shape[-1])) - embed_out = torch.cat(out_list, dim=0) - elif embed_key is not None and embed_key in embed: - embed_out = embed[embed_key] - else: - values = embed.values() - embed_out = next(iter(values)) - return embed_out - - -class SDTokenizer: - """#### Class representing a Stable Diffusion tokenizer.""" - - def __init__( - self, - tokenizer_path: str = None, - max_length: int = 77, - pad_with_end: bool = True, - embedding_directory: str = None, - embedding_size: int = 768, - embedding_key: str = "clip_l", - tokenizer_class: type = CLIPTokenizerFast, - has_start_token: bool = True, - pad_to_max_length: bool = True, - min_length: int = None, - ): - """#### Initialize the SDTokenizer. - - #### Args: - - `tokenizer_path` (str, optional): The path to the tokenizer. Defaults to None. - - `max_length` (int, optional): The maximum length of the input. Defaults to 77. - - `pad_with_end` (bool, optional): Whether to pad with the end token. Defaults to True. - - `embedding_directory` (str, optional): The directory for embeddings. Defaults to None. - - `embedding_size` (int, optional): The size of the embeddings. Defaults to 768. - - `embedding_key` (str, optional): The key for the embeddings. Defaults to "clip_l". - - `tokenizer_class` (type, optional): The tokenizer class. Defaults to CLIPTokenizer. - - `has_start_token` (bool, optional): Whether the tokenizer has a start token. Defaults to True. - - `pad_to_max_length` (bool, optional): Whether to pad to the maximum length. Defaults to True. - - `min_length` (int, optional): The minimum length of the input. Defaults to None. - """ - if tokenizer_path is None: - tokenizer_path = "_internal/sd1_tokenizer/" - self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path) - self.max_length = max_length - self.min_length = min_length - - empty = self.tokenizer("")["input_ids"] - if has_start_token: - self.tokens_start = 1 - self.start_token = empty[0] - self.end_token = empty[1] - else: - self.tokens_start = 0 - self.start_token = None - self.end_token = empty[0] - self.pad_with_end = pad_with_end - self.pad_to_max_length = pad_to_max_length - - vocab = self.tokenizer.get_vocab() - self.inv_vocab = {v: k for k, v in vocab.items()} - self.embedding_directory = embedding_directory - self.max_word_length = 8 - self.embedding_identifier = "embedding:" - self.embedding_size = embedding_size - self.embedding_key = embedding_key - - def _try_get_embedding(self, embedding_name: str) -> tuple: - """#### Try to get an embedding. - - #### Args: - - `embedding_name` (str): The name of the embedding. - - #### Returns: - - `tuple`: The embedding and any leftover text. - """ - embed = load_embed( - embedding_name, - self.embedding_directory, - self.embedding_size, - self.embedding_key, - ) - if embed is None: - stripped = embedding_name.strip(",") - if len(stripped) < len(embedding_name): - embed = load_embed( - stripped, - self.embedding_directory, - self.embedding_size, - self.embedding_key, - ) - return (embed, embedding_name[len(stripped) :]) - return (embed, "") - - def tokenize_with_weights(self, text: str, return_word_ids: bool = False) -> list: - """#### Tokenize text with weights. - - #### Args: - - `text` (str): The input text. - - `return_word_ids` (bool, optional): Whether to return word IDs. Defaults to False. - - #### Returns: - - `list`: The tokenized text with weights. - """ - if self.pad_with_end: - pad_token = self.end_token - else: - pad_token = 0 - - text = escape_important(text) - parsed_weights = token_weights(text, 1.0) - - # tokenize words - tokens = [] - for weighted_segment, weight in parsed_weights: - to_tokenize = ( - unescape_important(weighted_segment).replace("\n", " ").split(" ") - ) - to_tokenize = [x for x in to_tokenize if x != ""] - for word in to_tokenize: - # if we find an embedding, deal with the embedding - if ( - word.startswith(self.embedding_identifier) - and self.embedding_directory is not None - ): - embedding_name = word[len(self.embedding_identifier) :].strip("\n") - embed, leftover = self._try_get_embedding(embedding_name) - if embed is None: - logging.warning( - f"warning, embedding:{embedding_name} does not exist, ignoring" - ) - else: - if len(embed.shape) == 1: - tokens.append([(embed, weight)]) - else: - tokens.append( - [(embed[x], weight) for x in range(embed.shape[0])] - ) - print("loading ", embedding_name) - # if we accidentally have leftover text, continue parsing using leftover, else move on to next word - if leftover != "": - word = leftover - else: - continue - # parse word - tokens.append( - [ - (t, weight) - for t in self.tokenizer(word)["input_ids"][ - self.tokens_start : -1 - ] - ] - ) - - # reshape token array to CLIP input size - batched_tokens = [] - batch = [] - if self.start_token is not None: - batch.append((self.start_token, 1.0, 0)) - batched_tokens.append(batch) - for i, t_group in enumerate(tokens): - # determine if we're going to try and keep the tokens in a single batch - is_large = len(t_group) >= self.max_word_length - - while len(t_group) > 0: - if len(t_group) + len(batch) > self.max_length - 1: - remaining_length = self.max_length - len(batch) - 1 - # break word in two and add end token - if is_large: - batch.extend( - [(t, w, i + 1) for t, w in t_group[:remaining_length]] - ) - batch.append((self.end_token, 1.0, 0)) - t_group = t_group[remaining_length:] - # add end token and pad - else: - batch.append((self.end_token, 1.0, 0)) - if self.pad_to_max_length: - batch.extend([(pad_token, 1.0, 0)] * (remaining_length)) - # start new batch - batch = [] - if self.start_token is not None: - batch.append((self.start_token, 1.0, 0)) - batched_tokens.append(batch) - else: - batch.extend([(t, w, i + 1) for t, w in t_group]) - t_group = [] - - # fill last batch - batch.append((self.end_token, 1.0, 0)) - if self.pad_to_max_length: - batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch))) - if self.min_length is not None and len(batch) < self.min_length: - batch.extend([(pad_token, 1.0, 0)] * (self.min_length - len(batch))) - - if not return_word_ids: - batched_tokens = [[(t, w) for t, w, _ in x] for x in batched_tokens] - - return batched_tokens - - def untokenize(self, token_weight_pair: list) -> list: - """#### Untokenize a list of token-weight pairs. - - #### Args: - - `token_weight_pair` (list): The list of token-weight pairs. - - #### Returns: - - `list`: The untokenized list. - """ - return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) - - -class SD1Tokenizer: - """#### Class representing the SD1Tokenizer.""" - - def __init__(self, embedding_directory: str = None, clip_name: str = "l", tokenizer: type = SDTokenizer): - """#### Initialize the SD1Tokenizer. - - #### Args: - - `embedding_directory` (str, optional): The directory for embeddings. Defaults to None. - - `clip_name` (str, optional): The name of the CLIP model. Defaults to "l". - - `tokenizer` (type, optional): The tokenizer class. Defaults to SDTokenizer. - """ - self.clip_name = clip_name - self.clip = "clip_{}".format(self.clip_name) - setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory)) - - def tokenize_with_weights(self, text: str, return_word_ids: bool = False) -> dict: - """#### Tokenize text with weights. - - #### Args: - - `text` (str): The input text. - - `return_word_ids` (bool, optional): Whether to return word IDs. Defaults to False. - - #### Returns: - - `dict`: The tokenized text with weights. - """ - out = {} - out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights( - text, return_word_ids - ) - return out - - def untokenize(self, token_weight_pair: list) -> list: - """#### Untokenize a list of token-weight pairs. - - #### Args: - - `token_weight_pair` (list): The list of token-weight pairs. - - #### Returns: - - `list`: The untokenized list. - """ +import logging +import os +import traceback +import torch +from transformers import CLIPTokenizerFast + +def model_options_long_clip(sd, tokenizer_data, model_options): + w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None) + if w is None: + w = sd.get("text_model.embeddings.position_embedding.weight", None) + return tokenizer_data, model_options + +def parse_parentheses(string: str) -> list: + """#### Parse a string with nested parentheses. + + #### Args: + - `string` (str): The input string. + + #### Returns: + - `list`: The parsed list of strings. + """ + result = [] + current_item = "" + nesting_level = 0 + for char in string: + if char == "(": + if nesting_level == 0: + if current_item: + result.append(current_item) + current_item = "(" + else: + current_item = "(" + else: + current_item += char + nesting_level += 1 + elif char == ")": + nesting_level -= 1 + if nesting_level == 0: + result.append(current_item + ")") + current_item = "" + else: + current_item += char + else: + current_item += char + if current_item: + result.append(current_item) + return result + + +def token_weights(string: str, current_weight: float) -> list: + """#### Parse a string into tokens with weights. + + #### Args: + - `string` (str): The input string. + - `current_weight` (float): The current weight. + + #### Returns: + - `list`: The list of token-weight pairs. + """ + a = parse_parentheses(string) + out = [] + for x in a: + weight = current_weight + if len(x) >= 2 and x[-1] == ")" and x[0] == "(": + x = x[1:-1] + xx = x.rfind(":") + weight *= 1.1 + if xx > 0: + try: + weight = float(x[xx + 1 :]) + x = x[:xx] + except: + pass + out += token_weights(x, weight) + else: + out += [(x, current_weight)] + return out + + +def escape_important(text: str) -> str: + """#### Escape important characters in a string. + + #### Args: + - `text` (str): The input text. + + #### Returns: + - `str`: The escaped text. + """ + text = text.replace("\\)", "\0\1") + text = text.replace("\\(", "\0\2") + return text + + +def unescape_important(text: str) -> str: + """#### Unescape important characters in a string. + + #### Args: + - `text` (str): The input text. + + #### Returns: + - `str`: The unescaped text. + """ + text = text.replace("\0\1", ")") + text = text.replace("\0\2", "(") + return text + + +def expand_directory_list(directories: list) -> list: + """#### Expand a list of directories to include all subdirectories. + + #### Args: + - `directories` (list): The list of directories. + + #### Returns: + - `list`: The expanded list of directories. + """ + dirs = set() + for x in directories: + dirs.add(x) + for root, subdir, file in os.walk(x, followlinks=True): + dirs.add(root) + return list(dirs) + + +def load_embed(embedding_name: str, embedding_directory: list, embedding_size: int, embed_key: str = None) -> torch.Tensor: + """#### Load an embedding from a directory. + + #### Args: + - `embedding_name` (str): The name of the embedding. + - `embedding_directory` (list): The list of directories to search. + - `embedding_size` (int): The size of the embedding. + - `embed_key` (str, optional): The key for the embedding. Defaults to None. + + #### Returns: + - `torch.Tensor`: The loaded embedding. + """ + if isinstance(embedding_directory, str): + embedding_directory = [embedding_directory] + + embedding_directory = expand_directory_list(embedding_directory) + + valid_file = None + for embed_dir in embedding_directory: + embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name)) + embed_dir = os.path.abspath(embed_dir) + try: + if os.path.commonpath((embed_dir, embed_path)) != embed_dir: + continue + except: + continue + if not os.path.isfile(embed_path): + extensions = [".safetensors", ".pt", ".bin"] + for x in extensions: + t = embed_path + x + if os.path.isfile(t): + valid_file = t + break + else: + valid_file = embed_path + if valid_file is not None: + break + + if valid_file is None: + return None + + embed_path = valid_file + + embed_out = None + + try: + if embed_path.lower().endswith(".safetensors"): + import safetensors.torch + + embed = safetensors.torch.load_file(embed_path, device="cpu") + else: + if "weights_only" in torch.load.__code__.co_varnames: + embed = torch.load(embed_path, weights_only=True, map_location="cpu") + else: + embed = torch.load(embed_path, map_location="cpu") + except Exception: + logging.warning( + "{}\n\nerror loading embedding, skipping loading: {}".format( + traceback.format_exc(), embedding_name + ) + ) + return None + + if embed_out is None: + if "string_to_param" in embed: + values = embed["string_to_param"].values() + embed_out = next(iter(values)) + elif isinstance(embed, list): + out_list = [] + for x in range(len(embed)): + for k in embed[x]: + t = embed[x][k] + if t.shape[-1] != embedding_size: + continue + out_list.append(t.reshape(-1, t.shape[-1])) + embed_out = torch.cat(out_list, dim=0) + elif embed_key is not None and embed_key in embed: + embed_out = embed[embed_key] + else: + values = embed.values() + embed_out = next(iter(values)) + return embed_out + + +class SDTokenizer: + """#### Class representing a Stable Diffusion tokenizer.""" + + def __init__( + self, + tokenizer_path: str = None, + max_length: int = 77, + pad_with_end: bool = True, + embedding_directory: str = None, + embedding_size: int = 768, + embedding_key: str = "clip_l", + tokenizer_class: type = CLIPTokenizerFast, + has_start_token: bool = True, + pad_to_max_length: bool = True, + min_length: int = None, + ): + """#### Initialize the SDTokenizer. + + #### Args: + - `tokenizer_path` (str, optional): The path to the tokenizer. Defaults to None. + - `max_length` (int, optional): The maximum length of the input. Defaults to 77. + - `pad_with_end` (bool, optional): Whether to pad with the end token. Defaults to True. + - `embedding_directory` (str, optional): The directory for embeddings. Defaults to None. + - `embedding_size` (int, optional): The size of the embeddings. Defaults to 768. + - `embedding_key` (str, optional): The key for the embeddings. Defaults to "clip_l". + - `tokenizer_class` (type, optional): The tokenizer class. Defaults to CLIPTokenizer. + - `has_start_token` (bool, optional): Whether the tokenizer has a start token. Defaults to True. + - `pad_to_max_length` (bool, optional): Whether to pad to the maximum length. Defaults to True. + - `min_length` (int, optional): The minimum length of the input. Defaults to None. + """ + if tokenizer_path is None: + tokenizer_path = "_internal/sd1_tokenizer/" + self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path) + self.max_length = max_length + self.min_length = min_length + + empty = self.tokenizer("")["input_ids"] + if has_start_token: + self.tokens_start = 1 + self.start_token = empty[0] + self.end_token = empty[1] + else: + self.tokens_start = 0 + self.start_token = None + self.end_token = empty[0] + self.pad_with_end = pad_with_end + self.pad_to_max_length = pad_to_max_length + + vocab = self.tokenizer.get_vocab() + self.inv_vocab = {v: k for k, v in vocab.items()} + self.embedding_directory = embedding_directory + self.max_word_length = 8 + self.embedding_identifier = "embedding:" + self.embedding_size = embedding_size + self.embedding_key = embedding_key + + def _try_get_embedding(self, embedding_name: str) -> tuple: + """#### Try to get an embedding. + + #### Args: + - `embedding_name` (str): The name of the embedding. + + #### Returns: + - `tuple`: The embedding and any leftover text. + """ + embed = load_embed( + embedding_name, + self.embedding_directory, + self.embedding_size, + self.embedding_key, + ) + if embed is None: + stripped = embedding_name.strip(",") + if len(stripped) < len(embedding_name): + embed = load_embed( + stripped, + self.embedding_directory, + self.embedding_size, + self.embedding_key, + ) + return (embed, embedding_name[len(stripped) :]) + return (embed, "") + + def tokenize_with_weights(self, text: str, return_word_ids: bool = False) -> list: + """#### Tokenize text with weights. + + #### Args: + - `text` (str): The input text. + - `return_word_ids` (bool, optional): Whether to return word IDs. Defaults to False. + + #### Returns: + - `list`: The tokenized text with weights. + """ + if self.pad_with_end: + pad_token = self.end_token + else: + pad_token = 0 + + text = escape_important(text) + parsed_weights = token_weights(text, 1.0) + + # tokenize words + tokens = [] + for weighted_segment, weight in parsed_weights: + to_tokenize = ( + unescape_important(weighted_segment).replace("\n", " ").split(" ") + ) + to_tokenize = [x for x in to_tokenize if x != ""] + for word in to_tokenize: + # if we find an embedding, deal with the embedding + if ( + word.startswith(self.embedding_identifier) + and self.embedding_directory is not None + ): + embedding_name = word[len(self.embedding_identifier) :].strip("\n") + embed, leftover = self._try_get_embedding(embedding_name) + if embed is None: + logging.warning( + f"warning, embedding:{embedding_name} does not exist, ignoring" + ) + else: + if len(embed.shape) == 1: + tokens.append([(embed, weight)]) + else: + tokens.append( + [(embed[x], weight) for x in range(embed.shape[0])] + ) + print("loading ", embedding_name) + # if we accidentally have leftover text, continue parsing using leftover, else move on to next word + if leftover != "": + word = leftover + else: + continue + # parse word + tokens.append( + [ + (t, weight) + for t in self.tokenizer(word)["input_ids"][ + self.tokens_start : -1 + ] + ] + ) + + # reshape token array to CLIP input size + batched_tokens = [] + batch = [] + if self.start_token is not None: + batch.append((self.start_token, 1.0, 0)) + batched_tokens.append(batch) + for i, t_group in enumerate(tokens): + # determine if we're going to try and keep the tokens in a single batch + is_large = len(t_group) >= self.max_word_length + + while len(t_group) > 0: + if len(t_group) + len(batch) > self.max_length - 1: + remaining_length = self.max_length - len(batch) - 1 + # break word in two and add end token + if is_large: + batch.extend( + [(t, w, i + 1) for t, w in t_group[:remaining_length]] + ) + batch.append((self.end_token, 1.0, 0)) + t_group = t_group[remaining_length:] + # add end token and pad + else: + batch.append((self.end_token, 1.0, 0)) + if self.pad_to_max_length: + batch.extend([(pad_token, 1.0, 0)] * (remaining_length)) + # start new batch + batch = [] + if self.start_token is not None: + batch.append((self.start_token, 1.0, 0)) + batched_tokens.append(batch) + else: + batch.extend([(t, w, i + 1) for t, w in t_group]) + t_group = [] + + # fill last batch + batch.append((self.end_token, 1.0, 0)) + if self.pad_to_max_length: + batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch))) + if self.min_length is not None and len(batch) < self.min_length: + batch.extend([(pad_token, 1.0, 0)] * (self.min_length - len(batch))) + + if not return_word_ids: + batched_tokens = [[(t, w) for t, w, _ in x] for x in batched_tokens] + + return batched_tokens + + def untokenize(self, token_weight_pair: list) -> list: + """#### Untokenize a list of token-weight pairs. + + #### Args: + - `token_weight_pair` (list): The list of token-weight pairs. + + #### Returns: + - `list`: The untokenized list. + """ + return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) + + +class SD1Tokenizer: + """#### Class representing the SD1Tokenizer.""" + + def __init__(self, embedding_directory: str = None, clip_name: str = "l", tokenizer: type = SDTokenizer): + """#### Initialize the SD1Tokenizer. + + #### Args: + - `embedding_directory` (str, optional): The directory for embeddings. Defaults to None. + - `clip_name` (str, optional): The name of the CLIP model. Defaults to "l". + - `tokenizer` (type, optional): The tokenizer class. Defaults to SDTokenizer. + """ + self.clip_name = clip_name + self.clip = "clip_{}".format(self.clip_name) + setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory)) + + def tokenize_with_weights(self, text: str, return_word_ids: bool = False) -> dict: + """#### Tokenize text with weights. + + #### Args: + - `text` (str): The input text. + - `return_word_ids` (bool, optional): Whether to return word IDs. Defaults to False. + + #### Returns: + - `dict`: The tokenized text with weights. + """ + out = {} + out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights( + text, return_word_ids + ) + return out + + def untokenize(self, token_weight_pair: list) -> list: + """#### Untokenize a list of token-weight pairs. + + #### Args: + - `token_weight_pair` (list): The list of token-weight pairs. + + #### Returns: + - `list`: The untokenized list. + """ return getattr(self, self.clip).untokenize(token_weight_pair) \ No newline at end of file diff --git a/modules/StableFast/StableFast.py b/modules/StableFast/StableFast.py index dda2f7b77bb083c4e27c6a740e71b46f842f2394..4a74d09512c5f75473a29b7687921f609a58595d 100644 --- a/modules/StableFast/StableFast.py +++ b/modules/StableFast/StableFast.py @@ -1,274 +1,274 @@ -import contextlib -import functools -import logging -from dataclasses import dataclass - -import torch - -try: - from sfast.compilers.diffusion_pipeline_compiler import CompilationConfig - from sfast.compilers.diffusion_pipeline_compiler import ( - _enable_xformers, - _modify_model, - ) - from sfast.cuda.graphs import make_dynamic_graphed_callable - from sfast.jit import utils as jit_utils - from sfast.jit.trace_helper import trace_with_kwargs -except: - pass - - -def hash_arg(arg): - # micro optimization: bool obj is an instance of int - if isinstance(arg, (str, int, float, bytes)): - return arg - if isinstance(arg, (tuple, list)): - return tuple(map(hash_arg, arg)) - if isinstance(arg, dict): - return tuple( - sorted( - ((hash_arg(k), hash_arg(v)) for k, v in arg.items()), key=lambda x: x[0] - ) - ) - return type(arg) - - -class ModuleFactory: - def get_converted_kwargs(self): - return self.converted_kwargs - - -import torch as th -import torch.nn as nn -import copy - - -class BaseModelApplyModelModule(torch.nn.Module): - def __init__(self, func, module): - super().__init__() - self.func = func - self.module = module - - def forward( - self, - input_x, - timestep, - c_concat=None, - c_crossattn=None, - y=None, - control=None, - transformer_options={}, - ): - kwargs = {"y": y} - - new_transformer_options = {} - - return self.func( - input_x, - timestep, - c_concat=c_concat, - c_crossattn=c_crossattn, - control=control, - transformer_options=new_transformer_options, - **kwargs, - ) - - -class BaseModelApplyModelModuleFactory(ModuleFactory): - kwargs_name = ( - "input_x", - "timestep", - "c_concat", - "c_crossattn", - "y", - "control", - ) - - def __init__(self, callable, kwargs) -> None: - self.callable = callable - self.unet_config = callable.__self__.model_config.unet_config - self.kwargs = kwargs - self.patch_module = {} - self.patch_module_parameter = {} - self.converted_kwargs = self.gen_converted_kwargs() - - def gen_converted_kwargs(self): - converted_kwargs = {} - for arg_name, arg in self.kwargs.items(): - if arg_name in self.kwargs_name: - converted_kwargs[arg_name] = arg - - transformer_options = self.kwargs.get("transformer_options", {}) - patches = transformer_options.get("patches", {}) - - patch_module = {} - patch_module_parameter = {} - - new_transformer_options = {} - new_transformer_options["patches"] = patch_module_parameter - - self.patch_module = patch_module - self.patch_module_parameter = patch_module_parameter - return converted_kwargs - - def gen_cache_key(self): - key_kwargs = {} - for k, v in self.converted_kwargs.items(): - key_kwargs[k] = v - - patch_module_cache_key = {} - return ( - self.callable.__class__.__qualname__, - hash_arg(self.unet_config), - hash_arg(key_kwargs), - hash_arg(patch_module_cache_key), - ) - - @contextlib.contextmanager - def converted_module_context(self): - module = BaseModelApplyModelModule(self.callable, self.callable.__self__) - yield (module, self.converted_kwargs) - - -logger = logging.getLogger() - - -@dataclass -class TracedModuleCacheItem: - module: object - patch_id: int - device: str - - -class LazyTraceModule: - traced_modules = {} - - def __init__(self, config=None, patch_id=None, **kwargs_) -> None: - self.config = config - self.patch_id = patch_id - self.kwargs_ = kwargs_ - self.modify_model = functools.partial( - _modify_model, - enable_cnn_optimization=config.enable_cnn_optimization, - prefer_lowp_gemm=config.prefer_lowp_gemm, - enable_triton=config.enable_triton, - enable_triton_reshape=config.enable_triton, - memory_format=config.memory_format, - ) - self.cuda_graph_modules = {} - - def ts_compiler( - self, - m, - ): - with torch.jit.optimized_execution(True): - if self.config.enable_jit_freeze: - # raw freeze causes Tensor reference leak - # because the constant Tensors in the GraphFunction of - # the compilation unit are never freed. - m.eval() - m = jit_utils.better_freeze(m) - self.modify_model(m) - - if self.config.enable_cuda_graph: - m = make_dynamic_graphed_callable(m) - return m - - def __call__(self, model_function, /, **kwargs): - module_factory = BaseModelApplyModelModuleFactory(model_function, kwargs) - kwargs = module_factory.get_converted_kwargs() - key = module_factory.gen_cache_key() - - traced_module = self.cuda_graph_modules.get(key) - if traced_module is None: - with module_factory.converted_module_context() as (m_model, m_kwargs): - logger.info( - f'Tracing {getattr(m_model, "__name__", m_model.__class__.__name__)}' - ) - traced_m, call_helper = trace_with_kwargs( - m_model, None, m_kwargs, **self.kwargs_ - ) - - traced_m = self.ts_compiler(traced_m) - traced_module = call_helper(traced_m) - self.cuda_graph_modules[key] = traced_module - - return traced_module(**kwargs) - - -def build_lazy_trace_module(config, device, patch_id): - config.enable_cuda_graph = config.enable_cuda_graph and device.type == "cuda" - - if config.enable_xformers: - _enable_xformers(None) - - return LazyTraceModule( - config=config, - patch_id=patch_id, - check_trace=True, - strict=True, - ) - - -def gen_stable_fast_config(): - config = CompilationConfig.Default() - try: - import xformers - - config.enable_xformers = True - except ImportError: - print("xformers not installed, skip") - - # CUDA Graph is suggested for small batch sizes. - # After capturing, the model only accepts one fixed image size. - # If you want the model to be dynamic, don't enable it. - config.enable_cuda_graph = False - # config.enable_jit_freeze = False - return config - - -class StableFastPatch: - def __init__(self, model, config): - self.model = model - self.config = config - self.stable_fast_model = None - - def __call__(self, model_function, params): - input_x = params.get("input") - timestep_ = params.get("timestep") - c = params.get("c") - - if self.stable_fast_model is None: - self.stable_fast_model = build_lazy_trace_module( - self.config, - input_x.device, - id(self), - ) - - return self.stable_fast_model( - model_function, input_x=input_x, timestep=timestep_, **c - ) - - def to(self, device): - if type(device) == torch.device: - if self.config.enable_cuda_graph or self.config.enable_jit_freeze: - if device.type == "cpu": - del self.stable_fast_model - self.stable_fast_model = None - print( - "\33[93mWarning: Your graphics card doesn't have enough video memory to keep the model. If you experience a noticeable delay every time you start sampling, please consider disable enable_cuda_graph.\33[0m" - ) - return self - - -class ApplyStableFastUnet: - def apply_stable_fast(self, model, enable_cuda_graph): - config = gen_stable_fast_config() - - if config.memory_format is not None: - model.model.to(memory_format=config.memory_format) - - patch = StableFastPatch(model, config) - model_stable_fast = model.clone() - model_stable_fast.set_model_unet_function_wrapper(patch) +import contextlib +import functools +import logging +from dataclasses import dataclass + +import torch + +try: + from sfast.compilers.diffusion_pipeline_compiler import CompilationConfig + from sfast.compilers.diffusion_pipeline_compiler import ( + _enable_xformers, + _modify_model, + ) + from sfast.cuda.graphs import make_dynamic_graphed_callable + from sfast.jit import utils as jit_utils + from sfast.jit.trace_helper import trace_with_kwargs +except: + pass + + +def hash_arg(arg): + # micro optimization: bool obj is an instance of int + if isinstance(arg, (str, int, float, bytes)): + return arg + if isinstance(arg, (tuple, list)): + return tuple(map(hash_arg, arg)) + if isinstance(arg, dict): + return tuple( + sorted( + ((hash_arg(k), hash_arg(v)) for k, v in arg.items()), key=lambda x: x[0] + ) + ) + return type(arg) + + +class ModuleFactory: + def get_converted_kwargs(self): + return self.converted_kwargs + + +import torch as th +import torch.nn as nn +import copy + + +class BaseModelApplyModelModule(torch.nn.Module): + def __init__(self, func, module): + super().__init__() + self.func = func + self.module = module + + def forward( + self, + input_x, + timestep, + c_concat=None, + c_crossattn=None, + y=None, + control=None, + transformer_options={}, + ): + kwargs = {"y": y} + + new_transformer_options = {} + + return self.func( + input_x, + timestep, + c_concat=c_concat, + c_crossattn=c_crossattn, + control=control, + transformer_options=new_transformer_options, + **kwargs, + ) + + +class BaseModelApplyModelModuleFactory(ModuleFactory): + kwargs_name = ( + "input_x", + "timestep", + "c_concat", + "c_crossattn", + "y", + "control", + ) + + def __init__(self, callable, kwargs) -> None: + self.callable = callable + self.unet_config = callable.__self__.model_config.unet_config + self.kwargs = kwargs + self.patch_module = {} + self.patch_module_parameter = {} + self.converted_kwargs = self.gen_converted_kwargs() + + def gen_converted_kwargs(self): + converted_kwargs = {} + for arg_name, arg in self.kwargs.items(): + if arg_name in self.kwargs_name: + converted_kwargs[arg_name] = arg + + transformer_options = self.kwargs.get("transformer_options", {}) + patches = transformer_options.get("patches", {}) + + patch_module = {} + patch_module_parameter = {} + + new_transformer_options = {} + new_transformer_options["patches"] = patch_module_parameter + + self.patch_module = patch_module + self.patch_module_parameter = patch_module_parameter + return converted_kwargs + + def gen_cache_key(self): + key_kwargs = {} + for k, v in self.converted_kwargs.items(): + key_kwargs[k] = v + + patch_module_cache_key = {} + return ( + self.callable.__class__.__qualname__, + hash_arg(self.unet_config), + hash_arg(key_kwargs), + hash_arg(patch_module_cache_key), + ) + + @contextlib.contextmanager + def converted_module_context(self): + module = BaseModelApplyModelModule(self.callable, self.callable.__self__) + yield (module, self.converted_kwargs) + + +logger = logging.getLogger() + + +@dataclass +class TracedModuleCacheItem: + module: object + patch_id: int + device: str + + +class LazyTraceModule: + traced_modules = {} + + def __init__(self, config=None, patch_id=None, **kwargs_) -> None: + self.config = config + self.patch_id = patch_id + self.kwargs_ = kwargs_ + self.modify_model = functools.partial( + _modify_model, + enable_cnn_optimization=config.enable_cnn_optimization, + prefer_lowp_gemm=config.prefer_lowp_gemm, + enable_triton=config.enable_triton, + enable_triton_reshape=config.enable_triton, + memory_format=config.memory_format, + ) + self.cuda_graph_modules = {} + + def ts_compiler( + self, + m, + ): + with torch.jit.optimized_execution(True): + if self.config.enable_jit_freeze: + # raw freeze causes Tensor reference leak + # because the constant Tensors in the GraphFunction of + # the compilation unit are never freed. + m.eval() + m = jit_utils.better_freeze(m) + self.modify_model(m) + + if self.config.enable_cuda_graph: + m = make_dynamic_graphed_callable(m) + return m + + def __call__(self, model_function, /, **kwargs): + module_factory = BaseModelApplyModelModuleFactory(model_function, kwargs) + kwargs = module_factory.get_converted_kwargs() + key = module_factory.gen_cache_key() + + traced_module = self.cuda_graph_modules.get(key) + if traced_module is None: + with module_factory.converted_module_context() as (m_model, m_kwargs): + logger.info( + f'Tracing {getattr(m_model, "__name__", m_model.__class__.__name__)}' + ) + traced_m, call_helper = trace_with_kwargs( + m_model, None, m_kwargs, **self.kwargs_ + ) + + traced_m = self.ts_compiler(traced_m) + traced_module = call_helper(traced_m) + self.cuda_graph_modules[key] = traced_module + + return traced_module(**kwargs) + + +def build_lazy_trace_module(config, device, patch_id): + config.enable_cuda_graph = config.enable_cuda_graph and device.type == "cuda" + + if config.enable_xformers: + _enable_xformers(None) + + return LazyTraceModule( + config=config, + patch_id=patch_id, + check_trace=True, + strict=True, + ) + + +def gen_stable_fast_config(): + config = CompilationConfig.Default() + try: + import xformers + + config.enable_xformers = True + except ImportError: + print("xformers not installed, skip") + + # CUDA Graph is suggested for small batch sizes. + # After capturing, the model only accepts one fixed image size. + # If you want the model to be dynamic, don't enable it. + config.enable_cuda_graph = False + # config.enable_jit_freeze = False + return config + + +class StableFastPatch: + def __init__(self, model, config): + self.model = model + self.config = config + self.stable_fast_model = None + + def __call__(self, model_function, params): + input_x = params.get("input") + timestep_ = params.get("timestep") + c = params.get("c") + + if self.stable_fast_model is None: + self.stable_fast_model = build_lazy_trace_module( + self.config, + input_x.device, + id(self), + ) + + return self.stable_fast_model( + model_function, input_x=input_x, timestep=timestep_, **c + ) + + def to(self, device): + if type(device) == torch.device: + if self.config.enable_cuda_graph or self.config.enable_jit_freeze: + if device.type == "cpu": + del self.stable_fast_model + self.stable_fast_model = None + print( + "\33[93mWarning: Your graphics card doesn't have enough video memory to keep the model. If you experience a noticeable delay every time you start sampling, please consider disable enable_cuda_graph.\33[0m" + ) + return self + + +class ApplyStableFastUnet: + def apply_stable_fast(self, model, enable_cuda_graph): + config = gen_stable_fast_config() + + if config.memory_format is not None: + model.model.to(memory_format=config.memory_format) + + patch = StableFastPatch(model, config) + model_stable_fast = model.clone() + model_stable_fast.set_model_unet_function_wrapper(patch) return (model_stable_fast,) \ No newline at end of file diff --git a/modules/UltimateSDUpscale/RDRB.py b/modules/UltimateSDUpscale/RDRB.py index be83facb5af462121a9a33d540d3ff0a554d11cc..abbbca964b626c2ed7e9b02a77fa54afa706dc6e 100644 --- a/modules/UltimateSDUpscale/RDRB.py +++ b/modules/UltimateSDUpscale/RDRB.py @@ -1,471 +1,471 @@ -from collections import OrderedDict -import functools -import math -import re -from typing import Union, Dict -import torch -import torch.nn as nn -from modules.UltimateSDUpscale import USDU_util - - -class RRDB(nn.Module): - """#### Residual in Residual Dense Block (RRDB) class. - - #### Args: - - `nf` (int): Number of filters. - - `kernel_size` (int, optional): Kernel size. Defaults to 3. - - `gc` (int, optional): Growth channel. Defaults to 32. - - `stride` (int, optional): Stride. Defaults to 1. - - `bias` (bool, optional): Whether to use bias. Defaults to True. - - `pad_type` (str, optional): Padding type. Defaults to "zero". - - `norm_type` (str, optional): Normalization type. Defaults to None. - - `act_type` (str, optional): Activation type. Defaults to "leakyrelu". - - `mode` (USDU_util.ConvMode, optional): Convolution mode. Defaults to "CNA". - - `_convtype` (str, optional): Convolution type. Defaults to "Conv2D". - - `_spectral_norm` (bool, optional): Whether to use spectral normalization. Defaults to False. - - `plus` (bool, optional): Whether to use the plus variant. Defaults to False. - - `c2x2` (bool, optional): Whether to use 2x2 convolution. Defaults to False. - """ - - def __init__( - self, - nf: int, - kernel_size: int = 3, - gc: int = 32, - stride: int = 1, - bias: bool = True, - pad_type: str = "zero", - norm_type: str = None, - act_type: str = "leakyrelu", - mode: USDU_util.ConvMode = "CNA", - _convtype: str = "Conv2D", - _spectral_norm: bool = False, - plus: bool = False, - c2x2: bool = False, - ) -> None: - super(RRDB, self).__init__() - self.RDB1 = ResidualDenseBlock_5C( - nf, - kernel_size, - gc, - stride, - bias, - pad_type, - norm_type, - act_type, - mode, - plus=plus, - c2x2=c2x2, - ) - self.RDB2 = ResidualDenseBlock_5C( - nf, - kernel_size, - gc, - stride, - bias, - pad_type, - norm_type, - act_type, - mode, - plus=plus, - c2x2=c2x2, - ) - self.RDB3 = ResidualDenseBlock_5C( - nf, - kernel_size, - gc, - stride, - bias, - pad_type, - norm_type, - act_type, - mode, - plus=plus, - c2x2=c2x2, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass of the RRDB. - - #### Args: - - `x` (torch.Tensor): Input tensor. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - out = self.RDB1(x) - out = self.RDB2(out) - out = self.RDB3(out) - return out * 0.2 + x - - -class ResidualDenseBlock_5C(nn.Module): - """#### Residual Dense Block with 5 Convolutions (ResidualDenseBlock_5C) class. - - #### Args: - - `nf` (int, optional): Number of filters. Defaults to 64. - - `kernel_size` (int, optional): Kernel size. Defaults to 3. - - `gc` (int, optional): Growth channel. Defaults to 32. - - `stride` (int, optional): Stride. Defaults to 1. - - `bias` (bool, optional): Whether to use bias. Defaults to True. - - `pad_type` (str, optional): Padding type. Defaults to "zero". - - `norm_type` (str, optional): Normalization type. Defaults to None. - - `act_type` (str, optional): Activation type. Defaults to "leakyrelu". - - `mode` (USDU_util.ConvMode, optional): Convolution mode. Defaults to "CNA". - - `plus` (bool, optional): Whether to use the plus variant. Defaults to False. - - `c2x2` (bool, optional): Whether to use 2x2 convolution. Defaults to False. - """ - - def __init__( - self, - nf: int = 64, - kernel_size: int = 3, - gc: int = 32, - stride: int = 1, - bias: bool = True, - pad_type: str = "zero", - norm_type: str = None, - act_type: str = "leakyrelu", - mode: USDU_util.ConvMode = "CNA", - plus: bool = False, - c2x2: bool = False, - ) -> None: - super(ResidualDenseBlock_5C, self).__init__() - - self.conv1x1 = None - - self.conv1 = USDU_util.conv_block( - nf, - gc, - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=act_type, - mode=mode, - c2x2=c2x2, - ) - self.conv2 = USDU_util.conv_block( - nf + gc, - gc, - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=act_type, - mode=mode, - c2x2=c2x2, - ) - self.conv3 = USDU_util.conv_block( - nf + 2 * gc, - gc, - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=act_type, - mode=mode, - c2x2=c2x2, - ) - self.conv4 = USDU_util.conv_block( - nf + 3 * gc, - gc, - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=act_type, - mode=mode, - c2x2=c2x2, - ) - last_act = None - self.conv5 = USDU_util.conv_block( - nf + 4 * gc, - nf, - 3, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=last_act, - mode=mode, - c2x2=c2x2, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass of the ResidualDenseBlock_5C. - - #### Args: - - `x` (torch.Tensor): Input tensor. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - x1 = self.conv1(x) - x2 = self.conv2(torch.cat((x, x1), 1)) - x3 = self.conv3(torch.cat((x, x1, x2), 1)) - x4 = self.conv4(torch.cat((x, x1, x2, x3), 1)) - x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) - return x5 * 0.2 + x - - -class RRDBNet(nn.Module): - """#### Residual in Residual Dense Block Network (RRDBNet) class. - - #### Args: - - `state_dict` (dict): State dictionary. - - `norm` (str, optional): Normalization type. Defaults to None. - - `act` (str, optional): Activation type. Defaults to "leakyrelu". - - `upsampler` (str, optional): Upsampler type. Defaults to "upconv". - - `mode` (USDU_util.ConvMode, optional): Convolution mode. Defaults to "CNA". - """ - - def __init__( - self, - state_dict: Dict[str, torch.Tensor], - norm: str = None, - act: str = "leakyrelu", - upsampler: str = "upconv", - mode: USDU_util.ConvMode = "CNA", - ) -> None: - super(RRDBNet, self).__init__() - self.model_arch = "ESRGAN" - self.sub_type = "SR" - - self.state = state_dict - self.norm = norm - self.act = act - self.upsampler = upsampler - self.mode = mode - - self.state_map = { - # currently supports old, new, and newer RRDBNet arch _internal - # ESRGAN, BSRGAN/RealSR, Real-ESRGAN - "model.0.weight": ("conv_first.weight",), - "model.0.bias": ("conv_first.bias",), - "model.1.sub./NB/.weight": ("trunk_conv.weight", "conv_body.weight"), - "model.1.sub./NB/.bias": ("trunk_conv.bias", "conv_body.bias"), - r"model.1.sub.\1.RDB\2.conv\3.0.\4": ( - r"RRDB_trunk\.(\d+)\.RDB(\d)\.conv(\d+)\.(weight|bias)", - r"body\.(\d+)\.rdb(\d)\.conv(\d+)\.(weight|bias)", - ), - } - self.num_blocks = self.get_num_blocks() - self.plus = any("conv1x1" in k for k in self.state.keys()) - - self.state = self.new_to_old_arch(self.state) - - self.key_arr = list(self.state.keys()) - - self.in_nc: int = self.state[self.key_arr[0]].shape[1] - self.out_nc: int = self.state[self.key_arr[-1]].shape[0] - - self.scale: int = self.get_scale() - self.num_filters: int = self.state[self.key_arr[0]].shape[0] - - c2x2 = False - - self.supports_fp16 = True - self.supports_bfp16 = True - self.min_size_restriction = None - - self.shuffle_factor = None - - upsample_block = { - "upconv": USDU_util.upconv_block, - }.get(self.upsampler) - upsample_blocks = [ - upsample_block( - in_nc=self.num_filters, - out_nc=self.num_filters, - act_type=self.act, - c2x2=c2x2, - ) - for _ in range(int(math.log(self.scale, 2))) - ] - - self.model = USDU_util.sequential( - # fea conv - USDU_util.conv_block( - in_nc=self.in_nc, - out_nc=self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - c2x2=c2x2, - ), - USDU_util.ShortcutBlock( - USDU_util.sequential( - # rrdb blocks - *[ - RRDB( - nf=self.num_filters, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=self.norm, - act_type=self.act, - mode="CNA", - plus=self.plus, - c2x2=c2x2, - ) - for _ in range(self.num_blocks) - ], - # lr conv - USDU_util.conv_block( - in_nc=self.num_filters, - out_nc=self.num_filters, - kernel_size=3, - norm_type=self.norm, - act_type=None, - mode=self.mode, - c2x2=c2x2, - ), - ) - ), - *upsample_blocks, - # hr_conv0 - USDU_util.conv_block( - in_nc=self.num_filters, - out_nc=self.num_filters, - kernel_size=3, - norm_type=None, - act_type=self.act, - c2x2=c2x2, - ), - # hr_conv1 - USDU_util.conv_block( - in_nc=self.num_filters, - out_nc=self.out_nc, - kernel_size=3, - norm_type=None, - act_type=None, - c2x2=c2x2, - ), - ) - - self.load_state_dict(self.state, strict=False) - - def new_to_old_arch(self, state: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: - """#### Convert new architecture state dictionary to old architecture. - - #### Args: - - `state` (dict): State dictionary. - - #### Returns: - - `dict`: Converted state dictionary. - """ - # add nb to state keys - for kind in ("weight", "bias"): - self.state_map[f"model.1.sub.{self.num_blocks}.{kind}"] = self.state_map[ - f"model.1.sub./NB/.{kind}" - ] - del self.state_map[f"model.1.sub./NB/.{kind}"] - - old_state = OrderedDict() - for old_key, new_keys in self.state_map.items(): - for new_key in new_keys: - if r"\1" in old_key: - for k, v in state.items(): - sub = re.sub(new_key, old_key, k) - if sub != k: - old_state[sub] = v - else: - if new_key in state: - old_state[old_key] = state[new_key] - - # upconv layers - max_upconv = 0 - for key in state.keys(): - match = re.match(r"(upconv|conv_up)(\d)\.(weight|bias)", key) - if match is not None: - _, key_num, key_type = match.groups() - old_state[f"model.{int(key_num) * 3}.{key_type}"] = state[key] - max_upconv = max(max_upconv, int(key_num) * 3) - - # final layers - for key in state.keys(): - if key in ("HRconv.weight", "conv_hr.weight"): - old_state[f"model.{max_upconv + 2}.weight"] = state[key] - elif key in ("HRconv.bias", "conv_hr.bias"): - old_state[f"model.{max_upconv + 2}.bias"] = state[key] - elif key in ("conv_last.weight",): - old_state[f"model.{max_upconv + 4}.weight"] = state[key] - elif key in ("conv_last.bias",): - old_state[f"model.{max_upconv + 4}.bias"] = state[key] - - # Sort by first numeric value of each layer - def compare(item1: str, item2: str) -> int: - parts1 = item1.split(".") - parts2 = item2.split(".") - int1 = int(parts1[1]) - int2 = int(parts2[1]) - return int1 - int2 - - sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare)) - - # Rebuild the output dict in the right order - out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys) - - return out_dict - - def get_scale(self, min_part: int = 6) -> int: - """#### Get the scale factor. - - #### Args: - - `min_part` (int, optional): Minimum part. Defaults to 6. - - #### Returns: - - `int`: Scale factor. - """ - n = 0 - for part in list(self.state): - parts = part.split(".")[1:] - if len(parts) == 2: - part_num = int(parts[0]) - if part_num > min_part and parts[1] == "weight": - n += 1 - return 2**n - - def get_num_blocks(self) -> int: - """#### Get the number of blocks. - - #### Returns: - - `int`: Number of blocks. - """ - nbs = [] - state_keys = self.state_map[r"model.1.sub.\1.RDB\2.conv\3.0.\4"] + ( - r"model\.\d+\.sub\.(\d+)\.RDB(\d+)\.conv(\d+)\.0\.(weight|bias)", - ) - for state_key in state_keys: - for k in self.state: - m = re.search(state_key, k) - if m: - nbs.append(int(m.group(1))) - if nbs: - break - return max(*nbs) + 1 - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass of the RRDBNet. - - #### Args: - - `x` (torch.Tensor): Input tensor. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - return self.model(x) - - -PyTorchSRModels = (RRDBNet,) -PyTorchSRModel = Union[RRDBNet,] - -PyTorchModels = (*PyTorchSRModels,) +from collections import OrderedDict +import functools +import math +import re +from typing import Union, Dict +import torch +import torch.nn as nn +from modules.UltimateSDUpscale import USDU_util + + +class RRDB(nn.Module): + """#### Residual in Residual Dense Block (RRDB) class. + + #### Args: + - `nf` (int): Number of filters. + - `kernel_size` (int, optional): Kernel size. Defaults to 3. + - `gc` (int, optional): Growth channel. Defaults to 32. + - `stride` (int, optional): Stride. Defaults to 1. + - `bias` (bool, optional): Whether to use bias. Defaults to True. + - `pad_type` (str, optional): Padding type. Defaults to "zero". + - `norm_type` (str, optional): Normalization type. Defaults to None. + - `act_type` (str, optional): Activation type. Defaults to "leakyrelu". + - `mode` (USDU_util.ConvMode, optional): Convolution mode. Defaults to "CNA". + - `_convtype` (str, optional): Convolution type. Defaults to "Conv2D". + - `_spectral_norm` (bool, optional): Whether to use spectral normalization. Defaults to False. + - `plus` (bool, optional): Whether to use the plus variant. Defaults to False. + - `c2x2` (bool, optional): Whether to use 2x2 convolution. Defaults to False. + """ + + def __init__( + self, + nf: int, + kernel_size: int = 3, + gc: int = 32, + stride: int = 1, + bias: bool = True, + pad_type: str = "zero", + norm_type: str = None, + act_type: str = "leakyrelu", + mode: USDU_util.ConvMode = "CNA", + _convtype: str = "Conv2D", + _spectral_norm: bool = False, + plus: bool = False, + c2x2: bool = False, + ) -> None: + super(RRDB, self).__init__() + self.RDB1 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + self.RDB2 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + self.RDB3 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass of the RRDB. + + #### Args: + - `x` (torch.Tensor): Input tensor. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + out = self.RDB1(x) + out = self.RDB2(out) + out = self.RDB3(out) + return out * 0.2 + x + + +class ResidualDenseBlock_5C(nn.Module): + """#### Residual Dense Block with 5 Convolutions (ResidualDenseBlock_5C) class. + + #### Args: + - `nf` (int, optional): Number of filters. Defaults to 64. + - `kernel_size` (int, optional): Kernel size. Defaults to 3. + - `gc` (int, optional): Growth channel. Defaults to 32. + - `stride` (int, optional): Stride. Defaults to 1. + - `bias` (bool, optional): Whether to use bias. Defaults to True. + - `pad_type` (str, optional): Padding type. Defaults to "zero". + - `norm_type` (str, optional): Normalization type. Defaults to None. + - `act_type` (str, optional): Activation type. Defaults to "leakyrelu". + - `mode` (USDU_util.ConvMode, optional): Convolution mode. Defaults to "CNA". + - `plus` (bool, optional): Whether to use the plus variant. Defaults to False. + - `c2x2` (bool, optional): Whether to use 2x2 convolution. Defaults to False. + """ + + def __init__( + self, + nf: int = 64, + kernel_size: int = 3, + gc: int = 32, + stride: int = 1, + bias: bool = True, + pad_type: str = "zero", + norm_type: str = None, + act_type: str = "leakyrelu", + mode: USDU_util.ConvMode = "CNA", + plus: bool = False, + c2x2: bool = False, + ) -> None: + super(ResidualDenseBlock_5C, self).__init__() + + self.conv1x1 = None + + self.conv1 = USDU_util.conv_block( + nf, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv2 = USDU_util.conv_block( + nf + gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv3 = USDU_util.conv_block( + nf + 2 * gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv4 = USDU_util.conv_block( + nf + 3 * gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + last_act = None + self.conv5 = USDU_util.conv_block( + nf + 4 * gc, + nf, + 3, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=last_act, + mode=mode, + c2x2=c2x2, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass of the ResidualDenseBlock_5C. + + #### Args: + - `x` (torch.Tensor): Input tensor. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + x1 = self.conv1(x) + x2 = self.conv2(torch.cat((x, x1), 1)) + x3 = self.conv3(torch.cat((x, x1, x2), 1)) + x4 = self.conv4(torch.cat((x, x1, x2, x3), 1)) + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + return x5 * 0.2 + x + + +class RRDBNet(nn.Module): + """#### Residual in Residual Dense Block Network (RRDBNet) class. + + #### Args: + - `state_dict` (dict): State dictionary. + - `norm` (str, optional): Normalization type. Defaults to None. + - `act` (str, optional): Activation type. Defaults to "leakyrelu". + - `upsampler` (str, optional): Upsampler type. Defaults to "upconv". + - `mode` (USDU_util.ConvMode, optional): Convolution mode. Defaults to "CNA". + """ + + def __init__( + self, + state_dict: Dict[str, torch.Tensor], + norm: str = None, + act: str = "leakyrelu", + upsampler: str = "upconv", + mode: USDU_util.ConvMode = "CNA", + ) -> None: + super(RRDBNet, self).__init__() + self.model_arch = "ESRGAN" + self.sub_type = "SR" + + self.state = state_dict + self.norm = norm + self.act = act + self.upsampler = upsampler + self.mode = mode + + self.state_map = { + # currently supports old, new, and newer RRDBNet arch _internal + # ESRGAN, BSRGAN/RealSR, Real-ESRGAN + "model.0.weight": ("conv_first.weight",), + "model.0.bias": ("conv_first.bias",), + "model.1.sub./NB/.weight": ("trunk_conv.weight", "conv_body.weight"), + "model.1.sub./NB/.bias": ("trunk_conv.bias", "conv_body.bias"), + r"model.1.sub.\1.RDB\2.conv\3.0.\4": ( + r"RRDB_trunk\.(\d+)\.RDB(\d)\.conv(\d+)\.(weight|bias)", + r"body\.(\d+)\.rdb(\d)\.conv(\d+)\.(weight|bias)", + ), + } + self.num_blocks = self.get_num_blocks() + self.plus = any("conv1x1" in k for k in self.state.keys()) + + self.state = self.new_to_old_arch(self.state) + + self.key_arr = list(self.state.keys()) + + self.in_nc: int = self.state[self.key_arr[0]].shape[1] + self.out_nc: int = self.state[self.key_arr[-1]].shape[0] + + self.scale: int = self.get_scale() + self.num_filters: int = self.state[self.key_arr[0]].shape[0] + + c2x2 = False + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + self.shuffle_factor = None + + upsample_block = { + "upconv": USDU_util.upconv_block, + }.get(self.upsampler) + upsample_blocks = [ + upsample_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + act_type=self.act, + c2x2=c2x2, + ) + for _ in range(int(math.log(self.scale, 2))) + ] + + self.model = USDU_util.sequential( + # fea conv + USDU_util.conv_block( + in_nc=self.in_nc, + out_nc=self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + c2x2=c2x2, + ), + USDU_util.ShortcutBlock( + USDU_util.sequential( + # rrdb blocks + *[ + RRDB( + nf=self.num_filters, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=self.norm, + act_type=self.act, + mode="CNA", + plus=self.plus, + c2x2=c2x2, + ) + for _ in range(self.num_blocks) + ], + # lr conv + USDU_util.conv_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + kernel_size=3, + norm_type=self.norm, + act_type=None, + mode=self.mode, + c2x2=c2x2, + ), + ) + ), + *upsample_blocks, + # hr_conv0 + USDU_util.conv_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + kernel_size=3, + norm_type=None, + act_type=self.act, + c2x2=c2x2, + ), + # hr_conv1 + USDU_util.conv_block( + in_nc=self.num_filters, + out_nc=self.out_nc, + kernel_size=3, + norm_type=None, + act_type=None, + c2x2=c2x2, + ), + ) + + self.load_state_dict(self.state, strict=False) + + def new_to_old_arch(self, state: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + """#### Convert new architecture state dictionary to old architecture. + + #### Args: + - `state` (dict): State dictionary. + + #### Returns: + - `dict`: Converted state dictionary. + """ + # add nb to state keys + for kind in ("weight", "bias"): + self.state_map[f"model.1.sub.{self.num_blocks}.{kind}"] = self.state_map[ + f"model.1.sub./NB/.{kind}" + ] + del self.state_map[f"model.1.sub./NB/.{kind}"] + + old_state = OrderedDict() + for old_key, new_keys in self.state_map.items(): + for new_key in new_keys: + if r"\1" in old_key: + for k, v in state.items(): + sub = re.sub(new_key, old_key, k) + if sub != k: + old_state[sub] = v + else: + if new_key in state: + old_state[old_key] = state[new_key] + + # upconv layers + max_upconv = 0 + for key in state.keys(): + match = re.match(r"(upconv|conv_up)(\d)\.(weight|bias)", key) + if match is not None: + _, key_num, key_type = match.groups() + old_state[f"model.{int(key_num) * 3}.{key_type}"] = state[key] + max_upconv = max(max_upconv, int(key_num) * 3) + + # final layers + for key in state.keys(): + if key in ("HRconv.weight", "conv_hr.weight"): + old_state[f"model.{max_upconv + 2}.weight"] = state[key] + elif key in ("HRconv.bias", "conv_hr.bias"): + old_state[f"model.{max_upconv + 2}.bias"] = state[key] + elif key in ("conv_last.weight",): + old_state[f"model.{max_upconv + 4}.weight"] = state[key] + elif key in ("conv_last.bias",): + old_state[f"model.{max_upconv + 4}.bias"] = state[key] + + # Sort by first numeric value of each layer + def compare(item1: str, item2: str) -> int: + parts1 = item1.split(".") + parts2 = item2.split(".") + int1 = int(parts1[1]) + int2 = int(parts2[1]) + return int1 - int2 + + sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare)) + + # Rebuild the output dict in the right order + out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys) + + return out_dict + + def get_scale(self, min_part: int = 6) -> int: + """#### Get the scale factor. + + #### Args: + - `min_part` (int, optional): Minimum part. Defaults to 6. + + #### Returns: + - `int`: Scale factor. + """ + n = 0 + for part in list(self.state): + parts = part.split(".")[1:] + if len(parts) == 2: + part_num = int(parts[0]) + if part_num > min_part and parts[1] == "weight": + n += 1 + return 2**n + + def get_num_blocks(self) -> int: + """#### Get the number of blocks. + + #### Returns: + - `int`: Number of blocks. + """ + nbs = [] + state_keys = self.state_map[r"model.1.sub.\1.RDB\2.conv\3.0.\4"] + ( + r"model\.\d+\.sub\.(\d+)\.RDB(\d+)\.conv(\d+)\.0\.(weight|bias)", + ) + for state_key in state_keys: + for k in self.state: + m = re.search(state_key, k) + if m: + nbs.append(int(m.group(1))) + if nbs: + break + return max(*nbs) + 1 + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass of the RRDBNet. + + #### Args: + - `x` (torch.Tensor): Input tensor. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + return self.model(x) + + +PyTorchSRModels = (RRDBNet,) +PyTorchSRModel = Union[RRDBNet,] + +PyTorchModels = (*PyTorchSRModels,) PyTorchModel = Union[PyTorchSRModel] \ No newline at end of file diff --git a/modules/UltimateSDUpscale/USDU_upscaler.py b/modules/UltimateSDUpscale/USDU_upscaler.py index 47c58babaa19755d3fae94986f96304de4c45044..6ec570e77d2761e6a52ddb9dc21848141f7f40ce 100644 --- a/modules/UltimateSDUpscale/USDU_upscaler.py +++ b/modules/UltimateSDUpscale/USDU_upscaler.py @@ -1,182 +1,185 @@ -import logging as logger -import torch -from PIL import Image - -from modules.Device import Device -from modules.UltimateSDUpscale import RDRB -from modules.UltimateSDUpscale import image_util -from modules.Utilities import util - - -def load_state_dict(state_dict: dict) -> RDRB.PyTorchModel: - """#### Load a state dictionary into a PyTorch model. - - #### Args: - - `state_dict` (dict): The state dictionary. - - #### Returns: - - `RDRB.PyTorchModel`: The loaded PyTorch model. - """ - logger.debug("Loading state dict into pytorch model arch") - state_dict_keys = list(state_dict.keys()) - if "params_ema" in state_dict_keys: - state_dict = state_dict["params_ema"] - model = RDRB.RRDBNet(state_dict) - return model - - -class UpscaleModelLoader: - """#### Class for loading upscale models.""" - - def load_model(self, model_name: str) -> tuple: - """#### Load an upscale model. - - #### Args: - - `model_name` (str): The name of the model. - - #### Returns: - - `tuple`: The loaded model. - """ - model_path = f"./_internal/ESRGAN/{model_name}" - sd = util.load_torch_file(model_path, safe_load=True) - if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: - sd = util.state_dict_prefix_replace(sd, {"module.": ""}) - out = load_state_dict(sd).eval() - return (out,) - - -class ImageUpscaleWithModel: - """#### Class for upscaling images with a model.""" - - def upscale(self, upscale_model: torch.nn.Module, image: torch.Tensor) -> tuple: - """#### Upscale an image using a model. - - #### Args: - - `upscale_model` (torch.nn.Module): The upscale model. - - `image` (torch.Tensor): The input image tensor. - - #### Returns: - - `tuple`: The upscaled image tensor. - """ - device = torch.device(torch.cuda.current_device()) - upscale_model.to(device) - in_img = image.movedim(-1, -3).to(device) - Device.get_free_memory(device) - - tile = 512 - overlap = 32 - - oom = True - while oom: - steps = in_img.shape[0] * image_util.get_tiled_scale_steps( - in_img.shape[3], - in_img.shape[2], - tile_x=tile, - tile_y=tile, - overlap=overlap, - ) - pbar = util.ProgressBar(steps) - s = image_util.tiled_scale( - in_img, - lambda a: upscale_model(a), - tile_x=tile, - tile_y=tile, - overlap=overlap, - upscale_amount=upscale_model.scale, - pbar=pbar, - ) - oom = False - - upscale_model.cpu() - s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0) - return (s,) - - -def torch_gc() -> None: - """#### Perform garbage collection for PyTorch.""" - pass - - -class Script: - """#### Class representing a script.""" - pass - - -class Options: - """#### Class representing options.""" - - img2img_background_color: str = "#ffffff" # Set to white for now - - -class State: - """#### Class representing the state.""" - - interrupted: bool = False - - def begin(self) -> None: - """#### Begin the state.""" - pass - - def end(self) -> None: - """#### End the state.""" - pass - - -opts = Options() -state = State() - -# Will only ever hold 1 upscaler -sd_upscalers = [None] -actual_upscaler = None - -# Batch of images to upscale -batch = None - - -if not hasattr(Image, "Resampling"): # For older versions of Pillow - Image.Resampling = Image - - -class Upscaler: - """#### Class for upscaling images.""" - - def _upscale(self, img: Image.Image, scale: float) -> Image.Image: - """#### Upscale an image. - - #### Args: - - `img` (Image.Image): The input image. - - `scale` (float): The scale factor. - - #### Returns: - - `Image.Image`: The upscaled image. - """ - global actual_upscaler - tensor = image_util.pil_to_tensor(img) - image_upscale_node = ImageUpscaleWithModel() - (upscaled,) = image_upscale_node.upscale(actual_upscaler, tensor) - return image_util.tensor_to_pil(upscaled) - - def upscale(self, img: Image.Image, scale: float, selected_model: str = None) -> Image.Image: - """#### Upscale an image with a selected model. - - #### Args: - - `img` (Image.Image): The input image. - - `scale` (float): The scale factor. - - `selected_model` (str, optional): The selected model. Defaults to None. - - #### Returns: - - `Image.Image`: The upscaled image. - """ - global batch - batch = [self._upscale(img, scale) for img in batch] - return batch[0] - - -class UpscalerData: - """#### Class for storing upscaler data.""" - - name: str = "" - data_path: str = "" - - def __init__(self): +import logging as logger +import torch +from PIL import Image + +from modules.Device import Device +from modules.UltimateSDUpscale import RDRB +from modules.UltimateSDUpscale import image_util +from modules.Utilities import util + + +def load_state_dict(state_dict: dict) -> RDRB.PyTorchModel: + """#### Load a state dictionary into a PyTorch model. + + #### Args: + - `state_dict` (dict): The state dictionary. + + #### Returns: + - `RDRB.PyTorchModel`: The loaded PyTorch model. + """ + logger.debug("Loading state dict into pytorch model arch") + state_dict_keys = list(state_dict.keys()) + if "params_ema" in state_dict_keys: + state_dict = state_dict["params_ema"] + model = RDRB.RRDBNet(state_dict) + return model + + +class UpscaleModelLoader: + """#### Class for loading upscale models.""" + + def load_model(self, model_name: str) -> tuple: + """#### Load an upscale model. + + #### Args: + - `model_name` (str): The name of the model. + + #### Returns: + - `tuple`: The loaded model. + """ + model_path = f"./_internal/ESRGAN/{model_name}" + sd = util.load_torch_file(model_path, safe_load=True) + if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: + sd = util.state_dict_prefix_replace(sd, {"module.": ""}) + out = load_state_dict(sd).eval() + return (out,) + + +class ImageUpscaleWithModel: + """#### Class for upscaling images with a model.""" + + def upscale(self, upscale_model: torch.nn.Module, image: torch.Tensor) -> tuple: + """#### Upscale an image using a model. + + #### Args: + - `upscale_model` (torch.nn.Module): The upscale model. + - `image` (torch.Tensor): The input image tensor. + + #### Returns: + - `tuple`: The upscaled image tensor. + """ + if torch.cuda.is_available(): + device = torch.device(torch.cuda.current_device()) + else: + device = torch.device("cpu") + upscale_model.to(device) + in_img = image.movedim(-1, -3).to(device) + Device.get_free_memory(device) + + tile = 512 + overlap = 32 + + oom = True + while oom: + steps = in_img.shape[0] * image_util.get_tiled_scale_steps( + in_img.shape[3], + in_img.shape[2], + tile_x=tile, + tile_y=tile, + overlap=overlap, + ) + pbar = util.ProgressBar(steps) + s = image_util.tiled_scale( + in_img, + lambda a: upscale_model(a), + tile_x=tile, + tile_y=tile, + overlap=overlap, + upscale_amount=upscale_model.scale, + pbar=pbar, + ) + oom = False + + upscale_model.cpu() + s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0) + return (s,) + + +def torch_gc() -> None: + """#### Perform garbage collection for PyTorch.""" + pass + + +class Script: + """#### Class representing a script.""" + pass + + +class Options: + """#### Class representing options.""" + + img2img_background_color: str = "#ffffff" # Set to white for now + + +class State: + """#### Class representing the state.""" + + interrupted: bool = False + + def begin(self) -> None: + """#### Begin the state.""" + pass + + def end(self) -> None: + """#### End the state.""" + pass + + +opts = Options() +state = State() + +# Will only ever hold 1 upscaler +sd_upscalers = [None] +actual_upscaler = None + +# Batch of images to upscale +batch = None + + +if not hasattr(Image, "Resampling"): # For older versions of Pillow + Image.Resampling = Image + + +class Upscaler: + """#### Class for upscaling images.""" + + def _upscale(self, img: Image.Image, scale: float) -> Image.Image: + """#### Upscale an image. + + #### Args: + - `img` (Image.Image): The input image. + - `scale` (float): The scale factor. + + #### Returns: + - `Image.Image`: The upscaled image. + """ + global actual_upscaler + tensor = image_util.pil_to_tensor(img) + image_upscale_node = ImageUpscaleWithModel() + (upscaled,) = image_upscale_node.upscale(actual_upscaler, tensor) + return image_util.tensor_to_pil(upscaled) + + def upscale(self, img: Image.Image, scale: float, selected_model: str = None) -> Image.Image: + """#### Upscale an image with a selected model. + + #### Args: + - `img` (Image.Image): The input image. + - `scale` (float): The scale factor. + - `selected_model` (str, optional): The selected model. Defaults to None. + + #### Returns: + - `Image.Image`: The upscaled image. + """ + global batch + batch = [self._upscale(img, scale) for img in batch] + return batch[0] + + +class UpscalerData: + """#### Class for storing upscaler data.""" + + name: str = "" + data_path: str = "" + + def __init__(self): self.scaler = Upscaler() \ No newline at end of file diff --git a/modules/UltimateSDUpscale/USDU_util.py b/modules/UltimateSDUpscale/USDU_util.py index a59361d1ce5cbe97ac88cb954bc8ed95a7847ee1..9a2abe79b75b9d17c7be3c78084e49bfe2efa8ba 100644 --- a/modules/UltimateSDUpscale/USDU_util.py +++ b/modules/UltimateSDUpscale/USDU_util.py @@ -1,173 +1,173 @@ -from typing import Literal -import torch -import torch.nn as nn - -ConvMode = Literal["CNA", "NAC", "CNAC"] - -def act(act_type: str, inplace: bool = True, neg_slope: float = 0.2, n_prelu: int = 1) -> nn.Module: - """#### Get the activation layer. - - #### Args: - - `act_type` (str): The type of activation. - - `inplace` (bool, optional): Whether to perform the operation in-place. Defaults to True. - - `neg_slope` (float, optional): The negative slope for LeakyReLU. Defaults to 0.2. - - `n_prelu` (int, optional): The number of PReLU parameters. Defaults to 1. - - #### Returns: - - `nn.Module`: The activation layer. - """ - act_type = act_type.lower() - layer = nn.LeakyReLU(neg_slope, inplace) - return layer - -def get_valid_padding(kernel_size: int, dilation: int) -> int: - """#### Get the valid padding for a convolutional layer. - - #### Args: - - `kernel_size` (int): The size of the kernel. - - `dilation` (int): The dilation rate. - - #### Returns: - - `int`: The valid padding. - """ - kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) - padding = (kernel_size - 1) // 2 - return padding - -def sequential(*args: nn.Module) -> nn.Sequential: - """#### Create a sequential container. - - #### Args: - - `*args` (nn.Module): The modules to include in the sequential container. - - #### Returns: - - `nn.Sequential`: The sequential container. - """ - modules = [] - for module in args: - if isinstance(module, nn.Sequential): - for submodule in module.children(): - modules.append(submodule) - elif isinstance(module, nn.Module): - modules.append(module) - return nn.Sequential(*modules) - -def conv_block( - in_nc: int, - out_nc: int, - kernel_size: int, - stride: int = 1, - dilation: int = 1, - groups: int = 1, - bias: bool = True, - pad_type: str = "zero", - norm_type: str | None = None, - act_type: str | None = "relu", - mode: ConvMode = "CNA", - c2x2: bool = False, -) -> nn.Sequential: - """#### Create a convolutional block. - - #### Args: - - `in_nc` (int): The number of input channels. - - `out_nc` (int): The number of output channels. - - `kernel_size` (int): The size of the kernel. - - `stride` (int, optional): The stride of the convolution. Defaults to 1. - - `dilation` (int, optional): The dilation rate. Defaults to 1. - - `groups` (int, optional): The number of groups. Defaults to 1. - - `bias` (bool, optional): Whether to include a bias term. Defaults to True. - - `pad_type` (str, optional): The type of padding. Defaults to "zero". - - `norm_type` (str | None, optional): The type of normalization. Defaults to None. - - `act_type` (str | None, optional): The type of activation. Defaults to "relu". - - `mode` (ConvMode, optional): The mode of the convolution. Defaults to "CNA". - - `c2x2` (bool, optional): Whether to use 2x2 convolutions. Defaults to False. - - #### Returns: - - `nn.Sequential`: The convolutional block. - """ - assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode) - padding = get_valid_padding(kernel_size, dilation) - padding = padding if pad_type == "zero" else 0 - - c = nn.Conv2d( - in_nc, - out_nc, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - bias=bias, - groups=groups, - ) - a = act(act_type) if act_type else None - if mode in ("CNA", "CNAC"): - return sequential(None, c, None, a) - -def upconv_block( - in_nc: int, - out_nc: int, - upscale_factor: int = 2, - kernel_size: int = 3, - stride: int = 1, - bias: bool = True, - pad_type: str = "zero", - norm_type: str | None = None, - act_type: str = "relu", - mode: str = "nearest", - c2x2: bool = False, -) -> nn.Sequential: - """#### Create an upsampling convolutional block. - - #### Args: - - `in_nc` (int): The number of input channels. - - `out_nc` (int): The number of output channels. - - `upscale_factor` (int, optional): The upscale factor. Defaults to 2. - - `kernel_size` (int, optional): The size of the kernel. Defaults to 3. - - `stride` (int, optional): The stride of the convolution. Defaults to 1. - - `bias` (bool, optional): Whether to include a bias term. Defaults to True. - - `pad_type` (str, optional): The type of padding. Defaults to "zero". - - `norm_type` (str | None, optional): The type of normalization. Defaults to None. - - `act_type` (str, optional): The type of activation. Defaults to "relu". - - `mode` (str, optional): The mode of upsampling. Defaults to "nearest". - - `c2x2` (bool, optional): Whether to use 2x2 convolutions. Defaults to False. - - #### Returns: - - `nn.Sequential`: The upsampling convolutional block. - """ - upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode) - conv = conv_block( - in_nc, - out_nc, - kernel_size, - stride, - bias=bias, - pad_type=pad_type, - norm_type=norm_type, - act_type=act_type, - c2x2=c2x2, - ) - return sequential(upsample, conv) - -class ShortcutBlock(nn.Module): - """#### Elementwise sum the output of a submodule to its input.""" - - def __init__(self, submodule: nn.Module): - """#### Initialize the ShortcutBlock. - - #### Args: - - `submodule` (nn.Module): The submodule to apply. - """ - super(ShortcutBlock, self).__init__() - self.sub = submodule - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - output = x + self.sub(x) +from typing import Literal +import torch +import torch.nn as nn + +ConvMode = Literal["CNA", "NAC", "CNAC"] + +def act(act_type: str, inplace: bool = True, neg_slope: float = 0.2, n_prelu: int = 1) -> nn.Module: + """#### Get the activation layer. + + #### Args: + - `act_type` (str): The type of activation. + - `inplace` (bool, optional): Whether to perform the operation in-place. Defaults to True. + - `neg_slope` (float, optional): The negative slope for LeakyReLU. Defaults to 0.2. + - `n_prelu` (int, optional): The number of PReLU parameters. Defaults to 1. + + #### Returns: + - `nn.Module`: The activation layer. + """ + act_type = act_type.lower() + layer = nn.LeakyReLU(neg_slope, inplace) + return layer + +def get_valid_padding(kernel_size: int, dilation: int) -> int: + """#### Get the valid padding for a convolutional layer. + + #### Args: + - `kernel_size` (int): The size of the kernel. + - `dilation` (int): The dilation rate. + + #### Returns: + - `int`: The valid padding. + """ + kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) + padding = (kernel_size - 1) // 2 + return padding + +def sequential(*args: nn.Module) -> nn.Sequential: + """#### Create a sequential container. + + #### Args: + - `*args` (nn.Module): The modules to include in the sequential container. + + #### Returns: + - `nn.Sequential`: The sequential container. + """ + modules = [] + for module in args: + if isinstance(module, nn.Sequential): + for submodule in module.children(): + modules.append(submodule) + elif isinstance(module, nn.Module): + modules.append(module) + return nn.Sequential(*modules) + +def conv_block( + in_nc: int, + out_nc: int, + kernel_size: int, + stride: int = 1, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + pad_type: str = "zero", + norm_type: str | None = None, + act_type: str | None = "relu", + mode: ConvMode = "CNA", + c2x2: bool = False, +) -> nn.Sequential: + """#### Create a convolutional block. + + #### Args: + - `in_nc` (int): The number of input channels. + - `out_nc` (int): The number of output channels. + - `kernel_size` (int): The size of the kernel. + - `stride` (int, optional): The stride of the convolution. Defaults to 1. + - `dilation` (int, optional): The dilation rate. Defaults to 1. + - `groups` (int, optional): The number of groups. Defaults to 1. + - `bias` (bool, optional): Whether to include a bias term. Defaults to True. + - `pad_type` (str, optional): The type of padding. Defaults to "zero". + - `norm_type` (str | None, optional): The type of normalization. Defaults to None. + - `act_type` (str | None, optional): The type of activation. Defaults to "relu". + - `mode` (ConvMode, optional): The mode of the convolution. Defaults to "CNA". + - `c2x2` (bool, optional): Whether to use 2x2 convolutions. Defaults to False. + + #### Returns: + - `nn.Sequential`: The convolutional block. + """ + assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode) + padding = get_valid_padding(kernel_size, dilation) + padding = padding if pad_type == "zero" else 0 + + c = nn.Conv2d( + in_nc, + out_nc, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias, + groups=groups, + ) + a = act(act_type) if act_type else None + if mode in ("CNA", "CNAC"): + return sequential(None, c, None, a) + +def upconv_block( + in_nc: int, + out_nc: int, + upscale_factor: int = 2, + kernel_size: int = 3, + stride: int = 1, + bias: bool = True, + pad_type: str = "zero", + norm_type: str | None = None, + act_type: str = "relu", + mode: str = "nearest", + c2x2: bool = False, +) -> nn.Sequential: + """#### Create an upsampling convolutional block. + + #### Args: + - `in_nc` (int): The number of input channels. + - `out_nc` (int): The number of output channels. + - `upscale_factor` (int, optional): The upscale factor. Defaults to 2. + - `kernel_size` (int, optional): The size of the kernel. Defaults to 3. + - `stride` (int, optional): The stride of the convolution. Defaults to 1. + - `bias` (bool, optional): Whether to include a bias term. Defaults to True. + - `pad_type` (str, optional): The type of padding. Defaults to "zero". + - `norm_type` (str | None, optional): The type of normalization. Defaults to None. + - `act_type` (str, optional): The type of activation. Defaults to "relu". + - `mode` (str, optional): The mode of upsampling. Defaults to "nearest". + - `c2x2` (bool, optional): Whether to use 2x2 convolutions. Defaults to False. + + #### Returns: + - `nn.Sequential`: The upsampling convolutional block. + """ + upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode) + conv = conv_block( + in_nc, + out_nc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + c2x2=c2x2, + ) + return sequential(upsample, conv) + +class ShortcutBlock(nn.Module): + """#### Elementwise sum the output of a submodule to its input.""" + + def __init__(self, submodule: nn.Module): + """#### Initialize the ShortcutBlock. + + #### Args: + - `submodule` (nn.Module): The submodule to apply. + """ + super(ShortcutBlock, self).__init__() + self.sub = submodule + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + output = x + self.sub(x) return output \ No newline at end of file diff --git a/modules/UltimateSDUpscale/UltimateSDUpscale.py b/modules/UltimateSDUpscale/UltimateSDUpscale.py index d22a47e7877ce9edc6eaf63b84c38c461ee4b0f1..9cd62e7dae030e0465a817d510c19110a8217bc0 100644 --- a/modules/UltimateSDUpscale/UltimateSDUpscale.py +++ b/modules/UltimateSDUpscale/UltimateSDUpscale.py @@ -1,1019 +1,1019 @@ -from modules.AutoEncoders import VariationalAE -from modules.sample import sampling -from modules.UltimateSDUpscale import USDU_upscaler, image_util -import torch -from PIL import ImageFilter, ImageDraw, Image -from enum import Enum -import math - -# taken from https://github.com/ssitu/ComfyUI_UltimateSDUpscale - -state = USDU_upscaler.state - -class UnsupportedModel(Exception): - """#### Exception raised for unsupported models.""" - pass - - -class StableDiffusionProcessing: - """#### Class representing the processing of Stable Diffusion images.""" - - def __init__( - self, - init_img: Image.Image, - model: torch.nn.Module, - positive: str, - negative: str, - vae: VariationalAE.VAE, - seed: int, - steps: int, - cfg: float, - sampler_name: str, - scheduler: str, - denoise: float, - upscale_by: float, - uniform_tile_mode: bool, - ): - """ - #### Initialize the StableDiffusionProcessing class. - - #### Args: - - `init_img` (Image.Image): The initial image. - - `model` (torch.nn.Module): The model. - - `positive` (str): The positive prompt. - - `negative` (str): The negative prompt. - - `vae` (VariationalAE.VAE): The variational autoencoder. - - `seed` (int): The seed. - - `steps` (int): The number of steps. - - `cfg` (float): The CFG scale. - - `sampler_name` (str): The sampler name. - - `scheduler` (str): The scheduler. - - `denoise` (float): The denoise strength. - - `upscale_by` (float): The upscale factor. - - `uniform_tile_mode` (bool): Whether to use uniform tile mode. - """ - # Variables used by the USDU script - self.init_images = [init_img] - self.image_mask = None - self.mask_blur = 0 - self.inpaint_full_res_padding = 0 - self.width = init_img.width - self.height = init_img.height - - self.model = model - self.positive = positive - self.negative = negative - self.vae = vae - self.seed = seed - self.steps = steps - self.cfg = cfg - self.sampler_name = sampler_name - self.scheduler = scheduler - self.denoise = denoise - - # Variables used only by this script - self.init_size = init_img.width, init_img.height - self.upscale_by = upscale_by - self.uniform_tile_mode = uniform_tile_mode - - # Other required A1111 variables for the USDU script that is currently unused in this script - self.extra_generation_params = {} - - -class Processed: - """#### Class representing the processed images.""" - - def __init__( - self, p: StableDiffusionProcessing, images: list, seed: int, info: str - ): - """ - #### Initialize the Processed class. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - - `images` (list): The list of images. - - `seed` (int): The seed. - - `info` (str): The information string. - """ - self.images = images - self.seed = seed - self.info = info - - def infotext(self, p: StableDiffusionProcessing, index: int) -> str: - """ - #### Get the information text. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - - `index` (int): The index. - - #### Returns: - - `str`: The information text. - """ - return None - - -def fix_seed(p: StableDiffusionProcessing) -> None: - """ - #### Fix the seed for reproducibility. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - """ - pass - - -def process_images(p: StableDiffusionProcessing, pipeline: bool = False) -> Processed: - """ - #### Process the images. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - - #### Returns: - - `Processed`: The processed images. - """ - # Where the main image generation happens in A1111 - - # Setup - image_mask = p.image_mask.convert("L") - init_image = p.init_images[0] - - # Locate the white region of the mask outlining the tile and add padding - crop_region = image_util.get_crop_region(image_mask, p.inpaint_full_res_padding) - - x1, y1, x2, y2 = crop_region - crop_width = x2 - x1 - crop_height = y2 - y1 - crop_ratio = crop_width / crop_height - p_ratio = p.width / p.height - if crop_ratio > p_ratio: - target_width = crop_width - target_height = round(crop_width / p_ratio) - else: - target_width = round(crop_height * p_ratio) - target_height = crop_height - crop_region, _ = image_util.expand_crop( - crop_region, - image_mask.width, - image_mask.height, - target_width, - target_height, - ) - tile_size = p.width, p.height - - # Blur the mask - if p.mask_blur > 0: - image_mask = image_mask.filter(ImageFilter.GaussianBlur(p.mask_blur)) - - # Crop the images to get the tiles that will be used for generation - tiles = [img.crop(crop_region) for img in USDU_upscaler.batch] - - # Assume the same size for all images in the batch - initial_tile_size = tiles[0].size - - # Resize if necessary - for i, tile in enumerate(tiles): - if tile.size != tile_size: - tiles[i] = tile.resize(tile_size, Image.Resampling.LANCZOS) - - # Crop conditioning - positive_cropped = image_util.crop_cond( - p.positive, crop_region, p.init_size, init_image.size, tile_size - ) - negative_cropped = image_util.crop_cond( - p.negative, crop_region, p.init_size, init_image.size, tile_size - ) - - # Encode the image - vae_encoder = VariationalAE.VAEEncode() - batched_tiles = torch.cat([image_util.pil_to_tensor(tile) for tile in tiles], dim=0) - (latent,) = vae_encoder.encode(p.vae, batched_tiles) - - # Generate samples - (samples,) = sampling.common_ksampler( - p.model, - p.seed, - p.steps, - p.cfg, - p.sampler_name, - p.scheduler, - positive_cropped, - negative_cropped, - latent, - denoise=p.denoise, - pipeline=pipeline - ) - - # Decode the sample - vae_decoder = VariationalAE.VAEDecode() - (decoded,) = vae_decoder.decode(p.vae, samples) - - # Convert the sample to a PIL image - tiles_sampled = [image_util.tensor_to_pil(decoded, i) for i in range(len(decoded))] - - for i, tile_sampled in enumerate(tiles_sampled): - init_image = USDU_upscaler.batch[i] - - # Resize back to the original size - if tile_sampled.size != initial_tile_size: - tile_sampled = tile_sampled.resize( - initial_tile_size, Image.Resampling.LANCZOS - ) - - # Put the tile into position - image_tile_only = Image.new("RGBA", init_image.size) - image_tile_only.paste(tile_sampled, crop_region[:2]) - - # Add the mask as an alpha channel - # Must make a copy due to the possibility of an edge becoming black - temp = image_tile_only.copy() - image_mask = image_mask.resize(temp.size) - temp.putalpha(image_mask) - temp.putalpha(image_mask) - image_tile_only.paste(temp, image_tile_only) - - # Add back the tile to the initial image according to the mask in the alpha channel - result = init_image.convert("RGBA") - result.alpha_composite(image_tile_only) - - # Convert back to RGB - result = result.convert("RGB") - USDU_upscaler.batch[i] = result - - processed = Processed(p, [USDU_upscaler.batch[0]], p.seed, None) - return processed - - -class USDUMode(Enum): - """#### Enum representing the modes for Ultimate SD Upscale.""" - LINEAR = 0 - CHESS = 1 - NONE = 2 - - -class USDUSFMode(Enum): - """#### Enum representing the seam fix modes for Ultimate SD Upscale.""" - NONE = 0 - BAND_PASS = 1 - HALF_TILE = 2 - HALF_TILE_PLUS_INTERSECTIONS = 3 - - -class USDUpscaler: - """#### Class representing the Ultimate SD Upscaler.""" - - def __init__( - self, - p: StableDiffusionProcessing, - image: Image.Image, - upscaler_index: int, - save_redraw: bool, - save_seams_fix: bool, - tile_width: int, - tile_height: int, - ) -> None: - """ - #### Initialize the USDUpscaler class. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - - `image` (Image.Image): The image. - - `upscaler_index` (int): The upscaler index. - - `save_redraw` (bool): Whether to save the redraw. - - `save_seams_fix` (bool): Whether to save the seams fix. - - `tile_width` (int): The tile width. - - `tile_height` (int): The tile height. - """ - self.p: StableDiffusionProcessing = p - self.image: Image = image - self.scale_factor = math.ceil( - max(p.width, p.height) / max(image.width, image.height) - ) - self.upscaler = USDU_upscaler.sd_upscalers[upscaler_index] - self.redraw = USDURedraw() - self.redraw.save = save_redraw - self.redraw.tile_width = tile_width if tile_width > 0 else tile_height - self.redraw.tile_height = tile_height if tile_height > 0 else tile_width - self.seams_fix = USDUSeamsFix() - self.seams_fix.save = save_seams_fix - self.seams_fix.tile_width = tile_width if tile_width > 0 else tile_height - self.seams_fix.tile_height = tile_height if tile_height > 0 else tile_width - self.initial_info = None - self.rows = math.ceil(self.p.height / self.redraw.tile_height) - self.cols = math.ceil(self.p.width / self.redraw.tile_width) - - def get_factor(self, num: int) -> int: - """ - #### Get the factor for a given number. - - #### Args: - - `num` (int): The number. - - #### Returns: - - `int`: The factor. - """ - if num == 1: - return 2 - if num % 4 == 0: - return 4 - if num % 3 == 0: - return 3 - if num % 2 == 0: - return 2 - return 0 - - def get_factors(self) -> None: - """ - #### Get the list of scale factors. - """ - scales = [] - current_scale = 1 - current_scale_factor = self.get_factor(self.scale_factor) - while current_scale < self.scale_factor: - current_scale_factor = self.get_factor(self.scale_factor // current_scale) - scales.append(current_scale_factor) - current_scale = current_scale * current_scale_factor - self.scales = enumerate(scales) - - def upscale(self) -> None: - """ - #### Upscale the image. - """ - # Log info - print(f"Canva size: {self.p.width}x{self.p.height}") - print(f"Image size: {self.image.width}x{self.image.height}") - print(f"Scale factor: {self.scale_factor}") - # Get list with scale factors - self.get_factors() - # Upscaling image over all factors - for index, value in self.scales: - print(f"Upscaling iteration {index + 1} with scale factor {value}") - self.image = self.upscaler.scaler.upscale( - self.image, value, self.upscaler.data_path - ) - # Resize image to set values - self.image = self.image.resize( - (self.p.width, self.p.height), resample=Image.LANCZOS - ) - - def setup_redraw(self, redraw_mode: int, padding: int, mask_blur: int) -> None: - """ - #### Set up the redraw. - - #### Args: - - `redraw_mode` (int): The redraw mode. - - `padding` (int): The padding. - - `mask_blur` (int): The mask blur. - """ - self.redraw.mode = USDUMode(redraw_mode) - self.redraw.enabled = self.redraw.mode != USDUMode.NONE - self.redraw.padding = padding - self.p.mask_blur = mask_blur - - def setup_seams_fix( - self, padding: int, denoise: float, mask_blur: int, width: int, mode: int - ) -> None: - """ - #### Set up the seams fix. - - #### Args: - - `padding` (int): The padding. - - `denoise` (float): The denoise strength. - - `mask_blur` (int): The mask blur. - - `width` (int): The width. - - `mode` (int): The mode. - """ - self.seams_fix.padding = padding - self.seams_fix.denoise = denoise - self.seams_fix.mask_blur = mask_blur - self.seams_fix.width = width - self.seams_fix.mode = USDUSFMode(mode) - self.seams_fix.enabled = self.seams_fix.mode != USDUSFMode.NONE - - def calc_jobs_count(self) -> None: - """ - #### Calculate the number of jobs. - """ - redraw_job_count = (self.rows * self.cols) if self.redraw.enabled else 0 - seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols - global state - state.job_count = redraw_job_count + seams_job_count - - def print_info(self) -> None: - """ - #### Print the information. - """ - print(f"Tile size: {self.redraw.tile_width}x{self.redraw.tile_height}") - print(f"Tiles amount: {self.rows * self.cols}") - print(f"Grid: {self.rows}x{self.cols}") - print(f"Redraw enabled: {self.redraw.enabled}") - print(f"Seams fix mode: {self.seams_fix.mode.name}") - - def add_extra_info(self) -> None: - """ - #### Add extra information. - """ - self.p.extra_generation_params["Ultimate SD upscale upscaler"] = ( - self.upscaler.name - ) - self.p.extra_generation_params["Ultimate SD upscale tile_width"] = ( - self.redraw.tile_width - ) - self.p.extra_generation_params["Ultimate SD upscale tile_height"] = ( - self.redraw.tile_height - ) - self.p.extra_generation_params["Ultimate SD upscale mask_blur"] = ( - self.p.mask_blur - ) - self.p.extra_generation_params["Ultimate SD upscale padding"] = ( - self.redraw.padding - ) - - def process(self, pipeline) -> None: - """ - #### Process the image. - """ - USDU_upscaler.state.begin() - self.calc_jobs_count() - self.result_images = [] - if self.redraw.enabled: - self.image = self.redraw.start(self.p, self.image, self.rows, self.cols, pipeline) - self.initial_info = self.redraw.initial_info - self.result_images.append(self.image) - - if self.seams_fix.enabled: - self.image = self.seams_fix.start(self.p, self.image, self.rows, self.cols, pipeline) - self.initial_info = self.seams_fix.initial_info - self.result_images.append(self.image) - USDU_upscaler.state.end() - - -class USDURedraw: - """#### Class representing the redraw functionality for Ultimate SD Upscale.""" - - def init_draw(self, p: StableDiffusionProcessing, width: int, height: int) -> tuple: - """ - #### Initialize the draw. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - - `width` (int): The width. - - `height` (int): The height. - - #### Returns: - - `tuple`: The mask and draw objects. - """ - p.inpaint_full_res = True - p.inpaint_full_res_padding = self.padding - p.width = math.ceil((self.tile_width + self.padding) / 64) * 64 - p.height = math.ceil((self.tile_height + self.padding) / 64) * 64 - mask = Image.new("L", (width, height), "black") - draw = ImageDraw.Draw(mask) - return mask, draw - - def calc_rectangle(self, xi: int, yi: int) -> tuple: - """ - #### Calculate the rectangle coordinates. - - #### Args: - - `xi` (int): The x index. - - `yi` (int): The y index. - - #### Returns: - - `tuple`: The rectangle coordinates. - """ - x1 = xi * self.tile_width - y1 = yi * self.tile_height - x2 = xi * self.tile_width + self.tile_width - y2 = yi * self.tile_height + self.tile_height - - return x1, y1, x2, y2 - - def linear_process( - self, p: StableDiffusionProcessing, image: Image.Image, rows: int, cols: int, pipeline: bool = False - ) -> Image.Image: - """ - #### Perform linear processing. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - - `image` (Image.Image): The image. - - `rows` (int): The number of rows. - - `cols` (int): The number of columns. - - #### Returns: - - `Image.Image`: The processed image. - """ - global state - mask, draw = self.init_draw(p, image.width, image.height) - for yi in range(rows): - for xi in range(cols): - if state.interrupted: - break - draw.rectangle(self.calc_rectangle(xi, yi), fill="white") - p.init_images = [image] - p.image_mask = mask - processed = process_images(p, pipeline) - draw.rectangle(self.calc_rectangle(xi, yi), fill="black") - if len(processed.images) > 0: - image = processed.images[0] - - p.width = image.width - p.height = image.height - self.initial_info = processed.infotext(p, 0) - - return image - - def start(self, p: StableDiffusionProcessing, image: Image.Image, rows: int, cols: int, pipeline: bool = False) -> Image.Image: - """#### Start the redraw. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - - `image` (Image.Image): The image. - - `rows` (int): The number of rows. - - `cols` (int): The number of columns. - - #### Returns: - - `Image.Image`: The processed image. - """ - self.initial_info = None - return self.linear_process(p, image, rows, cols, pipeline=pipeline) - - -class USDUSeamsFix: - """#### Class representing the seams fix functionality for Ultimate SD Upscale.""" - - def init_draw(self, p: StableDiffusionProcessing) -> None: - """#### Initialize the draw. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - """ - self.initial_info = None - p.width = math.ceil((self.tile_width + self.padding) / 64) * 64 - p.height = math.ceil((self.tile_height + self.padding) / 64) * 64 - - def half_tile_process( - self, p: StableDiffusionProcessing, image: Image.Image, rows: int, cols: int, pipeline: bool = False - ) -> Image.Image: - """#### Perform half-tile processing. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - - `image` (Image.Image): The image. - - `rows` (int): The number of rows. - - `cols` (int): The number of columns. - - #### Returns: - - `Image.Image`: The processed image. - """ - global state - self.init_draw(p) - processed = None - - gradient = Image.linear_gradient("L") - row_gradient = Image.new("L", (self.tile_width, self.tile_height), "black") - row_gradient.paste( - gradient.resize( - (self.tile_width, self.tile_height // 2), resample=Image.BICUBIC - ), - (0, 0), - ) - row_gradient.paste( - gradient.rotate(180).resize( - (self.tile_width, self.tile_height // 2), resample=Image.BICUBIC - ), - (0, self.tile_height // 2), - ) - col_gradient = Image.new("L", (self.tile_width, self.tile_height), "black") - col_gradient.paste( - gradient.rotate(90).resize( - (self.tile_width // 2, self.tile_height), resample=Image.BICUBIC - ), - (0, 0), - ) - col_gradient.paste( - gradient.rotate(270).resize( - (self.tile_width // 2, self.tile_height), resample=Image.BICUBIC - ), - (self.tile_width // 2, 0), - ) - - p.denoising_strength = self.denoise - p.mask_blur = self.mask_blur - - for yi in range(rows - 1): - for xi in range(cols): - p.width = self.tile_width - p.height = self.tile_height - p.inpaint_full_res = True - p.inpaint_full_res_padding = self.padding - mask = Image.new("L", (image.width, image.height), "black") - mask.paste( - row_gradient, - ( - xi * self.tile_width, - yi * self.tile_height + self.tile_height // 2, - ), - ) - - p.init_images = [image] - p.image_mask = mask - processed = process_images(p, pipeline) - if len(processed.images) > 0: - image = processed.images[0] - - for yi in range(rows): - for xi in range(cols - 1): - p.width = self.tile_width - p.height = self.tile_height - p.inpaint_full_res = True - p.inpaint_full_res_padding = self.padding - mask = Image.new("L", (image.width, image.height), "black") - mask.paste( - col_gradient, - ( - xi * self.tile_width + self.tile_width // 2, - yi * self.tile_height, - ), - ) - - p.init_images = [image] - p.image_mask = mask - processed = process_images(p, pipeline) - if len(processed.images) > 0: - image = processed.images[0] - - p.width = image.width - p.height = image.height - if processed is not None: - self.initial_info = processed.infotext(p, 0) - - return image - - def start( - self, p: StableDiffusionProcessing, image: Image.Image, rows: int, cols: int, pipeline: bool = False - ) -> Image.Image: - """#### Start the seams fix process. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - - `image` (Image.Image): The image. - - `rows` (int): The number of rows. - - `cols` (int): The number of columns. - - #### Returns: - - `Image.Image`: The processed image. - """ - return self.half_tile_process(p, image, rows, cols, pipeline=pipeline) - - -class Script(USDU_upscaler.Script): - """#### Class representing the script for Ultimate SD Upscale.""" - - def run( - self, - p: StableDiffusionProcessing, - _: None, - tile_width: int, - tile_height: int, - mask_blur: int, - padding: int, - seams_fix_width: int, - seams_fix_denoise: float, - seams_fix_padding: int, - upscaler_index: int, - save_upscaled_image: bool, - redraw_mode: int, - save_seams_fix_image: bool, - seams_fix_mask_blur: int, - seams_fix_type: int, - target_size_type: int, - custom_width: int, - custom_height: int, - custom_scale: float, - pipeline: bool = False, - ) -> Processed: - """#### Run the script. - - #### Args: - - `p` (StableDiffusionProcessing): The processing object. - - `_` (None): Unused parameter. - - `tile_width` (int): The tile width. - - `tile_height` (int): The tile height. - - `mask_blur` (int): The mask blur. - - `padding` (int): The padding. - - `seams_fix_width` (int): The seams fix width. - - `seams_fix_denoise` (float): The seams fix denoise strength. - - `seams_fix_padding` (int): The seams fix padding. - - `upscaler_index` (int): The upscaler index. - - `save_upscaled_image` (bool): Whether to save the upscaled image. - - `redraw_mode` (int): The redraw mode. - - `save_seams_fix_image` (bool): Whether to save the seams fix image. - - `seams_fix_mask_blur` (int): The seams fix mask blur. - - `seams_fix_type` (int): The seams fix type. - - `target_size_type` (int): The target size type. - - `custom_width` (int): The custom width. - - `custom_height` (int): The custom height. - - `custom_scale` (float): The custom scale. - - #### Returns: - - `Processed`: The processed images. - """ - # Init - fix_seed(p) - USDU_upscaler.torch_gc() - - p.do_not_save_grid = True - p.do_not_save_samples = True - p.inpaint_full_res = False - - p.inpainting_fill = 1 - p.n_iter = 1 - p.batch_size = 1 - - seed = p.seed - - # Init image - init_img = p.init_images[0] - init_img = image_util.flatten( - init_img, USDU_upscaler.opts.img2img_background_color - ) - - p.width = math.ceil((init_img.width * custom_scale) / 64) * 64 - p.height = math.ceil((init_img.height * custom_scale) / 64) * 64 - - # Upscaling - upscaler = USDUpscaler( - p, - init_img, - upscaler_index, - save_upscaled_image, - save_seams_fix_image, - tile_width, - tile_height, - ) - upscaler.upscale() - - # Drawing - upscaler.setup_redraw(redraw_mode, padding, mask_blur) - upscaler.setup_seams_fix( - seams_fix_padding, - seams_fix_denoise, - seams_fix_mask_blur, - seams_fix_width, - seams_fix_type, - ) - upscaler.print_info() - upscaler.add_extra_info() - upscaler.process(pipeline=pipeline) - result_images = upscaler.result_images - - return Processed( - p, - result_images, - seed, - upscaler.initial_info if upscaler.initial_info is not None else "", - ) - - -# Upscaler -old_init = USDUpscaler.__init__ - - -def new_init( - self: USDUpscaler, - p: StableDiffusionProcessing, - image: Image.Image, - upscaler_index: int, - save_redraw: bool, - save_seams_fix: bool, - tile_width: int, - tile_height: int, -) -> None: - """#### Initialize the USDUpscaler class with new settings. - - #### Args: - - `self` (USDUpscaler): The USDUpscaler instance. - - `p` (StableDiffusionProcessing): The processing object. - - `image` (Image.Image): The image. - - `upscaler_index` (int): The upscaler index. - - `save_redraw` (bool): Whether to save the redraw. - - `save_seams_fix` (bool): Whether to save the seams fix. - - `tile_width` (int): The tile width. - - `tile_height` (int): The tile height. - """ - p.width = math.ceil((image.width * p.upscale_by) / 8) * 8 - p.height = math.ceil((image.height * p.upscale_by) / 8) * 8 - old_init( - self, - p, - image, - upscaler_index, - save_redraw, - save_seams_fix, - tile_width, - tile_height, - ) - - -USDUpscaler.__init__ = new_init - -# Redraw -old_setup_redraw = USDURedraw.init_draw - - -def new_setup_redraw( - self: USDURedraw, p: StableDiffusionProcessing, width: int, height: int -) -> tuple: - """#### Set up the redraw with new settings. - - #### Args: - - `self` (USDURedraw): The USDURedraw instance. - - `p` (StableDiffusionProcessing): The processing object. - - `width` (int): The width. - - `height` (int): The height. - - #### Returns: - - `tuple`: The mask and draw objects. - """ - mask, draw = old_setup_redraw(self, p, width, height) - p.width = math.ceil((self.tile_width + self.padding) / 8) * 8 - p.height = math.ceil((self.tile_height + self.padding) / 8) * 8 - return mask, draw - - -USDURedraw.init_draw = new_setup_redraw - -# Seams fix -old_setup_seams_fix = USDUSeamsFix.init_draw - - -def new_setup_seams_fix(self: USDUSeamsFix, p: StableDiffusionProcessing) -> None: - """#### Set up the seams fix with new settings. - - #### Args: - - `self` (USDUSeamsFix): The USDUSeamsFix instance. - - `p` (StableDiffusionProcessing): The processing object. - """ - old_setup_seams_fix(self, p) - p.width = math.ceil((self.tile_width + self.padding) / 8) * 8 - p.height = math.ceil((self.tile_height + self.padding) / 8) * 8 - - -USDUSeamsFix.init_draw = new_setup_seams_fix - -# Make the script upscale on a batch of images instead of one image -old_upscale = USDUpscaler.upscale - - -def new_upscale(self: USDUpscaler) -> None: - """#### Upscale a batch of images. - - #### Args: - - `self` (USDUpscaler): The USDUpscaler instance. - """ - old_upscale(self) - USDU_upscaler.batch = [self.image] + [ - img.resize((self.p.width, self.p.height), resample=Image.LANCZOS) - for img in USDU_upscaler.batch[1:] - ] - - -USDUpscaler.upscale = new_upscale -MAX_RESOLUTION = 8192 -# The modes available for Ultimate SD Upscale -MODES = { - "Linear": USDUMode.LINEAR, - "Chess": USDUMode.CHESS, - "None": USDUMode.NONE, -} -# The seam fix modes -SEAM_FIX_MODES = { - "None": USDUSFMode.NONE, - "Band Pass": USDUSFMode.BAND_PASS, - "Half Tile": USDUSFMode.HALF_TILE, - "Half Tile + Intersections": USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS, -} - - -class UltimateSDUpscale: - """#### Class representing the Ultimate SD Upscale functionality.""" - - def upscale( - self, - image: torch.Tensor, - model: torch.nn.Module, - positive: str, - negative: str, - vae: VariationalAE.VAE, - upscale_by: float, - seed: int, - steps: int, - cfg: float, - sampler_name: str, - scheduler: str, - denoise: float, - upscale_model: any, - mode_type: str, - tile_width: int, - tile_height: int, - mask_blur: int, - tile_padding: int, - seam_fix_mode: str, - seam_fix_denoise: float, - seam_fix_mask_blur: int, - seam_fix_width: int, - seam_fix_padding: int, - force_uniform_tiles: bool, - pipeline: bool = False, - ) -> tuple: - """#### Upscale the image. - - #### Args: - - `image` (torch.Tensor): The image tensor. - - `model` (torch.nn.Module): The model. - - `positive` (str): The positive prompt. - - `negative` (str): The negative prompt. - - `vae` (VariationalAE.VAE): The variational autoencoder. - - `upscale_by` (float): The upscale factor. - - `seed` (int): The seed. - - `steps` (int): The number of steps. - - `cfg` (float): The CFG scale. - - `sampler_name` (str): The sampler name. - - `scheduler` (str): The scheduler. - - `denoise` (float): The denoise strength. - - `upscale_model` (any): The upscale model. - - `mode_type` (str): The mode type. - - `tile_width` (int): The tile width. - - `tile_height` (int): The tile height. - - `mask_blur` (int): The mask blur. - - `tile_padding` (int): The tile padding. - - `seam_fix_mode` (str): The seam fix mode. - - `seam_fix_denoise` (float): The seam fix denoise strength. - - `seam_fix_mask_blur` (int): The seam fix mask blur. - - `seam_fix_width` (int): The seam fix width. - - `seam_fix_padding` (int): The seam fix padding. - - `force_uniform_tiles` (bool): Whether to force uniform tiles. - - #### Returns: - - `tuple`: The resulting tensor. - """ - # Set up A1111 patches - - # Upscaler - # An object that the script works with - USDU_upscaler.sd_upscalers[0] = USDU_upscaler.UpscalerData() - # Where the actual upscaler is stored, will be used when the script upscales using the Upscaler in UpscalerData - USDU_upscaler.actual_upscaler = upscale_model - - # Set the batch of images - USDU_upscaler.batch = [image_util.tensor_to_pil(image, i) for i in range(len(image))] - - # Processing - sdprocessing = StableDiffusionProcessing( - image_util.tensor_to_pil(image), - model, - positive, - negative, - vae, - seed, - steps, - cfg, - sampler_name, - scheduler, - denoise, - upscale_by, - force_uniform_tiles, - ) - - # Running the script - script = Script() - script.run( - p=sdprocessing, - _=None, - tile_width=tile_width, - tile_height=tile_height, - mask_blur=mask_blur, - padding=tile_padding, - seams_fix_width=seam_fix_width, - seams_fix_denoise=seam_fix_denoise, - seams_fix_padding=seam_fix_padding, - upscaler_index=0, - save_upscaled_image=False, - redraw_mode=MODES[mode_type], - save_seams_fix_image=False, - seams_fix_mask_blur=seam_fix_mask_blur, - seams_fix_type=SEAM_FIX_MODES[seam_fix_mode], - target_size_type=2, - custom_width=None, - custom_height=None, - custom_scale=upscale_by, - pipeline=pipeline, - ) - - # Return the resulting images - images = [image_util.pil_to_tensor(img) for img in USDU_upscaler.batch] - tensor = torch.cat(images, dim=0) +from modules.AutoEncoders import VariationalAE +from modules.sample import sampling +from modules.UltimateSDUpscale import USDU_upscaler, image_util +import torch +from PIL import ImageFilter, ImageDraw, Image +from enum import Enum +import math + +# taken from https://github.com/ssitu/ComfyUI_UltimateSDUpscale + +state = USDU_upscaler.state + +class UnsupportedModel(Exception): + """#### Exception raised for unsupported models.""" + pass + + +class StableDiffusionProcessing: + """#### Class representing the processing of Stable Diffusion images.""" + + def __init__( + self, + init_img: Image.Image, + model: torch.nn.Module, + positive: str, + negative: str, + vae: VariationalAE.VAE, + seed: int, + steps: int, + cfg: float, + sampler_name: str, + scheduler: str, + denoise: float, + upscale_by: float, + uniform_tile_mode: bool, + ): + """ + #### Initialize the StableDiffusionProcessing class. + + #### Args: + - `init_img` (Image.Image): The initial image. + - `model` (torch.nn.Module): The model. + - `positive` (str): The positive prompt. + - `negative` (str): The negative prompt. + - `vae` (VariationalAE.VAE): The variational autoencoder. + - `seed` (int): The seed. + - `steps` (int): The number of steps. + - `cfg` (float): The CFG scale. + - `sampler_name` (str): The sampler name. + - `scheduler` (str): The scheduler. + - `denoise` (float): The denoise strength. + - `upscale_by` (float): The upscale factor. + - `uniform_tile_mode` (bool): Whether to use uniform tile mode. + """ + # Variables used by the USDU script + self.init_images = [init_img] + self.image_mask = None + self.mask_blur = 0 + self.inpaint_full_res_padding = 0 + self.width = init_img.width + self.height = init_img.height + + self.model = model + self.positive = positive + self.negative = negative + self.vae = vae + self.seed = seed + self.steps = steps + self.cfg = cfg + self.sampler_name = sampler_name + self.scheduler = scheduler + self.denoise = denoise + + # Variables used only by this script + self.init_size = init_img.width, init_img.height + self.upscale_by = upscale_by + self.uniform_tile_mode = uniform_tile_mode + + # Other required A1111 variables for the USDU script that is currently unused in this script + self.extra_generation_params = {} + + +class Processed: + """#### Class representing the processed images.""" + + def __init__( + self, p: StableDiffusionProcessing, images: list, seed: int, info: str + ): + """ + #### Initialize the Processed class. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + - `images` (list): The list of images. + - `seed` (int): The seed. + - `info` (str): The information string. + """ + self.images = images + self.seed = seed + self.info = info + + def infotext(self, p: StableDiffusionProcessing, index: int) -> str: + """ + #### Get the information text. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + - `index` (int): The index. + + #### Returns: + - `str`: The information text. + """ + return None + + +def fix_seed(p: StableDiffusionProcessing) -> None: + """ + #### Fix the seed for reproducibility. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + """ + pass + + +def process_images(p: StableDiffusionProcessing, pipeline: bool = False) -> Processed: + """ + #### Process the images. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + + #### Returns: + - `Processed`: The processed images. + """ + # Where the main image generation happens in A1111 + + # Setup + image_mask = p.image_mask.convert("L") + init_image = p.init_images[0] + + # Locate the white region of the mask outlining the tile and add padding + crop_region = image_util.get_crop_region(image_mask, p.inpaint_full_res_padding) + + x1, y1, x2, y2 = crop_region + crop_width = x2 - x1 + crop_height = y2 - y1 + crop_ratio = crop_width / crop_height + p_ratio = p.width / p.height + if crop_ratio > p_ratio: + target_width = crop_width + target_height = round(crop_width / p_ratio) + else: + target_width = round(crop_height * p_ratio) + target_height = crop_height + crop_region, _ = image_util.expand_crop( + crop_region, + image_mask.width, + image_mask.height, + target_width, + target_height, + ) + tile_size = p.width, p.height + + # Blur the mask + if p.mask_blur > 0: + image_mask = image_mask.filter(ImageFilter.GaussianBlur(p.mask_blur)) + + # Crop the images to get the tiles that will be used for generation + tiles = [img.crop(crop_region) for img in USDU_upscaler.batch] + + # Assume the same size for all images in the batch + initial_tile_size = tiles[0].size + + # Resize if necessary + for i, tile in enumerate(tiles): + if tile.size != tile_size: + tiles[i] = tile.resize(tile_size, Image.Resampling.LANCZOS) + + # Crop conditioning + positive_cropped = image_util.crop_cond( + p.positive, crop_region, p.init_size, init_image.size, tile_size + ) + negative_cropped = image_util.crop_cond( + p.negative, crop_region, p.init_size, init_image.size, tile_size + ) + + # Encode the image + vae_encoder = VariationalAE.VAEEncode() + batched_tiles = torch.cat([image_util.pil_to_tensor(tile) for tile in tiles], dim=0) + (latent,) = vae_encoder.encode(p.vae, batched_tiles) + + # Generate samples + (samples,) = sampling.common_ksampler( + p.model, + p.seed, + p.steps, + p.cfg, + p.sampler_name, + p.scheduler, + positive_cropped, + negative_cropped, + latent, + denoise=p.denoise, + pipeline=pipeline + ) + + # Decode the sample + vae_decoder = VariationalAE.VAEDecode() + (decoded,) = vae_decoder.decode(p.vae, samples) + + # Convert the sample to a PIL image + tiles_sampled = [image_util.tensor_to_pil(decoded, i) for i in range(len(decoded))] + + for i, tile_sampled in enumerate(tiles_sampled): + init_image = USDU_upscaler.batch[i] + + # Resize back to the original size + if tile_sampled.size != initial_tile_size: + tile_sampled = tile_sampled.resize( + initial_tile_size, Image.Resampling.LANCZOS + ) + + # Put the tile into position + image_tile_only = Image.new("RGBA", init_image.size) + image_tile_only.paste(tile_sampled, crop_region[:2]) + + # Add the mask as an alpha channel + # Must make a copy due to the possibility of an edge becoming black + temp = image_tile_only.copy() + image_mask = image_mask.resize(temp.size) + temp.putalpha(image_mask) + temp.putalpha(image_mask) + image_tile_only.paste(temp, image_tile_only) + + # Add back the tile to the initial image according to the mask in the alpha channel + result = init_image.convert("RGBA") + result.alpha_composite(image_tile_only) + + # Convert back to RGB + result = result.convert("RGB") + USDU_upscaler.batch[i] = result + + processed = Processed(p, [USDU_upscaler.batch[0]], p.seed, None) + return processed + + +class USDUMode(Enum): + """#### Enum representing the modes for Ultimate SD Upscale.""" + LINEAR = 0 + CHESS = 1 + NONE = 2 + + +class USDUSFMode(Enum): + """#### Enum representing the seam fix modes for Ultimate SD Upscale.""" + NONE = 0 + BAND_PASS = 1 + HALF_TILE = 2 + HALF_TILE_PLUS_INTERSECTIONS = 3 + + +class USDUpscaler: + """#### Class representing the Ultimate SD Upscaler.""" + + def __init__( + self, + p: StableDiffusionProcessing, + image: Image.Image, + upscaler_index: int, + save_redraw: bool, + save_seams_fix: bool, + tile_width: int, + tile_height: int, + ) -> None: + """ + #### Initialize the USDUpscaler class. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + - `image` (Image.Image): The image. + - `upscaler_index` (int): The upscaler index. + - `save_redraw` (bool): Whether to save the redraw. + - `save_seams_fix` (bool): Whether to save the seams fix. + - `tile_width` (int): The tile width. + - `tile_height` (int): The tile height. + """ + self.p: StableDiffusionProcessing = p + self.image: Image = image + self.scale_factor = math.ceil( + max(p.width, p.height) / max(image.width, image.height) + ) + self.upscaler = USDU_upscaler.sd_upscalers[upscaler_index] + self.redraw = USDURedraw() + self.redraw.save = save_redraw + self.redraw.tile_width = tile_width if tile_width > 0 else tile_height + self.redraw.tile_height = tile_height if tile_height > 0 else tile_width + self.seams_fix = USDUSeamsFix() + self.seams_fix.save = save_seams_fix + self.seams_fix.tile_width = tile_width if tile_width > 0 else tile_height + self.seams_fix.tile_height = tile_height if tile_height > 0 else tile_width + self.initial_info = None + self.rows = math.ceil(self.p.height / self.redraw.tile_height) + self.cols = math.ceil(self.p.width / self.redraw.tile_width) + + def get_factor(self, num: int) -> int: + """ + #### Get the factor for a given number. + + #### Args: + - `num` (int): The number. + + #### Returns: + - `int`: The factor. + """ + if num == 1: + return 2 + if num % 4 == 0: + return 4 + if num % 3 == 0: + return 3 + if num % 2 == 0: + return 2 + return 0 + + def get_factors(self) -> None: + """ + #### Get the list of scale factors. + """ + scales = [] + current_scale = 1 + current_scale_factor = self.get_factor(self.scale_factor) + while current_scale < self.scale_factor: + current_scale_factor = self.get_factor(self.scale_factor // current_scale) + scales.append(current_scale_factor) + current_scale = current_scale * current_scale_factor + self.scales = enumerate(scales) + + def upscale(self) -> None: + """ + #### Upscale the image. + """ + # Log info + print(f"Canva size: {self.p.width}x{self.p.height}") + print(f"Image size: {self.image.width}x{self.image.height}") + print(f"Scale factor: {self.scale_factor}") + # Get list with scale factors + self.get_factors() + # Upscaling image over all factors + for index, value in self.scales: + print(f"Upscaling iteration {index + 1} with scale factor {value}") + self.image = self.upscaler.scaler.upscale( + self.image, value, self.upscaler.data_path + ) + # Resize image to set values + self.image = self.image.resize( + (self.p.width, self.p.height), resample=Image.LANCZOS + ) + + def setup_redraw(self, redraw_mode: int, padding: int, mask_blur: int) -> None: + """ + #### Set up the redraw. + + #### Args: + - `redraw_mode` (int): The redraw mode. + - `padding` (int): The padding. + - `mask_blur` (int): The mask blur. + """ + self.redraw.mode = USDUMode(redraw_mode) + self.redraw.enabled = self.redraw.mode != USDUMode.NONE + self.redraw.padding = padding + self.p.mask_blur = mask_blur + + def setup_seams_fix( + self, padding: int, denoise: float, mask_blur: int, width: int, mode: int + ) -> None: + """ + #### Set up the seams fix. + + #### Args: + - `padding` (int): The padding. + - `denoise` (float): The denoise strength. + - `mask_blur` (int): The mask blur. + - `width` (int): The width. + - `mode` (int): The mode. + """ + self.seams_fix.padding = padding + self.seams_fix.denoise = denoise + self.seams_fix.mask_blur = mask_blur + self.seams_fix.width = width + self.seams_fix.mode = USDUSFMode(mode) + self.seams_fix.enabled = self.seams_fix.mode != USDUSFMode.NONE + + def calc_jobs_count(self) -> None: + """ + #### Calculate the number of jobs. + """ + redraw_job_count = (self.rows * self.cols) if self.redraw.enabled else 0 + seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols + global state + state.job_count = redraw_job_count + seams_job_count + + def print_info(self) -> None: + """ + #### Print the information. + """ + print(f"Tile size: {self.redraw.tile_width}x{self.redraw.tile_height}") + print(f"Tiles amount: {self.rows * self.cols}") + print(f"Grid: {self.rows}x{self.cols}") + print(f"Redraw enabled: {self.redraw.enabled}") + print(f"Seams fix mode: {self.seams_fix.mode.name}") + + def add_extra_info(self) -> None: + """ + #### Add extra information. + """ + self.p.extra_generation_params["Ultimate SD upscale upscaler"] = ( + self.upscaler.name + ) + self.p.extra_generation_params["Ultimate SD upscale tile_width"] = ( + self.redraw.tile_width + ) + self.p.extra_generation_params["Ultimate SD upscale tile_height"] = ( + self.redraw.tile_height + ) + self.p.extra_generation_params["Ultimate SD upscale mask_blur"] = ( + self.p.mask_blur + ) + self.p.extra_generation_params["Ultimate SD upscale padding"] = ( + self.redraw.padding + ) + + def process(self, pipeline) -> None: + """ + #### Process the image. + """ + USDU_upscaler.state.begin() + self.calc_jobs_count() + self.result_images = [] + if self.redraw.enabled: + self.image = self.redraw.start(self.p, self.image, self.rows, self.cols, pipeline) + self.initial_info = self.redraw.initial_info + self.result_images.append(self.image) + + if self.seams_fix.enabled: + self.image = self.seams_fix.start(self.p, self.image, self.rows, self.cols, pipeline) + self.initial_info = self.seams_fix.initial_info + self.result_images.append(self.image) + USDU_upscaler.state.end() + + +class USDURedraw: + """#### Class representing the redraw functionality for Ultimate SD Upscale.""" + + def init_draw(self, p: StableDiffusionProcessing, width: int, height: int) -> tuple: + """ + #### Initialize the draw. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + - `width` (int): The width. + - `height` (int): The height. + + #### Returns: + - `tuple`: The mask and draw objects. + """ + p.inpaint_full_res = True + p.inpaint_full_res_padding = self.padding + p.width = math.ceil((self.tile_width + self.padding) / 64) * 64 + p.height = math.ceil((self.tile_height + self.padding) / 64) * 64 + mask = Image.new("L", (width, height), "black") + draw = ImageDraw.Draw(mask) + return mask, draw + + def calc_rectangle(self, xi: int, yi: int) -> tuple: + """ + #### Calculate the rectangle coordinates. + + #### Args: + - `xi` (int): The x index. + - `yi` (int): The y index. + + #### Returns: + - `tuple`: The rectangle coordinates. + """ + x1 = xi * self.tile_width + y1 = yi * self.tile_height + x2 = xi * self.tile_width + self.tile_width + y2 = yi * self.tile_height + self.tile_height + + return x1, y1, x2, y2 + + def linear_process( + self, p: StableDiffusionProcessing, image: Image.Image, rows: int, cols: int, pipeline: bool = False + ) -> Image.Image: + """ + #### Perform linear processing. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + - `image` (Image.Image): The image. + - `rows` (int): The number of rows. + - `cols` (int): The number of columns. + + #### Returns: + - `Image.Image`: The processed image. + """ + global state + mask, draw = self.init_draw(p, image.width, image.height) + for yi in range(rows): + for xi in range(cols): + if state.interrupted: + break + draw.rectangle(self.calc_rectangle(xi, yi), fill="white") + p.init_images = [image] + p.image_mask = mask + processed = process_images(p, pipeline) + draw.rectangle(self.calc_rectangle(xi, yi), fill="black") + if len(processed.images) > 0: + image = processed.images[0] + + p.width = image.width + p.height = image.height + self.initial_info = processed.infotext(p, 0) + + return image + + def start(self, p: StableDiffusionProcessing, image: Image.Image, rows: int, cols: int, pipeline: bool = False) -> Image.Image: + """#### Start the redraw. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + - `image` (Image.Image): The image. + - `rows` (int): The number of rows. + - `cols` (int): The number of columns. + + #### Returns: + - `Image.Image`: The processed image. + """ + self.initial_info = None + return self.linear_process(p, image, rows, cols, pipeline=pipeline) + + +class USDUSeamsFix: + """#### Class representing the seams fix functionality for Ultimate SD Upscale.""" + + def init_draw(self, p: StableDiffusionProcessing) -> None: + """#### Initialize the draw. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + """ + self.initial_info = None + p.width = math.ceil((self.tile_width + self.padding) / 64) * 64 + p.height = math.ceil((self.tile_height + self.padding) / 64) * 64 + + def half_tile_process( + self, p: StableDiffusionProcessing, image: Image.Image, rows: int, cols: int, pipeline: bool = False + ) -> Image.Image: + """#### Perform half-tile processing. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + - `image` (Image.Image): The image. + - `rows` (int): The number of rows. + - `cols` (int): The number of columns. + + #### Returns: + - `Image.Image`: The processed image. + """ + global state + self.init_draw(p) + processed = None + + gradient = Image.linear_gradient("L") + row_gradient = Image.new("L", (self.tile_width, self.tile_height), "black") + row_gradient.paste( + gradient.resize( + (self.tile_width, self.tile_height // 2), resample=Image.BICUBIC + ), + (0, 0), + ) + row_gradient.paste( + gradient.rotate(180).resize( + (self.tile_width, self.tile_height // 2), resample=Image.BICUBIC + ), + (0, self.tile_height // 2), + ) + col_gradient = Image.new("L", (self.tile_width, self.tile_height), "black") + col_gradient.paste( + gradient.rotate(90).resize( + (self.tile_width // 2, self.tile_height), resample=Image.BICUBIC + ), + (0, 0), + ) + col_gradient.paste( + gradient.rotate(270).resize( + (self.tile_width // 2, self.tile_height), resample=Image.BICUBIC + ), + (self.tile_width // 2, 0), + ) + + p.denoising_strength = self.denoise + p.mask_blur = self.mask_blur + + for yi in range(rows - 1): + for xi in range(cols): + p.width = self.tile_width + p.height = self.tile_height + p.inpaint_full_res = True + p.inpaint_full_res_padding = self.padding + mask = Image.new("L", (image.width, image.height), "black") + mask.paste( + row_gradient, + ( + xi * self.tile_width, + yi * self.tile_height + self.tile_height // 2, + ), + ) + + p.init_images = [image] + p.image_mask = mask + processed = process_images(p, pipeline) + if len(processed.images) > 0: + image = processed.images[0] + + for yi in range(rows): + for xi in range(cols - 1): + p.width = self.tile_width + p.height = self.tile_height + p.inpaint_full_res = True + p.inpaint_full_res_padding = self.padding + mask = Image.new("L", (image.width, image.height), "black") + mask.paste( + col_gradient, + ( + xi * self.tile_width + self.tile_width // 2, + yi * self.tile_height, + ), + ) + + p.init_images = [image] + p.image_mask = mask + processed = process_images(p, pipeline) + if len(processed.images) > 0: + image = processed.images[0] + + p.width = image.width + p.height = image.height + if processed is not None: + self.initial_info = processed.infotext(p, 0) + + return image + + def start( + self, p: StableDiffusionProcessing, image: Image.Image, rows: int, cols: int, pipeline: bool = False + ) -> Image.Image: + """#### Start the seams fix process. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + - `image` (Image.Image): The image. + - `rows` (int): The number of rows. + - `cols` (int): The number of columns. + + #### Returns: + - `Image.Image`: The processed image. + """ + return self.half_tile_process(p, image, rows, cols, pipeline=pipeline) + + +class Script(USDU_upscaler.Script): + """#### Class representing the script for Ultimate SD Upscale.""" + + def run( + self, + p: StableDiffusionProcessing, + _: None, + tile_width: int, + tile_height: int, + mask_blur: int, + padding: int, + seams_fix_width: int, + seams_fix_denoise: float, + seams_fix_padding: int, + upscaler_index: int, + save_upscaled_image: bool, + redraw_mode: int, + save_seams_fix_image: bool, + seams_fix_mask_blur: int, + seams_fix_type: int, + target_size_type: int, + custom_width: int, + custom_height: int, + custom_scale: float, + pipeline: bool = False, + ) -> Processed: + """#### Run the script. + + #### Args: + - `p` (StableDiffusionProcessing): The processing object. + - `_` (None): Unused parameter. + - `tile_width` (int): The tile width. + - `tile_height` (int): The tile height. + - `mask_blur` (int): The mask blur. + - `padding` (int): The padding. + - `seams_fix_width` (int): The seams fix width. + - `seams_fix_denoise` (float): The seams fix denoise strength. + - `seams_fix_padding` (int): The seams fix padding. + - `upscaler_index` (int): The upscaler index. + - `save_upscaled_image` (bool): Whether to save the upscaled image. + - `redraw_mode` (int): The redraw mode. + - `save_seams_fix_image` (bool): Whether to save the seams fix image. + - `seams_fix_mask_blur` (int): The seams fix mask blur. + - `seams_fix_type` (int): The seams fix type. + - `target_size_type` (int): The target size type. + - `custom_width` (int): The custom width. + - `custom_height` (int): The custom height. + - `custom_scale` (float): The custom scale. + + #### Returns: + - `Processed`: The processed images. + """ + # Init + fix_seed(p) + USDU_upscaler.torch_gc() + + p.do_not_save_grid = True + p.do_not_save_samples = True + p.inpaint_full_res = False + + p.inpainting_fill = 1 + p.n_iter = 1 + p.batch_size = 1 + + seed = p.seed + + # Init image + init_img = p.init_images[0] + init_img = image_util.flatten( + init_img, USDU_upscaler.opts.img2img_background_color + ) + + p.width = math.ceil((init_img.width * custom_scale) / 64) * 64 + p.height = math.ceil((init_img.height * custom_scale) / 64) * 64 + + # Upscaling + upscaler = USDUpscaler( + p, + init_img, + upscaler_index, + save_upscaled_image, + save_seams_fix_image, + tile_width, + tile_height, + ) + upscaler.upscale() + + # Drawing + upscaler.setup_redraw(redraw_mode, padding, mask_blur) + upscaler.setup_seams_fix( + seams_fix_padding, + seams_fix_denoise, + seams_fix_mask_blur, + seams_fix_width, + seams_fix_type, + ) + upscaler.print_info() + upscaler.add_extra_info() + upscaler.process(pipeline=pipeline) + result_images = upscaler.result_images + + return Processed( + p, + result_images, + seed, + upscaler.initial_info if upscaler.initial_info is not None else "", + ) + + +# Upscaler +old_init = USDUpscaler.__init__ + + +def new_init( + self: USDUpscaler, + p: StableDiffusionProcessing, + image: Image.Image, + upscaler_index: int, + save_redraw: bool, + save_seams_fix: bool, + tile_width: int, + tile_height: int, +) -> None: + """#### Initialize the USDUpscaler class with new settings. + + #### Args: + - `self` (USDUpscaler): The USDUpscaler instance. + - `p` (StableDiffusionProcessing): The processing object. + - `image` (Image.Image): The image. + - `upscaler_index` (int): The upscaler index. + - `save_redraw` (bool): Whether to save the redraw. + - `save_seams_fix` (bool): Whether to save the seams fix. + - `tile_width` (int): The tile width. + - `tile_height` (int): The tile height. + """ + p.width = math.ceil((image.width * p.upscale_by) / 8) * 8 + p.height = math.ceil((image.height * p.upscale_by) / 8) * 8 + old_init( + self, + p, + image, + upscaler_index, + save_redraw, + save_seams_fix, + tile_width, + tile_height, + ) + + +USDUpscaler.__init__ = new_init + +# Redraw +old_setup_redraw = USDURedraw.init_draw + + +def new_setup_redraw( + self: USDURedraw, p: StableDiffusionProcessing, width: int, height: int +) -> tuple: + """#### Set up the redraw with new settings. + + #### Args: + - `self` (USDURedraw): The USDURedraw instance. + - `p` (StableDiffusionProcessing): The processing object. + - `width` (int): The width. + - `height` (int): The height. + + #### Returns: + - `tuple`: The mask and draw objects. + """ + mask, draw = old_setup_redraw(self, p, width, height) + p.width = math.ceil((self.tile_width + self.padding) / 8) * 8 + p.height = math.ceil((self.tile_height + self.padding) / 8) * 8 + return mask, draw + + +USDURedraw.init_draw = new_setup_redraw + +# Seams fix +old_setup_seams_fix = USDUSeamsFix.init_draw + + +def new_setup_seams_fix(self: USDUSeamsFix, p: StableDiffusionProcessing) -> None: + """#### Set up the seams fix with new settings. + + #### Args: + - `self` (USDUSeamsFix): The USDUSeamsFix instance. + - `p` (StableDiffusionProcessing): The processing object. + """ + old_setup_seams_fix(self, p) + p.width = math.ceil((self.tile_width + self.padding) / 8) * 8 + p.height = math.ceil((self.tile_height + self.padding) / 8) * 8 + + +USDUSeamsFix.init_draw = new_setup_seams_fix + +# Make the script upscale on a batch of images instead of one image +old_upscale = USDUpscaler.upscale + + +def new_upscale(self: USDUpscaler) -> None: + """#### Upscale a batch of images. + + #### Args: + - `self` (USDUpscaler): The USDUpscaler instance. + """ + old_upscale(self) + USDU_upscaler.batch = [self.image] + [ + img.resize((self.p.width, self.p.height), resample=Image.LANCZOS) + for img in USDU_upscaler.batch[1:] + ] + + +USDUpscaler.upscale = new_upscale +MAX_RESOLUTION = 8192 +# The modes available for Ultimate SD Upscale +MODES = { + "Linear": USDUMode.LINEAR, + "Chess": USDUMode.CHESS, + "None": USDUMode.NONE, +} +# The seam fix modes +SEAM_FIX_MODES = { + "None": USDUSFMode.NONE, + "Band Pass": USDUSFMode.BAND_PASS, + "Half Tile": USDUSFMode.HALF_TILE, + "Half Tile + Intersections": USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS, +} + + +class UltimateSDUpscale: + """#### Class representing the Ultimate SD Upscale functionality.""" + + def upscale( + self, + image: torch.Tensor, + model: torch.nn.Module, + positive: str, + negative: str, + vae: VariationalAE.VAE, + upscale_by: float, + seed: int, + steps: int, + cfg: float, + sampler_name: str, + scheduler: str, + denoise: float, + upscale_model: any, + mode_type: str, + tile_width: int, + tile_height: int, + mask_blur: int, + tile_padding: int, + seam_fix_mode: str, + seam_fix_denoise: float, + seam_fix_mask_blur: int, + seam_fix_width: int, + seam_fix_padding: int, + force_uniform_tiles: bool, + pipeline: bool = False, + ) -> tuple: + """#### Upscale the image. + + #### Args: + - `image` (torch.Tensor): The image tensor. + - `model` (torch.nn.Module): The model. + - `positive` (str): The positive prompt. + - `negative` (str): The negative prompt. + - `vae` (VariationalAE.VAE): The variational autoencoder. + - `upscale_by` (float): The upscale factor. + - `seed` (int): The seed. + - `steps` (int): The number of steps. + - `cfg` (float): The CFG scale. + - `sampler_name` (str): The sampler name. + - `scheduler` (str): The scheduler. + - `denoise` (float): The denoise strength. + - `upscale_model` (any): The upscale model. + - `mode_type` (str): The mode type. + - `tile_width` (int): The tile width. + - `tile_height` (int): The tile height. + - `mask_blur` (int): The mask blur. + - `tile_padding` (int): The tile padding. + - `seam_fix_mode` (str): The seam fix mode. + - `seam_fix_denoise` (float): The seam fix denoise strength. + - `seam_fix_mask_blur` (int): The seam fix mask blur. + - `seam_fix_width` (int): The seam fix width. + - `seam_fix_padding` (int): The seam fix padding. + - `force_uniform_tiles` (bool): Whether to force uniform tiles. + + #### Returns: + - `tuple`: The resulting tensor. + """ + # Set up A1111 patches + + # Upscaler + # An object that the script works with + USDU_upscaler.sd_upscalers[0] = USDU_upscaler.UpscalerData() + # Where the actual upscaler is stored, will be used when the script upscales using the Upscaler in UpscalerData + USDU_upscaler.actual_upscaler = upscale_model + + # Set the batch of images + USDU_upscaler.batch = [image_util.tensor_to_pil(image, i) for i in range(len(image))] + + # Processing + sdprocessing = StableDiffusionProcessing( + image_util.tensor_to_pil(image), + model, + positive, + negative, + vae, + seed, + steps, + cfg, + sampler_name, + scheduler, + denoise, + upscale_by, + force_uniform_tiles, + ) + + # Running the script + script = Script() + script.run( + p=sdprocessing, + _=None, + tile_width=tile_width, + tile_height=tile_height, + mask_blur=mask_blur, + padding=tile_padding, + seams_fix_width=seam_fix_width, + seams_fix_denoise=seam_fix_denoise, + seams_fix_padding=seam_fix_padding, + upscaler_index=0, + save_upscaled_image=False, + redraw_mode=MODES[mode_type], + save_seams_fix_image=False, + seams_fix_mask_blur=seam_fix_mask_blur, + seams_fix_type=SEAM_FIX_MODES[seam_fix_mode], + target_size_type=2, + custom_width=None, + custom_height=None, + custom_scale=upscale_by, + pipeline=pipeline, + ) + + # Return the resulting images + images = [image_util.pil_to_tensor(img) for img in USDU_upscaler.batch] + tensor = torch.cat(images, dim=0) return (tensor,) \ No newline at end of file diff --git a/modules/UltimateSDUpscale/image_util.py b/modules/UltimateSDUpscale/image_util.py index b367003e4edbfcce4a86930d31bff57c94b24b07..036836cade5d4aeb502abc657c091e6c945dbd2b 100644 --- a/modules/UltimateSDUpscale/image_util.py +++ b/modules/UltimateSDUpscale/image_util.py @@ -1,265 +1,265 @@ -import math -import numpy as np -import torch -from PIL import Image - - -def get_tiled_scale_steps(width: int, height: int, tile_x: int, tile_y: int, overlap: int) -> int: - """#### Calculate the number of steps required for tiled scaling. - - #### Args: - - `width` (int): The width of the image. - - `height` (int): The height of the image. - - `tile_x` (int): The width of each tile. - - `tile_y` (int): The height of each tile. - - `overlap` (int): The overlap between tiles. - - #### Returns: - - `int`: The number of steps required for tiled scaling. - """ - return math.ceil((height / (tile_y - overlap))) * math.ceil( - (width / (tile_x - overlap)) - ) - - -@torch.inference_mode() -def tiled_scale( - samples: torch.Tensor, - function: callable, - tile_x: int = 64, - tile_y: int = 64, - overlap: int = 8, - upscale_amount: float = 4, - out_channels: int = 3, - pbar: any = None, -) -> torch.Tensor: - """#### Perform tiled scaling on a batch of samples. - - #### Args: - - `samples` (torch.Tensor): The input samples. - - `function` (callable): The function to apply to each tile. - - `tile_x` (int, optional): The width of each tile. Defaults to 64. - - `tile_y` (int, optional): The height of each tile. Defaults to 64. - - `overlap` (int, optional): The overlap between tiles. Defaults to 8. - - `upscale_amount` (float, optional): The upscale amount. Defaults to 4. - - `out_channels` (int, optional): The number of output channels. Defaults to 3. - - `pbar` (any, optional): The progress bar. Defaults to None. - - #### Returns: - - `torch.Tensor`: The scaled output tensor. - """ - output = torch.empty( - ( - samples.shape[0], - out_channels, - round(samples.shape[2] * upscale_amount), - round(samples.shape[3] * upscale_amount), - ), - device="cpu", - ) - for b in range(samples.shape[0]): - s = samples[b : b + 1] - out = torch.zeros( - ( - s.shape[0], - out_channels, - round(s.shape[2] * upscale_amount), - round(s.shape[3] * upscale_amount), - ), - device="cpu", - ) - out_div = torch.zeros( - ( - s.shape[0], - out_channels, - round(s.shape[2] * upscale_amount), - round(s.shape[3] * upscale_amount), - ), - device="cpu", - ) - for y in range(0, s.shape[2], tile_y - overlap): - for x in range(0, s.shape[3], tile_x - overlap): - s_in = s[:, :, y : y + tile_y, x : x + tile_x] - - ps = function(s_in).cpu() - mask = torch.ones_like(ps) - feather = round(overlap * upscale_amount) - for t in range(feather): - mask[:, :, t : 1 + t, :] *= (1.0 / feather) * (t + 1) - mask[:, :, mask.shape[2] - 1 - t : mask.shape[2] - t, :] *= ( - 1.0 / feather - ) * (t + 1) - mask[:, :, :, t : 1 + t] *= (1.0 / feather) * (t + 1) - mask[:, :, :, mask.shape[3] - 1 - t : mask.shape[3] - t] *= ( - 1.0 / feather - ) * (t + 1) - out[ - :, - :, - round(y * upscale_amount) : round((y + tile_y) * upscale_amount), - round(x * upscale_amount) : round((x + tile_x) * upscale_amount), - ] += ps * mask - out_div[ - :, - :, - round(y * upscale_amount) : round((y + tile_y) * upscale_amount), - round(x * upscale_amount) : round((x + tile_x) * upscale_amount), - ] += mask - - output[b : b + 1] = out / out_div - return output - - -def flatten(img: Image.Image, bgcolor: str) -> Image.Image: - """#### Replace transparency with a background color. - - #### Args: - - `img` (Image.Image): The input image. - - `bgcolor` (str): The background color. - - #### Returns: - - `Image.Image`: The image with transparency replaced by the background color. - """ - if img.mode in ("RGB"): - return img - return Image.alpha_composite(Image.new("RGBA", img.size, bgcolor), img).convert( - "RGB" - ) - - -BLUR_KERNEL_SIZE = 15 - - -def tensor_to_pil(img_tensor: torch.Tensor, batch_index: int = 0) -> Image.Image: - """#### Convert a tensor to a PIL image. - - #### Args: - - `img_tensor` (torch.Tensor): The input tensor. - - `batch_index` (int, optional): The batch index. Defaults to 0. - - #### Returns: - - `Image.Image`: The converted PIL image. - """ - img_tensor = img_tensor[batch_index].unsqueeze(0) - i = 255.0 * img_tensor.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8).squeeze()) - return img - - -def pil_to_tensor(image: Image.Image) -> torch.Tensor: - """#### Convert a PIL image to a tensor. - - #### Args: - - `image` (Image.Image): The input PIL image. - - #### Returns: - - `torch.Tensor`: The converted tensor. - """ - image = np.array(image).astype(np.float32) / 255.0 - image = torch.from_numpy(image).unsqueeze(0) - return image - - -def get_crop_region(mask: Image.Image, pad: int = 0) -> tuple: - """#### Get the coordinates of the white rectangular mask region. - - #### Args: - - `mask` (Image.Image): The input mask image in 'L' mode. - - `pad` (int, optional): The padding to apply. Defaults to 0. - - #### Returns: - - `tuple`: The coordinates of the crop region. - """ - coordinates = mask.getbbox() - if coordinates is not None: - x1, y1, x2, y2 = coordinates - else: - x1, y1, x2, y2 = mask.width, mask.height, 0, 0 - # Apply padding - x1 = max(x1 - pad, 0) - y1 = max(y1 - pad, 0) - x2 = min(x2 + pad, mask.width) - y2 = min(y2 + pad, mask.height) - return fix_crop_region((x1, y1, x2, y2), (mask.width, mask.height)) - - -def fix_crop_region(region: tuple, image_size: tuple) -> tuple: - """#### Remove the extra pixel added by the get_crop_region function. - - #### Args: - - `region` (tuple): The crop region coordinates. - - `image_size` (tuple): The size of the image. - - #### Returns: - - `tuple`: The fixed crop region coordinates. - """ - image_width, image_height = image_size - x1, y1, x2, y2 = region - if x2 < image_width: - x2 -= 1 - if y2 < image_height: - y2 -= 1 - return x1, y1, x2, y2 - - -def expand_crop(region: tuple, width: int, height: int, target_width: int, target_height: int) -> tuple: - """#### Expand a crop region to a specified target size. - - #### Args: - - `region` (tuple): The crop region coordinates. - - `width` (int): The width of the image. - - `height` (int): The height of the image. - - `target_width` (int): The desired width of the crop region. - - `target_height` (int): The desired height of the crop region. - - #### Returns: - - `tuple`: The expanded crop region coordinates and the target size. - """ - x1, y1, x2, y2 = region - actual_width = x2 - x1 - actual_height = y2 - y1 - - # Try to expand region to the right of half the difference - width_diff = target_width - actual_width - x2 = min(x2 + width_diff // 2, width) - # Expand region to the left of the difference including the pixels that could not be expanded to the right - width_diff = target_width - (x2 - x1) - x1 = max(x1 - width_diff, 0) - # Try the right again - width_diff = target_width - (x2 - x1) - x2 = min(x2 + width_diff, width) - - # Try to expand region to the bottom of half the difference - height_diff = target_height - actual_height - y2 = min(y2 + height_diff // 2, height) - # Expand region to the top of the difference including the pixels that could not be expanded to the bottom - height_diff = target_height - (y2 - y1) - y1 = max(y1 - height_diff, 0) - # Try the bottom again - height_diff = target_height - (y2 - y1) - y2 = min(y2 + height_diff, height) - - return (x1, y1, x2, y2), (target_width, target_height) - - -def crop_cond(cond: list, region: tuple, init_size: tuple, canvas_size: tuple, tile_size: tuple, w_pad: int = 0, h_pad: int = 0) -> list: - """#### Crop conditioning data to match a specific region. - - #### Args: - - `cond` (list): The conditioning data. - - `region` (tuple): The crop region coordinates. - - `init_size` (tuple): The initial size of the image. - - `canvas_size` (tuple): The size of the canvas. - - `tile_size` (tuple): The size of the tile. - - `w_pad` (int, optional): The width padding. Defaults to 0. - - `h_pad` (int, optional): The height padding. Defaults to 0. - - #### Returns: - - `list`: The cropped conditioning data. - """ - cropped = [] - for emb, x in cond: - cond_dict = x.copy() - n = [emb, cond_dict] - cropped.append(n) +import math +import numpy as np +import torch +from PIL import Image + + +def get_tiled_scale_steps(width: int, height: int, tile_x: int, tile_y: int, overlap: int) -> int: + """#### Calculate the number of steps required for tiled scaling. + + #### Args: + - `width` (int): The width of the image. + - `height` (int): The height of the image. + - `tile_x` (int): The width of each tile. + - `tile_y` (int): The height of each tile. + - `overlap` (int): The overlap between tiles. + + #### Returns: + - `int`: The number of steps required for tiled scaling. + """ + return math.ceil((height / (tile_y - overlap))) * math.ceil( + (width / (tile_x - overlap)) + ) + + +@torch.inference_mode() +def tiled_scale( + samples: torch.Tensor, + function: callable, + tile_x: int = 64, + tile_y: int = 64, + overlap: int = 8, + upscale_amount: float = 4, + out_channels: int = 3, + pbar: any = None, +) -> torch.Tensor: + """#### Perform tiled scaling on a batch of samples. + + #### Args: + - `samples` (torch.Tensor): The input samples. + - `function` (callable): The function to apply to each tile. + - `tile_x` (int, optional): The width of each tile. Defaults to 64. + - `tile_y` (int, optional): The height of each tile. Defaults to 64. + - `overlap` (int, optional): The overlap between tiles. Defaults to 8. + - `upscale_amount` (float, optional): The upscale amount. Defaults to 4. + - `out_channels` (int, optional): The number of output channels. Defaults to 3. + - `pbar` (any, optional): The progress bar. Defaults to None. + + #### Returns: + - `torch.Tensor`: The scaled output tensor. + """ + output = torch.empty( + ( + samples.shape[0], + out_channels, + round(samples.shape[2] * upscale_amount), + round(samples.shape[3] * upscale_amount), + ), + device="cpu", + ) + for b in range(samples.shape[0]): + s = samples[b : b + 1] + out = torch.zeros( + ( + s.shape[0], + out_channels, + round(s.shape[2] * upscale_amount), + round(s.shape[3] * upscale_amount), + ), + device="cpu", + ) + out_div = torch.zeros( + ( + s.shape[0], + out_channels, + round(s.shape[2] * upscale_amount), + round(s.shape[3] * upscale_amount), + ), + device="cpu", + ) + for y in range(0, s.shape[2], tile_y - overlap): + for x in range(0, s.shape[3], tile_x - overlap): + s_in = s[:, :, y : y + tile_y, x : x + tile_x] + + ps = function(s_in).cpu() + mask = torch.ones_like(ps) + feather = round(overlap * upscale_amount) + for t in range(feather): + mask[:, :, t : 1 + t, :] *= (1.0 / feather) * (t + 1) + mask[:, :, mask.shape[2] - 1 - t : mask.shape[2] - t, :] *= ( + 1.0 / feather + ) * (t + 1) + mask[:, :, :, t : 1 + t] *= (1.0 / feather) * (t + 1) + mask[:, :, :, mask.shape[3] - 1 - t : mask.shape[3] - t] *= ( + 1.0 / feather + ) * (t + 1) + out[ + :, + :, + round(y * upscale_amount) : round((y + tile_y) * upscale_amount), + round(x * upscale_amount) : round((x + tile_x) * upscale_amount), + ] += ps * mask + out_div[ + :, + :, + round(y * upscale_amount) : round((y + tile_y) * upscale_amount), + round(x * upscale_amount) : round((x + tile_x) * upscale_amount), + ] += mask + + output[b : b + 1] = out / out_div + return output + + +def flatten(img: Image.Image, bgcolor: str) -> Image.Image: + """#### Replace transparency with a background color. + + #### Args: + - `img` (Image.Image): The input image. + - `bgcolor` (str): The background color. + + #### Returns: + - `Image.Image`: The image with transparency replaced by the background color. + """ + if img.mode in ("RGB"): + return img + return Image.alpha_composite(Image.new("RGBA", img.size, bgcolor), img).convert( + "RGB" + ) + + +BLUR_KERNEL_SIZE = 15 + + +def tensor_to_pil(img_tensor: torch.Tensor, batch_index: int = 0) -> Image.Image: + """#### Convert a tensor to a PIL image. + + #### Args: + - `img_tensor` (torch.Tensor): The input tensor. + - `batch_index` (int, optional): The batch index. Defaults to 0. + + #### Returns: + - `Image.Image`: The converted PIL image. + """ + img_tensor = img_tensor[batch_index].unsqueeze(0) + i = 255.0 * img_tensor.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8).squeeze()) + return img + + +def pil_to_tensor(image: Image.Image) -> torch.Tensor: + """#### Convert a PIL image to a tensor. + + #### Args: + - `image` (Image.Image): The input PIL image. + + #### Returns: + - `torch.Tensor`: The converted tensor. + """ + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image).unsqueeze(0) + return image + + +def get_crop_region(mask: Image.Image, pad: int = 0) -> tuple: + """#### Get the coordinates of the white rectangular mask region. + + #### Args: + - `mask` (Image.Image): The input mask image in 'L' mode. + - `pad` (int, optional): The padding to apply. Defaults to 0. + + #### Returns: + - `tuple`: The coordinates of the crop region. + """ + coordinates = mask.getbbox() + if coordinates is not None: + x1, y1, x2, y2 = coordinates + else: + x1, y1, x2, y2 = mask.width, mask.height, 0, 0 + # Apply padding + x1 = max(x1 - pad, 0) + y1 = max(y1 - pad, 0) + x2 = min(x2 + pad, mask.width) + y2 = min(y2 + pad, mask.height) + return fix_crop_region((x1, y1, x2, y2), (mask.width, mask.height)) + + +def fix_crop_region(region: tuple, image_size: tuple) -> tuple: + """#### Remove the extra pixel added by the get_crop_region function. + + #### Args: + - `region` (tuple): The crop region coordinates. + - `image_size` (tuple): The size of the image. + + #### Returns: + - `tuple`: The fixed crop region coordinates. + """ + image_width, image_height = image_size + x1, y1, x2, y2 = region + if x2 < image_width: + x2 -= 1 + if y2 < image_height: + y2 -= 1 + return x1, y1, x2, y2 + + +def expand_crop(region: tuple, width: int, height: int, target_width: int, target_height: int) -> tuple: + """#### Expand a crop region to a specified target size. + + #### Args: + - `region` (tuple): The crop region coordinates. + - `width` (int): The width of the image. + - `height` (int): The height of the image. + - `target_width` (int): The desired width of the crop region. + - `target_height` (int): The desired height of the crop region. + + #### Returns: + - `tuple`: The expanded crop region coordinates and the target size. + """ + x1, y1, x2, y2 = region + actual_width = x2 - x1 + actual_height = y2 - y1 + + # Try to expand region to the right of half the difference + width_diff = target_width - actual_width + x2 = min(x2 + width_diff // 2, width) + # Expand region to the left of the difference including the pixels that could not be expanded to the right + width_diff = target_width - (x2 - x1) + x1 = max(x1 - width_diff, 0) + # Try the right again + width_diff = target_width - (x2 - x1) + x2 = min(x2 + width_diff, width) + + # Try to expand region to the bottom of half the difference + height_diff = target_height - actual_height + y2 = min(y2 + height_diff // 2, height) + # Expand region to the top of the difference including the pixels that could not be expanded to the bottom + height_diff = target_height - (y2 - y1) + y1 = max(y1 - height_diff, 0) + # Try the bottom again + height_diff = target_height - (y2 - y1) + y2 = min(y2 + height_diff, height) + + return (x1, y1, x2, y2), (target_width, target_height) + + +def crop_cond(cond: list, region: tuple, init_size: tuple, canvas_size: tuple, tile_size: tuple, w_pad: int = 0, h_pad: int = 0) -> list: + """#### Crop conditioning data to match a specific region. + + #### Args: + - `cond` (list): The conditioning data. + - `region` (tuple): The crop region coordinates. + - `init_size` (tuple): The initial size of the image. + - `canvas_size` (tuple): The size of the canvas. + - `tile_size` (tuple): The size of the tile. + - `w_pad` (int, optional): The width padding. Defaults to 0. + - `h_pad` (int, optional): The height padding. Defaults to 0. + + #### Returns: + - `list`: The cropped conditioning data. + """ + cropped = [] + for emb, x in cond: + cond_dict = x.copy() + n = [emb, cond_dict] + cropped.append(n) return cropped \ No newline at end of file diff --git a/modules/Utilities/Enhancer.py b/modules/Utilities/Enhancer.py index 1b75cc63329759116cc710a5f0d9b59126b81c6b..4fa45ad9875988230a078a3d1f549ce5686cab36 100644 --- a/modules/Utilities/Enhancer.py +++ b/modules/Utilities/Enhancer.py @@ -1,75 +1,75 @@ -import ollama -import os - -from modules.Utilities import util - - -def enhance_prompt(p: str) -> str: - """#### Enhance a text-to-image prompt using Ollama. - - #### Args: - - `p` (str, optional): The prompt. Defaults to `None`. - - #### Returns: - - `str`: The enhanced prompt - """ - - # Load the prompt from the file - prompt = util.load_parameters_from_file()[0] - if p is None: - pass - else: - prompt = p - print(prompt) - response = ollama.chat( - model="deepseek-r1", - messages=[ - { - "role": "user", - "content": f"""Your goal is to generate a text-to-image prompt based on a user's input, detailing their desired final outcome for an image. The user will provide specific details about the characteristics, features, or elements they want the image to include. The prompt should guide the generation of an image that aligns with the user's desired outcome. - - Generate a text-to-image prompt by arranging the following blocks in a single string, separated by commas: - - Image Type: [Specify desired image type] - - Aesthetic or Mood: [Describe desired aesthetic or mood] - - Lighting Conditions: [Specify desired lighting conditions] - - Composition or Framing: [Provide details about desired composition or framing] - - Background: [Specify desired background elements or setting] - - Colors: [Mention any specific colors or color palette] - - Objects or Elements: [List specific objects or features] - - Style or Artistic Influence: [Mention desired artistic style or influence] - - Subject's Appearance: [Describe appearance of main subject] - - Ensure the blocks are arranged in order of visual importance, from the most significant to the least significant, to effectively guide image generation, a block can be surrounded by parentheses to gain additionnal significance. - - This is an example of a user's input: "a beautiful blonde lady in lingerie sitting in seiza in a seducing way with a focus on her assets" - - And this is an example of a desired output: "portrait| serene and mysterious| soft, diffused lighting| close-up shot, emphasizing facial features| simple and blurred background| earthy tones with a hint of warm highlights| renaissance painting| a beautiful lady with freckles and dark makeup" - - Here is the user's input: {prompt} - - Write the prompt in the same style as the example above, in a single line , with absolutely no additional information, words or symbols other than the enhanced prompt. - - Output:""", - }, - ], - ) - content = response["message"]["content"] - print("here's the enhanced prompt :", content) - - if "" in content and "" in content: - # Get everything after - enhanced = content.split("")[-1].strip() - else: - enhanced = content.strip() - print("here's the enhanced prompt:", enhanced) - os.system("ollama stop deepseek-r1") - return "masterpiece, best quality, (extremely detailed CG unity 8k wallpaper, masterpiece, best quality, ultra-detailed, best shadow), high contrast, (best illumination), ((cinematic light)), hyper detail, dramatic light, depth of field," + enhanced +import ollama +import os + +from modules.Utilities import util + + +def enhance_prompt(p: str) -> str: + """#### Enhance a text-to-image prompt using Ollama. + + #### Args: + - `p` (str, optional): The prompt. Defaults to `None`. + + #### Returns: + - `str`: The enhanced prompt + """ + + # Load the prompt from the file + prompt = util.load_parameters_from_file()[0] + if p is None: + pass + else: + prompt = p + print(prompt) + response = ollama.chat( + model="deepseek-r1", + messages=[ + { + "role": "user", + "content": f"""Your goal is to generate a text-to-image prompt based on a user's input, detailing their desired final outcome for an image. The user will provide specific details about the characteristics, features, or elements they want the image to include. The prompt should guide the generation of an image that aligns with the user's desired outcome. + + Generate a text-to-image prompt by arranging the following blocks in a single string, separated by commas: + + Image Type: [Specify desired image type] + + Aesthetic or Mood: [Describe desired aesthetic or mood] + + Lighting Conditions: [Specify desired lighting conditions] + + Composition or Framing: [Provide details about desired composition or framing] + + Background: [Specify desired background elements or setting] + + Colors: [Mention any specific colors or color palette] + + Objects or Elements: [List specific objects or features] + + Style or Artistic Influence: [Mention desired artistic style or influence] + + Subject's Appearance: [Describe appearance of main subject] + + Ensure the blocks are arranged in order of visual importance, from the most significant to the least significant, to effectively guide image generation, a block can be surrounded by parentheses to gain additionnal significance. + + This is an example of a user's input: "a beautiful blonde lady in lingerie sitting in seiza in a seducing way with a focus on her assets" + + And this is an example of a desired output: "portrait| serene and mysterious| soft, diffused lighting| close-up shot, emphasizing facial features| simple and blurred background| earthy tones with a hint of warm highlights| renaissance painting| a beautiful lady with freckles and dark makeup" + + Here is the user's input: {prompt} + + Write the prompt in the same style as the example above, in a single line , with absolutely no additional information, words or symbols other than the enhanced prompt. + + Output:""", + }, + ], + ) + content = response["message"]["content"] + print("here's the enhanced prompt :", content) + + if "" in content and "" in content: + # Get everything after + enhanced = content.split("")[-1].strip() + else: + enhanced = content.strip() + print("here's the enhanced prompt:", enhanced) + os.system("ollama stop deepseek-r1") + return "masterpiece, best quality, (extremely detailed CG unity 8k wallpaper, masterpiece, best quality, ultra-detailed, best shadow), high contrast, (best illumination), ((cinematic light)), hyper detail, dramatic light, depth of field," + enhanced diff --git a/modules/Utilities/Latent.py b/modules/Utilities/Latent.py index 14539ae074fb9b59e61b01cdba073b111517541b..bed145809408559a28ac5f0f0df651d4bedea88a 100644 --- a/modules/Utilities/Latent.py +++ b/modules/Utilities/Latent.py @@ -1,210 +1,210 @@ -from typing import Dict, Tuple -import torch -from modules.Device import Device -from modules.Utilities import util - -class LatentFormat: - """#### Base class for latent formats. - - #### Attributes: - - `scale_factor` (float): The scale factor for the latent format. - - #### Returns: - - `LatentFormat`: A latent format object. - """ - - scale_factor: float = 1.0 - latent_channels: int = 4 - - def process_in(self, latent: torch.Tensor) -> torch.Tensor: - """#### Process the latent input, by multiplying it by the scale factor. - - #### Args: - - `latent` (torch.Tensor): The latent tensor. - - #### Returns: - - `torch.Tensor`: The processed latent tensor. - """ - return latent * self.scale_factor - - def process_out(self, latent: torch.Tensor) -> torch.Tensor: - """#### Process the latent output, by dividing it by the scale factor. - - #### Args: - - `latent` (torch.Tensor): The latent tensor. - - #### Returns: - - `torch.Tensor`: The processed latent tensor. - """ - return latent / self.scale_factor - -class SD15(LatentFormat): - """#### SD15 latent format. - - #### Args: - - `LatentFormat` (LatentFormat): The base latent format class. - """ - latent_channels: int = 4 - def __init__(self, scale_factor: float = 0.18215): - """#### Initialize the SD15 latent format. - - #### Args: - - `scale_factor` (float, optional): The scale factor. Defaults to 0.18215. - """ - self.scale_factor = scale_factor - self.latent_rgb_factors = [ - # R G B - [0.3512, 0.2297, 0.3227], - [0.3250, 0.4974, 0.2350], - [-0.2829, 0.1762, 0.2721], - [-0.2120, -0.2616, -0.7177], - ] - self.taesd_decoder_name = "taesd_decoder" - -class SD3(LatentFormat): - latent_channels = 16 - - def __init__(self): - """#### Initialize the SD3 latent format.""" - self.scale_factor = 1.5305 - self.shift_factor = 0.0609 - self.latent_rgb_factors = [ - [-0.0645, 0.0177, 0.1052], - [0.0028, 0.0312, 0.0650], - [0.1848, 0.0762, 0.0360], - [0.0944, 0.0360, 0.0889], - [0.0897, 0.0506, -0.0364], - [-0.0020, 0.1203, 0.0284], - [0.0855, 0.0118, 0.0283], - [-0.0539, 0.0658, 0.1047], - [-0.0057, 0.0116, 0.0700], - [-0.0412, 0.0281, -0.0039], - [0.1106, 0.1171, 0.1220], - [-0.0248, 0.0682, -0.0481], - [0.0815, 0.0846, 0.1207], - [-0.0120, -0.0055, -0.0867], - [-0.0749, -0.0634, -0.0456], - [-0.1418, -0.1457, -0.1259], - ] - self.taesd_decoder_name = "taesd3_decoder" - - def process_in(self, latent: torch.Tensor) -> torch.Tensor: - """#### Process the latent input, by multiplying it by the scale factor and subtracting the shift factor. - - #### Args: - - `latent` (torch.Tensor): The latent tensor. - - #### Returns: - - `torch.Tensor`: The processed latent tensor. - """ - return (latent - self.shift_factor) * self.scale_factor - - def process_out(self, latent: torch.Tensor) -> torch.Tensor: - """#### Process the latent output, by dividing it by the scale factor and adding the shift factor. - - #### Args: - - `latent` (torch.Tensor): The latent tensor. - - #### Returns: - - `torch.Tensor`: The processed latent tensor. - """ - return (latent / self.scale_factor) + self.shift_factor - - -class Flux1(SD3): - latent_channels = 16 - - def __init__(self): - """#### Initialize the Flux1 latent format.""" - self.scale_factor = 0.3611 - self.shift_factor = 0.1159 - self.latent_rgb_factors = [ - [-0.0404, 0.0159, 0.0609], - [0.0043, 0.0298, 0.0850], - [0.0328, -0.0749, -0.0503], - [-0.0245, 0.0085, 0.0549], - [0.0966, 0.0894, 0.0530], - [0.0035, 0.0399, 0.0123], - [0.0583, 0.1184, 0.1262], - [-0.0191, -0.0206, -0.0306], - [-0.0324, 0.0055, 0.1001], - [0.0955, 0.0659, -0.0545], - [-0.0504, 0.0231, -0.0013], - [0.0500, -0.0008, -0.0088], - [0.0982, 0.0941, 0.0976], - [-0.1233, -0.0280, -0.0897], - [-0.0005, -0.0530, -0.0020], - [-0.1273, -0.0932, -0.0680], - ] - self.taesd_decoder_name = "taef1_decoder" - - def process_in(self, latent: torch.Tensor) -> torch.Tensor: - """#### Process the latent input, by multiplying it by the scale factor and subtracting the shift factor. - - #### Args: - - `latent` (torch.Tensor): The latent tensor. - - #### Returns: - - `torch.Tensor`: The processed latent tensor. - """ - return (latent - self.shift_factor) * self.scale_factor - - def process_out(self, latent: torch.Tensor) -> torch.Tensor: - """#### Process the latent output, by dividing it by the scale factor and adding the shift factor. - - #### Args: - - `latent` (torch.Tensor): The latent tensor. - - #### Returns: - - `torch.Tensor`: The processed latent tensor. - """ - return (latent / self.scale_factor) + self.shift_factor - -class EmptyLatentImage: - """#### A class to generate an empty latent image. - - #### Args: - - `Device` (Device): The device to use for the latent image. - """ - - def __init__(self): - """#### Initialize the EmptyLatentImage class.""" - self.device = Device.intermediate_device() - - def generate( - self, width: int, height: int, batch_size: int = 1 - ) -> Tuple[Dict[str, torch.Tensor]]: - """#### Generate an empty latent image - - #### Args: - - `width` (int): The width of the latent image. - - `height` (int): The height of the latent image. - - `batch_size` (int, optional): The batch size. Defaults to 1. - - #### Returns: - - `Tuple[Dict[str, torch.Tensor]]`: The generated latent image. - """ - latent = torch.zeros( - [batch_size, 4, height // 8, width // 8], device=self.device - ) - return ({"samples": latent},) - -def fix_empty_latent_channels(model, latent_image): - """#### Fix the empty latent image channels. - - #### Args: - - `model` (Model): The model object. - - `latent_image` (torch.Tensor): The latent image. - - #### Returns: - - `torch.Tensor`: The fixed latent image. - """ - latent_channels = model.get_model_object( - "latent_format" - ).latent_channels # Resize the empty latent image so it has the right number of channels - if ( - latent_channels != latent_image.shape[1] - and torch.count_nonzero(latent_image) == 0 - ): - latent_image = util.repeat_to_batch_size(latent_image, latent_channels, dim=1) +from typing import Dict, Tuple +import torch +from modules.Device import Device +from modules.Utilities import util + +class LatentFormat: + """#### Base class for latent formats. + + #### Attributes: + - `scale_factor` (float): The scale factor for the latent format. + + #### Returns: + - `LatentFormat`: A latent format object. + """ + + scale_factor: float = 1.0 + latent_channels: int = 4 + + def process_in(self, latent: torch.Tensor) -> torch.Tensor: + """#### Process the latent input, by multiplying it by the scale factor. + + #### Args: + - `latent` (torch.Tensor): The latent tensor. + + #### Returns: + - `torch.Tensor`: The processed latent tensor. + """ + return latent * self.scale_factor + + def process_out(self, latent: torch.Tensor) -> torch.Tensor: + """#### Process the latent output, by dividing it by the scale factor. + + #### Args: + - `latent` (torch.Tensor): The latent tensor. + + #### Returns: + - `torch.Tensor`: The processed latent tensor. + """ + return latent / self.scale_factor + +class SD15(LatentFormat): + """#### SD15 latent format. + + #### Args: + - `LatentFormat` (LatentFormat): The base latent format class. + """ + latent_channels: int = 4 + def __init__(self, scale_factor: float = 0.18215): + """#### Initialize the SD15 latent format. + + #### Args: + - `scale_factor` (float, optional): The scale factor. Defaults to 0.18215. + """ + self.scale_factor = scale_factor + self.latent_rgb_factors = [ + # R G B + [0.3512, 0.2297, 0.3227], + [0.3250, 0.4974, 0.2350], + [-0.2829, 0.1762, 0.2721], + [-0.2120, -0.2616, -0.7177], + ] + self.taesd_decoder_name = "taesd_decoder" + +class SD3(LatentFormat): + latent_channels = 16 + + def __init__(self): + """#### Initialize the SD3 latent format.""" + self.scale_factor = 1.5305 + self.shift_factor = 0.0609 + self.latent_rgb_factors = [ + [-0.0645, 0.0177, 0.1052], + [0.0028, 0.0312, 0.0650], + [0.1848, 0.0762, 0.0360], + [0.0944, 0.0360, 0.0889], + [0.0897, 0.0506, -0.0364], + [-0.0020, 0.1203, 0.0284], + [0.0855, 0.0118, 0.0283], + [-0.0539, 0.0658, 0.1047], + [-0.0057, 0.0116, 0.0700], + [-0.0412, 0.0281, -0.0039], + [0.1106, 0.1171, 0.1220], + [-0.0248, 0.0682, -0.0481], + [0.0815, 0.0846, 0.1207], + [-0.0120, -0.0055, -0.0867], + [-0.0749, -0.0634, -0.0456], + [-0.1418, -0.1457, -0.1259], + ] + self.taesd_decoder_name = "taesd3_decoder" + + def process_in(self, latent: torch.Tensor) -> torch.Tensor: + """#### Process the latent input, by multiplying it by the scale factor and subtracting the shift factor. + + #### Args: + - `latent` (torch.Tensor): The latent tensor. + + #### Returns: + - `torch.Tensor`: The processed latent tensor. + """ + return (latent - self.shift_factor) * self.scale_factor + + def process_out(self, latent: torch.Tensor) -> torch.Tensor: + """#### Process the latent output, by dividing it by the scale factor and adding the shift factor. + + #### Args: + - `latent` (torch.Tensor): The latent tensor. + + #### Returns: + - `torch.Tensor`: The processed latent tensor. + """ + return (latent / self.scale_factor) + self.shift_factor + + +class Flux1(SD3): + latent_channels = 16 + + def __init__(self): + """#### Initialize the Flux1 latent format.""" + self.scale_factor = 0.3611 + self.shift_factor = 0.1159 + self.latent_rgb_factors = [ + [-0.0404, 0.0159, 0.0609], + [0.0043, 0.0298, 0.0850], + [0.0328, -0.0749, -0.0503], + [-0.0245, 0.0085, 0.0549], + [0.0966, 0.0894, 0.0530], + [0.0035, 0.0399, 0.0123], + [0.0583, 0.1184, 0.1262], + [-0.0191, -0.0206, -0.0306], + [-0.0324, 0.0055, 0.1001], + [0.0955, 0.0659, -0.0545], + [-0.0504, 0.0231, -0.0013], + [0.0500, -0.0008, -0.0088], + [0.0982, 0.0941, 0.0976], + [-0.1233, -0.0280, -0.0897], + [-0.0005, -0.0530, -0.0020], + [-0.1273, -0.0932, -0.0680], + ] + self.taesd_decoder_name = "taef1_decoder" + + def process_in(self, latent: torch.Tensor) -> torch.Tensor: + """#### Process the latent input, by multiplying it by the scale factor and subtracting the shift factor. + + #### Args: + - `latent` (torch.Tensor): The latent tensor. + + #### Returns: + - `torch.Tensor`: The processed latent tensor. + """ + return (latent - self.shift_factor) * self.scale_factor + + def process_out(self, latent: torch.Tensor) -> torch.Tensor: + """#### Process the latent output, by dividing it by the scale factor and adding the shift factor. + + #### Args: + - `latent` (torch.Tensor): The latent tensor. + + #### Returns: + - `torch.Tensor`: The processed latent tensor. + """ + return (latent / self.scale_factor) + self.shift_factor + +class EmptyLatentImage: + """#### A class to generate an empty latent image. + + #### Args: + - `Device` (Device): The device to use for the latent image. + """ + + def __init__(self): + """#### Initialize the EmptyLatentImage class.""" + self.device = Device.intermediate_device() + + def generate( + self, width: int, height: int, batch_size: int = 1 + ) -> Tuple[Dict[str, torch.Tensor]]: + """#### Generate an empty latent image + + #### Args: + - `width` (int): The width of the latent image. + - `height` (int): The height of the latent image. + - `batch_size` (int, optional): The batch size. Defaults to 1. + + #### Returns: + - `Tuple[Dict[str, torch.Tensor]]`: The generated latent image. + """ + latent = torch.zeros( + [batch_size, 4, height // 8, width // 8], device=self.device + ) + return ({"samples": latent},) + +def fix_empty_latent_channels(model, latent_image): + """#### Fix the empty latent image channels. + + #### Args: + - `model` (Model): The model object. + - `latent_image` (torch.Tensor): The latent image. + + #### Returns: + - `torch.Tensor`: The fixed latent image. + """ + latent_channels = model.get_model_object( + "latent_format" + ).latent_channels # Resize the empty latent image so it has the right number of channels + if ( + latent_channels != latent_image.shape[1] + and torch.count_nonzero(latent_image) == 0 + ): + latent_image = util.repeat_to_batch_size(latent_image, latent_channels, dim=1) return latent_image \ No newline at end of file diff --git a/modules/Utilities/upscale.py b/modules/Utilities/upscale.py index c9e5f588fbbc18447639ba461ae1cebbb19c1261..4dd83c0554243e7d5ff24bcc8bd4707a234eac31 100644 --- a/modules/Utilities/upscale.py +++ b/modules/Utilities/upscale.py @@ -1,166 +1,166 @@ -from typing import List -import torch - - -def bislerp(samples: torch.Tensor, width: int, height: int) -> torch.Tensor: - """#### Perform bilinear interpolation on samples. - - #### Args: - - `samples` (torch.Tensor): The input samples. - - `width` (int): The target width. - - `height` (int): The target height. - - #### Returns: - - `torch.Tensor`: The interpolated samples. - """ - - def slerp(b1: torch.Tensor, b2: torch.Tensor, r: torch.Tensor) -> torch.Tensor: - """#### Perform spherical linear interpolation between two vectors. - - #### Args: - - `b1` (torch.Tensor): The first vector. - - `b2` (torch.Tensor): The second vector. - - `r` (torch.Tensor): The interpolation ratio. - - #### Returns: - - `torch.Tensor`: The interpolated vector. - """ - - c = b1.shape[-1] - - # norms - b1_norms = torch.norm(b1, dim=-1, keepdim=True) - b2_norms = torch.norm(b2, dim=-1, keepdim=True) - - # normalize - b1_normalized = b1 / b1_norms - b2_normalized = b2 / b2_norms - - # zero when norms are zero - b1_normalized[b1_norms.expand(-1, c) == 0.0] = 0.0 - b2_normalized[b2_norms.expand(-1, c) == 0.0] = 0.0 - - # slerp - dot = (b1_normalized * b2_normalized).sum(1) - omega = torch.acos(dot) - so = torch.sin(omega) - - # technically not mathematically correct, but more pleasing? - res = (torch.sin((1.0 - r.squeeze(1)) * omega) / so).unsqueeze( - 1 - ) * b1_normalized + (torch.sin(r.squeeze(1) * omega) / so).unsqueeze( - 1 - ) * b2_normalized - res *= (b1_norms * (1.0 - r) + b2_norms * r).expand(-1, c) - - # edge cases for same or polar opposites - res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] - res[dot < 1e-5 - 1] = (b1 * (1.0 - r) + b2 * r)[dot < 1e-5 - 1] - return res - - def generate_bilinear_data( - length_old: int, length_new: int, device: torch.device - ) -> List[torch.Tensor]: - """#### Generate bilinear data for interpolation. - - #### Args: - - `length_old` (int): The old length. - - `length_new` (int): The new length. - - `device` (torch.device): The device to use. - - #### Returns: - - `torch.Tensor`: The ratios. - - `torch.Tensor`: The first coordinates. - - `torch.Tensor`: The second coordinates. - """ - coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape( - (1, 1, 1, -1) - ) - coords_1 = torch.nn.functional.interpolate( - coords_1, size=(1, length_new), mode="bilinear" - ) - ratios = coords_1 - coords_1.floor() - coords_1 = coords_1.to(torch.int64) - - coords_2 = ( - torch.arange(length_old, dtype=torch.float32, device=device).reshape( - (1, 1, 1, -1) - ) - + 1 - ) - coords_2[:, :, :, -1] -= 1 - coords_2 = torch.nn.functional.interpolate( - coords_2, size=(1, length_new), mode="bilinear" - ) - coords_2 = coords_2.to(torch.int64) - return ratios, coords_1, coords_2 - - orig_dtype = samples.dtype - samples = samples.float() - n, c, h, w = samples.shape - h_new, w_new = (height, width) - - # linear w - ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) - coords_1 = coords_1.expand((n, c, h, -1)) - coords_2 = coords_2.expand((n, c, h, -1)) - ratios = ratios.expand((n, 1, h, -1)) - - pass_1 = samples.gather(-1, coords_1).movedim(1, -1).reshape((-1, c)) - pass_2 = samples.gather(-1, coords_2).movedim(1, -1).reshape((-1, c)) - ratios = ratios.movedim(1, -1).reshape((-1, 1)) - - result = slerp(pass_1, pass_2, ratios) - result = result.reshape(n, h, w_new, c).movedim(-1, 1) - - # linear h - ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device) - coords_1 = coords_1.reshape((1, 1, -1, 1)).expand((n, c, -1, w_new)) - coords_2 = coords_2.reshape((1, 1, -1, 1)).expand((n, c, -1, w_new)) - ratios = ratios.reshape((1, 1, -1, 1)).expand((n, 1, -1, w_new)) - - pass_1 = result.gather(-2, coords_1).movedim(1, -1).reshape((-1, c)) - pass_2 = result.gather(-2, coords_2).movedim(1, -1).reshape((-1, c)) - ratios = ratios.movedim(1, -1).reshape((-1, 1)) - - result = slerp(pass_1, pass_2, ratios) - result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) - return result.to(orig_dtype) - - -def common_upscale(samples: List, width: int, height: int) -> torch.Tensor: - """#### Upscales the given samples to the specified width and height using the specified method and crop settings. - #### Args: - - `samples` (list): The list of samples to be upscaled. - - `width` (int): The target width for the upscaled samples. - - `height` (int): The target height for the upscaled samples. - #### Returns: - - `torch.Tensor`: The upscaled samples. - """ - s = samples - return bislerp(s, width, height) - - -class LatentUpscale: - """#### A class to upscale latent codes.""" - - def upscale(self, samples: dict, width: int, height: int) -> tuple: - """#### Upscales the given latent codes. - - #### Args: - - `samples` (dict): The latent codes to be upscaled. - - `width` (int): The target width for the upscaled samples. - - `height` (int): The target height for the upscaled samples. - - #### Returns: - - `tuple`: The upscaled samples. - """ - if width == 0 and height == 0: - s = samples - else: - s = samples.copy() - width = max(64, width) - height = max(64, height) - - s["samples"] = common_upscale(samples["samples"], width // 8, height // 8) - return (s,) +from typing import List +import torch + + +def bislerp(samples: torch.Tensor, width: int, height: int) -> torch.Tensor: + """#### Perform bilinear interpolation on samples. + + #### Args: + - `samples` (torch.Tensor): The input samples. + - `width` (int): The target width. + - `height` (int): The target height. + + #### Returns: + - `torch.Tensor`: The interpolated samples. + """ + + def slerp(b1: torch.Tensor, b2: torch.Tensor, r: torch.Tensor) -> torch.Tensor: + """#### Perform spherical linear interpolation between two vectors. + + #### Args: + - `b1` (torch.Tensor): The first vector. + - `b2` (torch.Tensor): The second vector. + - `r` (torch.Tensor): The interpolation ratio. + + #### Returns: + - `torch.Tensor`: The interpolated vector. + """ + + c = b1.shape[-1] + + # norms + b1_norms = torch.norm(b1, dim=-1, keepdim=True) + b2_norms = torch.norm(b2, dim=-1, keepdim=True) + + # normalize + b1_normalized = b1 / b1_norms + b2_normalized = b2 / b2_norms + + # zero when norms are zero + b1_normalized[b1_norms.expand(-1, c) == 0.0] = 0.0 + b2_normalized[b2_norms.expand(-1, c) == 0.0] = 0.0 + + # slerp + dot = (b1_normalized * b2_normalized).sum(1) + omega = torch.acos(dot) + so = torch.sin(omega) + + # technically not mathematically correct, but more pleasing? + res = (torch.sin((1.0 - r.squeeze(1)) * omega) / so).unsqueeze( + 1 + ) * b1_normalized + (torch.sin(r.squeeze(1) * omega) / so).unsqueeze( + 1 + ) * b2_normalized + res *= (b1_norms * (1.0 - r) + b2_norms * r).expand(-1, c) + + # edge cases for same or polar opposites + res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] + res[dot < 1e-5 - 1] = (b1 * (1.0 - r) + b2 * r)[dot < 1e-5 - 1] + return res + + def generate_bilinear_data( + length_old: int, length_new: int, device: torch.device + ) -> List[torch.Tensor]: + """#### Generate bilinear data for interpolation. + + #### Args: + - `length_old` (int): The old length. + - `length_new` (int): The new length. + - `device` (torch.device): The device to use. + + #### Returns: + - `torch.Tensor`: The ratios. + - `torch.Tensor`: The first coordinates. + - `torch.Tensor`: The second coordinates. + """ + coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape( + (1, 1, 1, -1) + ) + coords_1 = torch.nn.functional.interpolate( + coords_1, size=(1, length_new), mode="bilinear" + ) + ratios = coords_1 - coords_1.floor() + coords_1 = coords_1.to(torch.int64) + + coords_2 = ( + torch.arange(length_old, dtype=torch.float32, device=device).reshape( + (1, 1, 1, -1) + ) + + 1 + ) + coords_2[:, :, :, -1] -= 1 + coords_2 = torch.nn.functional.interpolate( + coords_2, size=(1, length_new), mode="bilinear" + ) + coords_2 = coords_2.to(torch.int64) + return ratios, coords_1, coords_2 + + orig_dtype = samples.dtype + samples = samples.float() + n, c, h, w = samples.shape + h_new, w_new = (height, width) + + # linear w + ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) + coords_1 = coords_1.expand((n, c, h, -1)) + coords_2 = coords_2.expand((n, c, h, -1)) + ratios = ratios.expand((n, 1, h, -1)) + + pass_1 = samples.gather(-1, coords_1).movedim(1, -1).reshape((-1, c)) + pass_2 = samples.gather(-1, coords_2).movedim(1, -1).reshape((-1, c)) + ratios = ratios.movedim(1, -1).reshape((-1, 1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h, w_new, c).movedim(-1, 1) + + # linear h + ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device) + coords_1 = coords_1.reshape((1, 1, -1, 1)).expand((n, c, -1, w_new)) + coords_2 = coords_2.reshape((1, 1, -1, 1)).expand((n, c, -1, w_new)) + ratios = ratios.reshape((1, 1, -1, 1)).expand((n, 1, -1, w_new)) + + pass_1 = result.gather(-2, coords_1).movedim(1, -1).reshape((-1, c)) + pass_2 = result.gather(-2, coords_2).movedim(1, -1).reshape((-1, c)) + ratios = ratios.movedim(1, -1).reshape((-1, 1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) + return result.to(orig_dtype) + + +def common_upscale(samples: List, width: int, height: int) -> torch.Tensor: + """#### Upscales the given samples to the specified width and height using the specified method and crop settings. + #### Args: + - `samples` (list): The list of samples to be upscaled. + - `width` (int): The target width for the upscaled samples. + - `height` (int): The target height for the upscaled samples. + #### Returns: + - `torch.Tensor`: The upscaled samples. + """ + s = samples + return bislerp(s, width, height) + + +class LatentUpscale: + """#### A class to upscale latent codes.""" + + def upscale(self, samples: dict, width: int, height: int) -> tuple: + """#### Upscales the given latent codes. + + #### Args: + - `samples` (dict): The latent codes to be upscaled. + - `width` (int): The target width for the upscaled samples. + - `height` (int): The target height for the upscaled samples. + + #### Returns: + - `tuple`: The upscaled samples. + """ + if width == 0 and height == 0: + s = samples + else: + s = samples.copy() + width = max(64, width) + height = max(64, height) + + s["samples"] = common_upscale(samples["samples"], width // 8, height // 8) + return (s,) diff --git a/modules/Utilities/util.py b/modules/Utilities/util.py index e920859ac88d39fd6d10fe39df17b050dc5d6412..69809d76cbe78fcaa0bb151b561cb7afd022b972 100644 --- a/modules/Utilities/util.py +++ b/modules/Utilities/util.py @@ -1,628 +1,628 @@ -import importlib -from inspect import isfunction -import itertools -import logging -import math -import os -import pickle -import safetensors.torch -import torch - - -def append_dims(x: torch.Tensor, target_dims: int) -> torch.Tensor: - """#### Appends dimensions to the end of a tensor until it has target_dims dimensions. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `target_dims` (int): The target number of dimensions. - - #### Returns: - - `torch.Tensor`: The expanded tensor. - """ - dims_to_append = target_dims - x.ndim - expanded = x[(...,) + (None,) * dims_to_append] - return expanded.detach().clone() if expanded.device.type == "mps" else expanded - - -def to_d(x: torch.Tensor, sigma: torch.Tensor, denoised: torch.Tensor) -> torch.Tensor: - """#### Convert a tensor to a denoised tensor. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `sigma` (torch.Tensor): The noise level. - - `denoised` (torch.Tensor): The denoised tensor. - - #### Returns: - - `torch.Tensor`: The converted tensor. - """ - return (x - denoised) / append_dims(sigma, x.ndim) - - -def load_torch_file(ckpt: str, safe_load: bool = False, device: str = None) -> dict: - """#### Load a PyTorch checkpoint file. - - #### Args: - - `ckpt` (str): The path to the checkpoint file. - - `safe_load` (bool, optional): Whether to use safe loading. Defaults to False. - - `device` (str, optional): The device to load the checkpoint on. Defaults to None. - - #### Returns: - - `dict`: The loaded checkpoint. - """ - if device is None: - device = torch.device("cpu") - if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"): - sd = safetensors.torch.load_file(ckpt, device=device.type) - else: - if safe_load: - if "weights_only" not in torch.load.__code__.co_varnames: - logging.warning( - "Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely." - ) - safe_load = False - if safe_load: - pl_sd = torch.load(ckpt, map_location=device, weights_only=True) - else: - pl_sd = torch.load(ckpt, map_location=device) - if "global_step" in pl_sd: - logging.debug(f"Global Step: {pl_sd['global_step']}") - if "state_dict" in pl_sd: - sd = pl_sd["state_dict"] - else: - sd = pl_sd - return sd - - -def calculate_parameters(sd: dict, prefix: str = "") -> dict: - """#### Calculate the parameters of a state dictionary. - - #### Args: - - `sd` (dict): The state dictionary. - - `prefix` (str, optional): The prefix for the parameters. Defaults to "". - - #### Returns: - - `dict`: The calculated parameters. - """ - params = 0 - for k in sd.keys(): - if k.startswith(prefix): - params += sd[k].nelement() - return params - - -def state_dict_prefix_replace( - state_dict: dict, replace_prefix: str, filter_keys: bool = False -) -> dict: - """#### Replace the prefix of keys in a state dictionary. - - #### Args: - - `state_dict` (dict): The state dictionary. - - `replace_prefix` (str): The prefix to replace. - - `filter_keys` (bool, optional): Whether to filter keys. Defaults to False. - - #### Returns: - - `dict`: The updated state dictionary. - """ - if filter_keys: - out = {} - else: - out = state_dict - for rp in replace_prefix: - replace = list( - map( - lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp) :])), - filter(lambda a: a.startswith(rp), state_dict.keys()), - ) - ) - for x in replace: - w = state_dict.pop(x[0]) - out[x[1]] = w - return out - - -def repeat_to_batch_size( - tensor: torch.Tensor, batch_size: int, dim: int = 0 -) -> torch.Tensor: - """#### Repeat a tensor to match a specific batch size. - - #### Args: - - `tensor` (torch.Tensor): The input tensor. - - `batch_size` (int): The target batch size. - - `dim` (int, optional): The dimension to repeat. Defaults to 0. - - #### Returns: - - `torch.Tensor`: The repeated tensor. - """ - if tensor.shape[dim] > batch_size: - return tensor.narrow(dim, 0, batch_size) - elif tensor.shape[dim] < batch_size: - return tensor.repeat( - dim * [1] - + [math.ceil(batch_size / tensor.shape[dim])] - + [1] * (len(tensor.shape) - 1 - dim) - ).narrow(dim, 0, batch_size) - return tensor - - -def set_attr(obj: object, attr: str, value: any) -> any: - """#### Set an attribute of an object. - - #### Args: - - `obj` (object): The object. - - `attr` (str): The attribute name. - - `value` (any): The value to set. - - #### Returns: - - `prev`: The previous attribute value. - """ - attrs = attr.split(".") - for name in attrs[:-1]: - obj = getattr(obj, name) - prev = getattr(obj, attrs[-1]) - setattr(obj, attrs[-1], value) - return prev - - -def set_attr_param(obj: object, attr: str, value: any) -> any: - """#### Set an attribute parameter of an object. - - #### Args: - - `obj` (object): The object. - - `attr` (str): The attribute name. - - `value` (any): The value to set. - - #### Returns: - - `prev`: The previous attribute value. - """ - return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False)) - - -def copy_to_param(obj: object, attr: str, value: any) -> None: - """#### Copy a value to a parameter of an object. - - #### Args: - - `obj` (object): The object. - - `attr` (str): The attribute name. - - `value` (any): The value to set. - """ - attrs = attr.split(".") - for name in attrs[:-1]: - obj = getattr(obj, name) - prev = getattr(obj, attrs[-1]) - prev.data.copy_(value) - - -def get_obj_from_str(string: str, reload: bool = False) -> object: - """#### Get an object from a string. - - #### Args: - - `string` (str): The string. - - `reload` (bool, optional): Whether to reload the module. Defaults to False. - - #### Returns: - - `object`: The object. - """ - module, cls = string.rsplit(".", 1) - if reload: - module_imp = importlib.import_module(module) - importlib.reload(module_imp) - return getattr(importlib.import_module(module, package=None), cls) - - -def get_attr(obj: object, attr: str) -> any: - """#### Get an attribute of an object. - - #### Args: - - `obj` (object): The object. - - `attr` (str): The attribute name. - - #### Returns: - - `obj`: The attribute value. - """ - attrs = attr.split(".") - for name in attrs: - obj = getattr(obj, name) - return obj - - -def lcm(a: int, b: int) -> int: - """#### Calculate the least common multiple (LCM) of two numbers. - - #### Args: - - `a` (int): The first number. - - `b` (int): The second number. - - #### Returns: - - `int`: The LCM of the two numbers. - """ - return abs(a * b) // math.gcd(a, b) - - -def get_full_path(folder_name: str, filename: str) -> str: - """#### Get the full path of a file in a folder. - - Args: - folder_name (str): The folder name. - filename (str): The filename. - - Returns: - str: The full path of the file. - """ - global folder_names_and_paths - folders = folder_names_and_paths[folder_name] - filename = os.path.relpath(os.path.join("/", filename), "/") - for x in folders[0]: - full_path = os.path.join(x, filename) - if os.path.isfile(full_path): - return full_path - - -def zero_module(module: torch.nn.Module) -> torch.nn.Module: - """#### Zero out the parameters of a module. - - #### Args: - - `module` (torch.nn.Module): The module. - - #### Returns: - - `torch.nn.Module`: The zeroed module. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - -def append_zero(x: torch.Tensor) -> torch.Tensor: - """#### Append a zero to the end of a tensor. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The tensor with a zero appended. - """ - return torch.cat([x, x.new_zeros([1])]) - - -def exists(val: any) -> bool: - """#### Check if a value exists. - - #### Args: - - `val` (any): The value. - - #### Returns: - - `bool`: Whether the value exists. - """ - return val is not None - - -def default(val: any, d: any) -> any: - """#### Get the default value of a variable. - - #### Args: - - `val` (any): The value. - - `d` (any): The default value. - - #### Returns: - - `any`: The default value if the value does not exist. - """ - if exists(val): - return val - return d() if isfunction(d) else d - - -def write_parameters_to_file( - prompt_entry: str, neg: str, width: int, height: int, cfg: int -) -> None: - """#### Write parameters to a file. - - #### Args: - - `prompt_entry` (str): The prompt entry. - - `neg` (str): The negative prompt entry. - - `width` (int): The width. - - `height` (int): The height. - - `cfg` (int): The CFG. - """ - with open("./_internal/prompt.txt", "w") as f: - f.write(f"prompt: {prompt_entry}") - f.write(f"neg: {neg}") - f.write(f"w: {int(width)}\n") - f.write(f"h: {int(height)}\n") - f.write(f"cfg: {int(cfg)}\n") - - -def load_parameters_from_file() -> tuple: - """#### Load parameters from a file. - - #### Returns: - - `str`: The prompt entry. - - `str`: The negative prompt entry. - - `int`: The width. - - `int`: The height. - - `int`: The CFG. - """ - with open("./_internal/prompt.txt", "r") as f: - lines = f.readlines() - parameters = {} - for line in lines: - # Skip empty lines - if line.strip() == "": - continue - key, value = line.split(": ") - parameters[key] = value.strip() - prompt = parameters["prompt"] - neg = parameters["neg"] - width = int(parameters["w"]) - height = int(parameters["h"]) - cfg = int(parameters["cfg"]) - return prompt, neg, width, height, cfg - - -PROGRESS_BAR_ENABLED = True -PROGRESS_BAR_HOOK = None - - -class ProgressBar: - """#### Class representing a progress bar.""" - - def __init__(self, total: int): - global PROGRESS_BAR_HOOK - self.total = total - self.current = 0 - self.hook = PROGRESS_BAR_HOOK - - -def get_tiled_scale_steps( - width: int, height: int, tile_x: int, tile_y: int, overlap: int -) -> int: - """#### Get the number of steps for tiled scaling. - - #### Args: - - `width` (int): The width. - - `height` (int): The height. - - `tile_x` (int): The tile width. - - `tile_y` (int): The tile height. - - `overlap` (int): The overlap. - - #### Returns: - - `int`: The number of steps. - """ - rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap)) - cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap)) - return rows * cols - - -@torch.inference_mode() -def tiled_scale_multidim( - samples: torch.Tensor, - function, - tile: tuple = (64, 64), - overlap: int = 8, - upscale_amount: int = 4, - out_channels: int = 3, - output_device: str = "cpu", - downscale: bool = False, - index_formulas: any = None, - pbar: any = None, -): - """#### Scale an image using a tiled approach. - - #### Args: - - `samples` (torch.Tensor): The input samples. - - `function` (function): The scaling function. - - `tile` (tuple, optional): The tile size. Defaults to (64, 64). - - `overlap` (int, optional): The overlap. Defaults to 8. - - `upscale_amount` (int, optional): The upscale amount. Defaults to 4. - - `out_channels` (int, optional): The number of output channels. Defaults to 3. - - `output_device` (str, optional): The output device. Defaults to "cpu". - - `downscale` (bool, optional): Whether to downscale. Defaults to False. - - `index_formulas` (any, optional): The index formulas. Defaults to None. - - `pbar` (any, optional): The progress bar. Defaults to None. - - #### Returns: - - `torch.Tensor`: The scaled image. - """ - dims = len(tile) - - if not (isinstance(upscale_amount, (tuple, list))): - upscale_amount = [upscale_amount] * dims - - if not (isinstance(overlap, (tuple, list))): - overlap = [overlap] * dims - - if index_formulas is None: - index_formulas = upscale_amount - - if not (isinstance(index_formulas, (tuple, list))): - index_formulas = [index_formulas] * dims - - def get_upscale(dim: int, val: int) -> int: - """#### Get the upscale value. - - #### Args: - - `dim` (int): The dimension. - - `val` (int): The value. - - #### Returns: - - `int`: The upscaled value. - """ - up = upscale_amount[dim] - if callable(up): - return up(val) - else: - return up * val - - def get_downscale(dim: int, val: int) -> int: - """#### Get the downscale value. - - #### Args: - - `dim` (int): The dimension. - - `val` (int): The value. - - #### Returns: - - `int`: The downscaled value. - """ - up = upscale_amount[dim] - if callable(up): - return up(val) - else: - return val / up - - def get_upscale_pos(dim: int, val: int) -> int: - """#### Get the upscaled position. - - #### Args: - - `dim` (int): The dimension. - - `val` (int): The value. - - #### Returns: - - `int`: The upscaled position. - """ - up = index_formulas[dim] - if callable(up): - return up(val) - else: - return up * val - - def get_downscale_pos(dim: int, val: int) -> int: - """#### Get the downscaled position. - - #### Args: - - `dim` (int): The dimension. - - `val` (int): The value. - - #### Returns: - - `int`: The downscaled position. - """ - up = index_formulas[dim] - if callable(up): - return up(val) - else: - return val / up - - if downscale: - get_scale = get_downscale - get_pos = get_downscale_pos - else: - get_scale = get_upscale - get_pos = get_upscale_pos - - def mult_list_upscale(a: list) -> list: - """#### Multiply a list by the upscale amount. - - #### Args: - - `a` (list): The list. - - #### Returns: - - `list`: The multiplied list. - """ - out = [] - for i in range(len(a)): - out.append(round(get_scale(i, a[i]))) - return out - - output = torch.empty( - [samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), - device=output_device, - ) - - for b in range(samples.shape[0]): - s = samples[b : b + 1] - - # handle entire input fitting in a single tile - if all(s.shape[d + 2] <= tile[d] for d in range(dims)): - output[b : b + 1] = function(s).to(output_device) - if pbar is not None: - pbar.update(1) - continue - - out = torch.zeros( - [s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), - device=output_device, - ) - out_div = torch.zeros( - [s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), - device=output_device, - ) - - positions = [ - range(0, s.shape[d + 2] - overlap[d], tile[d] - overlap[d]) - if s.shape[d + 2] > tile[d] - else [0] - for d in range(dims) - ] - - for it in itertools.product(*positions): - s_in = s - upscaled = [] - - for d in range(dims): - pos = max(0, min(s.shape[d + 2] - overlap[d], it[d])) - l = min(tile[d], s.shape[d + 2] - pos) - s_in = s_in.narrow(d + 2, pos, l) - upscaled.append(round(get_pos(d, pos))) - - ps = function(s_in).to(output_device) - mask = torch.ones_like(ps) - - for d in range(2, dims + 2): - feather = round(get_scale(d - 2, overlap[d - 2])) - if feather >= mask.shape[d]: - continue - for t in range(feather): - a = (t + 1) / feather - mask.narrow(d, t, 1).mul_(a) - mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a) - - o = out - o_d = out_div - for d in range(dims): - o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2]) - o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2]) - - o.add_(ps * mask) - o_d.add_(mask) - - if pbar is not None: - pbar.update(1) - - output[b : b + 1] = out / out_div - return output - - -def tiled_scale( - samples: torch.Tensor, - function, - tile_x: int = 64, - tile_y: int = 64, - overlap: int = 8, - upscale_amount: int = 4, - out_channels: int = 3, - output_device: str = "cpu", - pbar: any = None, -): - """#### Scale an image using a tiled approach. - - #### Args: - - `samples` (torch.Tensor): The input samples. - - `function` (function): The scaling function. - - `tile_x` (int, optional): The tile width. Defaults to 64. - - `tile_y` (int, optional): The tile height. Defaults to 64. - - `overlap` (int, optional): The overlap. Defaults to 8. - - `upscale_amount` (int, optional): The upscale amount. Defaults to 4. - - `out_channels` (int, optional): The number of output channels. Defaults to 3. - - `output_device` (str, optional): The output device. Defaults to "cpu". - - `pbar` (any, optional): The progress bar. Defaults to None. - - #### Returns: - - The scaled image. - """ - return tiled_scale_multidim( - samples, - function, - (tile_y, tile_x), - overlap=overlap, - upscale_amount=upscale_amount, - out_channels=out_channels, - output_device=output_device, - pbar=pbar, - ) +import importlib +from inspect import isfunction +import itertools +import logging +import math +import os +import pickle +import safetensors.torch +import torch + + +def append_dims(x: torch.Tensor, target_dims: int) -> torch.Tensor: + """#### Appends dimensions to the end of a tensor until it has target_dims dimensions. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `target_dims` (int): The target number of dimensions. + + #### Returns: + - `torch.Tensor`: The expanded tensor. + """ + dims_to_append = target_dims - x.ndim + expanded = x[(...,) + (None,) * dims_to_append] + return expanded.detach().clone() if expanded.device.type == "mps" else expanded + + +def to_d(x: torch.Tensor, sigma: torch.Tensor, denoised: torch.Tensor) -> torch.Tensor: + """#### Convert a tensor to a denoised tensor. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `sigma` (torch.Tensor): The noise level. + - `denoised` (torch.Tensor): The denoised tensor. + + #### Returns: + - `torch.Tensor`: The converted tensor. + """ + return (x - denoised) / append_dims(sigma, x.ndim) + + +def load_torch_file(ckpt: str, safe_load: bool = False, device: str = None) -> dict: + """#### Load a PyTorch checkpoint file. + + #### Args: + - `ckpt` (str): The path to the checkpoint file. + - `safe_load` (bool, optional): Whether to use safe loading. Defaults to False. + - `device` (str, optional): The device to load the checkpoint on. Defaults to None. + + #### Returns: + - `dict`: The loaded checkpoint. + """ + if device is None: + device = torch.device("cpu") + if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"): + sd = safetensors.torch.load_file(ckpt, device=device.type) + else: + if safe_load: + if "weights_only" not in torch.load.__code__.co_varnames: + logging.warning( + "Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely." + ) + safe_load = False + if safe_load: + pl_sd = torch.load(ckpt, map_location=device, weights_only=True) + else: + pl_sd = torch.load(ckpt, map_location=device) + if "global_step" in pl_sd: + logging.debug(f"Global Step: {pl_sd['global_step']}") + if "state_dict" in pl_sd: + sd = pl_sd["state_dict"] + else: + sd = pl_sd + return sd + + +def calculate_parameters(sd: dict, prefix: str = "") -> dict: + """#### Calculate the parameters of a state dictionary. + + #### Args: + - `sd` (dict): The state dictionary. + - `prefix` (str, optional): The prefix for the parameters. Defaults to "". + + #### Returns: + - `dict`: The calculated parameters. + """ + params = 0 + for k in sd.keys(): + if k.startswith(prefix): + params += sd[k].nelement() + return params + + +def state_dict_prefix_replace( + state_dict: dict, replace_prefix: str, filter_keys: bool = False +) -> dict: + """#### Replace the prefix of keys in a state dictionary. + + #### Args: + - `state_dict` (dict): The state dictionary. + - `replace_prefix` (str): The prefix to replace. + - `filter_keys` (bool, optional): Whether to filter keys. Defaults to False. + + #### Returns: + - `dict`: The updated state dictionary. + """ + if filter_keys: + out = {} + else: + out = state_dict + for rp in replace_prefix: + replace = list( + map( + lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp) :])), + filter(lambda a: a.startswith(rp), state_dict.keys()), + ) + ) + for x in replace: + w = state_dict.pop(x[0]) + out[x[1]] = w + return out + + +def repeat_to_batch_size( + tensor: torch.Tensor, batch_size: int, dim: int = 0 +) -> torch.Tensor: + """#### Repeat a tensor to match a specific batch size. + + #### Args: + - `tensor` (torch.Tensor): The input tensor. + - `batch_size` (int): The target batch size. + - `dim` (int, optional): The dimension to repeat. Defaults to 0. + + #### Returns: + - `torch.Tensor`: The repeated tensor. + """ + if tensor.shape[dim] > batch_size: + return tensor.narrow(dim, 0, batch_size) + elif tensor.shape[dim] < batch_size: + return tensor.repeat( + dim * [1] + + [math.ceil(batch_size / tensor.shape[dim])] + + [1] * (len(tensor.shape) - 1 - dim) + ).narrow(dim, 0, batch_size) + return tensor + + +def set_attr(obj: object, attr: str, value: any) -> any: + """#### Set an attribute of an object. + + #### Args: + - `obj` (object): The object. + - `attr` (str): The attribute name. + - `value` (any): The value to set. + + #### Returns: + - `prev`: The previous attribute value. + """ + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + setattr(obj, attrs[-1], value) + return prev + + +def set_attr_param(obj: object, attr: str, value: any) -> any: + """#### Set an attribute parameter of an object. + + #### Args: + - `obj` (object): The object. + - `attr` (str): The attribute name. + - `value` (any): The value to set. + + #### Returns: + - `prev`: The previous attribute value. + """ + return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False)) + + +def copy_to_param(obj: object, attr: str, value: any) -> None: + """#### Copy a value to a parameter of an object. + + #### Args: + - `obj` (object): The object. + - `attr` (str): The attribute name. + - `value` (any): The value to set. + """ + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + prev.data.copy_(value) + + +def get_obj_from_str(string: str, reload: bool = False) -> object: + """#### Get an object from a string. + + #### Args: + - `string` (str): The string. + - `reload` (bool, optional): Whether to reload the module. Defaults to False. + + #### Returns: + - `object`: The object. + """ + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def get_attr(obj: object, attr: str) -> any: + """#### Get an attribute of an object. + + #### Args: + - `obj` (object): The object. + - `attr` (str): The attribute name. + + #### Returns: + - `obj`: The attribute value. + """ + attrs = attr.split(".") + for name in attrs: + obj = getattr(obj, name) + return obj + + +def lcm(a: int, b: int) -> int: + """#### Calculate the least common multiple (LCM) of two numbers. + + #### Args: + - `a` (int): The first number. + - `b` (int): The second number. + + #### Returns: + - `int`: The LCM of the two numbers. + """ + return abs(a * b) // math.gcd(a, b) + + +def get_full_path(folder_name: str, filename: str) -> str: + """#### Get the full path of a file in a folder. + + Args: + folder_name (str): The folder name. + filename (str): The filename. + + Returns: + str: The full path of the file. + """ + global folder_names_and_paths + folders = folder_names_and_paths[folder_name] + filename = os.path.relpath(os.path.join("/", filename), "/") + for x in folders[0]: + full_path = os.path.join(x, filename) + if os.path.isfile(full_path): + return full_path + + +def zero_module(module: torch.nn.Module) -> torch.nn.Module: + """#### Zero out the parameters of a module. + + #### Args: + - `module` (torch.nn.Module): The module. + + #### Returns: + - `torch.nn.Module`: The zeroed module. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def append_zero(x: torch.Tensor) -> torch.Tensor: + """#### Append a zero to the end of a tensor. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The tensor with a zero appended. + """ + return torch.cat([x, x.new_zeros([1])]) + + +def exists(val: any) -> bool: + """#### Check if a value exists. + + #### Args: + - `val` (any): The value. + + #### Returns: + - `bool`: Whether the value exists. + """ + return val is not None + + +def default(val: any, d: any) -> any: + """#### Get the default value of a variable. + + #### Args: + - `val` (any): The value. + - `d` (any): The default value. + + #### Returns: + - `any`: The default value if the value does not exist. + """ + if exists(val): + return val + return d() if isfunction(d) else d + + +def write_parameters_to_file( + prompt_entry: str, neg: str, width: int, height: int, cfg: int +) -> None: + """#### Write parameters to a file. + + #### Args: + - `prompt_entry` (str): The prompt entry. + - `neg` (str): The negative prompt entry. + - `width` (int): The width. + - `height` (int): The height. + - `cfg` (int): The CFG. + """ + with open("./_internal/prompt.txt", "w") as f: + f.write(f"prompt: {prompt_entry}") + f.write(f"neg: {neg}") + f.write(f"w: {int(width)}\n") + f.write(f"h: {int(height)}\n") + f.write(f"cfg: {int(cfg)}\n") + + +def load_parameters_from_file() -> tuple: + """#### Load parameters from a file. + + #### Returns: + - `str`: The prompt entry. + - `str`: The negative prompt entry. + - `int`: The width. + - `int`: The height. + - `int`: The CFG. + """ + with open("./_internal/prompt.txt", "r") as f: + lines = f.readlines() + parameters = {} + for line in lines: + # Skip empty lines + if line.strip() == "": + continue + key, value = line.split(": ") + parameters[key] = value.strip() + prompt = parameters["prompt"] + neg = parameters["neg"] + width = int(parameters["w"]) + height = int(parameters["h"]) + cfg = int(parameters["cfg"]) + return prompt, neg, width, height, cfg + + +PROGRESS_BAR_ENABLED = True +PROGRESS_BAR_HOOK = None + + +class ProgressBar: + """#### Class representing a progress bar.""" + + def __init__(self, total: int): + global PROGRESS_BAR_HOOK + self.total = total + self.current = 0 + self.hook = PROGRESS_BAR_HOOK + + +def get_tiled_scale_steps( + width: int, height: int, tile_x: int, tile_y: int, overlap: int +) -> int: + """#### Get the number of steps for tiled scaling. + + #### Args: + - `width` (int): The width. + - `height` (int): The height. + - `tile_x` (int): The tile width. + - `tile_y` (int): The tile height. + - `overlap` (int): The overlap. + + #### Returns: + - `int`: The number of steps. + """ + rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap)) + cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap)) + return rows * cols + + +@torch.inference_mode() +def tiled_scale_multidim( + samples: torch.Tensor, + function, + tile: tuple = (64, 64), + overlap: int = 8, + upscale_amount: int = 4, + out_channels: int = 3, + output_device: str = "cpu", + downscale: bool = False, + index_formulas: any = None, + pbar: any = None, +): + """#### Scale an image using a tiled approach. + + #### Args: + - `samples` (torch.Tensor): The input samples. + - `function` (function): The scaling function. + - `tile` (tuple, optional): The tile size. Defaults to (64, 64). + - `overlap` (int, optional): The overlap. Defaults to 8. + - `upscale_amount` (int, optional): The upscale amount. Defaults to 4. + - `out_channels` (int, optional): The number of output channels. Defaults to 3. + - `output_device` (str, optional): The output device. Defaults to "cpu". + - `downscale` (bool, optional): Whether to downscale. Defaults to False. + - `index_formulas` (any, optional): The index formulas. Defaults to None. + - `pbar` (any, optional): The progress bar. Defaults to None. + + #### Returns: + - `torch.Tensor`: The scaled image. + """ + dims = len(tile) + + if not (isinstance(upscale_amount, (tuple, list))): + upscale_amount = [upscale_amount] * dims + + if not (isinstance(overlap, (tuple, list))): + overlap = [overlap] * dims + + if index_formulas is None: + index_formulas = upscale_amount + + if not (isinstance(index_formulas, (tuple, list))): + index_formulas = [index_formulas] * dims + + def get_upscale(dim: int, val: int) -> int: + """#### Get the upscale value. + + #### Args: + - `dim` (int): The dimension. + - `val` (int): The value. + + #### Returns: + - `int`: The upscaled value. + """ + up = upscale_amount[dim] + if callable(up): + return up(val) + else: + return up * val + + def get_downscale(dim: int, val: int) -> int: + """#### Get the downscale value. + + #### Args: + - `dim` (int): The dimension. + - `val` (int): The value. + + #### Returns: + - `int`: The downscaled value. + """ + up = upscale_amount[dim] + if callable(up): + return up(val) + else: + return val / up + + def get_upscale_pos(dim: int, val: int) -> int: + """#### Get the upscaled position. + + #### Args: + - `dim` (int): The dimension. + - `val` (int): The value. + + #### Returns: + - `int`: The upscaled position. + """ + up = index_formulas[dim] + if callable(up): + return up(val) + else: + return up * val + + def get_downscale_pos(dim: int, val: int) -> int: + """#### Get the downscaled position. + + #### Args: + - `dim` (int): The dimension. + - `val` (int): The value. + + #### Returns: + - `int`: The downscaled position. + """ + up = index_formulas[dim] + if callable(up): + return up(val) + else: + return val / up + + if downscale: + get_scale = get_downscale + get_pos = get_downscale_pos + else: + get_scale = get_upscale + get_pos = get_upscale_pos + + def mult_list_upscale(a: list) -> list: + """#### Multiply a list by the upscale amount. + + #### Args: + - `a` (list): The list. + + #### Returns: + - `list`: The multiplied list. + """ + out = [] + for i in range(len(a)): + out.append(round(get_scale(i, a[i]))) + return out + + output = torch.empty( + [samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), + device=output_device, + ) + + for b in range(samples.shape[0]): + s = samples[b : b + 1] + + # handle entire input fitting in a single tile + if all(s.shape[d + 2] <= tile[d] for d in range(dims)): + output[b : b + 1] = function(s).to(output_device) + if pbar is not None: + pbar.update(1) + continue + + out = torch.zeros( + [s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), + device=output_device, + ) + out_div = torch.zeros( + [s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), + device=output_device, + ) + + positions = [ + range(0, s.shape[d + 2] - overlap[d], tile[d] - overlap[d]) + if s.shape[d + 2] > tile[d] + else [0] + for d in range(dims) + ] + + for it in itertools.product(*positions): + s_in = s + upscaled = [] + + for d in range(dims): + pos = max(0, min(s.shape[d + 2] - overlap[d], it[d])) + l = min(tile[d], s.shape[d + 2] - pos) + s_in = s_in.narrow(d + 2, pos, l) + upscaled.append(round(get_pos(d, pos))) + + ps = function(s_in).to(output_device) + mask = torch.ones_like(ps) + + for d in range(2, dims + 2): + feather = round(get_scale(d - 2, overlap[d - 2])) + if feather >= mask.shape[d]: + continue + for t in range(feather): + a = (t + 1) / feather + mask.narrow(d, t, 1).mul_(a) + mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a) + + o = out + o_d = out_div + for d in range(dims): + o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2]) + o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2]) + + o.add_(ps * mask) + o_d.add_(mask) + + if pbar is not None: + pbar.update(1) + + output[b : b + 1] = out / out_div + return output + + +def tiled_scale( + samples: torch.Tensor, + function, + tile_x: int = 64, + tile_y: int = 64, + overlap: int = 8, + upscale_amount: int = 4, + out_channels: int = 3, + output_device: str = "cpu", + pbar: any = None, +): + """#### Scale an image using a tiled approach. + + #### Args: + - `samples` (torch.Tensor): The input samples. + - `function` (function): The scaling function. + - `tile_x` (int, optional): The tile width. Defaults to 64. + - `tile_y` (int, optional): The tile height. Defaults to 64. + - `overlap` (int, optional): The overlap. Defaults to 8. + - `upscale_amount` (int, optional): The upscale amount. Defaults to 4. + - `out_channels` (int, optional): The number of output channels. Defaults to 3. + - `output_device` (str, optional): The output device. Defaults to "cpu". + - `pbar` (any, optional): The progress bar. Defaults to None. + + #### Returns: + - The scaled image. + """ + return tiled_scale_multidim( + samples, + function, + (tile_y, tile_x), + overlap=overlap, + upscale_amount=upscale_amount, + out_channels=out_channels, + output_device=output_device, + pbar=pbar, + ) diff --git a/modules/WaveSpeed/fbcache_nodes.py b/modules/WaveSpeed/fbcache_nodes.py index 5e8b4a7861b0897b2d9c46420f21f6cd90798612..8b8051b1a85216f44ebfffc746db131bf2217bb3 100644 --- a/modules/WaveSpeed/fbcache_nodes.py +++ b/modules/WaveSpeed/fbcache_nodes.py @@ -1,201 +1,201 @@ -import contextlib -import unittest -import torch - -from . import first_block_cache - - -class ApplyFBCacheOnModel: - - def patch( - self, - model, - object_to_patch, - residual_diff_threshold, - max_consecutive_cache_hits=-1, - start=0.0, - end=1.0, - ): - if residual_diff_threshold <= 0.0 or max_consecutive_cache_hits == 0: - return (model, ) - - # first_block_cache.patch_get_output_data() - - using_validation = max_consecutive_cache_hits >= 0 or start > 0 or end < 1 - if using_validation: - model_sampling = model.get_model_object("model_sampling") - start_sigma, end_sigma = (float( - model_sampling.percent_to_sigma(pct)) for pct in (start, end)) - del model_sampling - - @torch.compiler.disable() - def validate_use_cache(use_cached): - nonlocal consecutive_cache_hits - use_cached = use_cached and end_sigma <= current_timestep <= start_sigma - use_cached = use_cached and (max_consecutive_cache_hits < 0 - or consecutive_cache_hits - < max_consecutive_cache_hits) - consecutive_cache_hits = consecutive_cache_hits + 1 if use_cached else 0 - return use_cached - else: - validate_use_cache = None - - prev_timestep = None - prev_input_state = None - current_timestep = None - consecutive_cache_hits = 0 - - def reset_cache_state(): - # Resets the cache state and hits/time tracking variables. - nonlocal prev_input_state, prev_timestep, consecutive_cache_hits - prev_input_state = prev_timestep = None - consecutive_cache_hits = 0 - first_block_cache.set_current_cache_context( - first_block_cache.create_cache_context()) - - def ensure_cache_state(model_input: torch.Tensor, timestep: float): - # Validates the current cache state and hits/time tracking variables - # and triggers a reset if necessary. Also updates current_timestep. - nonlocal current_timestep - input_state = (model_input.shape, model_input.dtype, model_input.device) - need_reset = ( - prev_timestep is None or - prev_input_state != input_state or - first_block_cache.get_current_cache_context() is None or - timestep >= prev_timestep - ) - if need_reset: - reset_cache_state() - current_timestep = timestep - - def update_cache_state(model_input: torch.Tensor, timestep: float): - # Updates the previous timestep and input state validation variables. - nonlocal prev_timestep, prev_input_state - prev_timestep = timestep - prev_input_state = (model_input.shape, model_input.dtype, model_input.device) - - model = model[0].clone() - diffusion_model = model.get_model_object(object_to_patch) - - if diffusion_model.__class__.__name__ in ("UNetModel", "Flux"): - - if diffusion_model.__class__.__name__ == "UNetModel": - create_patch_function = first_block_cache.create_patch_unet_model__forward - elif diffusion_model.__class__.__name__ == "Flux": - create_patch_function = first_block_cache.create_patch_flux_forward_orig - else: - raise ValueError( - f"Unsupported model {diffusion_model.__class__.__name__}") - - patch_forward = create_patch_function( - diffusion_model, - residual_diff_threshold=residual_diff_threshold, - validate_can_use_cache_function=validate_use_cache, - ) - - def model_unet_function_wrapper(model_function, kwargs): - try: - input = kwargs["input"] - timestep = kwargs["timestep"] - c = kwargs["c"] - t = timestep[0].item() - - ensure_cache_state(input, t) - - with patch_forward(): - result = model_function(input, timestep, **c) - update_cache_state(input, t) - return result - except Exception as exc: - reset_cache_state() - raise exc from None - else: - is_non_native_ltxv = False - if diffusion_model.__class__.__name__ == "LTXVTransformer3D": - is_non_native_ltxv = True - diffusion_model = diffusion_model.transformer - - double_blocks_name = None - single_blocks_name = None - if hasattr(diffusion_model, "transformer_blocks"): - double_blocks_name = "transformer_blocks" - elif hasattr(diffusion_model, "double_blocks"): - double_blocks_name = "double_blocks" - elif hasattr(diffusion_model, "joint_blocks"): - double_blocks_name = "joint_blocks" - else: - raise ValueError( - f"No double blocks found for {diffusion_model.__class__.__name__}" - ) - - if hasattr(diffusion_model, "single_blocks"): - single_blocks_name = "single_blocks" - - if is_non_native_ltxv: - original_create_skip_layer_mask = getattr( - diffusion_model, "create_skip_layer_mask", None) - if original_create_skip_layer_mask is not None: - # original_double_blocks = getattr(diffusion_model, - # double_blocks_name) - - def new_create_skip_layer_mask(self, *args, **kwargs): - # with unittest.mock.patch.object(self, double_blocks_name, - # original_double_blocks): - # return original_create_skip_layer_mask(*args, **kwargs) - # return original_create_skip_layer_mask(*args, **kwargs) - raise RuntimeError( - "STG is not supported with FBCache yet") - - diffusion_model.create_skip_layer_mask = new_create_skip_layer_mask.__get__( - diffusion_model) - - cached_transformer_blocks = torch.nn.ModuleList([ - first_block_cache.CachedTransformerBlocks( - None if double_blocks_name is None else getattr( - diffusion_model, double_blocks_name), - None if single_blocks_name is None else getattr( - diffusion_model, single_blocks_name), - residual_diff_threshold=residual_diff_threshold, - validate_can_use_cache_function=validate_use_cache, - cat_hidden_states_first=diffusion_model.__class__.__name__ - == "HunyuanVideo", - return_hidden_states_only=diffusion_model.__class__. - __name__ == "LTXVModel" or is_non_native_ltxv, - clone_original_hidden_states=diffusion_model.__class__. - __name__ == "LTXVModel", - return_hidden_states_first=diffusion_model.__class__. - __name__ != "OpenAISignatureMMDITWrapper", - accept_hidden_states_first=diffusion_model.__class__. - __name__ != "OpenAISignatureMMDITWrapper", - ) - ]) - dummy_single_transformer_blocks = torch.nn.ModuleList() - - def model_unet_function_wrapper(model_function, kwargs): - try: - input = kwargs["input"] - timestep = kwargs["timestep"] - c = kwargs["c"] - t = timestep[0].item() - - ensure_cache_state(input, t) - - with unittest.mock.patch.object( - diffusion_model, - double_blocks_name, - cached_transformer_blocks, - ), unittest.mock.patch.object( - diffusion_model, - single_blocks_name, - dummy_single_transformer_blocks, - ) if single_blocks_name is not None else contextlib.nullcontext( - ): - result = model_function(input, timestep, **c) - update_cache_state(input, t) - return result - except Exception as exc: - reset_cache_state() - raise exc from None - - model.set_model_unet_function_wrapper(model_unet_function_wrapper) - return (model, ) +import contextlib +import unittest +import torch + +from . import first_block_cache + + +class ApplyFBCacheOnModel: + + def patch( + self, + model, + object_to_patch, + residual_diff_threshold, + max_consecutive_cache_hits=-1, + start=0.0, + end=1.0, + ): + if residual_diff_threshold <= 0.0 or max_consecutive_cache_hits == 0: + return (model, ) + + # first_block_cache.patch_get_output_data() + + using_validation = max_consecutive_cache_hits >= 0 or start > 0 or end < 1 + if using_validation: + model_sampling = model.get_model_object("model_sampling") + start_sigma, end_sigma = (float( + model_sampling.percent_to_sigma(pct)) for pct in (start, end)) + del model_sampling + + @torch.compiler.disable() + def validate_use_cache(use_cached): + nonlocal consecutive_cache_hits + use_cached = use_cached and end_sigma <= current_timestep <= start_sigma + use_cached = use_cached and (max_consecutive_cache_hits < 0 + or consecutive_cache_hits + < max_consecutive_cache_hits) + consecutive_cache_hits = consecutive_cache_hits + 1 if use_cached else 0 + return use_cached + else: + validate_use_cache = None + + prev_timestep = None + prev_input_state = None + current_timestep = None + consecutive_cache_hits = 0 + + def reset_cache_state(): + # Resets the cache state and hits/time tracking variables. + nonlocal prev_input_state, prev_timestep, consecutive_cache_hits + prev_input_state = prev_timestep = None + consecutive_cache_hits = 0 + first_block_cache.set_current_cache_context( + first_block_cache.create_cache_context()) + + def ensure_cache_state(model_input: torch.Tensor, timestep: float): + # Validates the current cache state and hits/time tracking variables + # and triggers a reset if necessary. Also updates current_timestep. + nonlocal current_timestep + input_state = (model_input.shape, model_input.dtype, model_input.device) + need_reset = ( + prev_timestep is None or + prev_input_state != input_state or + first_block_cache.get_current_cache_context() is None or + timestep >= prev_timestep + ) + if need_reset: + reset_cache_state() + current_timestep = timestep + + def update_cache_state(model_input: torch.Tensor, timestep: float): + # Updates the previous timestep and input state validation variables. + nonlocal prev_timestep, prev_input_state + prev_timestep = timestep + prev_input_state = (model_input.shape, model_input.dtype, model_input.device) + + model = model[0].clone() + diffusion_model = model.get_model_object(object_to_patch) + + if diffusion_model.__class__.__name__ in ("UNetModel", "Flux"): + + if diffusion_model.__class__.__name__ == "UNetModel": + create_patch_function = first_block_cache.create_patch_unet_model__forward + elif diffusion_model.__class__.__name__ == "Flux": + create_patch_function = first_block_cache.create_patch_flux_forward_orig + else: + raise ValueError( + f"Unsupported model {diffusion_model.__class__.__name__}") + + patch_forward = create_patch_function( + diffusion_model, + residual_diff_threshold=residual_diff_threshold, + validate_can_use_cache_function=validate_use_cache, + ) + + def model_unet_function_wrapper(model_function, kwargs): + try: + input = kwargs["input"] + timestep = kwargs["timestep"] + c = kwargs["c"] + t = timestep[0].item() + + ensure_cache_state(input, t) + + with patch_forward(): + result = model_function(input, timestep, **c) + update_cache_state(input, t) + return result + except Exception as exc: + reset_cache_state() + raise exc from None + else: + is_non_native_ltxv = False + if diffusion_model.__class__.__name__ == "LTXVTransformer3D": + is_non_native_ltxv = True + diffusion_model = diffusion_model.transformer + + double_blocks_name = None + single_blocks_name = None + if hasattr(diffusion_model, "transformer_blocks"): + double_blocks_name = "transformer_blocks" + elif hasattr(diffusion_model, "double_blocks"): + double_blocks_name = "double_blocks" + elif hasattr(diffusion_model, "joint_blocks"): + double_blocks_name = "joint_blocks" + else: + raise ValueError( + f"No double blocks found for {diffusion_model.__class__.__name__}" + ) + + if hasattr(diffusion_model, "single_blocks"): + single_blocks_name = "single_blocks" + + if is_non_native_ltxv: + original_create_skip_layer_mask = getattr( + diffusion_model, "create_skip_layer_mask", None) + if original_create_skip_layer_mask is not None: + # original_double_blocks = getattr(diffusion_model, + # double_blocks_name) + + def new_create_skip_layer_mask(self, *args, **kwargs): + # with unittest.mock.patch.object(self, double_blocks_name, + # original_double_blocks): + # return original_create_skip_layer_mask(*args, **kwargs) + # return original_create_skip_layer_mask(*args, **kwargs) + raise RuntimeError( + "STG is not supported with FBCache yet") + + diffusion_model.create_skip_layer_mask = new_create_skip_layer_mask.__get__( + diffusion_model) + + cached_transformer_blocks = torch.nn.ModuleList([ + first_block_cache.CachedTransformerBlocks( + None if double_blocks_name is None else getattr( + diffusion_model, double_blocks_name), + None if single_blocks_name is None else getattr( + diffusion_model, single_blocks_name), + residual_diff_threshold=residual_diff_threshold, + validate_can_use_cache_function=validate_use_cache, + cat_hidden_states_first=diffusion_model.__class__.__name__ + == "HunyuanVideo", + return_hidden_states_only=diffusion_model.__class__. + __name__ == "LTXVModel" or is_non_native_ltxv, + clone_original_hidden_states=diffusion_model.__class__. + __name__ == "LTXVModel", + return_hidden_states_first=diffusion_model.__class__. + __name__ != "OpenAISignatureMMDITWrapper", + accept_hidden_states_first=diffusion_model.__class__. + __name__ != "OpenAISignatureMMDITWrapper", + ) + ]) + dummy_single_transformer_blocks = torch.nn.ModuleList() + + def model_unet_function_wrapper(model_function, kwargs): + try: + input = kwargs["input"] + timestep = kwargs["timestep"] + c = kwargs["c"] + t = timestep[0].item() + + ensure_cache_state(input, t) + + with unittest.mock.patch.object( + diffusion_model, + double_blocks_name, + cached_transformer_blocks, + ), unittest.mock.patch.object( + diffusion_model, + single_blocks_name, + dummy_single_transformer_blocks, + ) if single_blocks_name is not None else contextlib.nullcontext( + ): + result = model_function(input, timestep, **c) + update_cache_state(input, t) + return result + except Exception as exc: + reset_cache_state() + raise exc from None + + model.set_model_unet_function_wrapper(model_unet_function_wrapper) + return (model, ) diff --git a/modules/WaveSpeed/first_block_cache.py b/modules/WaveSpeed/first_block_cache.py index a4c9c96cdc246dcf52cdf293d68d581a495ef70d..c72767297544122646520e2217c4f65ba4159d42 100644 --- a/modules/WaveSpeed/first_block_cache.py +++ b/modules/WaveSpeed/first_block_cache.py @@ -1,824 +1,824 @@ -import contextlib -import dataclasses -import unittest -from collections import defaultdict -from typing import DefaultDict, Dict - -import torch - -from modules.AutoEncoders.ResBlock import forward_timestep_embed1 -from modules.NeuralNetwork.unet import apply_control1 -from modules.sample.sampling_util import timestep_embedding - - -@dataclasses.dataclass -class CacheContext: - buffers: Dict[str, torch.Tensor] = dataclasses.field(default_factory=dict) - incremental_name_counters: DefaultDict[str, int] = dataclasses.field( - default_factory=lambda: defaultdict(int)) - - def get_incremental_name(self, name=None): - if name is None: - name = "default" - idx = self.incremental_name_counters[name] - self.incremental_name_counters[name] += 1 - return f"{name}_{idx}" - - def reset_incremental_names(self): - self.incremental_name_counters.clear() - - @torch.compiler.disable() - def get_buffer(self, name): - return self.buffers.get(name) - - @torch.compiler.disable() - def set_buffer(self, name, buffer): - self.buffers[name] = buffer - - def clear_buffers(self): - self.buffers.clear() - - -@torch.compiler.disable() -def get_buffer(name): - cache_context = get_current_cache_context() - assert cache_context is not None, "cache_context must be set before" - return cache_context.get_buffer(name) - - -@torch.compiler.disable() -def set_buffer(name, buffer): - cache_context = get_current_cache_context() - assert cache_context is not None, "cache_context must be set before" - cache_context.set_buffer(name, buffer) - - -_current_cache_context = None - - -def create_cache_context(): - return CacheContext() - - -def get_current_cache_context(): - return _current_cache_context - - -def set_current_cache_context(cache_context=None): - global _current_cache_context - _current_cache_context = cache_context - - -@contextlib.contextmanager -def cache_context(cache_context): - global _current_cache_context - old_cache_context = _current_cache_context - _current_cache_context = cache_context - try: - yield - finally: - _current_cache_context = old_cache_context - - -# def patch_get_output_data(): -# import execution - -# get_output_data = getattr(execution, "get_output_data", None) -# if get_output_data is None: -# return - -# if getattr(get_output_data, "_patched", False): -# return - -# def new_get_output_data(*args, **kwargs): -# out = get_output_data(*args, **kwargs) -# cache_context = get_current_cache_context() -# if cache_context is not None: -# cache_context.clear_buffers() -# set_current_cache_context(None) -# return out - -# new_get_output_data._patched = True -# execution.get_output_data = new_get_output_data - - -@torch.compiler.disable() -def are_two_tensors_similar(t1, t2, *, threshold): - if t1.shape != t2.shape: - return False - mean_diff = (t1 - t2).abs().mean() - mean_t1 = t1.abs().mean() - diff = mean_diff / mean_t1 - return diff.item() < threshold - - -@torch.compiler.disable() -def apply_prev_hidden_states_residual(hidden_states, - encoder_hidden_states=None): - hidden_states_residual = get_buffer("hidden_states_residual") - assert hidden_states_residual is not None, "hidden_states_residual must be set before" - hidden_states = hidden_states_residual + hidden_states - hidden_states = hidden_states.contiguous() - - if encoder_hidden_states is None: - return hidden_states - - encoder_hidden_states_residual = get_buffer( - "encoder_hidden_states_residual") - if encoder_hidden_states_residual is None: - encoder_hidden_states = None - else: - encoder_hidden_states = encoder_hidden_states_residual + encoder_hidden_states - encoder_hidden_states = encoder_hidden_states.contiguous() - - return hidden_states, encoder_hidden_states - - -@torch.compiler.disable() -def get_can_use_cache(first_hidden_states_residual, - threshold, - parallelized=False): - prev_first_hidden_states_residual = get_buffer( - "first_hidden_states_residual") - can_use_cache = prev_first_hidden_states_residual is not None and are_two_tensors_similar( - prev_first_hidden_states_residual, - first_hidden_states_residual, - threshold=threshold, - ) - return can_use_cache - - -class CachedTransformerBlocks(torch.nn.Module): - - def __init__( - self, - transformer_blocks, - single_transformer_blocks=None, - *, - residual_diff_threshold, - validate_can_use_cache_function=None, - return_hidden_states_first=True, - accept_hidden_states_first=True, - cat_hidden_states_first=False, - return_hidden_states_only=False, - clone_original_hidden_states=False, - ): - super().__init__() - self.transformer_blocks = transformer_blocks - self.single_transformer_blocks = single_transformer_blocks - self.residual_diff_threshold = residual_diff_threshold - self.validate_can_use_cache_function = validate_can_use_cache_function - self.return_hidden_states_first = return_hidden_states_first - self.accept_hidden_states_first = accept_hidden_states_first - self.cat_hidden_states_first = cat_hidden_states_first - self.return_hidden_states_only = return_hidden_states_only - self.clone_original_hidden_states = clone_original_hidden_states - - def forward(self, *args, **kwargs): - img_arg_name = None - if "img" in kwargs: - img_arg_name = "img" - elif "hidden_states" in kwargs: - img_arg_name = "hidden_states" - txt_arg_name = None - if "txt" in kwargs: - txt_arg_name = "txt" - elif "context" in kwargs: - txt_arg_name = "context" - elif "encoder_hidden_states" in kwargs: - txt_arg_name = "encoder_hidden_states" - if self.accept_hidden_states_first: - if args: - img = args[0] - args = args[1:] - else: - img = kwargs.pop(img_arg_name) - if args: - txt = args[0] - args = args[1:] - else: - txt = kwargs.pop(txt_arg_name) - else: - if args: - txt = args[0] - args = args[1:] - else: - txt = kwargs.pop(txt_arg_name) - if args: - img = args[0] - args = args[1:] - else: - img = kwargs.pop(img_arg_name) - hidden_states = img - encoder_hidden_states = txt - if self.residual_diff_threshold <= 0.0: - for block in self.transformer_blocks: - if txt_arg_name == "encoder_hidden_states": - hidden_states = block( - hidden_states, - *args, - encoder_hidden_states=encoder_hidden_states, - **kwargs) - else: - if self.accept_hidden_states_first: - hidden_states = block(hidden_states, - encoder_hidden_states, *args, - **kwargs) - else: - hidden_states = block(encoder_hidden_states, - hidden_states, *args, **kwargs) - if not self.return_hidden_states_only: - hidden_states, encoder_hidden_states = hidden_states - if not self.return_hidden_states_first: - hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states - if self.single_transformer_blocks is not None: - hidden_states = torch.cat( - [hidden_states, encoder_hidden_states] - if self.cat_hidden_states_first else - [encoder_hidden_states, hidden_states], - dim=1) - for block in self.single_transformer_blocks: - hidden_states = block(hidden_states, *args, **kwargs) - hidden_states = hidden_states[:, - encoder_hidden_states.shape[1]:] - if self.return_hidden_states_only: - return hidden_states - else: - return ((hidden_states, encoder_hidden_states) - if self.return_hidden_states_first else - (encoder_hidden_states, hidden_states)) - - original_hidden_states = hidden_states - if self.clone_original_hidden_states: - original_hidden_states = original_hidden_states.clone() - first_transformer_block = self.transformer_blocks[0] - if txt_arg_name == "encoder_hidden_states": - hidden_states = first_transformer_block( - hidden_states, - *args, - encoder_hidden_states=encoder_hidden_states, - **kwargs) - else: - if self.accept_hidden_states_first: - hidden_states = first_transformer_block( - hidden_states, encoder_hidden_states, *args, **kwargs) - else: - hidden_states = first_transformer_block( - encoder_hidden_states, hidden_states, *args, **kwargs) - if not self.return_hidden_states_only: - hidden_states, encoder_hidden_states = hidden_states - if not self.return_hidden_states_first: - hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states - first_hidden_states_residual = hidden_states - original_hidden_states - del original_hidden_states - - can_use_cache = get_can_use_cache( - first_hidden_states_residual, - threshold=self.residual_diff_threshold, - ) - if self.validate_can_use_cache_function is not None: - can_use_cache = self.validate_can_use_cache_function(can_use_cache) - - torch._dynamo.graph_break() - if can_use_cache: - del first_hidden_states_residual - hidden_states, encoder_hidden_states = apply_prev_hidden_states_residual( - hidden_states, encoder_hidden_states) - else: - set_buffer("first_hidden_states_residual", - first_hidden_states_residual) - del first_hidden_states_residual - ( - hidden_states, - encoder_hidden_states, - hidden_states_residual, - encoder_hidden_states_residual, - ) = self.call_remaining_transformer_blocks( - hidden_states, - encoder_hidden_states, - *args, - txt_arg_name=txt_arg_name, - **kwargs) - set_buffer("hidden_states_residual", hidden_states_residual) - if encoder_hidden_states_residual is not None: - set_buffer("encoder_hidden_states_residual", - encoder_hidden_states_residual) - torch._dynamo.graph_break() - - if self.return_hidden_states_only: - return hidden_states - else: - return ((hidden_states, encoder_hidden_states) - if self.return_hidden_states_first else - (encoder_hidden_states, hidden_states)) - - def call_remaining_transformer_blocks(self, - hidden_states, - encoder_hidden_states, - *args, - txt_arg_name=None, - **kwargs): - original_hidden_states = hidden_states - original_encoder_hidden_states = encoder_hidden_states - if self.clone_original_hidden_states: - original_hidden_states = original_hidden_states.clone() - original_encoder_hidden_states = original_encoder_hidden_states.clone( - ) - for block in self.transformer_blocks[1:]: - if txt_arg_name == "encoder_hidden_states": - hidden_states = block( - hidden_states, - *args, - encoder_hidden_states=encoder_hidden_states, - **kwargs) - else: - if self.accept_hidden_states_first: - hidden_states = block(hidden_states, encoder_hidden_states, - *args, **kwargs) - else: - hidden_states = block(encoder_hidden_states, hidden_states, - *args, **kwargs) - if not self.return_hidden_states_only: - hidden_states, encoder_hidden_states = hidden_states - if not self.return_hidden_states_first: - hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states - if self.single_transformer_blocks is not None: - hidden_states = torch.cat([hidden_states, encoder_hidden_states] - if self.cat_hidden_states_first else - [encoder_hidden_states, hidden_states], - dim=1) - for block in self.single_transformer_blocks: - hidden_states = block(hidden_states, *args, **kwargs) - if self.cat_hidden_states_first: - hidden_states, encoder_hidden_states = hidden_states.split( - [ - hidden_states.shape[1] - - encoder_hidden_states.shape[1], - encoder_hidden_states.shape[1] - ], - dim=1) - else: - encoder_hidden_states, hidden_states = hidden_states.split( - [ - encoder_hidden_states.shape[1], - hidden_states.shape[1] - encoder_hidden_states.shape[1] - ], - dim=1) - - hidden_states_shape = hidden_states.shape - hidden_states = hidden_states.flatten().contiguous().reshape( - hidden_states_shape) - - if encoder_hidden_states is not None: - encoder_hidden_states_shape = encoder_hidden_states.shape - encoder_hidden_states = encoder_hidden_states.flatten().contiguous( - ).reshape(encoder_hidden_states_shape) - - hidden_states_residual = hidden_states - original_hidden_states - if encoder_hidden_states is None: - encoder_hidden_states_residual = None - else: - encoder_hidden_states_residual = encoder_hidden_states - original_encoder_hidden_states - return hidden_states, encoder_hidden_states, hidden_states_residual, encoder_hidden_states_residual - - -# Based on 90f349f93df3083a507854d7fc7c3e1bb9014e24 -def create_patch_unet_model__forward(model, - *, - residual_diff_threshold, - validate_can_use_cache_function=None): - - def call_remaining_blocks(self, transformer_options, control, - transformer_patches, hs, h, *args, **kwargs): - original_hidden_states = h - - for id, module in enumerate(self.input_blocks): - if id < 2: - continue - transformer_options["block"] = ("input", id) - h = forward_timestep_embed1(module, h, *args, **kwargs) - h = apply_control1(h, control, 'input') - if "input_block_patch" in transformer_patches: - patch = transformer_patches["input_block_patch"] - for p in patch: - h = p(h, transformer_options) - - hs.append(h) - if "input_block_patch_after_skip" in transformer_patches: - patch = transformer_patches["input_block_patch_after_skip"] - for p in patch: - h = p(h, transformer_options) - - transformer_options["block"] = ("middle", 0) - if self.middle_block is not None: - h = forward_timestep_embed1(self.middle_block, h, *args, **kwargs) - h = apply_control1(h, control, 'middle') - - for id, module in enumerate(self.output_blocks): - transformer_options["block"] = ("output", id) - hsp = hs.pop() - hsp = apply_control1(hsp, control, 'output') - - if "output_block_patch" in transformer_patches: - patch = transformer_patches["output_block_patch"] - for p in patch: - h, hsp = p(h, hsp, transformer_options) - - h = torch.cat([h, hsp], dim=1) - del hsp - if len(hs) > 0: - output_shape = hs[-1].shape - else: - output_shape = None - h = forward_timestep_embed1(module, h, *args, output_shape, - **kwargs) - hidden_states_residual = h - original_hidden_states - return h, hidden_states_residual - - def unet_model__forward(self, - x, - timesteps=None, - context=None, - y=None, - control=None, - transformer_options={}, - **kwargs): - """ - Apply the model to an input batch. - :param x: an [N x C x ...] Tensor of inputs. - :param timesteps: a 1-D batch of timesteps. - :param context: conditioning plugged in via crossattn - :param y: an [N] Tensor of labels, if class-conditional. - :return: an [N x C x ...] Tensor of outputs. - """ - transformer_options["original_shape"] = list(x.shape) - transformer_options["transformer_index"] = 0 - transformer_patches = transformer_options.get("patches", {}) - - num_video_frames = kwargs.get("num_video_frames", - self.default_num_video_frames) - image_only_indicator = kwargs.get("image_only_indicator", None) - time_context = kwargs.get("time_context", None) - - assert (y is not None) == ( - self.num_classes is not None - ), "must specify y if and only if the model is class-conditional" - hs = [] - t_emb = timestep_embedding(timesteps, - self.model_channels, - repeat_only=False).to(x.dtype) - emb = self.time_embed(t_emb) - - if "emb_patch" in transformer_patches: - patch = transformer_patches["emb_patch"] - for p in patch: - emb = p(emb, self.model_channels, transformer_options) - - if self.num_classes is not None: - assert y.shape[0] == x.shape[0] - emb = emb + self.label_emb(y) - - can_use_cache = False - - h = x - for id, module in enumerate(self.input_blocks): - if id >= 2: - break - transformer_options["block"] = ("input", id) - if id == 1: - original_h = h - h = forward_timestep_embed1( - module, - h, - emb, - context, - transformer_options, - time_context=time_context, - num_video_frames=num_video_frames, - image_only_indicator=image_only_indicator) - h = apply_control1(h, control, 'input') - if "input_block_patch" in transformer_patches: - patch = transformer_patches["input_block_patch"] - for p in patch: - h = p(h, transformer_options) - - hs.append(h) - if "input_block_patch_after_skip" in transformer_patches: - patch = transformer_patches["input_block_patch_after_skip"] - for p in patch: - h = p(h, transformer_options) - - if id == 1: - first_hidden_states_residual = h - original_h - can_use_cache = get_can_use_cache( - first_hidden_states_residual, - threshold=residual_diff_threshold, - ) - if validate_can_use_cache_function is not None: - can_use_cache = validate_can_use_cache_function( - can_use_cache) - if not can_use_cache: - set_buffer("first_hidden_states_residual", - first_hidden_states_residual) - del first_hidden_states_residual - - torch._dynamo.graph_break() - if can_use_cache: - h = apply_prev_hidden_states_residual(h) - else: - h, hidden_states_residual = call_remaining_blocks( - self, - transformer_options, - control, - transformer_patches, - hs, - h, - emb, - context, - transformer_options, - time_context=time_context, - num_video_frames=num_video_frames, - image_only_indicator=image_only_indicator) - set_buffer("hidden_states_residual", hidden_states_residual) - torch._dynamo.graph_break() - - h = h.type(x.dtype) - - if self.predict_codebook_ids: - return self.id_predictor(h) - else: - return self.out(h) - - new__forward = unet_model__forward.__get__(model) - - @contextlib.contextmanager - def patch__forward(): - with unittest.mock.patch.object(model, "_forward", new__forward): - yield - - return patch__forward - - -# Based on 90f349f93df3083a507854d7fc7c3e1bb9014e24 -def create_patch_flux_forward_orig(model, - *, - residual_diff_threshold, - validate_can_use_cache_function=None): - from torch import Tensor - - def call_remaining_blocks(self, blocks_replace, control, img, txt, vec, pe, - attn_mask, ca_idx, timesteps, transformer_options): - original_hidden_states = img - - extra_block_forward_kwargs = {} - if attn_mask is not None: - extra_block_forward_kwargs["attn_mask"] = attn_mask - - for i, block in enumerate(self.double_blocks): - if i < 1: - continue - if ("double_block", i) in blocks_replace: - - def block_wrap(args): - out = {} - out["img"], out["txt"] = block( - img=args["img"], - txt=args["txt"], - vec=args["vec"], - pe=args["pe"], - **extra_block_forward_kwargs) - return out - - out = blocks_replace[("double_block", - i)]({ - "img": img, - "txt": txt, - "vec": vec, - "pe": pe, - **extra_block_forward_kwargs - }, { - "original_block": block_wrap, - "transformer_options": transformer_options - }) - txt = out["txt"] - img = out["img"] - else: - img, txt = block(img=img, - txt=txt, - vec=vec, - pe=pe, - **extra_block_forward_kwargs) - - if control is not None: # Controlnet - control_i = control.get("input") - if i < len(control_i): - add = control_i[i] - if add is not None: - img += add - - # PuLID attention - if getattr(self, "pulid_data", {}): - if i % self.pulid_double_interval == 0: - # Will calculate influence of all pulid nodes at once - for _, node_data in self.pulid_data.items(): - if torch.any((node_data['sigma_start'] >= timesteps) - & (timesteps >= node_data['sigma_end'])): - img = img + node_data['weight'] * self.pulid_ca[ - ca_idx](node_data['embedding'], img) - ca_idx += 1 - - img = torch.cat((txt, img), 1) - - for i, block in enumerate(self.single_blocks): - if ("single_block", i) in blocks_replace: - - def block_wrap(args): - out = {} - out["img"] = block(args["img"], - vec=args["vec"], - pe=args["pe"], - **extra_block_forward_kwargs) - return out - - out = blocks_replace[("single_block", - i)]({ - "img": img, - "vec": vec, - "pe": pe, - **extra_block_forward_kwargs - }, { - "original_block": block_wrap, - "transformer_options": transformer_options - }) - img = out["img"] - else: - img = block(img, vec=vec, pe=pe, **extra_block_forward_kwargs) - - if control is not None: # Controlnet - control_o = control.get("output") - if i < len(control_o): - add = control_o[i] - if add is not None: - img[:, txt.shape[1]:, ...] += add - - # PuLID attention - if getattr(self, "pulid_data", {}): - real_img, txt = img[:, txt.shape[1]:, - ...], img[:, :txt.shape[1], ...] - if i % self.pulid_single_interval == 0: - # Will calculate influence of all nodes at once - for _, node_data in self.pulid_data.items(): - if torch.any((node_data['sigma_start'] >= timesteps) - & (timesteps >= node_data['sigma_end'])): - real_img = real_img + node_data[ - 'weight'] * self.pulid_ca[ca_idx]( - node_data['embedding'], real_img) - ca_idx += 1 - img = torch.cat((txt, real_img), 1) - - img = img[:, txt.shape[1]:, ...] - - img = img.contiguous() - hidden_states_residual = img - original_hidden_states - return img, hidden_states_residual - - def forward_orig( - self, - img: Tensor, - img_ids: Tensor, - txt: Tensor, - txt_ids: Tensor, - timesteps: Tensor, - y: Tensor, - guidance: Tensor = None, - control=None, - transformer_options={}, - attn_mask: Tensor = None, - ) -> Tensor: - patches_replace = transformer_options.get("patches_replace", {}) - if img.ndim != 3 or txt.ndim != 3: - raise ValueError( - "Input img and txt tensors must have 3 dimensions.") - - # running on sequences img - img = self.img_in(img) - vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) - if self.params.guidance_embed: - if guidance is None: - raise ValueError( - "Didn't get guidance strength for guidance distilled model." - ) - vec = vec + self.guidance_in( - timestep_embedding(guidance, 256).to(img.dtype)) - - vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) - txt = self.txt_in(txt) - - ids = torch.cat((txt_ids, img_ids), dim=1) - pe = self.pe_embedder(ids) - - ca_idx = 0 - extra_block_forward_kwargs = {} - if attn_mask is not None: - extra_block_forward_kwargs["attn_mask"] = attn_mask - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.double_blocks): - if i >= 1: - break - if ("double_block", i) in blocks_replace: - - def block_wrap(args): - out = {} - out["img"], out["txt"] = block( - img=args["img"], - txt=args["txt"], - vec=args["vec"], - pe=args["pe"], - **extra_block_forward_kwargs) - return out - - out = blocks_replace[("double_block", - i)]({ - "img": img, - "txt": txt, - "vec": vec, - "pe": pe, - **extra_block_forward_kwargs - }, { - "original_block": block_wrap, - "transformer_options": transformer_options - }) - txt = out["txt"] - img = out["img"] - else: - img, txt = block(img=img, - txt=txt, - vec=vec, - pe=pe, - **extra_block_forward_kwargs) - - if control is not None: # Controlnet - control_i = control.get("input") - if i < len(control_i): - add = control_i[i] - if add is not None: - img += add - - # PuLID attention - if getattr(self, "pulid_data", {}): - if i % self.pulid_double_interval == 0: - # Will calculate influence of all pulid nodes at once - for _, node_data in self.pulid_data.items(): - if torch.any((node_data['sigma_start'] >= timesteps) - & (timesteps >= node_data['sigma_end'])): - img = img + node_data['weight'] * self.pulid_ca[ - ca_idx](node_data['embedding'], img) - ca_idx += 1 - - if i == 0: - first_hidden_states_residual = img - can_use_cache = get_can_use_cache( - first_hidden_states_residual, - threshold=residual_diff_threshold, - ) - if validate_can_use_cache_function is not None: - can_use_cache = validate_can_use_cache_function( - can_use_cache) - if not can_use_cache: - set_buffer("first_hidden_states_residual", - first_hidden_states_residual) - del first_hidden_states_residual - - torch._dynamo.graph_break() - if can_use_cache: - img = apply_prev_hidden_states_residual(img) - else: - img, hidden_states_residual = call_remaining_blocks( - self, - blocks_replace, - control, - img, - txt, - vec, - pe, - attn_mask, - ca_idx, - timesteps, - transformer_options, - ) - set_buffer("hidden_states_residual", hidden_states_residual) - torch._dynamo.graph_break() - - img = self.final_layer(img, - vec) # (N, T, patch_size ** 2 * out_channels) - return img - - new_forward_orig = forward_orig.__get__(model) - - @contextlib.contextmanager - def patch_forward_orig(): - with unittest.mock.patch.object(model, "forward_orig", - new_forward_orig): - yield - - return patch_forward_orig +import contextlib +import dataclasses +import unittest +from collections import defaultdict +from typing import DefaultDict, Dict + +import torch + +from modules.AutoEncoders.ResBlock import forward_timestep_embed1 +from modules.NeuralNetwork.unet import apply_control1 +from modules.sample.sampling_util import timestep_embedding + + +@dataclasses.dataclass +class CacheContext: + buffers: Dict[str, torch.Tensor] = dataclasses.field(default_factory=dict) + incremental_name_counters: DefaultDict[str, int] = dataclasses.field( + default_factory=lambda: defaultdict(int)) + + def get_incremental_name(self, name=None): + if name is None: + name = "default" + idx = self.incremental_name_counters[name] + self.incremental_name_counters[name] += 1 + return f"{name}_{idx}" + + def reset_incremental_names(self): + self.incremental_name_counters.clear() + + @torch.compiler.disable() + def get_buffer(self, name): + return self.buffers.get(name) + + @torch.compiler.disable() + def set_buffer(self, name, buffer): + self.buffers[name] = buffer + + def clear_buffers(self): + self.buffers.clear() + + +@torch.compiler.disable() +def get_buffer(name): + cache_context = get_current_cache_context() + assert cache_context is not None, "cache_context must be set before" + return cache_context.get_buffer(name) + + +@torch.compiler.disable() +def set_buffer(name, buffer): + cache_context = get_current_cache_context() + assert cache_context is not None, "cache_context must be set before" + cache_context.set_buffer(name, buffer) + + +_current_cache_context = None + + +def create_cache_context(): + return CacheContext() + + +def get_current_cache_context(): + return _current_cache_context + + +def set_current_cache_context(cache_context=None): + global _current_cache_context + _current_cache_context = cache_context + + +@contextlib.contextmanager +def cache_context(cache_context): + global _current_cache_context + old_cache_context = _current_cache_context + _current_cache_context = cache_context + try: + yield + finally: + _current_cache_context = old_cache_context + + +# def patch_get_output_data(): +# import execution + +# get_output_data = getattr(execution, "get_output_data", None) +# if get_output_data is None: +# return + +# if getattr(get_output_data, "_patched", False): +# return + +# def new_get_output_data(*args, **kwargs): +# out = get_output_data(*args, **kwargs) +# cache_context = get_current_cache_context() +# if cache_context is not None: +# cache_context.clear_buffers() +# set_current_cache_context(None) +# return out + +# new_get_output_data._patched = True +# execution.get_output_data = new_get_output_data + + +@torch.compiler.disable() +def are_two_tensors_similar(t1, t2, *, threshold): + if t1.shape != t2.shape: + return False + mean_diff = (t1 - t2).abs().mean() + mean_t1 = t1.abs().mean() + diff = mean_diff / mean_t1 + return diff.item() < threshold + + +@torch.compiler.disable() +def apply_prev_hidden_states_residual(hidden_states, + encoder_hidden_states=None): + hidden_states_residual = get_buffer("hidden_states_residual") + assert hidden_states_residual is not None, "hidden_states_residual must be set before" + hidden_states = hidden_states_residual + hidden_states + hidden_states = hidden_states.contiguous() + + if encoder_hidden_states is None: + return hidden_states + + encoder_hidden_states_residual = get_buffer( + "encoder_hidden_states_residual") + if encoder_hidden_states_residual is None: + encoder_hidden_states = None + else: + encoder_hidden_states = encoder_hidden_states_residual + encoder_hidden_states + encoder_hidden_states = encoder_hidden_states.contiguous() + + return hidden_states, encoder_hidden_states + + +@torch.compiler.disable() +def get_can_use_cache(first_hidden_states_residual, + threshold, + parallelized=False): + prev_first_hidden_states_residual = get_buffer( + "first_hidden_states_residual") + can_use_cache = prev_first_hidden_states_residual is not None and are_two_tensors_similar( + prev_first_hidden_states_residual, + first_hidden_states_residual, + threshold=threshold, + ) + return can_use_cache + + +class CachedTransformerBlocks(torch.nn.Module): + + def __init__( + self, + transformer_blocks, + single_transformer_blocks=None, + *, + residual_diff_threshold, + validate_can_use_cache_function=None, + return_hidden_states_first=True, + accept_hidden_states_first=True, + cat_hidden_states_first=False, + return_hidden_states_only=False, + clone_original_hidden_states=False, + ): + super().__init__() + self.transformer_blocks = transformer_blocks + self.single_transformer_blocks = single_transformer_blocks + self.residual_diff_threshold = residual_diff_threshold + self.validate_can_use_cache_function = validate_can_use_cache_function + self.return_hidden_states_first = return_hidden_states_first + self.accept_hidden_states_first = accept_hidden_states_first + self.cat_hidden_states_first = cat_hidden_states_first + self.return_hidden_states_only = return_hidden_states_only + self.clone_original_hidden_states = clone_original_hidden_states + + def forward(self, *args, **kwargs): + img_arg_name = None + if "img" in kwargs: + img_arg_name = "img" + elif "hidden_states" in kwargs: + img_arg_name = "hidden_states" + txt_arg_name = None + if "txt" in kwargs: + txt_arg_name = "txt" + elif "context" in kwargs: + txt_arg_name = "context" + elif "encoder_hidden_states" in kwargs: + txt_arg_name = "encoder_hidden_states" + if self.accept_hidden_states_first: + if args: + img = args[0] + args = args[1:] + else: + img = kwargs.pop(img_arg_name) + if args: + txt = args[0] + args = args[1:] + else: + txt = kwargs.pop(txt_arg_name) + else: + if args: + txt = args[0] + args = args[1:] + else: + txt = kwargs.pop(txt_arg_name) + if args: + img = args[0] + args = args[1:] + else: + img = kwargs.pop(img_arg_name) + hidden_states = img + encoder_hidden_states = txt + if self.residual_diff_threshold <= 0.0: + for block in self.transformer_blocks: + if txt_arg_name == "encoder_hidden_states": + hidden_states = block( + hidden_states, + *args, + encoder_hidden_states=encoder_hidden_states, + **kwargs) + else: + if self.accept_hidden_states_first: + hidden_states = block(hidden_states, + encoder_hidden_states, *args, + **kwargs) + else: + hidden_states = block(encoder_hidden_states, + hidden_states, *args, **kwargs) + if not self.return_hidden_states_only: + hidden_states, encoder_hidden_states = hidden_states + if not self.return_hidden_states_first: + hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states + if self.single_transformer_blocks is not None: + hidden_states = torch.cat( + [hidden_states, encoder_hidden_states] + if self.cat_hidden_states_first else + [encoder_hidden_states, hidden_states], + dim=1) + for block in self.single_transformer_blocks: + hidden_states = block(hidden_states, *args, **kwargs) + hidden_states = hidden_states[:, + encoder_hidden_states.shape[1]:] + if self.return_hidden_states_only: + return hidden_states + else: + return ((hidden_states, encoder_hidden_states) + if self.return_hidden_states_first else + (encoder_hidden_states, hidden_states)) + + original_hidden_states = hidden_states + if self.clone_original_hidden_states: + original_hidden_states = original_hidden_states.clone() + first_transformer_block = self.transformer_blocks[0] + if txt_arg_name == "encoder_hidden_states": + hidden_states = first_transformer_block( + hidden_states, + *args, + encoder_hidden_states=encoder_hidden_states, + **kwargs) + else: + if self.accept_hidden_states_first: + hidden_states = first_transformer_block( + hidden_states, encoder_hidden_states, *args, **kwargs) + else: + hidden_states = first_transformer_block( + encoder_hidden_states, hidden_states, *args, **kwargs) + if not self.return_hidden_states_only: + hidden_states, encoder_hidden_states = hidden_states + if not self.return_hidden_states_first: + hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states + first_hidden_states_residual = hidden_states - original_hidden_states + del original_hidden_states + + can_use_cache = get_can_use_cache( + first_hidden_states_residual, + threshold=self.residual_diff_threshold, + ) + if self.validate_can_use_cache_function is not None: + can_use_cache = self.validate_can_use_cache_function(can_use_cache) + + torch._dynamo.graph_break() + if can_use_cache: + del first_hidden_states_residual + hidden_states, encoder_hidden_states = apply_prev_hidden_states_residual( + hidden_states, encoder_hidden_states) + else: + set_buffer("first_hidden_states_residual", + first_hidden_states_residual) + del first_hidden_states_residual + ( + hidden_states, + encoder_hidden_states, + hidden_states_residual, + encoder_hidden_states_residual, + ) = self.call_remaining_transformer_blocks( + hidden_states, + encoder_hidden_states, + *args, + txt_arg_name=txt_arg_name, + **kwargs) + set_buffer("hidden_states_residual", hidden_states_residual) + if encoder_hidden_states_residual is not None: + set_buffer("encoder_hidden_states_residual", + encoder_hidden_states_residual) + torch._dynamo.graph_break() + + if self.return_hidden_states_only: + return hidden_states + else: + return ((hidden_states, encoder_hidden_states) + if self.return_hidden_states_first else + (encoder_hidden_states, hidden_states)) + + def call_remaining_transformer_blocks(self, + hidden_states, + encoder_hidden_states, + *args, + txt_arg_name=None, + **kwargs): + original_hidden_states = hidden_states + original_encoder_hidden_states = encoder_hidden_states + if self.clone_original_hidden_states: + original_hidden_states = original_hidden_states.clone() + original_encoder_hidden_states = original_encoder_hidden_states.clone( + ) + for block in self.transformer_blocks[1:]: + if txt_arg_name == "encoder_hidden_states": + hidden_states = block( + hidden_states, + *args, + encoder_hidden_states=encoder_hidden_states, + **kwargs) + else: + if self.accept_hidden_states_first: + hidden_states = block(hidden_states, encoder_hidden_states, + *args, **kwargs) + else: + hidden_states = block(encoder_hidden_states, hidden_states, + *args, **kwargs) + if not self.return_hidden_states_only: + hidden_states, encoder_hidden_states = hidden_states + if not self.return_hidden_states_first: + hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states + if self.single_transformer_blocks is not None: + hidden_states = torch.cat([hidden_states, encoder_hidden_states] + if self.cat_hidden_states_first else + [encoder_hidden_states, hidden_states], + dim=1) + for block in self.single_transformer_blocks: + hidden_states = block(hidden_states, *args, **kwargs) + if self.cat_hidden_states_first: + hidden_states, encoder_hidden_states = hidden_states.split( + [ + hidden_states.shape[1] - + encoder_hidden_states.shape[1], + encoder_hidden_states.shape[1] + ], + dim=1) + else: + encoder_hidden_states, hidden_states = hidden_states.split( + [ + encoder_hidden_states.shape[1], + hidden_states.shape[1] - encoder_hidden_states.shape[1] + ], + dim=1) + + hidden_states_shape = hidden_states.shape + hidden_states = hidden_states.flatten().contiguous().reshape( + hidden_states_shape) + + if encoder_hidden_states is not None: + encoder_hidden_states_shape = encoder_hidden_states.shape + encoder_hidden_states = encoder_hidden_states.flatten().contiguous( + ).reshape(encoder_hidden_states_shape) + + hidden_states_residual = hidden_states - original_hidden_states + if encoder_hidden_states is None: + encoder_hidden_states_residual = None + else: + encoder_hidden_states_residual = encoder_hidden_states - original_encoder_hidden_states + return hidden_states, encoder_hidden_states, hidden_states_residual, encoder_hidden_states_residual + + +# Based on 90f349f93df3083a507854d7fc7c3e1bb9014e24 +def create_patch_unet_model__forward(model, + *, + residual_diff_threshold, + validate_can_use_cache_function=None): + + def call_remaining_blocks(self, transformer_options, control, + transformer_patches, hs, h, *args, **kwargs): + original_hidden_states = h + + for id, module in enumerate(self.input_blocks): + if id < 2: + continue + transformer_options["block"] = ("input", id) + h = forward_timestep_embed1(module, h, *args, **kwargs) + h = apply_control1(h, control, 'input') + if "input_block_patch" in transformer_patches: + patch = transformer_patches["input_block_patch"] + for p in patch: + h = p(h, transformer_options) + + hs.append(h) + if "input_block_patch_after_skip" in transformer_patches: + patch = transformer_patches["input_block_patch_after_skip"] + for p in patch: + h = p(h, transformer_options) + + transformer_options["block"] = ("middle", 0) + if self.middle_block is not None: + h = forward_timestep_embed1(self.middle_block, h, *args, **kwargs) + h = apply_control1(h, control, 'middle') + + for id, module in enumerate(self.output_blocks): + transformer_options["block"] = ("output", id) + hsp = hs.pop() + hsp = apply_control1(hsp, control, 'output') + + if "output_block_patch" in transformer_patches: + patch = transformer_patches["output_block_patch"] + for p in patch: + h, hsp = p(h, hsp, transformer_options) + + h = torch.cat([h, hsp], dim=1) + del hsp + if len(hs) > 0: + output_shape = hs[-1].shape + else: + output_shape = None + h = forward_timestep_embed1(module, h, *args, output_shape, + **kwargs) + hidden_states_residual = h - original_hidden_states + return h, hidden_states_residual + + def unet_model__forward(self, + x, + timesteps=None, + context=None, + y=None, + control=None, + transformer_options={}, + **kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + transformer_options["original_shape"] = list(x.shape) + transformer_options["transformer_index"] = 0 + transformer_patches = transformer_options.get("patches", {}) + + num_video_frames = kwargs.get("num_video_frames", + self.default_num_video_frames) + image_only_indicator = kwargs.get("image_only_indicator", None) + time_context = kwargs.get("time_context", None) + + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, + self.model_channels, + repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + + if "emb_patch" in transformer_patches: + patch = transformer_patches["emb_patch"] + for p in patch: + emb = p(emb, self.model_channels, transformer_options) + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + can_use_cache = False + + h = x + for id, module in enumerate(self.input_blocks): + if id >= 2: + break + transformer_options["block"] = ("input", id) + if id == 1: + original_h = h + h = forward_timestep_embed1( + module, + h, + emb, + context, + transformer_options, + time_context=time_context, + num_video_frames=num_video_frames, + image_only_indicator=image_only_indicator) + h = apply_control1(h, control, 'input') + if "input_block_patch" in transformer_patches: + patch = transformer_patches["input_block_patch"] + for p in patch: + h = p(h, transformer_options) + + hs.append(h) + if "input_block_patch_after_skip" in transformer_patches: + patch = transformer_patches["input_block_patch_after_skip"] + for p in patch: + h = p(h, transformer_options) + + if id == 1: + first_hidden_states_residual = h - original_h + can_use_cache = get_can_use_cache( + first_hidden_states_residual, + threshold=residual_diff_threshold, + ) + if validate_can_use_cache_function is not None: + can_use_cache = validate_can_use_cache_function( + can_use_cache) + if not can_use_cache: + set_buffer("first_hidden_states_residual", + first_hidden_states_residual) + del first_hidden_states_residual + + torch._dynamo.graph_break() + if can_use_cache: + h = apply_prev_hidden_states_residual(h) + else: + h, hidden_states_residual = call_remaining_blocks( + self, + transformer_options, + control, + transformer_patches, + hs, + h, + emb, + context, + transformer_options, + time_context=time_context, + num_video_frames=num_video_frames, + image_only_indicator=image_only_indicator) + set_buffer("hidden_states_residual", hidden_states_residual) + torch._dynamo.graph_break() + + h = h.type(x.dtype) + + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) + + new__forward = unet_model__forward.__get__(model) + + @contextlib.contextmanager + def patch__forward(): + with unittest.mock.patch.object(model, "_forward", new__forward): + yield + + return patch__forward + + +# Based on 90f349f93df3083a507854d7fc7c3e1bb9014e24 +def create_patch_flux_forward_orig(model, + *, + residual_diff_threshold, + validate_can_use_cache_function=None): + from torch import Tensor + + def call_remaining_blocks(self, blocks_replace, control, img, txt, vec, pe, + attn_mask, ca_idx, timesteps, transformer_options): + original_hidden_states = img + + extra_block_forward_kwargs = {} + if attn_mask is not None: + extra_block_forward_kwargs["attn_mask"] = attn_mask + + for i, block in enumerate(self.double_blocks): + if i < 1: + continue + if ("double_block", i) in blocks_replace: + + def block_wrap(args): + out = {} + out["img"], out["txt"] = block( + img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + **extra_block_forward_kwargs) + return out + + out = blocks_replace[("double_block", + i)]({ + "img": img, + "txt": txt, + "vec": vec, + "pe": pe, + **extra_block_forward_kwargs + }, { + "original_block": block_wrap, + "transformer_options": transformer_options + }) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=vec, + pe=pe, + **extra_block_forward_kwargs) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + # PuLID attention + if getattr(self, "pulid_data", {}): + if i % self.pulid_double_interval == 0: + # Will calculate influence of all pulid nodes at once + for _, node_data in self.pulid_data.items(): + if torch.any((node_data['sigma_start'] >= timesteps) + & (timesteps >= node_data['sigma_end'])): + img = img + node_data['weight'] * self.pulid_ca[ + ca_idx](node_data['embedding'], img) + ca_idx += 1 + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + if ("single_block", i) in blocks_replace: + + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + **extra_block_forward_kwargs) + return out + + out = blocks_replace[("single_block", + i)]({ + "img": img, + "vec": vec, + "pe": pe, + **extra_block_forward_kwargs + }, { + "original_block": block_wrap, + "transformer_options": transformer_options + }) + img = out["img"] + else: + img = block(img, vec=vec, pe=pe, **extra_block_forward_kwargs) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1]:, ...] += add + + # PuLID attention + if getattr(self, "pulid_data", {}): + real_img, txt = img[:, txt.shape[1]:, + ...], img[:, :txt.shape[1], ...] + if i % self.pulid_single_interval == 0: + # Will calculate influence of all nodes at once + for _, node_data in self.pulid_data.items(): + if torch.any((node_data['sigma_start'] >= timesteps) + & (timesteps >= node_data['sigma_end'])): + real_img = real_img + node_data[ + 'weight'] * self.pulid_ca[ca_idx]( + node_data['embedding'], real_img) + ca_idx += 1 + img = torch.cat((txt, real_img), 1) + + img = img[:, txt.shape[1]:, ...] + + img = img.contiguous() + hidden_states_residual = img - original_hidden_states + return img, hidden_states_residual + + def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + control=None, + transformer_options={}, + attn_mask: Tensor = None, + ) -> Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + if img.ndim != 3 or txt.ndim != 3: + raise ValueError( + "Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError( + "Didn't get guidance strength for guidance distilled model." + ) + vec = vec + self.guidance_in( + timestep_embedding(guidance, 256).to(img.dtype)) + + vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + ca_idx = 0 + extra_block_forward_kwargs = {} + if attn_mask is not None: + extra_block_forward_kwargs["attn_mask"] = attn_mask + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if i >= 1: + break + if ("double_block", i) in blocks_replace: + + def block_wrap(args): + out = {} + out["img"], out["txt"] = block( + img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + **extra_block_forward_kwargs) + return out + + out = blocks_replace[("double_block", + i)]({ + "img": img, + "txt": txt, + "vec": vec, + "pe": pe, + **extra_block_forward_kwargs + }, { + "original_block": block_wrap, + "transformer_options": transformer_options + }) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=vec, + pe=pe, + **extra_block_forward_kwargs) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + # PuLID attention + if getattr(self, "pulid_data", {}): + if i % self.pulid_double_interval == 0: + # Will calculate influence of all pulid nodes at once + for _, node_data in self.pulid_data.items(): + if torch.any((node_data['sigma_start'] >= timesteps) + & (timesteps >= node_data['sigma_end'])): + img = img + node_data['weight'] * self.pulid_ca[ + ca_idx](node_data['embedding'], img) + ca_idx += 1 + + if i == 0: + first_hidden_states_residual = img + can_use_cache = get_can_use_cache( + first_hidden_states_residual, + threshold=residual_diff_threshold, + ) + if validate_can_use_cache_function is not None: + can_use_cache = validate_can_use_cache_function( + can_use_cache) + if not can_use_cache: + set_buffer("first_hidden_states_residual", + first_hidden_states_residual) + del first_hidden_states_residual + + torch._dynamo.graph_break() + if can_use_cache: + img = apply_prev_hidden_states_residual(img) + else: + img, hidden_states_residual = call_remaining_blocks( + self, + blocks_replace, + control, + img, + txt, + vec, + pe, + attn_mask, + ca_idx, + timesteps, + transformer_options, + ) + set_buffer("hidden_states_residual", hidden_states_residual) + torch._dynamo.graph_break() + + img = self.final_layer(img, + vec) # (N, T, patch_size ** 2 * out_channels) + return img + + new_forward_orig = forward_orig.__get__(model) + + @contextlib.contextmanager + def patch_forward_orig(): + with unittest.mock.patch.object(model, "forward_orig", + new_forward_orig): + yield + + return patch_forward_orig diff --git a/modules/WaveSpeed/misc_nodes.py b/modules/WaveSpeed/misc_nodes.py index 22cfe1e098683b18366be039277949b7e0d24e9e..76f5dc8458e2196ced3235e52dbded5481a19cfb 100644 --- a/modules/WaveSpeed/misc_nodes.py +++ b/modules/WaveSpeed/misc_nodes.py @@ -1,62 +1,62 @@ -import importlib -import json - -from . import utils - - -class EnhancedCompileModel: - - def patch( - self, - model, - is_patcher, - object_to_patch, - compiler, - fullgraph, - dynamic, - mode, - options, - disable, - backend, - ): - utils.patch_optimized_module() - utils.patch_same_meta() - - import_path, function_name = compiler.rsplit(".", 1) - module = importlib.import_module(import_path) - compile_function = getattr(module, function_name) - - mode = mode if mode else None - options = json.loads(options) if options else None - - if compiler == "torch.compile" and backend == "inductor" and dynamic: - # TODO: Fix this - # File "pytorch/torch/_inductor/fx_passes/post_grad.py", line 643, in same_meta - # and statically_known_true(sym_eq(val1.size(), val2.size())) - # AttributeError: 'SymInt' object has no attribute 'size' - pass - - if is_patcher: - patcher = model[0].clone() - else: - patcher = model.patcher - patcher = patcher.clone() - - patcher.add_object_patch( - object_to_patch, - compile_function( - patcher.get_model_object(object_to_patch), - fullgraph=fullgraph, - dynamic=dynamic, - mode=mode, - options=options, - disable=disable, - backend=backend, - ), - ) - - if is_patcher: - return (patcher,) - else: - model.patcher = patcher - return (model,) +import importlib +import json + +from . import utils + + +class EnhancedCompileModel: + + def patch( + self, + model, + is_patcher, + object_to_patch, + compiler, + fullgraph, + dynamic, + mode, + options, + disable, + backend, + ): + utils.patch_optimized_module() + utils.patch_same_meta() + + import_path, function_name = compiler.rsplit(".", 1) + module = importlib.import_module(import_path) + compile_function = getattr(module, function_name) + + mode = mode if mode else None + options = json.loads(options) if options else None + + if compiler == "torch.compile" and backend == "inductor" and dynamic: + # TODO: Fix this + # File "pytorch/torch/_inductor/fx_passes/post_grad.py", line 643, in same_meta + # and statically_known_true(sym_eq(val1.size(), val2.size())) + # AttributeError: 'SymInt' object has no attribute 'size' + pass + + if is_patcher: + patcher = model[0].clone() + else: + patcher = model.patcher + patcher = patcher.clone() + + patcher.add_object_patch( + object_to_patch, + compile_function( + patcher.get_model_object(object_to_patch), + fullgraph=fullgraph, + dynamic=dynamic, + mode=mode, + options=options, + disable=disable, + backend=backend, + ), + ) + + if is_patcher: + return (patcher,) + else: + model.patcher = patcher + return (model,) diff --git a/modules/WaveSpeed/utils.py b/modules/WaveSpeed/utils.py index 79d63bc816d2ec88cb0b0566748b11325d2668ca..26e3b4321b2e174e95e1ab7ea526daf2ae5832df 100644 --- a/modules/WaveSpeed/utils.py +++ b/modules/WaveSpeed/utils.py @@ -1,127 +1,127 @@ -import contextlib -import unittest - -import torch - - - - -# wildcard trick is taken from pythongossss's -class AnyType(str): - - def __ne__(self, __value: object) -> bool: - return False - - -any_typ = AnyType("*") - - -def get_weight_dtype_inputs(): - return { - "weight_dtype": ( - [ - "default", - "float32", - "float64", - "bfloat16", - "float16", - "fp8_e4m3fn", - "fp8_e4m3fn_fast", - "fp8_e5m2", - ], - ), - } - - -def parse_weight_dtype(model_options, weight_dtype): - dtype = { - "float32": torch.float32, - "float64": torch.float64, - "bfloat16": torch.bfloat16, - "float16": torch.float16, - "fp8_e4m3fn": torch.float8_e4m3fn, - "fp8_e4m3fn_fast": torch.float8_e4m3fn, - "fp8_e5m2": torch.float8_e5m2, - }.get(weight_dtype, None) - if dtype is not None: - model_options["dtype"] = dtype - if weight_dtype == "fp8_e4m3fn_fast": - model_options["fp8_optimizations"] = True - return model_options - - -@contextlib.contextmanager -def disable_load_models_gpu(): - def foo(*args, **kwargs): - pass - from modules.Device import Device - with unittest.mock.patch.object(Device, "load_models_gpu", foo): - yield - - -def patch_optimized_module(): - try: - from torch._dynamo.eval_frame import OptimizedModule - except ImportError: - return - - if getattr(OptimizedModule, "_patched", False): - return - - def __getattribute__(self, name): - if name == "_orig_mod": - return object.__getattribute__(self, "_modules")[name] - if name in ( - "__class__", - "_modules", - "state_dict", - "load_state_dict", - "parameters", - "named_parameters", - "buffers", - "named_buffers", - "children", - "named_children", - "modules", - "named_modules", - ): - return getattr(object.__getattribute__(self, "_orig_mod"), name) - return object.__getattribute__(self, name) - - def __delattr__(self, name): - # unload_lora_weights() wants to del peft_config - return delattr(self._orig_mod, name) - - @classmethod - def __instancecheck__(cls, instance): - return isinstance(instance, OptimizedModule) or issubclass( - object.__getattribute__(instance, "__class__"), cls - ) - - OptimizedModule.__getattribute__ = __getattribute__ - OptimizedModule.__delattr__ = __delattr__ - OptimizedModule.__instancecheck__ = __instancecheck__ - OptimizedModule._patched = True - - -def patch_same_meta(): - try: - from torch._inductor.fx_passes import post_grad - except ImportError: - return - - same_meta = getattr(post_grad, "same_meta", None) - if same_meta is None: - return - - if getattr(same_meta, "_patched", False): - return - - def new_same_meta(a, b): - try: - return same_meta(a, b) - except Exception: - return False - - post_grad.same_meta = new_same_meta - new_same_meta._patched = True +import contextlib +import unittest + +import torch + + + + +# wildcard trick is taken from pythongossss's +class AnyType(str): + + def __ne__(self, __value: object) -> bool: + return False + + +any_typ = AnyType("*") + + +def get_weight_dtype_inputs(): + return { + "weight_dtype": ( + [ + "default", + "float32", + "float64", + "bfloat16", + "float16", + "fp8_e4m3fn", + "fp8_e4m3fn_fast", + "fp8_e5m2", + ], + ), + } + + +def parse_weight_dtype(model_options, weight_dtype): + dtype = { + "float32": torch.float32, + "float64": torch.float64, + "bfloat16": torch.bfloat16, + "float16": torch.float16, + "fp8_e4m3fn": torch.float8_e4m3fn, + "fp8_e4m3fn_fast": torch.float8_e4m3fn, + "fp8_e5m2": torch.float8_e5m2, + }.get(weight_dtype, None) + if dtype is not None: + model_options["dtype"] = dtype + if weight_dtype == "fp8_e4m3fn_fast": + model_options["fp8_optimizations"] = True + return model_options + + +@contextlib.contextmanager +def disable_load_models_gpu(): + def foo(*args, **kwargs): + pass + from modules.Device import Device + with unittest.mock.patch.object(Device, "load_models_gpu", foo): + yield + + +def patch_optimized_module(): + try: + from torch._dynamo.eval_frame import OptimizedModule + except ImportError: + return + + if getattr(OptimizedModule, "_patched", False): + return + + def __getattribute__(self, name): + if name == "_orig_mod": + return object.__getattribute__(self, "_modules")[name] + if name in ( + "__class__", + "_modules", + "state_dict", + "load_state_dict", + "parameters", + "named_parameters", + "buffers", + "named_buffers", + "children", + "named_children", + "modules", + "named_modules", + ): + return getattr(object.__getattribute__(self, "_orig_mod"), name) + return object.__getattribute__(self, name) + + def __delattr__(self, name): + # unload_lora_weights() wants to del peft_config + return delattr(self._orig_mod, name) + + @classmethod + def __instancecheck__(cls, instance): + return isinstance(instance, OptimizedModule) or issubclass( + object.__getattribute__(instance, "__class__"), cls + ) + + OptimizedModule.__getattribute__ = __getattribute__ + OptimizedModule.__delattr__ = __delattr__ + OptimizedModule.__instancecheck__ = __instancecheck__ + OptimizedModule._patched = True + + +def patch_same_meta(): + try: + from torch._inductor.fx_passes import post_grad + except ImportError: + return + + same_meta = getattr(post_grad, "same_meta", None) + if same_meta is None: + return + + if getattr(same_meta, "_patched", False): + return + + def new_same_meta(a, b): + try: + return same_meta(a, b) + except Exception: + return False + + post_grad.same_meta = new_same_meta + new_same_meta._patched = True diff --git a/modules/clip/CLIPTextModel.py b/modules/clip/CLIPTextModel.py index c049a581386b44a85214c4d541378858754a849b..1e4fea8f099235f14c87d1f1833667d1ef77bde9 100644 --- a/modules/clip/CLIPTextModel.py +++ b/modules/clip/CLIPTextModel.py @@ -1,163 +1,163 @@ -import torch - -class CLIPTextModel_(torch.nn.Module): - """#### The CLIPTextModel_ module.""" - def __init__( - self, - config_dict: dict, - dtype: torch.dtype, - device: torch.device, - operations: object, - ): - """#### Initialize the CLIPTextModel_ module. - - #### Args: - - `config_dict` (dict): The configuration dictionary. - - `dtype` (torch.dtype): The data type. - - `device` (torch.device): The device to use. - - `operations` (object): The operations object. - """ - num_layers = config_dict["num_hidden_layers"] - embed_dim = config_dict["hidden_size"] - heads = config_dict["num_attention_heads"] - intermediate_size = config_dict["intermediate_size"] - intermediate_activation = config_dict["hidden_act"] - num_positions = config_dict["max_position_embeddings"] - self.eos_token_id = config_dict["eos_token_id"] - - super().__init__() - from modules.clip.Clip import CLIPEmbeddings, CLIPEncoder - self.embeddings = CLIPEmbeddings( - embed_dim, - num_positions=num_positions, - dtype=dtype, - device=device, - operations=operations, - ) - self.encoder = CLIPEncoder( - num_layers, - embed_dim, - heads, - intermediate_size, - intermediate_activation, - dtype, - device, - operations, - ) - self.final_layer_norm = operations.LayerNorm( - embed_dim, dtype=dtype, device=device - ) - - def forward( - self, - input_tokens: torch.Tensor, - attention_mask: torch.Tensor = None, - intermediate_output: int = None, - final_layer_norm_intermediate: bool = True, - dtype: torch.dtype = torch.float32, - ) -> tuple: - """#### Forward pass for the CLIPTextModel_ module. - - #### Args: - - `input_tokens` (torch.Tensor): The input tokens. - - `attention_mask` (torch.Tensor, optional): The attention mask. Defaults to None. - - `intermediate_output` (int, optional): The intermediate output layer. Defaults to None. - - `final_layer_norm_intermediate` (bool, optional): Whether to apply final layer normalization to the intermediate output. Defaults to True. - - #### Returns: - - `tuple`: The output tensor, the intermediate output tensor, and the pooled output tensor. - """ - x = self.embeddings(input_tokens, dtype=dtype) - mask = None - if attention_mask is not None: - mask = 1.0 - attention_mask.to(x.dtype).reshape( - (attention_mask.shape[0], 1, -1, attention_mask.shape[-1]) - ).expand( - attention_mask.shape[0], - 1, - attention_mask.shape[-1], - attention_mask.shape[-1], - ) - mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) - - causal_mask = ( - torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device) - .fill_(float("-inf")) - .triu_(1) - ) - if mask is not None: - mask += causal_mask - else: - mask = causal_mask - - x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) - x = self.final_layer_norm(x) - if i is not None and final_layer_norm_intermediate: - i = self.final_layer_norm(i) - - pooled_output = x[ - torch.arange(x.shape[0], device=x.device), - ( - torch.round(input_tokens).to(dtype=torch.int, device=x.device) - == self.eos_token_id - ) - .int() - .argmax(dim=-1), - ] - return x, i, pooled_output - -class CLIPTextModel(torch.nn.Module): - """#### The CLIPTextModel module.""" - def __init__( - self, - config_dict: dict, - dtype: torch.dtype, - device: torch.device, - operations: object, - ): - """#### Initialize the CLIPTextModel module. - - #### Args: - - `config_dict` (dict): The configuration dictionary. - - `dtype` (torch.dtype): The data type. - - `device` (torch.device): The device to use. - - `operations` (object): The operations object. - """ - super().__init__() - self.num_layers = config_dict["num_hidden_layers"] - self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) - embed_dim = config_dict["hidden_size"] - self.text_projection = operations.Linear( - embed_dim, embed_dim, bias=False, dtype=dtype, device=device - ) - self.dtype = dtype - - def get_input_embeddings(self) -> torch.nn.Embedding: - """#### Get the input embeddings. - - #### Returns: - - `torch.nn.Embedding`: The input embeddings. - """ - return self.text_model.embeddings.token_embedding - - def set_input_embeddings(self, embeddings: torch.nn.Embedding) -> None: - """#### Set the input embeddings. - - #### Args: - - `embeddings` (torch.nn.Embedding): The input embeddings. - """ - self.text_model.embeddings.token_embedding = embeddings - - def forward(self, *args, **kwargs) -> tuple: - """#### Forward pass for the CLIPTextModel module. - - #### Args: - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `tuple`: The output tensors. - """ - x = self.text_model(*args, **kwargs) - out = self.text_projection(x[2]) +import torch + +class CLIPTextModel_(torch.nn.Module): + """#### The CLIPTextModel_ module.""" + def __init__( + self, + config_dict: dict, + dtype: torch.dtype, + device: torch.device, + operations: object, + ): + """#### Initialize the CLIPTextModel_ module. + + #### Args: + - `config_dict` (dict): The configuration dictionary. + - `dtype` (torch.dtype): The data type. + - `device` (torch.device): The device to use. + - `operations` (object): The operations object. + """ + num_layers = config_dict["num_hidden_layers"] + embed_dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + intermediate_size = config_dict["intermediate_size"] + intermediate_activation = config_dict["hidden_act"] + num_positions = config_dict["max_position_embeddings"] + self.eos_token_id = config_dict["eos_token_id"] + + super().__init__() + from modules.clip.Clip import CLIPEmbeddings, CLIPEncoder + self.embeddings = CLIPEmbeddings( + embed_dim, + num_positions=num_positions, + dtype=dtype, + device=device, + operations=operations, + ) + self.encoder = CLIPEncoder( + num_layers, + embed_dim, + heads, + intermediate_size, + intermediate_activation, + dtype, + device, + operations, + ) + self.final_layer_norm = operations.LayerNorm( + embed_dim, dtype=dtype, device=device + ) + + def forward( + self, + input_tokens: torch.Tensor, + attention_mask: torch.Tensor = None, + intermediate_output: int = None, + final_layer_norm_intermediate: bool = True, + dtype: torch.dtype = torch.float32, + ) -> tuple: + """#### Forward pass for the CLIPTextModel_ module. + + #### Args: + - `input_tokens` (torch.Tensor): The input tokens. + - `attention_mask` (torch.Tensor, optional): The attention mask. Defaults to None. + - `intermediate_output` (int, optional): The intermediate output layer. Defaults to None. + - `final_layer_norm_intermediate` (bool, optional): Whether to apply final layer normalization to the intermediate output. Defaults to True. + + #### Returns: + - `tuple`: The output tensor, the intermediate output tensor, and the pooled output tensor. + """ + x = self.embeddings(input_tokens, dtype=dtype) + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape( + (attention_mask.shape[0], 1, -1, attention_mask.shape[-1]) + ).expand( + attention_mask.shape[0], + 1, + attention_mask.shape[-1], + attention_mask.shape[-1], + ) + mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + + causal_mask = ( + torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device) + .fill_(float("-inf")) + .triu_(1) + ) + if mask is not None: + mask += causal_mask + else: + mask = causal_mask + + x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) + x = self.final_layer_norm(x) + if i is not None and final_layer_norm_intermediate: + i = self.final_layer_norm(i) + + pooled_output = x[ + torch.arange(x.shape[0], device=x.device), + ( + torch.round(input_tokens).to(dtype=torch.int, device=x.device) + == self.eos_token_id + ) + .int() + .argmax(dim=-1), + ] + return x, i, pooled_output + +class CLIPTextModel(torch.nn.Module): + """#### The CLIPTextModel module.""" + def __init__( + self, + config_dict: dict, + dtype: torch.dtype, + device: torch.device, + operations: object, + ): + """#### Initialize the CLIPTextModel module. + + #### Args: + - `config_dict` (dict): The configuration dictionary. + - `dtype` (torch.dtype): The data type. + - `device` (torch.device): The device to use. + - `operations` (object): The operations object. + """ + super().__init__() + self.num_layers = config_dict["num_hidden_layers"] + self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) + embed_dim = config_dict["hidden_size"] + self.text_projection = operations.Linear( + embed_dim, embed_dim, bias=False, dtype=dtype, device=device + ) + self.dtype = dtype + + def get_input_embeddings(self) -> torch.nn.Embedding: + """#### Get the input embeddings. + + #### Returns: + - `torch.nn.Embedding`: The input embeddings. + """ + return self.text_model.embeddings.token_embedding + + def set_input_embeddings(self, embeddings: torch.nn.Embedding) -> None: + """#### Set the input embeddings. + + #### Args: + - `embeddings` (torch.nn.Embedding): The input embeddings. + """ + self.text_model.embeddings.token_embedding = embeddings + + def forward(self, *args, **kwargs) -> tuple: + """#### Forward pass for the CLIPTextModel module. + + #### Args: + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `tuple`: The output tensors. + """ + x = self.text_model(*args, **kwargs) + out = self.text_projection(x[2]) return (x[0], x[1], out, x[2]) \ No newline at end of file diff --git a/modules/clip/Clip.py b/modules/clip/Clip.py index aaa9fd752de7914157ee09c99a8bd734e3afe47d..eb490d87a7a7c85a719e480134b6c0edd7d10610 100644 --- a/modules/clip/Clip.py +++ b/modules/clip/Clip.py @@ -1,623 +1,623 @@ -from enum import Enum -import logging -import torch - -from modules.Model import ModelPatcher -from modules.Attention import Attention -from modules.Device import Device -from modules.SD15 import SDToken -from modules.Utilities import util -from modules.clip import FluxClip -from modules.cond import cast - - -class CLIPAttention(torch.nn.Module): - """#### The CLIPAttention module.""" - def __init__( - self, - embed_dim: int, - heads: int, - dtype: torch.dtype, - device: torch.device, - operations: object, - ): - """#### Initialize the CLIPAttention module. - - #### Args: - - `embed_dim` (int): The embedding dimension. - - `heads` (int): The number of attention heads. - - `dtype` (torch.dtype): The data type. - - `device` (torch.device): The device to use. - - `operations` (object): The operations object. - """ - super().__init__() - - self.heads = heads - self.q_proj = operations.Linear( - embed_dim, embed_dim, bias=True, dtype=dtype, device=device - ) - self.k_proj = operations.Linear( - embed_dim, embed_dim, bias=True, dtype=dtype, device=device - ) - self.v_proj = operations.Linear( - embed_dim, embed_dim, bias=True, dtype=dtype, device=device - ) - - self.out_proj = operations.Linear( - embed_dim, embed_dim, bias=True, dtype=dtype, device=device - ) - - def forward( - self, - x: torch.Tensor, - mask: torch.Tensor = None, - optimized_attention: callable = None, - ) -> torch.Tensor: - """#### Forward pass for the CLIPAttention module. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `mask` (torch.Tensor, optional): The attention mask. Defaults to None. - - `optimized_attention` (callable, optional): The optimized attention function. Defaults to None. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - q = self.q_proj(x) - k = self.k_proj(x) - v = self.v_proj(x) - - out = optimized_attention(q, k, v, self.heads, mask) - return self.out_proj(out) - - -ACTIVATIONS = { - "quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), - "gelu": torch.nn.functional.gelu, -} - - -class CLIPMLP(torch.nn.Module): - """#### The CLIPMLP module. - (MLP stands for Multi-Layer Perceptron.)""" - def __init__( - self, - embed_dim: int, - intermediate_size: int, - activation: str, - dtype: torch.dtype, - device: torch.device, - operations: object, - ): - """#### Initialize the CLIPMLP module. - - #### Args: - - `embed_dim` (int): The embedding dimension. - - `intermediate_size` (int): The intermediate size. - - `activation` (str): The activation function. - - `dtype` (torch.dtype): The data type. - - `device` (torch.device): The device to use. - - `operations` (object): The operations object. - """ - super().__init__() - self.fc1 = operations.Linear( - embed_dim, intermediate_size, bias=True, dtype=dtype, device=device - ) - self.activation = ACTIVATIONS[activation] - self.fc2 = operations.Linear( - intermediate_size, embed_dim, bias=True, dtype=dtype, device=device - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the CLIPMLP module. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - x = self.fc1(x) - x = self.activation(x) - x = self.fc2(x) - return x - - -class CLIPLayer(torch.nn.Module): - """#### The CLIPLayer module.""" - def __init__( - self, - embed_dim: int, - heads: int, - intermediate_size: int, - intermediate_activation: str, - dtype: torch.dtype, - device: torch.device, - operations: object, - ): - """#### Initialize the CLIPLayer module. - - #### Args: - - `embed_dim` (int): The embedding dimension. - - `heads` (int): The number of attention heads. - - `intermediate_size` (int): The intermediate size. - - `intermediate_activation` (str): The intermediate activation function. - - `dtype` (torch.dtype): The data type. - - `device` (torch.device): The device to use. - - `operations` (object): The operations object. - """ - super().__init__() - self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) - self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) - self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) - self.mlp = CLIPMLP( - embed_dim, - intermediate_size, - intermediate_activation, - dtype, - device, - operations, - ) - - def forward( - self, - x: torch.Tensor, - mask: torch.Tensor = None, - optimized_attention: callable = None, - ) -> torch.Tensor: - """#### Forward pass for the CLIPLayer module. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `mask` (torch.Tensor, optional): The attention mask. Defaults to None. - - `optimized_attention` (callable, optional): The optimized attention function. Defaults to None. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) - x += self.mlp(self.layer_norm2(x)) - return x - - -class CLIPEncoder(torch.nn.Module): - """#### The CLIPEncoder module.""" - def __init__( - self, - num_layers: int, - embed_dim: int, - heads: int, - intermediate_size: int, - intermediate_activation: str, - dtype: torch.dtype, - device: torch.device, - operations: object, - ): - """#### Initialize the CLIPEncoder module. - - #### Args: - - `num_layers` (int): The number of layers. - - `embed_dim` (int): The embedding dimension. - - `heads` (int): The number of attention heads. - - `intermediate_size` (int): The intermediate size. - - `intermediate_activation` (str): The intermediate activation function. - - `dtype` (torch.dtype): The data type. - - `device` (torch.device): The device to use. - - `operations` (object): The operations object. - """ - super().__init__() - self.layers = torch.nn.ModuleList( - [ - CLIPLayer( - embed_dim, - heads, - intermediate_size, - intermediate_activation, - dtype, - device, - operations, - ) - for i in range(num_layers) - ] - ) - - def forward( - self, - x: torch.Tensor, - mask: torch.Tensor = None, - intermediate_output: int = None, - ) -> tuple: - """#### Forward pass for the CLIPEncoder module. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `mask` (torch.Tensor, optional): The attention mask. Defaults to None. - - `intermediate_output` (int, optional): The intermediate output layer. Defaults to None. - - #### Returns: - - `tuple`: The output tensor and the intermediate output tensor. - """ - optimized_attention = Attention.optimized_attention_for_device() - - if intermediate_output is not None: - if intermediate_output < 0: - intermediate_output = len(self.layers) + intermediate_output - - intermediate = None - for i, length in enumerate(self.layers): - x = length(x, mask, optimized_attention) - if i == intermediate_output: - intermediate = x.clone() - return x, intermediate - - -class CLIPEmbeddings(torch.nn.Module): - """#### The CLIPEmbeddings module.""" - def __init__( - self, - embed_dim: int, - vocab_size: int = 49408, - num_positions: int = 77, - dtype: torch.dtype = None, - device: torch.device = None, - operations: object = torch.nn, - ): - """#### Initialize the CLIPEmbeddings module. - - #### Args: - - `embed_dim` (int): The embedding dimension. - - `vocab_size` (int, optional): The vocabulary size. Defaults to 49408. - - `num_positions` (int, optional): The number of positions. Defaults to 77. - - `dtype` (torch.dtype, optional): The data type. Defaults to None. - - `device` (torch.device, optional): The device to use. Defaults to None. - """ - super().__init__() - self.token_embedding = operations.Embedding( - vocab_size, embed_dim, dtype=dtype, device=device - ) - self.position_embedding = operations.Embedding( - num_positions, embed_dim, dtype=dtype, device=device - ) - - def forward(self, input_tokens: torch.Tensor, dtype=torch.float32) -> torch.Tensor: - """#### Forward pass for the CLIPEmbeddings module. - - #### Args: - - `input_tokens` (torch.Tensor): The input tokens. - - `dtype` (torch.dtype, optional): The data type. Defaults to torch.float32. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - return self.token_embedding(input_tokens, out_dtype=dtype) + cast.cast_to( - self.position_embedding.weight, dtype=dtype, device=input_tokens.device - ) - - - -class CLIP: - """#### The CLIP class.""" - def __init__( - self, - target: object = None, - embedding_directory: str = None, - no_init: bool = False, - tokenizer_data={}, - parameters=0, - model_options={}, - ): - """#### Initialize the CLIP class. - - #### Args: - - `target` (object, optional): The target object. Defaults to None. - - `embedding_directory` (str, optional): The embedding directory. Defaults to None. - - `no_init` (bool, optional): Whether to skip initialization. Defaults to False. - """ - if no_init: - return - params = target.params.copy() - clip = target.clip - tokenizer = target.tokenizer - - load_device = model_options.get("load_device", Device.text_encoder_device()) - offload_device = model_options.get( - "offload_device", Device.text_encoder_offload_device() - ) - dtype = model_options.get("dtype", None) - if dtype is None: - dtype = Device.text_encoder_dtype(load_device) - - params["dtype"] = dtype - params["device"] = model_options.get( - "initial_device", - Device.text_encoder_initial_device( - load_device, offload_device, parameters * Device.dtype_size(dtype) - ), - ) - params["model_options"] = model_options - - self.cond_stage_model = clip(**(params)) - - # for dt in self.cond_stage_model.dtypes: - # if not Device.supports_cast(load_device, dt): - # load_device = offload_device - # if params["device"] != offload_device: - # self.cond_stage_model.to(offload_device) - # logging.warning("Had to shift TE back.") - - try: - self.tokenizer = tokenizer( - embedding_directory=embedding_directory, tokenizer_data=tokenizer_data - ) - except TypeError: - self.tokenizer = tokenizer( - embedding_directory=embedding_directory - ) - self.patcher = ModelPatcher.ModelPatcher( - self.cond_stage_model, - load_device=load_device, - offload_device=offload_device, - ) - if params["device"] == load_device: - Device.load_models_gpu([self.patcher], force_full_load=True, flux_enabled=True) - self.layer_idx = None - logging.debug( - "CLIP model load device: {}, offload device: {}, current: {}".format( - load_device, offload_device, params["device"] - ) - ) - - def clone(self) -> "CLIP": - """#### Clone the CLIP object. - - #### Returns: - - `CLIP`: The cloned CLIP object. - """ - n = CLIP(no_init=True) - n.patcher = self.patcher.clone() - n.cond_stage_model = self.cond_stage_model - n.tokenizer = self.tokenizer - n.layer_idx = self.layer_idx - return n - - def add_patches( - self, patches: list, strength_patch: float = 1.0, strength_model: float = 1.0 - ) -> None: - """#### Add patches to the model. - - #### Args: - - `patches` (list): The patches to add. - - `strength_patch` (float, optional): The strength of the patches. Defaults to 1.0. - - `strength_model` (float, optional): The strength of the model. Defaults to 1.0. - """ - return self.patcher.add_patches(patches, strength_patch, strength_model) - - def clip_layer(self, layer_idx: int) -> None: - """#### Set the clip layer. - - #### Args: - - `layer_idx` (int): The layer index. - """ - self.layer_idx = layer_idx - - def tokenize(self, text: str, return_word_ids: bool = False) -> list: - """#### Tokenize the input text. - - #### Args: - - `text` (str): The input text. - - `return_word_ids` (bool, optional): Whether to return word IDs. Defaults to False. - - #### Returns: - - `list`: The tokenized text. - """ - return self.tokenizer.tokenize_with_weights(text, return_word_ids) - - def encode_from_tokens(self, tokens: list, return_pooled: bool = False, return_dict: bool = False, flux_enabled:bool = False) -> tuple: - """#### Encode the input tokens. - - #### Args: - - `tokens` (list): The input tokens. - - `return_pooled` (bool, optional): Whether to return the pooled output. Defaults to False. - - `flux_enabled` (bool, optional): Whether to enable flux. Defaults to False. - - #### Returns: - - `tuple`: The encoded tokens and the pooled output. - """ - self.cond_stage_model.reset_clip_options() - - if self.layer_idx is not None: - self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) - - if return_pooled == "unprojected": - self.cond_stage_model.set_clip_options({"projected_pooled": False}) - - self.load_model(flux_enabled=flux_enabled) - o = self.cond_stage_model.encode_token_weights(tokens) - cond, pooled = o[:2] - if return_dict: - out = {"cond": cond, "pooled_output": pooled} - if len(o) > 2: - for k in o[2]: - out[k] = o[2][k] - return out - - if return_pooled: - return cond, pooled - return cond - - def load_sd(self, sd: dict, full_model: bool = False) -> None: - """#### Load the state dictionary. - - #### Args: - - `sd` (dict): The state dictionary. - - `full_model` (bool, optional): Whether to load the full model. Defaults to False. - """ - if full_model: - return self.cond_stage_model.load_state_dict(sd, strict=False) - else: - return self.cond_stage_model.load_sd(sd) - - def load_model(self, flux_enabled:bool = False) -> ModelPatcher: - """#### Load the model. - - #### Returns: - - `ModelPatcher`: The model patcher. - """ - Device.load_model_gpu(self.patcher, flux_enabled=flux_enabled) - return self.patcher - - def encode(self, text): - """#### Encode the input text. - - #### Args: - - `text` (str): The input text. - - #### Returns: - - `torch.Tensor`: The encoded text. - """ - tokens = self.tokenize(text) - return self.encode_from_tokens(tokens) - - def get_sd(self): - """#### Get the state dictionary. - - #### Returns: - - `dict`: The state dictionary. - """ - sd_clip = self.cond_stage_model.state_dict() - sd_tokenizer = self.tokenizer.state_dict() - for k in sd_tokenizer: - sd_clip[k] = sd_tokenizer[k] - return sd_clip - - def get_key_patches(self): - """#### Get the key patches. - - #### Returns: - - `list`: The key patches. - """ - return self.patcher.get_key_patches() - - -class CLIPType(Enum): - STABLE_DIFFUSION = 1 - SD3 = 3 - FLUX = 6 - -def load_text_encoder_state_dicts( - state_dicts=[], - embedding_directory=None, - clip_type=CLIPType.STABLE_DIFFUSION, - model_options={}, -): - """#### Load the text encoder state dictionaries. - - #### Args: - - `state_dicts` (list, optional): The state dictionaries. Defaults to []. - - `embedding_directory` (str, optional): The embedding directory. Defaults to None. - - `clip_type` (CLIPType, optional): The CLIP type. Defaults to CLIPType.STABLE_DIFFUSION. - - `model_options` (dict, optional): The model options. Defaults to {}. - - #### Returns: - - `CLIP`: The CLIP object. - """ - clip_data = state_dicts - - class EmptyClass: - pass - - for i in range(len(clip_data)): - if "text_projection" in clip_data[i]: - clip_data[i]["text_projection.weight"] = clip_data[i][ - "text_projection" - ].transpose( - 0, 1 - ) # old models saved with the CLIPSave node - - clip_target = EmptyClass() - clip_target.params = {} - if len(clip_data) == 2: - if clip_type == CLIPType.FLUX: - weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" - weight = clip_data[0].get(weight_name, clip_data[1].get(weight_name, None)) - dtype_t5 = None - if weight is not None: - dtype_t5 = weight.dtype - - clip_target.clip = FluxClip.flux_clip(dtype_t5=dtype_t5) - clip_target.tokenizer = FluxClip.FluxTokenizer - - parameters = 0 - tokenizer_data = {} - for c in clip_data: - parameters += util.calculate_parameters(c) - tokenizer_data, model_options = SDToken.model_options_long_clip( - c, tokenizer_data, model_options - ) - - clip = CLIP( - clip_target, - embedding_directory=embedding_directory, - parameters=parameters, - tokenizer_data=tokenizer_data, - model_options=model_options, - ) - for c in clip_data: - m, u = clip.load_sd(c) - if len(m) > 0: - logging.warning("clip missing: {}".format(m)) - - if len(u) > 0: - logging.debug("clip unexpected: {}".format(u)) - return clip - -class CLIPTextEncode: - """#### Text encoding class for the CLIP model.""" - def encode(self, clip: CLIP, text: str, flux_enabled: bool = False) -> tuple: - """#### Encode the input text. - - #### Args: - - `clip` (CLIP): The CLIP object. - - `text` (str): The input text. - - `flux_enabled` (bool, optional): Whether to enable flux. Defaults to False. - - #### Returns: - - `tuple`: The encoded text and the pooled output. - """ - tokens = clip.tokenize(text) - cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True, flux_enabled=flux_enabled) - return ([[cond, {"pooled_output": pooled}]],) - - -class CLIPSetLastLayer: - """#### Set the last layer class for the CLIP model.""" - def set_last_layer(self, clip: CLIP, stop_at_clip_layer: int) -> tuple: - """#### Set the last layer of the CLIP model. - - works same as Automatic1111 clip skip - - #### Args: - - `clip` (CLIP): The CLIP object. - - `stop_at_clip_layer` (int): The layer to stop at. - - #### Returns: - - `tuple`: Thefrom enum import Enum - """ - clip = clip.clone() - clip.clip_layer(stop_at_clip_layer) - return (clip,) - - -class ClipTarget: - """#### Target class for the CLIP model.""" - - def __init__(self, tokenizer: object, clip: object): - """#### Initialize the ClipTarget class. - - #### Args: - - `tokenizer` (object): The tokenizer. - - `clip` (object): The CLIP model. - """ - self.clip = clip - self.tokenizer = tokenizer - self.params = {} +from enum import Enum +import logging +import torch + +from modules.Model import ModelPatcher +from modules.Attention import Attention +from modules.Device import Device +from modules.SD15 import SDToken +from modules.Utilities import util +from modules.clip import FluxClip +from modules.cond import cast + + +class CLIPAttention(torch.nn.Module): + """#### The CLIPAttention module.""" + def __init__( + self, + embed_dim: int, + heads: int, + dtype: torch.dtype, + device: torch.device, + operations: object, + ): + """#### Initialize the CLIPAttention module. + + #### Args: + - `embed_dim` (int): The embedding dimension. + - `heads` (int): The number of attention heads. + - `dtype` (torch.dtype): The data type. + - `device` (torch.device): The device to use. + - `operations` (object): The operations object. + """ + super().__init__() + + self.heads = heads + self.q_proj = operations.Linear( + embed_dim, embed_dim, bias=True, dtype=dtype, device=device + ) + self.k_proj = operations.Linear( + embed_dim, embed_dim, bias=True, dtype=dtype, device=device + ) + self.v_proj = operations.Linear( + embed_dim, embed_dim, bias=True, dtype=dtype, device=device + ) + + self.out_proj = operations.Linear( + embed_dim, embed_dim, bias=True, dtype=dtype, device=device + ) + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor = None, + optimized_attention: callable = None, + ) -> torch.Tensor: + """#### Forward pass for the CLIPAttention module. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `mask` (torch.Tensor, optional): The attention mask. Defaults to None. + - `optimized_attention` (callable, optional): The optimized attention function. Defaults to None. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + + out = optimized_attention(q, k, v, self.heads, mask) + return self.out_proj(out) + + +ACTIVATIONS = { + "quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), + "gelu": torch.nn.functional.gelu, +} + + +class CLIPMLP(torch.nn.Module): + """#### The CLIPMLP module. + (MLP stands for Multi-Layer Perceptron.)""" + def __init__( + self, + embed_dim: int, + intermediate_size: int, + activation: str, + dtype: torch.dtype, + device: torch.device, + operations: object, + ): + """#### Initialize the CLIPMLP module. + + #### Args: + - `embed_dim` (int): The embedding dimension. + - `intermediate_size` (int): The intermediate size. + - `activation` (str): The activation function. + - `dtype` (torch.dtype): The data type. + - `device` (torch.device): The device to use. + - `operations` (object): The operations object. + """ + super().__init__() + self.fc1 = operations.Linear( + embed_dim, intermediate_size, bias=True, dtype=dtype, device=device + ) + self.activation = ACTIVATIONS[activation] + self.fc2 = operations.Linear( + intermediate_size, embed_dim, bias=True, dtype=dtype, device=device + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the CLIPMLP module. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + x = self.fc1(x) + x = self.activation(x) + x = self.fc2(x) + return x + + +class CLIPLayer(torch.nn.Module): + """#### The CLIPLayer module.""" + def __init__( + self, + embed_dim: int, + heads: int, + intermediate_size: int, + intermediate_activation: str, + dtype: torch.dtype, + device: torch.device, + operations: object, + ): + """#### Initialize the CLIPLayer module. + + #### Args: + - `embed_dim` (int): The embedding dimension. + - `heads` (int): The number of attention heads. + - `intermediate_size` (int): The intermediate size. + - `intermediate_activation` (str): The intermediate activation function. + - `dtype` (torch.dtype): The data type. + - `device` (torch.device): The device to use. + - `operations` (object): The operations object. + """ + super().__init__() + self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) + self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + self.mlp = CLIPMLP( + embed_dim, + intermediate_size, + intermediate_activation, + dtype, + device, + operations, + ) + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor = None, + optimized_attention: callable = None, + ) -> torch.Tensor: + """#### Forward pass for the CLIPLayer module. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `mask` (torch.Tensor, optional): The attention mask. Defaults to None. + - `optimized_attention` (callable, optional): The optimized attention function. Defaults to None. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) + x += self.mlp(self.layer_norm2(x)) + return x + + +class CLIPEncoder(torch.nn.Module): + """#### The CLIPEncoder module.""" + def __init__( + self, + num_layers: int, + embed_dim: int, + heads: int, + intermediate_size: int, + intermediate_activation: str, + dtype: torch.dtype, + device: torch.device, + operations: object, + ): + """#### Initialize the CLIPEncoder module. + + #### Args: + - `num_layers` (int): The number of layers. + - `embed_dim` (int): The embedding dimension. + - `heads` (int): The number of attention heads. + - `intermediate_size` (int): The intermediate size. + - `intermediate_activation` (str): The intermediate activation function. + - `dtype` (torch.dtype): The data type. + - `device` (torch.device): The device to use. + - `operations` (object): The operations object. + """ + super().__init__() + self.layers = torch.nn.ModuleList( + [ + CLIPLayer( + embed_dim, + heads, + intermediate_size, + intermediate_activation, + dtype, + device, + operations, + ) + for i in range(num_layers) + ] + ) + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor = None, + intermediate_output: int = None, + ) -> tuple: + """#### Forward pass for the CLIPEncoder module. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `mask` (torch.Tensor, optional): The attention mask. Defaults to None. + - `intermediate_output` (int, optional): The intermediate output layer. Defaults to None. + + #### Returns: + - `tuple`: The output tensor and the intermediate output tensor. + """ + optimized_attention = Attention.optimized_attention_for_device() + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.layers) + intermediate_output + + intermediate = None + for i, length in enumerate(self.layers): + x = length(x, mask, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + return x, intermediate + + +class CLIPEmbeddings(torch.nn.Module): + """#### The CLIPEmbeddings module.""" + def __init__( + self, + embed_dim: int, + vocab_size: int = 49408, + num_positions: int = 77, + dtype: torch.dtype = None, + device: torch.device = None, + operations: object = torch.nn, + ): + """#### Initialize the CLIPEmbeddings module. + + #### Args: + - `embed_dim` (int): The embedding dimension. + - `vocab_size` (int, optional): The vocabulary size. Defaults to 49408. + - `num_positions` (int, optional): The number of positions. Defaults to 77. + - `dtype` (torch.dtype, optional): The data type. Defaults to None. + - `device` (torch.device, optional): The device to use. Defaults to None. + """ + super().__init__() + self.token_embedding = operations.Embedding( + vocab_size, embed_dim, dtype=dtype, device=device + ) + self.position_embedding = operations.Embedding( + num_positions, embed_dim, dtype=dtype, device=device + ) + + def forward(self, input_tokens: torch.Tensor, dtype=torch.float32) -> torch.Tensor: + """#### Forward pass for the CLIPEmbeddings module. + + #### Args: + - `input_tokens` (torch.Tensor): The input tokens. + - `dtype` (torch.dtype, optional): The data type. Defaults to torch.float32. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + return self.token_embedding(input_tokens, out_dtype=dtype) + cast.cast_to( + self.position_embedding.weight, dtype=dtype, device=input_tokens.device + ) + + + +class CLIP: + """#### The CLIP class.""" + def __init__( + self, + target: object = None, + embedding_directory: str = None, + no_init: bool = False, + tokenizer_data={}, + parameters=0, + model_options={}, + ): + """#### Initialize the CLIP class. + + #### Args: + - `target` (object, optional): The target object. Defaults to None. + - `embedding_directory` (str, optional): The embedding directory. Defaults to None. + - `no_init` (bool, optional): Whether to skip initialization. Defaults to False. + """ + if no_init: + return + params = target.params.copy() + clip = target.clip + tokenizer = target.tokenizer + + load_device = model_options.get("load_device", Device.text_encoder_device()) + offload_device = model_options.get( + "offload_device", Device.text_encoder_offload_device() + ) + dtype = model_options.get("dtype", None) + if dtype is None: + dtype = Device.text_encoder_dtype(load_device) + + params["dtype"] = dtype + params["device"] = model_options.get( + "initial_device", + Device.text_encoder_initial_device( + load_device, offload_device, parameters * Device.dtype_size(dtype) + ), + ) + params["model_options"] = model_options + + self.cond_stage_model = clip(**(params)) + + # for dt in self.cond_stage_model.dtypes: + # if not Device.supports_cast(load_device, dt): + # load_device = offload_device + # if params["device"] != offload_device: + # self.cond_stage_model.to(offload_device) + # logging.warning("Had to shift TE back.") + + try: + self.tokenizer = tokenizer( + embedding_directory=embedding_directory, tokenizer_data=tokenizer_data + ) + except TypeError: + self.tokenizer = tokenizer( + embedding_directory=embedding_directory + ) + self.patcher = ModelPatcher.ModelPatcher( + self.cond_stage_model, + load_device=load_device, + offload_device=offload_device, + ) + if params["device"] == load_device: + Device.load_models_gpu([self.patcher], force_full_load=True, flux_enabled=True) + self.layer_idx = None + logging.debug( + "CLIP model load device: {}, offload device: {}, current: {}".format( + load_device, offload_device, params["device"] + ) + ) + + def clone(self) -> "CLIP": + """#### Clone the CLIP object. + + #### Returns: + - `CLIP`: The cloned CLIP object. + """ + n = CLIP(no_init=True) + n.patcher = self.patcher.clone() + n.cond_stage_model = self.cond_stage_model + n.tokenizer = self.tokenizer + n.layer_idx = self.layer_idx + return n + + def add_patches( + self, patches: list, strength_patch: float = 1.0, strength_model: float = 1.0 + ) -> None: + """#### Add patches to the model. + + #### Args: + - `patches` (list): The patches to add. + - `strength_patch` (float, optional): The strength of the patches. Defaults to 1.0. + - `strength_model` (float, optional): The strength of the model. Defaults to 1.0. + """ + return self.patcher.add_patches(patches, strength_patch, strength_model) + + def clip_layer(self, layer_idx: int) -> None: + """#### Set the clip layer. + + #### Args: + - `layer_idx` (int): The layer index. + """ + self.layer_idx = layer_idx + + def tokenize(self, text: str, return_word_ids: bool = False) -> list: + """#### Tokenize the input text. + + #### Args: + - `text` (str): The input text. + - `return_word_ids` (bool, optional): Whether to return word IDs. Defaults to False. + + #### Returns: + - `list`: The tokenized text. + """ + return self.tokenizer.tokenize_with_weights(text, return_word_ids) + + def encode_from_tokens(self, tokens: list, return_pooled: bool = False, return_dict: bool = False, flux_enabled:bool = False) -> tuple: + """#### Encode the input tokens. + + #### Args: + - `tokens` (list): The input tokens. + - `return_pooled` (bool, optional): Whether to return the pooled output. Defaults to False. + - `flux_enabled` (bool, optional): Whether to enable flux. Defaults to False. + + #### Returns: + - `tuple`: The encoded tokens and the pooled output. + """ + self.cond_stage_model.reset_clip_options() + + if self.layer_idx is not None: + self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) + + if return_pooled == "unprojected": + self.cond_stage_model.set_clip_options({"projected_pooled": False}) + + self.load_model(flux_enabled=flux_enabled) + o = self.cond_stage_model.encode_token_weights(tokens) + cond, pooled = o[:2] + if return_dict: + out = {"cond": cond, "pooled_output": pooled} + if len(o) > 2: + for k in o[2]: + out[k] = o[2][k] + return out + + if return_pooled: + return cond, pooled + return cond + + def load_sd(self, sd: dict, full_model: bool = False) -> None: + """#### Load the state dictionary. + + #### Args: + - `sd` (dict): The state dictionary. + - `full_model` (bool, optional): Whether to load the full model. Defaults to False. + """ + if full_model: + return self.cond_stage_model.load_state_dict(sd, strict=False) + else: + return self.cond_stage_model.load_sd(sd) + + def load_model(self, flux_enabled:bool = False) -> ModelPatcher: + """#### Load the model. + + #### Returns: + - `ModelPatcher`: The model patcher. + """ + Device.load_model_gpu(self.patcher, flux_enabled=flux_enabled) + return self.patcher + + def encode(self, text): + """#### Encode the input text. + + #### Args: + - `text` (str): The input text. + + #### Returns: + - `torch.Tensor`: The encoded text. + """ + tokens = self.tokenize(text) + return self.encode_from_tokens(tokens) + + def get_sd(self): + """#### Get the state dictionary. + + #### Returns: + - `dict`: The state dictionary. + """ + sd_clip = self.cond_stage_model.state_dict() + sd_tokenizer = self.tokenizer.state_dict() + for k in sd_tokenizer: + sd_clip[k] = sd_tokenizer[k] + return sd_clip + + def get_key_patches(self): + """#### Get the key patches. + + #### Returns: + - `list`: The key patches. + """ + return self.patcher.get_key_patches() + + +class CLIPType(Enum): + STABLE_DIFFUSION = 1 + SD3 = 3 + FLUX = 6 + +def load_text_encoder_state_dicts( + state_dicts=[], + embedding_directory=None, + clip_type=CLIPType.STABLE_DIFFUSION, + model_options={}, +): + """#### Load the text encoder state dictionaries. + + #### Args: + - `state_dicts` (list, optional): The state dictionaries. Defaults to []. + - `embedding_directory` (str, optional): The embedding directory. Defaults to None. + - `clip_type` (CLIPType, optional): The CLIP type. Defaults to CLIPType.STABLE_DIFFUSION. + - `model_options` (dict, optional): The model options. Defaults to {}. + + #### Returns: + - `CLIP`: The CLIP object. + """ + clip_data = state_dicts + + class EmptyClass: + pass + + for i in range(len(clip_data)): + if "text_projection" in clip_data[i]: + clip_data[i]["text_projection.weight"] = clip_data[i][ + "text_projection" + ].transpose( + 0, 1 + ) # old models saved with the CLIPSave node + + clip_target = EmptyClass() + clip_target.params = {} + if len(clip_data) == 2: + if clip_type == CLIPType.FLUX: + weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" + weight = clip_data[0].get(weight_name, clip_data[1].get(weight_name, None)) + dtype_t5 = None + if weight is not None: + dtype_t5 = weight.dtype + + clip_target.clip = FluxClip.flux_clip(dtype_t5=dtype_t5) + clip_target.tokenizer = FluxClip.FluxTokenizer + + parameters = 0 + tokenizer_data = {} + for c in clip_data: + parameters += util.calculate_parameters(c) + tokenizer_data, model_options = SDToken.model_options_long_clip( + c, tokenizer_data, model_options + ) + + clip = CLIP( + clip_target, + embedding_directory=embedding_directory, + parameters=parameters, + tokenizer_data=tokenizer_data, + model_options=model_options, + ) + for c in clip_data: + m, u = clip.load_sd(c) + if len(m) > 0: + logging.warning("clip missing: {}".format(m)) + + if len(u) > 0: + logging.debug("clip unexpected: {}".format(u)) + return clip + +class CLIPTextEncode: + """#### Text encoding class for the CLIP model.""" + def encode(self, clip: CLIP, text: str, flux_enabled: bool = False) -> tuple: + """#### Encode the input text. + + #### Args: + - `clip` (CLIP): The CLIP object. + - `text` (str): The input text. + - `flux_enabled` (bool, optional): Whether to enable flux. Defaults to False. + + #### Returns: + - `tuple`: The encoded text and the pooled output. + """ + tokens = clip.tokenize(text) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True, flux_enabled=flux_enabled) + return ([[cond, {"pooled_output": pooled}]],) + + +class CLIPSetLastLayer: + """#### Set the last layer class for the CLIP model.""" + def set_last_layer(self, clip: CLIP, stop_at_clip_layer: int) -> tuple: + """#### Set the last layer of the CLIP model. + + works same as Automatic1111 clip skip + + #### Args: + - `clip` (CLIP): The CLIP object. + - `stop_at_clip_layer` (int): The layer to stop at. + + #### Returns: + - `tuple`: Thefrom enum import Enum + """ + clip = clip.clone() + clip.clip_layer(stop_at_clip_layer) + return (clip,) + + +class ClipTarget: + """#### Target class for the CLIP model.""" + + def __init__(self, tokenizer: object, clip: object): + """#### Initialize the ClipTarget class. + + #### Args: + - `tokenizer` (object): The tokenizer. + - `clip` (object): The CLIP model. + """ + self.clip = clip + self.tokenizer = tokenizer + self.params = {} diff --git a/modules/clip/FluxClip.py b/modules/clip/FluxClip.py index 457f459b70b03d8777d464e26b50eecab09783cd..83b31fee8d9d6449da24b0fea1458ce16899901f 100644 --- a/modules/clip/FluxClip.py +++ b/modules/clip/FluxClip.py @@ -1,756 +1,756 @@ -import math -import os -import torch -from modules.Attention import Attention -from modules.Device import Device -from modules.SD15 import SDClip, SDToken -from modules.cond import cast -from transformers import T5TokenizerFast - -activations = { - "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"), - "relu": torch.nn.functional.relu, -} - -class T5DenseGatedActDense(torch.nn.Module): - """#### Dense Gated Activation Layer""" - def __init__(self, model_dim: int, ff_dim: int, ff_activation: str, dtype: torch.dtype, device: torch.device, operations): - """#### Initialize Dense Gated Activation Layer - - #### Args: - - `model_dim` (int): Model dimension. - - `ff_dim` (int): Feedforward dimension. - - `ff_activation` (str): Feedforward activation function. - - `dtype` (torch.dtype): Data type. - - `device` (torch.device): Device. - - `operations` (Operations): Operations. - """ - super().__init__() - self.wi_0 = operations.Linear( - model_dim, ff_dim, bias=False, dtype=dtype, device=device - ) - self.wi_1 = operations.Linear( - model_dim, ff_dim, bias=False, dtype=dtype, device=device - ) - self.wo = operations.Linear( - ff_dim, model_dim, bias=False, dtype=dtype, device=device - ) - # self.dropout = nn.Dropout(config.dropout_rate) - self.act = activations[ff_activation] - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward Pass - - #### Args: - - `x` (torch.Tensor): Input tensor. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - hidden_gelu = self.act(self.wi_0(x)) - hidden_linear = self.wi_1(x) - x = hidden_gelu * hidden_linear - # x = self.dropout(x) - x = self.wo(x) - return x - - -class T5LayerFF(torch.nn.Module): - """#### Feedforward Layer""" - def __init__( - self, model_dim: int, ff_dim: int, ff_activation: str, gated_act: bool, dtype: torch.dtype, device: torch.device, operations - ): - """#### Initialize Feedforward Layer - - #### Args: - - `model_dim` (int): Model dimension. - - `ff_dim` (int): Feedforward dimension. - - `ff_activation` (str): Feedforward activation function. - - `gated_act` (bool): Whether to use gated activation. - - `dtype` (torch.dtype): Data type. - - `device` (torch.device): Device. - - `operations` (Operations): Operations. - """ - super().__init__() - if gated_act: - self.DenseReluDense = T5DenseGatedActDense( - model_dim, ff_dim, ff_activation, dtype, device, operations - ) - - self.layer_norm = T5LayerNorm( - model_dim, dtype=dtype, device=device, operations=operations - ) - # self.dropout = nn.Dropout(config.dropout_rate) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward Pass - - #### Args: - - `x` (torch.Tensor): Input tensor. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - forwarded_states = self.layer_norm(x) - forwarded_states = self.DenseReluDense(forwarded_states) - # x = x + self.dropout(forwarded_states) - x += forwarded_states - return x - - -class T5Attention(torch.nn.Module): - """#### Attention Layer""" - def __init__( - self, - model_dim: int, - inner_dim: int, - num_heads: int, - relative_attention_bias: bool, - dtype: torch.dtype, - device: torch.device, - operations, - ): - """#### Initialize Attention Layer - - #### Args: - - `model_dim` (int): Model dimension. - - `inner_dim` (int): Inner dimension. - - `num_heads` (int): Number of attention heads. - - `relative_attention_bias` (bool): Whether to use relative attention bias. - - `dtype` (torch.dtype): Data type. - - `device` (torch.device): Device. - - `operations` (Operations): Operations. - """ - super().__init__() - - # Mesh TensorFlow initialization to avoid scaling before softmax - self.q = operations.Linear( - model_dim, inner_dim, bias=False, dtype=dtype, device=device - ) - self.k = operations.Linear( - model_dim, inner_dim, bias=False, dtype=dtype, device=device - ) - self.v = operations.Linear( - model_dim, inner_dim, bias=False, dtype=dtype, device=device - ) - self.o = operations.Linear( - inner_dim, model_dim, bias=False, dtype=dtype, device=device - ) - self.num_heads = num_heads - - self.relative_attention_bias = None - if relative_attention_bias: - self.relative_attention_num_buckets = 32 - self.relative_attention_max_distance = 128 - self.relative_attention_bias = operations.Embedding( - self.relative_attention_num_buckets, - self.num_heads, - device=device, - dtype=dtype, - ) - - @staticmethod - def _relative_position_bucket( - relative_position: torch.Tensor, bidirectional: bool = True, num_buckets: int = 32, max_distance: int = 128 - ) -> torch.Tensor: - """ - Adapted from Mesh Tensorflow: - https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 - - Translate relative position to a bucket number for relative attention. The relative position is defined as - memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to - position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for - small absolute relative_position and larger buckets for larger absolute relative_positions. All relative - positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. - This should allow for more graceful generalization to longer sequences than the model has been trained on - - #### Args: - - `relative_position` (torch.Tensor): Relative position tensor. - - `bidirectional` (bool): Whether the attention is bidirectional. - - `num_buckets` (int): Number of buckets. - - `max_distance` (int): Maximum distance. - - #### Returns: - - `torch.Tensor`: Bucketed relative positions. - """ - relative_buckets = 0 - if bidirectional: - num_buckets //= 2 - relative_buckets += (relative_position > 0).to(torch.long) * num_buckets - relative_position = torch.abs(relative_position) - else: - relative_position = -torch.min( - relative_position, torch.zeros_like(relative_position) - ) - # now relative_position is in the range [0, inf) - - # half of the buckets are for exact increments in positions - max_exact = num_buckets // 2 - is_small = relative_position < max_exact - - # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance - relative_position_if_large = max_exact + ( - torch.log(relative_position.float() / max_exact) - / math.log(max_distance / max_exact) - * (num_buckets - max_exact) - ).to(torch.long) - relative_position_if_large = torch.min( - relative_position_if_large, - torch.full_like(relative_position_if_large, num_buckets - 1), - ) - - relative_buckets += torch.where( - is_small, relative_position, relative_position_if_large - ) - return relative_buckets - - def compute_bias(self, query_length: int, key_length: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor: - """#### Compute binned relative position bias - - #### Args: - - `query_length` (int): Length of the query. - - `key_length` (int): Length of the key. - - `device` (torch.device): Device. - - `dtype` (torch.dtype): Data type. - - #### Returns: - - `torch.Tensor`: Computed bias. - """ - context_position = torch.arange(query_length, dtype=torch.long, device=device)[ - :, None - ] - memory_position = torch.arange(key_length, dtype=torch.long, device=device)[ - None, : - ] - relative_position = ( - memory_position - context_position - ) # shape (query_length, key_length) - relative_position_bucket = self._relative_position_bucket( - relative_position, # shape (query_length, key_length) - bidirectional=True, - num_buckets=self.relative_attention_num_buckets, - max_distance=self.relative_attention_max_distance, - ) - values = self.relative_attention_bias( - relative_position_bucket, out_dtype=dtype - ) # shape (query_length, key_length, num_heads) - values = values.permute([2, 0, 1]).unsqueeze( - 0 - ) # shape (1, num_heads, query_length, key_length) - return values - - def forward(self, x: torch.Tensor, mask: torch.Tensor = None, past_bias: torch.Tensor = None, optimized_attention = None) -> torch.Tensor: - """#### Forward Pass - - #### Args: - - `x` (torch.Tensor): Input tensor. - - `mask` (torch.Tensor, optional): Attention mask. Defaults to None. - - `past_bias` (torch.Tensor, optional): Past bias. Defaults to None. - - `optimized_attention` (callable, optional): Optimized attention function. Defaults to None. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - q = self.q(x) - k = self.k(x) - v = self.v(x) - if self.relative_attention_bias is not None: - past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype) - - if past_bias is not None: - if mask is not None: - mask = mask + past_bias - else: - mask = past_bias - - out = optimized_attention( - q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask - ) - return self.o(out), past_bias - - -class T5LayerSelfAttention(torch.nn.Module): - """#### Self-Attention Layer""" - def __init__( - self, - model_dim: int, - inner_dim: int, - ff_dim: int, - num_heads: int, - relative_attention_bias: bool, - dtype: torch.dtype, - device: torch.device, - operations, - ): - """#### Initialize Self-Attention Layer - - #### Args: - - `model_dim` (int): Model dimension. - - `inner_dim` (int): Inner dimension. - - `ff_dim` (int): Feedforward dimension. - - `num_heads` (int): Number of attention heads. - - `relative_attention_bias` (bool): Whether to use relative attention bias. - - `dtype` (torch.dtype): Data type. - - `device` (torch.device): Device. - - `operations` (Operations): Operations. - """ - super().__init__() - self.SelfAttention = T5Attention( - model_dim, - inner_dim, - num_heads, - relative_attention_bias, - dtype, - device, - operations, - ) - self.layer_norm = T5LayerNorm( - model_dim, dtype=dtype, device=device, operations=operations - ) - # self.dropout = nn.Dropout(config.dropout_rate) - - def forward(self, x: torch.Tensor, mask: torch.Tensor = None, past_bias: torch.Tensor = None, optimized_attention = None) -> torch.Tensor: - """#### Forward Pass - - #### Args: - - `x` (torch.Tensor): Input tensor. - - `mask` (torch.Tensor, optional): Attention mask. Defaults to None. - - `past_bias` (torch.Tensor, optional): Past bias. Defaults to None. - - `optimized_attention` (callable, optional): Optimized attention function. Defaults to None. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - self.layer_norm(x) - output, past_bias = self.SelfAttention( - self.layer_norm(x), - mask=mask, - past_bias=past_bias, - optimized_attention=optimized_attention, - ) - # x = x + self.dropout(attention_output) - x += output - return x, past_bias - - -class T5Block(torch.nn.Module): - """#### T5 Block""" - def __init__( - self, - model_dim: int, - inner_dim: int, - ff_dim: int, - ff_activation: str, - gated_act: bool, - num_heads: int, - relative_attention_bias: bool, - dtype: torch.dtype, - device: torch.device, - operations, - ): - """#### Initialize T5 Block - - #### Args: - - `model_dim` (int): Model dimension. - - `inner_dim` (int): Inner dimension. - - `ff_dim` (int): Feedforward dimension. - - `ff_activation` (str): Feedforward activation function. - - `gated_act` (bool): Whether to use gated activation. - - `num_heads` (int): Number of attention heads. - - `relative_attention_bias` (bool): Whether to use relative attention bias. - - `dtype` (torch.dtype): Data type. - - `device` (torch.device): Device. - - `operations` (Operations): Operations. - """ - super().__init__() - self.layer = torch.nn.ModuleList() - self.layer.append( - T5LayerSelfAttention( - model_dim, - inner_dim, - ff_dim, - num_heads, - relative_attention_bias, - dtype, - device, - operations, - ) - ) - self.layer.append( - T5LayerFF( - model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations - ) - ) - - def forward(self, x: torch.Tensor, mask: torch.Tensor = None, past_bias: torch.Tensor = None, optimized_attention = None) -> torch.Tensor: - """#### Forward Pass - - #### Args: - - `x` (torch.Tensor): Input tensor. - - `mask` (torch.Tensor, optional): Attention mask. Defaults to None. - - `past_bias` (torch.Tensor, optional): Past bias. Defaults to None. - - `optimized_attention` (callable, optional): Optimized attention function. Defaults to None. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention) - x = self.layer[-1](x) - return x, past_bias - - -class T5Stack(torch.nn.Module): - """#### T5 Stack""" - def __init__( - self, - num_layers: int, - model_dim: int, - inner_dim: int, - ff_dim: int, - ff_activation: str, - gated_act: bool, - num_heads: int, - relative_attention: bool, - dtype: torch.dtype, - device: torch.device, - operations, - ): - """#### Initialize T5 Stack - - #### Args: - - `num_layers` (int): Number of layers. - - `model_dim` (int): Model dimension. - - `inner_dim` (int): Inner dimension. - - `ff_dim` (int): Feedforward dimension. - - `ff_activation` (str): Feedforward activation function. - - `gated_act` (bool): Whether to use gated activation. - - `num_heads` (int): Number of attention heads. - - `relative_attention` (bool): Whether to use relative attention. - - `dtype` (torch.dtype): Data type. - - `device` (torch.device): Device. - - `operations` (Operations): Operations. - """ - super().__init__() - - self.block = torch.nn.ModuleList( - [ - T5Block( - model_dim, - inner_dim, - ff_dim, - ff_activation, - gated_act, - num_heads, - relative_attention_bias=((not relative_attention) or (i == 0)), - dtype=dtype, - device=device, - operations=operations, - ) - for i in range(num_layers) - ] - ) - self.final_layer_norm = T5LayerNorm( - model_dim, dtype=dtype, device=device, operations=operations - ) - # self.dropout = nn.Dropout(config.dropout_rate) - - def forward( - self, - x: torch.Tensor, - attention_mask: torch.Tensor = None, - intermediate_output: int = None, - final_layer_norm_intermediate: bool = True, - dtype: torch.dtype = None, - ) -> torch.Tensor: - """#### Forward Pass - - #### Args: - - `x` (torch.Tensor): Input tensor. - - `attention_mask` (torch.Tensor, optional): Attention mask. Defaults to None. - - `intermediate_output` (int, optional): Intermediate output index. Defaults to None. - - `final_layer_norm_intermediate` (bool, optional): Whether to apply final layer norm to intermediate output. Defaults to True. - - `dtype` (torch.dtype, optional): Data type. Defaults to None. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - mask = None - if attention_mask is not None: - mask = 1.0 - attention_mask.to(x.dtype).reshape( - (attention_mask.shape[0], 1, -1, attention_mask.shape[-1]) - ).expand( - attention_mask.shape[0], - 1, - attention_mask.shape[-1], - attention_mask.shape[-1], - ) - mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) - - intermediate = None - optimized_attention = Attention.optimized_attention_for_device() - past_bias = None - for i, l in enumerate(self.block): - x, past_bias = l(x, mask, past_bias, optimized_attention) - if i == intermediate_output: - intermediate = x.clone() - x = self.final_layer_norm(x) - if intermediate is not None and final_layer_norm_intermediate: - intermediate = self.final_layer_norm(intermediate) - return x, intermediate - -class T5(torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - """#### Initialize T5 Model - - #### Args: - - `config_dict` (dict): Configuration dictionary. - - `dtype` (torch.dtype): Data type. - - `device` (torch.device): Device. - - `operations` (Operations): Operations. - """ - super().__init__() - self.num_layers = config_dict["num_layers"] - model_dim = config_dict["d_model"] - - self.encoder = T5Stack( - self.num_layers, - model_dim, - model_dim, - config_dict["d_ff"], - config_dict["dense_act_fn"], - config_dict["is_gated_act"], - config_dict["num_heads"], - config_dict["model_type"] != "umt5", - dtype, - device, - operations, - ) - self.dtype = dtype - self.shared = operations.Embedding( - config_dict["vocab_size"], model_dim, device=device, dtype=dtype - ) - - def get_input_embeddings(self) -> torch.nn.Embedding: - """#### Get input embeddings - - #### Returns: - - `torch.nn.Embedding`: The input embeddings. - """ - return self.shared - - def set_input_embeddings(self, embeddings: torch.nn.Embedding) -> None: - """#### Set input embeddings - - #### Args: - - `embeddings` (torch.nn.Embedding): The input embeddings. - """ - self.shared = embeddings - - def forward(self, input_ids: torch.Tensor, *args, **kwargs) -> torch.Tensor: - """#### Forward pass - - #### Args: - - `input_ids` (torch.Tensor): Input tensor. - - `*args`: Additional arguments. - - `**kwargs`: Additional keyword arguments. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) - if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]: - x = torch.nan_to_num(x) # Fix for fp8 T5 base - return self.encoder(x, *args, **kwargs) - -class T5XXLModel(SDClip.SDClipModel): - def __init__( - self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={} - ): - """#### Initialize T5XXL Model - - #### Args: - - `device` (str, optional): Device. Defaults to "cpu". - - `layer` (str, optional): Layer. Defaults to "last". - - `layer_idx` (int, optional): Layer index. Defaults to None. - - `dtype` (torch.dtype, optional): Data type. Defaults to None. - - `model_options` (dict, optional): Model options. Defaults to {}. - """ - textmodel_json_config = os.path.join( - os.path.dirname(os.path.realpath(__file__)), - "./clip/t5_config_xxl.json", - ) - super().__init__( - device=device, - layer=layer, - layer_idx=layer_idx, - textmodel_json_config=textmodel_json_config, - dtype=dtype, - special_tokens={"end": 1, "pad": 0}, - model_class=T5, - model_options=model_options, - ) - -class T5XXLTokenizer(SDToken.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - """#### Initialize T5XXL Tokenizer - - #### Args: - - `embedding_directory` (str, optional): Embedding directory. Defaults to None. - - `tokenizer_data` (dict, optional): Tokenizer data. Defaults to {}. - """ - tokenizer_path = os.path.join( - os.path.dirname(os.path.realpath(__file__)), "./clip/t5_tokenizer" - ) - super().__init__( - tokenizer_path, - pad_with_end=False, - embedding_size=4096, - embedding_key="t5xxl", - tokenizer_class=T5TokenizerFast, - has_start_token=False, - pad_to_max_length=False, - max_length=99999999, - min_length=256, - ) - -class T5LayerNorm(torch.nn.Module): - def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None): - """#### Initialize T5 Layer Normalization - - #### Args: - - `hidden_size` (int): Hidden size. - - `eps` (float, optional): Epsilon. Defaults to 1e-6. - - `dtype` (torch.dtype, optional): Data type. Defaults to None. - - `device` (torch.device, optional): Device. Defaults to None. - - `operations` (Operations, optional): Operations. Defaults to None. - """ - super().__init__() - self.weight = torch.nn.Parameter( - torch.empty(hidden_size, dtype=dtype, device=device) - ) - self.variance_epsilon = eps - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass - - #### Args: - - `x` (torch.Tensor): Input tensor. - - #### Returns: - - `torch.Tensor`: Output tensor. - """ - variance = x.pow(2).mean(-1, keepdim=True) - x = x * torch.rsqrt(variance + self.variance_epsilon) - return cast.cast_to_input(self.weight, x) * x - -class FluxTokenizer: - def __init__(self, embedding_directory=None, tokenizer_data={}): - """#### Initialize Flux Tokenizer - - #### Args: - - `embedding_directory` (str, optional): Embedding directory. Defaults to None. - - `tokenizer_data` (dict, optional): Tokenizer data. Defaults to {}. - """ - clip_l_tokenizer_class = tokenizer_data.get( - "clip_l_tokenizer_class", SDToken.SDTokenizer - ) - self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) - self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) - - def tokenize_with_weights(self, text: str, return_word_ids=False) -> dict: - """#### Tokenize text with weights - - #### Args: - - `text` (str): Text to tokenize. - - `return_word_ids` (bool, optional): Whether to return word IDs. Defaults to False. - - #### Returns: - - `dict`: Tokenized text with weights. - """ - out = {} - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) - out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) - return out - -class FluxClipModel(torch.nn.Module): - def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}): - """#### Initialize FluxClip Model - - #### Args: - - `dtype_t5` (torch.dtype, optional): T5 data type. Defaults to None. - - `device` (str, optional): Device. Defaults to "cpu". - - `dtype` (torch.dtype, optional): Data type. Defaults to None. - - `model_options` (dict, optional): Model options. Defaults to {}. - """ - super().__init__() - dtype_t5 = Device.pick_weight_dtype(dtype_t5, dtype, device) - clip_l_class = model_options.get("clip_l_class", SDClip.SDClipModel) - self.clip_l = clip_l_class( - device=device, - dtype=dtype, - return_projected_pooled=False, - model_options=model_options, - ) - self.t5xxl = T5XXLModel( - device=device, dtype=dtype_t5, model_options=model_options - ) - self.dtypes = set([dtype, dtype_t5]) - - def reset_clip_options(self) -> None: - """#### Reset CLIP options""" - self.clip_l.reset_clip_options() - self.t5xxl.reset_clip_options() - - def encode_token_weights(self, token_weight_pairs: dict) -> tuple: - """#### Encode token weights - - #### Args: - - `token_weight_pairs` (dict): Token weight pairs. - - #### Returns: - - `tuple`: Encoded token weights. - """ - token_weight_pairs_l = token_weight_pairs["l"] - token_weight_pairs_t5 = token_weight_pairs["t5xxl"] - - t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5) - l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) - return t5_out, l_pooled - - def load_sd(self, sd: dict) -> None: - """#### Load state dictionary - - #### Args: - - `sd` (dict): State dictionary. - """ - if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: - return self.clip_l.load_sd(sd) - else: - return self.t5xxl.load_sd(sd) - -def flux_clip(dtype_t5=None): - """#### Create FluxClip Model - - #### Args: - - `dtype_t5` (torch.dtype, optional): T5 data type. Defaults to None. - - #### Returns: - - `FluxClipModel`: FluxClip Model class. - """ - class FluxClipModel_(FluxClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - """#### Initialize FluxClip Model - - #### Args: - - `device` (str, optional): Device. Defaults to "cpu". - - `dtype` (torch.dtype, optional): Data type. Defaults to None. - - `model_options` (dict, optional): Model options. Defaults to {}. - """ - super().__init__( - dtype_t5=dtype_t5, - device=device, - dtype=dtype, - model_options=model_options, - ) - +import math +import os +import torch +from modules.Attention import Attention +from modules.Device import Device +from modules.SD15 import SDClip, SDToken +from modules.cond import cast +from transformers import T5TokenizerFast + +activations = { + "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"), + "relu": torch.nn.functional.relu, +} + +class T5DenseGatedActDense(torch.nn.Module): + """#### Dense Gated Activation Layer""" + def __init__(self, model_dim: int, ff_dim: int, ff_activation: str, dtype: torch.dtype, device: torch.device, operations): + """#### Initialize Dense Gated Activation Layer + + #### Args: + - `model_dim` (int): Model dimension. + - `ff_dim` (int): Feedforward dimension. + - `ff_activation` (str): Feedforward activation function. + - `dtype` (torch.dtype): Data type. + - `device` (torch.device): Device. + - `operations` (Operations): Operations. + """ + super().__init__() + self.wi_0 = operations.Linear( + model_dim, ff_dim, bias=False, dtype=dtype, device=device + ) + self.wi_1 = operations.Linear( + model_dim, ff_dim, bias=False, dtype=dtype, device=device + ) + self.wo = operations.Linear( + ff_dim, model_dim, bias=False, dtype=dtype, device=device + ) + # self.dropout = nn.Dropout(config.dropout_rate) + self.act = activations[ff_activation] + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward Pass + + #### Args: + - `x` (torch.Tensor): Input tensor. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + hidden_gelu = self.act(self.wi_0(x)) + hidden_linear = self.wi_1(x) + x = hidden_gelu * hidden_linear + # x = self.dropout(x) + x = self.wo(x) + return x + + +class T5LayerFF(torch.nn.Module): + """#### Feedforward Layer""" + def __init__( + self, model_dim: int, ff_dim: int, ff_activation: str, gated_act: bool, dtype: torch.dtype, device: torch.device, operations + ): + """#### Initialize Feedforward Layer + + #### Args: + - `model_dim` (int): Model dimension. + - `ff_dim` (int): Feedforward dimension. + - `ff_activation` (str): Feedforward activation function. + - `gated_act` (bool): Whether to use gated activation. + - `dtype` (torch.dtype): Data type. + - `device` (torch.device): Device. + - `operations` (Operations): Operations. + """ + super().__init__() + if gated_act: + self.DenseReluDense = T5DenseGatedActDense( + model_dim, ff_dim, ff_activation, dtype, device, operations + ) + + self.layer_norm = T5LayerNorm( + model_dim, dtype=dtype, device=device, operations=operations + ) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward Pass + + #### Args: + - `x` (torch.Tensor): Input tensor. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + forwarded_states = self.layer_norm(x) + forwarded_states = self.DenseReluDense(forwarded_states) + # x = x + self.dropout(forwarded_states) + x += forwarded_states + return x + + +class T5Attention(torch.nn.Module): + """#### Attention Layer""" + def __init__( + self, + model_dim: int, + inner_dim: int, + num_heads: int, + relative_attention_bias: bool, + dtype: torch.dtype, + device: torch.device, + operations, + ): + """#### Initialize Attention Layer + + #### Args: + - `model_dim` (int): Model dimension. + - `inner_dim` (int): Inner dimension. + - `num_heads` (int): Number of attention heads. + - `relative_attention_bias` (bool): Whether to use relative attention bias. + - `dtype` (torch.dtype): Data type. + - `device` (torch.device): Device. + - `operations` (Operations): Operations. + """ + super().__init__() + + # Mesh TensorFlow initialization to avoid scaling before softmax + self.q = operations.Linear( + model_dim, inner_dim, bias=False, dtype=dtype, device=device + ) + self.k = operations.Linear( + model_dim, inner_dim, bias=False, dtype=dtype, device=device + ) + self.v = operations.Linear( + model_dim, inner_dim, bias=False, dtype=dtype, device=device + ) + self.o = operations.Linear( + inner_dim, model_dim, bias=False, dtype=dtype, device=device + ) + self.num_heads = num_heads + + self.relative_attention_bias = None + if relative_attention_bias: + self.relative_attention_num_buckets = 32 + self.relative_attention_max_distance = 128 + self.relative_attention_bias = operations.Embedding( + self.relative_attention_num_buckets, + self.num_heads, + device=device, + dtype=dtype, + ) + + @staticmethod + def _relative_position_bucket( + relative_position: torch.Tensor, bidirectional: bool = True, num_buckets: int = 32, max_distance: int = 128 + ) -> torch.Tensor: + """ + Adapted from Mesh Tensorflow: + https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 + + Translate relative position to a bucket number for relative attention. The relative position is defined as + memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to + position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for + small absolute relative_position and larger buckets for larger absolute relative_positions. All relative + positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. + This should allow for more graceful generalization to longer sequences than the model has been trained on + + #### Args: + - `relative_position` (torch.Tensor): Relative position tensor. + - `bidirectional` (bool): Whether the attention is bidirectional. + - `num_buckets` (int): Number of buckets. + - `max_distance` (int): Maximum distance. + + #### Returns: + - `torch.Tensor`: Bucketed relative positions. + """ + relative_buckets = 0 + if bidirectional: + num_buckets //= 2 + relative_buckets += (relative_position > 0).to(torch.long) * num_buckets + relative_position = torch.abs(relative_position) + else: + relative_position = -torch.min( + relative_position, torch.zeros_like(relative_position) + ) + # now relative_position is in the range [0, inf) + + # half of the buckets are for exact increments in positions + max_exact = num_buckets // 2 + is_small = relative_position < max_exact + + # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance + relative_position_if_large = max_exact + ( + torch.log(relative_position.float() / max_exact) + / math.log(max_distance / max_exact) + * (num_buckets - max_exact) + ).to(torch.long) + relative_position_if_large = torch.min( + relative_position_if_large, + torch.full_like(relative_position_if_large, num_buckets - 1), + ) + + relative_buckets += torch.where( + is_small, relative_position, relative_position_if_large + ) + return relative_buckets + + def compute_bias(self, query_length: int, key_length: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor: + """#### Compute binned relative position bias + + #### Args: + - `query_length` (int): Length of the query. + - `key_length` (int): Length of the key. + - `device` (torch.device): Device. + - `dtype` (torch.dtype): Data type. + + #### Returns: + - `torch.Tensor`: Computed bias. + """ + context_position = torch.arange(query_length, dtype=torch.long, device=device)[ + :, None + ] + memory_position = torch.arange(key_length, dtype=torch.long, device=device)[ + None, : + ] + relative_position = ( + memory_position - context_position + ) # shape (query_length, key_length) + relative_position_bucket = self._relative_position_bucket( + relative_position, # shape (query_length, key_length) + bidirectional=True, + num_buckets=self.relative_attention_num_buckets, + max_distance=self.relative_attention_max_distance, + ) + values = self.relative_attention_bias( + relative_position_bucket, out_dtype=dtype + ) # shape (query_length, key_length, num_heads) + values = values.permute([2, 0, 1]).unsqueeze( + 0 + ) # shape (1, num_heads, query_length, key_length) + return values + + def forward(self, x: torch.Tensor, mask: torch.Tensor = None, past_bias: torch.Tensor = None, optimized_attention = None) -> torch.Tensor: + """#### Forward Pass + + #### Args: + - `x` (torch.Tensor): Input tensor. + - `mask` (torch.Tensor, optional): Attention mask. Defaults to None. + - `past_bias` (torch.Tensor, optional): Past bias. Defaults to None. + - `optimized_attention` (callable, optional): Optimized attention function. Defaults to None. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + q = self.q(x) + k = self.k(x) + v = self.v(x) + if self.relative_attention_bias is not None: + past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype) + + if past_bias is not None: + if mask is not None: + mask = mask + past_bias + else: + mask = past_bias + + out = optimized_attention( + q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask + ) + return self.o(out), past_bias + + +class T5LayerSelfAttention(torch.nn.Module): + """#### Self-Attention Layer""" + def __init__( + self, + model_dim: int, + inner_dim: int, + ff_dim: int, + num_heads: int, + relative_attention_bias: bool, + dtype: torch.dtype, + device: torch.device, + operations, + ): + """#### Initialize Self-Attention Layer + + #### Args: + - `model_dim` (int): Model dimension. + - `inner_dim` (int): Inner dimension. + - `ff_dim` (int): Feedforward dimension. + - `num_heads` (int): Number of attention heads. + - `relative_attention_bias` (bool): Whether to use relative attention bias. + - `dtype` (torch.dtype): Data type. + - `device` (torch.device): Device. + - `operations` (Operations): Operations. + """ + super().__init__() + self.SelfAttention = T5Attention( + model_dim, + inner_dim, + num_heads, + relative_attention_bias, + dtype, + device, + operations, + ) + self.layer_norm = T5LayerNorm( + model_dim, dtype=dtype, device=device, operations=operations + ) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x: torch.Tensor, mask: torch.Tensor = None, past_bias: torch.Tensor = None, optimized_attention = None) -> torch.Tensor: + """#### Forward Pass + + #### Args: + - `x` (torch.Tensor): Input tensor. + - `mask` (torch.Tensor, optional): Attention mask. Defaults to None. + - `past_bias` (torch.Tensor, optional): Past bias. Defaults to None. + - `optimized_attention` (callable, optional): Optimized attention function. Defaults to None. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + self.layer_norm(x) + output, past_bias = self.SelfAttention( + self.layer_norm(x), + mask=mask, + past_bias=past_bias, + optimized_attention=optimized_attention, + ) + # x = x + self.dropout(attention_output) + x += output + return x, past_bias + + +class T5Block(torch.nn.Module): + """#### T5 Block""" + def __init__( + self, + model_dim: int, + inner_dim: int, + ff_dim: int, + ff_activation: str, + gated_act: bool, + num_heads: int, + relative_attention_bias: bool, + dtype: torch.dtype, + device: torch.device, + operations, + ): + """#### Initialize T5 Block + + #### Args: + - `model_dim` (int): Model dimension. + - `inner_dim` (int): Inner dimension. + - `ff_dim` (int): Feedforward dimension. + - `ff_activation` (str): Feedforward activation function. + - `gated_act` (bool): Whether to use gated activation. + - `num_heads` (int): Number of attention heads. + - `relative_attention_bias` (bool): Whether to use relative attention bias. + - `dtype` (torch.dtype): Data type. + - `device` (torch.device): Device. + - `operations` (Operations): Operations. + """ + super().__init__() + self.layer = torch.nn.ModuleList() + self.layer.append( + T5LayerSelfAttention( + model_dim, + inner_dim, + ff_dim, + num_heads, + relative_attention_bias, + dtype, + device, + operations, + ) + ) + self.layer.append( + T5LayerFF( + model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations + ) + ) + + def forward(self, x: torch.Tensor, mask: torch.Tensor = None, past_bias: torch.Tensor = None, optimized_attention = None) -> torch.Tensor: + """#### Forward Pass + + #### Args: + - `x` (torch.Tensor): Input tensor. + - `mask` (torch.Tensor, optional): Attention mask. Defaults to None. + - `past_bias` (torch.Tensor, optional): Past bias. Defaults to None. + - `optimized_attention` (callable, optional): Optimized attention function. Defaults to None. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention) + x = self.layer[-1](x) + return x, past_bias + + +class T5Stack(torch.nn.Module): + """#### T5 Stack""" + def __init__( + self, + num_layers: int, + model_dim: int, + inner_dim: int, + ff_dim: int, + ff_activation: str, + gated_act: bool, + num_heads: int, + relative_attention: bool, + dtype: torch.dtype, + device: torch.device, + operations, + ): + """#### Initialize T5 Stack + + #### Args: + - `num_layers` (int): Number of layers. + - `model_dim` (int): Model dimension. + - `inner_dim` (int): Inner dimension. + - `ff_dim` (int): Feedforward dimension. + - `ff_activation` (str): Feedforward activation function. + - `gated_act` (bool): Whether to use gated activation. + - `num_heads` (int): Number of attention heads. + - `relative_attention` (bool): Whether to use relative attention. + - `dtype` (torch.dtype): Data type. + - `device` (torch.device): Device. + - `operations` (Operations): Operations. + """ + super().__init__() + + self.block = torch.nn.ModuleList( + [ + T5Block( + model_dim, + inner_dim, + ff_dim, + ff_activation, + gated_act, + num_heads, + relative_attention_bias=((not relative_attention) or (i == 0)), + dtype=dtype, + device=device, + operations=operations, + ) + for i in range(num_layers) + ] + ) + self.final_layer_norm = T5LayerNorm( + model_dim, dtype=dtype, device=device, operations=operations + ) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward( + self, + x: torch.Tensor, + attention_mask: torch.Tensor = None, + intermediate_output: int = None, + final_layer_norm_intermediate: bool = True, + dtype: torch.dtype = None, + ) -> torch.Tensor: + """#### Forward Pass + + #### Args: + - `x` (torch.Tensor): Input tensor. + - `attention_mask` (torch.Tensor, optional): Attention mask. Defaults to None. + - `intermediate_output` (int, optional): Intermediate output index. Defaults to None. + - `final_layer_norm_intermediate` (bool, optional): Whether to apply final layer norm to intermediate output. Defaults to True. + - `dtype` (torch.dtype, optional): Data type. Defaults to None. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape( + (attention_mask.shape[0], 1, -1, attention_mask.shape[-1]) + ).expand( + attention_mask.shape[0], + 1, + attention_mask.shape[-1], + attention_mask.shape[-1], + ) + mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + + intermediate = None + optimized_attention = Attention.optimized_attention_for_device() + past_bias = None + for i, l in enumerate(self.block): + x, past_bias = l(x, mask, past_bias, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + x = self.final_layer_norm(x) + if intermediate is not None and final_layer_norm_intermediate: + intermediate = self.final_layer_norm(intermediate) + return x, intermediate + +class T5(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + """#### Initialize T5 Model + + #### Args: + - `config_dict` (dict): Configuration dictionary. + - `dtype` (torch.dtype): Data type. + - `device` (torch.device): Device. + - `operations` (Operations): Operations. + """ + super().__init__() + self.num_layers = config_dict["num_layers"] + model_dim = config_dict["d_model"] + + self.encoder = T5Stack( + self.num_layers, + model_dim, + model_dim, + config_dict["d_ff"], + config_dict["dense_act_fn"], + config_dict["is_gated_act"], + config_dict["num_heads"], + config_dict["model_type"] != "umt5", + dtype, + device, + operations, + ) + self.dtype = dtype + self.shared = operations.Embedding( + config_dict["vocab_size"], model_dim, device=device, dtype=dtype + ) + + def get_input_embeddings(self) -> torch.nn.Embedding: + """#### Get input embeddings + + #### Returns: + - `torch.nn.Embedding`: The input embeddings. + """ + return self.shared + + def set_input_embeddings(self, embeddings: torch.nn.Embedding) -> None: + """#### Set input embeddings + + #### Args: + - `embeddings` (torch.nn.Embedding): The input embeddings. + """ + self.shared = embeddings + + def forward(self, input_ids: torch.Tensor, *args, **kwargs) -> torch.Tensor: + """#### Forward pass + + #### Args: + - `input_ids` (torch.Tensor): Input tensor. + - `*args`: Additional arguments. + - `**kwargs`: Additional keyword arguments. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) + if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]: + x = torch.nan_to_num(x) # Fix for fp8 T5 base + return self.encoder(x, *args, **kwargs) + +class T5XXLModel(SDClip.SDClipModel): + def __init__( + self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={} + ): + """#### Initialize T5XXL Model + + #### Args: + - `device` (str, optional): Device. Defaults to "cpu". + - `layer` (str, optional): Layer. Defaults to "last". + - `layer_idx` (int, optional): Layer index. Defaults to None. + - `dtype` (torch.dtype, optional): Data type. Defaults to None. + - `model_options` (dict, optional): Model options. Defaults to {}. + """ + textmodel_json_config = os.path.join( + os.path.dirname(os.path.realpath(__file__)), + "./clip/t5_config_xxl.json", + ) + super().__init__( + device=device, + layer=layer, + layer_idx=layer_idx, + textmodel_json_config=textmodel_json_config, + dtype=dtype, + special_tokens={"end": 1, "pad": 0}, + model_class=T5, + model_options=model_options, + ) + +class T5XXLTokenizer(SDToken.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + """#### Initialize T5XXL Tokenizer + + #### Args: + - `embedding_directory` (str, optional): Embedding directory. Defaults to None. + - `tokenizer_data` (dict, optional): Tokenizer data. Defaults to {}. + """ + tokenizer_path = os.path.join( + os.path.dirname(os.path.realpath(__file__)), "./clip/t5_tokenizer" + ) + super().__init__( + tokenizer_path, + pad_with_end=False, + embedding_size=4096, + embedding_key="t5xxl", + tokenizer_class=T5TokenizerFast, + has_start_token=False, + pad_to_max_length=False, + max_length=99999999, + min_length=256, + ) + +class T5LayerNorm(torch.nn.Module): + def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None): + """#### Initialize T5 Layer Normalization + + #### Args: + - `hidden_size` (int): Hidden size. + - `eps` (float, optional): Epsilon. Defaults to 1e-6. + - `dtype` (torch.dtype, optional): Data type. Defaults to None. + - `device` (torch.device, optional): Device. Defaults to None. + - `operations` (Operations, optional): Operations. Defaults to None. + """ + super().__init__() + self.weight = torch.nn.Parameter( + torch.empty(hidden_size, dtype=dtype, device=device) + ) + self.variance_epsilon = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass + + #### Args: + - `x` (torch.Tensor): Input tensor. + + #### Returns: + - `torch.Tensor`: Output tensor. + """ + variance = x.pow(2).mean(-1, keepdim=True) + x = x * torch.rsqrt(variance + self.variance_epsilon) + return cast.cast_to_input(self.weight, x) * x + +class FluxTokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + """#### Initialize Flux Tokenizer + + #### Args: + - `embedding_directory` (str, optional): Embedding directory. Defaults to None. + - `tokenizer_data` (dict, optional): Tokenizer data. Defaults to {}. + """ + clip_l_tokenizer_class = tokenizer_data.get( + "clip_l_tokenizer_class", SDToken.SDTokenizer + ) + self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) + self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) + + def tokenize_with_weights(self, text: str, return_word_ids=False) -> dict: + """#### Tokenize text with weights + + #### Args: + - `text` (str): Text to tokenize. + - `return_word_ids` (bool, optional): Whether to return word IDs. Defaults to False. + + #### Returns: + - `dict`: Tokenized text with weights. + """ + out = {} + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) + return out + +class FluxClipModel(torch.nn.Module): + def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}): + """#### Initialize FluxClip Model + + #### Args: + - `dtype_t5` (torch.dtype, optional): T5 data type. Defaults to None. + - `device` (str, optional): Device. Defaults to "cpu". + - `dtype` (torch.dtype, optional): Data type. Defaults to None. + - `model_options` (dict, optional): Model options. Defaults to {}. + """ + super().__init__() + dtype_t5 = Device.pick_weight_dtype(dtype_t5, dtype, device) + clip_l_class = model_options.get("clip_l_class", SDClip.SDClipModel) + self.clip_l = clip_l_class( + device=device, + dtype=dtype, + return_projected_pooled=False, + model_options=model_options, + ) + self.t5xxl = T5XXLModel( + device=device, dtype=dtype_t5, model_options=model_options + ) + self.dtypes = set([dtype, dtype_t5]) + + def reset_clip_options(self) -> None: + """#### Reset CLIP options""" + self.clip_l.reset_clip_options() + self.t5xxl.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs: dict) -> tuple: + """#### Encode token weights + + #### Args: + - `token_weight_pairs` (dict): Token weight pairs. + + #### Returns: + - `tuple`: Encoded token weights. + """ + token_weight_pairs_l = token_weight_pairs["l"] + token_weight_pairs_t5 = token_weight_pairs["t5xxl"] + + t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5) + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + return t5_out, l_pooled + + def load_sd(self, sd: dict) -> None: + """#### Load state dictionary + + #### Args: + - `sd` (dict): State dictionary. + """ + if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + return self.clip_l.load_sd(sd) + else: + return self.t5xxl.load_sd(sd) + +def flux_clip(dtype_t5=None): + """#### Create FluxClip Model + + #### Args: + - `dtype_t5` (torch.dtype, optional): T5 data type. Defaults to None. + + #### Returns: + - `FluxClipModel`: FluxClip Model class. + """ + class FluxClipModel_(FluxClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + """#### Initialize FluxClip Model + + #### Args: + - `device` (str, optional): Device. Defaults to "cpu". + - `dtype` (torch.dtype, optional): Data type. Defaults to None. + - `model_options` (dict, optional): Model options. Defaults to {}. + """ + super().__init__( + dtype_t5=dtype_t5, + device=device, + dtype=dtype, + model_options=model_options, + ) + return FluxClipModel_ \ No newline at end of file diff --git a/modules/clip/clip/hydit_clip.json b/modules/clip/clip/hydit_clip.json index c41c7c1ff376407f42e3ff20ab26faaa98bb5e65..6fe8efc6b3a71b73848d7da2a85f52a8e6c82eec 100644 --- a/modules/clip/clip/hydit_clip.json +++ b/modules/clip/clip/hydit_clip.json @@ -1,35 +1,35 @@ -{ - "_name_or_path": "hfl/chinese-roberta-wwm-ext-large", - "architectures": [ - "BertModel" - ], - "attention_probs_dropout_prob": 0.1, - "bos_token_id": 0, - "classifier_dropout": null, - "directionality": "bidi", - "eos_token_id": 2, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.1, - "hidden_size": 1024, - "initializer_range": 0.02, - "intermediate_size": 4096, - "layer_norm_eps": 1e-12, - "max_position_embeddings": 512, - "model_type": "bert", - "num_attention_heads": 16, - "num_hidden_layers": 24, - "output_past": true, - "pad_token_id": 0, - "pooler_fc_size": 768, - "pooler_num_attention_heads": 12, - "pooler_num_fc_layers": 3, - "pooler_size_per_head": 128, - "pooler_type": "first_token_transform", - "position_embedding_type": "absolute", - "torch_dtype": "float32", - "transformers_version": "4.22.1", - "type_vocab_size": 2, - "use_cache": true, - "vocab_size": 47020 -} - +{ + "_name_or_path": "hfl/chinese-roberta-wwm-ext-large", + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "bos_token_id": 0, + "classifier_dropout": null, + "directionality": "bidi", + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "output_past": true, + "pad_token_id": 0, + "pooler_fc_size": 768, + "pooler_num_attention_heads": 12, + "pooler_num_fc_layers": 3, + "pooler_size_per_head": 128, + "pooler_type": "first_token_transform", + "position_embedding_type": "absolute", + "torch_dtype": "float32", + "transformers_version": "4.22.1", + "type_vocab_size": 2, + "use_cache": true, + "vocab_size": 47020 +} + diff --git a/modules/clip/clip/long_clipl.json b/modules/clip/clip/long_clipl.json index 5e2056ff37ec907462bac7a557e12bb728a15990..0a437520ae3fdbd6fff1d0a3cae27c5d21545b94 100644 --- a/modules/clip/clip/long_clipl.json +++ b/modules/clip/clip/long_clipl.json @@ -1,25 +1,25 @@ -{ - "_name_or_path": "openai/clip-vit-large-patch14", - "architectures": [ - "CLIPTextModel" - ], - "attention_dropout": 0.0, - "bos_token_id": 0, - "dropout": 0.0, - "eos_token_id": 49407, - "hidden_act": "quick_gelu", - "hidden_size": 768, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 248, - "model_type": "clip_text_model", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "pad_token_id": 1, - "projection_dim": 768, - "torch_dtype": "float32", - "transformers_version": "4.24.0", - "vocab_size": 49408 -} +{ + "_name_or_path": "openai/clip-vit-large-patch14", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 49407, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 248, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "projection_dim": 768, + "torch_dtype": "float32", + "transformers_version": "4.24.0", + "vocab_size": 49408 +} diff --git a/modules/clip/clip/mt5_config_xl.json b/modules/clip/clip/mt5_config_xl.json index 092fefd6e32dac566e443fc03eae53f6a8b57400..bb445b916a35cc414357dc24d0585d25afbf34a5 100644 --- a/modules/clip/clip/mt5_config_xl.json +++ b/modules/clip/clip/mt5_config_xl.json @@ -1,22 +1,22 @@ -{ - "d_ff": 5120, - "d_kv": 64, - "d_model": 2048, - "decoder_start_token_id": 0, - "dropout_rate": 0.1, - "eos_token_id": 1, - "dense_act_fn": "gelu_pytorch_tanh", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": true, - "layer_norm_epsilon": 1e-06, - "model_type": "mt5", - "num_decoder_layers": 24, - "num_heads": 32, - "num_layers": 24, - "output_past": true, - "pad_token_id": 0, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "vocab_size": 250112 -} +{ + "d_ff": 5120, + "d_kv": 64, + "d_model": 2048, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "mt5", + "num_decoder_layers": 24, + "num_heads": 32, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 250112 +} diff --git a/modules/clip/clip/sd1_clip_config.json b/modules/clip/clip/sd1_clip_config.json index 3ba8c6b5bc3d6389fb6c9e2c8231729ad9d663a4..4978eb744e32ca932af08db68b5712efd3ead770 100644 --- a/modules/clip/clip/sd1_clip_config.json +++ b/modules/clip/clip/sd1_clip_config.json @@ -1,25 +1,25 @@ -{ - "_name_or_path": "openai/clip-vit-large-patch14", - "architectures": [ - "CLIPTextModel" - ], - "attention_dropout": 0.0, - "bos_token_id": 0, - "dropout": 0.0, - "eos_token_id": 49407, - "hidden_act": "quick_gelu", - "hidden_size": 768, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 77, - "model_type": "clip_text_model", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "pad_token_id": 1, - "projection_dim": 768, - "torch_dtype": "float32", - "transformers_version": "4.24.0", - "vocab_size": 49408 -} +{ + "_name_or_path": "openai/clip-vit-large-patch14", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 49407, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "projection_dim": 768, + "torch_dtype": "float32", + "transformers_version": "4.24.0", + "vocab_size": 49408 +} diff --git a/modules/clip/clip/sd2_clip_config.json b/modules/clip/clip/sd2_clip_config.json index 00893cfdc9b00f8eb7cf5aaa9c343e7fcd298d82..bd31995f443dbabd12ecd4a84d676d8fd6f01e68 100644 --- a/modules/clip/clip/sd2_clip_config.json +++ b/modules/clip/clip/sd2_clip_config.json @@ -1,23 +1,23 @@ -{ - "architectures": [ - "CLIPTextModel" - ], - "attention_dropout": 0.0, - "bos_token_id": 0, - "dropout": 0.0, - "eos_token_id": 49407, - "hidden_act": "gelu", - "hidden_size": 1024, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 4096, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 77, - "model_type": "clip_text_model", - "num_attention_heads": 16, - "num_hidden_layers": 24, - "pad_token_id": 1, - "projection_dim": 1024, - "torch_dtype": "float32", - "vocab_size": 49408 -} +{ + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 49407, + "hidden_act": "gelu", + "hidden_size": 1024, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "pad_token_id": 1, + "projection_dim": 1024, + "torch_dtype": "float32", + "vocab_size": 49408 +} diff --git a/modules/clip/clip/t5_config_base.json b/modules/clip/clip/t5_config_base.json index 71f68327c27280ce150d0c8e92fd61eca0b52a63..5f280e958b5aafd921f3f9f9d2c6e0e01852dd63 100644 --- a/modules/clip/clip/t5_config_base.json +++ b/modules/clip/clip/t5_config_base.json @@ -1,22 +1,22 @@ -{ - "d_ff": 3072, - "d_kv": 64, - "d_model": 768, - "decoder_start_token_id": 0, - "dropout_rate": 0.1, - "eos_token_id": 1, - "dense_act_fn": "relu", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": false, - "layer_norm_epsilon": 1e-06, - "model_type": "t5", - "num_decoder_layers": 12, - "num_heads": 12, - "num_layers": 12, - "output_past": true, - "pad_token_id": 0, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "vocab_size": 32128 -} +{ + "d_ff": 3072, + "d_kv": 64, + "d_model": 768, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "relu", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": false, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 12, + "num_heads": 12, + "num_layers": 12, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 32128 +} diff --git a/modules/clip/clip/t5_config_xxl.json b/modules/clip/clip/t5_config_xxl.json index 28283b51a11bed6a874499f82d411c16cc646eb1..cfdbe43f0300a3f0a3308f56139686088e45ebbf 100644 --- a/modules/clip/clip/t5_config_xxl.json +++ b/modules/clip/clip/t5_config_xxl.json @@ -1,22 +1,22 @@ -{ - "d_ff": 10240, - "d_kv": 64, - "d_model": 4096, - "decoder_start_token_id": 0, - "dropout_rate": 0.1, - "eos_token_id": 1, - "dense_act_fn": "gelu_pytorch_tanh", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": true, - "layer_norm_epsilon": 1e-06, - "model_type": "t5", - "num_decoder_layers": 24, - "num_heads": 64, - "num_layers": 24, - "output_past": true, - "pad_token_id": 0, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "vocab_size": 32128 -} +{ + "d_ff": 10240, + "d_kv": 64, + "d_model": 4096, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 24, + "num_heads": 64, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 32128 +} diff --git a/modules/clip/clip/t5_pile_config_xl.json b/modules/clip/clip/t5_pile_config_xl.json index ee4e03f97a5b3a9927fc676816f210a364ee234b..68c6d9606668644099000683b6e181cdcec24044 100644 --- a/modules/clip/clip/t5_pile_config_xl.json +++ b/modules/clip/clip/t5_pile_config_xl.json @@ -1,22 +1,22 @@ -{ - "d_ff": 5120, - "d_kv": 64, - "d_model": 2048, - 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a/modules/clip/clip/t5_tokenizer/special_tokens_map.json b/modules/clip/clip/t5_tokenizer/special_tokens_map.json index 17ade346a1042cbe0c1436f5bedcbd85c099d582..fa0964ac27925e153d40d8afdc49f5fadb9b0a91 100644 --- a/modules/clip/clip/t5_tokenizer/special_tokens_map.json +++ b/modules/clip/clip/t5_tokenizer/special_tokens_map.json @@ -1,125 +1,125 @@ -{ - "additional_special_tokens": [ - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "" - ], - 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MAIBKDACA0R0AgMwEAQDHCgCAcRsAgKelAADTCgCAo40AAMwUAgCAuQAAgbkAAKeFAAAIDACAgmUAAIEbAICMNQAA8wsAgMzsHADN/AMAiRsAgK6tAADZCgCAkRsAgMzABgDN0AYAsL0BAMyQBwDfCgCAgckBAMwYHQDNIAIAhBEAAOsKAIDNuAYAzKwGAKEbAIDlCgCAgSkAALEbAICpGwCAo+0BAMxAHQDNEAIAuRsAgMEbAICBCQAAyRsAgMxAHQDN0AIAqNkBABQMAIDMkAcAzBwBAMxgBgDNZAYA8QoAgBwKAIDRGwCAkSkBAP0KAICBzR8A2RsAgPcKAIDpGwCA4RsAgMzEBgDNwAYAgTEAAIDZAAAfCgCAIgoAgIK5AQCDRQEAgLkBAIG5AQCGXQEA8R0AgIRdAQDpHQCAzcAAAMzwAACIARwAiXkBAAEeAICPVQEAjGEBAPkdAICB3R4AgRUfAJkbAICBXR8AjIEfAIdBHwDMGAMAzWgDAIBNHwCBpR8AJQoAgIOpHwCMFR8AjNEeACgKAICHtR8AgJUfAIGZHwCBEQAAg70fAICFHwCBiR8A8RsAgIQ9AACbDACAiZkfAPkbAICIBQAABgsAgAEcAICADQAAgf0AAAkcAICj2R8Ao3keAKOFAAAMCwCArTUfAKdhHgCnqR8AoQwAgIQNAACnDACAozUfACsKAICtiR8AhHEAAKchHwCxPR4AsYUfAJUMAIDhHQCAEgsAgLcLAIDMtBwAzbAcAFAMAICxQR8AVgwAgJwLAIAZHgCAER4AgCkeAIAhHgCAgLkeAIG5HgCCIQEAgzUBAIRhAQAxHgCAhokBAIe9AQCIkQEAiekBANkdAICL/QEAjOUBAIINAAAJHgCAj90BAIO5AQCRrQEAgb0BAIC9AQCAoQEAgaEBAPkLAID/CwCAhD0AABEcAICJlQEAm4EBAIHNHgCAzR4AzPwCAM3wAgCB5QAAGRwAgIHtAACjpQAAzJABAM1cAgCHHQAAGwsAgKj5AAAhHACAJwsAgFwMAIBiDACAKRwAgIQFAAAxHACAo9UAACELAIA5HACAgVEAAMz0AQDN0AEALQsAgIc9AABRHACAMwsAgEEcAIA/CwCAhwUAAFkcAIBJHACAh/EDAIHZAwCBmQMAgZEAAGEcAIB0DACAjPkDAMwkAQCHuQMAgfkDADkLAIDMZAIAgskDAIyZAwBpHACAh9EDAI+RAwCB3QYAkfUDAMwABADN7AMAh2UAABkdAIBLCwCAcRwAgHoMAIBFCwCAzBgBAIg5AACBHACAeRwAgMxcAwCMJQAALgoAgMwsAQCx/QAAozkDADEKAIA0CgCAoRwAgKdZAwDMdAMAiAkAAKNRAwCpHACAXQsAgINtDQCnnQAApq0AAKOdAACxDQMAzCgBANULAICntQAAprUAAMkLAIDMMAEAgdUHAMMLAIDMKAEAzwsAgEEeAIBjCwCArYkAAGkLAICAzQEAgd0BAMxEAQDNnB4AhPUBAL0LAIDMWAEAzUwBAIDtAQCB/QEAg7UAAGgMAICM3QEAbgwAgMwIHgCM8QYAzDgBAM08AQBRHgCAiREAAIEFBgBJHgCAYR4AgFkeAIBpHgCAgz0AAIAhAACBOQAAgDkAAIEhAAA5HgCAiRwAgMwoAQCB2QYAbwsAgIH9BgDMJAEAmRwAgJEcAICxHACAgCEBAIE1AQCjBQAAuRwAgMEcAIDJHACAzIwFAM1AAgC3HAMAdQsAgIfNBwDZHACA0RwAgB0dAIDNiAAAzJAAAIzdBQCjhQAAFgoAgMzgAgDhHACAiNUHAIFNAACATQAAUQsAgOkcAIBXCwCAkTkHADcKAICIxQcApQsAgIrJBwDxHACAmz0AAIflBwBxHgCAgYUHAICFBwA6CgCAgvkHAILVBgCDRQAAgMkGAIHdBgCG4QYAewsAgIRRAACJHgCAipUGAIuZBgCIeQAAiZ0GAK0MAICPWQcAjG0HAPkcAIDMgAMAzSQCALARBwA9CgCAgR4AgCEdAIB5HgCAhAsAgICNAACBnQAAzOwDAM3oBAABHQCAigsAgKNJBwCQCwCACR0AgKO9BwARHQCAGwAAgOcHAIALAACApKUHAOsEAICKBQCAAwAAgKhhBwDZDQCAZQAAgMgDAIAbCQCArWkHAIAtAQCBPQEAgl0BAINRAQCEYQEAuAQAgKwEAICHYQEAiK0BAIm1AQCKvQEAjykVALwFAIAdDACAzHgCAM3YBQCB3QEAgXEAAOQLAICC/QEAhBkAACMMAICH7QEAIAwAgMw0BADNMAQA5wsAgJ9pFQAmDACAjMkBAM34BADM8AIAsUkBACEHAICB1QAAoxUBAKCZFQBzCACARgcAgIT1AADMKAQAzSwEAMMIAICveQEAqH0BADENAICqaQEAUgkAgLQlAQC1KQEAowkBAAIMAIDqBgCA7gYAgLIFAQCzPQEAvPUAAL39AAC+2QAAOAgAgLgBAQC5AQEAugEBADwHAIBDBwCAhgwAALOdAwCyiQMAswgAgIC9AwBpBwCAbAcAgBIJAIDkBgCA5wYAgDUIAICJhQMAzOQHAL+hAwAFDACA1wwAgIxlAADN5AwAzCQMAIlBAACIVQAAi0UAAIpFAACFtQMAhLUDAIeVAwCGgQMAAQ0AgAQNAIAHDQCAmCwAABMAAICmyAAAzYwGAMyoBgCFaQAAFwAAgDEAAIBpAACAzPADAAcAAIA1AACA0QwAgLGVAAAlDQCAs5UAALKVAAA1DQCAOA0AgEANAIA7DQCALg0AgHUAAICmBgCAJQAAgJgJAIAdIQCAv1UDAEMNAIAZIQCAFSEAgGEgAIC4bAAAlGUNAJIAAgCcrQEAnaUBAJqJAQCbiQEAmJkBAJmJAQDMIAYAzQQGAMxABgDNXAYAzDwHAM04BwDMvAcAhXUAAIABDwCBDQ8AaSAAgLqZAQCFBQAAcSAAgFkgAIC+hQEAgSkPAIAlDwBlIACAgiEPAIUpAAC0pQEAhREAAG0gAICziQ8AsoUPALHJAQCwAQwAt4EPALbtAQC17QEAtO0BAIFlAQCAZQEAg2EBALi1DwDMPAsAhHkBAIDhDwCB3Q8AdSAAgF0gAIDMyAQAzbgEAIWtAACFFQAAISEAgDkhAIDM6BkAzbQZAKRdAQBGDQCAok0CAKPxDwCgVQEAod0PAH8IAIBuCQCAOwkAgO0eAIBsCQCA9R4AgHcJAIDxHgCAsQgAgJMNAACtHgCA+R4AgITVDACF6Q4AlGkAAIfdDgC1HgCAmbQCAL0eAIDFHgCAsR4AgD0hAIC5HgCAn3QBAMEeAICRGA0AgI0OAIGBDgCGhQ4AlYwDAISJDgCXRAIAghEAAKm4AACA0QAAge0AAMkeAIBJDQCA5R4AgIVZDwCDiQAAoTQNAIFFDgCASQ4A6R4AgKU0AQCFYQ8AzPAUAB0fAIC5xAUAzMgDAM3cAwCA3QAAgcEAACUfAIC/kAUAhREAALHsBwCA9QAAgcEAAKEgAIC1jAYALR8AgLdABgCA3Q4AgekOAMwoAgDNtAIAgM0OAIH5DgCFKQAAg4UBAIB1AQCBsQEAgPEBAIHVAQCpIACANR8AgIUFAACxIACAgJkBAIG9AQCCfQAAk9UBAJThAQCFDQAAmSAAgCEfAICACQAAgRkAACkfAICTrQEAlC0AAKUgAICFDQAAMR8AgIUFAACtIACAOR8AgIUpAACCGQAAhTUAAIDxAACB4QAAtSAAgJ0gAIBBIQCAhQUAAGEhAICDdQEAgO0BAIEpAQDM8AEAzbABAEwNAIBdIQCAWSEAgKMNAIBdHwCAZR8AgIA9AACBDQAAbR8AgHUfAICALQAAgR0AAIIVAABhHwCAzSwBAGkfAIBxHwCAeR8AgIjFAwClIQCAzJACAM28AgCE7QMATw0AgIb5AwCdHwCAgIEDAIH9AwCAPQAAgTUAAIFJAACAQQAAzdwBAIJBAAClHwCAoR8AgKkfAIDNMAEAlJ0DAI0hAIDN8AEAzAwBAIG5AwCAxQMAg6EDAJOlAwCArQAAgdUAAICdAACBqQAAiSEAgFINAICBwQAAgMkAAIC1AACBgQAAhSEAgINpBADMcAMAzbQDAIEhAIDNPAEApg0AgJMBBADNjAIAzPQCAIANAACBNQAAlNkGANEfAIDVHwCA2R8AgMwIAQDNHAEAgREAAIApAACpIQCAghkAAICRAQCBkQEAzWgFAMyUAgDMEAkAzSgWAMxYDgDNeA4AzBQNAM3YCgDMKAwAzYwNAMzgFwDM4AoAzDgLAM30CACFEQAAVQ0AgIBRBwCBUQcA4SAAgM2QDgCFBQAA6SAAgMzYDgDN7AEA8SAAgM0ADgCFGQAAzfAPAM08DgDNVA4AzGgBAM1sAQDZIACAYQgAgJSZBwDMwDsAgGEBAIHZAACFKQAAzWQOAMx4AQDNfAEAga0HAICtBwCFZQAAgp0HAIBRAQCBUQEAlOEHAM3AAACEeQEAk8UHAIZhAQDlIACAiCEBAIUNAADtIACAzRgBAMzYAADNtAAAgN0HAIHNBwCZHwCAhQkAAM0fAID1IACA/R8AgN0gAIAFIACADSAAgBUgAIAJIACAASAAgK0hAIARIACAGSAAgMy4AgDNHAMAgGUAAIF1AACCfQAAHSAAgIUJAACFQQAAASEAgKkNAICAmQYAgSEHAIUZAACDfQAACSEAgIVZAAD9IACA+SAAgIDNAACB2QAAjR4AgIURAACE6QAAlR4AgIblAABBIACAgDUAAIENAACdHgCAhR0AAEkgAIClHgCAhQUAAFEgAICAVQAAgW0AAIJ9AACTRQAAlA0AAIUNAAA5IACAkR4AgIAJAACBEQAAmR4AgIUdAABFIACAoR4AgIUFAABNIACAgOkBAIHxAQCCBQAAqR4AgIUJAACFCQAAVSAAgD0gAICAbQEAgXkBAIIZAACDpQEADSEAgIV1AACFBQAAESEAgAUhAIAhIACAzMgCAM3cAgCsDQCAzR4AgIA5AACBOQAA1R4AgN0eAIDRHgCA2R4AgIAdAACBDQAA4R4AgCUgAICAxQAAgdUAAM3AAADMJAIAgNUAAIHFAACFOQAAg8kAACUhAICvDQCAgNUAAIEJAACFBQAALSEAgP0eAICBIACAgAkAAIERAAAFHwCAk5kAAJS5AAANHwCAhWUAAIU9AACJIACAk10AABUfAICFEQAAzXAFAMx0BQCUATwAkSAAgHkgAIDNKAEAhSAAgI0gAICFGQAAlSAAgH0gAIA1IQCAKSEAgCkgAICFJQAAhTkAAMz4AgDNxAMAzTwBALINAICBlQMAgI0DAM3EAQCCpQMAhVEAAIVJAADMKAEAzSwBAM04AQDMPAEAgGk+AIFpPgBJIQCARSEAgM04PADMVDwAgdE8AJOdPgDMSAEAzcgCAM00AQBNIQCAlLk+AFgNAICAoT4AgaE+AIKhPgCIjTwAVSEAgIWtAACALQAAgSEAAIXVPwCVHwCAgO0AAIHxAACGpQAARR8AgISpAADNJAEAzSgBAE0fAICI+T4AhfE/AFUfAIBJHwCAhcU/AM0wAQDNEAEAzfQGAIDdAQCB6QEAzbwGAM1wBgDM4AYAzVwBAMxoBgDNkAYAzWQGAM14BgDMrAcAzagHAMzoBwDNyAcAgk0/AIP9AgCANQIAgekCAFEfAIBZHwCAgAU9AIV9AQBRIQCALSAAgM0UAQApDgCAge0BAIDhAQDNPAEAgs0BAM0sAQCCdQEAgW0BAIBZAQCAZQEAgcUAAIUfAIDNJAEAzTgBAILxAACB+QAAgFkBAIApAACBcQAAzBgBAM18AQDNLAEAjR8AgIEdAACAHQAAiR8AgJEfAIBxIQCAzSQBAMzkPQDNXA8AzegAAMwMAQCA1QEAgckBAIKZAACD5T8ACR8AgBEfAIAZHwCAMSEAgCMOAIB1IQCAPR8AgDEgAIBBHwCALA4AgIBNPwCBQT8AfR8AgGkhAICBHwCAZSEAgIAlPwCBKT8Ak5E/AIN9AAAmDgCAlEEAAMzYAgDNrAIAbSEAgJNVAACACQAAgR0AALUNAIB9IQCAlEEAAK0fAICAnQAAgaEAAIAdAACBEQAAhKUAALUfAICGpQAAvR8AgIjxAACC0QAAgdkAAIDNAACAJQAAgSkAAIIFAADFHwCAsR8AgLkfAIDBHwCAk7EAAJQRAADJHwCAgB0AAIEVAACAJQAAgS0AAII9AAB5IQCAgO0AAIHRAACCFQAAg4EAAIHQPQA1IACAzCACAM3cAQCFeAIAkSEAgC8OAICZIQCAiRgDAN0fAICALQAAgTUAAIAJAACBbQAA5R8AgMEgAICRsQAAkKkAAJPdOwCSAQQAlaUAAJSVOwDtHwCAlqEAAIUJAACTQQAAySAAgPUfAICFBQAA0SAAgJT1AAC5IACAgLkAAIHdAACC5QAA4R8AgOkfAICF6QAAgAkAAIE1AACFBQAAxSAAgPEfAICFHQAAzSAAgPkfAICFBQAA1SAAgLHBBQCwxQMAvSAAgLLFAwC12QUAtM0DAJ0hAICFOQAAuf0DAKEhAICVIQCAuw0AgM0NAIAXDgCAAR8AgAUOAIDTDQCAzIgCAAsOAIDN4D4AzZABAMwkAQBwDQCAjg0AgEEOAIB9DgCAgLEAAM3UPgDN5D4Agw4AgMy8PgDNuD4AgNEDAIHtAwCC/QMAhmkAAD4OAICFnQMAzTwBADgOAIDM6AIAzTw/AIjlAADNGAEAiQ4AgIhBAAA7DgCAdw4AgM0sAQCVDgCAgNUAAJsOAICG4QAAhukAAEcOAIDNJAEAoQ4AgM0QAQCI0QAAiCkAAMz4AgBNDgCAzfgCAMwkAQCnDgCAhS0DAMygPgDNbD4AgNUDAIHNAwCCAQMAg/kDAMxkAwDNzAIARA4AgM0kAQDMDAIAzQgCAIERAADMnAMAzLA+AM20PgDMxD4AzcA+AMyAPgDNuD4ArQ4AgMyEAgDMmD8AzVA+AMwgPgDNoD4AzQw/AM0wPwDNeD8AzQQ/AIhZAAC/DgCAzfgBAMzEAQBKDgCAxQ4AgMsOAIDMFAIAzAgBAM3IAQCIBQAA0Q4AgNcOAIDMKAIAuQ4AgIgNAACG0QAAgB0BAITNAACI9QAAzDwCAIQ1AQDMRAIAhikBAIAOAICIZQEAhg4AgKdEBQBiDgCAi+0AAIjtAACBDQAAiCUAAIZlAADMcAIAzXQCAMwwAgDN2AUAXA4AgIwOAICAOQAAXw4AgMzgBQB6DgCAzCgBAM0UAQCGJQAAiFUAAAgOAICGhDAAxA0AgIDVBwCG/QcAmA4AgMwkAgCIPQAAng4AgGsOAICIPQAApA4AgMxIAgDNeAIAUA4AgKoOAICXwAUAlnAFAJUYBQCAaQAAk1gFAIE5AACIZQAAkPg8AIZZAACeqAUAhEUAAGgOAIDM1AIAmrQFAIBdAACYrAUAp+wEAIgRAADM2AIAzdwCAKO8BACwDgCAzGACAMIOAIBuDgCAyA4AgK0IBADODgCAq/QEAMwsAgCIBQAA1A4AgLfoAwC2HAQAtSgEAMwAAgCzKAQAi3kAAIh9AACwdAQAhkEAAL6kAwCEdQAAiB0AANoOAIC6TAMAzNwDALj8AwCDqAIAiA0AALwOAICIFQAAh5QCAMw4AgBlDgCAzAQCAIvcAgCPDQAAcQ4AgI8ZAADMIAIAdA4AgI3wAgCIdQAAmCADAJksAwCPDgCAlA0AgMxMAgCWcAMAzCQCAIg9AACSDgCAzCwCAIgFAACzDgCAzCQCAIgNAAC2DgCAh/UAAKjUAwCpxAMA3Q4AgNlgAgDSDwCA1Q8AgNsPAICUNQAAkzEAANloAgDYDwCA2UwCAJQFAADeDwCAlSEAAJQpAABQEACAdBYAgEMXAIDSFgCA2WACADcXAIC12AMAtPADAJQ1AADZWAIAWhcAgJQFAADZVAIAlA0AADEXAIDgdAEAisgAALwVAACIyAAA4IACAIcXAICBoAAApOwCAKTIAgCoXAAAvA0AAJkXAIDghAIAvAUAAJ0XAICk+AIA4PQCALDMAwCV0AAAXRcAgLPgAwCmyAIAp2ACAJLYAABkFwCAvsEAAGsXAICXwQAAchcAgHkXAICAFwCAzXg/AMy8PwC+gA0AixcAgLx4DAC9gA0AuvQMALtUDAC49AwAkhcAgLYXAIC3uAwAuhcAgLWMDACyoAMAs6AMAKEXAICxQAMArnACAK9kAwC4BQMArUgDAKgXAICvFwCAqEQDAKnYAwDaFwCAp9gDAKRoAgCliAMAtjUDALc9AwCSyAIAtT0DAJldAQCYTQEAm2UBAJppAQCdZQEAnGUBAJ+FAQCemQEAh5wCAL6tAACWpQAAl70AAMw0BQDNjDcAzLg4AM2sOACflQEAth0AAJ2ZAQCc9QEAs7EBAK54AgDhFwCAvhcAgJk9AADFFwCAmxkAAJoJAADMFwCA0xcAgOBIAgCeCQAArFwCAK30AgD6FwCA9hcAgP4XAIDoFwCAh2ADAO8XAICvVAIAvhEAAJcFAAACGACA4KwCAAYYAICG+AMAh+wDAOC0AgAOGACAr0gCAK6QAgDgPAIAvg0AAAoYAICXGQAA4NgCAIaEAwCWEQAAvwAMAJ1tAACcYQAAEhgAgLFMAgCzUAIAlQ0AABYYAICGnAMA4MgCALMEAgCCBQAAIhgAgLNQAgCVDQAAJhgAgBoYAIAeGACA4LQCAIaMAwCH3AMAvg0AAJVpAACWeQAAKhgAgLToAgC1UAIAlwUAADIYAIDg1AIAtPQCAL4ZAADgoAIALhgAgODUAgCZjAMAt9QCAIoFAAA2GACAOhgAgIoVAAC3NAIAjx0AAD4YAIBCGACAswUAAEYYAICzBQAAWxgAgJwJAACdCQAATRgAgFQYAICMBQAAYhgAgG0YAIB0GACAexgAgJ9JAACCGACAiRgAgGYYAICQGACAlxgAgNkYAIDPGACA6hgAgOAYAICeGACAg8kBAIH5AQCsGACAsxgAgLoYAIDBGACAyBgAgKUYAICAtAIApYgDAOEIAgCuHQAA8RgAgLwJAACN9QEA9RgAgOEAAgCSlQEA45QQAJNFAACXiQEAhRQAAId4AQCGAAQARjoAgEo6AIBOOgCAUjoAgFY6AICdeQAA74xoAJyhAQBaOgCAXjoAgKKZAABiOgCAZjoAgGo6AIBuOgCAp4kAAHI6AIB2OgCAqUkBAHo6AICsqQAAfjoAgII6AICGOgCAsyUBAIo6AICOOgCAkjoAgLchAQC2OQEAtTEBAJY6AICaOgCAufkAALkRAQC4GQEAnjoAgKI6AICmOgCAqjoAgICwAQCEiAIArjoAgIPIAQCEVAMAhFwEALI6AICEXAUAgN0DAIEtAACCMQAAvjwCALo6AIC+OgCAh4gDAIacBACzLQMAwjoAgMY6AIC+AAQAvhwFALbRAwC12QMAyjoAgLv5AwC68QMAmljTAYTgBwC/xQMAvtkDAL3dAwC83QMAvgAYAKUFAwCmDQMAzjoAgIQcGADSOgCA1joAgKPxAwCsAQMArQEDAK4FAwCvGQMArKQbAq3cGgKqLQMAqyUDAL5MGQC+SBoA2joAgL6AGwC04BoCtdQdArYwHgLvCAIA3joAgOGgAQC6OBoC4/gCALoAAAC9ZBwCvvQcAr8AEAKRBNMBkOT2AeBEAQCSCD4C4joAgOY6AIDqOgCA7joAgL6sHADyOgCA9joAgPo6AID+OgCAAjsAgAY7AIAKOwCAgbBtAICAAQCDHFIAgth3AIUgmgCEkL4AhwjPAIaM5gCJbDcBiOAsAYsYfgGK2BMBjeClAYzwWgGP/OsBjliPAbDVFwCxAWgAso1rALOdawC0SWsAtZVvAA47AIDgcAEAEjsAgBY7AIAaOwCAHjsAgIAZAACBGQAAggUAACI7AIAqOwCAoaUCAKJJBwCjQQcApEEGAKXVGwCm3RsAp8EaAKgBHACp4R8AqkkfAKsBEACs9RMAra0TAK4BFACv+RcAqDEGAKkxBgCqTQYAq0UGAKxNBgCtmQYAro0GAK+FBgCGgAMAhxgDAC47AIAyOwCANjsAgDo7AIA+OwCAQjsAgLhtBwC5dQcAun0HALt1BwC8bQcAvc0HAL75BwC/+QcAsKkGALGFBgCyeQcAs3kHALRpBwC1aQcAtl0HALdVBwC2OgCAs8EGAEY7AIAmOwCAth0GAEo7AIBOOwCAtcEGALppBgC7RQYAUjsAgFY7AIC+qQcAv6kHALypBwC9qQcAo4UGAFo7AIBeOwCAYjsAgGY7AICmWQYApYUGAGo7AICrAQYAqi0GAG47AIByOwCAr+0HAK7tBwCt7QcArO0HAKjBBgCpLQEAqiUBAKs9AQCsJQEArS0BAK4lAQCvlQEAdjsAgHo7AIB+OwCAgjsAgIY7AICCvQAAgb0AAIC9AAC4nQEAua0BALqlAQC7bQAAvHUAAL19AAC+dQAAv20AALD1AQCx/QEAssEBALPBAQC0tQEAtb0BALa1AQC3rQEAijsAgI47AICSOwCAs6EBAJY7AIC1oQEAtqEBAJo7AICGgAEAh8QBALo9AQC7NQEAvBkBAL0ZAQC+fQEAv3UBAKPtAQCeOwCAojsAgKY7AICqOwCApu0BAKXtAQCuOwCAq3kBAKpxAQCyOwCAtjsAgK85AQCuMQEArVUBAKxVAQC6OwCAvjsAgMI7AIDGOwCAyjsAgOGsAQDOOwCA42AGANI7AIDWOwCA2jsAgO9UBgDeOwCA4jsAgL60GgDmOwCA6jsAgO47AICGaBwAh4wDAPI7AID2OwCA+jsAgP47AICAOQAAgTkAAIIFAAACPACACjwAgA48AIASPACAFjwAgKgdAwCpQQMAqkEDAKtBAwCsQQMArUkDAK5xAwCvcQMAhCAdABo8AIAePACAIjwAgCY8AIAqPACALjwAgDI8AIC46QAAufUAALr9AAC78QAAvJEAAL2RAAC+iQAAv4kAALDhAACx4QAAsuEAALPhAAC04QAAte0AALbZAAC32QAA4wwHAOEgBwDhMAEA4wgHADY8AIA6PACAPjwAgEI8AIBGPACASjwAgE48AIBSPACA75gHAFY8AIBaPACA74gHALOJAgBePACAYjwAgL6AGgBmPACAtokCALWJAgBqPACAu2UBALplAQBuPACAcjwAgL9pAQC+ZQEAvXUBALx1AQC3PQYAtj0GALU9BgC0IQYAszUGALI1BgCxAQYAsAkGAL9ZBgC+UQYAvVkGALxNBgC7bQYAunkGALlxBgC4eQYAgJ0AAIGtAACCpQAAejwAgH48AICCPACAhjwAgIo8AICvcQYArmkGAK1tBgCsbQYAq4EGAKqZBgCpkQYAqJkGAAY8AIB2PACAjjwAgKPFHQCSPACApcUdAKbFHQCWPACAhgADAIdkAwCqKR4AqykeAKw5HgCtOR4ArikeAK8lHgCzOR4AmjwAgJ48AICiPACApjwAgLb9HgC1/R4AqjwAgLvZHgC60R4ArjwAgLI8AIC/aR8AvmEfAL1pHwC8wR4AqPEeAKnxHgCq8R4Aq/EeAKw1HgCtPR4ArjUeAK8tHgC2PACAujwAgL48AIDCPACAxjwAgMo8AIDOPACA0jwAgLjlHwC57R8AuuUfALv5HwC86R8AvZEfAL6RHwC/jR8AsFUeALFdHgCyVR4As/0fALTlHwC17R8AtuUfALfdHwCjeR8A1jwAgNo8AIDePACA4jwAgKa9HwClvR8A5jwAgKuZHwCqkR8AhogAAIdMAQCvKR4AriEeAK0pHgCsgR8AgEkAAIFJAACCWQAAs5keAOo8AIC1iR4AtlEBAO48AIDyPACA9jwAgLotAQC7JQEAvD0BAL0lAQC+JQEAvxUBAKhNHgCpVR4Aql0eAKtVHgCsTR4ArZ0BAK6JAQCvgQEAhKwBAPo8AID+PACAAj0AgAY9AIAKPQCADj0AgBI9AIC4ZQEAuW0BALplAQC7fQEAvGUBAL1tAQC+ZQEAv9kAALClAQCxrQEAsqUBALO9AQC0rQEAtZ0BALaVAQC3XQEAo9UdABY9AIAaPQCAHj0AgCI9AICmHQIApcUdACY9AICraQIAqmECACo9AIAuPQCAr1kCAK5pAgCtaQIArHECADI9AIA2PQCAOj0AgD49AIBCPQCARj0AgEo9AIBOPQCAgDkAAIE5AACCBQAAUj0AgFo9AIBePQCAh0ADAIZcBACETAQAYj0AgGY9AICEBAUA4yABAGo9AIDhqAEAbj0AgO+UGgByPQCAdj0AgHo9AIB+PQCAgj0AgIY9AICKPQCAs6EDAI49AICSPQCAlj0AgJo9AIC2fQMAtX0DAJ49AIC7WQMAulEDAKI9AICmPQCAv/0AAL79AAC9/QAAvEEDAKhRAgCpWQIAqmkCAKtpAgCstQIArb0CAK61AgCvrQIAhKgHAKo9AICuPQCAsj0AgIKpAAC2PQCAgKkAAIGpAAC4aQEAuWkBALoJAQC7CQEAvBkBAL0ZAQC+CQEAvwkBALDVAgCx3QIAstUCALNpAQC0eQEAtXkBALZpAQC3YQEA4bgBAOHUHwDjOB8A4wwbALo9AIC+PQCAwj0AgMo9AIDOPQCA0j0AgNY9AIDaPQCAvjwJAN49AIDvhBsA74QbAKOhAgDiPQCAhugEAIe8BQDmPQCApn0CAKV9AgDqPQCAq1kCAKpRAgDuPQCA8j0AgK/9AQCu/QEArf0BAKxBAgCzhQYAxj0AgPY9AID6PQCA/j0AgLaJBgC1jQYAAj4AgLuRBgC6iQYABj4AgAo+AIC/9QYAvokGAL2BBgC8iQYADj4AgBI+AIAWPgCAGj4AgB4+AIAiPgCAJj4AgO+EHQAqPgCA4QAEAC4+AIDj/AQAgBEAAIEdAACCBQAAMj4AgKjxBgCp8QYAqg0GAKsFBgCsBQYArQkGAK49BgCvNQYANj4AgDo+AICGiAAAhxADAD4+AIBCPgCARj4AgEo+AIC4EQYAuRkGALohBgC7IQYAvPUHAL39BwC+9QcAv+kHALBNBgCxVQYAsl0GALNVBgC0TQYAtTEGALYxBgC3MQYAo4UHAE4+AIBSPgCAVj4AgFo+AICmiQcApY0HAF4+AICrkQcAqokHAGI+AIBmPgCAr/UHAK6JBwCtgQcArIkHAGo+AICz4QYAbj4AgHI+AIC25QYAdj4AgHo+AIC18QYAur0GALuNBgB+PgCAgj4AgL59AQC/ZQEAvJUGAL11AQCoHQYAqSUGAKotBgCrJQYArD0GAK0hBgCuXQYAr00GAIY+AICKPgCAjj4AgJI+AICWPgCAgrkDAIGxAwCAuQMAuO0BALmFAQC6jQEAu4UBALydAQC9hQEAvo0BAL+FAQCwPQYAsQ0GALIFBgCz5QEAtP0BALXlAQC25QEAt9UBAKOlBQCaPgCAnj4AgKI+AICqPgCApqEFAKW1BQCuPgCAq8kFAKr5BQCGCAwAhxwDAK8hAgCuOQIArTECAKzRBQCyPgCAs/ECALY+AIC6PgCAtlUDAL4+AIDCPgCAteECALpxAwC7eQMAxj4AgMo+AIC+MQMAvz0DALxRAwC9UQMAqCUCAKk1AgCqPQIAqzUCAKwtAgCtkQMArpEDAK+RAwDOPgCA0j4AgNY+AIDaPgCArAAAAN4+AIDiPgCA5j4AgLiZAwC5rQMAuqUDALttAwC8dQMAvX0DAL51AwC/bQMAsPEDALH5AwCywQMAs8EDALSxAwC1vQMAtrUDALepAwDqPgCA7j4AgPI+AID2PgCA+j4AgP4+AIACPwCA76gaAL5oDADhlAEABj8AgOMcBgCADQAAgXEAAIJxAAAKPwCAo/UDAA4/AIASPwCAhEwCABo/AICmUQIApeUDAB4/AICrfQIAqnUCAIbIDACHLA0ArzkCAK41AgCtVQIArFUCAOFQBgAiPwCA4xQHAITADAAmPwCAKj8AgC4/AIAyPwCANj8AgDo/AIA+PwCAQj8AgEY/AIBKPwCA73gbAL74DwBOPwCAUj8AgFY/AICzjQEAWj8AgLWZAQC2jQEAXj8AgFY9AIBiPwCAuoUBALtNAQC8VQEAvV0BAL5VAQC/SQEAo0EOABY/AIBmPwCAaj8AgG4/AICmQQ4ApVUOAHI/AICrgQ4AqkkOAHY/AIB6PwCAr4UOAK6ZDgCtkQ4ArJkOAIBtAACBCQAAgh0AAH4/AIDvGAkAgj8AgIY/AICKPwCA4zwNAI4/AIDhWAwAkj8AgIbQAACHvAMAlj8AgJo/AICokQ4AqZkOAKrJDgCrxQ4ArN0OAK3BDgCuwQ4Ar/UOAIToAACePwCAoj8AgKY/AICqPwCArj8AgLI/AIC2PwCAuMEPALnBDwC6wQ8Au8EPALzBDwC9wQ8AvsEPAL/1DwCwjQ4AsUUOALJNDgCzRQ4AtF0OALVBDgC2QQ4At0EOAKhRDgCpWQ4Aqo0OAKudDgCshQ4ArY0OAK6FDgCvvQ4Auj8AgL4/AIDCPwCAxj8AgMo/AIDOPwCA0j8AgNY/AIC4kQ4AuZkOALqtDgC7RQEAvF0BAL1FAQC+RQEAv3UBALDFDgCxzQ4AssUOALPdDgC0xQ4AtbUOALa9DgC3tQ4AswUOANo/AIDePwCA4j8AgOY/AIC2DQ4AtQ0OAOo/AIC7CQ4AugEOAO4/AIDyPwCAv3EOAL4BDgC9CQ4AvBEOAIJtAACjQQ4AgFUAAIFlAACmSQ4A+j8AgP4/AIClSQ4AqkUOAKtNDgCGSAAAh3gAAK5FDgCvNQ4ArFUOAK1NDgCoXQIAqWECAKplAgCrdQIArG0CAK2xAgCusQIAr7ECAITsBAACQACABkAAgApAAIAOQACAEkAAgBZAAIAaQACAuHEDALlxAwC6cQMAu3EDALzVAwC93QMAvtUDAL/NAwCw0QIAsdECALLRAgCz0QIAtFEDALVRAwC2UQMAt1EDAB5AAICz6QIAIkAAgL6ABAC2NQIAJkAAgCpAAIC14QIAuhECALsRAgAuQACAMkAAgL6RAwC/kQMAvAECAL0BAgA2QACAOkAAgKOlAgA+QACApa0CAEJAAIBGQACApnkCAEpAAIBOQACAq10CAKpdAgCtTQIArE0CAK/dAwCu3QMAqNUCAKndAgCqLQEAqyUBAKw9AQCtJQEAri0BAK8lAQBSQACAVkAAgFpAAIBeQACAYkAAgGpAAIBuQACAckAAgLiFAQC5iQEAup0BALuVAQC8sQEAvbEBAL55AAC/eQAAsF0BALHlAQCy4QEAs/kBALTpAQC13QEAttUBALe9AQDh8A4AdkAAgOMUDgB6QACAgb0AAIC9AAB+QACAgq0AAIYABACH7AUAgkAAgIZAAICKQACAjkAAgO9gDgCSQACAlkAAgJpAAICFXH0AnkAAgKJAAIDjZAEApkAAgOG0AQCqQACA76AOAK5AAICmPgCAhPgFALJAAIC2QACAukAAgLMlBgBmQACAvkAAgMJAAIDGQACAtiUGALU1BgDKQACAu6EGALoZBgDOQACA0kAAgL+ZBgC+rQYAva0GALy1BgCCbQAA7zAEAIBVAACBZQAAvlwDANZAAICG+AAAh2wDANpAAIDeQACA4kAAgOZAAIDqQACA40QEAO5AAIDhjAcAo6UGAPJAAID2QACA+kAAgP5AAICmpQYApbUGAAJBAICrIQYAqpkGAAZBAIAKQQCArxkGAK4tBgCtLQYArDUGAA5BAICz+QcAEkEAgBZBAIC2SQcAGkEAgB5BAIC1UQcAulEHALtRBwAiQQCAJkEAgL41BwC/OQcAvEUHAL09BwCoNQYAqT0GAKo1BgCriQYArJ0GAK2NBgCusQYAr7EGACpBAIAuQQCAMkEAgDZBAICADQAAgbEAAIKxAAA6QQCAuKEGALmtBgC6vQYAu7UGALytBgC9XQEAvlUBAL9NAQCw0QYAsdEGALLVBgCzrQYAtLUGALW5BgC2qQYAt6UGAKO9BgA+QQCAQkEAgISEAgC+kAEApg0GAKUVBgBKQQCAqxUGAKoVBgCGCAAAh3wBAK99BgCucQYArXkGAKwBBgBOQQCAs60BAFJBAIBWQQCAtqkBAFpBAIBeQQCAta0BALptAQC7dQEAYkEAgGZBAIC+XQEAvzUBALxlAQC9VQEAqGECAKlhAgCqYQIAq2ECAKxhAgCtbQIArp0CAK+VAgBqQQCAbkEAgHJBAIB2QQCAekEAgH5BAICCQQCAhkEAgLiVAgC5nQIAuqECALuhAgC8cQMAvXEDAL5xAwC/cQMAsO0CALH1AgCy9QIAs8UCALTdAgC1tQIAtrECALexAgCKQQCAjkEAgJJBAICj5QIAlkEAgKXlAgCm4QIAmkEAgJ5BAICiQQCAqiUCAKs9AgCsLQIArR0CAK4VAgCvfQIApkEAgKpBAICuQQCAhEB8AIAVAACBHQAAggUAALJBAIC+7HwAukEAgIZIfQCHCAMAvkEAgMJBAIDGQQCAykEAgKidAgCpxQIAqsECAKvBAgCsxQIArc0CAK7xAgCv8QIAzkEAgNJBAIDWQQCA2kEAgMkAAADeQQCA4kEAgOZBAIC4wQEAucEBALrBAQC73QEAvM0BAL31AQC+/QEAv50BALBBAQCxQQEAskEBALNBAQC0QQEAtUEBALZBAQC3QQEA4TgGAOpBAIDjaAYA7kEAgPJBAID2QQCA+kEAgISUfQC+rHwA/kEAgAJCAIAGQgCAvrh/AApCAIDvEAEADkIAgBJCAIAWQgCAGkIAgB5CAIDhkAEAIkIAgONEAAAqQgCAgS0AAIAtAADvgAAAgjkAAC5CAIAyQgCA9j8AgDZCAIDhsH8AtkEAgOPUfAA6QgCAJkIAgD5CAICGuAAAh9QCAEJCAIBGQgCASkIAgE5CAIBSQgCAVkIAgO8gfABaQgCAs4l9AF5CAIBiQgCAZkIAgGpCAIC2jX0AtY19AG5CAIC7RX4AukV+AHJCAIB2QgCAv0V+AL5FfgC9VX4AvFV+AKNJfQB6QgCAfkIAgIJCAICGQgCApk19AKVNfQCKQgCAq4V+AKqFfgCOQgCAkkIAgK+FfgCuhX4ArZV+AKyVfgCCbQAAszF+AIBVAACBZQAAtvF/AITcAwCWQgCAtSF+ALrNfwC70X8AhgAEAIfUAAC+dX8Av3l/ALzBfwC9wX8AqOV/AKn1fwCq/X8Aq/V/AKztfwCtNX4Arj1+AK81fgCaQgCAnkIAgKJCAICmQgCAqkIAgK5CAICyQgCAtkIAgLjZfgC54X4AuuF+ALvhfgC85X4Avel+AL6ZfgC/mX4AsE1+ALFRfgCyUX4As1F+ALT1fgC1+X4Atul+ALfpfgCjdX8AukIAgL5CAIDCQgCAxkIAgKa1fgClZX8AykIAgKuVfgCqiX4AzkIAgNJCAICvPX4ArjF+AK2FfgCshX4A1kIAgLMxfgDaQgCA3kIAgLbFAQDiQgCA5kIAgLXRAQC6yQEAu8kBAOpCAIDuQgCAvs0BAL+xAQC8yQEAvckBAKjdfQCp9X0Aqv19AKvxfQCsHQIArQECAK45AgCvOQIA8kIAgPZCAID6QgCA/kIAgIIFAAACQwCAgBEAAIERAAC4EQIAuRkCALohAgC7IQIAvNUCAL3dAgC+1QIAv80CALBJAgCxSQIAslkCALNZAgC0TQIAtTECALYxAgC3MQIAvgADAKNxfQCEiAIAvoAEAKaFAgAKQwCADkMAgKWRAgCqiQIAq4kCAIYoBACHDAMAro0CAK/xAgCsiQIArYkCABJDAICEyAMAhcwFALPlAwAWQwCAteUDALbtAwAaQwCAHkMAgCJDAIC6bQMAu2UDALx9AwC9ZQMAvmUDAL9VAwAmQwCAKkMAgL8ABACjJQIALkMAgKUlAgCmLQIAMkMAgDZDAIA6QwCAqq0CAKulAgCsvQIAraUCAK6lAgCvlQIAPkMAgEJDAIBGQwCASkMAgE5DAIDjzAMAUkMAgOGsAQBWQwCA7xwDAFpDAIBeQwCAYkMAgGZDAIBqQwCAbkMAgOFwfwBGQQCA4wR+AHJDAIB6QwCA4ZQBAH5DAIDjWAEAgNkAAIHZAACCJQAA7+R+AIJDAICGQwCA7+B+AIpDAICzAQEAjkMAgIboBwCHLAQAkkMAgLY1AQC1BQEAlkMAgLvxAAC64QAAmkMAgJ5DAIC/sQAAvtEAAL3ZAAC84QAABkMAgHZDAICiQwCApkMAgKEBBACgEQQAoxkAAKLFBACotQYAqb0GAKrpBgCr/QYArO0GAK3VBgCu3QYArz0HALBFBwCxVQcAslUHALNtBwC0dQcAtRUHALYdBwC3FQcAuC0HALk1BwC6MQcAuw0HALwZBwC9GQcAvgkHAL8JBwCjQQYAqkMAgK5DAICyQwCAtkMAgKZ1BgClRQYAukMAgKuxBwCqoQcAj8ltAL5DAICv8QcArpEHAK2ZBwCsoQcAld11AJTBdACXzXAAli1zAJFdaACQVWgAk9l0AJJNaQCd5XgAnB17AJ9tBwCeuXgAmR1/AJhVcACboXwAmvl8AIJhbACDhWkAwkMAgMZDAICGEXUAhxF1AISVaQCFjWgAij10AIvFcgDKQwCAzkMAgI7dfgCPMX0AjD1xAI2dcQCSGX0Ak716ANJDAIDvkAkAltUGAJdRBQCUXXkAlQl5AJpxBQCbvQUA1kMAgNpDAIDeQwCA4agFAJx5AQDjuAgAoYUBAOJDAICjqQ0AogEMAKUBCACkOQ0Ap6kJAKa9CQCppRUAqAEUAKsBFACq/RUArbkRAKyxEQCvARwArqEQALH9HACw5R0As+kZALIBGAC1ASQAtH0ZAIQUAAC+FAAAgI0AAIGVAACCbQAA6kMAgIZQDwCHZAAA7kMAgPJDAIC61QcAu90HALjBBwC5wQcAvjEEAL8xBAC88QcAvfEHALKtBwCztQcAsK0HALGlBwC2nQcAt/UHALSlBwC1lQcAqmkHAKtpBwCoaQcAqWkHAK5pBwCvaQcArGkHAK1pBwD2QwCA+kMAgP5DAIACRACABkQAgApEAIAORACAEkQAgKgRBQCpHQUAqjkFAKs5BQCsLQUArVEFAK5JBQCvQQUAFkQAgBpEAIAeRACAIkQAgCZEAIAqRACALkQAgDJEAIC4XQIAuWkCALrBAwC7wQMAvPkDAL35AwC+kQMAv7UDALAJBQCxCQUAsuECALPhAgC0dQIAtX0CALZ1AgC3bQIAs7EEAIQAAgC+BA0ANkQAgDpEAIC20QQAtaUEAD5EAIC7zQQAus0EAEJEAIBGRACAv7kDAL6xAwC9NQMAvDUDAEpEAICj9QQATkQAgFJEAICmlQQAWkQAgF5EAICl4QQAqokEAKuJBACHqA0AhswMAK71AwCv/QMArHEDAK1xAwDhUAYA4TQHAONAAADjWAcAgNEAAIHdAACC1QAAYkQAgGZEAIBqRACAbkQAgHJEAIB2RACAekQAgO+cAADvyAcAfkQAgIJEAICzNQIAhkQAgLW1AQCKRACAjkQAgLa1AQC+7AwAkkQAgLuRAQC6mQEAvVEBALyJAQC/UQEAvlkBAKjtDQCp/Q0AqvUNAKttDgCsdQ4ArX0OAK51DgCvbQ4AVkQAgJZEAICaRACAnkQAgKJEAICmRACAqkQAgK5EAIC49Q4Auf0OALr1DgC7QQ8AvEEPAL1JDwC+cQ8Av3EPALAVDgCxHQ4AshUOALPNDgC01Q4Atd0OALbVDgC3zQ4Ao30NALJEAIC2RACAukQAgL5EAICm/Q4Apf0OAMJEAICr2Q4AqtEOAISoAgDGRACArxkOAK4RDgCtGQ4ArMEOAIBNAACBVQAAglUAALNRDwDKRACAtXEPALZxDwDORACAhuAAAIcEAwC6XQ8Auy0PALw1DwC9OQ8Avi0PAL8lDwCoVQ4AqV0OAKqVDgCrrQ4ArLUOAK29DgCutQ4Ar60OANJEAIDWRACA2kQAgN5EAIDiRACA5kQAgOpEAIDuRACAuGkBALlpAQC6eQEAu3kBALxpAQC9aQEAvt0BAL/VAQCw1Q4AsaUOALKtDgCzoQ4AtKUOALWtDgC2nQ4At1kBAKMdDgDyRACA9kQAgOZDAID6RACApj0OAKU9DgD+RACAq2EOAKoRDgACRQCABkUAgK9pDgCuYQ4ArXUOAKx5DgAKRQCADkUAgBJFAIAWRQCAGkUAgB5FAIAiRQCAJkUAgIANAACBFQAAgh0AACpFAIAuRQCAMkUAgIR4AQC+FAAA4xQPADpFAIDh4A0AhAADAIawBACHFAMAPkUAgEJFAIBGRQCASkUAgE5FAIBSRQCA78APAFZFAIBaRQCAXkUAgGJFAIBmRQCAakUAgLNtAwBuRQCAtX0DALZ1AwByRQCAdkUAgHpFAIC6UQMAu1EDALz1AwC9/QMAvukDAL/hAwB+RQCAgkUAgIZFAICKRQCAjkUAgJJFAICWRQCAmkUAgKhxAgCpeQIAqokDAKuJAwCsmQMArZkDAK6JAwCviQMAsPkDALH5AwCyTQMAs0UDALRBAwC1SQMAtnEDALdxAwC4IQMAuSEDALohAwC7IQMAvCEDAL0hAwC+IQMAvyEDAICdAQCBEQAAghEAAIQEBQDvFAAAnkUAgKJFAIC+EAUA48gAAKpFAIDh0AEArkUAgLJFAIC2RQCAukUAgL5FAICqeQIAq3kCAIboBACHYAUArsECAK/JAgCs3QIArdUCAMJFAICjRQIAxkUAgMpFAICmXQIAzkUAgNJFAIClVQIA1kUAgNpFAIDeRQCA4kUAgOZFAIDqRQCA7kUAgO+EDgC+rAQA4dAOAPJFAIDjFAEA9kUAgPpFAID+RQCAAkYAgLPdAQAGRgCACkYAgA5GAIASRgCAtv0BALX9AQAaRgCAu90BALrdAQCE4AQAHkYAgL+hAQC+vQEAvb0BALy9AQCoBQYAqR0GAKoVBgCrLQYArDUGAK09BgCuNQYArykGAKZFAICC9QcAgeUHAIDlBwAWRgCAIkYAgIYcAACHsAMAuCUGALnFBgC6zQYAu8UGALzdBgC9xQYAvs0GAL/FBgCwWQYAsVkGALIpBgCzKQYAtDkGALUlBgC2JQYAtx0GAKOdBgAmRgCAKkYAgC5GAIAyRgCApr0GAKW9BgA2RgCAq50GAKqdBgA6RgCAPkYAgK/hBgCu/QYArf0GAKz9BgBCRgCAs/UHAEZGAIBKRgCAtu0HAE5GAIBSRgCAteUHALqNBwC7kQcAVkYAgFpGAIC+dQcAv30HALyBBwC9fQcAqCUGAKkpBgCqOQYAqzkGAKwpBgCtKQYArnkGAK91BgBeRgCAYkYAgGZGAIBqRgCAbkYAgHJGAIB2RgCAekYAgLjVBgC53QYAuuEGALv9BgC85QYAve0GAL7lBgC/mQYAsA0GALERBgCyEQYAs+0GALT1BgC1/QYAtvUGALftBgCjsQYAgi0AAIEVAACAsQAANkUAgKapBgCloQYAfkYAgKvVBgCqyQYAgkYAgL5oAQCvOQYArjEGAK05BgCsxQYAikYAgLPxAQCGaAAAh3wBALZdAQCORgCAkkYAgLVVAQC6SQEAu0kBAJZGAICaRgCAvj0BAL8hAQC8OQEAvTUBAJ5GAICiRgCAhAQDAL6AHACmRgCA4RwGAKpGAIDjAAYAvwguAK5GAICyRgCA78gHALZGAIC6RgCAvkYAgMJGAIDGRgCAykYAgKN9AgDORgCApdkCANJGAIDWRgCAptECANpGAIDeRgCAq8UCAKrFAgCtuQIArLUCAK+tAgCusQIAqW0FAKhZBQCrDQIAqrkCAK0dAgCsHQIArwUCAK4NAgC+aB0A4kYAgOZGAIDqRgCAgB0AAIEJAACCmQEA7kYAgLnhAwC4KQIAu+EDALrpAwC94QMAvPkDAL/hAwC+6QMAsU0CALBNAgCzIQIAsi0CALUlAgC0OQIAtxECALYlAgCowQIAqdECAKrRAgCr5QIArP0CAK0VAQCuHQEArw0BAPJGAID6RgCA/kYAgAJHAIAGRwCACkcAgA5HAIASRwCAuAUBALkJAQC6HQEAuxUBALwxAQC9MQEAvv0BAL/1AQCweQEAsUEBALJBAQCzXQEAtEUBALVNAQC2RQEAtz0BAIagHQCHxB0AFkcAgO/YAAAaRwCAHkcAgCJHAIDvxAYAhGwcAOH0BgAmRwCA47AGACpHAIDhlAEALkcAgONEBgCzGQIAMkcAgDZHAIA6RwCAhewsALbVAQC1NQIAPkcAgLvFAQC6/QEAQkcAgEZHAIC/yQEAvsEBAL3JAQC81QEAo9kdAPZGAIBKRwCATkcAgFJHAICmFR4ApfUdAFZHAICrBR4Aqj0eAFpHAIBeRwCArwkeAK4BHgCtCR4ArBUeAIBpAACBaQAAggUAAGJHAIBmRwCAakcAgIcQAwCGfAMAbkcAgHJHAIB2RwCAekcAgH5HAICCRwCAhkcAgIpHAICopR8Aqa0fAKqlHwCrvR8ArKUfAK2tHwCupR8ArxUfAI5HAICSRwCAlkcAgJpHAICeRwCAokcAgKZHAICqRwCAuA0fALkZHwC6IR8AuyEfALzZAAC92QAAvskAAL/BAACwcR8AsXEfALJxHwCzRR8AtEEfALVNHwC2PR8AtzUfALMtHgCuRwCAskcAgLZHAIC6RwCAti0eALUtHgC+RwCAu7UeALq1HgDCRwCAxkcAgL+JHgC+hR4AvZEeALylHgCCKQAAo2keAIAdAACBFQAApmkeAMpHAIDORwCApWkeAKrxHgCr8R4A0kcAgITgAQCuwR4Ar80eAKzhHgCt1R4AqNUBAKnlAQCq7QEAq+UBAKz9AQCt5QEAru0BAK/lAQC+oAEAhkYAgNZHAIDaRwCAhhAAAId0AQDeRwCA4kcAgLh9AQC5wQAAusEAALvBAAC8wQAAvckAAL7xAAC/8QAAsJ0BALFFAQCyTQEAs0UBALRdAQC1RQEAtk0BALdFAQDmRwCA6kcAgO5HAIDyRwCA9kcAgO80AgDv7B4A+kcAgOHwHQDj4AIA4zAeAOGEAQD+RwCAAkgAgAZIAIAKSACAsyUCAJQAAAAOSACAEkgAgBZIAIC2JQIAtTUCABpIAIC7wQIAuhkCAB5IAIAiSACAv8ECAL7ZAgC90QIAvNkCACZIAIAqSACALkgAgKPpAgAySACApfkCAKbpAgA2SACAOkgAgD5IAICq1QIAqw0CAKwVAgCtHQIArhUCAK8NAgCAYQAAgWEAAIIFAABCSACASkgAgIQABAC+FAQATkgAgIbABACHUAMAUkgAgFZIAIBaSACAXkgAgGJIAIBmSACAqK0CAKm9AgCqtQIAqw0BAKwVAQCtHQEArhUBAK8NAQCE7AQAakgAgG5IAIBySACAdkgAgHpIAIB+SACAgkgAgLgdAQC5LQEAuiUBALvNAQC81QEAvd0BAL7JAQC/wQEAsH0BALFVAQCyXQEAs1UBALRNAQC1PQEAtjUBALctAQDhGB4AhkgAgOM4HgCKSACAjkgAgJJIAICWSACAmkgAgJ5IAICiSACAvmAEAKZIAICBdQAAgHUAAO/gHwCCbQAAqkgAgK5IAICG6AQAh3wFALJIAIDhkAEAukgAgOOgAAC+SACAwkgAgMZIAIDvtAAAykgAgM5IAIDSSACA1kgAgLUFBgBGSACAtkgAgLYFBgDaSACA3kgAgLOlBQDiSACAvRkGALwRBgC/YQYAvhEGAOZIAIDqSACAuwkGALohBgCj/QUA7kgAgPJIAID2SACA+kgAgKZdBgClXQYA/kgAgKtRBgCqeQYAAkkAgAZJAICvOQYArkkGAK1BBgCsSQYAqFEGAKlZBgCqYQYAq2EGAKxhBgCtYQYArmEGAK9hBgAKSQCADkkAgBJJAIAWSQCAgA0AAIGxAQCCsQEAGkkAgLhNBwC5VQcAul0HALtVBwC8TQcAvXUHAL59BwC/cQcAsMUHALHNBwCyxQcAs90HALTFBwC1zQcAtsUHALd5BwCz6QcAHkkAgCJJAICEwAEAvtgBALbhBwC16QcAJkkAgLsJBgC6AQYAhogAAIesAQC/CQYAvgEGAL0JBgC8EQYAKkkAgKOtBwAuSQCAMkkAgKalBwA2SQCAOkkAgKWtBwCqRQYAq00GAD5JAIBCSQCArkUGAK9NBgCsVQYArU0GAKhZBgCpZQYAqm0GAKtlBgCsYQYArWEGAK5hBgCvYQYAhKwBAEZJAIBKSQCATkkAgFJJAIBWSQCAWkkAgF5JAIC4kQEAuZkBALqhAQC7oQEAvHEBAL1xAQC+cQEAv3EBALDxAQCx8QEAsvUBALPdAQC0xQEAtbEBALaxAQC3sQEAs+UFAGJJAIBmSQCAakkAgG5JAIC24QUAtekFAHJJAIC7NQIAujUCAHZJAIB6SQCAv3UCAL4BAgC9CQIAvCECAH5JAICjoQUAgkkAgIZJAICmpQUAikkAgI5JAIClrQUAqnECAKtxAgCSSQCAvigDAK5FAgCvMQIArGUCAK1NAgCA1QAAgd0AAILhAACaSQCA4yABAJ5JAIDhqAEAokkAgO80AgCmSQCAhggMAIdoAwCsAAAAqkkAgK5JAICySQCAs40DALZJAIC6SQCAhIAMAL5JAIC2vQMAtYEDAMJJAIC7TQMAuk0DAMZJAIDKSQCAv00DAL5NAwC9TQMAvE0DAKhBAgCpTQIAqkUCAKtZAgCsSQIArX0CAK51AgCvuQIAvmgNAM5JAIDSSQCA1kkAgIRsDADaSQCA3kkAgOJJAIC4TQEAuVUBALpVAQC7ZQEAvH0BAL0VAQC+EQEAvxEBALDJAgCxyQIAstkCALPZAgC0yQIAtckCALZ9AQC3dQEA4XgHAOOYAADjuAYA4VwGAOZJAIDqSQCA7kkAgPJJAID2SQCA+kkAgP5JAIACSgCA7AAAAO9cAADv6AYACkoAgIFpAACAYQAAo4UCAIJhAACliQIADkoAgBJKAICmtQIAhkAMAIfEDACrRQIAqkUCAK1FAgCsRQIAr0UCAK5FAgCojQ4AqZEOAKqVDgCrqQ4ArKUOAK2tDgCupQ4Ar9kOAAZKAIAWSgCAGkoAgB5KAIAiSgCAJkoAgCpKAIAuSgCAuHUPALl9DwC6dQ8Au90PALzFDwC9zQ8AvsUPAL/9DwCwqQ4AsbUOALK1DgCzhQ4AtJ0OALVRDwC2UQ8At1EPALMdDgAySgCANkoAgDpKAIA+SgCAti0OALUtDgBCSgCAu3EOALptDgBGSgCASkoAgL+VDwC+WQ4AvVEOALxhDgBOSgCAo1kOAFJKAIBWSgCApmkOAFpKAIBeSgCApWkOAKopDgCrNQ4AYkoAgGZKAICuHQ4Ar9EPAKwlDgCtFQ4AqL0OAKnRDgCq0Q4AqykBAKw5AQCtOQEArikBAK8pAQCADQAAgRUAAIIdAABqSgCAbkoAgHJKAIC+dAIAdkoAgLjtAQC5hQEAuoEBALuBAQC8hQEAvY0BAL6xAQC/sQEAsFkBALFZAQCy7QEAs+UBALT9AQC15QEAtuUBALfVAQB6SgCAtqkBALWhAQB+SgCAs0kOAIJKAICGOAAAh9wBAL8xAQC+KQEAvSEBALwpAQC7jQEAuo0BAJZJAICGSgCAoxkOAIpKAICOSgCAkkoAgJZKAICm+QEApfEBAJpKAICr3QEAqt0BAJ5KAICiSgCAr2EBAK55AQCtcQEArHkBAKZKAIDv3A8AqkoAgK5KAICySgCAtkoAgLpKAIC+SgCAwkoAgMZKAIDKSgCAzkoAgNJKAIDj6A4A1koAgOGMDgCAEQAAgREAAIIRAACEQAIA2koAgN5KAIDiSgCAvhADAIbABACHRAMA6koAgO5KAIDySgCA9koAgPpKAID+SgCA7yQCAAJLAIAGSwCACksAgA5LAIASSwCAFksAgBpLAICE7AQAHksAgCJLAIAmSwCA4+wCACpLAIDhOAEALksAgLNVAwAySwCANksAgDpLAIA+SwCAth0DALUdAwBCSwCAuwkDALo5AwBGSwCASksAgL/9AAC+/QAAvfkAALwRAwCogQIAqYkCAKqdAgCrsQIArNUCAK3dAgCu1QIAr80CAIDNAQCBCQAAghkAAE5LAIBSSwCAWksAgL5wBQBeSwCAuFkBALlZAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9lAQCwvQIAsY0CALKFAgCzbQEAtHkBALV5AQC2aQEAt2kBAIYgBACHCAUAYksAgGZLAIBqSwCAbksAgHJLAIDvXAAAhOwEAOFcDgB2SwCA44wOAHpLAIB+SwCAgksAgIZLAICjVQIAiksAgI5LAICSSwCAlksAgKYdAgClHQIAmksAgKsJAgCqOQIAnksAgKJLAICv/QEArv0BAK35AQCsEQIAqGkGAKlpBgCqeQYAq3kGAKxpBgCtaQYArp0GAK+VBgBWSwCApksAgKpLAICuSwCAsksAgLZLAIC6SwCAvksAgLj1BgC5+QYAuo0GALuFBgC8nQYAvYUGAL6FBgC/tQYAsO0GALH1BgCy/QYAs/UGALTtBgC10QYAttEGALfRBgCz8QYAghUAAIG1AACAtQAAwksAgLbpBgC14QYAvtQDALsxBgC6KQYAxksAgMpLAIC/FQYAvikGAL0hBgC8KQYAzksAgKO1BgCGyAAAh8gAAKatBgDSSwCA1ksAgKWlBgCqbQYAq3UGANpLAIDeSwCArm0GAK9RBgCsbQYArWUGAKg1BgCpOQYAqoEGAKuBBgCsgQYArYEGAK6BBgCvtQYA4ksAgOZLAIDqSwCA7ksAgPJLAID2SwCA+ksAgP5LAIC4nQYAua0GALqlBgC7aQEAvHkBAL15AQC+aQEAv2kBALDRBgCx0QYAstEGALPRBgC0tQYAtb0GALa1BgC3rQYAswkGAAJMAIAGTACACkwAgA5MAIC2AQYAtQkGABJMAIC7FQYAuhUGABZMAIAaTACAv3kGAL5xBgC9BQYAvAUGAB5MAICjTQYAIkwAgOZKAICmRQYAJkwAgCpMAIClTQYAqlEGAKtRBgAuTACAMkwAgK41BgCvPQYArEEGAK1BBgCB6QMAgN0DAISIAwCC4QMAhrA8AIeIAgC+VAMAOkwAgD5MAIBCTACARkwAgEpMAIBOTACAUkwAgFZMAIBaTACA4/AGAF5MAIDhMAYAhAA8AGJMAIBmTACAakwAgG5MAIByTACAhTQ9AHZMAIB6TACA77AHAH5MAICCTACAhkwAgIpMAICOTACAkkwAgL7EPACWTACAgp0BAIGdAQCAnQEAqA0CAKllAgCqfQIAq3UCAKxZAgCtWQIArpkDAK+ZAwCw6QMAsekDALL5AwCz+QMAtOkDALXpAwC2XQMAt1UDALhtAwC5dQMAunUDALtFAwC8XQMAvTUDAL4xAwC/KQMAmkwAgJ5MAICiTACAqkwAgOFgAwDv9AMA40QCAK5MAICyTACA4zwDAO/0NwDh/AEAtkwAgLpMAIC+TACAwkwAgIZkPwCHaD0AhTQhALOZAwDGTACAtb0DALa1AwDKTACAzkwAgNJMAIC6QQIAu0ECALxBAgC9QQIAvkECAL9BAgDWTACA2kwAgN5MAIDiTACA5kwAgOpMAIDuTACA7/gBAIRoPADhPAYA8kwAgOMcBgD2TACA+kwAgP5MAIACTQCAoxUDAAZNAIAKTQCADk0AgBJNAICmOQMApTEDABpNAICrzQIAqs0CAL5kPgAeTQCAr80CAK7NAgCtzQIArM0CAKgdPgCpJT4Aqi0+AKslPgCsPT4ArSU+AK4tPgCvJT4ApkwAgIL1PwCB5T8AgOU/ABZNAIAiTQCAhgAEAIecAwC4LT4AuTE+ALoxPgC7MT4AvNE+AL3RPgC+0T4Av80+ALBdPgCxIT4Asjk+ALM5PgC0KT4AtSk+ALYZPgC3FT4As6U+ACZNAIAqTQCALk0AgDJNAIC2pT4AtbU+ADZNAIC75T4Aupk+ADpNAIA+TQCAv+0+AL7tPgC97T4AvO0+AEJNAICj4T4ARk0AgEpNAICm4T4ATk0AgFJNAICl8T4Aqt0+AKuhPgBWTQCAWk0AgK6pPgCvqT4ArKk+AK2pPgCPBSUAsyU+AF5NAIBiTQCAtik+AGZNAIBqTQCAtSk+ALp9PgC7RT4Abk0AgHJNAIC+tT4Av70+ALxdPgC9vT4An304AJ5lOQCd8TgAnFE0AJtZNQCaUTUAmfEwAJgNMQCXZTEAlsEwAJVZLQCUTS0Ak+EsAJLZKQCRWSkAkPEoALSlGQC13RgAdk0AgIQIAACwkRUAsQEVALIBGACzvRkAgA0AAIGtAwCCpQMAek0AgKNhAACiHT0AoZk9AKBxPACkxQUApUEEAKYBCACn4QkANkwAgKH1AQCi6QEAo90FAKwBEACtxREArtkRAK85EACoZQgAqQEMAKrZDQCrCQ0AijEuAIuhMwB+TQCAgk0AgI65MwCPETYAjB0yAI1NMgCCJSYAg6krAL5kAwCEYAQAhqEvAIcVLgCEGSoAhZEqAJphPgCb7T4AhsgEAIfcAwCKTQCA4Vw+AJyJAwDjAD4Akmk2AJN5NwCOTQCA7xg+AJZNOwCXuT8AlME7AJVdOgCpnT0AqIk9AKu5PQCqrT0Arak9AKyhPQCvyT0ArqE9AL7oBACSTQCAlk0AgJpNAICeTQCAok0AgKZNAICqTQCAuVk9ALhRPQC7eT0AumU9AL1pPQC8YT0Avx09AL5hPQCxgT0AsLk9ALNpPQCyiT0AtXk9ALRxPQC3aT0AtnE9AKMhPACuTQCAsk0AgLZNAIC6TQCApi08AKUtPAC+TQCAq0E8AKp5PADCTQCAxk0AgK+5PACusTwArbk8AKxZPADKTQCAzk0AgLN9AwDSTQCAtdkDANZNAIDaTQCAttEDAN5NAIDiTQCAu8UDALrFAwC9uQMAvLUDAL+tAwC+sQMA5k0AgOpNAIDuTQCA71wDAIAVAACBHQAAgjEAAO+MPgCE7AQA4fw+APJNAIDjHD4A+k0AgOGUAQD+TQCA4yAAAKP1AwACTgCAh+gEAIZsBAAGTgCAplkDAKVRAwAKTgCAq00DAKpNAwAOTgCAEk4AgK8lAwCuOQMArTEDAKw9AwCGTQCA9k0AgBZOAIAaTgCAHk4AgCJOAIAmTgCAKk4AgKhxBgCpTQYAqo0GAKuFBgCsnQYArYUGAK6NBgCvhQYAsP0GALFBBwCyQQcAs0EHALRBBwC1SQcAtnEHALdxBwC4IQcAuSEHALolBwC7OQcAvCkHAL0VBwC+HQcAv/0HALMlBgAuTgCAMk4AgDZOAIA6TgCAtiUGALU1BgA+TgCAu6UHALoZBgBCTgCARk4AgL+tBwC+pQcAvbUHALy1BwBKTgCAo2EGAE5OAIBSTgCApmEGAFZOAIBaTgCApXEGAKpdBgCr4QcAXk4AgGJOAICu4QcAr+kHAKzxBwCt8QcAqLEGAKm9BgCqzQYAq90GAKzNBgCt/QYArvUGAK8VAQCA+QEAgc0BAILFAQC+ZAIAhpAAAIcAAQBqTgCAbk4AgLjRAQC52QEAuuEBALvhAQC8kQEAvZ0BAL6VAQC/iQEAsG0BALF1AQCyfQEAs3UBALRtAQC18QEAtvEBALfxAQCzRQYAZk4AgHJOAIB2TgCAek4AgLZ9BgC1RQYAfk4AgLuxAQC6qQEAgk4AgIZOAIC/NQEAvqkBAL2hAQC8qQEAik4AgKMBBgCOTgCAkk4AgKY5BgCWTgCAmk4AgKUBBgCq7QEAq/UBAJ5OAICiTgCAru0BAK9xAQCs7QEAreUBAOEoAQCmTgCA41ACAKpOAICuTgCAsk4AgLZOAIC6TgCAvk4AgMJOAIDGTgCAyk4AgIFxAACAGQAA75wCAIJ5AADOTgCA0k4AgITIAgCzxQMA2k4AgLXFAwC2xQMAvhADAIbADACHRAwAuqkDALulAwC8vQMAvaEDAL6hAwC/lQMArhEGAK8ZBgCsAQYArQEGAKqlBgCrEQYAqEU5AKlxOQDeTgCA4k4AgOZOAIDqTgCA7k4AgPJOAID2TgCA+k4AgL7tBwC/TQcAvNEHAL3lBwC63QcAu8EHALg1BgC51QcAtjkGALcNBgC0JQYAtTkGALIxBgCzPQYAsFEGALFRBgCoOQIAqTkCAKqBAgCrgQIArIECAK2JAgCusQIAr7ECAIRsDQD+TgCAvmANAAJPAIAGTwCACk8AgA5PAIASTwCAuE0BALlVAQC6XQEAu1UBALxNAQC9dQEAvn0BAL91AQCwoQIAsa0CALKlAgCzuQIAtKkCALWdAgC2lQIAt3kBAOFUBgDh1AcA4zgGAOOwBwAWTwCAGk8AgB5PAIAiTwCAhOQMACZPAIAqTwCALk8AgDJPAIA2TwCA72wAAO/kBwCjSQIAOk8AgD5PAIBCTwCASk8AgKZJAgClSQIATk8AgKspAgCqJQIAhkgMAIfcDACvGQIAri0CAK0tAgCsMQIAqFEOAKmlDgCqrQ4Aq6UOAKy9DgCtpQ4Arq0OAK+lDgCA5Q8Age0PAILlDwBGTwCAUk8AgFZPAIBaTwCAXk8AgLjVDwC53Q8AutUPALvpDwC8+Q8AvfkPAL7pDwC/6Q8AsN0OALFBDwCyRQ8As10PALRFDwC1TQ8AtkUPALftDwCzJQ4AYk8AgGZPAIBqTwCAbk8AgLYlDgC1NQ4Ack8AgLuFDwC6GQ4Adk8AgHpPAIC/iQ8AvoEPAL2JDwC8kQ8Afk8AgKNhDgCCTwCAhk8AgKZhDgCKTwCAjk8AgKVxDgCqXQ4Aq8EPAJJPAICWTwCArsUPAK/NDwCs1Q8Arc0PAKjRDgCp2Q4AqjkBAKs5AQCsKQEArSkBAK6dAQCvlQEAmk8AgJ5PAICiTwCApk8AgIANAACBtQAAgr0AAKpPAIC4lQEAuZ0BALqhAQC7oQEAvHEAAL1xAAC+cQAAv3EAALDtAQCx9QEAsvUBALPFAQC03QEAtbUBALaxAQC3sQEArk8AgLJPAICzuQEAvsACALWpAQC2TwCAuk8AgLahAQCGgAEAh8QBALs5AQC6IQEAvRkBALwpAQC/eQEAvhEBAKPxAQC+TwCA1k4AgMJPAIDGTwCApukBAKXhAQDKTwCAq3EBAKppAQDOTwCA0k8AgK8xAQCuWQEArVEBAKxhAQDWTwCA2k8AgN5PAIDiTwCA4agBAOZPAIDjQAIA6k8AgL8oFQDuTwCA73QCAPJPAID2TwCA+k8AgP5PAIACUACABlAAgON0DwCEiAMA4TQOAApQAIAOUACAElAAgBZQAICADQAAgRUAAIIRAAAaUACAHlAAgO+kDwAiUACAKlAAgKgZAwCpQQMAqkUDAKtdAwCsTQMArX0DAK51AwCvnQAAhaQVAL58AwCGCAQAhxwDAC5QAIAyUACANlAAgDpQAIC49QAAuf0AALr1AAC7jQAAvIEAAL2BAAC+gQAAv4EAALDlAACx7QAAsuUAALP5AAC07QAAtdEAALbVAAC3zQAAPlAAgEJQAIBGUACAs8ECAEpQAIC1yQIAtvECAE5QAIBSUACAVlAAgLotAQC7JQEAvD0BAL0hAQC+JQEAvxkBAKapAgCESAIAWlAAgKWRAgBeUACAo5kCAGJQAIBmUACArn0BAK9BAQCsZQEArXkBAKp1AQCrfQEAalAAgG5QAIByUACAdlAAgHpQAIB+UACA7+QAAIJQAICGUACAilAAgOMQDgCOUACA4VgOAJJQAICALQAAgREAAIIVAAC+sAUAs3UBAJpQAICHFAUAhmwEAJ5QAIC21QAAtWUBAKJQAIC7/QAAuvUAAKZQAICqUACAv6EAAL69AAC93QAAvN0AAKh9BgCptQYAqr0GAKu1BgCsrQYArRUHAK4dBwCvFQcAllAAgK5QAICyUACAtlAAgLpQAIC+UACAwlAAgMZQAIC4OQcAuTkHALrJBwC7yQcAvNkHAL3ZBwC+zQcAv8UHALBxBwCxeQcAskkHALNJBwC0OQcAtSUHALYhBwC3IQcAozUGAMpQAIDOUACA0lAAgNZQAICmlQcApSUGANpQAICrvQcAqrUHAN5QAIDiUACAr+EHAK79BwCtnQcArJ0HAOZQAIDqUACA7lAAgPJQAID2UACAgj0AAIE9AACAPQAA+lAAgP5QAIACUQCAhKADAL6kAwAGUQCAhvgAAIfgAACoxQYAqdUGAKrVBgCr5QYArP0GAK0xAQCuMQEArzEBAApRAIAOUQCAElEAgBZRAIAaUQCAHlEAgCJRAIAmUQCAuN0BALntAQC65QEAu40BALyVAQC9nQEAvpUBAL+NAQCwUQEAsVEBALJRAQCzUQEAtPUBALX9AQC29QEAt+0BALNdBgAqUQCALlEAgDJRAIA2UQCAtrEBALV1BgA6UQCAu5UBALqVAQA+UQCAQlEAgL85AQC+MQEAvYUBALyFAQClLQYARlEAgEpRAICm6QEATlEAgFJRAICjBQYAVlEAgK3dAQCs3QEAr2EBAK5pAQBaUQCAJlAAgKvNAQCqzQEAXlEAgGJRAICExAMAvwD0AGZRAICCPQAAgT0AAIA9AABqUQCAblEAgHJRAIC+YAMAelEAgH5RAICCUQCAhlEAgIbgHACHAAMA7wwHAIpRAICOUQCAklEAgJZRAICaUQCAnlEAgKJRAICmUQCAqlEAgOHABgCuUQCA4ywHALJRAIC2UQCAulEAgL5RAIDCUQCAxlEAgMpRAIDOUQCA0lEAgKiBAwCpgQMAqoEDAKuBAwCsgQMArYEDAK6BAwCvgQMAsEUDALFNAwCyRQMAs10DALRNAwC1fQMAtnUDALcZAwC4KQMAuTUDALo9AwC7MQMAvAEDAL31AAC+/QAAv+0AALMpAgDWUQCA2lEAgN5RAIDiUQCAtiECALUpAgCEUB0Au6kCALqhAgDqUQCA7lEAgL+ZAgC+qQIAvakCALyxAgCBTQAAgE0AAO+cAwCCXQAAhvAcAId4HQC+EB0A8lEAgPZRAID6UQCA/lEAgAJSAIDhkAEABlIAgONgAwAKUgCADlIAgBJSAIAWUgCAGlIAgB5SAIAiUgCAJlIAgO+UAQCE7BwA4XAGACpSAIDjUAEALlIAgDJSAIA2UgCAOlIAgKPpAgA+UgCAQlIAgEZSAIBKUgCApuECAKXpAgBOUgCAq2kCAKphAgBSUgCAvqgcAK9ZAgCuaQIArWkCAKxxAgCoMR4AqTEeAKoxHgCrMR4ArF0eAK1FHgCuTR4Ar0UeAOZRAICCzR8AgfUfAID9HwBWUgCAWlIAgIYcAACH+AMAuMUeALnNHgC6xR4Au90eALzFHgC9zR4AvsUeAL9ZHwCwPR4AsQUeALINHgCzBR4AtB0eALUBHgC2BR4At/0eALO5HgBeUgCAYlIAgGZSAIBqUgCAtsUeALXVHgBuUgCAu8EeALr5HgByUgCAdlIAgL/FHgC+2R4AvdEeALzZHgB6UgCAo/0eAH5SAICCUgCApoEeAIZSAICKUgCApZEeAKq9HgCrhR4AjlIAgJJSAICunR4Ar4EeAKydHgCtlR4AqCkeAKkpHgCqVR4Aq20eAKx1HgCtfR4ArnUeAK9pHgCWUgCAmlIAgJ5SAICiUgCAplIAgKpSAICuUgCAslIAgLjpHgC59R4Auv0eALv1HgC87R4AvZEeAL6RHgC/kR4AsB0eALHlHgCy7R4As+UeALT9HgC15R4Atu0eALflHgCz3R4AtlIAgLpSAIC+UgCAwlIAgLb9HgC1/R4AhFgBALshHgC62R4AvigAAMpSAIC/IR4AvjkeAL0xHgC8OR4AgU0AAIBNAACjlR4Agl0AAKW1HgDGUgCAzlIAgKa1HgB2UQCA0lIAgKtpHgCqkR4ArXkeAKxxHgCvaR4ArnEeAIYABACHRAMAs4ECANZSAIC1gQIA2lIAgN5SAIC2gQIAiAAAAOJSAIC74QIAuu0CAL3lAgC8+QIAv9ECAL7lAgDmUgCA6lIAgIREAwC+jAMA4UgCAO5SAIDjAAIA7/wfAPJSAIDhPB4A79wCAONgHwD2UgCA+lIAgP5SAIACUwCAqQUCAKixAgCrBQIAqgUCAK0NAgCsBQIArzUCAK41AgCEbAUABlMAgApTAIAOUwCAElMAgBZTAIAaUwCAHlMAgLnpAwC44QMAu/kDALrhAwC96QMAvOEDAL9dAwC+4QMAsSkCALAlAgCzPQIAsiECALUZAgC0LQIAt9kDALYRAgAiUwCAJlMAgCpTAICjhQMALlMAgKWFAwCmhQMAMlMAgDpTAIA+UwCAqukDAKvlAwCs/QMAreEDAK7hAwCv1QMAgEkAAIFVAACCVQAAo6kCAL6YBAClQQEApkEBAEJTAICG4AUAh+AFAKotAQCrOQEArBEBAK0FAQCuDQEArwUBAEZTAIBKUwCATlMAgO/cAABSUwCAVlMAgFpTAIDviB4AhCwHAOHsHgBeUwCA4xweAGJTAIDhlAEAZlMAgOMwAACzJQIAhWDmAGpTAIBuUwCAclMAgLbNAQC1zQEAdlMAgLu1AQC6oQEAelMAgH5TAIC/iQEAvoEBAL2JAQC8nQEANlMAgIJTAICGUwCAilMAgI5TAICSUwCAllMAgJpTAICoAQcAqQEHAKp1BwCrrQcArLUHAK29BwCuqQcAr6kHALDZBwCx7QcAsvkHALP1BwC0mQcAtZkHALaJBwC3gQcAuIkHALmJBwC6bQAAu2UAALx9AAC9ZQAAvm0AAL9lAACBCQAAgJkAAJ5TAICCHQAAolMAgKZTAICqUwCArlMAgKgNBQCpfQUAqk0FAKuhBgCspQYAra0GAK6dBgCv/QYAsIUGALGRBgCyqQYAs70GALSlBgC1rQYAtqUGALd5BgC4SQYAuUkGALpZBgC7WQYAvEkGAL1JBgC++QcAv/kHALNdBgCyUwCAhigCAIcsAQC2UwCAtp0GALWdBgC6UwCAu4kGALq9BgC+UwCAwlMAgL/9BgC+/QYAvYEGALyNBgDGUwCAoxkGAMpTAIDOUwCAptkGANJTAIDWUwCApdkGAKr5BgCrzQYA2lMAgN5TAICuuQYAr7kGAKzJBgCtxQYAqBkBAKkZAQCqjQAAq50AAKyNAACtvQAArrUAAK/dAADiUwCA5lMAgOpTAIDuUwCA8lMAgPZTAID6UwCA/lMAgLhpAAC5aQAAunkAALt5AAC8aQAAvWkAAL7dAwC/1QMAsKkAALGpAACyvQAAs7UAALSZAAC1mQAAtlkAALdZAAC+LAIAAlQAgAZUAIAKVACADlQAgBJUAIAaVACAHlQAgIAtAACBNQAAgj0AACJUAICGkAwAh+gCACZUAIAqVACAs0UDAC5UAIAyVACANlQAgDpUAIC2fQMAtUUDAD5UAIC7LQMAui0DAEJUAIBGVACAvx0DAL4dAwC9IQMAvCkDAKvNAwCqzQMASlQAgE5UAICv/QMArv0DAK3BAwCsyQMAo6UDAFJUAIBWVACAWlQAgF5UAICmnQMApaUDAGJUAIBmVACAalQAgG5UAIByVACAdlQAgII9AACBPQAAgD0AAHpUAIB+VACAglQAgIRgAwCG0AwAhzADAIpUAICOVACAvkQCAJJUAICWVACAmlQAgOEAAACeVACA46gGAKJUAICE7AwAplQAgO/QAwCqVACArlQAgLJUAIC2VACAulQAgLNtAQC+VACAwlQAgMZUAIDKVACAthEBALVlAQDOVACAuz0BALo1AQDSVACA1lQAgL/9AQC+/QEAvRUBALwVAQDaVACA4fwGAN5UAIDjPAcA4lQAgOZUAIDqVACA7lQAgPJUAIC+bAwA+lQAgP5UAIACVQCABlUAgApVAIDvFAYAgV0AAIBdAACj5QEAgm0AAKXtAQAOVQCAElUAgKaZAQCHqAwAhuQMAKu1AQCqvQEArZ0BAKydAQCvdQEArnUBAKgZDgCpGQ4AqiUOAKs1DgCsLQ4ArVEOAK5RDgCvUQ4AhlQAgPZUAIAWVQCAGlUAgB5VAIAiVQCAJlUAgCpVAIC47Q4AufUOALr1DgC7jQ4AvJUOAL2dDgC+lQ4Av40OALAxDgCxOQ4AsgEOALMBDgC0+Q4AtfkOALbdDgC31Q4AqHkOAKl5DgCqjQ8Aq4UPAKydDwCtgQ8AroUPAK+5DwAuVQCAMlUAgDZVAIA6VQCAPlUAgEJVAIBGVQCASlUAgLiRDwC5mQ8AuqEPALuhDwC8UQ8AvV0PAL5JDwC/SQ8AsM0PALHVDwCy3Q8As9UPALTNDwC1sQ8AtrEPALexDwCzBQ4ATlUAgFJVAIBWVQCAWlUAgLYBDgC1FQ4AXlUAgLsRDgC6CQ4AYlUAgISgAQC/dQ4AvgkOAL0BDgC8CQ4AgmkAAKNBDgCAWQAAgVEAAKZFDgC+WAEAZlUAgKVRDgCqTQ4Aq1UOAIbIAACHrAEArk0OAK8xDgCsTQ4ArUUOAGpVAIBuVQCAclUAgHZVAIB6VQCAflUAgBZUAICCVQCAqAkOAKkJDgCqGQ4AqxkOAKwJDgCtYQ4ArmEOAK+VAQCw7QEAsfUBALL9AQCz9QEAtO0BALV1AQC2fQEAt3UBALhNAQC5VQEAul0BALtVAQC8TQEAvfEAAL7xAAC/8QAAhlUAgIpVAICOVQCAklUAgJZVAIDj6A4AmlUAgOE0DgC+AAQA79wPAJ5VAICiVQCAplUAgKpVAICuVQCAslUAgLPxDQC2VQCAulUAgL5VAIDCVQCAtoENALXhDQDGVQCAu1ECALpJAgDKVQCAzlUAgL/RAgC+SQIAvUECALxJAgCjMQ0A0lUAgISIAwDaVQCA3lUAgKZBDQClIQ0A4lUAgKuRAgCqiQIA5lUAgOpVAICvEQIArokCAK2BAgCsiQIAgKkAAIGpAACCTQAA7lUAgOFkEgDjTAIA4wgLAOGsAQDyVQCA7zwCAO8YFgD2VQCAhlAGAIdIAwD6VQCA/lUAgKiBAgCpgQIAqoECAKuBAgCsgQIArYECAK6FAgCvHQEAAlYAgAZWAIAKVgCADlYAgBJWAIAWVgCAGlYAgIS4BQC4dQEAuX0BALp1AQC7CQEAvBkBAL0ZAQC+CQEAvwEBALBlAQCxbQEAsmUBALN9AQC0aQEAtV0BALZVAQC3TQEAHlYAgCJWAIAmVgCAKlYAgC5WAIAyVgCA7zQAAO/ADgDhXA4A4UwPAOOUAADjnA4ANlYAgIJlAACBfQAAgH0AADpWAIA+VgCAvsQHALNFAgBCVgCAtUUCALZNAgBKVgCAhkAGAIeQBAC67QEAu+UBALz9AQC95QEAvuEBAL/VAQCflQgAngUIAJ3dDQCcPQwAmzEMAJr1DQCZ7RAAmD0QAJfVEQCWsRUAlQUUAJTlFQCTtRkAkjEYAJE5GACQDRwAj2EcANZVAICz1QYATlYAgLX9BgBGVgCAUlYAgLaRBgBWVgCAWlYAgLuVBgC6lQYAvVUHALxVBwC/VQcAvlUHAF5WAIBiVgCAqo0GAKuFBgCsnQYArYUGAK6BBgCvtQYAhKgAAGZWAIBqVgCAoyUFAG5WAIClJQUApi0FAHJWAIB2VgCAelYAgH5WAICCVgCAhlYAgIpWAICOVgCAklYAgJZWAICaVgCAnlYAgKJWAICjqQUAotEEAKHZBACgZQUAgiEdAIM1HQCmVgCAqlYAgIaVGACH3RQAhBkZAIUZGQCKDRUAi7EUAK5WAICyVgCAjsURAI/VDACMzRAAjR0RAJJhDQCTdQ0AvkwAALpWAICWxQkAl80EAJSNDACVXQkAmkEFAJtBBQCGyP8Ah0wAAIFZAACAeQAAnCEEAIJRAAChxQEAvlYAgKMB/ACi2QEApRX9AKS1/QCnufkApgH4AKkJ+AColfkAqwX1AKqt9QCtsfEArAHwAK8d8ACurfEAseHtALAB7ACzAegAsv3sALVd6QC09ekAwlYAgMZWAIDKVgCAzlYAgNJWAIDWVgCA2lYAgN5WAIDiVgCA5lYAgKiNBACplQQAqpUEAKulBACsvQQArdkEAK75BACv8QQAhGz8AOpWAIDuVgCA8lYAgPZWAID6VgCA/lYAgAJXAIC4eQUAucUFALrNBQC7xQUAvN0FAL3FBQC+zQUAv+0FALCZBACxmQQAskkFALNJBQC0WQUAtVkFALZJBQC3SQUAox0EAL7M/AAGVwCAClcAgA5XAICmWQQApTUEABJXAICrXQQAql0EABZXAIAaVwCAr50FAK6dBQCtnQUArJ0FAB5XAICznQIAIlcAgCpXAIC2UQIALlcAgDJXAIC1uQIAukkCALtVAgCGSP0Ah8D8AL41AgC/PQIAvEUCAL09AgCo3QQAqUkDAKpRAwCrbQMArHUDAK2VAwCunQMAr7kDAICNAQCB5QEAguEBADZXAIA6VwCAPlcAgEJXAIBGVwCAuJUDALmdAwC6lQMAu60DALy1AwC9vQMAvrUDAL9VAgCwyQMAsdUDALLVAwCzrQMAtLUDALW9AwC2tQMAt60DAEpXAIBOVwCAo9EDAFJXAICl9QMAVlcAgFpXAICmHQMAXlcAgGJXAICrGQMAqgUDAK1xAwCsCQMAr3EDAK55AwDhKAcAZlcAgOPkBgBqVwCA4SgGAG5XAIDjaAEAclcAgHZXAIB6VwCA71gAAH5XAICCVwCAhlcAgO/IBgCKVwCAqE39AKmB/QCq0f0Aq9H9AKzx/QCt8f0ArvH9AK/x/QAmVwCAghEAAIEZAACA0f8AjlcAgJJXAICEdAMAvnQDALh1/gC5ff4AunX+ALvF/gC83f4AvcX+AL7F/gC/9f4AsJH9ALGR/QCykf0As5H9ALRV/gC1Xf4AtlX+ALdN/gCzWf0AllcAgIasAACHRAMAmlcAgLZx/QC1ef0AnlcAgLtV/QC6Vf0AolcAgKZXAIC/mf4AvpH+AL1F/QC8Rf0AqlcAgKMd/QCuVwCAslcAgKY1/QC2VwCAulcAgKU9/QCqEf0AqxH9AL5XAIDCVwCArtX+AK/d/gCsAf0ArQH9AKjN/wCp0f8AqtH/AKsh/gCsIf4ArSH+AK4h/gCvIf4AxlcAgMpXAIDOVwCA0lcAgNZXAIDaVwCA3lcAgOJXAIC4jf4AuZH+ALqV/gC7rf4AvLX+AL25/gC+qf4Av6n+ALDh/gCx4f4AsuX+ALP5/gC06f4AtdX+ALbd/gC3uf4As1n/AOZXAIC2VgCA6lcAgO5XAIC2of4Atan+APJXAIC7Jf4AuiX+APZXAID6VwCAvxH+AL4t/gC9Lf4AvDH+AIIZAACjHf8AgGUAAIEZAACm5f4A/lcAgAJYAICl7f4AqmH+AKth/gCEZAEAviAAAK5p/gCvVf4ArHX+AK1p/gAKWACA4zT+AA5YAIDhfP0AhrAEAIcIAwASWACAFlgAgBpYAIAeWACAhCQDAIQkBAAiWACA70j+ACZYAIAqWACAs+kCAC5YAIC+RAQAvkAFADJYAIC2nQIAtZkCADZYAIC7iQIAur0CADpYAIA+WACAv1kDAL5RAwC9WQMAvJECAKkdAgCoFQIAqyUCAKolAgCtWQIArFUCAK9NAgCuUQIAvmQGAEJYAIBGWACASlgAgE5YAIBSWACAVlgAgFpYAIC5+QMAuPEDALtNAwC68QMAvUEDALxZAwC/cQMAvkEDALEJAgCwPQIAs8kDALIBAgC12QMAtNEDALfJAwC20QMA4ZABAF5YAIDj8AAAYlgAgGZYAICCPQAAgT0AAIA9AABqWACAblgAgHJYAIB6WACAflgAgIJYAIDvLAAAhlgAgKPpAwCKWACAhugEAIdgBQCOWACApp0DAKWZAwCSWACAq4kDAKq9AwCWWACAmlgAgK9ZAgCuUQIArVkCAKyRAwCeWACAolgAgKZYAICqWACArlgAgLJYAIC2WACA71gBAISgBADhVP8AulgAgOOEAQC+WACAwlgAgMZYAIDKWACAs9kBAM5YAICFzBkA0lgAgNZYAIC28QEAtfkBANpYAIC7pQEAutkBAN5YAIDiWACAv50BAL6dAQC9pQEAvK0BAKgBBgCpDQYAqhEGAKsRBgCsMQYArTEGAK4pBgCvJQYAdlgAgILJBwCBwQcAgPEHAOZYAIDqWACAhhwAAIf8AwC47QYAufUGALr9BgC79QYAvO0GAL1RBwC+VQcAv00HALBdBgCxIQYAsjkGALMxBgC0GQYAtRkGALbdBgC31QYAo5kGAO5YAIDyWACA9lgAgPpYAICmsQYApbkGAP5YAICr5QYAqpkGAAJZAIAGWQCAr90GAK7dBgCt5QYArO0GAApZAICz8QcADlkAgBJZAIC2gQcAFlkAgBpZAIC1mQcAuo0HALtlBwAeWQCAIlkAgL59BwC/ZQcAvH0HAL11BwCoLQYAqTUGAKo9BgCrMQYArFUGAK1FBgCuRQYAr3UGACZZAIAqWQCALlkAgDJZAIA2WQCAOlkAgD5ZAIBCWQCAuOkGALn1BgC6/QYAu/UGALztBgC9kQYAvpUGAL+NBgCwDQYAseUGALLtBgCz5QYAtP0GALXlBgC27QYAt+UGAKO1BgBGWQCASlkAgE5ZAIBSWQCApsUGAKXdBgAGWACAqyEGAKrJBgBWWQCAWlkAgK8hBgCuOQYArTEGAKw5BgCASQAAgUkAAIJZAACzRQEAXlkAgLVFAQC2RQEAYlkAgIZAAACHZAAAuikBALslAQC8PQEAvSEBAL4hAQC/FQEAZlkAgGpZAICEBAMAvgAMAOMoBgDv4AIA4RAGAG5ZAIDvkAYA4zwCAHJZAIDh1AEAdlkAgHpZAIB+WQCAglkAgIZZAICKWQCAo8ECAI5ZAIClwQIAklkAgJZZAICmwQIAmlkAgJ5ZAICroQIAqq0CAK2lAgCsuQIAr5ECAK6lAgCpBQIAqLECAKsFAgCqBQIArQ0CAKwFAgCvNQIArjUCAISoDACiWQCAplkAgKpZAICuWQCAslkAgLZZAIC6WQCAuekDALjhAwC7+QMAuuEDAL3pAwC84QMAv10DAL7hAwCxKQIAsCUCALM9AgCyIQIAtRkCALQtAgC32QMAthECAKitAgCp1QIAqtUCAKsNAQCsFQEArQkBAK4xAQCvLQEAvlkAgMJZAIDKWQCAzlkAgNJZAIDWWQCA2lkAgN5ZAIC4IQEAuSEBALrtAQC75QEAvP0BAL3lAQC+7QEAv+UBALBVAQCxXQEAslUBALMtAQC0NQEAtTkBALYtAQC3JQEAgD0BAIGlAACCrQAA79QHAOJZAIDmWQCA6lkAgO8oBwC+LAwA4fQGAO5ZAIDjkAcA8lkAgOGUAQD2WQCA4wwGALMdAgD6WQCAh0QNAIZMDQD+WQCAtskBALXdAQACWgCAu9kBALrRAQAGWgCACloAgL+9AQC+sQEAvbkBALzBAQDGWQCADloAgBJaAIAWWgCAGloAgB5aAIAiWgCAJloAgKgJDwCpCQ8AqhkPAKsZDwCsCQ8ArQkPAK6pDwCvqQ8AsNkPALHtDwCy+Q8As/UPALSVDwC1hQ8AtoUPALe1DwC4jQ8AuWEAALphAAC7YQAAvGEAAL1hAAC+YQAAv2EAAKNdDQCCLQAAgRUAAIAdAAAqWgCApokOAKWdDgAuWgCAq5kOAKqRDgAyWgCANloAgK/9DgCu8Q4ArfkOAKyBDgA6WgCAs/UPAIboAwCHvAMAtu0PAD5aAIBCWgCAteUPALp5DwC7TQ8ARloAgEpaAIC+NQ8AvyUPALxJDwC9RQ8AozEOAE5aAIBSWgCAVloAgFpaAICmKQ4ApSEOAF5aAICriQ4Aqr0OAGJaAIBmWgCAr+EOAK7xDgCtgQ4ArI0OAGpaAIBuWgCAcloAgHZaAIB6WgCAfloAgIJaAICGWgCAiloAgI5aAICSWgCAlloAgIANAACB1QAAgt0AAJpaAICoQQEAqVEBAKpRAQCrZQEArH0BAK2RAACukQAAr5EAAJ5aAICiWgCAhGQBAL5kAQCGkAEAh4QAAKpaAICuWgCAuJEAALmRAAC6kQAAu5EAALyxAAC9sQAAvrEAAL+xAACw8QAAsfkAALLBAACzwQAAtLEAALWxAAC2sQAAt7EAALPZAgCyWgCAvnADAL5EBAC2WgCAthEDALX1AgC6WgCAuz0DALo1AwC+WgCAwloAgL91AwC+dQMAvRUDALwVAwDGWgCAo50CAMpaAIDOWgCAplUDANJaAIDWWgCApbECAKpxAwCreQMA2loAgN5aAICuMQMArzEDAKxRAwCtUQMAqDkDAKk5AwCqjQAAq50AAKyNAACtvQAArrUAAK/dAADiWgCA5loAgOpaAIDuWgCA8loAgPZaAID6WgCA/loAgLhpAAC5aQAAunkAALt5AAC8aQAAvWkAAL7ZAQC/2QEAsKkAALGpAACyvQAAs7UAALSZAAC1mQAAtlkAALdZAAACWwCABlsAgApbAIAOWwCA70QAABJbAICGmAUAh+QCAOOYAACEqAIA4fgBABpbAICAOQAAgTkAAIItAAAeWwCAs0UBACJbAIAmWwCAKlsAgC5bAIC2fQEAtUUBADJbAIC7LQEAui0BADZbAIA6WwCAvx0BAL4dAQC9IQEAvCkBAD5bAIDhUA4AQlsAgOM8DwBGWwCASlsAgE5bAIBSWwCAVlsAgFpbAIDjAAAAXlsAgGJbAIBmWwCAhPQFAO/kDgCuqQEAr6kBAKydAQCtlQEAqpkBAKuZAQBqWwCAblsAgKbJAQByWwCAdlsAgKXxAQCC/QcAo/EBAID9BwCB9QcAFlsAgHpbAIB+WwCAglsAgIZbAICKWwCAhrgDAIeQAwCoDQcAqRkHAKptBwCrZQcArH0HAK1lBwCuZQcAr1UHALAtBwCxxQcAssEHALPdBwC0xQcAtc0HALbFBwC3/QcAuMUHALnJBwC62QcAu9kHALypBwC9qQcAvp0HAL+VBwCzxQcAjlsAgJJbAICWWwCAmlsAgLbFBwC11QcAnlsAgLshBwC6yQcAolsAgKZbAIC/KQcAviEHAL0pBwC8NQcAqlsAgKOBBwCuWwCAslsAgKaBBwC2WwCAulsAgKWRBwCqjQcAq2UHAL5bAIDCWwCArmUHAK9tBwCscQcArW0HAKgVAQCpgQEAqoEBAKuBAQCsgQEArYkBAK6xAQCvsQEAxlsAgMpbAIDOWwCA0lsAgNZbAIDaWwCA3lsAgOJbAIC4ZQAAuW0AALplAAC7fQAAvGUAAL1tAAC+ZQAAv90AALChAQCxrQEAsqUBALO5AQC0qQEAtZ0BALaVAQC3XQAA5lsAgIIdAACBHQAAgB0AAOpbAIDuWwCA8lsAgL5YAQCErAIA9lsAgIcIAQCGjAEA+lsAgKZaAID+WwCAAlwAgLNJAQAGXACAClwAgA5cAIASXACAtkkBALVJAQAWXACAuykBALolAQAaXACAHlwAgL8ZAQC+LQEAvS0BALwxAQC+2AMAIlwAgO/4BgAmXACAKlwAgC5cAIDv4AIAMlwAgOGUAQA2XACA43QCADpcAIDhmAUAPlwAgOMMBwBCXACARlwAgEpcAICjwQIAhIwDAKXBAgBOXACAUlwAgKbBAgBWXACAWlwAgKuhAgCqrQIAraUCAKy5AgCvkQIArqUCAKgxAwCpPQMAqjUDAKtJAwCsWQMArVkDAK5JAwCvQQMAgMUAAIEJAACCGQAAXlwAgGJcAIBqXACAh2wDAIYcHAC47QAAufEAALr1AAC7jQAAvJUAAL2BAAC+gQAAv70AALAJAwCxCQMAsu0AALPhAAC04QAAteEAALblAAC32QAAblwAgHJcAIB2XACAs7ECAHpcAIC13QIAttUCAH5cAICCXACAhlwAgLrBAgC7wQIAvDUBAL05AQC+KQEAvykBAKaNAgCKXACAjlwAgKWFAgCSXACAo+kCAJZcAICaXACArnEBAK9xAQCsbQEArWEBAKqZAgCrmQIAnlwAgKJcAICmXACA4YQGAKpcAIDjJAYArlwAgOGUAQCyXACA4ywAAL7oHQC2XACAulwAgO/IAACE/B0AvvAcAL5cAIDvSAcAwlwAgMZcAIDKXACAzlwAgIEdAACAHQAA0lwAgIIFAACGQBwAh8QcANpcAIDeXACA4lwAgOZcAIDqXACA7lwAgKi1HgCpBR8Aqg0fAKsFHwCsAR8ArQkfAK45HwCvOR8A1lwAgPJcAID2XACA+lwAgP5cAIACXQCABl0AgApdAIC4yR8AudUfALrRHwC76R8AvPkfAL3tHwC+mR8Av5kfALAlHwCxLR8AsjkfALM1HwC0LR8AtQ0fALYFHwC3/R8As4UfAA5dAIASXQCAFl0AgBpdAIC2iR8AtYkfAB5dAIC76R8AuuEfACJdAIAmXQCAv8kfAL7pHwC94R8AvO0fACpdAICjwR8ALl0AgDJdAICmzR8ANl0AgDpdAIClzR8AqqUfAKutHwA+XQCAQl0AgK6tHwCvjR8ArKkfAK2lHwCo6R4AqekeAKr5HgCr+R4ArOkeAK3pHgCuPQEArzUBAID5AQCBzQEAgsUBAIRgAgBGXQCASl0AgIdoAQCGnAAAuNEBALnZAQC64QEAu+EBALyRAQC9nQEAvpUBAL+JAQCwTQEAsVUBALJdAQCzVQEAtE0BALXxAQC28QEAt/EBALNxHgBOXQCAUl0AgFZdAIBaXQCAtmkeALVhHgBeXQCAu5EBALqJAQBiXQCAZl0AgL81AQC+iQEAvYEBALyJAQBqXQCAZlwAgKM5HgBuXQCApSkeAHJdAIB2XQCApiEeAHpdAIB+XQCAq9kBAKrBAQCtyQEArMEBAK99AQCuwQEAgl0AgIZdAICKXQCAjl0AgJJdAICWXQCAml0AgJ5dAICiXQCApl0AgKpdAICuXQCAsl0AgLpdAIC+XQCAvnADAOHkHgCESAIA4+gfAIQABACAeQAAgXkAAIJpAADCXQCAhsAEAIdEAwDGXQCAyl0AgM5dAIDSXQCA7yAfANZdAIDaXQCA3l0AgOJdAIDvSAIA5l0AgOpdAIDuXQCA8l0AgL7oBAD2XQCA+l0AgP5dAIACXgCA4ZABAAZeAIDj6AIAs0kDAApeAIAOXgCAEl4AgBZeAIC2SQMAtUkDABpeAIC7LQMAuiUDAB5eAIAiXgCAvxUDAL4VAwC9IQMAvCkDAKg1AgCpgQIAqoECAKuBAgCsgQIArYkCAK6xAgCvsQIAgP0BAIHNAQCCxQEAKl4AgIaQBACHBAUALl4AgIRwBAC4SQEAuUkBALpZAQC7WQEAvEkBAL1JAQC+eQEAv3kBALChAgCxqQIAsr0CALO1AgC0kQIAtZECALZ5AQC3eQEAMl4AgDZeAIA6XgCAPl4AgEJeAIBGXgCASl4AgO/QHgC+6AQA4VweAE5eAIDjkAAAUl4AgFZeAIBaXgCAXl4AgKNJAgBiXgCAZl4AgGpeAIBuXgCApkkCAKVJAgByXgCAqy0CAKolAgB2XgCAel4AgK8VAgCuFQIArSECAKwpAgCoNQYAqT0GAKpVBgCrZQYArH0GAK1lBgCubQYAr2EGACZeAIB+XgCAgl4AgIZeAICADQAAgbEAAIKxAACKXgCAuOkGALnpBgC6+QYAu/UGALyVBgC9nQYAvpUGAL+NBgCw4QYAseEGALLhBgCz/QYAtOUGALXtBgC25QYAt9kGALPdBgCOXgCAkl4AgJZeAICaXgCAtuUGALX1BgCeXgCAuyUGALolBgCGmAAAh6wAAL8pBgC+IQYAvSkGALw1BgCiXgCAo5kGAKZeAICqXgCApqEGAK5eAICyXgCApbEGAKphBgCrYQYAtl4AgLpeAICuZQYAr20GAKxxBgCtbQYAqC0GAKk9BgCqiQYAq4kGAKyZBgCtmQYArokGAK+JBgC+XgCAwl4AgMZeAIDKXgCAzl4AgNJeAIDWXgCA2l4AgLiNBgC5lQYAupUGALulBgC8vQYAvXEBAL5xAQC/cQEAsPkGALHNBgCy2QYAs9kGALTJBgC1yQYAtr0GALe1BgCzAQYA3l4AgOJeAIDmXgCA6l4AgLYZBgC1EQYA7l4AgLsJBgC6PQYA8l4AgPZeAIC/DQYAvg0GAL0NBgC8DQYA+l4AgKNFBgC2XQCA/l4AgKZdBgACXwCAhFgAAKVVBgCqeQYAq00GAL5oAQAGXwCArkkGAK9JBgCsSQYArUkGAIDBAwCByQMAgt0DAKPNAgAKXwCApdkCAKbNAgAOXwCAhoANAIeUAwCqxQIAqw0DAKwVAwCtHQMArhUDAK8NAwDhnBcA4xgGAOMUAwDhNAYA7xgCABJfAIAWXwCAGl8AgOPQAgAeXwCA4VACACJfAIAmXwCA7ywGAO/kJQAqXwCArE0CAK1RAgCuUQIAr2UCAKgBAgCpCQIAqlkCAKtVAgCE7A0ALl8AgDJfAIA2XwCAvvgNADpfAIA+XwCAQl8AgLxRAwC9WQMAvmEDAL9hAwC47QMAuVEDALpRAwC7UQMAtM0DALXVAwC23QMAt9UDALAdAgCx1QMAst0DALPVAwDjyAAARl8AgOG4AQBKXwCAhFQPAE5fAIBSXwCAVl8AgKHpAgCgFQYAo6UDAKINAwDvIAAAWl8AgF5fAIBiXwCAZl8AgGpfAICFNCYAs40DAG5fAIC1mQMAto0DAHJfAICGwA8Ah5QNALqFAwC7TQIAvFUCAL1dAgC+VQIAv00CAHpfAIB+XwCAgl8AgIZfAICKXwCAjl8AgI/d6wDvxAYAvuAPAOGMBgCSXwCA44AGAID1AACB5QAAguUAAJZfAICZbR8AmMUfAJvJGwCaeRoAnXUaAJzFGwCf+QcAnhkGAJFpFgCQsesAk20XAJLNFwCV0RMAlGkSAJdREgCWzRMAg1XkAIJB5AB2XwCAml8AgIeNHQCGkRgAhTkYAISVGQCLERwAigUcAJ5fAICiXwCAj4UVAI6ZEACNORAAjJUdAJNRFACSRRQApl8AgKpfAICXYQkAlnUIAJWdCQCU+RUAm0EMAJqtDQCuXwCAsl8AgLZfAIC6XwCAvl8AgJzxDAChbQ0Awl8AgKMBBACihQAApZkEAKSRBACnGTgApsUFAKkJOACoKTgAq4k8AKoBPACtATAArB08AK8pMACunTAAseE0ALABNACzASgAsv00ALXZKAC00SgAxl8AgMpfAIDOXwCA0l8AgNZfAIDaXwCAgB0AAIEJAACC2QEA3l8AgKgRDwCpGQ8Aql0PAKtVDwCsTQ8ArXEPAK51DwCvbQ8A4l8AgOpfAICGiAAAhxABAO5fAIDyXwCA9l8AgPpfAIC4TQ4AuVEOALpRDgC7UQ4AvGUOAL1tDgC+ZQ4Avx0OALAdDwCxwQ8AssEPALPBDwC0xQ8Atc0PALbFDwC3eQ4As9UPAP5fAIACYACABmAAgApgAIC28Q8AtcUPAA5gAIC7BQ8AutkPABJgAIAWYACAvwkPAL4BDwC9FQ8AvBUPABpgAICjkQ8AHmAAgCJgAICmtQ8AJmAAgCpgAIClgQ8Aqp0PAKtBDwAuYACAMmAAgK5FDwCvTQ8ArFEPAK1RDwCogQ0AqYENAKqBDQCrgQ0ArIENAK2BDQCusQ0Ar6ENADZgAIA6YACAPmAAgEJgAIBGYACAgrkAAIG9AACAvQAAuDUCALk9AgC6zQIAu5UCALyNAgC9tQIAvr0CAL+1AgCwbQIAsU0CALJFAgCzJQIAtD0CALUdAgC2FQIAtw0CAEpgAIBOYACAswENAFJgAIC1AQ0AWmAAgISUAwC2CQ0AviwEAF5gAIC7gQIAuqECAL35AgC8mQIAv9ECAL7xAgBiYACAZmAAgGpgAICjRQ0AbmAAgKVFDQCmTQ0AcmAAgIbgBACHpAQAquUCAKvFAgCs3QIArb0CAK61AgCvlQIAqCUCAKk1AgCqPQIAqzUCAKwtAgCtkQIArpECAK+RAgB2YACAemAAgH5gAICCYACAzAAAAIZgAICKYACAjmAAgLiZAgC5rQIAuqUCALttAQC8dQEAvX0BAL51AQC/bQEAsPECALH5AgCywQIAs8ECALSxAgC1vQIAtrUCALepAgCSYACA44QOAJZgAIDh9A4AmmAAgJ5gAICiYACApmAAgIQgBQCqYACArmAAgLJgAIC2YACA7+wOALpgAIC+YACAs/UCAMJgAICG6AQAh4wEAL5cBAC2UQIAteUCAMpgAIC7fQIAunUCAM5gAIDSYACAvzkCAL41AgC9VQIAvFUCAKM1BQBWYACAxmAAgNZgAIDaYACAppEFAKUlBQDeYACAq70FAKq1BQDiYACA5mAAgK/5BQCu9QUArZUFAKyVBQCA+QcAgfkHAIKNBwCzjQYA6mAAgLWdBgC2iQYA7mAAgPJgAID2YACAuk0HALtFBwC8XQcAvUEHAL5BBwC/QQcA+mAAgP5gAIDmXwCAAmEAgAZhAIAKYQCADmEAgBJhAICoNQYAqQEGAKppBgCraQYArHkGAK1lBgCuZQYAr50HALDlBwCx7QcAsuUHALP5BwC06QcAtekHALZZBwC3VQcAuHEHALlxBwC6cQcAu3EHALxVBwC9XQcAvlUHAL9NBwCjwQcAFmEAgBphAIAeYQCAImEAgKbFBwCl0QcAJmEAgKsJBgCqAQYAKmEAgC5hAICvDQYArg0GAK0NBgCsEQYAgGkAAIFpAACCBQAAMmEAgL6YAQCEmAEANmEAgDphAICGADwAh8QBAD5hAIBCYQCARmEAgEphAIBOYQCAUmEAgKhdBgCpbQYAqmUGAKuBAQCsgQEArYkBAK6xAQCvsQEAVmEAgFphAIBeYQCAYmEAgGZhAIBqYQCAbmEAgHJhAIC4VQEAuV0BALpVAQC7yQAAvNkAAL3ZAAC+yQAAv8EAALCxAQCxuQEAsokBALOJAQC0cQEAtXEBALZ1AQC3bQEAs+0FAHZhAIB6YQCAfmEAgIJhAIC2CQIAtQkCAIZhAIC7fQIAunUCAIphAICOYQCAv7UCAL61AgC9XQIAvF0CAL5gAgCjqQUAkmEAgJZhAICmTQIAmmEAgJ5hAIClTQIAqjECAKs5AgCiYQCAhOADAK7xAgCv8QIArBkCAK0ZAgC+iDwAqmEAgKotAwCrJQMArD0DAK0lAwCuLQMAryUDAID1AACB/QAAgsEAAKPBAwCuYQCApcEDAKbBAwCyYQCAhmA8AIdUAwC2YQCAumEAgL5hAIDjqAIAwmEAgOGkAQDGYQCA71wCAMphAIDOYQCA0mEAgNZhAIDaYQCA3mEAgOJhAIDjjAcA5mEAgOE8BADqYQCA7mEAgPJhAID2YQCAhCACAPphAID+YQCAAmIAgAZiAIDvbAcACmIAgA5iAICzLQIAhEQ9ABJiAIAaYgCAHmIAgLYtAgC1LQIAImIAgLvJAgC6wQIAJmIAgCpiAIC/yQIAvsECAL3JAgC80QIA4XgHAOPAAADjOAYA4VwGAICpAACBqQAAgtEAAC5iAIAyYgCANmIAgL6kPAA6YgCAPmIAgO8cAADvkAYAQmIAgIZgPACHBD0ARmIAgLNxAQBKYgCAtRkBALYJAQBOYgCAUmIAgFZiAIC6AQEAuwEBALwBAQC9AQEAvgEBAL8BAQCohT4AqbU+AKq1PgCrxT4ArN0+AK3FPgCuwT4Ar/0+AFpiAIBeYgCAYmIAgGZiAIBqYgCAbmIAgHJiAIB2YgCAuFE/ALlRPwC6UT8Au1E/ALx1PwC9fT8AvnU/AL9tPwCwiT4AsYk+ALKZPgCzmT4AtIk+ALWJPgC2eT8At3U/AKZhAICjOT4AemIAgBZiAICmQT4AfmIAgIJiAIClUT4Aqkk+AKtJPgCGYgCAimIAgK5JPgCvST4ArEk+AK1JPgCASQAAgVEAAIJRAACzkT8AjmIAgLW5PwC2RT8AkmIAgIZAAACHBAMAukU/ALtdPwC8TT8AvT0/AL4pPwC/IT8AqE0+AKlVPgCqVT4Aq2U+AKx9PgCtiT4Arrk+AK+5PgCWYgCAmmIAgJ5iAICiYgCApmIAgKpiAICuYgCAsmIAgLhhAQC5YQEAumEBALthAQC8YQEAvWEBAL5hAQC/YQEAsM0+ALHVPgCy1T4As6U+ALShPgC1qT4Atpk+ALeZPgCj3T4AtmIAgLpiAIC+YgCAwmIAgKYJPgCl9T4AxmIAgKsRPgCqCT4AymIAgM5iAICvbT4ArmU+AK1xPgCsAT4A0mIAgNZiAIDaYgCA3mIAgOJiAIDmYgCA6mIAgO5iAICAOQAAgTkAAIIFAADyYgCAvrgBAIS4AQD6YgCA/mIAgKitAgCp1QIAqtUCAKstAwCsNQMArT0DAK41AwCvLQMAAmMAgAZjAIAKYwCADmMAgBJjAIAWYwCAGmMAgB5jAIC46QMAuekDALqJAwC7iQMAvJkDAL2ZAwC+iQMAv4kDALBVAwCxXQMAslUDALPpAwC0+QMAtfkDALbpAwC34QMAs10CACJjAICGKAQAh8wDACZjAIC2vQMAtb0DACpjAIC7mQMAupEDAC5jAIAyYwCAvz0DAL49AwC9PQMAvIEDAIUAFACjGQIANmMAgDpjAICm+QMAPmMAgEJjAICl+QMAqtUDAKvdAwBGYwCASmMAgK55AwCveQMArMUDAK15AwDjVD4A4dw/AOHQPgDjPD4ATmMAgO8cAABSYwCAVmMAgFpjAIDjwAAAXmMAgOHUAQDvYD4AYmMAgGpjAIDvRD8AgGEAAIFtAACCfQAAhAAFAIbwBACHnAUAvhAFAG5jAIByYwCAdmMAgHpjAIB+YwCAgmMAgIZjAICKYwCAjmMAgLiJPQC5iT0Aupk9ALuRPQC8uT0Avbk9AL7RPQC/0T0AsAU+ALENPgCyBT4Asx0+ALQFPgC1DT4AtgU+ALe5PQConT4Aqa0+AKqlPgCrvT4ArKU+AK2tPgCupT4Ar30+AISsBAC+rAQAkmMAgJZjAICaYwCAnmMAgKJjAICmYwCAqPkFAKn5BQCqKQYAqykGAKw5BgCtOQYArikGAK8pBgBmYwCAqmMAgK5jAICyYwCAtmMAgLpjAIC+YwCAwmMAgLiNBgC5kQYAupEGALulBgC8vQYAvUUHAL5BBwC/QQcAsFkGALFZBgCy7QYAs/0GALTtBgC13QYAttUGALe1BgCzoQYAxmMAgMpjAIDOYwCA0mMAgLa5BgC1sQYA2mMAgLudBgC6nQYA1mMAgPZiAIC/GQYAvikGAL0pBgC8OQYAglEAAKPlBgCAQQAAgUEAAKb9BgDeYwCA4mMAgKX1BgCq2QYAq9kGAIZIAACHbAAArm0GAK9dBgCsfQYArW0GAKg5BgCpWQYAqmkGAKtpBgCseQYArXkGAK5pBgCvaQYA5mMAgOpjAIDuYwCA8mMAgPZjAID6YwCA/mMAgAJkAIC4ZQEAuW0BALplAQC7fQEAvGUBAL1tAQC+ZQEAv9kBALAZBgCxGQYAsoEGALOBBgC0gQYAtYEGALaBBgC3gQYAs+EGAAZkAIAKZACADmQAgBJkAIC2+QYAtfEGABZkAIC73QYAut0GABpkAIAeZACAv0UGAL5FBgC9VQYAvFUGACJkAICjpQYAJmQAgCpkAICmvQYALmQAgDJkAICltQYAqpkGAKuZBgA2ZACAOmQAgK4BBgCvAQYArBEGAK0RBgConQIAqdECAKrRAgCrLQMArDUDAK09AwCuNQMAry0DAD5kAIBCZACAvmQCAEpkAIBOZACAUmQAgFZkAIBaZACAuOkDALnpAwC6iQMAu4UDALydAwC9gQMAvoEDAL+1AwCwVQMAsV0DALJVAwCz6QMAtPkDALX5AwC26QMAt+EDAIBtAwCBpQAAgq0AALNVAgBeZACAtbEDALaxAwBiZACAhOACAGZkAIC6nQMAu5UDALyNAwC9MQMAvjEDAL8xAwCjGQIAamQAgIVwaQBuZACAcmQAgKb9AwCl/QMAdmQAgKvZAwCq0QMAhkgMAIe8AwCvfQMArn0DAK19AwCswQMAemQAgH5kAICCZACAhmQAgO+wBgDvxAMAimQAgI5kAIDjfAYA45QDAOG4BwDh3AEAkmQAgJZkAICaZACAnmQAgKJkAICmZACAhEQCAL5YDQCADQAAgTUAAII9AACqZACArmQAgLJkAICGyAwAh1wNALpkAIC+ZACAwmQAgMZkAIDKZACAzmQAgNJkAIDWZACA2mQAgN5kAIDiZACA74AGAISsDQDh7AYA5mQAgONcBgDqZACA7mQAgPJkAID2ZACAs/UBAPpkAID+ZACAAmUAgAZlAIC2RQEAteUBAAplAIC7LQEAuiEBAA5lAIASZQCAv/UAAL71AAC9JQEAvC0BAKgtDgCpNQ4Aqj0OAKs1DgCsLQ4ArYUOAK6FDgCvuQ4AtmQAgBZlAIAaZQCAHmUAgIAZAACBGQAAggUAACJlAIC4WQ8AuVkPALp5DwC7eQ8AvGkPAL1pDwC+GQ8AvxkPALClDgCxqQ4AsrkOALOxDgC0cQ8AtXEPALZxDwC3cQ8Apb0OAL6IAwAqZQCAph0OACZlAIAuZQCAo60OADJlAICtfQ4ArHUOAK+tDwCurQ8ARmQAgDZlAICrdQ4AqnkOALO5DwA6ZQCAhmgAAIcMAwA+ZQCAtlEPALVZDwBCZQCAu3UPALp1DwBGZQCASmUAgL9FDwC+RQ8AvVEPALxlDwCocQ4AqXEOAKpxDgCrcQ4ArJEOAK2RDgCukQ4Ar5EOAE5lAIBSZQCAVmUAgFplAIBeZQCAYmUAgGZlAIBqZQCAuIUOALmNDgC6hQ4Au50OALyNDgC9vQ4AvrUOAL95AQCw8Q4AsfEOALLxDgCzxQ4AtMEOALXBDgC2wQ4At8EOAKP5DgBuZQCAcmUAgHZlAIB6ZQCAphEOAKUZDgB+ZQCAqzUOAKo1DgCCZQCAhmUAgK8FDgCuBQ4ArREOAKwlDgCADQAAgRUAAIIdAACKZQCAjmUAgJJlAICElAEAvpQBAIZABwCH5AAAmmUAgJ5lAICiZQCApmUAgKplAICuZQCAqIkCAKmRAgCqlQIAq7kCAKzVAgCtxQIArsUCAK/1AgCyZQCAtmUAgLplAIC+ZQCAvnwDAMJlAIDGZQCAymUAgLh9AwC5wQMAusEDALvBAwC8wQMAvckDAL7xAwC/8QMAsI0CALFFAwCyTQMAs0UDALRdAwC1RQMAtk0DALdFAwCzHQIAzmUAgNJlAIDWZQCA2mUAgLZFAgC1XQIA3mUAgLuBAwC6SQIA4mUAgOZlAIC/gQMAvpkDAL2RAwC8mQMA6mUAgKNZAgDuZQCA8mUAgKYBAgD2ZQCA+mUAgKUZAgCqDQIAq8UDAP5lAIACZgCArt0DAK/FAwCs3QMArdUDAIDZAQCB7QEAguUBAO+4DgAKZgCA4cQBAISYAgDj1AAADmYAgL7sBAASZgCA7wgAABZmAIDhxA8AGmYAgONkDgCGAAUAh2gFAB5mAICzvQIAImYAgLWtAgC2pQIAJmYAgCpmAIAuZgCAukEBALtBAQC8RQEAvU0BAL5FAQC/+QEAMmYAgDZmAIA6ZgCAPmYAgEJmAIBGZgCASmYAgO/gAQCEbAQA4dQOAE5mAIDjHA4AUmYAgFZmAIBaZgCAXmYAgKMxAgBiZgCAhCQHAGZmAIBqZgCApikCAKUhAgBuZgCAq80BAKrNAQByZgCAemYAgK91AQCuyQEArcEBAKzJAQCo6QUAqekFAKr5BQCr+QUArOkFAK3pBQCuOQYArzkGAAZmAICCzQcAgfUHAID9BwB2ZgCAfmYAgIYYAwCHkAMAuNEGALnZBgC64QYAu+EGALyRBgC9nQYAvpUGAL+JBgCwSQYAsUkGALJdBgCzVQYAtE0GALXxBgC28QYAt/EGALDhBwCx4QcAsgkHALMJBwC0GQcAtRkHALYJBwC3CQcAuDkHALkNBwC6GQcAuxkHALwJBwC9CQcAvn0HAL9xBwCCZgCAlmUAgIZmAICKZgCAjmYAgJJmAICWZgCAmmYAgKjxBwCpxQcAqsEHAKvdBwCsyQcArb0HAK6pBwCvoQcAsykGAJ5mAICiZgCApmYAgKpmAIC2XQYAtSEGAK5mAIC7RQYAukUGALJmAIC2ZgCAv70GAL69BgC9vQYAvL0GALpmAICjbQYAvmYAgMJmAICmGQYAxmYAgMpmAIClZQYAqgEGAKsBBgDOZgCA0mYAgK75BgCv+QYArPkGAK35BgCobQYAqbEBAKpJAQCrRQEArF0BAK1FAQCuTQEAr0UBANZmAICCHQAAgR0AAIAdAADaZgCA3mYAgOJmAIC+VAEAuIEAALmNAAC6hQAAu5kAALyJAAC9vQAAvrUAAL99AACwPQEAseEAALLhAACz4QAAtOEAALXpAAC20QAAt9EAALsFAwC62QIAhiwCAIcsAwC/DQMAvgUDAL0VAwC8FQMAs+ECAOpmAIDuZgCAhCwDAPJmAIC25QIAtfUCAPZmAICqnQIAq0EDAPpmAID+ZgCArkEDAK9JAwCsUQMArVEDAAJnAICjpQIABmcAgApnAICmoQIADmcAgBJnAIClsQIAqakAAKihAACrtQAAqr0AAK3dAACs3QAAr/EAAK79AAC+LBwAFmcAgBpnAIAeZwCAImcAgCZnAIAqZwCALmcAgLl9AAC4fQAAu80BALrNAQC93QEAvN0BAL/NAQC+zQEAsZUAALCJAACzTQAAspUAALVdAAC0XQAAt00AALZNAAAyZwCANmcAgDpnAIA+ZwCAQmcAgEZnAIBKZwCATmcAgIA5AACBOQAAggUAAFJnAIBaZwCAXmcAgIf4AgCGfB0A4bgEAL7IHADjQAYAYmcAgGZnAIBqZwCAbmcAgHJnAIB2ZwCAemcAgH5nAICCZwCAhmcAgIpnAIDvsAcAjmcAgJJnAICWZwCAmmcAgO/IAACeZwCAomcAgKZnAIDvQAYAqmcAgOH8BgCuZwCA4xwGALJnAIDhlAEAtmcAgONkBgCAEQAAgRkAAIIpAACz/QEAumcAgLWdAQC2lQEAvmcAgMJnAICEbB0AuoUBALuZAQC8iQEAvVEBAL5RAQC/UQEAozEeAFZnAIDGZwCAymcAgM5nAICmWR4ApVEeANJnAICrVR4AqkkeAIYIAwCHbAMAr50eAK6dHgCtnR4ArEUeANZnAICzCR8A2mcAgN5nAIC2CR8A4mcAgOZnAIC1CR8AugUfALsNHwDqZwCA7mcAgL4FHwC/CR8AvBUfAL0NHwCw5R8Ase0fALLlHwCz/R8AtOUfALXpHwC2GR8AtxkfALgpHwC5NR8Auj0fALs1HwC8ER8AvR0fAL4JHwC/BR8A8mcAgPZnAIDmZgCA+mcAgP5nAIACaACABmgAgApoAICo0R8AqdEfAKqlHwCrvR8ArKUfAK2tHwCupR8Ar50fAKNNHgAOaACAEmgAgBZoAIAaaACApk0eAKVNHgAeaACAq0keAKpBHgAiaACAJmgAgK9NHgCuQR4ArUkeAKxRHgCADQAAgRUAAIIdAAAqaACALmgAgDJoAICEtAEAvrQBAL/oAQA6aACAhkgHAIc0AACEvAYAPmgAgEJoAIC+tAYAqI0BAKmVAQCqlQEAq80BAKzZAQCt2QEArs0BAK/FAQBGaACASmgAgE5oAIBSaACAVmgAgFpoAIBeaACAYmgAgLgdAQC5wQAAusEAALvBAAC8wQAAvckAAL7xAAC/8QAAsIkBALGJAQCyKQEAsykBALQ9AQC1JQEAti0BALclAQC7bQIAum0CAGZoAIBqaACAv8ECAL7ZAgC93QIAvN0CALM9AgBuaACAcmgAgHZoAICE/AYAtnkCALVxAgB6aACAqikCAKspAgB+aACAgmgAgK6dAgCvhQIArJkCAK2ZAgCGaACAo3kCAIpoAICOaACApj0CAJJoAICWaACApTUCAIJtJwCDjSoAhqgFAIdsAwCGmS4Ah80vAIQRLgCFmS4AiiESAIspEgCaaACAnmgAgI6RFgCPHRYAjBESAI0RFgCScRoAk+UaAKJoAIDvlHYAlvEeAJflHgCUSRoAlRkeAJopAgCb4QIAqmgAgK5oAICyaACA4SASAJzxAgDjIBYAnyEfAJ7BHwCdmRsAnC0bAJuhGwCavRcAmTkXAJixFwCXiRMAlqkTAJWpEwCUdS4AkzkvAJIxLwCRsS8AkDUrAI+tJgDjeB8A0gAAAOFcHwCCmQEAtmgAgIDxAQCB8QEAvqgHALpoAIC+aACAwmgAgIS8BgDvLB8AxmgAgMpoAIDhpB4A48wAAON8HgDhvAEAzmgAgNJoAIDWaACAhJwGANpoAIC+bAYA3mgAgOJoAIDmaACA7xAAAO8EHgDqaACA7mgAgPJoAID2aACA+mgAgP5oAIACaQCABmkAgAppAICAPQAAgQkAAILJBwAOaQCAo/kDAKLxAwChMQMAoM0fALBJcQCxAXwAsgl8ALMhfQC0AXgAtRV4ADZoAICmaACAEmkAgL4oDgCGDAAAh4wDABZpAIAaaQCAHmkAgCJpAIAmaQCAoV0AAKJVAACjfQAApAEMAKUVDACm9QwApwEIAKghCACpxQgAqgF0AKsJdACsAXQArR11AK55cACveXAAqOUFAKnxBQCq8QUAqy0FAKw1BQCtPQUArjUFAK8tBQAqaQCALmkAgDJpAIA2aQCAOmkAgD5pAIBCaQCARmkAgLj9BgC5jQYAuoUGALutBgC8uQYAvbkGAL6tBgC/pQYAsFUFALFdBQCyVQUAs+UGALT9BgC10QYAttEGALfRBgCzeQQASmkAgE5pAIBSaQCAVmkAgLa9BAC1vQQAWmkAgLuZBAC6kQQAXmkAgGJpAIC/FQcAvjkHAL0xBwC8gQQAZmkAgKM9BABqaQCAbmkAgKb5BAByaQCAdmkAgKX5BACq1QQAq90EAHppAIB+aQCArn0HAK9RBwCsxQQArXUHAKhpBwCpaQcAqnkHAKvZBgCs9QYArf0GAK71BgCv5QYAgMkAAIHJAACCBQAAgmkAgIZwDwCHNAAAimkAgI5pAIC4fQYAuQUGALoNBgC7BQYAvB0GAL0FBgC+DQYAvwUGALCdBgCxdQYAsn0GALN1BgC0UQYAtV0GALZVBgC3TQYAs/EEAJJpAICWaQCAmmkAgJ5pAIC2fQUAtX0FAKJpAIC7sQUAulkFAKZpAICqaQCAv5kFAL6VBQC9oQUAvKkFAK5pAICjtQQAsmkAgLZpAICmOQUAumkAgL5pAIClOQUAqh0FAKv1BQDCaQCAxmkAgK7RBQCv3QUArO0FAK3lBQCpuQIAqLECAKvJAgCqsQIArTUCAKw1AgCvNQIArjUCAMppAIDOaQCA0mkAgNZpAIDaaQCA3mkAgOJpAIDmaQCAuekDALjZAwC7iQMAuuEDAL2dAwC8nQMAv4EDAL6JAwCxVQIAsFUCALNVAgCyVQIAtfkDALTxAwC36QMAtvEDALM9AwDqaQCA7mkAgPJpAID6aQCAtrEDALW5AwD+aQCAu5UDALqVAwCGiAwAh6ANAL85AgC+MQIAvYUDALyFAwACagCAo3kDAAZqAIAKagCApvUDAA5qAIASagCApf0DAKrRAwCr0QMAFmoAgBpqAICudQIAr30CAKzBAwCtwQMAgIUAAIGNAACChQAA79AGAOOwBwDj9AQA4QgHAOHsBADvOAYA7yAEAL6kDAAeagCAImoAgOGEAQAmagCA49wGACpqAIAuagCAhMANALPJAQAyagCAtdkBALbJAQA2agCAOmoAgD5qAIC6xQEAu60BALy5AQC9uQEAvq0BAL+lAQCwLQ4AsUUOALJBDgCzQQ4AtEUOALVNDgC2cQ4At3EOALiBDgC5gQ4AuoEOALuBDgC8gQ4AvYEOAL6BDgC/gQ4A9mkAgEJqAIBGagCASmoAgIZpAIBOagCAUmoAgFZqAICo2Q0AqdkNAKptDgCrZQ4ArH0OAK1lDgCuZQ4Ar1UOAKOFDgCCLQAAgRUAAIAdAABaagCApoUOAKWVDgBeagCAq+EOAKqJDgBiagCAZmoAgK/pDgCu4Q4ArfUOAKz1DgBqagCAs4UPAIZoAACHHAMAtoUPAG5qAIByagCAtZEPALqNDwC7SQ8AdmoAgHpqAIC+MQ8AvzEPALxJDwC9RQ8AqBEOAKkZDgCqSQ4Aq0UOAKxdDgCtQQ4ArkEOAK91DgB+agCAgmoAgIZqAICKagCAjmoAgJJqAICWagCAmmoAgLihDgC5oQ4Aug0BALsFAQC8HQEAvQEBAL4BAQC/AQEAsA0OALHJDgCy2Q4As9UOALSxDgC1sQ4AtqkOALehDgCjwQ4AnmoAgKJqAICmagCAqmoAgKbBDgCl1Q4ArmoAgKsNDgCqyQ4AsmoAgLZqAICvdQ4ArnUOAK0BDgCsDQ4AumoAgL5qAIDCagCAxmoAgIANAACBNQAAgj0AAMpqAIDOagCA0moAgISEAQC+hAEAhjAHAIf4AADaagCA3moAgKjBAgCp0QIAqtECAKvlAgCs/QIArTUDAK49AwCvNQMA4moAgOZqAIDqagCA7moAgPJqAID2agCA+moAgP5qAIC40QMAudkDALrhAwC74QMAvJEDAL2RAwC+kQMAv5EDALBNAwCxVQMAsl0DALNVAwC0TQMAtfEDALbxAwC38QMAu7EDALqpAwACawCAvoQDAL8VAwC+qQMAvaEDALypAwCzeQIABmsAgAprAIAOawCAEmsAgLaVAwC1VQIAFmsAgKrtAwCr9QMAGmsAgB5rAICu7QMAr1EDAKztAwCt5QMAImsAgKM9AgAmawCAKmsAgKbRAwAuawCAMmsAgKURAgA2awCAgiEAAIEVAACAFQAA7wQAAISUAgA6awCAPmsAgOPYAABCawCA4fgBAEprAIBOawCAUmsAgFZrAIBaawCAhmAFAIcIBQBeawCAs20BAGJrAIC1fQEAtnUBAGZrAIBqawCAbmsAgLpRAQC7UQEAvPkBAL3RAQC+0QEAv9EBAHJrAICjpQEAdmsAgHprAICmvQEAfmsAgIJrAICltQEAqpkBAKuZAQCGawCAimsAgK4ZAQCvGQEArDEBAK0ZAQCOawCA4fQOAJJrAIDjFA4A9AAAAOF8DACWawCA41AKAJprAICeawCAviAEAO8wDQCiawCApmsAgIQ0BADvrA4AsDkGALE5BgCygQYAs6kGALS5BgC1uQYAtqkGALehBgC46QYAuekGALrJBgC7xQYAvN0GAL3BBgC+wQYAvz0HAEZrAICCHQAAgR0AAIAdAACqawCArmsAgLJrAIDWagCAqJkFAKmZBQCqSQYAq0kGAKxZBgCtWQYArkkGAK9JBgCorQcAqbUHAKq9BwCrtQcArK0HAK3dBwCuyQcAr8EHALZrAIC6awCAhogDAIcQAwC+awCAwmsAgMZrAIDKawCAuG0HALkFBwC6AQcAuxUHALwxBwC9MQcAvikHAL8pBwCwgQcAsYEHALJpBwCzZQcAtH0HALVhBwC2YQcAt1UHALM1BgDOawCA0msAgNZrAIDaawCAtl0GALUlBgDeawCAu0UGALpFBgDiawCA5msAgL+lBgC+uQYAvbEGALy9BgDqawCAo3EGAO5rAIDyawCAphkGAPZrAID6awCApWEGAKoBBgCrAQYA/msAgAJsAICu/QYAr+EGAKz5BgCt9QYAqCUBAKk1AQCqPQEAqzUBAKwtAQCtkQAArpEAAK+RAAAGbACACmwAgA5sAIASbACAFmwAgIK9AwCBvQMAgL0DALiZAAC5rQAAuqUAALttAAC8dQAAvX0AAL51AAC/bQAAsPEAALH5AACywQAAs8EAALSxAAC1vQAAtrUAALepAAAabACAHmwAgCJsAICEgAIAvhwCACpsAICG+HwAh8wCAISsAwAubACAMmwAgDZsAIA6bACAPmwAgEJsAIBGbACAs/UCAEpsAIBObACAkgAAAFJsAIC2UQMAteUCAFZsAIC7fQMAunUDAFpsAIBebACAvzkDAL41AwC9VQMAvFUDAKM1AgBibACAZmwAgGpsAIBubACAppEDAKUlAgBybACAq70DAKq1AwB2bACAemwAgK/5AwCu9QMArZUDAKyVAwC+wAMAfmwAgIJsAICGbACAgA0AAIE1AACCPQAAimwAgI5sAICSbACAhsh8AIcAAwCabACAnmwAgKJsAICmbACAqmwAgK5sAICybACAtmwAgLpsAIC+bACAwmwAgO/0AwCE7HwA4ZQBAMZsAIDjMAMAymwAgM5sAIDSbACA1mwAgLNpAQDabACA3mwAgOJsAIDmbACAtmEBALVpAQDqbACAuykBALohAQDubACA8mwAgL8dAQC+HQEAvSUBALwtAQD2bACA+mwAgP5sAICjpQEAAm0AgKWlAQCmrQEAvlR8AIaAfACH7HwAqu0BAKvlAQCs4QEArekBAK7RAQCv0QEACm0AgOGcBgCEBH8A4yQGAOPUBgAObQCA4TAEABJtAIDvlAcAgnUAAIFhAACAaQAAFm0AgBptAIAebQCA7+wGALiNfgC5lX4AupV+ALulfgC8vX4AvdF+AL7RfgC/0X4AsGV+ALFtfgCyeX4As3F+ALRZfgC1WX4Atr1+ALe1fgCoVX4AqWF+AKphfgCrYX4ArGF+AK1hfgCuYX4Ar2F+ACJtAICWbACAJmwAgCZtAIAGbQCAKm0AgC5tAIAybQCAqHF+AKlxfgCqcX4Aq3F+AKyRfwCtkX8ArpF/AK+RfwA2bQCAOm0AgD5tAIBCbQCARm0AgEptAIBObQCAUm0AgLiFfwC5jX8AuoV/ALudfwC8jX8Avb1/AL61fwC/XX8AsPF/ALHxfwCy8X8As8V/ALTBfwC1wX8AtsF/ALfBfwCz+X8AVm0AgFptAIBebQCAYm0AgLYRfgC1GX4AZm0AgLs1fgC6NX4Aam0AgG5tAIC/BX4AvgV+AL0RfgC8JX4AghUAAKO9fwCAYQAAgWEAAKZVfgBybQCAvpABAKVdfgCqcX4Aq3F+AHZtAIB6bQCArkF+AK9BfgCsYX4ArVV+AKhBfgCpUX4AqlV+AKt9fgCsZX4ArW1+AK75AQCv8QEAhgAAAIc0AQB+bQCAgm0AgIZtAICKbQCAjm0AgJJtAIC4dQEAuX0BALp1AQC7yQAAvNkAAL3ZAAC+yQAAv8EAALCVAQCxnQEAspUBALNNAQC0VQEAtV0BALZVAQC3TQEAs919AJZtAICabQCAnm0AgKJtAIC27X0Ate19AKZtAIC7WQIAulECAKptAICubQCAv5kCAL6RAgC9mQIAvEECALJtAICjmX0Atm0AgLptAICmqX0Avm0AgMJtAIClqX0AqhUCAKsdAgDGbQCAym0AgK7VAgCv3QIArAUCAK3dAgDObQCA0m0AgNZtAIDabQCAgB0AAIEJAACCOQAA3m0AgOJtAIC+AAQA6m0AgO5tAIDybQCA9m0AgPptAID+bQCAhIwDAAJuAICHCAMAhuwEAAZuAIDviAIACm4AgA5uAICEbAQA4zQCABJuAIDhVAEAFm4AgBpuAIAebgCAIm4AgKhtAgCprQIAqqUCAKu9AgCspQIAra0CAK6lAgCvGQEAvqwEACZuAIAqbgCALm4AgDJuAIA2bgCAOm4AgD5uAIC4DQEAuREBALoRAQC7JQEAvD0BAL3VAQC+3QEAv9UBALBpAQCxaQEAsnkBALNxAQC0WQEAtVkBALY5AQC3NQEAsy0CAEJuAIBGbgCASm4AgE5uAIC2LQIAtS0CAFJuAIC7rQEAuq0BAFpuAIBebgCAv50BAL6dAQC9pQEAvK0BAIBNAACBVQAAglUAAO9sAABibgCA7+x/AO+8fgBmbgCA4RB/AOPUfwDj2H4A4ex/AGpuAIDhTH4Abm4AgOMkfgDmbQCAVm4AgKsFBgCqBQYArQ0GAKwFBgCvNQYArjUGAIYAAwCHKAMAo4UFAHJuAIClhQUAdm4AgHpuAICmhQUAs/EGAH5uAICCbgCAhm4AgIpuAIC26QYAteEGAI5uAIC7vQYAur0GAJJuAICWbgCAv4kGAL6BBgC9iQYAvJUGAKgpBgCpKQYAqjkGAKs5BgCsKQYArSkGAK5dBgCvTQYAmm4AgJ5uAICibgCApm4AgKpuAICubgCAsm4AgLZuAIC46QcAuekHALr5BwC7+QcAvOkHAL3pBwC+XQcAv1UHALA5BgCxOQYAsgEGALMdBgC0BQYAtQ0GALYFBgC32QcAo7EHAIItAACBFQAAgB0AALpuAICmqQcApaEHAL5uAICr/QcAqv0HAMJuAICEpAIAr8kHAK7BBwCtyQcArNUHAL7MAQCzlQYAxm4AgMpuAIC2qQYAzm4AgNJuAIC1rQYAulkBALshAQCGyAAAhwwBAL4hAQC/KQEAvDEBAL0xAQCoKQYAqSkGAKpZBgCrUQYArGEGAK1tBgCutQEAr6kBAITgAQDWbgCA2m4AgN5uAIDibgCA5m4AgOpuAIDubgCAuGEBALlhAQC6YQEAu2EBALxhAQC9YQEAvmEBAL9hAQCw2QEAsaEBALKhAQCzoQEAtKEBALWpAQC2kQEAt5EBAKPRBQDybgCA9m4AgPpuAID+bgCApu0FAKXpBQACbwCAq2UCAKodAgAGbwCACm8AgK9tAgCuZQIArXUCAKx1AgAObwCAEm8AgBZvAIAabwCAHm8AgCJvAIAmbwCAKm8AgIA9AACBCQAAghkAAC5vAIAybwCAOm8AgL48AwA+bwCAhgAMAIcUAwBCbwCAs9UDAEZvAIC1PQMAtjUDAEpvAIBObwCAv4wKALoRAwC7EQMAvLUAAL29AAC+tQAAv60AAFJvAIDjdAEAVm8AgOG8AQBabwCAXm8AgGJvAIBmbwCAam8AgG5vAIBybwCAdm8AgHpvAIDvdAIAfm8AgIJvAICoTQIAqVECAKpRAgCrqQIArLkCAK25AgCuqQIAr6kCAIRsDQCGbwCAim8AgI5vAICSbwCAlm8AgJpvAIC+dA0AuG0BALkFAQC6DQEAuwUBALwdAQC9BQEAvg0BAL8FAQCw2QIAsdkCALJtAQCzZQEAtH0BALVlAQC2ZQEAt1UBAOG4AQDhUAcA47QAAON8BwCAqQAAgQkAAII5AACebwCAom8AgKpvAICubwCAsm8AgO4AAAC2bwCA7wAAAO9kBgCGYAwAh+QMAKORAgC6bwCApXkCAL5vAIDCbwCApnECAMZvAIDKbwCAq1UCAKpVAgCt+QEArPEBAK/pAQCu8QEApm8AgDZvAIDObwCA0m8AgNZvAIDabwCA3m8AgOJvAICoVQ4AqVkOAKqhDgCrvQ4ArK0OAK2VDgCu+Q4Ar/UOALCRDgCxkQ4AspEOALORDgC0sQ4AtbEOALaxDgC3sQ4AuJEOALmdDgC6lQ4Au0kPALxZDwC9WQ8AvkkPAL9JDwCzCQ4A5m8AgOpvAIDubwCA8m8AgLY1DgC1BQ4A9m8AgLt1DgC6dQ4A+m8AgP5vAIC/VQ4AvlUOAL1lDgC8ZQ4AAnAAgKNNDgAGcACACnAAgKZxDgAOcACAEnAAgKVBDgCqMQ4AqzEOAISkAwC+pAMArhEOAK8RDgCsIQ4ArSEOAKilDgCprQ4AqqUOAKu5DgCs3Q4ArcEOAK7BDgCv/Q4AgO0BAIHxAQCC8QEAFnAAgIaQAQCHtAEAGnAAgB5wAIC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALCFDgCxbQEAsmUBALN9AQC0ZQEAtW0BALZlAQC3+QEAsy0OACJwAIAmcACAKnAAgC5wAIC2QQ4AtVUOADJwAIC7qQEAukEOADZwAIA6cACAv6kBAL6hAQC9qQEAvLEBAD5wAICjaQ4AQnAAgEZwAICmBQ4ASnAAgE5wAIClEQ4AqgUOAKvtAQBScACAVnAAgK7lAQCv7QEArPUBAK3tAQCoOQMAqTkDAKqNAwCrhQMArJ0DAK2FAwCuhQMAr7UDAFpwAIBecACAYnAAgGZwAIBqcACAbnAAgHJwAIB2cACAuGEAALlhAAC6YQAAu2EAALxhAAC9YQAAvmEAAL9hAACwzQMAsaUDALKhAwCzoQMAtKUDALWtAwC2kQMAt5EDAIANAACBEQAAghEAAHpwAIDv9AIAfnAAgIJwAIC+HAMA4xQCAISIAgDhgAEAinAAgI5wAICScACAh8gDAIY8BAC7AQMAumkDAJZwAICacACAvwkDAL4BAwC9FQMAvBUDALNlAwCecACAonAAgKZwAICqcACAtmUDALV1AwCucACAsnAAgLZwAIC6cACAo4kCAL5wAIClmQIApokCAMJwAICELAIAxnAAgKqFAgCr7QIArPkCAK35AgCu7QIAr+UCAMpwAIDOcACAvkQFAIRMBQDScACA1nAAgNpwAIDecACA4nAAgOZwAIDqcACA7nAAgIAZAACBGQAAggUAAPJwAIDhGA8A4VwOAOO4DgDjdAEA+nAAgP5wAIACcQCABnEAgIYABACHZAUACnEAgA5xAIAScQCAFnEAgO98DgDvqAEAs3UBABpxAIAecQCAInEAgCZxAIC2MQEAtRUBACpxAIC7HQEAuhUBAC5xAIAycQCAv+EAAL79AAC9/QAAvP0AAPZwAIA2cQCAOnEAgD5xAICGcACAQnEAgEZxAIBKcQCAqI0GAKmVBgCqnQYAq+UGAKz9BgCt0QYArtEGAK/RBgCwsQYAsbkGALJJBwCzSQcAtFkHALVFBwC2RQcAt3kHALghBwC5IQcAujkHALs5BwC8KQcAvSkHAL4ZBwC/GQcAozUGAE5xAIBScQCAVnEAgFpxAICmcQYApVUGAF5xAICrXQYAqlUGAGJxAIC+oAMAr6EHAK69BwCtvQcArL0HAIBRAACBWQAAgmEAALNVBwCF9AAAtX0HALZ1BwBmcQCAhgAcAIfkAQC6LQcAuyUHALw9BwC9JQcAviUHAL8VBwCokQYAqZEGAKqRBgCrkQYArLkGAK25BgCuqQYAr6kGAGpxAIBucQCAcnEAgHZxAICiIQEAozUBAKA5BQChEQQAuEkBALlJAQC6XQEAu1UBALxNAQC90QEAvtEBAL/RAQCwpQYAsa0GALKlBgCzvQYAtK0GALWdBgC2lQYAt3kBAKMZBgCPnXkAenEAgH5xAICCcQCApjkGAKUxBgCGcQCAq2kGAKphBgCKcQCAjnEAgK9ZBgCuaQYArWkGAKxxBgCeiQgAn8EFAJzJCQCdyQkAmqENAJu9DACYsQ0AmbkNAJahcQCXRXEAlEV1AJWxcQCSoXUAk7V1AJDleQCRzXkAil1yAItFcgCScQCAvoAcAI51DgCPZQ4AjLlyAI11DgCCOXoAgzl6AJZxAICacQCAhnF2AIeZdgCECXoAhW12AJptBwCbVQIAnnEAgKJxAICmcQCA4ZAAAJxZAgDjCBoAkgkPAJNlCgCqcQCA7zgWAJZ1BgCXdQYAlH0KAJU1CwCpjRYAqIUWAKsBEACqMRYArXESAKy1EgCvuS4ArgEsAKF9AgCucQCAo6EeAKKpHgClsRoApPUfAKflGwCmsRoAhMwDAIRMHACycQCAtnEAgLpxAIC+cQCAwnEAgMZxAICxASgAsNkuALONKgCy6SoAtfUmALQBJACEcB0AynEAgID9AQCBFQAAgh0AAL6AHADOcQCA0nEAgIe4AgCGPB0A2nEAgN5xAIDicQCA5nEAgOpxAIDucQCA8nEAgPZxAID6cQCA/nEAgAJyAIAGcgCA44ADAApyAIDhoAEADnIAgO+UAwAScgCAFnIAgBpyAIAecgCAInIAgCZyAIAqcgCALnIAgOE8BgAycgCA49AGADZyAIDhMAcAOnIAgOOsBgCAOQAAgRUAAIIdAADvHAYAPnIAgEJyAIC+uB8A7+gBALPpAgBKcgCAh8QcAIbsHABOcgCAtlkCALVRAgBScgCAu00CALpNAgBWcgCAWnIAgL+5AQC+2QEAvdEBALz1AQCjKR0A1nEAgEZyAIBecgCAYnIAgKaZHQClkR0AZnIAgKuNHQCqjR0AanIAgG5yAICveR4ArhkeAK0RHgCsNR4AcnIAgLNtHwB2cgCAenIAgLZlHwB+cgCAgnIAgLVtHwC6IR8AuyEfAIZyAICKcgCAviUfAL8pHwC8MR8AvTEfAKihHwCpoR8AqqEfAKuhHwCsoR8AraEfAK6hHwCvoR8AjnIAgJJyAICWcgCAmnIAgJ5yAICicgCApnIAgKpyAIC4rR8AubUfALq9HwC7tR8AvK0fAL1VHwC+UR8Av00fALChHwCxoR8AsqEfALOhHwC0pR8AtakfALadHwC3lR8AoykeAIIZAACBGQAAgLEBAK5yAICmIR4ApSkeALJyAICrZR4AqmUeAIaIAACH/AEAr20eAK5hHgCtdR4ArHUeALZyAICzmR4AunIAgL5yAIC2XQEAwnIAgMZyAIC1sR4AukkBALtJAQDKcgCAznIAgL49AQC/IQEAvDkBAL01AQCoRR4AqVUeAKpVHgCrZR4ArH0eAK2ZAQCuiQEAr4EBAISsAADScgCA1nIAgNpyAIDecgCA4nIAgOZyAIDqcgCAuK0BALllAQC6bQEAu2UBALx9AQC9ZQEAvm0BAL9lAQCwyQEAsckBALKpAQCzpQEAtL0BALWhAQC2oQEAt5UBALhpHAC5oRwAusEcALvBHAC8wRwAvcEcAL7BHAC/wRwAsIkfALGJHwCyIRwAswUcALQdHAC1fRwAtnUcALdtHACoYR8AqWEfAKphHwCrYR8ArNkfAK3ZHwCuyR8Ar8EfAO5yAIDycgCA9nIAgPpyAID+cgCAAnMAgAZzAIAKcwCADnMAgBJzAIC+AAQAo1EdABZzAICleR0AppUCABpzAIAecwCAInMAgKqBAgCrgQIArPECAK39AgCu9QIAr+kCACpzAIDh9AEALnMAgON8AQCATQAAgXUAAIJ9AAAycwCAhsAEAIekBAA2cwCAOnMAgD5zAIBCcwCARnMAgO+MAgCoSQIAqUkCAKpdAgCrVQIArHkCAK15AgCuvQIAr7UCAISgBQBKcwCATnMAgFJzAIC+vAQAVnMAgFpzAIBecwCAuC0BALk1AQC6PQEAuzUBALwtAQC91QEAvt0BAL/NAQCwzQIAsdUCALLdAgCz1QIAtM0CALUVAQC2HQEAtxUBAOGEHgDjbB8A41wfAOFYHgBicwCAZnMAgGpzAIBucwCAcnMAgHZzAIB6cwCAfnMAgOkAAADv9B4A70weAIJzAICzlQIAhnMAgIpzAICOcwCAknMAgLa5AgC1sQIAmnMAgLtRAgC6SQIAhsgEAIesBAC/kQEAvkkCAL1BAgC8SQIAJnMAgKNRBQCecwCAlnMAgKZ9BQCicwCApnMAgKV1BQCqjQUAq5UFAKpzAICucwCAro0FAK9VBgCsjQUArYUFAICJBwCBiQcAgpkHALORBgCycwCAtbkGALapBgC2cwCAunMAgL5zAIC6TQcAu0UHALxdBwC9QQcAvkEHAL9BBwCoQQYAqU0GAKpVBgCrZQYArH0GAK1lBgCubQYAr2UGAMJzAIDGcwCAynMAgM5zAIDScwCA1nMAgNpzAIDecwCAuFkHALlZBwC6aQcAu2kHALx5BwC9eQcAvmUHAL8ZBwCwxQcAsc0HALLFBwCz2QcAtMkHALXJBwC2aQcAt2kHAKPdBwDicwCA5nMAgOpzAIDucwCApuUHAKX1BwDycwCAqwkGAKoBBgD2cwCA+nMAgK8NBgCuDQYArQ0GAKwRBgCAbQAAgQkAAIIZAAD+cwCAAnQAgISYAQC+kAEABnQAgIbAAACH5AEACnQAgA50AIASdACAFnQAgBp0AIAedACAqF0GAKmNAQCqnQEAq5UBAKy5AQCtuQEArskBAK/BAQCEoAAAInQAgCZ0AIAqdACALnQAgDJ0AIA2dACAOnQAgLh5AQC5eQEAus0AALvFAAC83QAAvcUAAL7FAAC/9QAAsIEBALGBAQCySQEAs0kBALRZAQC1WQEAtkkBALdJAQCzFQIAPnQAgEJ0AIBGdACASnQAgLY5AgC1MQIATnQAgLtFAgC6RQIAUnQAgFZ0AIC/nQIAvp0CAL2dAgC8nQIAhXw+AKNRAgBadACAXnQAgKZ9AgBidACAZnQAgKV1AgCqAQIAqwECAGp0AIBudACArtkCAK/ZAgCs2QIArdkCAIDpAACB6QAAggUAAHJ0AIC+AAwAenQAgIeoAwCGvAwAfnQAgIJ0AICGdACAinQAgI50AICSdACAlnQAgJp0AICedACAonQAgKZ0AICqdACA42ABAK50AIDhoAEAsnQAgO+IAgC2dACAunQAgL50AIDCdACAxnQAgMp0AIDOdACAqGkCAKlpAgCqeQIAq3kCAKxpAgCtaQIArr0CAK+1AgC+rAwA0nQAgNZ0AIDadACAgB0AAIEJAACCqQAA3nQAgLhRAQC5WQEAumEBALthAQC8GQEAvRkBAL4NAQC/BQEAsM0CALHVAgCy3QIAs9UCALTNAgC1cQEAtnEBALdxAQDjxAAA4XwHAOF4BgDjvAYA4nQAgIQYDQCGuAwAhzwNAL4sDwDqdACA7nQAgPJ0AIDvEAAA9nQAgPp0AIDvdAYA/nQAgAJ1AIAGdQCAs70CAAp1AIC1rQIAtqUCAA51AIASdQCAFnUAgLpFAgC7XQIAvEUCAL1NAgC+RQIAv/kBAHZ0AIClfQ0ApnUNAOZ0AIAadQCAHnUAgCJ1AICjbQ0ArJUNAK2dDQCulQ0ArykOACZ1AIAqdQCAqpUNAKuNDQCz5Q4ALnUAgDJ1AIA2dQCAOnUAgLblDgC19Q4APnUAgLuhDgC62Q4AQnUAgEZ1AIC/pQ4AvrkOAL2xDgC8uQ4AqBUOAKklDgCqLQ4AqyUOAKw9DgCtJQ4Ari0OAK8lDgCADQAAgRUAAIIdAABKdQCATnUAgFJ1AICEMAMAVnUAgLgpDgC5KQ4AujkOALs5DgC8KQ4AvSkOAL79DwC/9Q8AsF0OALElDgCyLQ4AsyUOALQ9DgC1IQ4AtiUOALcZDgCjpQ8AWnUAgIYoAQCHTAEAXnUAgKalDwCltQ8AYnUAgKvhDwCqmQ8AZnUAgGp1AICv5Q8ArvkPAK3xDwCs+Q8AbnUAgLPpDgBydQCAdnUAgLaRDgB6dQCAfnUAgLXlDgC6sQ4Au7kOAIJ1AICGdQCAvmEBAL9hAQC8mQ4AvZkOAKglDgCpLQ4AqiUOAKs5DgCsKQ4ArVUOAK5dDgCvVQ4AinUAgI51AICSdQCAlnUAgJp1AICedQCAonUAgKZ1AIC49QEAuYEBALqBAQC7gQEAvIEBAL2JAQC+sQEAv7EBALAxDgCxOQ4AsgkOALMJDgC04QEAteEBALbhAQC3zQEAo60NAKp1AICudQCAsnUAgLZ1AICm1Q0ApaENALp1AICr/Q0AqvUNAL51AIDCdQCAryUCAK4lAgCt3Q0ArN0NAIBdAACBbQAAgmUAALNRAwC+nAMAtXkDALYZAwDKdQCAhOACAM51AIC6PQMAuzUDALwZAwC9GQMAvtkDAL/ZAwCohQMAqZUDAKqVAwCrpQMArL0DAK3VAwCu0QMAr9EDAIYABACHNAMAv6AzANJ1AIDWdQCA2nUAgN51AIDidQCAuHEDALlxAwC6cQMAu3EDALzVAAC93QAAvtUAAL/NAACwtQMAsb0DALKBAwCzgQMAtFEDALVRAwC2UQMAt1EDAO+oAwDmdQCA6nUAgO51AICEHAIA8nUAgPZ1AID6dQCAviwFAP51AIACdgCABnYAgONAAwAKdgCA4SgAAA52AICjXQIAEnYAgBZ2AIAadgCAHnYAgKYVAgCldQIAInYAgKs5AgCqMQIAJnYAgCp2AICv1QIArtUCAK0VAgCsFQIA4ygBAOEADwDhCA4A4wgOAID9AACBCQAAgjkAAC52AIAydgCAOnYAgD52AIBCdgCA7+gOAEZ2AIBKdgCA72QOALNtAQBOdgCAhugEAIcMBQBSdgCAtm0BALVtAQBWdgCAu+0AALrtAABadgCAXnYAgL/VAAC+6QAAveEAALzpAACoXQYAqWEGAKqlBgCrvQYArKUGAK2tBgCupQYArxkHADZ2AIBidgCAZnYAgGp2AIBudgCAcnYAgHZ2AIB6dgCAuHUHALl5BwC6DQcAuwUHALwdBwC9BQcAvgUHAL81BwCwaQcAsWkHALJ9BwCzdQcAtG0HALVRBwC2UQcAt1EHAKMtBgB+dgCAgnYAgIZ2AICKdgCApi0GAKUtBgCOdgCAq60HAKqtBwCSdgCAlnYAgK+VBwCuqQcAraEHAKypBwCADQAAgRUAAIIdAACadgCAnnYAgKJ2AICEVAMAvlwAAKZ2AICqdgCAhugAAIdMAwCudgCAsnYAgLZ2AIC6dgCAvnYAgOMEBADCdgCA4bQFAMZ2AIDKdgCAznYAgNJ2AIDWdgCA2nYAgN52AIDidgCA5nYAgO/sBADqdgCA7nYAgLPtBgDydgCA9nYAgPp2AID+dgCAtpEGALXhBgACdwCAu40GALqNBgAGdwCACncAgL9BAQC+WQEAvVEBALxZAQCoJQYAqS0GAKolBgCrOQYArCkGAK1RBgCuSQYAr0EGAIDNAACBCQAAghkAAA53AIASdwCAhCwBAL40AAAadwCAuP0BALlBAQC6QQEAu0EBALxBAQC9SQEAvnEBAL9xAQCwCQYAsQkGALLNAQCzxQEAtN0BALXFAQC2zQEAt8UBAIagPACHRAMAHncAgKOhBQAidwCApa0FAKbdBQAmdwCAKncAgL4oPACqwQUAq8EFAKwVAgCtHQIArhUCAK8NAgC2QQMALncAgDJ3AIC1sQIANncAgLOhAgA6dwCAPncAgL5FAwC/TQMAvHUDAL1NAwC6ZQMAu20DAEJ3AIBGdwCASncAgE53AIDGdQCAUncAgFZ3AIBadwCAXncAgGJ3AICoRQIAqVUCAKpdAgCrVQIArE0CAK21AwCusQMAr60DALDVAwCx3QMAstUDALPtAwC09QMAtf0DALb1AwC37QMAuNkDALnZAwC6rQMAu6UDALy9AwC9pQMAvqUDAL+VAwCj9QMAZncAgGp3AIBudwCAcncAgKYVAgCl5QMAdncAgKs5AgCqMQIAencAgH53AICvGQIArhECAK0ZAgCsIQIAgGkAAIFpAACCBQAAgncAgIp3AICOdwCAkncAgO8cAACEbAIA4ZQBAJZ3AIDjyAAAmncAgJ53AICGWDwAh1A9AKJ3AICmdwCAqncAgISEPQCudwCAsncAgLZ3AIDvuAEAvmw8AOF0BgC6dwCA42QBAL53AIDCdwCAxncAgMp3AICz0QEAzncAgNJ3AIDWdwCA2ncAgLaRAQC1+QEA3ncAgLu9AQC6vQEA4ncAgOZ3AIC/dQEAvnUBAL2FAQC8hQEAqL09AKkNPgCqGT4AqxE+AKwxPgCtUT4ArlE+AK9NPgCGdwCAgh0AAIEdAACAHQAA6ncAgO53AIDydwCA9ncAgLjVPgC53T4AutU+ALtJPwC8WT8AvVk/AL5JPwC/QT8AsDk+ALE5PgCyET4AsxE+ALTxPgC18T4AtvU+ALftPgCjkT4A+ncAgIYoAACHwAMA/ncAgKbRPgCluT4AAngAgKv9PgCq/T4ABngAgAp4AICvNT4ArjU+AK3FPgCsxT4ADngAgLOdPwASeACAFngAgLalPwAaeACAHngAgLWtPwC6aT8Au3U/ACJ4AIAmeACAvlk/AL9FPwC8bT8AvWU/ACp4AIAueACAMngAgDZ4AIDjYDwAOngAgOEAPQA+eACA7/w9AEJ4AIBGeACASngAgE54AIBSeACAVngAgFp4AICjGT4AghkAAIEZAACAcQAAXngAgKYhPgClKT4AYngAgKvxPgCq7T4AhCQBAL4kAQCvwT4Art0+AK3hPgCs6T4AqNE+AKnRPgCq0T4Aq+U+AKzhPgCt4T4Arhk+AK8ZPgCGAAAAh4QAAGp4AIBueACAcngAgHZ4AIB6eACAfngAgLh9PgC5AT4AugE+ALsBPgC8AT4AvQk+AL4xPgC/MT4AsGk+ALF1PgCyfT4As3U+ALRZPgC1RT4Atk0+ALdFPgCohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAIJ4AICGeACAingAgL8k5gGOeACAkngAgJZ4AICaeACAuFUDALlZAwC6bQMAu2UDALx9AwC9ZQMAvm0DAL9lAwCwtQIAsb0CALKBAgCzgQIAtHEDALVxAwC2cQMAt3EDALMdAgCeeACAongAgKZ4AICEiAMAtlUCALU1AgAWdwCAu3kCALpxAgCqeACArngAgL+1AwC+tQMAvVUCALxVAgCyeACAo1kCALZ4AIC6eACAphECAL54AIDCeACApXECAKo1AgCrPQIAxngAgMp4AICu8QMAr/EDAKwRAgCtEQIAqKkCAKmpAgCquQIAq7kCAKypAgCtqQIArjkBAK85AQCAzQEAgQkAAIIZAADOeACA0ngAgL64BQDaeACA3ngAgLjpAQC56QEAuokBALuFAQC8nQEAvYEBAL6BAQC/tQEAsEkBALFVAQCyXQEAs1UBALRNAQC18QEAtvEBALfxAQDvFAAA4ngAgIaoBQCH3AUA5ngAgIRYBADqeACA78Q+AO54AIDhxD4A8ngAgOMwPgDjyAAA9ngAgOEoAQD6eACAtn0CAP54AIACeQCAtXUCAAZ5AICzZQIACnkAgA55AIC+3QEAv2EBALzdAQC91QEAutkBALvFAQASeQCAFnkAgKOxBQDWeACAGnkAgB55AIAieQCApqkFAKWhBQAmeQCAqxEGAKoNBgAqeQCALnkAgK+1BgCuCQYArQEGAKwJBgAyeQCANnkAgDp5AIA+eQCAgBkAAIEZAACCBQAAQnkAgL5sAwBGeQCAhsgAAIccAwBKeQCATnkAgFJ5AIBWeQCAqLkHAKm5BwCqDQcAqx0HAKwJBwCtNQcArjEHAK8pBwCEqAMAWnkAgF55AIBieQCAZnkAgGp5AIBueQCAcnkAgLjJAAC5yQAAutkAALvRAAC8+QAAvfkAAL6ZAAC/mQAAsF0HALEhBwCyIQcAsz0HALQpBwC1KQcAtgEHALcBBwCzhQYAdnkAgHp5AIB+eQCAgnkAgLa1BgC1gQYAhnkAgLvlBgC6mQYAinkAgI55AIC/7QYAvu0GAL3pBgC89QYAknkAgJZ5AICaeQCAnnkAgKJ5AICmeQCAqnkAgO+QBACueQCA4dwGALJ5AIDj7AUAgCkAAIEVAACCEQAAvnwBAKMFBgC6eQCAhigAAIdMAQC+eQCApjUGAKUBBgDCeQCAq2UGAKoZBgDGeQCAynkAgK9tBgCubQYArWkGAKx1BgDOeQCAs70BANJ5AIDWeQCAtnkBANp5AIDeeQCAtXkBALpVAQC7XQEA4nkAgOZ5AIC++QAAv/kAALxFAQC9+QAAqHECAKlxAgCqcQIAq3ECAKy1AgCtvQIArrUCAK+tAgCE7AwA6nkAgO55AIDyeQCA9nkAgPp5AID+eQCAAnoAgLhpAwC5aQMAugkDALsJAwC8GQMAvRkDAL4JAwC/CQMAsNUCALHdAgCy1QIAs2kDALR5AwC1eQMAtmkDALdhAwAGegCACnoAgA56AICj9QIAEnoAgKUxAgCmMQIAFnoAgBp6AIAeegCAqh0CAKsVAgCsDQIArbEDAK6xAwCvsQMAgGEAAIFhAACCBQAAInoAgIbwDACHYAMAvhAMACp6AIBmeACALnoAgDJ6AIA2egCAOnoAgD56AIBCegCARnoAgKiFAgCplQIAqpUCAKulAgCsvQIArdUCAK7RAgCv0QIASnoAgE56AIBSegCAVnoAgFp6AIBeegCAYnoAgGZ6AIC4dQEAuX0BALp1AQC7zQEAvNUBAL3dAQC+yQEAv8EBALC1AgCxvQIAsoECALOBAgC0VQEAtV0BALZVAQC3TQEA4RAGAIRIDADjDAYAanoAgISYDABuegCAcnoAgHZ6AIB6egCAfnoAgIJ6AICGegCAgXUAAIB1AADvIAEAgnUAAIp6AICOegCAknoAgL7ADACFtA4A4RACAO9cAADjABYA4ZABAJp6AIDjWAEA7zwHAJ56AICiegCAhgAIAIe4DACznQ0AJnoAgKZ6AICqegCArnoAgLbVDQC1tQ0AsnoAgLv5DQC68Q0AtnoAgLp6AIC/GQ4AvhEOAL3VDQC81Q0AvnoAgKPZDQDCegCAxnoAgKaRDQDKegCAznoAgKXxDQCqtQ0Aq70NANJ6AIDWegCArlUOAK9dDgCskQ0ArZENAKhdDgCpYQ4AqmEOAKthDgCsYQ4ArWEOAK5hDgCvYQ4A2noAgN56AIDiegCA5noAgOp6AIDuegCA8noAgPZ6AIC4TQ8AuVEPALpRDwC7UQ8AvHEPAL1xDwC+cQ8Av3EPALDBDwCxwQ8AssEPALPBDwC0wQ8AtcEPALbBDwC3wQ8As+kPAPp6AIC+gAEA/noAgJZ6AIC24Q8AtekPAAJ7AIC7BQ4AugUOAAp7AIAGewCAvwUOAL4FDgC9FQ4AvBUOAIFNAACAQQAA72gNAIJRAACG8AcAh9QBAA57AIASewCAFnsAgIRwAQAaewCAHnsAgOHgDgAiewCA40gNACZ7AICjaQ8AKnsAgC57AIAyewCANnsAgKZhDwClaQ8AOnsAgKuFDgCqhQ4APnsAgEJ7AICvhQ4AroUOAK2VDgCslQ4ARnsAgLMxDgBKewCATnsAgLbBAQBSewCAVnsAgLXRAQC6zQEAu6UBAFp7AIBeewCAvqUBAL+tAQC8sQEAvbEBAI/dJgCj8Q0AYnsAgGZ7AICmAQIAansAgG57AIClEQIAqg0CAKtlAgByewCAviAEAK5lAgCvbQIArHECAK1xAgCfoQwAnnkKAJ1pCgCc0QgAm7E2AJp1NgCZ0TQAmOEyAJdtMgCWZTIAlTU/AJRhPgCTcT4AkjU7AJFxOgCQeToAgJUAAIGdAACCoQAAensAgO9EAgDhdA8AfnsAgOMcDwDj1AEAgnsAgOHgAQDvXAEAo7UCAKJBAACh3Q4AoLkOALWpAwCGewCAhMAEALahAwCG8AUAh+QEALOFAwCKewCAvXEDALxpAwC/QQMAvnEDAI57AIC2eQCAu3EDALp5AwCC3ScAgwE7AL6EBwC+wAYAhhE/AIcZPwCEETsAhV06AIp9PgCLJTMAknsAgJZ7AICOuTUAjxU3AIw1MwCNgTMAkqE3AJPZCQC+xBkAmnsAgJaxDQCXUQ8AlHkLAJVhCwCaBQ8Am5EBAJ57AICiewCApnsAgN0AAACcfQMAqnsAgOFIDwCuewCA4xwOALJ7AIC2ewCAunsAgL57AIDCewCAsUEXALChFwCzqesBsgHoAbUB7AG0EesB74wOAMZ7AICpxR8AqAEcAKsBEACqkR8ArdkTAKzREwCv2RcArgUTAKHxAgDKewCAo8kHAKLBAgClARgApGUHAKehGwCm+RsAqCkFAKldBQCqVQUAq20FAKx5BQCteQUArm0FAK9hBQB2ewCAznsAgNJ7AIDWewCAgA0AAIGxAACCsQAA2nsAgLiJBQC5iQUAup0FALuVBQC8uQUAvbkFAL5RBgC/UQYAsOUFALHtBQCy5QUAs/0FALTtBQC13QUAttUFALe9BQCj3QUA3nsAgOJ7AICEDAAA5nsAgKb5BQCl8QUA6nsAgKspBQCqIQUAhpgAAIegAACvGQUArikFAK0pBQCsMQUA7nsAgLNhBgDyewCA9nsAgLYhBgD6ewCA/nsAgLUBBgC6rQcAu40HAAJ8AIAGfACAvo0HAL9xBwC8lQcAvY0HAL65BQC/uQUAvLkFAL25BQC6uQUAu7kFALi5BQC5uQUAtkkFALdJBQC0fQUAtXUFALJ5BQCzeQUAsBUFALF9BQCuXQUAr20FAKxFBQCtXQUAqqUKAKtdBQCovQoAqa0KAAp8AIAOfACAEnwAgBZ8AIAafACAHnwAgCJ8AIAmfACAqA0HAKkdBwCqLQcAq0kHAKxNBwCtZQcArrEGAK+xBgAqfACALnwAgDJ8AIA2fACAOnwAgD58AIBCfACARnwAgLhVBgC5XQYAulUGALtxBgC8NQYAvfEBAL7xAQC/8QEAsK0GALGNBgCyhQYAs50GALSNBgC1cQYAtnUGALdtBgCjpQQAgi0AAIEVAACAHQAASnwAgKblBAClxQQATnwAgKtJBQCqaQUAUnwAgFp8AICvtQUArkkFAK1JBQCsUQUAhmAcAIcIAwBefACAs4UCAGJ8AIC1gQIAtoECAGZ8AIBqfACAbnwAgLoJAwC7CQMAvBkDAL0ZAwC+CQMAvwkDAKxVAgCtXQIArmECAK9hAgCoDQIAqVUCAKpRAgCrUQIAhKwDAHJ8AIB2fACAenwAgIT8HQB+fACAgnwAgIZ8AIC8cQMAvXEDAL5xAwC/cQMAuHEDALlxAwC6cQMAu3EDALSRAwC1kQMAtpEDALeRAwCwkQMAsZEDALKRAwCzkQMAinwAgI58AICSfACAlnwAgJp8AIDhpAEAnnwAgOOAAQC+aBwAonwAgKZ8AIDv2AYAqnwAgK58AICyfACAtnwAgKOJAwCCLQAAgRUAAIAdAAC6fACApo0DAKWNAwC+fACAqwUCAKoFAgDCfACAynwAgK8FAgCuBQIArRUCAKwVAgCGIBwAh8QdAM58AIDSfACA1nwAgNp8AIDefACA72wGAOJ8AIDhbAcA5nwAgON0BwDqfACA7nwAgPJ8AID2fACAs5EBAPp8AID+fACAAn0AgAZ9AIC2sQEAtbkBAAp9AIC7VQEAukkBAA59AIASfQCAv/UAAL71AAC9RQEAvEUBAKNRHgDGfACAFn0AgBp9AIAefQCApnEeAKV5HgAifQCAq5UeAKqJHgAmfQCAKn0AgK81HwCuNR8ArYUeAKyFHgCAbQAAgRUAAIIdAADv/BkALn0AgDJ9AIA2fQCAOn0AgIbAAACHrAMAPn0AgEJ9AIBGfQCA4SwcAEp9AIDjzBwAqK0eAKnNHgCq2R4Aq9EeAKzxHgCt8R4Arj0eAK81HgCE7AAATn0AgFJ9AIBWfQCAWn0AgF59AIBifQCAZn0AgLjRHwC53R8Auu0fALvlHwC84R8AveEfAL7hHwC/4R8AsE0eALFRHgCyUR4As1EeALTxHwC18R8AtvEfALfxHwCobR4AqY0eAKqFHgCrnR4ArIUeAK2NHgCuuR4Ar7UeAGp9AIBufQCAcn0AgHZ9AIB6fQCAfn0AgIJ9AICGfQCAuJ0eALmtHgC6pR4Au0UBALxdAQC9RQEAvkUBAL91AQCw0R4AsdEeALLRHgCz0R4AtLUeALW9HgC2tR4At60eALMNHgCKfQCAjn0AgJJ9AICWfQCAtg0eALUNHgCafQCAuxUeALoVHgCefQCAon0AgL95HgC+cR4AvQUeALwFHgCCbQAAo0keAIBVAACBZQAApkkeAL6cAQCqfQCApUkeAKpRHgCrUR4Ah3wAAIZMAACuNR4Arz0eAKxBHgCtQR4AqF0CAKltAgCqZQIAq30CAKxpAgCtsQIArrECAK+xAgCE7AQArn0AgLJ9AIC2fQCAun0AgL59AIDCfQCAxn0AgLhxAwC5cQMAunEDALtxAwC81QMAvd0DAL7VAwC/zQMAsNECALHRAgCy0QIAs9ECALRRAwC1UQMAtlEDALdRAwCz7QIAyn0AgM59AIC+gAQA0n0AgLYxAgC14QIA1n0AgLsVAgC6FQIA2n0AgN59AIC/lQMAvpUDAL0FAgC8BQIA4n0AgKOpAgDmfQCA6n0AgKZ1AgDufQCA8n0AgKWlAgCqUQIAq1ECAPZ9AID6fQCArtEDAK/RAwCsQQIArUECAKjZAgCpIQEAqiEBAKshAQCsIQEArSEBAK4hAQCvIQEA/n0AgAJ+AIAGfgCAviAEAAp+AIAOfgCAEn4AgBp+AIC4jQEAuZEBALqRAQC7pQEAvL0BAL11AAC+fQAAv3UAALDlAQCx7QEAsvkBALPxAQC02QEAtdkBALa5AQC3tQEA4RgeAB5+AIDjKB8AIn4AgIGlAACApQAAJn4AgIKlAACGAAQAh/QFACp+AIAufgCAMn4AgDZ+AIDvYB4AOn4AgD5+AIBCfgCAhfD0AUZ+AIBKfgCA42QBAE5+AIDhpAEAUn4AgO/IAABWfgCAWn4AgFZ8AICE/AUAXn4AgGJ+AICzKQYAFn4AgGZ+AIBqfgCAbn4AgLYhBgC1KQYAcn4AgLupBgC6oQYAdn4AgHp+AIC/nQYAvp0GAL2lBgC8rQYA4bQHAH5+AIDjeAQAgn4AgIB9AACBEQAAghUAAIZ+AICGwAAAh1gDAIp+AICOfgCAkn4AgJZ+AIDvDAQAmn4AgKOpBgCefgCAon4AgKZ+AICqfgCApqEGAKWpBgCufgCAqykGAKohBgCyfgCAtn4AgK8dBgCuHQYArSUGAKwtBgC6fgCAs0kHAL5+AIDCfgCAtn0HAMZ+AIDKfgCAtXUHALpdBwC7JQcAzn4AgNJ+AIC+IQcAvy0HALw9BwC9MQcAqD0GAKmBBgCqhQYAq5UGAKy5BgCtuQYArqkGAK+pBgDWfgCA2n4AgN5+AIDifgCA5n4AgIK5AACBsQAAgLkAALitBgC5vQYAurUGALtFAQC8XQEAvUUBAL5FAQC/dQEAsN0GALGlBgCyrQYAs6EGALShBgC1rQYAtpkGALeVBgCjDQYA6n4AgO5+AIDyfgCAhJgCAKY5BgClMQYAvpwBAKthBgCqGQYAhggAAId8AQCvaQYArmUGAK11BgCseQYA+n4AgLO1AQD+fgCAAn8AgLZVAQAGfwCACn8AgLWhAQC6cQEAu3kBAA5/AIASfwCAvjEBAL89AQC8UQEAvVEBAKhpAgCpaQIAqnkCAKt5AgCsbQIArZECAK6RAgCvkQIAFn8AgBp/AIAefwCAIn8AgCZ/AIAqfwCALn8AgDJ/AIC4mQIAua0CALqlAgC7bQMAvHUDAL19AwC+dQMAv20DALDxAgCx+QIAssECALPBAgC0sQIAtb0CALa1AgC3qQIANn8AgDp/AIA+fwCAo/0CAEJ/AICl6QIAph0CAEZ/AIBKfwCATn8AgKo5AgCrMQIArBkCAK0ZAgCueQIAr3UCAFJ/AIBWfwCAWn8AgIQADACAGQAAgQkAAII5AABefwCAYn8AgGp/AIBufwCAvuAMAHJ/AIB2fwCAhlgNAIcMAwCowQIAqc0CAKrFAgCr2QIArMkCAK39AgCu9QIArz0BAHp/AIB+fwCAgn8AgIZ/AICKfwCAjn8AgJJ/AIC+MAwAuMUBALnNAQC62QEAu9EBALzxAQC98QEAvpkBAL+ZAQCwRQEAsU0BALJFAQCzXQEAtEUBALVNAQC2RQEAt/0BAOE4BgCWfwCA42wGAJp/AICefwCAon8AgKZ/AICqfwCAhKgNAK5/AICyfwCAtn8AgL6wDwC6fwCA72wGAL5/AIDCfwCApn0AgMZ/AIDKfwCA41AAAM5/AIDhoAEA0n8AgO+EAADafwCAhyANAIZMDwCAPQAAgSEAAIIlAADefwCAs80NAGZ/AIDWfwCA4n8AgOZ/AIC2/Q0AtcENAOp/AIC7CQ4AugEOAO5/AIDyfwCAvwkOAL4BDgC9CQ4AvBEOAPZ/AIDjmAwA+n8AgOH8DwD+fwCAAoAAgAaAAIAKgACADoAAgBKAAIAWgACAGoAAgB6AAIDvYAwAIoAAgCaAAICjTQ0AKoAAgC6AAIAygACANoAAgKZ9DQClQQ0AOoAAgKuJDgCqgQ4APoAAgEKAAICviQ4AroEOAK2JDgCskQ4Agm0AALM1DgCAVQAAgWUAALb1DwCE3AMARoAAgLX9DwC60Q8Au9EPAIYABACH3AAAvn0PAL9lDwC8wQ8AvXkPAKjlDwCp7Q8AqvkPAKv5DwCsMQ4ArTEOAK4xDgCvMQ4ASoAAgE6AAIBSgACAVoAAgFqAAIBegACAYoAAgGaAAIC43Q4AueEOALrhDgC74Q4AvOUOAL3pDgC+mQ4Av5UOALBRDgCxUQ4AslEOALPpDgC0/Q4AteUOALbtDgC35Q4Ao3EPAGqAAIBugACAcoAAgHaAAICmsQ4ApbkOAHqAAICrlQ4AqpUOAH6AAICCgACAryEOAK45DgCtPQ4ArIUOAIaAAICzyQEAioAAgI6AAIC2+QEAkoAAgJaAAIC1wQEAuqkBALu1AQCagACAnoAAgL6tAQC/lQEAvK0BAL2lAQCo5Q0AqfkNAKoFAgCrHQIArA0CAK09AgCuNQIAr10CAKKAAICmgACAqoAAgK6AAICAGQAAgRkAAIIFAACygACAuC0CALk1AgC6MQIAuzECALzVAgC93QIAvtUCAL/NAgCwKQIAsTUCALI9AgCzNQIAtC0CALUVAgC2HQIAtxUCALqAAICEnAIAvoAAgKOBAgDCgACApYkCAKaxAgDGgACAhiAEAIfUAwCq4QIAq/0CAKzlAgCt7QIAruUCAK/dAgC29QMAvkQDAIWM/QG1/QMAyoAAgLP9AwDOgACA0oAAgL59AwC/TQMAvGUDAL19AwC6dQMAu30DANaAAIDagACA3oAAgOKAAICEBAIAoyUCAOaAAIClJQIApi0CAOqAAIDugACA8oAAgKqtAgCrpQIArL0CAK2lAgCupQIAr5UCAPaAAID6gACA/oAAgAKBAIAGgQCA48ADAAqBAIDhrAEADoEAgO9YAwASgQCAFoEAgIANAACB5QAAgu0AABqBAIDhYA8A40ABAOM4DgDheA4AHoEAgCKBAIC+lAUAKoEAgIYABACHZAUALoEAgDKBAIA2gQCA7/wOAO98DgA6gQCAs1EBAD6BAID2fgCAQoEAgEaBAIC2DQEAtQkBAEqBAIC74QAAuhkBAE6BAIBSgQCAv9EAAL7pAAC96QAAvPkAALaAAIAmgQCAVoEAgFqBAIBegQCAYoEAgGaBAIBqgQCAqKEGAKmtBgCquQYAq7EGAKzhBgCt7QYAruUGAK/FBgCwvQYAsUUHALJNBwCzXQcAtE0HALV1BwC2fQcAtx0HALglBwC5LQcAuiUHALs9BwC8KQcAvRUHAL4RBwC/EQcAoxEGAG6BAIBygQCAdoEAgHqBAICmTQYApUkGAH6BAICroQcAqlkGAIKBAICGgQCAr5EHAK6pBwCtqQcArLkHAIANAACBFQAAgh0AAIqBAICOgQCAkoEAgISUAwC+lAMAloEAgJqBAICGyAAAh4wAAJ6BAICigQCApoEAgKqBAIConQYAqa0GAKqlBgCrvQYArK0GAK3RBgCu1QYAr80GAK6BAICygQCAtoEAgLqBAIC+gQCAwoEAgMaBAIDKgQCAuF0BALnBAQC6wQEAu8EBALzBAQC9yQEAvvEBAL/xAQCwvQYAsY0GALKFBgCzZQEAtH0BALVlAQC2bQEAt2UBALMtBgDOgQCA0oEAgNaBAIDagQCAtlEGALUlBgDegQCAu0kGALp5BgDigQCA5oEAgL+hAQC+uQEAvbEBALxRBgDqgQCAo2kGAO6BAIDygQCAphUGAPaBAID6gQCApWEGAKo9BgCrDQYA/oEAgAKCAICu/QEAr+UBAKwVBgCt9QEAutUHALvdBwC4wQcAucEHAL4xBAC/MQQAvPEHAL3xBwCyrQcAs7UHALCtBwCxpQcAtp0HALf1BwC0pQcAtZUHAKppBwCraQcAqGkHAKlpBwCuaQcAr2kHAKxpBwCtaQcAgLkDAIGNAwCChQMAhKgDAIZQ/AGHCAMAvjQDAAqCAICoZQIAqXUCAKp9AgCrdQIArG0CAK21AwCuvQMAr7UDAA6CAIASggCAFoIAgBqCAIAeggCAIoIAgCaCAIAqggCAuFEDALlZAwC6YQMAu2EDALwRAwC9HQMAvhUDAL8JAwCwzQMAsdUDALLdAwCz1QMAtM0DALVxAwC2cQMAt3EDAC6CAIAyggCAs/0DADaCAIC17QMAOoIAgD6CAIC2PQIAQoIAgEaCAIC7GQIAugECAL0JAgC8AQIAv70CAL4BAgBKggCAToIAgITE/QG+wPwBUoIAgFaCAIBaggCA79wDAF6CAIDhlAEAYoIAgOMQAwBmggCAgu0AAIHtAACA7QAA4TgGAOE8BwDjQAEA45QGAGqCAIBuggCAcoIAgHqCAICGgPwBh+j9AX6CAICCggCAhoIAgIqCAIDvnAEA79wGAKM1AwCOggCAkoIAgJaCAICaggCApvUCAKUlAwCeggCAq9ECAKrJAgCiggCApoIAgK91AgCuyQIArcECAKzJAgB2ggCAqoIAgK6CAICyggCA76T9AbaCAIC6ggCAvoIAgON4/QHCggCA4UD8AcaCAIDKggCAzoIAgNKCAIDWggCAs+X+AYItAACBFQAAgB0AANqCAIC25f4BtfX+Ad6CAIC7Yf8Butn+AeKCAICE5AMAv2n/Ab5h/wG9df8BvHn/Aaj9/gGpJf4Bqi3+Aasl/gGsPf4BrSX+Aa4t/gGvJf4BviwAAOaCAICGiAAAh+wAAOqCAIDuggCA8oIAgPaCAIC4gf8BuYH/AbqZ/wG7mf8BvIn/Ab21/wG+sf8Bv63/AbBd/gGx5f8Bsu3/AbPh/wG05f8Bte3/AbbZ/wG32f8Bo6X/AfqCAID+ggCAAoMAgAaDAICmpf8BpbX/AQqDAICrIf4Bqpn/AQ6DAIASgwCAryn+Aa4h/gGtNf4BrDn+ARaDAICz6f4BGoMAgB6DAIC2lf4BIoMAgCaDAIC16f4BurH+Abu5/gEqgwCALoMAgL51AQC/fQEAvJH+Ab2R/gGoHf4BqS3+Aaol/gGrPf4BrCX+Aa1R/gGuUf4Br1H+ATKDAIA2gwCAOoMAgD6DAIBCgwCARoMAgEqDAIBOgwCAuNkBALnZAQC67QEAu+EBALzhAQC94QEAvuEBAL/hAQCwMf4BsTn+AbIB/gGzAf4BtPUBALX9AQC29QEAt+kBAKOt/QFSgwCAvkwDAFqDAIBegwCAptH9AaWt/QFigwCAq/39Aar1/QFmgwCAaoMAgK85AgCuMQIArdX9AazV/QGA+QMAgfkDAIJNAACFdCAAboMAgITYAwCE1AQAcoMAgIZABACHVAMAdoMAgHqDAIB+gwCAgoMAgIaDAIC+8AUAqDECAKkxAgCqMQIAqzECAKyVAwCtnQMArpUDAK+NAwCKgwCAjoMAgJKDAICWgwCAhHwHAJqDAICegwCAooMAgLipAwC5qQMAumkDALtpAwC8eQMAvXkDAL5pAwC/aQMAsP0DALHNAwCyxQMAs60DALS5AwC1uQMAtq0DALelAwCmgwCAqoMAgK6DAICygwCAtoMAgLqDAIDv6AMAvoMAgOGQAQDCgwCA42wDAMqDAICAJQAAgSkAAIIdAADOgwCAs/kDANKDAICGaAcAh1wFANaDAIC2XQIAtV0CANqDAIC7SQIAunkCAN6DAIDigwCAvz0CAL49AgC9OQIAvFECAOaDAIDhPP4BvkAGAOPwAQDqgwCA7oMAgPKDAID2gwCA+oMAgP6DAIAChACABoIAgAaEAIAKhACADoQAgO/kAQAShACAFoQAgKNxAwAahACApdUCAB6EAIAihACAptUCACaEAIAqhACAq8ECAKrxAgCtsQIArNkCAK+1AgCutQIA4dz8AcaDAIDjUAQA74gEAID1BwCBCQAAgj0AAC6EAICEJAEAMoQAgDaEAIA6hACAPoQAgOFMBADv5BwA43QEALNdBgBChACAhgAMAIfgAwBGhACAtgUGALV1BgBKhACAuxEGALoJBgBOhACAUoQAgL/VBgC+1QYAvQEGALwJBgCojQYAqZUGAKqVBgCrpQYArL0GAK3FBgCuxQYAr/UGAFaEAIBahACAXoQAgGKEAIBmhACAaoQAgG6EAIByhACAuHUGALl9BgC6dQYAu80HALzVBwC93QcAvtUHAL/NBwCwjQYAsZUGALKdBgCzlQYAtFEGALVRBgC2UQYAt1EGAKMdBwCPFewBdoQAgHqEAIB+hACApkUHAKU1BwCChACAq1EHAKpJBwCGhACAioQAgK+VBwCulQcArUEHAKxJBwCeRfkBn6X5AZyR/QGdTfkBmlX9AZtd/QGYBfEBmZX+AZal8gGXYfEBlG31AZU19QGS4ekBk4X2AZBV7AGRXekBsbEdALClHQCziRkAskEcALUBJAC09RkAjoQAgJKEAICWhACAgqkDAIGhAwCAaQAAohUFAKMFAgCgFQYAob0FAKHFAQCahACAo80NAKLlAQClAQgApN0NAKfRCQCm2QkAqQEUAKilCACrxRQAqs0VAK3REQCsARAArwEcAK51EQCCEe8BgynvAZ6EAICihACAhuH1AYcR9gGEOeoBhY3qAYp59gGL4fEBvqQMAKqEAICO+f0BjzH+AYw98gGNYfIBkkn+AZOd/gGHCAwAhmwMAJax+gGX+QUAlFn6AZVZ+gGaYQYAm8EGAK6EAICyhACAtoQAgLqEAICcyQEAvoQAgKitBQCpuQUAqs0FAKvdBQCszQUArf0FAK71BQCvHQUAwoQAgMaEAIDKhACAzoQAgNKEAIDWhACA2oQAgN6EAIC4dQUAuX0FALoJBQC7CQUAvB0FAL0BBQC+AQUAvz0FALBxBQCxcQUAsnEFALNxBQC0UQUAtVEFALZRBQC3TQUAs0UEAOKEAIDmhACA6oQAgO6EAIC2fQQAtUUEAPKEAIC7tQQAurUEAPaEAID6hACAv5UEAL6VBAC9pQQAvKUEAP6EAICjAQQAAoUAgAaFAICmOQQACoUAgA6FAIClAQQAqvEEAKvxBAAShQCAhOwNAK7RBACv0QQArOEEAK3hBADh0AYAhAwMAOMoBwC+AAwAGoUAgO9EAwCGuAwAhywNAB6FAIDjlAEAIoUAgOH8AQBWgwCAJoUAgO/IBgAqhQCALoUAgDKFAICzjQMANoUAgLWNAwA6hQCAPoUAgLa1AwBChQCARoUAgLtBAwC6SQMAvUEDALxZAwC/QQMAvkkDAKNFDACmhACAFoUAgEqFAIBOhQCApn0MAKVFDABShQCAq4kMAKqBDABWhQCAWoUAgK+JDACugQwArYkMAKyRDACAFQ8AgR0PAIIhDwCzIQ4AXoUAgLUhDgC2JQ4AYoUAgGaFAIBqhQCAusEOALvBDgC8wQ4AvcEOAL7BDgC/wQ4AqK0OAKntDgCq5Q4Aq/0OAKzlDgCt6Q4ArjkOAK85DgBuhQCAcoUAgHaFAIB6hQCAgB0AAIEJAACCvQEAfoUAgLjNDwC51Q8AutUPALvlDwC8/Q8AvZUPAL6RDwC/kQ8AsEkOALFJDgCyWQ4As1kOALRJDgC1SQ4Atv0PALf1DwCjbQ8AgoUAgL6EAQCKhQCAjoUAgKZpDwClbQ8AkoUAgKuNDwCqjQ8AhogAAIdsAQCvjQ8Aro0PAK2NDwCsjQ8AloUAgLPtDgCahQCAnoUAgLaRDgCihQCApoUAgLXhDgC6tQ4Au70OAKqFAICuhQCAvn0BAL9lAQC8mQ4AvZkOAKgRDgCpJQ4AqiEOAKs5DgCsLQ4ArVUOAK5dDgCvUQ4AhKgAALKFAIC2hQCAuoUAgL6FAIDChQCAxoUAgMqFAIC47QEAuZUBALqVAQC7rQEAvLUBAL11AQC+fQEAv3UBALA1DgCxPQ4AsgkOALMJDgC0/QEAteUBALblAQC31QEAo6kNAM6FAIDShQCA1oUAgNqFAICm1Q0ApaUNAN6FAICr+Q0AqvENAOKFAIDmhQCAryECAK45AgCt3Q0ArN0NAIANAACBFQAAgh0AAOqFAIDuhQCA8oUAgIeQAwCGfAQAvuwEAPqFAID+hQCAAoYAgAaGAIAKhgCADoYAgBKGAICyLQ4AszUOALAtDgCxJQ4Ati0OALedDwC0LQ4AtSUOALq9DwC7jQ8AuKUPALm9DwC+LQ8AvxUPALyVDwC9JQ8AFoYAgBqGAIAehgCAIoYAgCaGAIAqhgCALoYAgDKGAICqpQ4Aq7UOAKjFDgCp3Q4Arp0OAK9VDgCspQ4ArZUOAKgNAgCpFQIAqhUCAKtNAgCsWQIArVkCAK5NAgCvRQIAhKgFADaGAIA6hgCAPoYAgIS4BABChgCARoYAgEqGAIC4/QIAuUEBALpBAQC7QQEAvEEBAL1JAQC+cQEAv3EBALAJAgCxCQIAss0CALPFAgC03QIAtcUCALbNAgC3xQIA4dQPAOMQDgDj9A4A4QwOAE6GAIBShgCAVoYAgFqGAIBehgCAYoYAgL4kBABqhgCA7AAAAO9EAADvzA4AboYAgIJlAACz2QIAgFUAAIFtAAC2nQIAcoYAgHaGAIC1lQIAuokCALuJAgCGqAQAh+AEAL5dAgC/RQIAvF0CAL1VAgCjHQUA9oUAgGaGAIB6hgCAfoYAgKZZBQClUQUAgoYAgKtNBQCqTQUAhoYAgIqGAICvgQUArpkFAK2RBQCsmQUAjoYAgLMpBgCShgCAloYAgLYpBgCahgCAnoYAgLUpBgC6pQYAu60GAKKGAICmhgCAvqUGAL+tBgC8tQYAva0GAKjlBgCp7QYAquUGAKv9BgCs5QYAre0GAK7lBgCvXQYAqoYAgK6GAICyhgCAtoYAgLqGAIC+hgCAwoYAgMaGAIC46QcAuekHALr9BwC79QcAvO0HAL1FBwC+TQcAv0UHALAlBgCxLQYAsiUGALM9BgC0JQYAtS0GALYlBgC32QcAo20HAIItAACBFQAAgB0AAMqGAICmbQcApW0HAM6GAICr6QcAquEHANKGAIC+oAEAr+kHAK7hBwCt6QcArPEHANaGAICzkQYAhugAAIcsAQC2QQEA2oYAgN6GAIC1UQEAuk0BALslAQDihgCA5oYAgL4lAQC/LQEAvDEBAL0xAQCwrQEAscUBALLBAQCzwQEAtMUBALXNAQC28QEAt/EBALgBAQC5AQEAugEBALsBAQC8AQEAvQEBAL4BAQC/AQEA6oYAgO6GAIDyhgCA9oYAgIaFAID6hgCA/oYAgAKHAICoTQYAqVkGAKo9BgCrNQYArP0BAK3lAQCu5QEAr9UBAKPVBQAGhwCACocAgA6HAIAShwCApgUCAKUVAgAWhwCAq2ECAKoJAgAahwCAHocAgK9pAgCuYQIArXUCAKx1AgAihwCAJocAgCqHAIAuhwCAMocAgOFkBQA2hwCA4+wFAIARAACBEQAAghEAAO/0BgA6hwCAPocAgEKHAIC+MAMAhMQCAEqHAICz4QMAhMAcALVRAwBOhwCAUocAgLZZAwBWhwCAWocAgLtxAwC6eQMAvbUAALxpAwC/tQAAvrUAAF6HAIDhlAEAYocAgONcAgCGcBwAh0QDAGaHAIBqhwCAbocAgHKHAIB2hwCAeocAgH6HAICChwCAhocAgO94AgCoVQIAqV0CAKphAgCrYQIArNECAK3RAgCu0QIAr9ECAIqHAICOhwCAkocAgJaHAICahwCAnocAgKKHAICmhwCAuGkBALlpAQC6CQEAuwkBALwZAQC9GQEAvgkBAL8FAQCwtQIAsb0CALK1AgCzaQEAtHkBALV5AQC2aQEAt2EBAOHEBwDjpAYA47gGAOF8BgCADQAAgTUAAII9AACqhwCArocAgLKHAIC+4B0AuocAgL6HAIDvYAAA7+gGAMKHAICjqQIAxocAgMqHAIDOhwCA0ocAgKYRAgClGQIA1ocAgKs5AgCqMQIAhkgcAIfMHACv/QEArv0BAK39AQCsIQIAqIUeAKmRHgCqkR4Aq60eAKy1HgCt1R4ArtEeAK/FHgC2hwCA2ocAgN6HAIDihwCA5ocAgOqHAIDuhwCA8ocAgLhhHwC5YR8AumEfALthHwC8YR8AvWEfAL5hHwC/YR8AsL0eALGFHgCyjR4As4UeALSdHgC1hR4Ato0eALeFHgCzGR4A9ocAgPqHAID+hwCAAogAgLZVHgC1PR4ABogAgLtBHgC6eR4ACogAgA6IAIC/QR4AvlkeAL1RHgC8WR4AEogAgKNdHgAWiACAGogAgKYRHgAeiACAIogAgKV5HgCqPR4AqwUeAISkAwC+qAMArh0eAK8FHgCsHR4ArRUeAKitHgCptR4AqrUeAKvJHgCs2R4ArdkeAK7JHgCvwR4AgO0BAIHxAQCC8QEAJogAgIaQAACHdAEAKogAgC6IAIC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALBFAQCxTQEAskUBALNdAQC0RQEAtU0BALZFAQC3+QEAsz0eADKIAIA2iACAOogAgD6IAIC2WR4AtVEeAEKIAIC7iQEAuoEBAEaIAIBKiACAv4kBAL6BAQC9iQEAvJEBAE6IAIBSiACAo3UeAFaIAIClGR4AWogAgF6IAICmER4ARocAgGKIAICrwQEAqskBAK3BAQCs2QEAr8EBAK7JAQBmiACAaogAgG6IAIByiACAdogAgIQYAgB6iACAfogAgIKIAICGiACAiogAgI6IAICSiACAmogAgJ6IAIC+cAMAgGkAAIFpAACCeQAAhAAEAIbwBACHdAMAoogAgO8MHwCmiACA4aweAKqIAIDj8B4ArogAgLKIAIC2iACAuogAgL6IAIDCiACAxogAgMqIAIDvVAIAzogAgNKIAIDWiACA46QCANqIAIDhgAEA3ogAgOKIAIDmiACA6ogAgO6IAICzRQMA8ogAgPaIAID6iACA/ogAgLZFAwC1VQMAAokAgLshAwC6SQMAvqAEAAqJAIC/KQMAviEDAL01AwC8OQMAqDkCAKk5AgCqjQIAq4UCAKydAgCthQIAroUCAK+1AgCA7QEAgfUBAIL1AQAOiQCAhpAEAIcEBQASiQCAFokAgLhFAQC5TQEAukUBALtdAQC8SQEAvUkBAL55AQC/eQEAsM0CALGlAgCyrQIAs6ECALSlAgC1rQIAtp0CALd9AQAaiQCAHokAgCKJAIAmiQCAKokAgC6JAIAyiQCA74gBAITsBADhVB4ANokAgONUAQA6iQCAPokAgEKJAIBGiQCAo0UCAEqJAIBOiQCAUokAgFaJAICmRQIApVUCAFqJAICrIQIAqkkCAF6JAIBiiQCArykCAK4hAgCtNQIArDkCAKg1BgCpPQYAqlEGAKttBgCseQYArWUGAK5tBgCvZQYABokAgGaJAIBqiQCAbokAgIAZAACBGQAAggUAAHKJAIC45QYAuekGALr5BgC7+QYAvOkGAL3pBgC+nQYAv5UGALAdBgCx5QYAsu0GALPlBgC0/QYAteEGALbhBgC34QYAs9kGAL7QAwB2iQCAeokAgH6JAIC25QYAtfEGAIKJAIC7IQYAutkGAIaYAACHeAMAvyUGAL45BgC9MQYAvDkGAIaJAICjnQYAiokAgI6JAICmoQYAkokAgJaJAICltQYAqp0GAKtlBgCaiQCAnokAgK59BgCvYQYArH0GAK11BgCo7QcAqSkGAKoxBgCrMQYArJEGAK2RBgCukQYAr5EGAKKJAICmiQCAqokAgK6JAICyiQCAtokAgLqJAIC+iQCAuIUGALmNBgC6hQYAu50GALyNBgC9vQYAvrUGAL95AQCw8QYAsfEGALLxBgCzxQYAtMEGALXBBgC2wQYAt8EGALO5BgDCiQCAxokAgMqJAIDOiQCAthEGALUZBgDSiQCAuzUGALo1BgDWiQCA2okAgL8FBgC+BQYAvREGALwlBgClQQYA3okAgOKJAICmSQYAgRUAAIB5AACj4QYAghUAAK1JBgCsfQYAr10GAK5dBgCENAEAlogAgKttBgCqbQYAvswDAOqJAICzlQIA7okAgLXZAgDyiQCA9okAgLbRAgCGgAwAhzgDALvFAgC6xQIAvRUDALwVAwC/FQMAvhUDAPqJAID+iQCA71gGAIRAAwACigCABooAgAqKAIAOigCAEooAgBaKAIAaigCAHooAgOE4BgAiigCA4yQGAL5wDACsSQIArUkCAK5dAgCvVQIAqB0CAKkFAgCqBQIAq10CAISoDAAmigCAKooAgC6KAIC+vA0AMooAgDaKAIA6igCAvE0DAL1VAwC+VQMAv2UDALjpAwC56QMAul0DALtVAwC0yQMAtckDALbZAwC32QMAsBkCALEZAgCy2QMAs9kDAD6KAIDj5AAAQooAgOG8AQBGigCAgj0AAIE9AACAPQAASooAgE6KAIBSigCAWooAgF6KAIDvzAMAYooAgGaKAICj3QMAaooAgIboDACHYA0AbooAgKaZAwClkQMAcooAgKuNAwCqjQMAdooAgHqKAICvXQIArl0CAK1dAgCsXQIAfooAgIKKAICGigCAiooAgI6KAICSigCAlooAgO/gAQCEvAwA4YwGAJqKAIDjHAYAnooAgKKKAICmigCAqooAgLPVAQCuigCAsooAgLaKAIC6igCAtpEBALWZAQC+igCAu70BALq9AQDCigCAyooAgL+dAQC+nQEAvZ0BALydAQCoBQ4AqQkOAKodDgCrFQ4ArFEOAK1RDgCuSQ4Ar0kOAFaKAICCzQ8AgfUPAID9DwDGigCAzooAgIYcAACHsAMAuOkOALnpDgC6/Q4Au/UOALztDgC9VQ8AvlEPAL9NDwCwOQ4AsTkOALIJDgCzCQ4AtBkOALUZDgC2DQ4At9kOAKOVDgDSigCA1ooAgNqKAIDeigCAptEOAKXZDgDiigCAq/0OAKr9DgDmigCA6ooAgK/dDgCu3Q4Ard0OAKzdDgDuigCAs/0PAPKKAID2igCAtoEPAPqKAID+igCAtZkPALqNDwC7ZQ8AAosAgAaLAIC+fQ8Av2UPALx9DwC9dQ8AqC0OAKk1DgCqMQ4AqzEOAKxVDgCtRQ4ArkUOAK91DgAKiwCADosAgBKLAIAWiwCAGosAgB6LAIAiiwCAJosAgLjpDgC59Q4Auv0OALv1DgC87Q4AvZEOAL6RDgC/kQ4AsA0OALHlDgCy7Q4As+UOALT9DgC15Q4Atu0OALflDgCjuQ4Agi0AAIEVAACAHQAAKosAgKbFDgCl3Q4ALosAgKshDgCqyQ4AMosAgL4sAQCvIQ4ArjkOAK0xDgCsOQ4AOosAgLZVAQC1RQEANosAgLNVAQA+iwCAhngAAIdcAAC/OQEAvjEBAL0lAQC8JQEAuzEBALpZAQDmiQCAQosAgEaLAIBKiwCAhAQDAKOJAgBOiwCApZkCAKaJAgBSiwCAvyg5AFaLAICqhQIAq+0CAKz5AgCt+QIAru0CAK/lAgDjWAIA78AOAOGIAQBaiwCAXosAgGKLAIBmiwCAaosAgG6LAIByiwCAdosAgHqLAIDvKAIA4ygOAH6LAIDhRA4AqbUCAKhpDQCrAQIAqgkCAK0BAgCsGQIArzECAK4BAgC+AAQAgosAgIaLAICKiwCAjosAgJKLAICWiwCAmosAgLnlAwC45QMAu+UDALrlAwC95QMAvOUDAL/lAwC+5QMAsSECALBJAgCzJQIAsiUCALUpAgC0IQIAtxUCALYVAgCowQIAqdECAKr1AgCrDQEArBUBAK0FAQCuBQEArzkBAJ6LAICiiwCAqosAgK6LAICyiwCAtosAgLqLAIC+iwCAuC0BALk9AQC67QEAu+UBALz9AQC95QEAvu0BAL/lAQCwLQEAsTUBALI9AQCzNQEAtC0BALUVAQC2HQEAtxUBAIA9AQCBpQAAgq0AAO/YAACGsAUAh9gFAMKLAIDv1A8AhGwEAOH0DgDGiwCA4xwPAMqLAIDhlAEAzosAgOMMDgCzPQIA0osAgNaLAIDaiwCA3osAgLbFAQC13QEA4osAgLuxAQC6qQEA5osAgOqLAIC/kQEAvqkBAL2hAQC8qQEAposAgO6LAICqRQYAq10GAKxFBgCtTQYArkUGAK99BgDyiwCA9osAgPqLAICj0QUA/osAgKUxBgCmKQYAAowAgAaMAICCHQAAgR0AAIAdAAAKjACADowAgBKMAIC+lAMAFowAgBqMAICGSAMAh8wDAB6MAIAijACAJowAgCqMAICoqQcAqakHAKq5BwCruQcArKkHAK2pBwCuAQcArzUHAC6MAIAyjACANowAgDqMAIA+jACAQowAgEaMAIBKjACAuC0HALnBAAC66QAAu+kAALz5AAC95QAAvuUAAL+dAACwUQcAsV0HALItBwCzJQcAtD0HALUlBwC2JQcAtxUHALMxBgBOjACAUowAgFaMAIBajACAtikGALUhBgBejACAu5kGALqVBgBijACAZowAgL/hBgC++QYAvfEGALz5BgBqjACAo3UGAG6MAIByjACApm0GAHaMAIB6jACApWUGAKrRBgCr3QYAfowAgIKMAICuvQYAr6UGAKy9BgCttQYAqOUBAKn1AQCq/QEAq/UBAKztAQCtNQEArj0BAK81AQCA+QAAgc0AAILFAACEYAEAvngBAIqMAICHrAAAhpABALjRAAC52QAAuuEAALvhAAC8kQAAvZ0AAL6VAAC/iQAAsE0BALFVAQCyXQEAs1UBALRNAQC18QAAtvEAALfxAACzdQIAjowAgJKMAICWjACAmowAgLa1AgC1ZQIAnowAgLuRAgC6iQIAoowAgKaMAIC/NQMAvokCAL2BAgC8iQIAqowAgKMxAgCujACAhMADAKbxAgCyjACAtowAgKUhAgCqzQIAq9UCALqMAIC+jACArs0CAK9xAwCszQIArcUCAKuNAACqjQAAqY0AAKg5AwCvvQAArr0AAK2FAACsjQAAqgAAAKsAAADCjACAxowAgMqMAIDOjACA0owAgNaMAIC7fQAAun0AALl9AAC4fQAAv90BAL7dAQC93QEAvN0BALO5AACysQAAsaEAALCtAAC3XQAAtl0AALWVAAC0lQAA2owAgN6MAIDijACA5owAgIE1AACADQAA6owAgII1AAC+rD0A7owAgPKMAICFaD0A+owAgP6MAICGODwAh8ACALNJAQACjQCA0AAAAAaNAIAKjQCAtkkBALVJAQAOjQCAuykBALolAQASjQCAFo0AgL8dAQC+HQEAvSEBALwpAQDjNDYA4QwGAOGwAgDjPAYAGo0AgB6NAIAijQCAJo0AgIQsPwC+oD8AKo0AgC6NAIDvfDcAMo0AgDaNAIDvGAEAOo0AgD6NAICGaD4Ah8w/AEKNAIBGjQCASo0AgO+UAABOjQCA4ZQBAFKNAIDjUAAAVo0AgILpPwCB6T8AgPE/AKMJPgCPASQA9owAgFqNAIBejQCApgk+AKUJPgBijQCAq2k+AKplPgBmjQCAao0AgK9dPgCuXT4ArWE+AKxpPgCeYTgAn3U4AJzBNACdtTkAmqU1AJt1NACYeTAAmXExAJYhLQCXhTEAlG0sAJVlLACSeSgAk6UtAJBRJACReSgAsQ0UALAFFACzARgAslUUALV5GAC0tRgAbo0AgHKNAIB2jQCAeo0AgH6NAICCjQCAotE8AKMlAQCgdTkAob08AKHJAACGjQCAowEEAKLlAAClHQQApPUEAKf5CACmAQgAqQEMAKhtCACrzQwAqs0MAK3REACsARAAr9URAK7ZEACCBSUAgy0lAIqNAICOjQCAhsEsAIcRLQCEHSkAhRUpAIopLQCLZSwAko0AgJaNAICOHTAAj8E0AIzZMACNHTEAkmE1AJPNNQCajQCAno0AgJZhOQCXmTgAlKE4AJV9OQCaYT0AmwU9AKKNAICmjQCAqo0AgK6NAICc6QAAso0AgLaNAIC6jQCAvo0AgMKNAICGjACAxo0AgMqNAIDOjQCAqJE+AKmRPgCq7T4Aq+E+AKzhPgCt6T4ArtE+AK/RPgCwUT4AsVE+ALJRPgCzUT4AtHk+ALV5PgC2bT4At2U+ALghPgC5IT4Aujk+ALs5PgC8KT4AvRU+AL4RPgC/DT4AgJkDAIGZAwCCBQAA0o0AgL5UAwDhsD0A2o0AgONAPgCEOAIA3o0AgOKNAIDv9D8A5o0AgOqNAICGmAQAhxwDALMFPQCECAQA7o0AgPKNAID2jQCAtgk9ALUJPQD6jQCAu/U9ALr1PQD+jQCAAo4AgL/dPQC+3T0AveU9ALzlPQAGjgCACo4AgKPNPQC+xAQApcE9AA6OAIASjgCApsE9ABaOAIAajgCAqz09AKo9PQCtLT0ArC09AK8VPQCuFT0AtmkCAB6OAIAijgCAtWkCACaOAICzSQIAKo4AgC6OAIC+qQMAv6kDALzBAwC9wQMAuvkDALv5AwAyjgCANo4AgKgtAwCpnQMAqpUDAKutAwCstQMArb0DAK61AwCv2QMAgA0AAIEVAACCHQAAOo4AgD6OAIBCjgCAh7QFAIacBAC4MQIAuTECALo1AgC7zQIAvNUCAL3dAgC+1QIAv8kCALBpAgCxaQIAskECALNBAgC0OQIAtTkCALYRAgC3EQIASo4AgOM0PgBOjgCA4aw+AFKOAIDvfAMAVo4AgFqOAIBejgCA45QDAGKOAIDhfD4AZo4AgO/oPgBqjgCAbo4AgHKOAIB2jgCAo1UDAHqOAICldQMAfo4AgIKOAICmdQMAho4AgIqOAICr5QIAquUCAK3dAgCs3QIAr7UCAK61AgCoGQYAqSEGAKohBgCrPQYArCUGAK1dBgCuVQYAr00GAEaOAICOjgCAko4AgJaOAICajgCAno4AgKKOAICmjgCAuOUGALmBBgC6gQYAu50GALyJBgC9iQYAvqEGAL+hBgCwPQYAsQ0GALIFBgCz7QYAtPUGALXhBgC24QYAt90GALOpBgCCLQAAgRUAAIAdAACqjgCAtt0GALWtBgCujgCAu8kGALr5BgCyjgCAhOADAL8lBgC+MQYAvTkGALzRBgC+iAMAo+0GANaNAIC2jgCAppkGALqOAIC+jgCApekGAKq9BgCrjQYAhkgAAIdsAACudQYAr2EGAKyVBgCtfQYAqIEGAKmNBgCqmQYAq5UGAKyNBgCttQYArrEGAK+tBgDCjgCAxo4AgMqOAIDOjgCA0o4AgNaOAIDajgCA3o4AgLilBgC5YQEAumEBALthAQC8YQEAvWEBAL5hAQC/YQEAsNkGALHZBgCyqQYAs6kGALS9BgC1oQYAtqEGALedBgCzEQYA4o4AgOaOAIDqjgCA7o4AgLY1BgC1BQYA8o4AgLsdBgC6HQYA9o4AgPqOAIC/ZQYAvnkGAL19BgC8fQYA/o4AgKNVBgACjwCABo8AgKZxBgAKjwCADo8AgKVBBgCqWQYAq1kGABKPAIAWjwCArj0GAK8hBgCsOQYArTkGAKjVAgCp3QIAqikDAKspAwCsOQMArTkDAK4pAwCvKQMAGo8AgB6PAIAijwCAKo8AgC6PAIAyjwCAvrgDADaPAIC47QMAuYUDALqBAwC7gQMAvIUDAL2NAwC+sQMAv7EDALBZAwCxWQMAsu0DALPlAwC0/QMAteUDALblAwC31QMAgKEAAIGhAACCoQAAvoAMADqPAICEmAIAPo8AgEKPAICGAAwAh/QDAEaPAIBKjwCATo8AgFKPAIBWjwCAhLADALPhAwBajwCAXo8AgGKPAIBmjwCAtvkDALXxAwBqjwCAu90DALrdAwBujwCAco8AgL9hAwC+eQMAvXEDALx5AwB2jwCAeo8AgH6PAICjLQIAgo8AgKU9AgCmNQIAho8AgIqPAICOjwCAqhECAKsRAgCstQIArb0CAK61AgCvrQIA48QDAOMQBwDhuAEA4WwHAIBxAACBcQAAggUAAJKPAICGwAwAh1QNAJqPAICejwCA77ADAO8ABwCijwCApo8AgKqPAICujwCAso8AgLaPAIC6jwCAvo8AgMKPAIDvpAEAhKANAOGABgDGjwCA4xABAMqPAIDOjwCA0o8AgNaPAICz9QEA2o8AgN6PAIDijwCA5o8AgLZNAQC1SQEA6o8AgLtRAQC6SQEA7o8AgPKPAIC/OQEAvjEBAL1BAQC8SQEAqC0OAKk1DgCqPQ4AqzEOAKyBDgCtjQ4AroUOAK+1DgCWjwCA9o8AgPqPAID+jwCAgBkAAIEZAACCBQAAApAAgLidDgC5rQ4AuqUOALtNDwC8VQ8AvV0PAL5JDwC/QQ8AsM0OALHVDgCy3Q4As9UOALS1DgC1vQ4AtrUOALetDgCjtQ4AvogDAAaQAIAKkACADpAAgKYNDgClCQ4AEpAAgKsRDgCqCQ4AhggAAIdsAwCveQ4ArnEOAK0BDgCsCQ4AFpAAgBqQAIAekACAs7UPACKQAIC1VQ8Atl0PACaPAIAmkACAKpAAgLp5DwC7eQ8AvGkPAL1dDwC+SQ8Av0kPAKhpDgCpaQ4AqnEOAKtxDgCskQ4ArZEOAK6RDgCvkQ4ALpAAgDKQAIA2kACAOpAAgD6QAIBCkACARpAAgEqQAIC4hQ4AuY0OALqFDgC7nQ4AvI0OAL29DgC+tQ4Av3kBALDxDgCx8Q4AsvEOALPFDgC0wQ4AtcEOALbBDgC3wQ4Ao/kOAE6QAIBSkACAVpAAgFqQAICmEQ4ApRkOAF6QAICrNQ4AqjUOAGKQAIBmkACArwUOAK4FDgCtEQ4ArCUOAIANAACBFQAAgh0AAGqQAIBukACAcpAAgISUAQC+lAEAhkAHAIf0AAB6kACAfpAAgIKQAICGkACAipAAgI6QAICojQIAqZUCAKqVAgCrzQIArNUCAK3dAgCuyQIAr/0CAJKQAICWkACAmpAAgJ6QAIC/ABQAopAAgKaQAICqkACAuH0DALnBAwC6wQMAu8EDALzBAwC9yQMAvvEDAL/xAwCwhQIAsUUDALJNAwCzRQMAtF0DALVFAwC2TQMAt0UDALMdAgCukACAspAAgLaQAIC6kACAtl0CALVdAgC+kACAu4EDALpBAgDCkACAxpAAgL+BAwC+mQMAvZEDALyZAwDKkACAo1kCAM6QAIDSkACAphkCANaQAIDakACApRkCAKoFAgCrxQMA3pAAgOKQAICu3QMAr8UDAKzdAwCt1QMA6pAAgOPMAACEBAIA4bwBAIDJAQCB/QEAgvUBAL4QBQDukACAvigEAPKQAID2kACA+pAAgO8QAAD+kACAApEAgIbgBACH9AIABpEAgAqRAIDj/A8ADpEAgOHgDwASkQCA7xQPABaRAIAakQCAHpEAgCKRAIAmkQCAKpEAgC6RAIAykQCANpEAgDqRAIA+kQCAQpEAgEaRAIBKkQCA7+ABAIUEEgDh3A4ATpEAgOMcDgCAKQAAgR0AAIIFAABSkQCAszECAFqRAICEzAUAXpEAgGKRAIC2KQIAtSECAGaRAIC7zQEAus0BAGqRAIBukQCAv3UBAL7JAQC9wQEAvMkBAKjpBQCp6QUAqvkFAKv5BQCs6QUArekFAK45BgCvOQYA5pAAgFaRAICGiAAAhwADAHKRAIB2kQCAepEAgH6RAIC40QYAudkGALrhBgC74QYAvJEGAL2dBgC+lQYAv4kGALBJBgCxSQYAsl0GALNVBgC0TQYAtfEGALbxBgC38QYAo3EFAIKRAICGkQCAipEAgI6RAICmaQUApWEFAJKRAICrjQYAqo0GAJaRAICakQCArzUGAK6JBgCtgQYArIkGAJ6RAICikQCAs+EHAKaRAIC14QcAqpEAgK6RAIC25QcAdpAAgLKRAIC7vQcAuqEHAL2VBwC8qQcAv5UHAL6VBwCoAQYAqSUGAKohBgCrIQYArCEGAK0tBgCuJQYAr1UGALaRAICCHQAAgR0AAIAdAAC6kQCAvpEAgMKRAIC+MAEAuDkGALk5BgC6yQYAu8kGALzZBgC92QYAvskGAL/JBgCwLQYAsTEGALI1BgCzCQYAtBkGALUZBgC2CQYAtwkGAKOpBgCEjAIAhigfAIdEAQDKkQCApq0GAKWpBgDOkQCAq/UGAKrpBgDSkQCA1pEAgK/dBgCu3QYArd0GAKzhBgDakQCAsxUGAN6RAIDikQCAtj0GAOaRAIDqkQCAtTUGALrZAQC72QEA7pEAgPKRAIC+fQEAv2UBALx9AQC9dQEAqMUFAKnJBQCq2QUAq9EFAKz5BQCt+QUArikCAK8pAgD2kQCA+pEAgP6RAIACkgCAjAAAAAaSAIAKkgCADpIAgLjtAgC5hQIAuo0CALuBAgC8hQIAvY0CAL69AgC/fQMAsFkCALFZAgCy7QIAs+UCALT9AgC15QIAtuUCALfVAgCjUQUAEpIAgBaSAIAakgCAHpIAgKZ5BQClcQUAIpIAgKudAgCqnQIAJpIAgCqSAICvIQIArjkCAK0xAgCsOQIAghEAAC6SAICAZQAAgQkAADKSAIC+mAMAOpIAgD6SAICEJAMAQpIAgIdoAwCGjBwARpIAgEqSAIBOkgCAUpIAgFaSAIBakgCAs6ECAITAHAC10QIAXpIAgGKSAIC21QIAZpIAgGqSAIC7wQIAuvUCAL0RAQC82QIAvxEBAL4ZAQBukgCAcpIAgHaSAIB6kgCAfpIAgIKSAICGkgCA77gGAIqSAIDhnAQAjpIAgON0BgCSkgCAlpIAgJqSAICekgCAgPkAAIH5AACCBQAAopIAgL5YHACEWB8A71wAAO9ABgDhkAEA4fwGAOM8AADjdAYAqpIAgK6SAICGmBwAh/QcAKNpAgC+DB8AspIAgLaSAIC6kgCAph0CAKUZAgC+kgCAqwkCAKo9AgDCkgCAxpIAgK/ZAQCu0QEArdkBAKwRAgCokR0AqZkdAKqhHQCroR0ArNEdAK3dHQCu1R0Ar8kdADaSAICmkgCAypIAgM6SAIDSkgCA1pIAgNqSAIDekgCAuHkeALl5HgC6zR4Au8UeALzdHgC9xR4AvsUeAL/1HgCwuR0AsY0dALKFHQCzTR4AtFUeALVdHgC2VR4At0keALjNHwC51R8Aut0fALvVHwC88R8Avf0fAL7pHwC/6R8AsKUfALGxHwCysR8As40fALSVHwC19R8Atv0fALf1HwCoGR4AqRkeAKotHgCrPR4ArCUeAK0tHgCuJR4Ar90fAOKSAIDmkgCA6pIAgO6SAIDykgCAxpEAgPaSAID6kgCAs+UfAP6SAIACkwCABpMAgAqTAIC27R8Ate0fAA6TAIC7NR4AuiEeABKTAIAWkwCAv3EeAL4RHgC9GR4AvCUeAIJpAACjoR8AgFkAAIFRAACmqR8AGpMAgB6TAIClqR8AqmUeAKtxHgCGAAQAh+wBAK5VHgCvNR4ArGEeAK1dHgCoMR4AqTEeAKpBHgCrQR4ArEEeAK1JHgCucR4Ar3EeACKTAIAmkwCAKpMAgC6TAIAykwCANpMAgDqTAIA+kwCAuCkBALkpAQC6OQEAuzUBALwtAQC90QAAvtEAAL/RAACwyQEAsckBALLZAQCz2QEAtMkBALXJAQC2GQEAtxkBALPJHQBCkwCARpMAgEqTAIBOkwCAtskdALXJHQBSkwCAuw0CALoNAgBWkwCAWpMAgL8NAgC+DQIAvQ0CALwNAgBekwCAo40dAGKTAIBmkwCApo0dAGqTAIBukwCApY0dAKpJAgCrSQIAcpMAgHaTAICuSQIAr0kCAKxJAgCtSQIAgA0AAIERAACCEQAAepMAgO/MAgB+kwCAgpMAgISQAgDjLAIAvigDAOHYAQCKkwCAhhAEAIfUAwCOkwCAkpMAgLNhAwCWkwCAmpMAgJ6TAICikwCAtnkDALVxAwCmkwCAu10DALpdAwCqkwCArpMAgL/hAAC++QAAvfEAALz5AACjoQIAspMAgLaTAIC6kwCAvpMAgKa5AgClsQIAwpMAgKudAgCqnQIAxpMAgMqTAICvIQEArjkBAK0xAQCsOQEAzpMAgNKTAIDvZB8A1pMAgNqTAIDekwCA4pMAgOaTAICADQAAgREAAIIVAADqkwCA4eAcAO6TAIDjiB8A8pMAgISAAgC+jAUAh0gFAIYsBAD6kwCA/pMAgO+kHgDv9B4A4QAeAOFQHwDjLB4A47AeAAKUAIAGlACACpQAgA6UAIASlACAFpQAgISEBACzcQEAGpQAgLUdAQC2FQEAHpQAgCKUAIAmlACAugEBALsBAQC89QAAvf0AAL71AAC/7QAAqK0GAKm9BgCqtQYAq8kGAKzZBgCt2QYArskGAK/BBgAqlACALpQAgDKUAIA2lACAOpQAgD6UAIBClACARpQAgLhtBwC5BQcAug0HALsBBwC8AQcAvQEHAL4BBwC/AQcAsIkGALGJBgCybQcAs2UHALR9BwC1ZQcAtmUHALdVBwCGkwCAozkGAEqUAID2kwCApl0GAE6UAIBSlACApVUGAKpJBgCrSQYAVpQAgFqUAICuvQcAr6UHAKy9BwCttQcAgG0AAIEJAACCGQAAXpQAgGKUAIC+nAMAZpQAgGqUAICGQAAAh2AAAG6UAIBylACAdpQAgHqUAIB+lACAgpQAgKiRBgCpkQYAqrkGAKu5BgCsqQYArakGAK7ZBgCv2QYAhpQAgIqUAICOlACAkpQAgJaUAICalACAnpQAgKKUAIC4cQEAuXEBALpxAQC7cQEAvNkBAL3BAQC+wQEAv/UBALCxBgCxuQYAsokGALOJBgC0UQEAtVEBALZRAQC3UQEAszEGAKaUAICqlACArpQAgLKUAIC2KQYAtSEGALaUAIC7fQYAunUGALqUAIC+lACAv5UBAL6VAQC9XQYAvF0GAMKUAICjdQYAxpQAgMqUAICmbQYAzpQAgNKUAIClZQYAqjEGAKs5BgCErAEAvqABAK7RAQCv0QEArBkGAK0ZBgCo3QIAqe0CAKrlAgCr/QIArOUCAK3tAgCu5QIArz0DANqUAIDelACA4pQAgL5kDADmlACA6pQAgO6UAIDylACAuMkDALnJAwC62QMAu9EDALz5AwC9+QMAvpkDAL+VAwCwRQMAsU0DALJFAwCzXQMAtEUDALVNAwC2RQMAt/kDAIFVAwCASQMAs2UCAIJVAwC1ZQIA9pQAgPqUAIC2ZQIAhgAMAIfkAwC7gQMAuokDAL2BAwC8mQMAv4EDAL6JAwCjLQIA/pQAgAKVAIAGlQCACpUAgKYtAgClLQIADpUAgKvJAwCqwQMAEpUAgBaVAICvyQMArsEDAK3JAwCs0QMA49gGAOGsBwDhnAYA45wGABqVAICEWA0AHpUAgCKVAIAmlQCAKpUAgC6VAIAylQCA7xwBADaVAIA6lQCA70AGAIB5AACBFQAAghEAAIQADAA+lQCA46wAAEKVAIDhpAEASpUAgO9wAACGyAwAh6QNAE6VAIBSlQCAVpUAgFqVAIC6yQUAu8kFALilBQC5zQUAvvkFAL/5BQC8zQUAvcUFALKlBQCzrQUAsBEGALERBgC2rQUAt50FALS1BQC1rQUAqmEGAKthBgConQYAqZUGAK5hBgCvYQYArHEGAK1xBgBelQCAYpUAgGaVAIBqlQCAbpUAgHKVAIC+sAwAdpUAgKghDgCpIQ4AqiEOAKs9DgCsJQ4ArS0OAK4lDgCviQ4ARpUAgHqVAIB+lQCAgpUAgIaVAICKlQCAjpUAgJKVAIC4UQ8AuV0PALpVDwC7bQ8AvHUPAL19DwC+dQ8Av2kPALD5DgCxoQ4AsqEOALOhDgC0oQ4AtakOALaRDgC3kQ4As6kOAJaVAIDWlACAmpUAgJ6VAIC2rQ4Ata0OAKKVAIC7ZQ4Auj0OAKaVAICqlQCAv20OAL5lDgC9dQ4AvHUOAIIZAACj7Q4AgGUAAIEZAACm6Q4ArpUAgLKVAICl6Q4AqnkOAKshDgC2lQCAupUAgK4hDgCvKQ4ArDEOAK0xDgCoYQ4AqXUOAKp9DgCrdQ4ArG0OAK31DgCu/Q4Ar/UOAIaAAQCHpAEAvpUAgMKVAIDGlQCAypUAgM6VAIDSlQCAuHUBALl9AQC6dQEAu8kBALzdAQC9xQEAvsUBAL/1AQCwjQ4AsZUOALKdDgCzkQ4AtFUBALVdAQC2VQEAt00BALP1DgDWlQCA2pUAgN6VAIDilQCAtnUOALXlDgDmlQCAu1EOALpJDgDqlQCA7pUAgL+ZAQC+kQEAvUUOALxJDgDylQCAo7EOAPaVAID6lQCApjEOAP6VAIAClgCApaEOAKoNDgCrFQ4ABpYAgAqWAICu1QEAr90BAKwNDgCtAQ4AqO0CAKktAwCqJQMAqz0DAKwlAwCtLQMAriUDAK+ZAwAOlgCAEpYAgBaWAIAalgCAHpYAgCKWAIC+dAIAKpYAgLiNAwC5kQMAupEDALulAwC8vQMAvXUAAL59AAC/dQAAsOkDALHpAwCy+QMAs/EDALTZAwC12QMAtrkDALe1AwCArQAAgbUAAIK9AACzoQMALpYAgLWhAwC2oQMAMpYAgITgAgA2lgCAuiEDALshAwC8IQMAvSkDAL4RAwC/EQMAo+0DAIXABACFtG8AOpYAgD6WAICm7QMApe0DAEKWAICrbQMAqm0DAIZIBQCHbAMAr10DAK5dAwCtZQMArG0DAEaWAIDjAA4A71hsAOG0DwBKlgCATpYAgFKWAIBWlgCAoakDAKD9DwCjwQMAog0DAOHgAwDv4A8A4+QDAFqWAIBelgCAYpYAgIQEBAC+BAQAZpYAgO+UAwBqlgCAbpYAgHKWAIDj1AMAdpYAgOFUAAB6lgCAfpYAgIKWAICGlgCAgA0AAIEVAACCHQAAipYAgI6WAICSlgCAj5EbAO+cDgCE4AcA4dQOAJqWAIDj8A4AnpYAgKKWAICGGAcAh5AEAJnlFwCY5RcAm+kLAJo5CwCd/QoAnPELAJ9VDwCeXQ8AkSkfAJDNGwCTJR8Aks0fAJXREwCUKRMAlxkXAJZ1EwCM4RAAjSUQAI4tEACP+QwAJpYAgJaWAICKORQAi5UUAITpGACFBRgAhuUYAIfxFACmlgCAqpYAgIIxHACDFRwAnKkEAK6WAICylgCAtpYAgLqWAIC+lgCAmtEEAJt9BACUTQ0AleUIAJblCACXtQgAwpYAgMaWAICSWQwAk1kMAKGRAADKlgCAowF8AKKZAACluXwApJF8AKeZeACm4X0AqYF5AKiheACriXQAqgF0AK0BcACsWXQAr4VwAK6dcACx4WwAsAFsALMBaACyHWwAtfVoALT1aADOlgCA0pYAgNaWAIDalgCA3pYAgOKWAIDmlgCA6pYAgO6WAIDylgCAqD0HAKmVBwCqlQcAq6kHAKzdBwCtxQcArsUHAK8dBgD2lgCAgh0AAIEdAACAHQAA+pYAgP6WAIAClwCAvmABALgZBgC5GQYAuikGALslBgC8IQYAvSEGAL4hBgC/IQYAsHEGALFxBgCycQYAs3EGALRNBgC1NQYAtj0GALctBgCzHQcACpcAgIYoAACHqAAADpcAgLZFBwC1VQcAEpcAgLu1BgC6tQYAFpcAgBqXAIC/8QYAvokGAL2lBgC8pQYAHpcAgKNZBwAilwCAJpcAgKYBBwAqlwCALpcAgKURBwCq8QYAq/EGADKXAIA2lwCArs0GAK+1BgCs4QYAreEGAKipBQCptQUAqr0FAKs9AgCsJQIArVECAK5RAgCvUQIAOpcAgD6XAIBClwCARpcAgIQ8AwBKlwCATpcAgFKXAIC4pQIAua0CALqlAgC7vQIAvKUCAL2tAgC+pQIAv30DALAxAgCxMQIAshkCALMZAgC09QIAta0CALalAgC3nQIAVpcAgFqXAIBelwCAszkFAGKXAIC1oQIAtt0CAGaXAIBqlwCAbpcAgLr5AgC7+QIAvMECAL3BAgC+PQIAv2UCAHKXAICmgQIApf0CAHqXAICjZQUAvlh8AIbYfACHnHwArzkCAK5hAgCtnQIArJ0CAKulAgCqpQIAfpcAgIKXAICohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAIGFAQCAhQEAhpcAgILtAQCKlwCAjpcAgJKXAICWlwCAuHUBALl9AQC6dQEAu80BALzVAQC93QEAvskBAL/BAQCwtQIAsb0CALKBAgCzgQIAtFEBALVRAQC2UQEAt1EBAJqXAICelwCAopcAgKaXAIDhMAYA4WQHAOMoBgDjxAYAhCB9AKqXAIDvbAAA7xgGAK6XAICylwCAtpcAgLqXAICzXQIAvkh8AL6XAIDClwCAxpcAgLYVAgC1dQIAypcAgLs5AgC6MQIAzpcAgNKXAIC/1QEAvtUBAL0VAgC8FQIAo519AHaXAIDWlwCA2pcAgN6XAICm1X0ApbV9AOKXAICr+X0AqvF9AOaXAIDqlwCArxV+AK4VfgCt1X0ArNV9AIBNAACBVQAAglUAALOxfgDulwCAtWV/ALZtfwDylwCAhkADAIcEAwC66X8Au+l/ALz5fwC9+X8Avt1/AL/NfwD2lwCA+pcAgAaXAID+lwCAApgAgAaYAIAKmACADpgAgKhtfgCpXX4AqlV+AKuFfwCsgX8ArYF/AK6BfwCvgX8AsEF/ALFBfwCyQX8As0F/ALR1fwC1ZX8Atm1/ALdlfwC4XX8AuS1/ALolfwC7PX8AvC1/AL0dfwC+FX8Av/UAAKP9fwASmACAFpgAgBqYAIAemACApiF+AKUpfgAimACAq6V+AKqlfgAmmACAKpgAgK+BfgCukX4ArbV+AKy1fgAumACAMpgAgDaYAIA6mACAPpgAgEKYAIBGmACASpgAgIA9AACBCQAAghkAAE6YAIBSmACAhLgBAL6wAQBWmACAqK0BAKnVAQCq1QEAqw0BAKwVAQCtGQEArgkBAK8JAQCGAAQAhwQBAFqYAIBemACAYpgAgGaYAIBqmACAbpgAgLjtAAC5hQAAuo0AALuFAAC8nQAAvYUAAL6NAAC/hQAAsHkBALF5AQCy7QAAs+UAALT9AAC15QAAtuUAALfVAACzXQIAcpgAgHaYAIB6mACAfpgAgLaZAgC1nQIAgpgAgLu9AgC6vQIAhpgAgIqYAIC/IQMAvjkDAL0xAwC8OQMAvigDAKMZAgCOmACAkpgAgKbdAgCWmACAmpgAgKXZAgCq+QIAq/kCAJ6YAICimACArn0DAK9lAwCsfQMArXUDAL7IBACmmACAqpgAgL7EBQCumACAspgAgLaYAIC6mACAgD0AAIEJAACCGQAAvpgAgMKYAICEOAMAypgAgM6YAIDveAIA0pgAgIZIBACHVAMA1pgAgNqYAIDemACA4pgAgOaYAIDqmACA7pgAgPKYAIDjVAIA9pgAgOFAAQD6mACA/pgAgOMkfwACmQCA4Zx8AAaZAIAKmQCADpkAgBKZAICEbAUAFpkAgBqZAIAemQCAIpkAgO8YfwAmmQCAKpkAgLPxAgAumQCAMpkAgDqZAIA+mQCAtukCALXhAgBCmQCAu3EBALppAQCHoAUAhswEAL85AQC+WQEAvVEBALxhAQDhQH8ARpkAgOM4fgCEwAQAgtkAAO8UAACApQAAgdkAAEqZAIDjwAAATpkAgOHUAQBSmQCAVpkAgO+EfgBamQCAqs0BAKvVAQBemQCAYpkAgK79AQCvnQEArMUBAK31AQBmmQCAo1UCAGqZAIBumQCApk0CAHKZAIB2mQCApUUCAMaYAIA2mQCAepkAgH6ZAICCmQCAhpkAgIqZAICOmQCAqJkGAKmZBgCq7QYAq/0GAKzlBgCt7QYAruUGAK/dBgCwpQYAsa0GALKlBgCzuQYAtK0GALVVBwC2UQcAt00HALh1BwC5fQcAunUHALtJBwC8WQcAvVkHAL5JBwC/RQcAs0UGAJKZAICWmQCAmpkAgJ6ZAIC2TQYAtU0GAKKZAIC7SQYAukEGAIYIAACHjAAAv7EHAL5JBgC9TQYAvFEGAIJdAACjAQYAgEUAAIFdAACmCQYAqpkAgK6ZAIClCQYAqgUGAKsNBgCymQCAtpkAgK4NBgCv9QcArBUGAK0JBgCoTQYAqVUGAKpVBgCriQYArLEGAK29BgCuqQYAr6kGAKaZAIC6mQCAvpkAgMKZAIDGmQCAypkAgM6ZAIDSmQCAuEkBALlJAQC6WQEAu1kBALxJAQC9SQEAvt0BAL/VAQCw3QYAsa0GALKlBgCzjQYAtJkGALWZBgC2jQYAt4UGALPdBgDWmQCA2pkAgN6ZAIDimQCAtj0GALU5BgDmmQCAu2kGALoZBgDqmQCA7pkAgL9dBgC+XQYAvVkGALxxBgDymQCAo5kGAPaZAID6mQCApnkGAP6ZAIACmgCApX0GAKpdBgCrLQYABpoAgAqaAICuGQYArxkGAKw1BgCtHQYAqNUCAKndAgCq4QIAq+ECAKw1AwCtPQMArjUDAK8tAwCAzQMAgQkAAIIZAAAOmgCAEpoAgIQYAgC+dAMAGpoAgLjpAwC56QMAuokDALuFAwC8nQMAvYEDAL6BAwC/tQMAsFUDALFdAwCyVQMAs+kDALT5AwC1+QMAtukDALfhAwCGIAwAhxADAB6aAIAimgCAJpoAgCqaAIAumgCA71wCADKaAIDhFAAANpoAgOOIAgC++AwAOpoAgD6aAIBCmgCAu/kDALrxAwC+gA0ARpoAgL9dAwC+XQMAvV0DALzhAwCzCQIASpoAgE6aAIBSmgCAVpoAgLbdAwC13QMAWpoAgKipBgCpqQYAqrkGAKu5BgCsqQYArakGAK4dBQCvFQUAXpoAgGKaAIBmmgCAapoAgG6aAIBymgCAdpoAgHqaAIC4GQUAuS0FALolBQC7yQUAvNkFAL3FBQC+zQUAv8UFALBtBQCxdQUAsnUFALNFBQC0XQUAtT0FALY1BQC3KQUA4fQGAOFUBwDjFAYA47wGAIEJAACAqQAAfpoAgII5AACE7A0AgpoAgIeIDACGDAwAipoAgI6aAIDvzAcA78QHAKMpAwCSmgCAlpoAgJqaAICemgCApv0CAKX9AgCimgCAq9kCAKrRAgCmmgCAqpoAgK99AgCufQIArX0CAKzBAgCoPQ4AqY0OAKqFDgCrnQ4ArIUOAK2NDgCuuQ4Ar7UOAIaaAICumgCAspoAgLaaAIC6mgCAvpoAgMKaAIDGmgCAuL0OALllDwC6bQ8Au2UPALx9DwC9ZQ8Avm0PAL9lDwCw1Q4Asd0OALLVDgCzoQ4AtJUOALWdDgC2lQ4At40OALMNDgDKmgCAzpoAgNKaAIDWmgCAtg0OALUNDgDamgCAuxkOALoRDgDemgCAFpoAgL9ZDgC+UQ4AvXUOALwBDgDimgCAo0kOAOaaAIDqmgCApkkOAO6aAIDymgCApUkOAKpVDgCrXQ4AhKQDAPaaAICuFQ4Arx0OAKxFDgCtMQ4AqLEOAKmxDgCqzQ4Aq8UOAKzdDgCtxQ4ArsUOAK/1DgCA7QEAgfEBAILxAQD6mgCAhpABAIe0AQD+mgCAApsAgLjFAQC5zQEAusUBALvdAQC8zQEAvf0BAL6ZAQC/lQEAsI0OALFBAQCyQQEAs0EBALRBAQC1QQEAtkEBALdBAQCzRQ4ABpsAgAqbAIAOmwCAEpsAgLZFDgC1VQ4AFpsAgLuFAQC6SQ4AGpsAgB6bAIC/hQEAvoUBAL2VAQC8lQEAIpsAgKMBDgAmmwCAKpsAgKYBDgAumwCAMpsAgKURDgCqDQ4Aq8EBADabAIA6mwCArsEBAK/BAQCs0QEArdEBAKgtAwCpPQMAqjUDAKuJAwCsmQMArZkDAK6JAwCvgQMAPpsAgEKbAIBGmwCASpsAgE6bAIBSmwCAVpsAgFqbAIC4rQMAuWUAALptAAC7ZQAAvH0AAL1lAAC+bQAAv2UAALDJAwCxyQMAsqkDALOlAwC0vQMAtaEDALahAwC3lQMAgL0AAIEJAACCGQAAXpsAgGKbAIC+2AMAapsAgG6bAICErAIAcpsAgIfoAwCGDAQAdpsAgHqbAIB+mwCAgpsAgLP9AwCGmwCAipsAgI6bAICSmwCAtlkDALVRAwCWmwCAu00DALpNAwCamwCAnpsAgL8lAwC+OQMAvTEDALw9AwCimwCAppsAgKqbAICumwCA71gPALKbAIC2mwCAupsAgOOQDgC+mwCA4bAPAMKbAIDGmwCAypsAgM6bAIDSmwCAgHUAAIF9AACCdQAAhBgFAO88AwDamwCAvhQFAN6bAIDj0AMA4psAgOFAAADmmwCAhtAEAIdYBQDqmwCA7psAgPKbAID2mwCA+psAgP6bAIACnACABpwAgAqcAIDvrA8AhOwEAOEQDgAOnACA41QBABKcAIAWnACAGpwAgB6cAICj/QIAIpwAgCacAIAqnACALpwAgKZZAgClUQIAMpwAgKtNAgCqTQIANpwAgDqcAICvJQIArjkCAK0xAgCsPQIAqJkGAKmZBgCqrQYAq70GAKylBgCtrQYArqUGAK/ZBgDWmwCAghEAAIEZAACAwQcAPpwAgEKcAIC+cAMARpwAgLhJBwC5SQcAul0HALtVBwC8TQcAvXEHAL51BwC/bQcAsKkGALGpBgCyuQYAs7EGALSZBgC1mQYAtnkHALd5BwC1NQYASpwAgE6cAIC2NQYAhjAAAIdcAwCzPQYAUpwAgL19BgC8dQYAv0UGAL5FBgBmmwCAVpwAgLt1BgC6dQYAo2UGAFqcAIBenACAYpwAgGacAICmbQYApW0GAGqcAICrLQYAqi0GAG6cAIBynACArx0GAK4dBgCtJQYArC0GAKhVBgCpWQYAqm0GAKthBgCsaQYArWkGAK6ZBgCvmQYAdpwAgHqcAIB+nACAgpwAgIacAICKnACAjpwAgJKcAIC4+QYAufkGALqNBgC7hQYAvJ0GAL2FBgC+hQYAv7UGALDpBgCx6QYAsvkGALP5BgC06QYAtd0GALbJBgC3yQYAs+UGAJacAICanACAnpwAgKKcAIC26QYAteEGAKacAIC7LQYAui0GAKqcAICunACAvxkGAL4tBgC9LQYAvC0GAIIVAACjoQYAgGEAAIFhAACmrQYAspwAgL6QAQClpQYAqmkGAKtpBgCEpAEAupwAgK5pBgCvXQYArGkGAK1pBgCohQIAqY0CAKqVAgCruQIArNUCAK3dAgCu1QIAr80CAIaAHACHZAMAvpwAgL5gAwDCnACAxpwAgMqcAIDOnACAuHUDALl9AwC6dQMAu8kDALzZAwC92QMAvskDAL/BAwCwvQIAsY0CALKFAgCzTQMAtFUDALVdAwC2VQMAt00DALMdAgDSnACAhAgDANacAIDanACAtl0CALVdAgDenACAu0kCALp5AgDinACA5pwAgL+ZAwC+kQMAvZkDALxRAgCwAAAAo1kCAOqcAIDunACAphkCAPKcAID2nACApRkCAKo9AgCrDQIA+pwAgP6cAICu1QMAr90DAKwVAgCt3QMAAp0AgAadAIAKnQCA76wGAA6dAIASnQCAFp0AgBqdAIC+6BwAHp0AgCKdAIAqnQCALp0AgOGABwAynQCA42AGAIBdAACBYQAAgmEAALN9AQA2nQCAtW0BALZlAQA6nQCAhiAdAIdYHQC6+QEAu/EBALzZAQC92QEAvrEBAL+xAQDvoAAAPp0AgEKdAIBGnQCASp0AgE6dAIBSnQCA71wBAIRsHADhzAYAVp0AgOMcBgDjSAAAWp0AgOEwAQBenQCAo/EBAGKdAICFABQAZp0AgGqdAICm6QEApeEBAG6dAICrfQEAqnUBAHKdAIB2nQCArz0BAK49AQCtVQEArFUBAKjtHQCpLR4AqjkeAKs5HgCsKR4ArSkeAK6dHgCvkR4AJp0AgHqdAIB+nQCAgp0AgIadAICC+QAAgfEAAID9AAC4qR4AuakeALpJHwC7SR8AvFkfAL1FHwC+TR8Av0UfALDxHgCx+R4AssEeALPBHgC0uR4AtbkeALatHgC3pR4AsBEfALERHwCyER8AsyUfALQlHwC1KR8Atl0fALdRHwC4cR8AuXkfALpBHwC7QR8AvJUAAL2dAAC+lQAAv40AAIqdAIC2nACAjp0AgJKdAICWnQCAmp0AgIb4AwCH0AAAqM0fAKnVHwCq0R8Aq70fAKytHwCtcR8ArnEfAK9xHwCzOR4Anp0AgKKdAICmnQCAqp0AgLaRHgC1RR4Arp0AgLu1HgC6tR4Asp0AgLadAIC/jR4AvoEeAL2RHgC8pR4Aup0AgKN9HgC+nQCAwp0AgKbVHgDGnQCAyp0AgKUBHgCq8R4Aq/EeAM6dAIDSnQCArsUeAK/JHgCs4R4ArdUeAKhVAQCpgQAAqoEAAKuBAACsgQAArYkAAK6xAACvsQAA1p0AgNqdAIDenQCA4p0AgOadAIDqnQCA7p0AgPKdAIC4ZQAAuW0AALplAAC7fQAAvGUAAL1tAAC+ZQAAv90DALChAACxrQAAsqUAALO5AAC0qQAAtZ0AALaVAAC3XQAA9p0AgIIdAACBHQAAgB0AAPqdAID+nQCAAp4AgL4UAgAKngCAhKgCAA6eAIASngCAFp4AgBqeAIAengCAjwAAALNJAwAingCAhugEAIesAgAmngCAtkkDALVJAwAqngCAuykDALolAwAungCAMp4AgL8ZAwC+LQMAvS0DALwxAwA2ngCAo40DADqeAIA+ngCApo0DAEKeAIBGngCApY0DAKrhAwCr7QMASp4AgE6eAICu6QMAr90DAKz1AwCt6QMAvoQDAFKeAIBWngCAWp4AgF6eAIBingCAZp4AgGqeAICAPQAAgQkAAIIZAABungCAcp4AgHqeAICENAMAfp4AgLMtAQCCngCAh8wCAIZMBQCGngCAti0BALUtAQCKngCAu0kBALp5AQCOngCAkp4AgL+9AQC+vQEAvbkBALxRAQDheB8Alp4AgOPQHwCangCAnp4AgOGUAQCingCA42gDAKaeAICqngCArp4AgO+IAwCyngCAtp4AgO+sHwC6ngCAvp4AgMKeAIDGngCAyp4AgM6eAIDSngCA1p4AgO9EHgDangCA4dweAN6eAIDjHB4A4p4AgOqeAIDungCA8p4AgIFpAACAZQAAo+UBAIJ9AACl5QEA9p4AgIQUBACm5QEAvigEAPqeAICrgQEAqrEBAK1xAQCsmQEAr3UBAK51AQCoIQYAqS0GAKolBgCrPQYArCUGAK0tBgCuXQYAr00GAHaeAIDmngCAhggDAIeMAwD+ngCAAp8AgAafAIAKnwCAuOkGALnpBgC6jQYAu4UGALydBgC9hQYAvo0GAL+FBgCwPQYAsQ0GALIFBgCz7QYAtPkGALX5BgC27QYAt+UGALDNBwCx1QcAstEHALPtBwC09QcAtf0HALbpBwC36QcAuN0HALklBwC6LQcAuyUHALw9BwC9JQcAvi0HAL8lBwAOnwCAEp8AgAaeAIAWnwCAGp8AgB6fAIAinwCAJp8AgKgVBgCpGQYAqu0HAKv9BwCs7QcArd0HAK7VBwCvuQcAswUGACqfAIAunwCAMp8AgDafAIC2PQYAtQUGADqfAIC7cQYAumkGAD6fAIBCnwCAv1kGAL5RBgC9WQYAvGUGAEafAICjQQYASp8AgE6fAICmeQYAUp8AgIS0AQClQQYAqi0GAKs1BgC+gAEAWp8AgK4VBgCvHQYArCEGAK0dBgCoNQYAqT0GAKo1BgCrWQYArHUGAK2lAQCurQEAr6UBAIDpAACB6QAAgv0AAL8kAQCGMA8Ah+QAAF6fAIBinwCAuMUAALnNAAC6xQAAu90AALzNAAC9/QAAvvUAAL+dAACw3QEAsSUBALItAQCzIQEAtCEBALUhAQC2IQEAtyEBALvBAgC6OQIAZp8AgGqfAIC/xQIAvsUCAL3VAgC82QIAs50FAG6fAIBynwCAdp8AgIwAAAC2BQIAtd0FAHqfAICqfQIAq4UCAH6fAICCnwCAroECAK+BAgCsnQIArZECAIafAICj2QUAip8AgI6fAICmQQIAkp8AgJafAIClmQUAgpFqAIORagCanwCAnp8AgIa5FgCH6RcAhBEWAIWZFgCKoRIAi6ESAKKfAICmnwCAjpEeAI9ZHgCMmRMAjREeAJJxGgCT5RoAqp8AgO/oJACW8QYAlwUGAJTlGgCVGQYAmikCAJvFAgCunwCAsp8AgLafAIDhKBsAnN0CAOMgDwCfIQcAnsEHAJ01GwCcLRsAm6EbAJr5HwCZOR8AmLEfAJcBEgCWIRMAlSkTAJRRFgCTGRcAkjEXAJGxFwCQKWsAj1FrAOOsBwCEBA0A4RwHAIANAACBNQAAgj0AALqfAIC+nwCAwp8AgL4gDQDKnwCAzp8AgO9MBwCGWAwAh2ANANKfAIDWnwCA2p8AgN6fAICEXA8A4p8AgO8IAADvhAYA4ZABAOGwBgDj4AAA42QGAOafAIDqnwCA7p8AgPKfAID2nwCA+p8AgL4ADwCEQA4A/p8AgAKgAIAGoACACqAAgA6gAIASoACAFqAAgBqgAICj1QMAotUDAKExAwCgLQcAVp8AgMafAIAeoACAIqAAgCagAICCmQAAgZEAAICZAACoTQ0AqZ0NAKqVDQCrJQ4ArD0OAK0RDgCuEQ4ArxEOALB9DgCxDQ4AsgUOALMtDgC0OQ4AtTkOALYtDgC3JQ4AuOkOALnpDgC6wQ4Au8EOALy5DgC9nQ4AvpUOAL+NDgCzPQ0AKqAAgC6gAIAyoACANqAAgLaxDgC1lQ4AOqAAgLvpDgC6mQ4AhogAAIfkAAC/3Q4Avt0OAL3ZDgC88Q4APqAAgKN5DQC+hAEAhIAGAKb1DgBCoACARqAAgKXRDgCq3Q4Aq60OAEqgAIBOoACArpkOAK+ZDgCstQ4ArZ0OALIFNQCzGTQAsG0wALENNQBSoACAVqAAgLQBKAC1PSkAWqAAgF6gAIBioACAZqAAgGqgAIBuoACAcqAAgHagAICiRQEAo9UBAHqgAIChTQEAps0FAKcBOACkAQQApX0FAKoBPACrRT0AqEk5AKnlOQCudTEAr30xAKxdPQCtATAAqO0OAKn1DgCqCQ4AqwkOAKwZDgCtGQ4Arg0OAK8tDgB+oACAgqAAgIagAICKoACAjqAAgJKgAICWoACAmqAAgLgdDgC5JQ4Aui0OALslDgC8PQ4Avd0BAL7VAQC/zQEAsFUOALFdDgCyVQ4Asy0OALQ1DgC1JQ4Ati0OALclDgCzgQ0AnqAAgKKgAICqoACArqAAgLaZDQC1kQ0AvlQEALuZDQC6kQ0AhogEAIe8AwC/4Q0AvvENAL35DQC8gQ0AgkkAAKPFDQCA9QMAgUkAAKbdDQCyoACAtqAAgKXVDQCq1Q0Aq90NALqgAIC+oACArrUNAK+lDQCsxQ0Arb0NAKgdAgCpRQIAql0CAKtVAgCseQIArXkCAK6JAwCviQMAwqAAgMagAIDKoACAzqAAgIT8BQDSoACA1qAAgNqgAIC4iQMAuWUDALptAwC7ZQMAvH0DAL1lAwC+bQMAv2UDALDBAwCxwQMAssEDALPBAwC0wQMAtcEDALbBAwC3wQMA3qAAgOKgAIDmoACA6qAAgO6gAIDhpAEA8qAAgOPADgC+aAQA9qAAgPqgAIDvHAEA/qAAgAKhAIAGoQCACqEAgLOVAwAOoQCAEqEAgBqhAIAeoQCAtrkDALWxAwAioQCAu0UCALpFAgCGqAQAh6QFAL9FAgC+RQIAvVUCALxVAgDh4A4A4SwMAOMIDgDj1A4AgK0AAIHRAACC0QAAJqEAgCqhAIAuoQCAMqEAgDahAIA6oQCAPqEAgO+IDgDvLA4AoxUDAEKhAICFxCsARqEAgEqhAICmOQMApTEDAE6hAICrxQIAqsUCAFKhAIBWoQCAr8UCAK7FAgCt1QIArNUCAKgNBgCpFQYAql0GAKtVBgCseQYArXkGAK65BgCvuQYAFqEAgFqhAIBeoQCAYqEAgGahAIBqoQCAbqEAgHKhAIC4TQcAuVUHALpRBwC7aQcAvHkHAL1lBwC+bQcAv2UHALDJBgCxyQYAst0GALPVBgC0zQYAtXUHALZ9BwC3dQcAs9UGAHahAIB6oQCAfqEAgIKhAIC2+QYAtfEGAIahAIC7DQYAug0GAIYIAACHLAAAv7EHAL4JBgC9AQYAvAkGAIJRAACjkQYAgEEAAIFBAACmvQYAiqEAgI6hAICltQYAqkkGAKtJBgCSoQCAlqEAgK5NBgCv9QcArE0GAK1FBgCwsQYAsbEGALLNBgCzwQYAtMEGALXJBgC28QYAt/EGALgFAQC5DQEAugUBALsdAQC8BQEAvQ0BAL4FAQC/uQEAmqEAgJ6hAICioQCApqEAgKqhAICuoQCApqAAgLKhAICoLQYAqTUGAKo1BgCr8QYArNEGAK3RBgCu0QYAr9EGALPdBgC2oQCAuqEAgL6hAIDCoQCAtjEGALU5BgDGoQCAuxUGALoVBgDKoQCAzqEAgL9tBgC+ZQYAvXUGALx5BgDSoQCAo5kGANahAIDaoQCApnUGAN6hAIDioQCApX0GAKpRBgCrUQYA5qEAgOqhAICuIQYArykGAKw9BgCtMQYAqNUCAKndAgCq4QIAq+ECAKxRAwCtUQMArlEDAK9RAwDuoQCA8qEAgL7sAwD6oQCA/qEAgAKiAIAGogCACqIAgLjpAwC56QMAuokDALuFAwC8nQMAvYEDAL6BAwC/tQMAsDEDALExAwCyNQMAs+kDALT5AwC1+QMAtukDALfhAwCAbQMAgaUAAIKtAACzZQIADqIAgLXVAwC23QMAEqIAgITgAgAWogCAuvkDALv5AwC87QMAvTEDAL4xAwC/MQMAh+wDAIZkPACyAAAAGqIAgB6iAIDjCAQAIqIAgOHsBgAmogCA7wAGACqiAIAuogCAMqIAgDaiAIA6ogCAPqIAgEKiAIBGogCASqIAgE6iAIDjoAMAUqIAgOGoAQBWogCA7/ADAIIdAACBHQAAgB0AAFqiAIBeogCAYqIAgGqiAIC+TD0AbqIAgKOhAwC+QDwApRECAHKiAIB2ogCAphkCAIRsAgB6ogCAqz0CAKo9AgCt9QIArCkCAK/1AgCu9QIAhkA8AIe0PQB+ogCAgqIAgIaiAICKogCAjqIAgO9EBgCSogCA4dQGAJaiAIDjDAcAmqIAgJ6iAICiogCApqIAgLP1AQCqogCArqIAgLKiAIC2ogCAtkUBALXlAQC6ogCAuzEBALopAQC+ogCAwqIAgL8dAQC+HQEAvRkBALwlAQCoLT4AqTU+AKo9PgCrNT4ArC0+AK2FPgCuhT4Ar7k+AGaiAIDGogCAyqIAgM6iAICAGQAAgRkAAIIFAADSogCAuLk+ALm5PgC6ST8Au0k/ALxZPwC9WT8Avk0/AL9BPwCwrT4AsbU+ALKxPgCzjT4AtJk+ALWZPgC2iT4At4k+AKO1PgCEjAIA1qIAgNqiAIDeogCApgU+AKWlPgDiogCAq3E+AKppPgCGCAAAh2gDAK9dPgCuXT4ArVk+AKxlPgDmogCAs5E/AOqiAIDuogCAtlk/APKiAID2ogCAtbk/ALp1PwC7fT8A+qIAgP6iAIC+QT8Av0E/ALxZPwC9VT8AsJU+ALGdPgCyqT4As6U+ALShPgC1oT4AtqE+ALehPgC45T4Aue0+ALrlPgC7/T4AvO0+AL3dPgC+1T4AvxkBAAKjAIAGowCACqMAgA6jAIASowCA9qEAgBajAIAaowCAqF0+AKkhPgCqPT4AqzU+AKwVPgCt/T4ArvU+AK/tPgCj1T4AHqMAgCKjAIAmowCAKqMAgKYdPgCl/T4ALqMAgKs5PgCqMT4AMqMAgDajAICvBT4ArgU+AK0RPgCsHT4AgREAAIANAAA6owCAghkAAD6jAIBCowCAhJQBAL4QAACGQAcAhwABAEqjAIBOowCAUqMAgFajAIBaowCAXqMAgKiNAgCplQIAqpUCAKvNAgCs2QIArdkCAK7NAgCvxQIAYqMAgGajAIBqowCAbqMAgIwAAAByowCAdqMAgHqjAIC4HQMAucEDALrBAwC7wQMAvMEDAL3JAwC+8QMAv/EDALCJAgCxiQIAsikDALMpAwC0OQMAtTkDALYpAwC3JQMAsx0CAH6jAICCowCAhqMAgIqjAIC2WQIAtVECAI6jAIC7TQIAuk0CAJKjAICWowCAv/0DAL79AwC9/QMAvP0DAJqjAICeowCAoqMAgKajAIDhDD4AqqMAgOOoPwCuowCAgT0AAIAxAADvUD8Agh0AALKjAIC++AQAhhgFAIdMAwCEDAIA48wAALqjAIDhvAEAvqMAgMKjAIDGowCAyqMAgM6jAICELAUA0qMAgNajAIDaowCA7xAAAN6jAIDiowCAo90DAOajAIDqowCA7qMAgPKjAICmmQMApZEDAPajAICrjQMAqo0DAPqjAID+owCArz0CAK49AgCtPQIArD0CAAKkAIAGpACACqQAgA6kAIASpACAFqQAgBqkAIDvKD4AHqQAgOE8PgAipACA4zgBAIApAACBFQAAghEAACqkAICzMQIAvsgEAITABAAupACAMqQAgLYpAgC1IQIANqQAgLvNAQC6zQEAOqQAgD6kAIC/dQEAvskBAL3BAQC8yQEAqOkFAKnpBQCq+QUAq/kFAKzpBQCt6QUArjkGAK85BgC2owCAJqQAgIaIAACHQAMAQqQAgEakAIBKpACATqQAgLjRBgC52QYAuuEGALvhBgC8kQYAvZEGAL6RBgC/kQYAsEkGALFJBgCyXQYAs1UGALRNBgC18QYAtvEGALfxBgCjcQUAUqQAgFakAIBapACAXqQAgKZpBQClYQUAYqQAgKuNBgCqjQYAZqQAgGqkAICvNQYArokGAK2BBgCsiQYAbqQAgLPRBwBypACAdqQAgLbxBwB6pACAfqQAgLXBBwC60QcAu90HAIKkAICGpACAvrkHAL+5BwC8xQcAvbkHALhpBgC5aQYAuokGALuJBgC8mQYAvZkGAL6JBgC/iQYAsBEGALEdBgCyFQYAs2kGALR5BgC1eQYAtmkGALdhBgCoSQYAqVUGAKpdBgCrVQYArE0GAK11BgCucQYAr3EGAEajAICCHQAAgR0AAIAdAACKpACAjqQAgJKkAIC+cAEAo5UGAJqkAICGKAAAh0gBAJ6kAICmtQYApYUGAKKkAICrmQYAqpUGAKakAICqpACAr/0GAK79BgCt/QYArIEGAK6kAICzFQYAsqQAgLakAIC2PQYAuqQAgL6kAIC1NQYAutkBALvZAQDCpACAxqQAgL59AQC/ZQEAvH0BAL11AQCovQUAqckFAKrZBQCr0QUArPkFAK35BQCuKQIArykCAMqkAIDOpACA0qQAgNakAICMAAAA2qQAgN6kAIDipACAuO0CALmFAgC6gQIAu4ECALyFAgC9jQIAvrECAL+xAgCwWQIAsVkCALLtAgCz5QIAtP0CALXlAgC25QIAt9UCAKNRBQDmpACA6qQAgO6kAIDypACApnkFAKVxBQD2pACAq50CAKqdAgD6pACA/qQAgK8hAgCuOQIArTECAKw5AgCBbQAAgG0AAAKlAICCBQAAvlwMAAqlAIAOpQCA79AGAITsAwDhHAUAEqUAgOP8BwAWpQCAGqUAgIbYDACHvAwAqIUCAKmVAgCqlQIAq6UCAKy9AgCt1QIArtECAK/RAgAepQCAIqUAgCalAIAqpQCALqUAgDKlAIA2pQCAOqUAgLh1AQC5fQEAunUBALvJAQC82QEAvdkBAL7JAQC/wQEAsLUCALG9AgCygQIAs4ECALRRAQC1UQEAtlEBALdRAQA+pQCAhAQNAEKlAIBGpQCAvhwMAEqlAIDvHAAA76AGAOGQAQDhRAcA43AGAOOYBgBOpQCAUqUAgFalAIBapQCAs10CAF6lAIBipQCAZqUAgGqlAIC2FQIAtXUCAG6lAIC7OQIAujECAHKlAIB6pQCAv9UBAL7VAQC9FQIAvBUCAKOdDQAGpQCAdqUAgH6lAICCpQCAptUNAKW1DQCGpQCAq/kNAKrxDQCGCAMAh2ADAK8VDgCuFQ4ArdUNAKzVDQCAkQ8AgZkPAIKhDwCzpQ4AiqUAgLWhDgC2eQ8AjqUAgJKlAICWpQCAukUPALtdDwC8RQ8AvU0PAL5FDwC//Q8AqFUOAKldDgCqYQ4Aq30OAKxlDgCttQ8Arr0PAK+1DwCapQCAnqUAgKKlAICmpQCAqqUAgK6lAICypQCAtqUAgLhVDwC5dQ8Aun0PALt1DwC8bQ8AvREPAL4RDwC/EQ8AsM0PALHVDwCy3Q8As9UPALTNDwC1dQ8AtnEPALdxDwCj6Q8AuqUAgL6lAIDCpQCAxqUAgKY1DgCl7Q8AyqUAgKsRDgCqCQ4AzqUAgNKlAICvsQ4ArgkOAK0BDgCsCQ4A1qUAgIIdAACBHQAAgB0AANqlAIDepQCA4qUAgL6UAQCErAEA5qUAgIfgAQCGzAAA6qUAgO6lAIDypQCAlqQAgKhtDgCpiQEAqpkBAKuRAQCswQEArckBAK75AQCv+QEAhKAAAPalAID6pQCA/qUAgAKmAIAGpgCACqYAgA6mAIC4xQAAuc0AALrFAAC73QAAvM0AAL39AAC+9QAAv50AALBBAQCxQQEAskEBALNBAQC0QQEAtUEBALZBAQC3QQEAsxECABKmAIAWpgCAGqYAgB6mAIC2SQIAtUkCACKmAIC7hQIAuoUCACamAIAqpgCAv4UCAL6FAgC9lQIAvJUCAIU8GgCjVQIALqYAgDKmAICmDQIANqYAgDqmAIClDQIAqsECAKvBAgA+pgCAQqYAgK7BAgCvwQIArNECAK3RAgCCGQAARqYAgIAZAACBGQAASqYAgE6mAIBSpgCAWqYAgL4ABABepgCAYqYAgGamAIBqpgCAbqYAgHKmAIB2pgCA7+gOAHqmAICG6AQAh1ADAH6mAICCpgCA74ACAIamAIDhlAEAiqYAgONYAQCOpgCA4wAOAJKmAIDhaA0AlqYAgKhxAgCpcQIAqnECAKupAgCsuQIArbkCAK6pAgCvqQIAhKwFAJqmAICepgCAoqYAgKamAICqpgCArqYAgLKmAIC4bQEAuQ0BALoFAQC7GQEAvAkBAL09AQC+NQEAv9kBALDZAgCx2QIAsm0BALNlAQC0fQEAtWUBALZlAQC3VQEA4WAPAOP0AADjHA4A4bwBALamAICCOQAAgTEAAIA9AAC6pgCAvigEAL6mAIDCpgCAvjwHAO8QAADv0A4AyqYAgIbgBACHyAQAzqYAgLO1AgDSpgCAtX0CALZ1AgDWpgCA2qYAgN6mAIC6UQIAu1ECALz1AQC9/QEAvvUBAL/tAQBWpgCAxqYAgKqxBQCrsQUArBUGAK0dBgCuFQYArw0GAOKmAIDmpgCA6qYAgKNVBQDupgCApZ0FAKaVBQDypgCAs+kGAPamAID6pgCA/qYAgAKnAIC24QYAtekGAAanAIC7sQYAuqEGAAqnAIAOpwCAv50GAL6RBgC9pQYAvKkGAKgdBgCpIQYAqiEGAKshBgCsIQYArSEGAK4hBgCvIQYAEqcAgBanAIAapwCAHqcAgCKnAIAmpwCAKqcAgC6nAIC45QcAue0HALrlBwC7/QcAvOUHAL3tBwC+5QcAv00HALAlBgCxNQYAsj0GALMxBgC0FQYAtRkGALYNBgC3AQYAo6kHAIIVAACBtQEAgLUBADKnAICmoQcApakHADanAICr8QcAquEHAISgAgA6pwCAr90HAK7RBwCt5QcArOkHAD6nAICzlQYAhugAAIcYAQC2tQYAQqcAgEanAIC1vQYAukkBALtVAQBKpwCATqcAgL45AQC/OQEAvEUBAL05AQCoPQYAqU0GAKpZBgCrUQYArHEGAK1xBgCuuQEAr7kBAISsAQBSpwCAVqcAgFqnAIBepwCAYqcAgGanAIBqpwCAuKkBALmpAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9pAQCwyQEAsdUBALLVAQCzqQEAtLkBALW5AQC2qQEAt6EBAKPRBQBupwCAcqcAgHanAIB6pwCApvEFAKX5BQB+pwCAqxECAKoNAgCCpwCAhqcAgK99AgCufQIArX0CAKwBAgCKpwCAjqcAgJKnAICWpwCAgTEAAIANAACapwCAgjkAAJ6nAICipwCAviQDAKqnAICupwCAsqcAgIbYHACHTAMAtqcAgLqnAIC+pwCAhMAcAOMgAQDCpwCA4cgBAManAIDvMAIAyqcAgM6nAIDSpwCA1qcAgNqnAIDepwCA4qcAgLOVAwDmpwCA6qcAgO6nAIDypwCAtrkDALWxAwD2pwCAu1EDALpJAwD6pwCA/qcAgL/1AAC+SQMAvUEDALxJAwCoLQIAqUUCAKpdAgCrVQIArHkCAK15AgCuvQIAr7UCAL5oHQACqACABqgAgAqoAICAHQAAgQkAAIKpAAAOqACAuFEBALlZAQC6YQEAu2EBALwRAQC9EQEAvhEBAL8RAQCwzQIAsdUCALLdAgCz1QIAtM0CALVxAQC2cQEAt3EBAOFYBgDhVAcA47AAAOO8BgASqACAGqgAgIYYHACHVB0AHqgAgCKoAIAmqACAKqgAgL74HAAuqACA7/AGAO/gBgCjlQIAMqgAgDaoAIA6qACAPqgAgKa5AgClsQIAQqgAgKtRAgCqSQIARqgAgEqoAICv9QEArkkCAK1BAgCsSQIAqG0eAKl1HgCqfR4Aq40eAKyVHgCtnR4Aro0eAK+BHgAWqACATqgAgFKoAIBWqACAWqgAgF6oAIBiqACAZqgAgLiJHgC5iR4AupkeALuRHgC8uR4AvbkeAL59HwC/dR8AsMUeALHNHgCyxR4As90eALTFHgC1zR4AtsUeALe5HgCz9R4AaqgAgG6oAIByqACAdqgAgLYdHgC1HR4AeqgAgLsJHgC6AR4AfqgAgIKoAIC/CR4AvgEeAL0JHgC8ER4Agm0AAKOxHgCAVQAAgWUAAKZZHgCEmAMAv9ABAKVZHgCqRR4Aq00eAIYABACHmAEArkUeAK9NHgCsVR4ArU0eAIqoAICOqACAhCQAAJKoAICWqACAmqgAgKanAICGqACAqLUeAKmFHgCqjR4Aq4UeAKydHgCtgR4Arv0eAK/1HgCwjR4AsZUeALKVHgCzpR4AtL0eALVxAQC2cQEAt3EBALhRAQC5UQEAulEBALtRAQC89QEAvf0BAL71AQC/7QEAsyUeAL4IBwCeqACAoqgAgKaoAIC2IR4AtTUeAKqoAIC7cR4AumkeAK6oAICyqACAv5UBAL5ZHgC9UR4AvGEeALaoAICjYR4AuqgAgL6oAICmZR4AwqgAgMaoAIClcR4Aqi0eAKs1HgDKqACAzqgAgK4dHgCv0QEArCUeAK0VHgDhVBoA0qgAgONcCgDWqACA2qgAgN6oAIDiqACA5qgAgOqoAIC+qAUA7qgAgPKoAICPMSoA+qgAgO/E+wD+qACAk2EuAJIdLwCR2SoAkEkqAJfZEgCWdRIAlQ0TAJTBLgCbHRsAmkEWAJlJFgCYDRcAn3EeAJ4RGwCdcRoAnHkaAKOhAgCinQMAoZUfAKCJHgDjiAEA4wgeAOFoAADh/B4A79wBAO98HwC1if4AtAH8ALMB+gCylfoAsQH4ALAR9gCv4fYArgH0AK0l8gCs7fIAqwHwAKrpDwCp1Q4AqN0OAKcBDACmyQoApe0KAKQBCACj4QYAovEGAKHlAwACqQCAggErAIMBKwAGqQCACqkAgIYxLwCHiS8AhIkrAIVFLgCKdRIAiwUTAIYIBQCHbAUAjhEXAI8RFwCMsRMAjV0WAJI9GgCTQRsAhMgFAIQABwCWUR8Al1EfAJRRGwCVORoAmn0eAJt9AgAOqQCAEqkAgIFZAQCAVQEAnFkDAIJRAQC+yAcAFqkAgBqpAIAeqQCAIqkAgCapAIAqqQCA79QeAC6pAIDhJB4AMqkAgONoAQA2qQCAOqkAgD6pAIBCqQCAu2kCALpZAgBGqQCASqkAgL8dAgC+HQIAvRkCALxxAgCz7QIATqkAgFKpAIBWqQCAWqkAgLZ9AgC17QIAXqkAgKMNBQD2qACAYqkAgGqpAIBmqQCApp0FAKUNBQBuqQCAq4kFAKq5BQCGCAMAh3wDAK/9BQCu/QUArfkFAKyRBQCAsQcAgbkHAIJBAACzsQYAcqkAgLVZBwC2MQcAdqkAgHqpAIB+qQCAuuEHALvhBwC84QcAveEHAL7hBwC/3QcAqLUGAKm5BgCqdQYAq4UHAKydBwCt/QcArvUHAK8ZBwCCqQCAhqkAgIqpAICOqQCAkqkAgJapAICaqQCAnqkAgLh1BwC5fQcAunUHALsFBwC8HQcAvTEHAL4xBwC/MQcAsGkHALFpBwCyeQcAs3kHALRpBwC1VQcAtlEHALdNBwCj/QcAoqkAgKapAICqqQCArqkAgKZ9BgClFQYAsqkAgKutBgCqrQYAtqkAgLqpAICvkQYArq0GAK2tBgCsrQYAvqkAgMKpAIDGqQCAyqkAgIAdAACBCQAAgjkAAM6pAIDSqQCA2qkAgIbIAACHpAEA3qkAgOKpAIDmqQCA6qkAgKiNAQCpmQEAqtkBAKvRAQCs8QEArfEBAK45AQCvOQEAhKAAAO6pAIDyqQCA9qkAgPqpAID+qQCAAqoAgAaqAIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+nQAAv5UAALBJAQCxSQEAslkBALNZAQC0SQEAtUkBALb9AAC39QAAugUEALsJBAC44QcAueEHAL4JBAC/CQQAvAkEAL0JBACyjQcAs+UHALC1BwCxhQcAtuUHALftBwC08QcAtfEHAKpNBwCrVQcAqEkHAKlJBwCu3QcAr8UHAKxNBwCt1QcACqoAgA6qAIASqgCAFqoAgBqqAIAeqgCAIqoAgCaqAICz0QIAKqoAgC6qAIC+AAwAMqoAgLbxAgC1+QIANqoAgLsNAgC6DQIAOqoAgD6qAIC/DQIAvg0CAL0NAgC8DQIAghUAAKOVAgCAYQAAgWEAAKa1AgBCqgCASqoAgKW9AgCqSQIAq0kCAIbIDACHrAwArkkCAK9JAgCsSQIArUkCAKhlAgCpdQIAqn0CAKt1AgCsbQIArbECAK6xAgCvsQIAhKANAE6qAIBSqgCAVqoAgFqqAIBeqgCAYqoAgGaqAIC4MQEAuTEBALoxAQC7MQEAvNUBAL3dAQC+yQEAv8EBALDRAgCx0QIAstECALPRAgC0EQEAtREBALYRAQC3EQEA4bAGAGqqAIDj0AYAhEAPAG6qAIDhpAEAcqoAgOPABgB2qgCAeqoAgH6qAIDv1AYA7AAAAIKqAIDvZAcAhqoAgIqqAICOqgCAkqoAgLO5AgCWqgCAtakCALZ9AgCaqgCAnqoAgKKqAIC6WQIAu1kCALxJAgC9SQIAvpkBAL+ZAQCjdQ0ARqoAgKaqAICqqgCArqoAgKaxDQClZQ0AsqoAgKuVDQCqlQ0AvqQDALaqAICvVQ4ArlUOAK2FDQCshQ0AgE0AAIFVAACCVQAAs2UPALqqAIC1ZQ8Atm0PAL6qAICGQAMAhxQDALrtDwC7/Q8AvOkPAL3VDwC+3Q8Av9UPAKhZDgCpoQ8AqqEPAKuhDwCsoQ8AraEPAK6hDwCvoQ8AwqoAgMaqAIDKqgCAzqoAgNKqAIDWqgCA2qoAgN6qAIC4AQ8AuQEPALoBDwC7HQ8AvA0PAL01DwC+PQ8Av9UAALBlDwCxdQ8AsnEPALNNDwC0VQ8AtV0PALZNDwC3QQ8AoykOAOKqAIDmqgCA6qoAgO6qAICmIQ4ApSkOAPKqAICrsQ4AqqEOAPaqAID6qgCAr5kOAK6RDgCtmQ4ArKUOAP6qAIACqwCABqsAgAqrAIDvJA0ADqsAgBKrAIAWqwCA49AOABqrAIDhGA4AHqsAgIAVAACBGQAAggUAACKrAICo0QEAqdkBAKopAQCrKQEArDkBAK05AQCuKQEArykBAL5oAQAqqwCAhsgBAIesAAAuqwCAMqsAgDarAIA6qwCAuO0AALmFAAC6jQAAu4UAALydAAC9gQAAvoEAAL+BAACwWQEAsVkBALLtAACz5QAAtP0AALXlAAC25QAAt9UAALOhAgA+qwCAQqsAgEarAIBKqwCAtrkCALWxAgBOqwCAu50CALqdAgBSqwCAVqsAgL8hAwC+OQMAvTEDALw5AwCF+PUAo+UCAFqrAIBeqwCApv0CAGKrAIBmqwCApfUCAKrZAgCr2QIAaqsAgG6rAICufQMAr2UDAKx9AwCtdQMAuOkAALnpAAC6aQAAu2kAALx5AAC9ZQAAvm0AAL9lAACwsQAAsbkAALKBAACzgQAAtPkAALX5AAC27QAAt+UAAKhlAwCpdQMAqn0DAKt1AwCsbQMArdEAAK7RAACv0QAAcqsAgHarAIB6qwCA1qkAgH6rAICCqwCAhqsAgIqrAICA/QEAgQkAAIIZAACOqwCAkqsAgL5EAgCaqwCAnqsAgISsAgCiqwCAh/gCAIasBQCmqwCAqqsAgK6rAICyqwCAs/UCALarAIC6qwCAvqsAgMKrAIC2UQEAteUCAMarAIC7fQEAunUBAMqrAIDOqwCAvz0BAL49AQC9VQEAvFUBAOFwDwDSqwCA47gOAITABQDvyAAA1qsAgNqrAIDeqwCA4zwOAOKrAIDh0AEA5qsAgIR0BwDqqwCA72gBAO6rAIDyqwCApXkCAKbNAQD2qwCAgCEAAIEhAACC3QcAo2kCAKzJAQCtyQEArqEBAK+hAQD6qwCA/qsAgKrpAQCr4QEAlqsAgAKsAIC+QAIABqwAgIYwAwCHMAMACqwAgA6sAICoOQcAqTkHAKoNBwCrHQcArAUHAK0NBwCuBQcAr3kHALAJBwCxCQcAshkHALMRBwC0OQcAtTkHALbdBwC3yQcAuPkHALn5BwC6zQcAu8EHALzFBwC9yQcAvrkHAL+xBwCzpQcAEqwAgBasAIAarACAHqwAgLatBwC1rQcAIqwAgLvtBwC67QcAJqwAgCqsAIC/3QcAvt0HAL3lBwC87QcALqwAgKPhBwAyrACANqwAgKbpBwA6rACAPqwAgKXpBwCqqQcAq6kHAEKsAIBGrACArpkHAK+ZBwCsqQcAraEHAEqsAIBOrACAUqwAgFasAIBarACAXqwAgGKsAIBmrACAgREAAIANAABqrACAghkAAG6sAIByrACAvuQBAHasAICG4AAAhxgBAHqsAIB+rACAgqwAgIasAICKrACA77AEAI6sAIDh1AYAkqwAgONcBACWrACAmqwAgJ6sAICirACAqJkBAKmZAQCqDQEAqwUBAKwdAQCtBQEArgUBAK81AQCEiAEApqwAgKqsAICurACAsqwAgLasAIC6rACAvqwAgLjBAAC5wQAAusEAALvBAAC8wQAAvcEAAL7BAAC/wQAAsE0BALElAQCyIQEAsyEBALQlAQC1LQEAthEBALcRAQDCrACAxqwAgLONAgDKrACAtZ0CAM6sAIDSrACAto0CANasAIDarACAu+kCALqBAgC9/QIAvP0CAL/hAgC+6QIA3qwAgKbVAgClxQIAvggDAKPVAgCCLQAAgRkAAIB5AACvuQIArrECAK2lAgCspQIAq7ECAKrZAgDirACA6qwAgO80AgDurACAhxgDAIYs/ADyrACA9qwAgPqsAID+rACAAq0AgAatAIAKrQCADq0AgOMAAQASrQCA4eABABatAIC6tQMAu70DABqtAIAerQCAvnkDAL95AwC8pQMAvXkDACarAICztQMAIq0AgCatAIC2kQMAKq0AgC6tAIC1pQMAqEkCAKlJAgCqWQIAq1kCAKxJAgCtdQIArnECAK9tAgC+aP0AvqT/ADKtAIA2rQCAOq0AgD6tAIBCrQCARq0AgLj5AgC5+QIAukkBALtJAQC8XQEAvUEBAL5BAQC/fQEAsBUCALEdAgCyFQIAs8kCALTZAgC12QIAtskCALfJAgDjIAYA4bAGAOGAAQDjEAYAgA0AAIE1AACCPQAASq0AgE6tAIBSrQCAWq0AgF6tAIDvcAAAYq0AgGatAIDvTAEAhIz9AGqtAICjmQIAbq0AgKWJAgByrQCAdq0AgKa9AgCGwPwAh+T8AKuRAgCqmQIArVUCAKyJAgCvVQIArlUCAKh9/gCpgf4Aqpn+AKuZ/gCsif4ArYn+AK65/gCvuf4AVq0AgHqtAIB+rQCAgq0AgIatAICKrQCAjq0AgJKtAIC4tf4Aub3+ALph/wC7Yf8AvGH/AL1h/wC+Yf8Av2H/ALDJ/gCxyf4Ast3+ALPR/gC0uf4Atbn+ALaR/gC3kf4AsxH+AJatAICarQCAnq0AgKKtAIC2Cf4AtQH+AKatAIC7Df4Aug3+AKqtAICurQCAv33+AL59/gC9Bf4AvAn+ALKtAICjVf4Atq0AgLqtAICmTf4Avq0AgMKtAIClRf4Aqkn+AKtJ/gCEKAMAxq0AgK45/gCvOf4ArE3+AK1B/gCAzQEAgdEBAILRAQCzuf4Ayq0AgLXR/gC21f4Azq0AgIZgAQCHYAEAug0BALsFAQC8HQEAvQUBAL4NAQC/BQEA0q0AgNatAIDarQCA3q0AgOKtAIDhwP0A5q0AgOOM/ADqrQCA7q0AgPKtAIDvtPwA9q0AgPqtAID+rQCAAq4AgKgp/gCpKf4Aqj3+AKs1/gCsVf4ArVn+AK5N/gCvRf4ABq4AgAquAIAOrgCAEq4AgBauAIAargCAHq4AgCKuAIC4SQEAuUkBALpZAQC7UQEAvHkBAL15AQC+GQEAvxUBALDFAQCxzQEAssUBALPdAQC0xQEAtc0BALbFAQC3eQEAJq4AgCquAIAurgCAo7n9ADKuAICl0f0AptX9AITQAwBBrgCAvuACAKoNAgCrBQIArB0CAK0FAgCuDQIArwUCAIFJAACAQQAAowkDAIJdAAClGQMARa4AgEmuAICmEQMAhsAEAIfkAwCrDQMAqg0DAK0BAwCsHQMArwEDAK4JAwCw4QMAseEDALLhAwCz/QMAtOUDALXtAwC25QMAtz0DALgFAwC5DQMAugUDALsdAwC8BQMAvQ0DAL4FAwC/vQAATa4AgFGuAIBVrgCAWa4AgOasAIBdrgCAYa4AgGWuAICo8QMAqfkDAKqpAwCrqQMArLkDAK25AwCuqQMAr6UDALNBAgBprgCAba4AgHGuAIB1rgCAtlkCALVRAgB5rgCAu0UCALpFAgB9rgCAga4AgL9JAgC+QQIAvUkCALxVAgCFrgCAia4AgI2uAICRrgCA74wDAJWuAICZrgCAna4AgONsAwChrgCA4VAAAKWuAICprgCAvngFALGuAICEcAIAgOUAAIHpAACC+QAAta4AgIawBACHVAUAua4AgO9A/gC9rgCA4Vz+AMGuAIDjVAEAxa4AgMmuAIDNrgCA0a4AgLOZAQDVrgCA2a4AgN2uAIDhrgCAth0BALUdAQDlrgCAuz0BALo9AQDprgCA7a4AgL/hAAC++QAAvfEAALz5AACoIQYAqVEGAKpRBgCrzQYArNUGAK3dBgCu1QYAr8kGAK2uAIDxrgCA9a4AgPmuAID9rgCAAa8AgAWvAIAJrwCAuG0HALkFBwC6DQcAuwUHALwdBwC9AQcAvgEHAL8BBwCwuQYAsbkGALJtBwCzZQcAtH0HALVlBwC2ZQcAt1UHAKPZBgANrwCAEa8AgBWvAIAZrwCApl0GAKVdBgCEnAIAq30GAKp9BgC+JAMAHa8AgK+hBwCuuQcArbEHAKy5BwCASQAAgUkAAIJZAACzVQcAIa8AgLV9BwC2aQcAJa8AgIZAAACHVAMAulUHALspBwC8OQcAvTkHAL4pBwC/IQcAo5kGACmvAIAtrwCAMa8AgDWvAICmpQYApbEGADmvAICr5QYAqpkGAD2vAIBBrwCAr+0GAK7lBgCt9QYArPUGAOE4BQBFrwCA4yQEAEmvAIBNrwCAUa8AgFWvAIBZrwCAXa8AgGGvAIBlrwCAaa8AgG2vAIBxrwCA7/QEAHWvAICo+QYAqQkGAKoRBgCrLQYArDkGAK0lBgCuLQYAryUGAHmvAIB9rwCAga8AgIWvAICAGQAAgRkAAIIFAACJrwCAuOUBALntAQC65QEAu/0BALzlAQC97QEAvuUBAL9ZAQCwXQYAsSEGALIhBgCzIQYAtCEGALUpBgC2EQYAtxEGAKjRAgCp2QIAqg0DAKsFAwCsHQMArQUDAK4FAwCvNQMAvmQCAJGvAICVrwCAma8AgJ2vAIChrwCApa8AgKmvAIC4JQMAuS0DALolAwC7PQMAvCUDAL0pAwC++QMAv/kDALBNAwCxIQMAsiUDALM9AwC0JQMAtS0DALYlAwC3HQMAs4UDAITIAgCtrwCAhAgDALGvAIC2hQMAtZUDALWvAIC75QMAuokDAIYIDACHnAMAv+kDAL7hAwC96QMAvPEDAIXsCgA2rgCAo80DALmvAICl3QMAva8AgMGvAICmzQMAxa8AgMmvAICrrQMAqsEDAK2hAwCsuQMAr6EDAK6pAwDNrwCA0a8AgNWvAIDZrwCA78gDAN2vAIDhrwCA5a8AgOO0AwDprwCA4dABAO2vAICADQAAgXUAAIJ9AADxrwCA9a8AgPmvAICzZQEAvgQCALVlAQABsACABbAAgLZlAQCGQA0Ah1gNALv1AQC6/QEAvaUBALy5AQC/mQEAvqUBAAmwAIANsACAEbAAgIQADAAVsACAGbAAgB2wAIDvzAEAIbAAgOEsBgAlsACA4yABAOwAAAApsACALbAAgDGwAIA1sACAo+kBADmwAIA9sACApukBAEGwAIBFsACApekBAKpxAQCreQEASbAAgE2wAICuKQEArxUBAKw1AQCtKQEAqCUOAKktDgCqJQ4Aqz0OAKwlDgCtLQ4AriUOAK+VDgD9rwCAUbAAgFWwAIBZsACAXbAAgIKdAACBnQAAgJ0AALhFDwC5TQ8AukUPALtZDwC8SQ8AvUkPAL59DwC/cQ8AsPEOALH5DgCypQ4As7kOALSpDgC1lQ4Atp0OALd9DwCo1Q8Aqd0PAKoJDwCrCQ8ArBkPAK0FDwCuDQ8ArwUPAGGwAIBlsACAabAAgL6gAwBtsACAcbAAgId4AwCGEAAAuBUPALkdDwC6IQ8AuyEPALz1AAC9/QAAvvUAAL/tAACwQQ8AsU0PALJdDwCzVQ8AtE0PALU1DwC2MQ8AtzEPAHWwAIDvsAwAebAAgH2wAICBsACAhbAAgImwAICNsACAkbAAgJWwAICZsACAnbAAgKGwAIDjqA0ApbAAgOGMDQCzwQ4AqbAAgK2wAICxsACAtbAAgLbFDgC10Q4AubAAgLvJDgC6xQ4AvbAAgMGwAIC/sQ4AvskOAL3BDgC8yQ4AowEOAMWwAIDJsACAzbAAgNGwAICmBQ4ApREOANWwAICrCQ4AqgUOANmwAICErAIAr3EOAK4JDgCtAQ4ArAkOAIBRAACBWQAAgmEAALPFAAC+zAEAtcUAALbNAADhsACAhkAHAIcUAQC6yQAAu8kAALzZAAC92QAAvskAAL/FAACrDQMAqg0DAKkJAwCouQIArw0DAK4NAwCtDQMArA0DAL5gAwDlsACA6bAAgO2wAIDxsACA9bAAgPmwAIC+MAUAuykDALoZAwC5GQMAuAEDAL/dAwC+3QMAvd0DALwxAwCzTQMAsk0DALFNAwCwTQMAtzkDALYxAwC1QQMAtE0DAP2wAICmkQMApZkDAAGxAICjmQMABbEAgAmxAIANsQCAr5kDAK6VAwCthQMArIUDAKuVAwCqlQMAja8AgBGxAIAVsQCAGbEAgB2xAIAhsQCAJbEAgCmxAIAtsQCAMbEAgDWxAIA5sQCAPbEAgEGxAICAHQAAgQkAAIL9AQBFsQCAvwgHAEmxAIBRsQCA7yQAAFWxAICElAIAWbEAgF2xAICH4AIAhgQFAL4AGABhsQCAZbEAgOGQAQBpsQCA44AAAG2xAIBxsQCAdbEAgLNlAQB5sQCAtWUBALZtAQB9sQCAgbEAgIWxAIC65QEAu/kBALzpAQC96QEAvsUBAL+9AQCJsQCAjbEAgJGxAIC+xBkAlbEAgJmxAICdsQCA78gBAKGxAIDh3A4ApbEAgOMwDgCpsQCArbEAgLGxAICEMAQAgHkAAIEVAACCFQAAo+UBALWxAICl5QEApu0BALmxAICGQAYAh5AHAKplAQCreQEArGkBAK1pAQCuRQEArz0BAKjdBQCpIQYAqiEGAKshBgCsIQYArSEGAK4hBgCvnQYATbEAgL2xAIDBsQCAhDABAMWxAIDJsQCAzbEAgNGxAIC4jQYAuZUGALqdBgC7lQYAvI0GAL21BgC+vQYAv7UGALDtBgCx8QYAsvEGALPxBgC0zQYAtbUGALa9BgC3tQYAqIkHAKmVBwCqkQcAq5EHAKy9BwCtpQcArqEHAK/dBwDVsQCA2bEAgN2xAIDhsQCA5bEAgOmxAIDtsQCA8bEAgLhJBwC5VQcAul0HALtVBwC8cQcAvX0HAL5pBwC/aQcAsKUHALGtBwCyuQcAs7EHALSRBwC1kQcAtnkHALd5BwD1sQCA+bEAgP2xAIABsgCA78gFAOHACQAFsgCA48AZAOMkBAAJsgCA4dAGAO/cKACinQMAoxUBAKAZBQChjQUAs1kGAA2yAIARsgCAFbIAgBmyAIC2ZQYAtXUGAB2yAIC7KQYAuiEGACGyAIAlsgCAvxUGAL4VBgC9JQYAvC0GAKOZBgCPmfwAKbIAgDGyAIA1sgCApqUGAKW1BgA5sgCAq+kGAKrhBgCGKB8Ah5wAAK/VBgCu1QYAreUGAKztBgCebQkAn30HAJwNCwCd7QkAmvENAJs5DQCY5fAAmQ0PAJbh8QCX6fEAlMX1AJUN8wCSHfcAk/H1AJD9+QCR7fkAgh3/AIMB+gA9sgCAQbIAgIYV9gCHOfYAhAn6AIXx9ACKwfAAiyXyAEWyAIBJsgCAjuEMAI8VDgCMNfIAjQHzAJKtDgCTgQgATbIAgFGyAICW6QQAl3UGAJR5CgCV8QoAmtEGAJvJAABVsgCAWbIAgIEdAwCAHQMAnFkCAIL1AwCrARAAqpUWAKmNFgCojRYAr5UuAK4BLACt/RIArJkSAKOlHgCipR4AoY0CAN2wAICnGRoAppUaAKUBGACknR8AXbIAgGGyAIBlsgCAabIAgG2yAIBxsgCAdbIAgHmyAICz5SoAsuUqALGtLwCw5S4AfbIAgIGyAIC1ASQAtBEqAKgpAwCpNQMAqj0DAKs1AwCsLQMArbUDAK69AwCvtQMAhbIAgImyAICNsgCAkbIAgIAdAACBCQAAgrkAAJWyAIC4TQIAuV0CALptAgC7CQIAvBkCAL0ZAgC+CQIAvwECALDNAwCx1QMAst0DALPVAwC0zQMAtXUCALZ9AgC3dQIAmbIAgITIHQChsgCAvgwfAKWyAICpsgCA70gGAO9YBwDhWAYA4ZgGAOOUAQDjAAYAhhAcAId8HQC+9B4ArbIAgLGyAIC2ZQMAtfUDALWyAICz5QMAubIAgL2yAIDBsgCAv+ECAL5ZAwC9UQMAvFkDALtBAwC6WQMAxbIAgMmyAIAtsgCAnbIAgM2yAIDRsgCA1bIAgNmyAIDdsgCA4bIAgKitHQCptR0AqrUdAKslHgCsPR4ArR0eAK4VHgCvdR4AsA0eALEtHgCyJR4As40eALSVHgC1nR4AtpUeALeNHgC4tR4Aub0eALq1HgC7nR4AvIUeAL1VHwC+XR8Av1UfALMdHQDlsgCA6bIAgO2yAIDxsgCAtr0eALWVHgD1sgCAu8keALrpHgD5sgCA/bIAgL95HgC+cR4AvXkeALzRHgCCKQAAo1kdAIAdAACBFQAApvkeAAGzAIAFswCApdEeAKqtHgCrjR4ACbMAgITgAwCuNR4Arz0eAKyVHgCtPR4AqIkeAKmVHgCqnR4Aq7EeAKzRHgCt2R4Ars0eAK/FHgANswCAEbMAgIaIAACHbAEAFbMAgBmzAIAdswCAIbMAgLhdAQC5wQEAusEBALvBAQC8wQEAvckBAL7xAQC/8QEAsL0eALGdHgCylR4As2UBALR9AQC1ZQEAtm0BALdlAQCqLR0AqzUdACWzAIApswCAri0dAK+VHACsLR0ArSUdAISMAQCjkR0ALbMAgDGzAICmER0ANbMAgDmzAIClgR0As1UeAD2zAIBBswCARbMAgEmzAIC2GR4AtRkeAE2zAIC7GR4AujkeAFGzAIBVswCAv+EBAL75AQC98QEAvAEeAFmzAIBdswCAYbMAgKOZHQBlswCApdUdAKbVHQBpswCAbbMAgHGzAICq9R0Aq9UdAKzNHQCtPQIArjUCAK8tAgCAZQAAgRUAAIIdAACEAAQAdbMAgHmzAICHcAMAhvwEAIGzAICFswCAibMAgI2zAICRswCAlbMAgJmzAICdswCAvsgEAKGzAIClswCAqbMAgK2zAICxswCAtbMAgO/cHwC5swCA4ZQBAL2zAIDjHAEAwbMAgMWzAIDJswCAzbMAgLt1AwC6aQMAvkgGANGzAIC/HQMAvh0DAL0dAwC8ZQMAs9UDANWzAIDZswCA3bMAgOGzAIC2fQMAtcUDAIRwBQCoJQIAqTUCAKo9AgCrNQIArC0CAK2dAgCulQIAr7UCAIIVAADlswCAgNkBAIEJAADEAAAA6bMAgPGzAID1swCAuKkCALmpAgC6SQEAu0kBALxZAQC9RQEAvkUBAL99AQCwzQIAsdECALLRAgCzqQIAtLkCALW5AgC2qQIAt6ECAOEoHgDhNBwA43QBAOMYHgD5swCA/bMAgIa4BACHVAUAhDgHAAG0AIAFtACACbQAgL6sBwANtACA78weAO/IGgCj9QIAEbQAgBW0AIAZtACAHbQAgKZdAgCl5QIAIbQAgKtVAgCqSQIAJbQAgCm0AICvPQIArj0CAK09AgCsRQIAqGEGAKlhBgCqYQYAq2EGAKxhBgCtYQYArmEGAK9hBgDtswCALbQAgDG0AIA1tACAObQAgD20AIBBtACARbQAgLjxBgC58QYAuvEGALvxBgC8nQYAvbEGAL6xBgC/sQYAsOUGALHtBgCy5QYAs/0GALTlBgC17QYAttkGALfVBgCz6QYASbQAgE20AIBRtACAVbQAgLbhBgC16QYAWbQAgLspBgC6IQYAXbQAgGG0AIC/KQYAviEGAL0pBgC8MQYAgl0AAKOtBgCARQAAgV0AAKalBgBltACAabQAgKWtBgCqZQYAq20GAIYADACHQAMArmUGAK9tBgCsdQYArW0GAG20AIDvfAUAcbQAgHW0AIB5tACAfbQAgIG0AICFtACAibQAgI20AICRtACAlbQAgJm0AIDjaAUAnbQAgOF4BQCz0QYAobQAgKW0AICptACArbQAgLb9BgC1/QYAsbQAgLupBgC6oQYAtbQAgLm0AIC/mQYAvqkGAL2pBgC8sQYAqLkGAKm5BgCqGQYAqxkGAKw1BgCtPQYArjUGAK8pBgC9tACAgh0AAIEdAACAHQAAwbQAgMW0AIDJtACA0bQAgLjpAQC56QEAuvkBALv5AQC86QEAvekBAL5dAQC/VQEAsCUGALEtBgCyJQYAsz0GALQtBgC1HQYAthUGALfZAQCGgAwAh+QCANW0AICjnQUA2bQAgKWxBQCmsQUA3bQAgOG0AIDltACAqu0FAKvlBQCs/QUAreUFAK7lBQCv1QUAtk0DAOm0AICExAMAtUUDAO20AICzjQIA8bQAgPW0AIC+SQMAv0kDALxJAwC9SQMAumkDALtpAwD5tACA/bQAgAG1AICmiQMApYEDAAW1AICjSQIACbUAgA21AIARtQCAr40DAK6NAwCtjQMArI0DAKutAwCqrQMAfbMAgBW1AIAZtQCAHbUAgIW0PQAhtQCAJbUAgCm1AIAttQCAMbUAgIA9AACBCQAAgh0AADW1AIC+sAMAObUAgIc4AwCG3AwAQbUAgEW1AIBJtQCATbUAgFG1AIDvXAYAVbUAgFm1AIC+6AwA45QGAF21AIDh3AEAYbUAgGW1AIBptQCAbbUAgLNRAQBxtQCAdbUAgHm1AIB9tQCAtnEBALV5AQCBtQCAuz0BALo9AQCFtQCAibUAgL/9AQC+9QEAvQUBALwFAQCNtQCAkbUAgJW1AICEQAwAmbUAgJ21AIChtQCA76wHAKW1AIDhJAYAqbUAgONABwCGkAwAh/wMALG1AIC1tQCAgFkAAIFlAACCYQAAo90BALm1AICl9QEApv0BAL21AIDBtQCAxbUAgKqxAQCrsQEArIkBAK2JAQCueQEAr3EBAM20AIA9tQCAybUAgM21AICttQCA0bUAgNW1AIDZtQCAqJ0NAKktDgCqOQ4AqzEOAKwRDgCtEQ4Arn0OAK9tDgCwGQ4AsRkOALIxDgCzMQ4AtNEOALXZDgC2zQ4At8UOALj9DgC52Q4AuqkOALupDgC8vQ4AvaUOAL6tDgC/pQ4AqIEPAKmBDwCqgQ8Aq4EPAKyBDwCtjQ8AroUPAK+1DwDdtQCA4bUAgOW1AIDptQCA7bUAgPG1AID1tQCA+bUAgLidDwC5rQ8AuqUPALtNDwC8VQ8AvV0PAL5JDwC/SQ8AsNEPALHRDwCy0Q8As9EPALS1DwC1vQ8AtrUPALetDwCzCQ4A/bUAgAG2AIAFtgCACbYAgLYNDgC1CQ4ADbYAgLsVDgC6FQ4AEbYAgBW2AIC/eQ4AvnEOAL0FDgC8BQ4AghUAAKNNDgCAYQAAgWEAAKZJDgAZtgCAvhABAKVNDgCqUQ4Aq1EOAIQkAQAhtgCArjUOAK89DgCsQQ4ArUEOAKg5DgCpOQ4AqlkOAKtRDgCscQ4ArXEOAK6RAQCvkQEAhgAAAIeEAAAltgCAKbYAgC22AIAxtgCANbYAgDm2AIC4dQEAuX0BALp1AQC7yQAAvNkAAL3ZAAC+yQAAv8EAALD1AQCx/QEAsvUBALNNAQC0VQEAtV0BALZVAQC3TQEAuk0PALtVDwC4TQ8AuUUPAL59DwC/tQ8AvEUPAL11DwCyAQ8AswEPALAxDwCxMQ8AtgEPALcNDwC0EQ8AtREPAKqZDgCrRQ8AqOUOAKmZDgCuQQ8Ar0EPAKxRDwCtUQ8APbYAgEG2AIBFtgCASbYAgE22AIBRtgCAVbYAgFm2AICzUQ0AXbYAgGG2AIBltgCAabYAgLZxDQC1eQ0AbbYAgLu5AgC6sQIAcbYAgHW2AIC/GQIAvhECAL0ZAgC8oQIAebYAgKMVDQB9tgCAgbYAgKY1DQCFtgCAibYAgKU9DQCq9QIAq/0CAIToAwCRtgCArlUCAK9dAgCs5QIArV0CAKhtAgCprQIAqqUCAKu9AgCspQIAra0CAK6lAgCvfQEAgO0BAIHxAQCC8QEAvqAFAJW2AICZtgCAh2gFAIYcBQC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALAFAQCxDQEAsgUBALMdAQC0BQEAtQ0BALYFAQC3+QEA4WQPAOGcDwDjFA4A49QPAJ22AIDhPA4AobYAgOPkAAC+rAQApbYAgKm2AIDvDAAArbYAgLG2AIDvYA4A77QPALW2AIC5tgCAhEQEALNhAgC9tgCAtWECALZhAgDBtgCAxbYAgMm2AIC6jQEAu4UBALydAQC9hQEAvo0BAL+FAQCjrQUAjbYAgM22AIDRtgCA1bYAgKatBQClrQUA2bYAgKtJBgCqQQYA3bYAgOG2AICvSQYArkEGAK1JBgCsUQYA5bYAgOm2AIDttgCA8bYAgIAdAACBCQAAgjkAAPW2AID5tgCA/bYAgIbIAACHIAMAAbcAgAW3AIAJtwCADbcAgKhtBgCptQcAqr0HAKsdBwCsCQcArTEHAK4xBwCvLQcAhKgDABG3AIAVtwCAGbcAgB23AIAhtwCAJbcAgCm3AIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+nQAAv5UAALBVBwCxJQcAsi0HALM9BwC0LQcAtRUHALYdBwC39QAALbcAgOG8BgAxtwCA4/QFADW3AIA5twCAPbcAgEG3AIBFtwCASbcAgE23AIBRtwCAVbcAgFm3AIBdtwCA7+gEALN1BgCCLQAAgRUAAIAdAABhtwCAtvEGALXBBgBltwCAu6EGALrRBgBptwCAvmwBAL+RBgC+qQYAvakGALy5BgCjtQYAcbcAgIYoAACHTAEAdbcAgKYxBgClAQYAebcAgKthBgCqEQYAfbcAgIG3AICvUQYArmkGAK1pBgCseQYAhbcAgLO9AQCJtwCAjbcAgLZ5AQCRtwCAlbcAgLV5AQC6VQEAu10BAJm3AICdtwCAvvkAAL/lAAC8RQEAvf0AAKhxAgCpcQIAqnECAKtxAgCstQIArb0CAK61AgCvrQIAhOw8AKG3AICltwCAqbcAgK23AICxtwCAtbcAgLm3AIC4XQMAuWUDALptAwC7ZQMAvH0DAL1lAwC+bQMAv2UDALDVAgCx3QIAstUCALNtAwC0eQMAtWUDALZtAwC3ZQMAHbYAgL23AIDBtwCAo/UCAMW3AIClMQIApjECAMm3AIDNtwCA0bcAgKodAgCrFQIArA0CAK21AwCusQMAr60DAIBlAACBCQAAghkAANW3AIDZtwCA4bcAgL4QPADltwCAhsA8AIcgAwDptwCA7bcAgPG3AID1twCA+bcAgP23AICohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAAG4AIAFuACACbgAgA24AIARuACAFbgAgBm4AIAduACAuHUBALl9AQC6dQEAu8kBALzZAQC9xQEAvsUBAL/9AQCwtQIAsb0CALKBAgCzgQIAtFUBALVdAQC2VQEAt00BAOGkBgAhuACA41AGAL6APACEHDwAvoA/ACW4AIApuACALbgAgDG4AIA1uACAObgAgD24AIBBuACA7+AGAEW4AICBfQAAgHEAAEm4AICCBQAAUbgAgFW4AIDvTAAAWbgAgOGQAQBduACA41gBAGG4AIBluACAabgAgIZYPwCH/DwAs509AN23AIBNuACAbbgAgHG4AIC21T0AtbU9AHW4AIC7+T0AuvE9AHm4AIB9uACAvxk+AL4RPgC91T0AvNU9AIG4AICj2T0AhbgAgIm4AICmkT0AjbgAgJG4AICl8T0AqrU9AKu9PQCVuACAmbgAgK5VPgCvXT4ArJE9AK2RPQCoVT4AqVk+AKphPgCrYT4ArGE+AK1hPgCuYT4Ar2E+AISoAwCduACAobgAgKW4AICpuACArbgAgLG4AIC1uACAuEU/ALldPwC6VT8Au20/ALx1PwC9fT8AvnU/AL9tPwCwwT8AscE/ALLBPwCzwT8AtME/ALXBPwC2wT8At8E/AIC5AQCBuQEAggUAALm4AIDhgD4AwbgAgOMoPQDFuACAhoAAAIcEAQDvCD0AybgAgM24AIDRuACA1bgAgNm4AICzqT8AvbgAgN24AIDhuACA5bgAgLahPwC1qT8A6bgAgLtFPgC6RT4A7bgAgPG4AIC/RT4AvkU+AL1VPgC8VT4Ao2k/APW4AID5uACA/bgAgAG5AICmYT8ApWk/AAW5AICrhT4AqoU+AAm5AIANuQCAr4U+AK6FPgCtlT4ArJU+ABG5AICzGT4AFbkAgBm5AIC2IT4AHbkAgCG5AIC1MT4AuvEBALv5AQAluQCAKbkAgL6xAQC/vQEAvNEBAL3RAQCo0T0AqdE9AKrVPQCr6T0ArP09AK3lPQCu7T0ArxECAID5AwCBzQMAgsUDAIQkAwC+AAQAMbkAgIesAwCGvAQAuBkCALktAgC6JQIAu+kCALz5AgC9+QIAvukCAL/pAgCwcQIAsXkCALJBAgCzQQIAtDECALU9AgC2NQIAtykCAKVtPQA1uQCAObkAgKZ9PQA9uQCAbbcAgKNFPQBBuQCArY0CAKyNAgCv4QIAru0CAKwAAABFuQCAq6UCAKqtAgDh+AEASbkAgOP0AgCEwAQATbkAgFG5AIBVuQCAWbkAgF25AIBhuQCAZbkAgGm5AIBtuQCAcbkAgO8wAgB1uQCAqBUCAKkZAgCqJQIAqz0CAKwlAgCtLQIAriUCAK9VAgB5uQCAfbkAgIG5AICFuQCAibkAgI25AICEsAQAkbkAgLjRAgC52QIAuuECALvhAgC8kQIAvZ0CAL6VAgC/iQIAsC0CALE1AgCyNQIAswUCALQdAgC18QIAtvECALfxAgDheD8A4zQBAOMIPgDhbD4AgQkAAICpAACVuQCAgj0AAJm5AIChuQCApbkAgL4gBACpuQCA79g+AO/MPgCtuQCAsbkAgLPpAgCG6AQAh8AEALbpAgC1uQCAubkAgLXpAgC6rQIAu7UCAL25AIDBuQCAvp0CAL9xAgC8pQIAvZUCAC25AICduQCAxbkAgMm5AIDNuQCA0bkAgNW5AIDZuQCAqBUGAKmhBgCqoQYAq70GAKytBgCtgQYArv0GAK/tBgCwlQYAsZ0GALKVBgCzrQYAtLUGALW9BgC2tQYAt60GALiVBgC5mQYAukkHALtJBwC8WQcAvVkHAL5JBwC/SQcArN0FAK3tBQCu5QUArwkFAN25AIDhuQCAqtUFAKvNBQDluQCApZEFAKaRBQDpuQCA7bkAgPG5AID1uQCAo5EFALNJBgD5uQCA/bkAgAG6AIAFugCAtmEGALVFBgAJugCAuzkGALoxBgC+ZAAADboAgL8ZBgC+EQYAvRkGALwhBgCjiQcAgtkBAIHZAQCAwQEAEboAgKahBwClhQcAFboAgKv5BwCq8QcAhggBAId8AQCv2QcArtEHAK3ZBwCs4QcAGboAgLP1BgAdugCAIboAgLaFBgAlugCAKboAgLWdBgC6jQYAu20BAC26AIAxugCAvmUBAL9tAQC8dQEAvW0BAKglBgCpLQYAqjkGAKsxBgCsUQYArUEGAK5BBgCvdQYANboAgDm6AIA9ugCAQboAgEW6AIBJugCATboAgFG6AIC4VQEAuWUBALplAQC7fQEAvGUBAL1tAQC+HQEAvxUBALANBgCx7QEAsuUBALP9AQC05QEAte0BALblAQC3bQEAo7EFAFW6AIBZugCAvkgDAL5YDACmwQUApdkFAF26AICrKQIAqskFAGG6AIBlugCArykCAK4hAgCtKQIArDECAGm6AIBtugCAcboAgHW6AICAGQAAgRkAAIIFAAB5ugCAhKwDAIG6AICHGAMAhswMAIW6AICJugCAjboAgJG6AICokQMAqZkDAKrJAwCrxQMArN0DAK3BAwCuwQMAr/UDAJW6AICZugCAnboAgKG6AIClugCAqboAgK26AICxugCAuH0DALnBAAC6wQAAu9EAALz5AAC9+QAAvpkAAL+ZAACwjQMAsUUDALJNAwCzRQMAtF0DALVFAwC2TQMAt0UDALNBAgC1ugCAuboAgL8EDwC9ugCAtkECALVVAgDBugCAu4ECALpJAgDFugCAyboAgL+BAgC+mQIAvZECALyZAgDNugCA0boAgNW6AIDZugCA76QDAN26AIDhugCA5boAgOMQAwDpugCA4VgAAIQgDQCAKQAAgSkAAIIdAADxugCA4VAGAOGgBwDjoAYA41AHAIWUDAD1ugCA70gbAPm6AIDhJAIA/boAgONwGgABuwCABbsAgAm7AIDvqAEA7+gGAIagDwCHDA0Ao4kCAA27AIClnQIAEbsAgBW7AICmiQIAGbsAgB27AICrSQIAqoECAK1ZAgCsUQIAr0kCAK5RAgCoZQ4AqXUOAKp9DgCrdQ4ArG0OAK21DgCuvQ4Ar7UOAO26AIAhuwCAJbsAgCm7AIAtuwCAOLsAgDy7AIBAuwCAuF0PALltDwC6ZQ8Auw0PALwVDwC9HQ8AvhUPAL8JDwCwzQ4AsdUOALLdDgCz1Q4AtM0OALVxDwC2cQ8At20PALP1DgBEuwCASLsAgEy7AIBQuwCAtjUOALXlDgBUuwCAuxEOALoJDgBYuwCAXLsAgL+1DwC+CQ4AvQEOALwJDgCCFQAAo7EOAIBhAACBYQAApnEOAGC7AIC+EAEApaEOAKpNDgCrVQ4AaLsAgIQgAQCuTQ4Ar/EPAKxNDgCtRQ4An0UIAJ4NCQCdDQkAnJkLAJt1NQCaETUAmZk3AJgNMQCXJTEAliUxAJWBPQCUDT0Ak4k/AJIVOACRPTkAkD05AI9lJQDvrA0AhgAEAIegAQBsuwCAcLsAgHS7AIDv6AEAeLsAgOE0AgB8uwCA4zQBAIC7AIDjCAwAhLsAgOEIDQChoQEAiLsAgKMJBQCibQMApc0EAKQRBQCnHRkAph0ZAKmhHQCoORkAq+kcAKqpHQCtkREArAEQAK8BFACuUREAsfkVALDlFQCz6WkAsgFoALUBbAC0eWkAjLsAgJC7AICUuwCAmLsAgJy7AICguwCAowkDAKIZDQCh/Q0AoP0NAIIlJgCDBToApLsAgKi7AICGqTwAhzU+AIQdOgCFPTsAiok+AIslMgCsuwCAsLsAgI6xNACPMTYAjD0yAI0tMgCSJTYAk9EIAIREAwC+wAQAlhULAJdVDgCUXQoAlVUKAJplDgCbiQ4AtLsAgLi7AIC8uwCAwLsAgJyBAADEuwCAuLUCALm9AgC6tQIAuwkCALwZAgC9GQIAvgkCAL8BAgCwdQ0AsX0NALJJDQCzSQ0AtJUCALWdAgC2lQIAt40CAKi9DQCpUQ0AqlUNAKtpDQCsfQ0ArWUNAK5tDQCvEQ0AZLsAgILtAQCBHQAAgB0AAMi7AIDMuwCAfboAgL5wBQCznQwAhIwFANC7AIDYuwCA3LsAgLalDAC1tQwA4LsAgLv5DAC68QwAhigFAIcgBQC/GQMAvhEDAL3dDAC83QwA5LsAgKPZDADouwCA7LsAgKbhDADwuwCA9LsAgKXxDACqtQwAq70MAPi7AID8uwCArlUDAK9dAwCsmQwArZkMAAC8AIAEvACACLwAgAy8AIAQvACAFLwAgBi8AIDvvAEAHLwAgOF8DgAgvACA41ABACS8AIAovACALLwAgDC8AICzlQIANLwAgDi8AIA8vACAQLwAgLa9AgC1uQIASLwAgLs5AgC6YQIAhsgEAIesBAC/GQIAvhECAL0ZAgC8IQIAo1UFAILVBwCBxQcAgMUHAEy8AICmfQUApXkFAFC8AICr+QUAqqEFAFS8AIBYvACAr9kFAK7RBQCt2QUArOEFAFy8AICzWQcAYLwAgGS8AIC2HQcAaLwAgGy8AIC1FQcAugkHALsJBwBwvACAdLwAgL75BwC/+QcAvPkHAL35BwDUuwCARLwAgHi8AIB8vACAgLwAgIS8AICIvACAjLwAgKitBwCptQcAqrUHAKvtBwCs+QcArfkHAK7tBwCv5QcAsKkHALGpBwCySQcAs0kHALRZBwC1WQcAtkkHALdJBwC4eQcAuUUHALpBBwC7XQcAvEUHAL1NBwC+RQcAvzkHAKMdBgCQvACAlLwAgJi8AICcvACAplkGAKVRBgCgvACAq00GAKpNBgCkvACAqLwAgK+9BgCuvQYArb0GAKy9BgCAbQAAgQkAAIIZAACsvACAsLwAgISYAQC+kAEAtLwAgIYAHACHxAEAuLwAgLy8AIDAvACAxLwAgMi8AIDMvACAqF0GAKmVAQCqlQEAq6UBAKy9AQCt1QEArtEBAK/RAQDQvACA1LwAgNi8AIDcvACA4LwAgOS8AIDovACA7LwAgLhZAQC5WQEAus0AALvFAAC83QAAvcUAAL7FAAC/9QAAsLUBALG9AQCygQEAs4EBALR5AQC1eQEAtmkBALdpAQCzHQIA8LwAgPS8AIC+gBwA+LwAgLZVAgC1NQIA/LwAgLt5AgC6cQIAAL0AgAS9AIC/vQIAvr0CAL1VAgC8VQIACL0AgKNZAgAMvQCAEL0AgKYRAgAUvQCAGL0AgKVxAgCqNQIAqz0CABy9AIAgvQCArvkCAK/5AgCsEQIArRECACi9AIAsvQCAvgQdAL4AHgAwvQCANL0AgDi9AIA8vQCAgPkAAIHNAACCxQAAhCADAIawHACHlAMAQL0AgES9AIBIvQCATL0AgFC9AIBUvQCA42wCAFi9AIDhoAEAXL0AgO8UAgBgvQCAZL0AgGi9AIBsvQCAcL0AgHS9AIB4vQCA4fAGAOE0BgDjTAAA4xgGAHy9AICAvQCAhL0AgIi9AICAPQAAgQkAAIIZAACMvQCAkL0AgIS8HQDvmAAA7zgHALMxAgDRAAAAh9gdAIZsHACYvQCAtikCALUhAgCcvQCAu80CALrNAgCgvQCApL0AgL/NAgC+zQIAvc0CALzNAgCyXQYAs2UGALANBgCxVQYAtn0GALedBQC0fQYAtXUGALqNBQC7zQUAuKUFALmFBQC+xQUAv8kFALzVBQC9zQUAqL0AgKy9AICwvQCAtL0AgLi9AIC8vQCAwL0AgMS9AICqtQYAq70GAKgBBwCpvQYAroEGAK+NBgCsmQYArZUGAKNxHQDIvQCAzL0AgNC9AIDUvQCApmkdAKVhHQDYvQCAq40dAKqNHQDcvQCA4L0AgK+NHQCujR0ArY0dAKyNHQDkvQCAs9UeAOi9AIDsvQCAts0eAPC9AID0vQCAtcUeALqhHgC7oR4A+L0AgPy9AIC+pR4Av6keALyxHgC9sR4AJL0AgJS9AIAAvgCAhAQDAID5AACB+QAAghEAAAS+AICoIR4AqSEeAKo5HgCrOR4ArCkeAK0pHgCuAR4ArwEeALABHgCxAR4AsgEeALMBHgC0BR4AtQkeALY9HgC3NR4AuA0eALkVHgC6HR4AuxUeALwNHgC95R8Avu0fAL/lHwCjkR8ACL4AgIYoAQCHSAEADL4AgKaJHwClgR8AEL4AgKvlHwCq5R8AFL4AgBi+AICv7R8AruEfAK31HwCs9R8AHL4AgLMtHgAgvgCAJL4AgLaVHgAovgCALL4AgLWdHgC6sR4Au7EeADC+AIA0vgCAvnUBAL99AQC8oR4AvaEeAKjRHgCp2R4AquEeAKvhHgCsUR4ArVEeAK5RHgCvUR4AOL4AgDy+AIBAvgCARL4AgEi+AIBMvgCAUL4AgFS+AIC43QEAue0BALrlAQC7jQEAvJkBAL2ZAQC+jQEAv4UBALAxHgCxMR4AsjEeALMxHgC09QEAtf0BALb1AQC37QEAo2kdAFi+AIBcvgCAYL4AgGS+AICm0R0ApdkdAGi+AICr9R0AqvUdAGy+AIBwvgCArzkCAK4xAgCt5R0ArOUdAIFpAACAWQAAvgAEAIJhAAB4vgCAfL4AgIC+AICEvgCAhOwDAIi+AICHiAMAhuwEAIy+AICQvgCAlL4AgJi+AICohQMAqZUDAKqVAwCrpQMArL0DAK3VAwCu0QMAr9EDAJy+AICgvgCApL4AgKi+AICsvgCAsL4AgLS+AIC4vgCAuHEDALlxAwC6cQMAu3EDALzVAAC93QAAvtUAAL/NAACwtQMAsb0DALKBAwCzgQMAtFEDALVRAwC2UQMAt1EDAOFUHgDhrB8A45QBAOMoHgDjYAMAvL4AgOEIAADAvgCA75ADAMS+AIDIvgCAzL4AgNC+AIDUvgCA70wfAO9MHwCzXQIA2L4AgNy+AIDgvgCA6L4AgLYVAgC1dQIA7L4AgLs5AgC6MQIAhCQFAL7gBAC/1QIAvtUCAL0VAgC8FQIAuJEdALmZHQC6oR0Au6EdALzRHQC93R0AvtUdAL/JHQCwCR4AsQkeALIZHgCzGR4AtAkeALUJHgC2vR0At7UdAKipHgCpqR4AqrkeAKu5HgCsqR4ArakeAK55HgCveR4AgKUAAIGtAACCpQAA8L4AgIbQBACH+AQA9L4AgPi+AIB0vgCA5L4AgPy+AIAAvwCABL8AgAi/AIAMvwCAEL8AgKhxBgCpcQYAqnEGAKtxBgCsVQYArUUGAK5NBgCvRQYAsD0GALHlBgCy7QYAs+UGALT9BgC15QYAtu0GALflBgC43QYAuXEHALp1BwC7SQcAvFkHAL1ZBwC+SQcAv0kHALPZBgAUvwCAGL8AgBy/AIAgvwCAtuUGALX9BgAkvwCAuwEGALrZBgAovwCALL8AgL8BBgC+GQYAvREGALwZBgAwvwCAo9kFADS/AIA4vwCAppEFADy/AIBAvwCApfEFAKq1BQCrvQUARL8AgEi/AICuUQUAr1EFAKyRBQCtkQUAo1kHAIIZAACBGQAAgOEBAEy/AICmZQcApX0HAFC/AICrgQcAqlkHAISgAgC+rAEAr4EHAK6ZBwCtkQcArJkHAFS/AICzqQYAhugAAIcsAQC2WQEAWL8AgFy/AIC1oQYAunUBALt9AQBgvwCAZL8AgL75AQC/+QEAvGUBAL35AQCo0QYAqdkGAKplBgCrdQYArG0GAK2dAQCulQEAr40BAITsAQBovwCAbL8AgHC/AIB0vwCAeL8AgHy/AICAvwCAuGkBALlpAQC6CQEAuwUBALwdAQC9AQEAvgEBAL81AQCw9QEAsf0BALL1AQCzaQEAtHkBALV5AQC2aQEAt2EBAIS/AICIvwCAjL8AgKPhBQCQvwCApekFAKYRAgCUvwCAmL8AgJy/AICqPQIAqzUCAKwtAgCtsQIArrECAK+xAgCgvwCApL8AgL4EAwCEAAwAqL8AgKy/AICwvwCAtL8AgIANAACBFQAAgh0AALi/AIC8vwCAwL8AgIdEAwCG3AwAs+kDAMi/AIDMvwCA0L8AgNS/AIC2PQMAtT0DANi/AIC7GQMAuhEDANy/AIDgvwCAv7kAAL6xAAC9uQAAvAEDAOS/AIDhlAEA6L8AgON8AQDsvwCA8L8AgPS/AID4vwCA/L8AgADAAIAEwACACMAAgAzAAIAQwACAFMAAgO9MAgCoVQIAqV0CAKphAgCrYQIArLUCAK29AgCutQIAr60CAL5oDQAYwACAHMAAgCDAAIAkwACAgq0AAIGtAACArQAAuGEBALlhAQC6CQEAuwkBALwBAQC9AQEAvgEBAL8BAQCw1QIAsd0CALLVAgCzbQEAtHUBALV9AQC2aQEAt2EBAOFoBgDh8AcA47AAAOP0BgAowACALMAAgDDAAIA4wACAPMAAgEDAAIBEwACASMAAgL78DABMwACA72wAAO8oBgCjqQIAUMAAgIZoDACHBA0AVMAAgKZ9AgClfQIAWMAAgKtZAgCqUQIAXMAAgGDAAICv+QEArvEBAK35AQCsQQIAqIUOAKmNDgCqhQ4Aq50OAKyNDgCtvQ4ArrUOAK/dDgA0wACAZMAAgGjAAIBswACAcMAAgHTAAIB4wACAfMAAgLitDgC5tQ4Aur0OALu1DgC8dQ8AvX0PAL51DwC/bQ8AsKkOALG1DgCyvQ4As7UOALStDgC1lQ4Atp0OALeVDgCzDQ4AgMAAgITAAICIwACAjMAAgLY9DgC1BQ4AkMAAgLtxDgC6bQ4AlMAAgJjAAIC/UQ4AvmkOAL1hDgC8aQ4AghkAAKNJDgCAZQAAgRkAAKZ5DgCcwACAoMAAgKVBDgCqKQ4AqzUOAIS8AwCkwACAri0OAK8VDgCsLQ4ArSUOAKidDgCppQ4Aqq0OAKulDgCsvQ4AraEOAK7dDgCvzQ4AhiABAIdkAQCowACArMAAgLDAAIC0wACAuMAAgLzAAIC4eQEAuXkBALrNAQC7xQEAvN0BAL3FAQC+xQEAv/UBALC9DgCxjQ4AsoUOALNJAQC0WQEAtVkBALZJAQC3SQEAtS0OAMDAAIDEwACAtjkOAMjAAIDMwACAsz0OANDAAIC9hQEAvEkOAL+FAQC+hQEA1MAAgMS/AIC7UQ4AumEOAKNlDgDYwACA3MAAgODAAIDkwACApmEOAKV1DgDowACAqwkOAKo5DgDswACA8MAAgK/dAQCu3QEArd0BAKwRDgD0wACA+MAAgO/QDwD8wACAAMEAgATBAIAIwQCADMEAgBDBAIC+aAMAGMEAgBzBAIDhVA4AIMEAgONkDgAkwQCAgFkAAIFZAACCaQAAhIwDAIbwBACHFAMAKMEAgCzBAIAwwQCANMEAgDjBAIA8wQCAQMEAgETBAIBIwQCATMEAgFDBAIBUwQCAWMEAgFzBAIBgwQCAZMEAgGjBAIBswQCAqIkDAKmJAwCqmQMAq5kDAKyJAwCtiQMArj0DAK81AwCwUQMAsVEDALJVAwCzfQMAtBUDALUdAwC2FQMAtw0DALg9AwC5DQMAugUDALvtAAC89QAAvfkAAL7pAAC/6QAAcMEAgHTBAIB4wQCAsz0CAHzBAIC1LQIAtiUCAIDBAIC+aAUAiMEAgLq5AgC7uQIAvK0CAL2FAgC+/QIAv/UCAIBJAACBVQAAglUAAIQABQDvjAMAvhgEAId0BQCG/AQA4zwDAIzBAIDhUAAAkMEAgJTBAICYwQCAnMEAgKDBAICkwQCAqMEAgKzBAICwwQCAtMEAgLjBAIC8wQCA79QOAL4oBgDhdA4AwMEAgONUAQDEwQCAyMEAgMzBAIDQwQCAo/ECANTBAIDYwQCA3MEAgODBAICm6QIApeECAOTBAICrdQIAqnUCAOjBAIDswQCArzkCAK4xAgCtSQIArGECAKgpBgCpKQYAqj0GAKsxBgCsSQYArUkGAK55BgCveQYAhMEAgIIVAACBxQcAgMUHAPDBAICEaAMA9MEAgPjBAIC4yQYAuckGALrZBgC72QYAvMkGAL3JBgC+WQcAv1kHALAJBgCxCQYAshkGALMZBgC0CQYAtQkGALb5BgC3+QYAs7UGAPzBAICGrAAAh0ADAADCAIC2yQYAtcEGAATCAIC7zQYAus0GAAjCAIAMwgCAv80GAL7NBgC9zQYAvM0GABDCAICj8QYAFMIAgBjCAICmjQYAHMIAgCDCAIClhQYAqokGAKuJBgAkwgCAKMIAgK6JBgCviQYArIkGAK2JBgCoJQYAqWEGAKplBgCrfQYArGUGAK1tBgCuZQYAr50GACzCAIAwwgCANMIAgDjCAIA8wgCAQMIAgETCAIBIwgCAuPUGALn9BgC69QYAu4kGALyZBgC9mQYAvokGAL+BBgCw5QYAse0GALLlBgCz/QYAtOUGALXtBgC20QYAt80GAEzCAIC2/QYAtf0GAFDCAICz/QYAVMIAgFjCAIBcwgCAvzkGAL4xBgC9OQYAvCEGALs5BgC6MQYAFMEAgGDCAICjrQYAgnkAAIFVAACAVQAAhFwBAKatBgClrQYAaMIAgKtpBgCqYQYAhkh/AIfkAACvaQYArmEGAK1pBgCscQYAbMIAgO/cBwBwwgCAdMIAgHjCAIB8wgCAgMIAgITCAICIwgCAhKADAIzCAIC/JHkAkMIAgONoBwCUwgCA4XQGALPRAgCYwgCAvgQDAISAfQCcwgCAtvkCALXxAgCgwgCAu7UCALqpAgCkwgCAqMIAgL9RAwC+mQIAvZECALylAgCpBQIAqLkCAKsVAgCqHQIArT0CAKw9AgCvUQIArl0CAL5ofQCswgCAsMIAgLTCAIC4wgCAvMIAgMDCAIDEwgCAufEDALjpAwC78QMAuvkDAL1RAwC86QMAv00DAL5RAwCxNQIAsCkCALMBAgCyNQIAtdEDALQZAgC30QMAttkDAIIpAACjlQMAgB0AAIEVAACmvQMAyMIAgMzCAICltQMAqu0DAKvxAwDQwgCA2MIAgK7dAwCvFQIArOEDAK3VAwCGYH0Ah3h9ALNBAQCEAH8AtUEBANzCAIDgwgCAtkkBAOTCAIDowgCAu0EBALpNAQC9SQEAvEUBAL8pAQC+OQEA7MIAgO/cBgDwwgCA9MIAgPjCAID8wgCAAMMAgO8wBgCELH4A4eAGAATDAIDjiAEACMMAgON0AAAMwwCA4SwBAKPJAQAQwwCAFMMAgIVweQAYwwCApsEBAKXJAQAcwwCAq8kBAKrFAQAgwwCAJMMAgK+hAQCusQEArcEBAKzNAQCo3X0AqQV+AKoBfgCrAX4ArAF+AK0BfgCuAX4ArwF+ANTCAIAowwCALMMAgDDDAIA0wwCAgp0AAIGdAACAnQAAuC1+ALnhfgC64X4Au+F+ALzhfgC94X4AvuF+AL/hfgCwQX4AsU1+ALJZfgCzVX4AtDV+ALUlfgC2JX4AtxV+AKitfwCp0X8AqtF/AKvtfwCs9X8ArRV/AK4RfwCvEX8AOMMAgDzDAIBAwwCARMMAgIbwAwCHuAAASMMAgEzDAIC4EX8AuRl/ALohfwC7IX8AvPUAAL39AAC+9QAAv+0AALBxfwCxcX8AsnF/ALNFfwC0QX8AtU1/ALY9fwC3NX8As1l+AFDDAIBUwwCAWMMAgFzDAIC2lX4AtX1+AGDDAIC7tX4AurV+AGTDAIBowwCAv4l+AL6FfgC9kX4AvKV+AGzDAICjHX4AcMMAgHTDAICm0X4AeMMAgHzDAIClOX4AqvF+AKvxfgCAwwCAhMMAgK7BfgCvzX4ArOF+AK3VfgCwrQAAscUAALLBAACzwQAAtMUAALXNAAC28QAAt/EAALhhAAC5YQAAumEAALt9AAC8ZQAAvW0AAL5lAAC/vQMAiMMAgIzDAICQwwCAZMIAgJTDAICYwwCAnMMAgKDDAICoWQEAqVkBAKrtAACr5QAArP0AAK3lAACu5QAAr9UAAKTDAICCHQAAgR0AAIAdAACowwCArMMAgLDDAIC+VAIAhoAEAIfsAgC4wwCAvMMAgMDDAIDEwwCAyMMAgL54AwDjdH4AzMMAgOG4fQDQwwCA1MMAgNjDAIDcwwCA4MMAgOTDAIDowwCA7MMAgPDDAIDvwH4A9MMAgPjDAID8wwCAs4UDAADEAIAExACACMQAgAzEAIC2hQMAtZUDABDEAIC74QMAuokDAL4kBgAUxACAv+kDAL7hAwC99QMAvPUDAIIpAACjwQMAgB0AAIEVAACmwQMAGMQAgBzEAICl0QMAqs0DAKulAwAgxACAheAFAK6lAwCvrQMArLEDAK2xAwDh+AMAKMQAgONcHwAsxACA7/QDADDEAICGPAcAh6wCAON8fgA0xACA4YABADjEAIA8xACAQMQAgO/kEwBExACAs3EBAEjEAIBMxACAUMQAgFTEAIC2EQEAtWEBAFjEAIC7OQEAujEBAFzEAIBgxACAvxkBAL4RAQC9GQEAvCEBAGTEAIBoxACAbMQAgHDEAIB0xACAeMQAgHzEAIDvxH8AgMQAgOH8fgCExACA4/B/AIANAACBdQAAgn0AAIjEAICMxACAkMQAgKP5AQC+AAgApekBAJjEAICcxACAppkBAISoBQCgxACAq7EBAKq5AQCtkQEArKkBAK+RAQCumQEAqCkGAKkpBgCqOQYAqzkGAKwpBgCtUQYArlUGAK9NBgAkxACAhCABAKTEAICUxACAo+EBAKKZBAChGQQAoPEFALg5BgC5OQYAus0GALvFBgC83QYAvcUGAL7FBgC/8QYAsDUGALE9BgCyNQYAsw0GALQVBgC1HQYAthUGALcJBgCPoWwAs5EHAIYoAQCHfAMAtqEHAKjEAICsxACAtbEHALrlBwC77QcAsMQAgLTEAIC+7QcAv90HALz1BwC97QcAn/l4AJ7leACdcXkAnCF8AJvxfACaYX0AmZlxAJjZcACX4XAAlnl0AJVtdACUbXQAk61pAJJxaACReWgAkB1uAIIhbQCD5W8AuMQAgLzEAICGTWgAh5V1AISZaQCFmWkAiqV1AIu5dQDAxACAxMQAgI5xcACPgXwAjDlxAI05cQCSYX0Ak6l9AMjEAIDMxACAlml5AJeZBACU4XgAlX15AJpBBQCbyQUA0MQAgNTEAIDYxACA3MQAgJypAADgxACAo4ENAKKpAQChqQEA5MQAgKexCQCmAQgApU0NAKSZDQCrkRUAqoUVAKkBFACocQkArx0QAK7pEQCtvREArAEQALMBGACy8RwAscEdALDJHQC0wwCA6MQAgLXhGAC0/RkA7MQAgPDEAID0xACA+MQAgIAdAACBCQAAgv0DAPzEAICjFQUAAMUAgIaIDACHPAMACMUAgKYlBQClNQUADMUAgKtpBQCqYQUAEMUAgBTFAICvWQUArmkFAK1pBQCscQUAGMUAgBzFAICEBAwAIMUAgCTFAIDhbAYAKMUAgOPsewAsxQCAMMUAgDTFAIDvqAYAOMUAgDzFAIBAxQCARMUAgKmNBQCogQUAq60FAKqZBQCtoQUArLkFAK+lBQCuqQUAhGgNAEjFAIBMxQCAUMUAgFTFAIBYxQCAXMUAgL70DAC5SQUAuEEFALtZBQC6QQUAvUkFALxBBQC/cQUAvn0FALGpBQCwoQUAs7kFALKhBQC1mQUAtKkFALd5BQC2kQUAqNUEAKndBACq7QQAqyUDAKyFAwCtjQMArrEDAK+xAwBgxQCAZMUAgGjFAIBsxQCAgBkAAIEZAACCBQAAcMUAgLgxAgC5MQIAujUCALvBAgC8hQIAvbUCAL69AgC/tQIAsGkCALFpAgCyQQIAs0ECALQ5AgC1OQIAthECALcRAgCGoAwAh0wNAHjFAIB8xQCA76QGAIDFAICExQCA78wHAOOUAQDhpAYA4TgBAONcBgCIxQCAjMUAgJDFAICUxQCAmMUAgJzFAICzLQQAoMUAgLVFAwCkxQCAqMUAgLZFAwCsxQCAsMUAgLvlAgC65QIAvd0CALzdAgC/tQIAvrUCAATFAIB0xQCAtMUAgLjFAIC8xQCAwMUAgMTFAIDIxQCAqDEOAKk5DgCqAQ4AqwEOAKxxDgCtcQ4ArnUOAK9tDgCwGQ4AsSUOALItDgCzJQ4AtCEOALUhDgC2IQ4AtyEOALjFDgC5zQ4AusUOALvdDgC8xQ4Avc0OAL5ZDwC/WQ8As6kOAMzFAIDQxQCA1MUAgNjFAIC20Q4AtdkOANzFAIC7wQ4Auv0OAODFAIC+LAAAv8UOAL7FDgC90Q4AvNkOAIJpAACj7Q4AgFkAAIFRAACmlQ4A5MUAgOjFAIClnQ4AqrkOAKuFDgCGyAAAh6wAAK6BDgCvgQ4ArJ0OAK2VDgDsxQCAs5EOAPDFAID0xQCAtqUOAPjFAID8xQCAta0OALrhDgC74Q4AAMYAgATGAIC+6Q4Av9UOALz1DgC96Q4Ao6UKAAjGAIAMxgCAEMYAgBTGAICmzQ0Apc0NABjGAICrbQwAqm0MABzGAIAgxgCArz0MAK49DACtVQwArFUMAKgJDgCpCQ4Aqh0OAKsVDgCsIQ4ArSEOAK4hDgCvIQ4AJMYAgCjGAIAsxgCAMMYAgDTGAIA4xgCAPMYAgEDGAIC4zQEAudUBALrdAQC71QEAvM0BAL1RAQC+UQEAv1EBALAhDgCxIQ4AsiUOALM5DgC0KQ4AtRUOALYdDgC39QEARMYAgEjGAIBMxgCAo5kNAFDGAIClpQ0Apq0NAL7cAgCE7AMAWMYAgKrpDQCr6Q0ArP0NAK3hDQCu4Q0Ar90NAIBFAACBTQAAglkAAKNFAwBcxgCApUEDAKZBAwBgxgCAhsAEAIcAAwCqLQMAqyUDAKw9AwCtJQMAriUDAK8VAwCoWQIAqYUDAKqBAwCrgQMArIUDAK2NAwCusQMAr7EDAGTGAIBoxgCAbMYAgHDGAIB0xgCAeMYAgHzGAICAxgCAuGUDALltAwC6ZQMAu30DALxlAwC9bQMAvmUDAL/dAACwpQMAsa0DALKlAwCzvQMAtK0DALWdAwC2lQMAt10DALMJAgCExgCAiMYAgIzGAICQxgCAtg0CALUNAgCUxgCAu2kCALphAgCYxgCAnMYAgL9ZAgC+aQIAvWkCALxxAgCgxgCApMYAgKjGAICsxgCA4aABALDGAIDjaAMAtMYAgIEVAACAFQAA74wDAIIVAAC4xgCAvMYAgMDGAIC+cAUA4RgOAOGUDwDjOA8A49QPAISUAgDIxgCAzMYAgNDGAIDUxgCA2MYAgNzGAIDgxgCA5MYAgOjGAIDv7AEA7/gPAIZgBACHBAUAs5UBAITMBQC1dQEA7MYAgPDGAIC2dQEA9MYAgPjGAIC7UQEAulkBAL31AAC8SQEAv/UAAL71AACoJQYAqVUGAKpVBgCrrQYArLUGAK29BgCutQYAr60GAMTGAID8xgCAAMcAgATHAIAIxwCADMcAgBDHAIAUxwCAuGkHALlpBwC6CQcAuwkHALwZBwC9GQcAvg0HAL8BBwCw1QYAsd0GALLVBgCzaQcAtHkHALV5BwC2aQcAt2EHAKPdBgAYxwCAHMcAgCDHAIAkxwCApj0GAKU9BgAoxwCAqxkGAKoRBgAsxwCAMMcAgK+9BwCuvQcArb0HAKwBBgCAXQAAgW0AAIJlAACzUQcAvtgDALVxBwC2cQcANMcAgIbgAACHFAMAul0HALs5BwC8KQcAvRUHAL4dBwC/2QAAqJUGAKmdBgCqlQYAq60GAKy1BgCtvQYArrUGAK+tBgA4xwCAPMcAgEDHAIBExwCASMcAgEzHAIBQxwCAVMcAgLhxAQC5cQEAunEBALtxAQC81QEAvd0BAL7VAQC/zQEAsNUGALGxBgCysQYAs40GALSVBgC1UQEAtlEBALdRAQBYxwCAoxkGAFzHAIBgxwCApjkGAFTGAIBkxwCApTkGAKoVBgCrcQYAaMcAgGzHAICuVQYAr5EBAKxhBgCtXQYAcMcAgHTHAIB4xwCAfMcAgIDHAICExwCAiMcAgIzHAICQxwCAlMcAgJjHAICcxwCAgBkAAIEZAACCBQAAoMcAgISAAgC+gAMAhwwDAIasHADhaAYAqMcAgOOYBwCsxwCAsMcAgLTHAIDvrAcAuMcAgLzHAIDAxwCAxMcAgMjHAIDMxwCA0McAgNTHAICzZQMA2McAgLVlAwC2bQMA3McAgODHAIDkxwCAuukDALvlAwC8/QMAve0DAL7RAwC/0QMA6McAgOzHAIDwxwCA9McAgPjHAID8xwCAAMgAgATIAICogQMAqYEDAKqBAwCrgQMArIEDAK2BAwCugQMAr4EDALBBAwCxTQMAskUDALNVAwC0eQMAtXkDALYZAwC3GQMAuCkDALkpAwC6OQMAuzkDALwpAwC9KQMAvhkDAL8ZAwCBGQAAgBEAAKMhAgCCLQAApSECAAjIAIAMyACApikCABDIAIAYyACAq6ECAKqtAgCtqQIArLkCAK+VAgCulQIAhEwCAL5IHQCHZB0AhuwcAONAAwAcyACA4aABACDIAIDvnAMAJMgAgCjIAIAsyACAMMgAgDTIAIA4yACAPMgAgEDIAIBEyACASMgAgEzIAIBQyACAVMgAgFjIAIDvtAEAhKgdAOF8BgBcyACA43AGAGDIAIBkyACAaMgAgGzIAICz4QEAcMgAgHTIAIB4yACAfMgAgLblAQC19QEAgMgAgLuhAQC62QEAvuQcAIjIAIC/rQEAvqUBAL2xAQC8uQEAqBUeAKkZHgCqKR4AqykeAKw9HgCtJR4Ari0eAK8lHgAUyACAgvkfAIH5HwCA4R8AhMgAgIzIAICGHAAAh7ADALjBHgC5wR4AusEeALvBHgC8wR4AvcEeAL7BHgC/wR4AsF0eALElHgCyLR4AsyUeALQhHgC1KR4AthkeALcZHgCjoR4AkMgAgJTIAICYyACAnMgAgKalHgCltR4AoMgAgKvhHgCqmR4ApMgAgKjIAICv7R4AruUeAK3xHgCs+R4ArMgAgLOZHwCwyACAtMgAgLa9HwC4yACAvMgAgLW1HwC6mR8Au5kfAMDIAIDEyACAvnkfAL95HwC8eR8AvXkfAKglHgCpUR4AqlUeAKtpHgCseR4ArXkeAK5pHgCvaR4AyMgAgMzIAIDQyACA1MgAgNjIAIDcyACA4MgAgOTIAIC42R4Aue0eALr5HgC7+R4AvOkeAL3pHgC+nR4Av5UeALAZHgCxGR4AsukeALPpHgC0+R4AtfkeALbpHgC36R4Ao90eAIIpAACBFQAAgB0AAOjIAICm+R4ApfEeAOzIAICr3R4Aqt0eAKTHAIDwyACArz0eAK49HgCtPR4ArD0eAITIAgCzQQEAvgwBAPjIAIC2QQEA/MgAgADJAIC1UQEAuk0BALslAQCGSAAAh1ABAL4lAQC/LQEAvDEBAL0xAQAEyQCACMkAgIQEAwC+gAQADMkAgO+oHwAQyQCAFMkAgL8oMQDjdB8AGMkAgOE4HgAcyQCAIMkAgCTJAIAoyQCALMkAgDDJAICjzQIANMkAgKXdAgA4yQCAPMkAgKbNAgBAyQCARMkAgKupAgCqwQIArb0CAKy9AgCvoQIArqkCAKm1AgCoaR0AqwECAKoJAgCtAQIArBkCAK8xAgCuAQIAhGwFAEjJAIBMyQCAUMkAgFTJAICCnQEAgZ0BAICdAQC55QMAuOUDALvlAwC65QMAveUDALzlAwC/5QMAvuUDALEhAgCwSQIAsyUCALIlAgC1KQIAtCECALcVAgC2FQIAqM0CAKnRAgCq0QIAqw0BAKwVAQCtBQEArgEBAK8BAQBYyQCAXMkAgGDJAIBoyQCAvvgEAGzJAIBwyQCAdMkAgLgVAQC5HQEAuikBALspAQC89QEAvf0BAL71AQC/7QEAsEkBALFVAQCyXQEAs1UBALRNAQC1NQEAtj0BALcxAQCGoAUAh8gFAHjJAIDvvAAAfMkAgIDJAICEyQCA74weAIQsBwDh8B4AiMkAgOMcHgCMyQCA4ZQBAJDJAIDjbAAAsxkCAJTJAICYyQCAnMkAgIQACAC2xQEAtd0BAKDJAIC70QEAus0BAKTJAICoyQCAv7EBAL7JAQC9wQEAvMkBAKPZBQBkyQCArMkAgLDJAIC0yQCApgUGAKUdBgC4yQCAqxEGAKoNBgC8yQCAwMkAgK9xBgCuCQYArQEGAKwJBgDEyQCAgh0AAIEdAACAHQAAyMkAgMzJAIDQyQCA1MkAgIZAAwCHxAMA2MkAgNzJAIDgyQCA5MkAgOjJAIDsyQCAqK0HAKmxBwCqsQcAq7EHAKwZBwCtBQcArg0HAK8FBwDwyQCA9MkAgPjJAID8yQCAAMoAgATKAIAIygCADMoAgLgtBwC5zQAAusUAALvdAAC8zQAAvf0AAL71AAC/nQAAsEkHALFVBwCyUQcAsykHALQ5BwC1OQcAtiUHALcVBwCzOQYAEMoAgBTKAIAYygCAHMoAgLaFBgC1kQYAIMoAgLuRBgC6jQYAJMoAgCjKAIC//QYAvv0GAL39BgC8hQYALMoAgKN9BgAwygCANMoAgKbBBgA4ygCAPMoAgKXVBgCqyQYAq9UGAEDKAIC+bAEArrkGAK+5BgCswQYArbkGAKjpAQCp6QEAqvkBAKv5AQCs6QEArekBAK45AQCvOQEAgPUAAIH9AACCwQAARMoAgIYQAACHdAEASMoAgPTIAIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+kQAAv5EAALBJAQCxSQEAslkBALNZAQC0SQEAtUkBALb9AAC39QAA7/QGAEzKAIBQygCAVMoAgO8wAgBYygCAXMoAgGDKAIDj4AcAZMoAgOGAAQBoygCA4ygGAGzKAIDhyAUAcMoAgLMxAgB0ygCAeMoAgJYAAAB8ygCAtikCALUhAgCAygCAu80CALrNAgCEygCAiMoAgL/NAgC+zQIAvc0CALzNAgCMygCAkMoAgJTKAICj/QIAmMoAgKXtAgCm5QIAnMoAgKDKAICkygCAqgECAKsBAgCsAQIArQECAK4BAgCvAQIAgA0AAIEVAACCHQAAqMoAgKzKAICwygCAvlQMALjKAICGwAwAhyQDALzKAIDAygCAxMoAgMjKAIDMygCA0MoAgKi5AgCpAQEAqgEBAKsBAQCsBQEArQ0BAK4FAQCvOQEAhKgNANTKAIDYygCA3MoAgODKAIDkygCA6MoAgOzKAIC4LQEAucUBALrNAQC7xQEAvMEBAL3JAQC++QEAv/kBALBNAQCxUQEAslUBALMpAQC0OQEAtSUBALYlAQC3FQEA4RgGAPDKAIDjOAcA9MoAgPjKAIC+WAwA/MoAgADLAICEbA8ABMsAgL5gDwAIywCADMsAgBDLAIDvcAYAFMsAgIAVAACBGQAAgi0AAITMDwDjYAYAGMsAgOGgAQAcywCA73QAACDLAICGyAwAh/wMACjLAIAsywCAMMsAgDTLAICjCQ4AtMoAgCTLAIA4ywCAPMsAgKYNDgClDQ4AQMsAgKsVDgCqCQ4ARMsAgEjLAICvYQ4Arn0OAK19DgCsAQ4ATMsAgLOpDgBQywCAVMsAgLapDgBYywCAXMsAgLWpDgC6SQ8Au0kPAGDLAIBkywCAvkkPAL9JDwC8SQ8AvUkPAKhdDgCpbQ4AqmUOAKt9DgCsZQ4ArW0OAK5lDgCvuQ8AaMsAgGzLAIBwywCAdMsAgHjLAIB8ywCAgMsAgITLAIC4UQ8AuV0PALpVDwC7aQ8AvH0PAL1lDwC+bQ8Av2EPALDJDwCxyQ8AstkPALPZDwC0yQ8AtckPALZ9DwC3cQ8AiMsAgLURDwC2EQ8AjMsAgIARAACBGQAAgikAALMVDwC8HQ8AvWEPAL5hDwC/fQ8AkMsAgJTLAIC6FQ8AuwkPAKOtDwCYywCAhugAAIfIAQCcywCApq0PAKWtDwCgywCAq00OAKpNDgCkywCAqMsAgK9NDgCuTQ4ArU0OAKxNDgCocQ4AqXEOAKpxDgCrcQ4ArJ0BAK2FAQCuhQEAr7UBAL7sAACsywCAsMsAgLTLAIC4ywCAvMsAgMDLAIDEywCAuGEBALlhAQC6YQEAu2EBALxhAQC9YQEAvmEBAL9hAQCwzQEAsaUBALKhAQCzoQEAtKUBALWtAQC2kQEAt5EBALP5DQDIywCAzMsAgNDLAIDUywCAtgUCALUVAgDYywCAu2ECALoJAgDcywCA4MsAgL9pAgC+YQIAvXUCALx1AgDkywCAo70NAOjLAIDsywCApkECAPDLAID0ywCApVECAKpNAgCrJQIA+MsAgPzLAICuJQIAry0CAKwxAgCtMQIAge0AAIDtAADv0AEAgh0AAADMAIAIzACAhjgEAIdQAwAMzACAEMwAgBTMAIAYzACA4eABABzMAIDjZA8AIMwAgCTMAIAozACALMwAgLORAwAwzACAtbkDALZ9AwA0zACAOMwAgDzMAIC6WQMAu1kDALxJAwC9SQMAvv0AAL/1AACoRQIAqVUCAKpVAgCrZQIArH0CAK2xAgCusQIAr7ECAL5oBQBAzACARMwAgEjMAIBMzACAUMwAgFTMAIBYzACAuF0BALltAQC6ZQEAuw0BALwZAQC9GQEAvg0BAL8FAQCw0QIAsdECALLRAgCz0QIAtHUBALV9AQC2dQEAt20BAOF4DwDjNA4A47gOAOF8DgBczACAYMwAgGTMAIBozACAbMwAgHDMAIB4zACAfMwAgIDMAIDv5A4A79QOAITMAICjnQIAgmEAAIFpAACAUQAAhJwFAKZxAgCltQIAiMwAgKtVAgCqVQIAhkgEAIfMBACv+QEArvEBAK1FAgCsRQIAqJUGAKmlBgCqrQYAq6UGAKy9BgCtoQYArqUGAK/dBgB0zACAjMwAgJDMAICUzACAmMwAgJzMAICgzACApMwAgLhtBwC5dQcAun0HALt1BwC8bQcAvcUHAL7NBwC/xQcAsKUGALGtBgCyuQYAs7EGALSRBgC1kQYAtl0HALdVBwCzJQYAqMwAgKzMAICwzACAtMwAgLYhBgC1NQYAuMwAgLtpBgC6YQYAvMwAgMDMAIC/VQYAvlUGAL1lBgC8bQYAxMwAgKNhBgDIzACAzMwAgKZlBgDQzACA1MwAgKVxBgCqJQYAqy0GANjMAIDczACArhEGAK8RBgCsKQYArSEGAKipBgCpqQYAqrkGAKuxBgCszQYArTEBAK4xAQCvMQEAgMkBAIHJAQCCBQAA4MwAgL54AgCEeAIA5MwAgOjMAIC43QEAue0BALrlAQC7jQEAvJkBAL2ZAQC+jQEAv4UBALBRAQCxUQEAslEBALNRAQC09QEAtf0BALb1AQC37QEAszEGAOzMAICGKAAAh9wBAPDMAIC2sQEAtUUGAPTMAIC7lQEAupUBAPjMAID8zACAvzkBAL4xAQC9hQEAvIUBAATMAICjdQYAAM0AgATNAICm9QEACM0AgAzNAIClAQYAqtEBAKvRAQAQzQCAFM0AgK51AQCvfQEArMEBAK3BAQAYzQCAHM0AgCDNAIAkzQCAKM0AgCzNAIAwzQCANM0AgDjNAIA8zQCAQM0AgETNAIBIzQCATM0AgFDNAIC+cAMAhQA8AOHEBgCERAIA44wHAIBhAACBYQAAgmEAAO9oAwCFRDwA4RACAFjNAIDj2CsAhlA9AIf0AwBczQCA76QHAGDNAIDvQAIAZM0AgGjNAIBszQCAcM0AgHTNAIB4zQCAhDw8AHzNAICAzQCAhM0AgIjNAIDj7AIAjM0AgOEsAQCzUQMAkM0AgJTNAICYzQCAnM0AgLZ5AwC1cQMAoM0AgLs5AwC6MQMApM0AgKjNAIC/9QAAvvUAAL0VAwC8FQMAqD0CAKmBAgCqmQIAq5ECAKy5AgCtuQIArtECAK/RAgCEqD8Avqg/AKzNAICwzQCAtM0AgLjNAIC8zQCAwM0AgLhRAQC5UQEAulEBALtRAQC8cQEAvXEBAL5xAQC/cQEAsLUCALG9AgCygQIAs4ECALRxAQC1cQEAtnEBALdxAQCAtQAAgb0AAIK1AADIzQCAhrA/AIfgPADMzQCA71QAAL4sPgDhVAYA0M0AgOOIAADUzQCA2M0AgNzNAIDgzQCAo1ECAOTNAIC/2CYA6M0AgOzNAICmeQIApXECAPDNAICrOQIAqjECAPTNAID4zQCAr/UBAK71AQCtFQIArBUCAJAtJACRBSgAkg0oAJPZKACUhS0AlTUsAJbFLACXtTEAmAEwAJkVMACalTUAmyk0AJxtNACdmTUAnj04AJ81OABUzQCAttU+ALXFPgDEzQCAs9E+APzNAIAAzgCABM4AgL/ZPgC+1T4AvcU+ALzFPgC71T4Auuk+AAjOAICPXSQAqeUJAKgVCACrBQwAqg0MAK0BEACsAQwAr0EQAK69EACh4QAADM4AgKMBBACi4QAApZ0EAKSVBACnuQgApgEIAKD1OQChBT0Aouk8AKP1PQAQzgCAFM4AgBjOAIAczgCAscEUALABFACzARgAsn0UALXVGAC01RgAIM4AgCTOAICCISUAgyklACjOAIAszgCAhsUpAIeBLACEGSkAhRkpAIoBLQCL+S0AMM4AgDjOAICOATEAj4k0AIyRMACNHTEAkkU1AJMZNQCG6AcAh+wBAJZZOQCXYTgAlPU0AJVZOQCaoTwAm0U9ADzOAIBAzgCAgX0AAIB9AACcQTwAglUAAKjpPwCp/T8Aqgk/AKsFPwCsHT8ArQU/AK4NPwCvBT8ARM4AgEjOAIBMzgCAUM4AgFTOAIBYzgCAXM4AgGDOAIC4DT8AuRU/ALoVPwC7JT8AvD0/AL39PgC+9T4Av+0+ALB9PwCxQT8AskE/ALNBPwC0QT8AtU0/ALY9PwC3NT8Ao4E8AGTOAIBozgCAbM4AgHDOAICmhTwApZU8AHTOAICrhTwAqrk8AHjOAIB8zgCAr4k8AK6FPACtlTwArJU8AITIAwCz7T0AgM4AgITOAIC26T0AiM4AgIzOAIC16T0Auq09ALu1PQCQzgCAlM4AgL6dPQC/IQIAvKU9AL2VPQCoDT0AqR09AKohPQCrPT0ArCU9AK0tPQCuJT0Ar1k9AIANAACBFQAAgh0AAJjOAICczgCAoM4AgKjOAIC+uAMAuLkCALlhAgC6GQIAuxkCALwJAgC9CQIAviECAL8hAgCwLT0AsTU9ALI1PQCzBT0AtB09ALWhAgC2oQIAt6ECAKOpPACszgCAhigFAIfsAgCwzgCApq08AKWtPAC0zgCAq/E8AKrpPAC4zgCAvM4AgK9lAwCu2TwArdE8AKzhPADAzgCAsykCAMTOAIDIzgCAtvkCAMzOAIDQzgCAtfkCALrVAgC73QIA1M4AgNjOAIC+eQEAv3kBALzFAgC9eQEA3M4AgODOAICj5QIA5M4AgKU1AgDozgCA7M4AgKY1AgDwzgCA9M4AgKsRAgCqGQIArbUBAKwJAgCvtQEArrUBAOPwPgDhrD8A4UA+AON8PwD4zgCA/M4AgADPAIAEzwCAgA0AAIERAACCEQAACM8AgO+oPgAMzwCAEM8AgO8gPgCoLQUAqW0FAKplBQCrrQUArLUFAK29BQCutQUAr60FAKTOAICE6AMAvuADABTPAICGEAMAh5gDABjPAIAczwCAuGkGALlpBgC6AQYAuwEGALwFBgC9DQYAvjEGAL8xBgCw1QUAsd0FALLVBQCzaQYAtHkGALV5BgC2aQYAt2EGAKg5BgCpgQcAqpkHAKuRBwCsuQcArbkHAK7ZBwCv1QcAIM8AgCTPAIA0zgCAKM8AgCzPAIAwzwCANM8AgDjPAIC4VQcAuV0HALppBwC7aQcAvAEHAL0BBwC+AQcAvwEHALCtBwCxsQcAsrEHALOFBwC0nQcAtXUHALZ9BwC3cQcAsxEGADzPAIBAzwCARM8AgEjPAIC2OQYAtTEGAEzPAIC7dQYAumkGAFDPAIBUzwCAv7EGAL5ZBgC9UQYAvGUGAFjPAICjVQYAXM8AgGDPAICmfQYAZM8AgGjPAICldQYAqi0GAKsxBgBszwCAcM8AgK4dBgCv9QYArCEGAK0VBgCouQEAqbkBAKopAQCrKQEArD0BAK0lAQCuLQEAryUBAHTPAICCHQAAgR0AAIAdAAB4zwCAfM8AgIDPAIC+cAEAuIEAALmNAAC6hQAAu5kAALyJAAC9vQAAvrUAAL99AACwXQEAseEAALLhAACz4QAAtOEAALXpAAC20QAAt9EAAITIAgCzpQIAhzgDAIYoAgC2oQIAiM8AgIzPAIC1sQIAup0CALshAwC+bAMAkM8AgL4hAwC/KQMAvDEDAL0xAwCj4QIAlM8AgJjPAICczwCAoM8AgKblAgCl9QIApM8AgKtlAwCq2QIAqM8AgKzPAICvbQMArmUDAK11AwCsdQMAqZkAAKiRAACrzQAAqqEAAK3dAACs3QAAr8UAAK7NAAC+LA0AsM8AgLTPAIC4zwCAvM8AgMDPAIDEzwCAyM8AgLnBAQC4eQAAu8EBALrJAQC9wQEAvNkBAL/FAQC+xQEAsY0AALCNAACzQQAAskkAALVBAAC0WQAAt0EAALZJAADMzwCA0M8AgNTPAIDYzwCA3M8AgO9QBwDgzwCA5M8AgL74DwDjdAcA6M8AgOF8BACAGQAAgQkAAIJ5AADszwCA8M8AgLNpAQD4zwCAhMQCALYdAQD8zwCAANAAgLUVAQC6CQEAuwkBAIboDQCH6A0Avt0BAL/FAQC83QEAvdUBAATQAIAI0ACADNAAgBDQAIDv1AAAFNAAgBjQAIDvTAEA47ADAOG0BgDhgAEA45gBABzQAIAg0ACAJNAAgCjQAIAs0ACAMNAAgKPlAQCEwA0ApZkBADTQAIA40ACAppEBADzQAIBA0ACAq4UBAKqFAQCtWQEArFEBAK9JAQCuUQEA9M8AgETQAIBI0ACATNAAgFDQAIBU0ACAWNAAgFzQAICoaQ8AqXEPAKpxDwCrrQ8ArLUPAK29DwCutQ8Ar6kPALDZDwCx9Q8Asv0PALP1DwC07Q8AtZUPALadDwC3iQ8AuLkPALmFDwC6jQ8Au2kAALx5AAC9eQAAvmkAAL9pAACBnQAAgJ0AAGDQAICCBQAAZNAAgGjQAIBs0ACAcNAAgIaAAwCH9AMAdNAAgHjQAIB80ACAgNAAgITQAICEzwCAs5kPAIjQAICM0ACAkNAAgJTQAIC2XQ8AtV0PAJjQAIC7UQ8Aun0PAJzQAICg0ACAvzEPAL5JDwC9QQ8AvEkPAKNZDgCk0ACAqNAAgKzQAICw0ACApp0OAKWdDgC00ACAq5EOAKq9DgC40ACAvNAAgK/xDgCuiQ4ArYEOAKyJDgDA0ACAxNAAgMjQAIDM0ACAgBkAAIEZAACCBQAA0NAAgISgAQDU0ACAh+gBAIYABADY0ACA3NAAgODQAIDk0ACAqBUBAKkdAQCqFQEAqyUBAKw9AQCtJQEAri0BAK8lAQDo0ACA7NAAgPDQAID00ACA+NAAgPzQAIAA0QCABNEAgLjJAAC5yQAAutkAALvRAAC8+QAAvfkAAL6ZAAC/mQAAsCUBALEtAQCyJQEAsz0BALQtAQC1HQEAthUBALf5AAAI0QCADNEAgBDRAICzkQIAFNEAgLW5AgC2qQIAGNEAgBzRAIAg0QCAuu0CALvlAgC8/QIAveUCAL7lAgC/1QIApvECACTRAIAo0QCApeECACzRAICjyQIAMNEAgDTRAICuvQIAr40CAKylAgCtvQIAqrUCAKu9AgA40QCAPNEAgID5AACB+QAAggUAAEDRAIC+yAMAhBgDAEjRAIBM0QCAUNEAgFTRAIBY0QCAXNEAgGDRAIBk0QCAhhgEAIecAwBo0QCAbNEAgHDRAIB00QCAeNEAgHzRAIDvsAIAgNEAgOGUAQCE0QCA42wCAIjRAICM0QCAkNEAgJTRAICY0QCA79APAJzRAICg0QCApNEAgKjRAIDhrAEArNEAgONsAACAMQAAgT0AAIIdAADv9A4A42wOALDRAIDhLA8AvnAFALM5AgCEDAUAhugEAIdgBQDcAAAAtvECALX5AgC40QCAu9UCALrVAgC80QCAwNEAgL91AQC+dQEAvcUCALzFAgDE0QCA4fQOAMjRAIDjUA4AzNEAgNDRAIDU0QCA2NEAgNzRAIDg0QCA5NEAgOjRAIDs0QCA8NEAgPTRAIDv5A8ApmUCAPjRAID80QCApW0CAADSAICjrQIABNIAgAjSAICu4QEAr+EBAKxRAgCtUQIAqkECAKtBAgAM0gCAENIAgKiZBgCpmQYAqqkGAKupBgCsuQYArbkGAK6pBgCvqQYAFNIAgIIdAACBHQAAgB0AABjSAIAc0gCAINIAgL50AwC4rQYAubUGALq9BgC7tQYAvK0GAL1RBwC+UQcAv1EHALChBgCxoQYAsqEGALOhBgC0oQYAtaEGALalBgC3mQYARNEAgLMlBgCExAMAtNEAgLY9BgAk0gCAKNIAgLU1BgC6YQYAu2EGAIYIAACHiAAAvmEGAL9hBgC8cQYAvXEGAKNhBgAs0gCAMNIAgDTSAIA40gCApnkGAKVxBgA80gCAqyUGAKolBgBA0gCARNIAgK8lBgCuJQYArTUGAKw1BgCoXQYAqW0GAKplBgCrjQYArJkGAK2FBgCujQYAr4UGAEjSAIBM0gCAUNIAgFTSAIBY0gCAXNIAgGDSAIBk0gCAuIUGALmNBgC6mQYAu5UGALyNBgC9rQYAvqUGAL99AQCw/QYAscUGALLNBgCzxQYAtN0GALXFBgC2zQYAt8UGALPtBgBo0gCAbNIAgHDSAIB00gCAtgUGALURBgB40gCAuwEGALo5BgB80gCAgNIAgL8BBgC+GQYAvREGALwZBgCE0gCAo6kGAIjSAICM0gCApkEGAJDSAICElAEApVUGAKp9BgCrRQYAvqABAJjSAICuXQYAr0UGAKxdBgCtVQYAqJkCAKnBAgCqwQIAq8ECAKzBAgCtyQIArvECAK/xAgCB7QMAgO0DAJzSAICC+QMAhpAcAId0AwCg0gCApNIAgLjFAwC5zQMAusUDALvdAwC8zQMAvf0DAL71AwC/nQMAsEEDALFBAwCyQQMAs0EDALRBAwC1QQMAtkEDALdBAwCzSQIAqNIAgKzSAICw0gCAtNIAgLZJAgC1SQIAuNIAgLuFAwC6hQMAvNIAgMDSAIC/hQMAvoUDAL2VAwC8lQMAxNIAgKMNAgDI0gCAzNIAgKYNAgDQ0gCA1NIAgKUNAgCqwQMAq8EDANjSAIDc0gCArsEDAK/BAwCs0QMArdEDAOOYAQDhpAcA4VgGAONYBgDhoAEA4NIAgOPQAADk0gCA6NIAgOzSAIDvOAAA8NIAgO/0AQD00gCA+NIAgO/4BgCAeQAAgRUAAIIdAACEAB0A/NIAgADTAIC+EB0ACNMAgIbAHACHrB0ADNMAgBDTAIAU0wCAGNMAgBzTAIAg0wCAu8UFALqhBQC5qQUAuJEFAL/NBQC+zQUAvckFALzVBQCzHQYAsh0GALEdBgCwHQYAt6EFALa9BQC1vQUAtL0FAKu9BgCqvQYAqb0GAKi9BgCvfQYArn0GAK19BgCsfQYAJNMAgCjTAIAs0wCAMNMAgDTTAIA40wCAPNMAgEDTAICo7R0AqS0eAKoxHgCrMR4ArJUeAK2dHgCulR4Ar40eAATTAIBE0wCASNMAgEzTAIBQ0wCAVNMAgFjTAIBc0wCAuKkeALmpHgC6XR8Au1EfALxxHwC9cR8AvnUfAL9pHwCw/R4Asc0eALLFHgCzrR4AtLkeALW5HgC2rR4At6UeALO5HgBg0wCAZNMAgGjTAICU0gCAth0eALUdHgBs0wCAuwkeALo5HgBw0wCAhOADAL99HgC+fR4AvXkeALwRHgCCaQAAo/0eAIBFAACBUQAAplkeAL6cAwB00wCApVkeAKp9HgCrTR4AhkgAAIdsAACuOR4ArzkeAKxVHgCtPR4AqF0eAKltHgCqZR4Aq30eAKxlHgCtbR4ArmUeAK/9HgB40wCAfNMAgIDTAICE0wCAiNMAgIzTAICQ0wCAlNMAgLhpAQC5aQEAunkBALt5AQC8aQEAvWkBAL7dAQC/1QEAsIUeALGNHgCyhR4As50eALSFHgC1jR4AtoUeALdZAQCz7R4AmNMAgJzTAICg0wCApNMAgLbtHgC17R4AqNMAgLtJHgC6QR4ArNMAgLDTAIC/SR4AvkEeAL1JHgC8UR4AtNMAgKOpHgC40wCAvNMAgKapHgDA0wCAxNMAgKWpHgCqBR4Aqw0eAMjTAIDM0wCArgUeAK8NHgCsFR4ArQ0eAKghAwCpIQMAqiEDAKshAwCsIQMArSEDAK4hAwCvIQMA0NMAgNTTAIDY0wCAvmACANzTAIDg0wCA6NMAgOzTAIC4iQMAuYkDALqdAwC7lQMAvLkDAL25AwC+eQAAv3kAALDlAwCx7QMAsuUDALP9AwC07QMAtd0DALbVAwC3vQMAgKkAAIG1AACCvQAAs6UDAPDTAIC1pQMAtq0DAPTTAICE4AIA+NMAgLotAwC7JQMAvD0DAL0lAwC+JQMAvxUDAKPpAwD80wCAhmgEAIeAAwAA1ACApuEDAKXpAwAE1ACAq2kDAKphAwAI1ACADNQAgK9ZAwCuaQMArWkDAKxxAwAQ1ACAFNQAgBjUAIAc1ACAINQAgOE8HwAk1ACA40AeACjUAIAs1ACAMNQAgO+MHgA01ACAONQAgDzUAIBA1ACARNQAgIIlAACBEQAAgB0AAEjUAIDj5AMATNQAgOGsAQBQ1ACA77ADAIRkAgC+YAUAhtAEAIdEBQBY1ACAXNQAgGDUAIBk1ACAaNQAgGzUAIBw1ACAdNQAgHjUAIDvsAEAhKQFAOHcHgB81ACA4xABAIDUAICE1ACAiNQAgIzUAICzUQEAkNQAgJTUAICY1ACAnNQAgLYRAQC1fQEAoNQAgLsNAQC6DQEApNQAgKjUAIC//QAAvv0AAL39AAC8/QAAqDkGAKk5BgCqmQYAq5EGAKy1BgCt0QYArskGAK/BBgBU1ACArNQAgLDUAIC01ACAgA0AAIGxAACCsQAAuNQAgLhhBwC5YQcAumEHALt9BwC8ZQcAvW0HAL5lBwC/HQcAsIkGALGJBgCyaQcAs2kHALR5BwC1eQcAtmkHALdlBwCjEQYAvNQAgMDUAIC+gAMAxNQAgKZRBgClPQYAyNQAgKtNBgCqTQYAhggAAId8AwCvvQcArr0HAK29BwCsvQcAzNQAgNDUAICzSQcA1NQAgLVZBwDY1ACA3NQAgLZRBwDg1ACA5NMAgLtBBwC6dQcAvUUHALxFBwC/RQcAvkUHAKh5BgCpeQYAqokGAKuJBgCsmQYArZkGAK6JBgCviQYA5NQAgOjUAIDs1ACA8NQAgPTUAID41ACA/NQAgADVAIC4jQYAuZUGALqVBgC7pQYAvL0GAL1xAQC+cQEAv3EBALD5BgCxzQYAstkGALPZBgC0yQYAtckGALa9BgC3tQYAowEGAATVAIAI1QCADNUAgBDVAICmGQYApREGABTVAICrCQYAqj0GABjVAIAc1QCArw0GAK4NBgCtDQYArA0GACDVAIAk1QCAKNUAgCzVAICAGQAAgRkAAIIFAAAw1QCAhKwBAL6sAQCH6AAAhkwPADjVAIA81QCAQNUAgETVAIConQIAqcUCAKrNAgCrwQIArMUCAK3NAgCu+QIArz0DAEjVAIBM1QCAUNUAgFTVAIC+PAwAWNUAgFzVAIBg1QCAuMkDALnJAwC62QMAu9EDALz5AwC9+QMAvpkDAL+ZAwCwRQMAsU0DALJFAwCzXQMAtEUDALVNAwC2RQMAt/kDALNFAgBk1QCAaNUAgGzVAIBw1QCAtk0CALVNAgB01QCAu4kDALqBAwB41QCAfNUAgL+JAwC+gQMAvYkDALyRAwCA1QCAowECAITVAICI1QCApgkCAIzVAICQ1QCApQkCAKrFAwCrzQMAlNUAgJjVAICuxQMAr80DAKzVAwCtzQMAgO0BAIEVAACCEQAAhAACAJzVAIDhpAEAoNUAgOPsAACo1QCArNUAgLDVAIDvMAAAtNUAgLjVAIC81QCAwNUAgIbgDACH9AIAxNUAgMjVAIDM1QCA0NUAgO/MBgDU1QCA4bAHANjVAIDjEAYA3NUAgODVAIDk1QCA6NUAgOzVAIDw1QCA9NUAgPjVAID81QCAANYAgATWAIAI1gCA7+gBAIUYDwDhzAYADNYAgOMcBgCAKQAAgR0AAIIFAAAQ1gCAszkCAITMDQCGaA8Ah/wMAOHQ0gO28QEAtfkBABjWAIC72QEAutEBAL7kDAAc1gCAv30BAL59AQC9fQEAvMEBAKjxDQCp8Q0AqvENAKvxDQCsMQ4ArTEOAK4xDgCvMQ4ApNUAgBTWAIAg1gCAJNYAgCjWAIAs1gCAMNYAgDTWAIC46Q4AuekOALqJDgC7hQ4AvJ0OAL2BDgC+gQ4Av7UOALBVDgCxXQ4AslUOALPpDgC0+Q4AtfkOALbpDgC34Q4Ao3kNADjWAIA81gCAQNYAgETWAICmsQ4ApbkOAEjWAICrmQ4AqpEOAEzWAIBQ1gCArz0OAK49DgCtPQ4ArIEOAFTWAICz7Q8AWNYAgFzWAIC26Q8AYNYAgGTWAIC16Q8Auq0PALu1DwA01QCAaNYAgL6VDwC/mQ8AvK0PAL2hDwCoIQ4AqSEOAKohDgCrPQ4ArCUOAK0tDgCuJQ4Ar1UOAGzWAIBw1gCAdNYAgHjWAICAHQAAgQkAAIK9AAB81gCAuDkOALk5DgC6yQ4Au8kOALzZDgC92Q4AvskOAL/JDgCwLQ4AsTUOALI9DgCzMQ4AtBUOALUZDgC2CQ4AtwkOAKOpDgCA1gCAhIACAL6AAQCFAAQApq0OAKWtDgCI1gCAq/EOAKrpDgCGKAcAhxgAAK/dDgCu0Q4AreUOAKzpDgCM1gCAs+0BAJDWAICU1gCAtuUBAJjWAICc1gCAte0BALplAQC7bQEAoNYAgKTWAIC+bQEAv10BALx1AQC9bQEAqN0NAKnpDQCqIQIAqyECAKwhAgCtIQIAriECAK8hAgCo1gCArNYAgLDWAIC01gCAohECAKMRAgCgqQ4AodUCALiJAgC5iQIAup0CALuVAgC8vQIAvXUDAL59AwC/dQMAsOUCALHtAgCy5QIAs/0CALTtAgC13QIAttUCALe9AgCjqQIAj8UaALjWAIC81gCAwNYAgKahAgClqQIAxNYAgKspAgCqIQIAyNYAgMzWAICvGQIArikCAK0pAgCsMQIAniUOAJ/lDgCc6QoAnRUKAJpFFgCbRQoAmFkWAJlRFgCWcRIAl4ETAJRVEgCV7RIAktEeAJPZHgCQtRoAkVUeAISpHwCFJR8AhiUfAIexEwDQ1gCA1NYAgIJZGwCDURsAjEUSAI2lFwCOpRcAj7kXAIA5+wHY1gCAijkTAIutEwCUmQsAlaEPAJZpDwCX3Q8A3NYAgO+cDwCSyQsAk30LAJxFAwDjeA4A4NYAgOGYDADk1gCAhHgCAJqRAwCbXQMA4QQAAL6IBQDj3OoD6NYAgOzWAIDw1gCA7+wAAO+MDgDhcA4A4fwOAOMwAADjeA4AgSEAAIA5AADvtO0DgikAALMJAgD41gCAhmgEAIcsBQD81gCAtg0CALUNAgAA1wCAu8UBALrFAQAE1wCACNcAgL99AQC+fQEAvdUBALzVAQCE1gCA9NYAgAzXAIAQ1wCAFNcAgBjXAIAc1wCAINcAgKi9BQCp5QUAquEFAKvhBQCs5QUAre0FAK7RBQCv0QUAsGEGALFhBgCyYQYAs2EGALTZBgC12QYAtskGALfBBgC4yQYAuckGALp5BwC7eQcAvEUHAL0lBwC+EQcAvw0HAKNJBQAk1wCAKNcAgCzXAIAw1wCApk0FAKVNBQA01wCAq4UGAKqFBgA41wCAPNcAgK89BgCuPQYArZUGAKyVBgBA1wCARNcAgEjXAIBM1wCAUNcAgFTXAIBY1wCAXNcAgIA5AACBOQAAggUAAGDXAIC+uAMAhLgDAGjXAIBs1wCAqMUGAKnVBgCq1QYAq+UGAKz9BgCtHQEArhUBAK8NAQBk1wCAcNcAgIaIAQCHHAEAdNcAgHjXAIB81wCAgNcAgLjpAQC56QEAuokBALuJAQC8mQEAvZkBAL6JAQC/iQEAsHUBALF9AQCydQEAs+kBALT5AQC1+QEAtukBALfhAQCzXQYAhNcAgIjXAICM1wCAhLwBALadAQC1dQYAkNcAgLu5AQC6sQEAlNcAgJjXAIC/PQEAvj0BAL09AQC8oQEAnNcAgKMZBgCg1wCApNcAgKbZAQCo1wCArNcAgKUxBgCq9QEAq/0BALDXAIC01wCArnkBAK95AQCs5QEArXkBAKj5AgCp+QIAqi0DAKs9AwCsJQMArS0DAK4lAwCvmQMAuNcAgLzXAIDA1wCAxNcAgIANAACBsQAAgrEAAMjXAIC4lQMAuZ0DALqhAwC7oQMAvHEAAL1xAAC+cQAAv3EAALDpAwCx6QMAsvUDALPFAwC03QMAtbUDALaxAwC3sQMAvswDAMzXAIDQ1wCA2NcAgNzXAIDg1wCA5NcAgO/kAgDo1wCA4ZQBAOzXAIDjLAEA8NcAgPTXAICHGAMAhhz8A7tNAwC6TQMA+NcAgPzXAIC/EQMAvnkDAL1xAwC8QQMAs8UDAITo/AMA2ACABNgAgAjYAIC2zQMAtc0DAAzYAICkAfwDpSX/A6bZ/wOnAfgDENgAgKEVAwCiHQMAoz0CAKwR9wOtAfADri3zA68B8wOoEfsDqZn7A6oB9AOrHfcDtAHoA7Vl6wO+xPwDhMT8A7AB7AOxVe8Dsk3vA7Nx7gMU2ACAGNgAgBzYAIAg2ACAJNgAgCjYAIAs2ACAMNgAgOFQBgDhNAQA42wBAOPoBgA02ACAONgAgDzYAIBA2ACAgDUAAIE9AACCNQAASNgAgEzYAIBQ2ACA77ABAO/ABgCj5QIAVNgAgIbo/AOHfP0DWNgAgKbtAgCl7QIAXNgAgKttAgCqbQIAYNgAgGTYAICvMQIArlkCAK1RAgCsYQIAqI3+A6mV/gOqnf4Dq5X+A6yx/gOtvf4Drqn+A6+p/gNE2ACAaNgAgGzYAIBw2ACAdNgAgHjYAIB82ACAgNgAgLgl/wO5Lf8DuiX/A7s9/wO8Jf8DvS3/A74l/wO/zf8DsKn+A7Gp/gOygf4Ds4H+A7SB/gO1if4Dtmn/A7cd/wOE2ACA4SD8A4jYAIDjePwDjNgAgJDYAICU2ACAmNgAgJzYAICg2ACApNgAgKjYAICAHQAAgXEAAIJxAADvDP0Ds1X+A6zYAICw2ACAvkAAALTYAIC2ff4DtXn+A7jYAIC7Lf4Dui3+A4boAACHrAAAvw3+A74F/gO9Ff4DvBX+A6OV/wO82ACAwNgAgMTYAIDI2ACApr3/A6W5/wPM2ACAq+3/A6rt/wPQ2ACA1NgAgK/N/wOuxf8DrdX/A6zV/wPY2ACAs/H+A9zYAIDg2ACAto3+A+TYAIDo2ACAtY3+A7pFAQC7TQEA7NgAgPDYAIC+RQEAv00BALxVAQC9TQEAqC3+A6k1/gOqPf4Dq0n+A6xB/gOtSf4DrnH+A69x/gP02ACA+NgAgPzYAIAA2QCABNkAgAjZAIAM2QCAENkAgLhJAQC5VQEAul0BALtVAQC8TQEAvXUBAL59AQC/dQEAsMUBALHNAQCyxQEAs90BALTFAQC1zQEAtsUBALd9AQCjtf0DFNkAgBjZAICExAMAHNkAgKbJ/QOlyf0DINkAgKsJAgCqAQIAKNkAgL7sAgCvCQIArgECAK0JAgCsEQIAgEkAAIFVAACCVQAAo0UDACzZAIClRQMApkUDADDZAICGwAQAhxQDAKopAwCrJQMArD0DAK0hAwCuIQMArxUDADTZAIA42QCAPNkAgEDZAIBE2QCASNkAgEzZAIBQ2QCAqH0CAKmhAwCqoQMAq6EDAKyhAwCtqQMArpEDAK+RAwCwgQMAsY0DALKFAwCzmQMAtIkDALW9AwC2tQMAt30DALhFAwC5TQMAukUDALtdAwC8RQMAvU0DAL5FAwC/+QAA1NcAgLMNAgBU2QCAWNkAgLYNAgBc2QCAYNkAgLUNAgC6YQIAu20CAGTZAIBo2QCAvmkCAL9dAgC8dQIAvWkCAGzZAIBw2QCAdNkAgHjZAIB82QCA4aQBAIDZAIDjQAMAhNkAgIjZAICM2QCA77gDAIAVAACBHQAAggUAAJDZAICEgAIAvsgFAIcYBQCGLAQAmNkAgJzZAICg2QCA76gBAKTZAIDhdP4DqNkAgOPw/gOs2QCAsNkAgLTZAIC42QCAvNkAgMDZAIDE2QCAs5EBAMjZAIC1UQEAtlEBAMzZAIDQ2QCA1NkAgLp9AQC7dQEAvG0BAL39AAC+9QAAv+kAAKgpBgCpVQYAqlUGAKuNBgCslQYArZ0GAK6VBgCvjQYAlNkAgNjZAIDc2QCA4NkAgOTZAIDo2QCA7NkAgPDZAIC4bQcAuQUHALoNBwC7BQcAvB0HAL0FBwC+AQcAvz0HALD1BgCx/QYAsvUGALNlBwC0fQcAtWEHALZhBwC3VQcA4xAFAPTZAIDh8AQA+NkAgIAdAACBCQAAgjkAAPzZAIAA2gCAhOgDAL7gAwAE2gCA78wFAAjaAICHOAAAhhgAAKOdBgAM2gCAENoAgBTaAIAY2gCApl0GAKVdBgAc2gCAq3kGAKpxBgAg2gCAJNoAgK/lBwCu+QcArfEHAKxhBgCokQYAqZEGAKqRBgCrrQYArLkGAK2lBgCurQYAr6UGACjaAIAs2gCAMNoAgDTaAIA42gCAPNoAgEDaAIBE2gCAuGUBALltAQC6ZQEAu30BALxlAQC9bQEAvmUBAL/ZAQCw3QYAsaUGALKtBgCzpQYAtKEGALWpBgC2mQYAt5kGALMZBgBI2gCATNoAgFDaAIBU2gCAtiUGALUxBgBY2gCAu2EGALoZBgBc2gCAYNoAgL9tBgC+ZQYAvXEGALx5BgBk2gCAo10GAGjaAIBs2gCApmEGAHDaAICEmAEApXUGAKpdBgCrJQYAvqQBAHjaAICuIQYArykGAKw9BgCtNQYAqcUCAKixAgCrxQIAqsUCAK3NAgCsxQIAr/UCAK71AgB82gCAgNoAgITaAICI2gCAjNoAgJDaAICU2gCAmNoAgLnJAwC4wQMAu9kDALrBAwC9+QMAvMkDAL+ZAwC+8QMAsUUDALBFAwCzRQMAskUDALVFAwC0RQMAt0UDALZFAwCASQMAgUkDAIJdAwCzRQIAvtwMALVFAgC2RQIAnNoAgIYADACH5AMAuokDALuJAwC8mQMAvZkDAL6JAwC/iQMAowkCAKDaAICk2gCAqNoAgKzaAICmCQIApQkCALDaAICrxQMAqsUDALTaAIC42gCAr8UDAK7FAwCt1QMArNUDALzaAIDA2gCAxNoAgCTZAIDvAAAAyNoAgMzaAIDQ2gCA4+gAANTaAIDhjAEA2NoAgNzaAIDg2gCA6NoAgOzaAICAbQAAgXUAAIJ9AACEQAIAhvAMAId4DQDw2gCA9NoAgPjaAID82gCAANsAgATbAIAI2wCADNsAgBDbAIAU2wCAGNsAgBzbAIAg2wCAJNsAgCjbAIAs2wCAMNsAgO/MAQCE7AwA4TAGADTbAIDjGAEAONsAgDzbAIBA2wCARNsAgLPlAQBI2wCAhIQPAEzbAIBQ2wCAtuUBALX1AQBY2wCAu30BALrZAQC+oAwAXNsAgL8hAQC+OQEAvTEBALw5AQCo7Q0AqSUOAKotDgCrJQ4ArD0OAK0lDgCuLQ4AryUOAOTaAICC9Q8AgeUPAIDpDwBU2wCAYNsAgIaYAACHDAMAuK0OALlFDwC6TQ8Au0UPALxFDwC9TQ8AvkUPAL95DwCwXQ4AsfkOALKtDgCzpQ4AtL0OALWlDgC2pQ4At5UOAGTbAIDv7AwAaNsAgGzbAIBw2wCAdNsAgHjbAIB82wCAvugAAIDbAICE2wCAiNsAgIzbAIDj6A0AkNsAgOEEDACj5Q4AlNsAgJjbAICc2wCAoNsAgKblDgCl9Q4ApNsAgKt9DgCq2Q4AqNsAgKzbAICvIQ4ArjkOAK0xDgCsOQ4AqDkOAKk5DgCqUQ4Aq1EOAKxxDgCtcQ4ArnEOAK9xDgCw2wCAtNsAgLjbAIC82wCAgBkAAIEZAACCBQAAwNsAgLjRDgC50Q4AutEOALvlDgC84Q4AveEOAL7hDgC/4Q4AsBEOALERDgCyEQ4AsxEOALTxDgC18Q4AtvEOALfxDgCz2Q4AyNsAgIYoAACHuAAAzNsAgLbxDgC1+Q4A0NsAgLvVDgC61Q4A1NsAgNjbAIC/NQ4AvjUOAL3FDgC8xQ4A3NsAgKOdDgDg2wCA5NsAgKa1DgDo2wCA7NsAgKW9DgCqkQ4Aq5EOAPDbAID02wCArnEOAK9xDgCsgQ4ArYEOAKjdDQCp6Q0Aqj0CAKuNAgCsmQIArZkCAK6JAgCviQIAvqwEAPjbAID82wCAhCADAADcAIAE3ACACNwAgAzcAIC4iQIAuYkCALqZAgC7kQIAvLkCAL25AgC+eQMAv3kDALD5AgCx+QIAss0CALPFAgC03QIAtcUCALbBAgC3uQIAs7UCABDcAIAU3ACAGNwAgBzcAIC2GQIAtRECACDcAIC7PQIAuj0CACTcAIAo3ACAvwECAL4ZAgC9EQIAvBkCACzcAICj8QIAMNwAgDjcAICmXQIAPNwAgEDcAIClVQIAqnkCAKt5AgCGSAUAh6wEAK5dAgCvRQIArF0CAK1VAgCohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAETcAIBI3ACATNwAgFDcAICB8QEAgJkBAHTaAICC9QEAuHkBALl5AQC6zQEAu8UBALzdAQC9xQEAvsUBAL/1AQCwtQIAsb0CALKBAgCzgQIAtFUBALVdAQC2SQEAt0kBAFTcAIBY3ACAXNwAgO/UAQCEEAUAYNwAgGTcAIDvjA4AvuwFAOHsDgBo3ACA4xwOAGzcAIDhlAEAcNwAgONkDgCzXQIAdNwAgHjcAIB83ACAgNwAgLYVAgC1dQIAhNwAgLs5AgC6MQIAiNwAgIzcAIC/2QEAvtEBAL0VAgC8FQIAo50FADTcAICQ3ACAlNwAgJjcAICm1QUApbUFAJzcAICr+QUAqvEFAKDcAICk3ACArxkGAK4RBgCt1QUArNUFAIBRAACBWQAAgmEAALOVBgCo3ACAtXEHALZxBwCs3ACAhkADAIdUAwC67QcAu+UHALzlBwC97QcAvtEHAL/NBwCw3ACAtNwAgLjcAIC83ACAwNwAgMTcAIDvQAQAyNwAgOEwBwDM3ACA45QEANDcAIDU3ACA2NwAgNzcAIDg3ACAoxkGAOTcAIDo3ACA7NwAgPDcAICm/QcApf0HAPTcAICraQcAqmEHAPjcAID83ACAr0EHAK5dBwCtYQcArGkHAKjNBwCp0QcAqtEHAKstBgCsNQYArT0GAK41BgCvnQYAAN0AgATdAIAI3QCADN0AgIAZAACBGQAAggUAABDdAIC4iQYAuYkGALqZBgC7kQYAvLkGAL25BgC+UQEAv1EBALDlBgCx7QYAsv0GALP1BgC02QYAtcUGALbBBgC3uQYAqNEBAKnZAQCqCQEAqwkBAKwZAQCtGQEArgkBAK8JAQCEYAEAvnwBAIeoAACGjAEAGN0AgBzdAIAg3QCAJN0AgLgJAQC5CQEAuhkBALsRAQC8OQEAvTkBAL75AAC/+QAAsH0BALFBAQCyRQEAs10BALRFAQC1TQEAtkUBALc5AQAo3QCALN0AgDDdAICzjQIANN0AgLWdAgC2lQIAON0AgDzdAIBA3QCAurUCALuJAgC8nQIAvYUCAL6NAgC/hQIAps0CAETdAIBI3QCApcUCAEzdAICj1QIAUN0AgFTdAICu1QIAr90CAKzFAgCt3QIAqu0CAKvRAgCE9AMAWN0AgKgxAwCpMQMAqjEDAKsxAwCskQAArZEAAK6RAACvjQAAXN0AgGDdAIBk3QCAaN0AgGzdAIBw3QCAdN0AgHjdAIC4vQAAuWUAALptAAC7ZQAAvH0AAL1lAAC+bQAAv2UAALD9AACxxQAAss0AALOpAAC0uQAAtaUAALahAAC3oQAAgL0BAIEJAACCGQAAfN0AgIDdAIC+WAIAhxQdAIacHQCEbB0AxNsAgIjdAICM3QCAvrwcAJDdAICU3QCAmN0AgLP5AgCc3QCAoN0AgKTdAICo3QCAtlEBALVZAQC+3B8Au0EBALp5AQCs3QCAsN0AgL8hAQC+PQEAvT0BALxZAQDhcAcAtN0AgOMIBgC43QCA78wAALzdAIDA3QCAxN0AgOMQAADI3QCA4dABAMzdAICGkBwAh/QcAO/gBgDQ3QCAo3kCANTdAIDY3QCA3N0AgODdAICm0QEApdkBAOTdAICrwQEAqvkBAOjdAIDs3QCAr6EBAK69AQCtvQEArNkBAITdAICCFQAAgeUfAIDlHwDw3QCA9N0AgPjdAID83QCAqAkfAKkJHwCqHR8AqxUfAKwNHwCtcR8ArnEfAK9xHwCwER8AsS0fALIlHwCzyR8AtN0fALXBHwC2wR8At8EfALjFHwC5yR8AutUfALupHwC8uR8AvbkfAL6pHwC/oR8As7UfAADeAIAE3gCACN4AgAzeAIC20R8AtaUfABDeAIC7yR8AuvUfABTeAIAY3gCAvyUfAL45HwC9PR8AvNEfABzeAIAg3gCAJN4AgCjeAIAs3gCA4WAfADDeAIDjtBwANN4AgDjeAIA83gCA7wAdAEDeAIBE3gCASN4AgEzeAICjNR4AUN4AgFTeAIBY3gCAXN4AgKZRHgClJR4AYN4AgKtJHgCqdR4AhKgCAGTeAICvpR4ArrkeAK29HgCsUR4AgE0AAIFVAACCVQAAs8kBAGjeAIC12QEAtskBAGzeAICGoAAAhwQBALrFAQC7rQEAvLUBAL29AQC+tQEAv60BAKiZAQCpmQEAqg0BAKsFAQCsHQEArQUBAK4FAQCvNQEAcN4AgHTeAIB43gCAfN4AgIDeAICE3gCAiN4AgIzeAIC4JQEAuS0BALo5AQC7OQEAvCkBAL0pAQC+3QAAv9UAALBNAQCxJQEAsi0BALMlAQC0PQEAtSUBALYhAQC3HQEAkN4AgJTeAICY3gCAo4kCAJzeAIClmQIApokCAKDeAICk3gCAqN4AgKqFAgCr7QIArPUCAK39AgCu9QIAr+0CAKzeAICw3gCAtN4AgIRAAgC43gCAvN4AgMDeAIDE3gCAgA0AAIEVAACCHQAAyN4AgMzeAIDQ3gCAh7QDAIbcBAC+zAMA2N4AgNzeAIDg3gCA7+gCAOTeAIDo3gCA7N4AgOP8AgDw3gCA4dABAPTeAID43gCA/N4AgADfAIAE3wCAs2EDAAjfAIAM3wCAEN8AgBTfAIC2eQMAtXEDABjfAIC7XQMAul0DABzfAIAg3wCAv+EAAL79AAC9/QAAvP0AALC5AgCxuQIAsgkBALMJAQC0GQEAtQUBALYFAQC3PQEAuAUBALllAQC6bQEAu2UBALxhAQC9YQEAvmEBAL9hAQCFXAcAJN8AgCjfAIAs3wCAFN0AgDDfAIA03wCAON8AgKgxAgCpOQIAqskCAKvJAgCs2QIArdkCAK7JAgCvyQIAhMwFAOGAHgA83wCA47weAOE4HgBA3wCA46AAAL4QBABI3wCATN8AgO8MHgBQ3wCAVN8AgFjfAIBc3wCA73QeAKNhAgCCUQAAgUEAAICRAABg3wCApnkCAKVxAgBk3wCAq10CAKpdAgCGyAQAhzwFAK/hAQCu/QEArf0BAKz9AQCohQYAqY0GAKqFBgCrmQYArIkGAK2JBgCuvQYAr7EGAETfAIBo3wCAbN8AgHDfAIB03wCAeN8AgHzfAICA3wCAuJ0GALmtBgC6pQYAuwkHALwZBwC9GQcAvg0HAL8FBwCw0QYAsdEGALLRBgCz0QYAtLUGALW9BgC2tQYAt60GALMNBgCE3wCAiN8AgIzfAICQ3wCAtgkGALUBBgCU3wCAuxUGALoVBgCY3wCAnN8AgL95BgC+cQYAvQUGALwFBgCg3wCA4aAEAKTfAIDjXAUAgA0AAIE1AACCPQAAqN8AgKzfAICw3wCAhGADAL5sAAC/8AEAhZAAALTfAIDvmAUAo40HAIQIAACGAAwAh4wAALjfAICmiQcApYEHALzfAICrlQcAqpUHAMDfAIDE3wCAr/kHAK7xBwCthQcArIUHAMjfAICz6QYAzN8AgNDfAIC26QYA1N8AgNjfAIC16QYAukUBALtNAQDc3wCA4N8AgL5FAQC/TQEAvFUBAL1NAQCoIQYAqSEGAKolBgCrPQYArCUGAK0tBgCuSQYAr0EGAOTfAIDo3wCA7N8AgPDfAID03wCA+N8AgPzfAIAA4ACAuEkBALlJAQC6WQEAu1EBALx5AQC9eQEAvhkBAL8VAQCwxQEAsc0BALLFAQCz3QEAtMUBALXNAQC2xQEAt3kBAATgAIAI4ACADOAAgKOhBQAQ4ACApaEFAKahBQAU4ACAjyHqAxjgAICqDQIAqwUCAKwdAgCtBQIArg0CAK8FAgCX7RIAlmUSAJVFEQCUnRYAk3EWAJJVFQCReesDkFnqA59hBgCeNQUAnUUaAJxpGgCbVRkAmkUeAJlZHgCYRR0A4WAAABzgAIDjTD4AIOAAgKOxAgCi1QEAobUHAKCJBgCxATgAsAk+ALOVOgCyjToAtbUmALQBJADvaDoAvjAMAKnJNgCowTYAqwEwAKrhNwCtzTMArPUyAK/5PgCuATwAoRkCACjgAICjbQ4Aom0OAKX1CgCkAQgAp4ULAKaZCgCGAA0Ah0QNAIIJ6wODCesDhDHqA4UVFACGORcAh80XAISgDQAs4ACAiiUQAIsNEwCMnRMAjQ0cAI4ZHwCPDR8A1N4AgO8AAwCSbRgAk0kbAJR9GwCVBQQAllkHAJdJBwAw4ACANOAAgJpFBgCbLQAAnFEDAONgAAA44ACA4WwAAIClAQCBAQEAggUBAL4ADAA84ACAQOAAgETgAIDviAEASOAAgOFUBgBM4ACA41QBAFDgAIBU4ACAWOAAgFzgAICz6QIAYOAAgGTgAIBo4ACAbOAAgLadAgC1mQIAcOAAgLuJAgC6vQIAdOAAgHjgAIC/WQIAvlECAL1ZAgC8kQIAoykNAHzgAICA4ACAhOAAgIjgAICmXQ0ApVkNAIzgAICrSQ0Aqn0NAJDgAICY4ACAr5kNAK6RDQCtmQ0ArFENAIBRAACBWQAAgmEAALMtDwCc4ACAtS0PALbJDwCg4ACAhkADAIcIAwC6yQ8Au8UPALzBDwC9wQ8AvsEPAL/BDwAk4ACAlOAAgKTgAICo4ACArOAAgLDgAIC04ACAuOAAgKhFDgCpgQ8AqskPAKvJDwCsyQ8ArSUPAK4tDwCvJQ8AsGEPALFtDwCyeQ8As3kPALRpDwC1aQ8Ath0PALcVDwC4LQ8AuTUPALo1DwC7BQ8AvB0PAL3xAAC+8QAAv/EAAKNhDgC84ACAhMQBAMDgAIDE4ACApoUOAKVhDgDI4ACAq4kOAKqFDgDM4ACA0OAAgK+NDgCujQ4ArY0OAKyNDgDU4ACA2OAAgNzgAIDg4ACA5OAAgOjgAIDs4ACA8OAAgPTgAICCHQAAgR0AAIAdAAD44ACA/OAAgADhAIC+tAEAqK0BAKnVAQCq1QEAqwUBAKwdAQCtBQEArg0BAK8FAQCGgAEAhxgBAAjhAIAM4QCAEOEAgBThAIAY4QCAHOEAgLiFAAC5jQAAuoUAALudAAC8hQAAvY0AAL6FAAC/vQAAsH0BALHhAACy5QAAs/0AALTtAAC13QAAttUAALe9AACzXQIAIOEAgCThAIAo4QCALOEAgLaFAgC1lQIAMOEAgLslAwC6uQIANOEAgDjhAIC/GQMAvikDAL0pAwC8MQMAvswEAKMZAgA84QCAQOEAgKbBAgBE4QCASOEAgKXRAgCq/QIAq2EDAEzhAIBQ4QCArm0DAK9dAwCsdQMArW0DAKgpAwCpKQMAqjkDAKs5AwCsKQMArSkDAK6dAACvlQAAVOEAgFjhAIBc4QCAYOEAgGThAICCqQEAga0BAICtAQC4mQAAua0AALqlAAC7bQAAvHUAAL19AAC+dQAAv20AALDtAACx9QAAsvUAALPFAAC03QAAtb0AALa1AAC3qQAA4XgBAOEcDgDjEAAA4zwOAGjhAIBs4QCAvhQEAHDhAICErAIAeOEAgId4BQCGDAUAfOEAgIDhAIDvvAAA70gOALPxAgCE4QCAiOEAgIzhAICQ4QCAtukCALXhAgCU4QCAu3EBALppAQCY4QCAhKAEAL85AQC+WQEAvVEBALxhAQCc4QCAhIwEAKDhAICEADgApOEAgKjhAICs4QCAsOEAgKqJDgCriQ4AqLkOAKmxDgCu/Q4Ar+EOAKz5DgCt9Q4Asq0OALNlDgCwkQ4AsaUOALZ9DgC3ZQ4AtH0OALV1DgC6XQ4Au+UNALhdDgC5VQ4AvuENAL/pDQC8/Q0AvfUNAKOxBQB04QCAtOEAgLjhAIC84QCApqkFAKWhBQDA4QCAqzEGAKopBgDE4QCAyOEAgK95BgCuGQYArREGAKwhBgDM4QCA0OEAgNThAIDY4QCAgB0AAIEJAACCOQAA3OEAgODhAIDk4QCAhsgAAIcMAwDo4QCA7OEAgPDhAID04QCAqKUHAKm1BwCqvQcAq8kHAKzZBwCt2QcArskHAK/BBwC+oAAA+OEAgPzhAIAA4gCABOIAgAjiAIAM4gCAEOIAgLjNAAC51QAAutUAALvlAAC8/QAAvZUAAL6dAAC/lQAAsIkHALFlBwCyYQcAs30HALRlBwC1bQcAtmUHALf1AACzNQYAFOIAgBjiAIAc4gCAIOIAgLZZBgC1UQYAJOIAgLuhBgC6TQYAKOIAgCziAIC/qQYAvqEGAL2pBgC8tQYAMOIAgDTiAIDv8AUAOOIAgDziAIBA4gCAROIAgEjiAICAPQAAgQkAAIIdAABM4gCA4cgGAFDiAIDjSAQAVOIAgKO1BgBY4gCAhigAAIdAAQBc4gCAptkGAKXRBgBg4gCAqyEGAKrNBgBk4gCAaOIAgK8pBgCuIQYArSkGAKw1BgBs4gCAs70BAHDiAIB04gCAtnkBAHjiAIB84gCAtXkBALpVAQC7XQEAgOIAgITiAIC++QAAv/kAALxFAQC9+QAAqHECAKlxAgCqcQIAq3ECAKy1AgCtvQIArrUCAK+tAgC+rDwAiOIAgIziAICQ4gCAlOIAgJjiAICc4gCAoOIAgLhpAwC5aQMAugkDALsJAwC8HQMAvQUDAL4NAwC/BQMAsNUCALHdAgCy1QIAs2kDALR5AwC1eQMAtmkDALdhAwCk4gCAqOIAgKziAICj9QIAsOIAgKUxAgCmMQIAtOIAgLjiAIC84gCAqh0CAKsVAgCsDQIArbEDAK6xAwCvsQMA7xgCAIIVAACBbQAAgG0AAMDiAIDI4gCAhvg8AIcYAwDM4gCA0OIAgNTiAIDY4gCA42wHAAThAIDhaAEA3OIAgKiFAgCplQIAqpUCAKulAgCsvQIArdUCAK7RAgCv0QIA4OIAgOTiAIDo4gCA7OIAgPDiAID04gCA+OIAgPziAIC4dQEAuX0BALp1AQC7zQEAvNUBAL3dAQC+yQEAv8EBALC1AgCxvQIAsoECALOBAgC0VQEAtV0BALZVAQC3TQEA4bQGAADjAIDj9AYABOMAgIQYPQAI4wCADOMAgBDjAIAU4wCAGOMAgBzjAIAg4wCAJOMAgCjjAIDvWAYALOMAgIF9AACAcQAAMOMAgIIFAAA44wCAPOMAgO+AAQC+VDwA4ZABAEDjAIDjfAYAROMAgEjjAIBM4wCAhtg8AIf0PACjnT0AxOIAgDTjAIBQ4wCAVOMAgKbVPQCltT0AWOMAgKv5PQCq8T0AXOMAgGDjAICvGT4ArhE+AK3VPQCs1T0AZOMAgLOhPgBo4wCAbOMAgLatPgBw4wCAdOMAgLWxPgC6ST8Au0k/AHjjAIB84wCAvkk/AL9JPwC8ST8AvUk/AKhVPgCpZT4Aqm0+AKtlPgCsfT4ArWk+AK65PwCvuT8AgOMAgITjAICI4wCAjOMAgJDjAICU4wCAmOMAgJzjAIC4VT8AuV0/ALpVPwC7bT8AvHU/AL19PwC+dT8Av20/ALDJPwCxyT8Astk/ALPZPwC0yT8Atck/ALZ9PwC3cT8AghUAAKPhPwCAsQEAgbEBAKbtPwCg4wCAvtABAKXxPwCqCT4Aqwk+AITkAQCk4wCArgk+AK8JPgCsCT4ArQk+ALPdPACo4wCAhugAAIfMAQCs4wCAtpU8ALX1PACw4wCAu7k8ALqxPAC04wCAuOMAgL9ZPwC+UT8AvZU8ALyVPACoUT4AqVE+AKptPgCrYT4ArGE+AK1hPgCulQEAr40BAISgAQC84wCAwOMAgMTjAIDI4wCAzOMAgNDjAIDU4wCAuKkBALmpAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9pAQCw/QEAsc0BALLFAQCzrQEAtLkBALW5AQC2rQEAt6UBALPlPQDY4wCA3OMAgODjAIDk4wCAtuE9ALXpPQDo4wCAuwkCALo5AgDs4wCA8OMAgL99AgC+fQIAvXkCALwRAgD04wCAo6E9APjjAID84wCApqU9AADkAIAE5ACApa09AKp9AgCrTQIACOQAgAzkAICuOQIArzkCAKxVAgCtPQIAgOkAAIHpAACCHQAAvsADAO/kAgAQ5ACAh1QDAIY8BADjEAEAGOQAgOH4AQAc5ACAIOQAgCTkAIAo5ACALOQAgDDkAIA05ACAOOQAgLORAwA85ACAtbkDALZ9AwBA5ACAROQAgEjkAIC6WQMAu1kDALxJAwC9SQMAvv0AAL/1AACoRQIAqVUCAKpVAgCrZQIArH0CAK2xAgCusQIAr7ECAIRsBQBM5ACAUOQAgFTkAIBY5ACAXOQAgL5wBQBg5ACAuF0BALltAQC6ZQEAuw0BALwZAQC9GQEAvg0BAL8FAQCw0QIAsdECALLRAgCz0QIAtHUBALV9AQC2dQEAt20BAOFAPwDjvAAA4wg+AOFsPgBk5ACAaOQAgGzkAIBw5ACAdOQAgHjkAIB85ACAgOQAgL5sBwDvVAAA75w+AIjkAICjnQIAgmkAAIFhAACAaQAAjOQAgKZxAgCltQIAkOQAgKtVAgCqVQIAhsgEAIfsBACv+QEArvEBAK1FAgCsRQIAqKUGAKmpBgCquQYAq7kGAKypBgCtqQYArtkGAK/ZBgCE5ACAlOQAgJjkAICc5ACAoOQAgKTkAICo5ACArOQAgLhxBwC5cQcAunUHALvdBwC8xQcAvc0HAL7FBwC//QcAsKkGALG1BgCytQYAs40GALSVBgC1UQcAtlEHALdRBwCzMQYAsOQAgLTkAIC45ACAvOQAgLYpBgC1IQYAwOQAgLtxBgC6bQYAxOQAgMjkAIC/lQcAvlEGAL1ZBgC8YQYAzOQAgKN1BgDQ5ACA1OQAgKZtBgDY5ACA3OQAgKVlBgCqKQYAqzUGAODkAIDk5ACArhUGAK/RBwCsJQYArR0GAIANAACBFQAAgh0AAOjkAIDs5ACA8OQAgITcAQD05ACAhoAAAIcgAQD45ACA/OQAgADlAIAE5QCACOUAgAzlAIAQ5QCA43QEABTlAIDhyAUAGOUAgBzlAIAg5QCAJOUAgCjlAIAs5QCAMOUAgDTlAIA45QCA77QEADzlAIBA5QCAqD0GAKlVBgCqVQYAq6kBAKy5AQCtuQEArqkBAK+pAQCErAEAROUAgEjlAIBM5QCAUOUAgFTlAIBY5QCAXOUAgLhtAQC5BQEAugEBALsBAQC8BQEAvQ0BAL4xAQC/MQEAsNkBALHZAQCybQEAs2UBALR9AQC1ZQEAtmUBALdVAQCBvQMAgL0DALPVBQCCGQAAtTkCAGDlAIC+VAMAtjECAGjlAIBs5QCAuxUCALoVAgC9uQIAvLECAL+pAgC+sQIAcOUAgKZpAgClYQIAhAAMAKONBQB05QCAhvgMAId8AwCv8QIArukCAK3hAgCs6QIAq00CAKpNAgB45QCAfOUAgIDlAICE5QCAiOUAgIzlAIDjIAEAkOUAgOGgAQCU5QCA70ACAJjlAICc5QCAoOUAgKTlAICo5QCArOUAgLDlAICz8QMAtOUAgBTkAIC45QCAvOUAgLbpAwC14QMAwOUAgLu1AwC6tQMAxOUAgMjlAIC/lQMAvpUDAL2lAwC8pQMAqCkCAKkpAgCqOQIAqzkCAKwpAgCtKQIArlkCAK9VAgCAzQEAgQkAAIIZAADM5QCA0OUAgL58DQCHtA0AhhwMALgxAgC5PQIAujUCALvpAgC8+QIAvfkCAL7pAgC/6QIAsDECALExAgCyMQIAszECALQRAgC1EQIAthECALcRAgDY5QCA3OUAgODlAIDk5QCA6OUAgOzlAIDw5QCA79QGAPTlAIDhVAYA+OUAgOOkAACsDBUA/OUAgADmAIAE5gCAo/ECAAjmAIAM5gCAEOYAgBTmAICm6QIApeECABjmAICrtQIAqrUCABzmAIAg5gCAr5UCAK6VAgCtpQIArKUCAKghDgCpIQ4AqkkOAKtZDgCsaQ4ArWkOAK6ZDgCvmQ4A1OUAgCTmAIAo5gCALOYAgDDmAIA05gCAOOYAgDzmAIC49Q4Auf0OALr1DgC7iQ4AvJ0OAL2FDgC+hQ4Av7UOALDpDgCx6Q4Asv0OALPxDgC01Q4Atd0OALbVDgC3zQ4As8EOAIIVAACBtQAAgLUAAEDmAIC26Q4AteEOAL4QAAC7LQ4Aui0OAIRkAwBE5gCAvxkOAL4RDgC9JQ4AvCkOAEjmAICjhQ4AhogAAIdsAwCmrQ4ATOYAgFDmAIClpQ4AqmkOAKtpDgBU5gCAWOYAgK5VDgCvXQ4ArG0OAK1hDgCziQ4AXOYAgGDmAIBk5gCAaOYAgLaBDgC1iQ4AbOYAgLuVDgC6jQ4AcOYAgHTmAIC/+Q4AvvEOAL2FDgC8hQ4AeOYAgHzmAICA5gCAhOYAgOMMDQCI5gCA4RgNAIzmAIDvrAwAkOYAgJTmAICY5gCAnOYAgKDmAICk5gCAqOYAgKgBDgCpAQ4AqgEOAKsBDgCsAQ4ArQEOAK4BDgCvPQ4AgN0AAIEJAACCGQAArOYAgLDmAICEPAEAvnQAALjmAIC4HQ4AuS0OALolDgC76QEAvPkBAL35AQC+6QEAv+kBALBJDgCxUQ4AslEOALNRDgC0NQ4AtT0OALY1DgC3LQ4Ao4kNALzmAICGrAQAhzwDAMDmAICmgQ0ApYkNAMTmAICrlQ0Aqo0NAMjmAIDM5gCAr/kNAK7xDQCthQ0ArIUNANDmAICznQIAhEgDAL5ABAC2VQMA1OYAgNjmAIC1sQIAunEDALt5AwDc5gCA4OYAgL4xAwC/MQMAvFEDAL1RAwCwkQMAsZkDALKhAwCzoQMAtNEDALXRAwC20QMAt9EDALj1AwC5+QMAus0DALvFAwC83QMAvcUDAL7NAwC/xQMA5OYAgOjmAIDs5gCA8OYAgIV8GQD05gCA+OYAgGTlAICoIQIAqTECAKoxAgCrBQIArB0CAK3xAwCu8QMAr/EDAPzmAIAA5wCABOcAgAjnAIDvUAAADOcAgBDnAIAU5wCA44QAABjnAIDh+AEAHOcAgIAVAACBGQAAggUAACDnAICjmQMAKOcAgIZoBACHYAUALOcAgKZRAgCltQMAMOcAgKt9AgCqdQIANOcAgDjnAICvNQIArjUCAK1VAgCsVQIAPOcAgEDnAIBE5wCASOcAgEznAIBQ5wCAVOcAgO/4AQC+bAQA4YAOAFjnAIDjFAEAXOcAgGDnAIBk5wCAaOcAgGznAIBw5wCAdOcAgLPdAQB45wCAtf0BALb1AQB85wCAgOcAgITnAIC6sQEAu4UBALydAQC9NQEAvj0BAL81AQCpBQYAqLkFAKsVBgCqHQYArT0GAKw9BgCvTQYArl0GACTnAICCHQAAgR0AAIAdAACI5wCAjOcAgJDnAICU5wCAuUEHALidBgC7QQcAukkHAL1FBwC8WQcAv0UHAL5FBwCxCQYAsD0GALOpBgCyAQYAtbkGALSxBgC3rQYAtrEGAKORBgCEjAIAhigAAIfAAwCY5wCAprkGAKWxBgCc5wCAq8kGAKr9BgCg5wCApOcAgK95BgCucQYArXkGAKzRBgCo5wCAs5kHAKznAICw5wCAtlEHALTnAIC45wCAtbEHALptBwC7dQcAvOcAgMDnAIC+WQcAv0UHALxtBwC9ZQcAxOcAgMjnAIDM5wCA0OcAgNTnAIDY5wCA3OcAgO+oBQDg5wCA4TQFAOTnAIDjdAUA6OcAgOznAIDw5wCA9OcAgKMdBgCCLQAAgRUAAIAdAAD45wCAptUGAKU1BgD85wCAq/EGAKrpBgAA6ACAhCgBAK/BBgCu3QYAreEGAKzpBgCoxQYAqdUGAKrVBgCr5QYArP0GAK0VBgCuHQYArxUGAL7sAQAI6ACAhggAAIcgAAAM6ACAEOgAgBToAIAY6ACAuH0GALkFBgC6DQYAuwUGALwBBgC9CQYAvjkGAL85BgCwbQYAsXUGALJ9BgCzdQYAtFkGALVFBgC2TQYAt0UGAKiRAgCpmQIAqqECAKuhAgCs0QIArd0CAK7VAgCvyQIAHOgAgCDoAIAk6ACAvyweACjoAIAs6ACAMOgAgDToAIC4VQMAuV0DALppAwC7ZQMAvGEDAL1hAwC+YQMAv2EDALC5AgCxjQIAsoUCALNtAwC0dQMAtX0DALZ1AwC3bQMAOOgAgDzoAICzIQIAQOgAgLVRAgCEiAMAROgAgLZVAgC05gCAvigcALtBAgC6dQIAvbEDALxZAgC/sQMAvrkDAKNpAgBI6ACATOgAgFDoAIBU6ACAph0CAKUZAgBY6ACAqwkCAKo9AgBc6ACAYOgAgK/5AwCu8QMArfkDAKwRAgCopQIAqbUCAKq9AgCrtQIArK0CAK01AQCuPQEArzUBAL4sHABk6ACAaOgAgGzoAIBw6ACAeOgAgIdoHQCGHB0AuIUBALmNAQC6hQEAu50BALyNAQC9vQEAvrUBAL95AACwUQEAsVEBALJRAQCzUQEAtPEBALXxAQC29QEAt+UBAO/YAACCtQAAgaUAAIClAAB86ACAgOgAgIToAIDvxAYAiOgAgOH0BgCM6ACA4zgBAOPMAACQ6ACA4SgBAJToAICY6ACAtuUBALV1AgCEQBwAs2UCAJzoAICg6ACApOgAgL9lAQC+ZQEAvdUBALzVAQC7xQEAusUBAKjoAICs6ACAo7UdAHToAICw6ACAtOgAgLjoAICmNR4ApaUdALzoAICrFR4AqhUeAMDoAIDE6ACAr7UeAK61HgCtBR4ArAUeAMjoAIDM6ACA0OgAgNToAICADQAAgTUAAII9AADY6ACA3OgAgODoAIC1BQAAcRoAgOG0AgCs2AIAtQUAAHUaAICotR8AqRUfAKodHwCrFR8ArDEfAK09HwCuLR8AryEfAOG0AgCs2AIAtQUAAHkaAIDhtAIArNgCALUFAAB9GgCAuNEAALnZAAC64QAAu+EAALyRAAC9kQAAvpEAAL+RAACwIR8AsTEfALIxHwCzMR8AtAkfALUJHwC28QAAt/EAAOG0AgCs3AIA71QdALUdAACBGgCA4bwCAKzQAgC1KQAAoyUBAKKRAwChFR0AoA0dAOGAHgCFGgCA47wdAOHEAgCz1R4AtQkAAKzYAgCJGgCA4bwCALb9HgC1+R4ArOACALu1HgC6pR4AtQUAAI0aAIC/jR4Avo0eAL2lHgC8pR4AoxUeAOG8AgCs0AIAtREAAI9pJQCmPR4ApTkeAJEaAICrdR4AqmUeAOG0AgCseAEAr00eAK5NHgCtZR4ArGUeAJvdFACa5RUAmQEXAJjhEACfcR8AnnkZAJ35GQCcARsAk+UtAJIRLwCRbSkAkG0pAJf5EQCW8REAlYUsAJSZLQC1JQAA4ZQCAILxJgCDjSoAhJUqAIXhLACGHS4Ah3kuAKy0AgCVGgCAilUvAIspEgCMORIAjRkTAI7xFACPHRYAtQUAAJkaAICSVRcAk5EYAJRxGgCV+RoAlvkcAJd9HgCC4AMAkwsAgJpVHgCb2QAAnHUCAIMMAICzDACAuIkKAKwBBACthQYAroEGAMwQAgDMfAMAtgwAgJ0aAIDCDACAxQwAgMgMAIAACwCAgaUyArwMAIAE6ACAmpUGAJtVIwK8kQYAvbEAAL6RBgC/rQYAuOkGALmVBgC6kQYAoRoAgLTBBgC1zQYAts0GALfdBgCw/QYAseUGALKdAACz5QYAhVTHA6UaAICH/AAAuAEKAK0aAIDpDACAsRoAgIyRcwCNpAEAzPACAL4NAIDBDQCAiRQAALgZCgCLDAAAGg4AgFMOAIC5DACAvwwAgBkKAICRwAEAywwAgLhtCgDODACA1AwAgNoMAIDdDACA4AwAgLUaAIAoDQCA5gwAgLkaAIDhpB4AKw0AgONUHgCvIXMAzCgCAO8MAIDsDACA8gwAgPUMAID4DACAzIACAJS4AwD7DACAkhQCAO9gHgCQAAIA/gwAgAoNAIC48QoADQ0AgJ8LAIAQDQCAiSkLABMNAICpGgCAvDABAL/EAQC+7AEAFg0AgMzsAgC4xQoAukQBAK0JAIAZDQCAygYAgN8GAIDyBgCAHA0AgPoGAIAfDQCACgcAgC0HAIAYBwCA9gcAgC8HAICpDQCAOgcAgK8NAIBKBwCAtXkAAGcHAIC3cSoCcgcAgLFhAAB0BwCAsw0pAo0HAIC96QAAoAcAgPoHAICtBwCAuRkrAsMHAIC7WRQCHwgAgFoJAIA8CACALw4AgFsIAIA5AACAgQgAgHEAAIDHCACAKwAAgCAJAIA9AACAXAkAgEMAAIBeCQCARQgAgGoIAIBJAACAAAgAgFMAAIB5CQCAWQAAgCINAIBfAACAuw0iAtANAIDMFDYCHwAAgL9lAAC+EQAAvW0AAOUHAICAaQEAgXUBAIJxAQCD3SEChGkHAIWBBwCGgQcAh3EBAIihAQCJrQEAirUHAIuNBwCMlQcAjaUBAE8AAICPpQEAkOEBAJHtBwCSsSECk/0HAJSNBwCVUQYAlvEBAJfZAQCY0QEAmXUGAJp9BgCb1QEAnGkGAJ2ZFAKeUQYAn1EGAKB1FAKhuQYAokkBAKOFLQKkIQEApS0BAKZ1FAKntQYAqKERAqlRFAKqlQYAsSEAgMy8NQLNPDUCbQAAgKoDAICsAwCArwMAgL0hAIDEIQCA2yEAgOIhAIDJAACADwAAgLihBgC6BgCAtwYAgMwAAIDOIQCAtQMAgN0FAIAYBgCAugUCALvVAgC46QUAuf0FAL7JAgC/5RcCvA0CAL0BAgCy4QUAs+EFALCNBQCxnQUAtuUFALfpBQC09QUAte0FAKo9BQCrwQUAqD0FAKk1BQCuzQUAr/UFAKzNBQCtxQUAoj0FAKMFBQCg1QIAoTkFAKYdBQCnBQUApB0FAKUVBQC/BgCAm8EFAD4GAIBVBgCAnt0FAJ8xBACcUQIAndUFAHIGAICJBgCApAMAgDAiAIDbAACAoAMAgI8HAIDuBwCA8gcAgJAJAIACCACABggAgJYLAICUCQCArwoAgG8HAICLBwCAlwcAgKIHAICqBwCAqgkAgPsOAIASDwCAHw8AgMwEMwLNsDACzCAzAs3gMALMEDACzGgwAsxYMALNjDACzGgxAs0UMQLM1DECzRQ2AsxwIALN0CcCzDA2AswkMQLMDDwCzWg/AswYPwLNND8CzBg9As3AMgLMRDwCzBg5Asw4MgLNqDICzIgyAs34MwLMfDMCzUAzAswoMwLNCDMCzMghAs0kJgLMrCYCzEA4AsyYJQLNyDoCzBwkAs0QJALMhDsCzag7AsysJQLNvDoCzKw4Asz4JwLM4DgCzXQ4AicPAID2BgCAYQ0AgIgNAIDNICoCzBwrAqoGAIAsIgCAzKQgAs2gJwLMOCYCygQAgMw4OgLNPDsCzBA5As1gPgLMoAMAvj0NAL3tLALWBACAu1UjAgQJAIC5PSICzwYAgNkHAIClBACAoA0AgLIEAIBvBQCA9AYAgL4EAIB1BQCAr70MAK6ZLgKtpQwAwgUAgKvFIgIDBgCAxAQAgCMGAIDQBACAyAUAgCkGAIBdBgCAowEYAqAEAIAaBwCAHQcAgJ9dDACeUQwAnUUMACcHAICbWSECrwcAgLEHAIC0BwCAuAcAgCoHAIDOBwCA0AcAgJMtJgLTBwCAbAgAgG8IAICPBQwAjnEMAI1lDAB5CACAi0UgAmAJAICJNS8CYwkAgGcJAIB8CACAcAkAgHMJAIC9AwCAACIAgIFdDACAYQwAgAABAIEYAACCAAQABCIAgIQQBwCFFAYAhuQIAIc8AgCILAUAiaQFAIoAeAAIIgCAjCQAAAwiAIAUIgCAECIAgLgRAACRxHsAkkh6AJNMeQAcIgCAzOgCAJbwCQC4OQAAkMAJACQiAICS8AkAzPgCAJS0CQC4DQAAKCIAgMwcAgC4BQAANCIAgMzkAgC4HQAAOCIAgDwiAIBDIgCAWiIAgKiMCACp5HsAYSIAgKvUBgDM5AIAuA0AAGsiAIDMlAIAbyIAgLGAewC4CQAAuBUAAMz8AgC15AgAcyIAgMzYAgB3IgCAuAUAALqcBQC7XAUAvAB8AL30fwC++H0Av/xyAIAJOgKBDToCggE6AoMFOgKEGToChR06AoYROgKHFToCiCk6AoktOgKKIToCiyU6Aow5OgKNPToCjjE6Ao81OgLM8AIAkekPAIMiAIDMzAIAuBkAAH8iAIDM3AIAl+UPALg1AAC4DQAAjyIAgMz8AgC4BQAAkyIAgMwwAgCXIgCAzNACAJsiAICfIgCAzIgCAKQtDwClVQ8Apl0PAMyUAgCoqToCqa06ArjVAACjIgCAuDUAAKciAIDMUAMAr7U6AswsAwCrIgCAzBgDALMFDwC0HQ8AzyIAgLYJDwC3CQ8Avmh9ALhtAAC4RQAAzDgDALwpDwDTIgCAviUPAMxYAwCH5Q4AzOg6Ari9AQC4yQEAzPA1As2kMwLMgCICzXwlAs2UNgLMBCkCzew7AsxkOgK45QEAuMEBAInVDgCI1Q4Al7EOALgNAACvIgCAsyIAgLciAIC4GQAAuyIAgNciAICfaTsC2yIAgL8iAIC4PQAAzMQCAMz4AgDDIgCAxyIAgLjZAADLIgCA3yIAgLjRAADjIgCAuPEAAMzMMwLnIgCAuMkAAMzoMwLrIgCAuNUAAKllAAC4yQAAzNgCAKq5BgC3TQ0Atk0NALU1DgC0NQ4AuFUAABUjAICxGQ8AsCkOAL/1AwC+UQ0AvVkNALw1DAC7XQ0Aul0NALldDQC4XQ0AgL0KAIHFCgCCFQQAg8kKAMx8BQCF3QoAhtUKAIfNCgDMVAUAifEKAIq5CACLDQgAjBEIAI0VCACOtScCj+UKAJBpCACRbQgAknEIAJNtJALMEAUAlR0IAJaFCgDMEAUAzDQFAJk9CACaiQoAmw0IAJwRCACdFQgAzEgFAMwQAgCgZQoAoW0KAKJlCgC4BQcApLEEAMzoAgCmsQQAuA0HAKiBBADM/AIAqpkIAKtdCgCsuQgArakEALglBwCvNQgAsNEIALHxBADMwAIAs40IALQpKAK1IQoAtiEKALchCgC4IQsAuSUIALhBBwC7KQsAvA0dAr3dDwC+MQsAvzELAIDdCgAZIwCAnKF9ANADAIDpAwCAhRkJAIaZCQCHlQkAiOEJAIklJQICBACAGwQAgC4EAIBBBACAVAQAgGcEAICQrQoAkUkFAJJtBQCTYQUAlGEFAJVtBQCWZQUAlxEFAJg1BQCZPQUAmjUFAJsNBQCcFQUAnR0FAJ4VBQCfCQUAoKkJAKH9BQCi9QUAowEFAKQFBQClDQUApgUFAKc9BQCoBQUAqQ0FAKoFBQCrGQUArIkJAK2pBQCutQkAr/0JALABCQCxfQUAsnUFALMBBQC0aQkAtQEFALYFBQC3PQUAuAUFALnhJQK6AQUAuwEFALzRJQK9PQkAvnkJAL9dCQCDMAUAoXgHAJ+xfgB6BACApHgHAKVIBwCNBACA8wQAgIt8BADdAACAEwEAgIhIBAAcAQCAIAEAgCQBAIAoAQCALAEAgDABAICyAAcAs/wHADQBAIDhAACAtuQHALfwBwDmAACA6wAAgLrgBwC7nAcAvIgHAL2oBwDwAACAs8F+AKPMBAD1AACA+gAAgIMABAD/AACAhXQEAKUgBAAEAQCAiEwEAAkBAIAOAQCAFwEAgK8tBwCNxAcArSEHAKwpBwDNAwCA8AQAgI8FAICwZQcA4gUAgB0GAIBDBgCAWgYAgHcGAICOBgCA0wMAgOwDAIAFBACAHgQAgDEEAIC8fAQAgt0rAoPlKwKA/QoAgfkrAoaZCQCHmQkAhOEKAIXhCgCKiQkAi4kJAIiJCQCJiQkAjoUJAEQEAICM4QgAjY0JAJK5KwKTQScCkJkrApHFCwCWyQsAl3UnApTFDQCV0SQCmskLAJvZKgKYyQsAmXkHAFcEAIBqBACAnP0LAH0EAICQBACA9gQAgKABAICkAQCAqAEAgONkAgCsAQCAsAEAgLQBAIDvvAcAqBEJALgBAIC8AQCAwAEAgMQBAIDIAQCAzAEAgNABAIDUAQCA2AEAgNwBAIDgAQCA5AEAgOgBAIDsAQCA8AEAgPQBAID4AQCA/AEAgAACAICCnH4ABAIAgKD1VAKh2VQCoulUAqP1dQCk7XUApZ12AKaVdgCnvXYAqIV2AKkpfQCqOX0AqwV9AKwdfQCtBX0Arg19AK8FfQCwfX0AsUl+ALJRfgCzUX4AtHV+ALV9fgC2aX4At2l+ALhZfgC5WX4Auil+ALspfgC8IX4AvSF+AL4ZfgC/GX4AkgcAgDkJAIDXBwCATSIAgLQNAAC1NQAAtj0AAKIGAICsBgCArwYAgAMjAIAJIwCAvSV4ALy1WALGMQCALjoAgJkqAIC9KgCAySoAgNkqAIDhKgCA7SoAgPUqAID9KgCACSsAgF0rAIB1KwCAhSsAgJUrAIClKwCAtSsAgNUrAICAeX8AgYF/AIKBfwCDnX8AhI1/AIWxfwCGsX8Ah7F/AIjhfwCJ4X8AiuF/AIv9fwCM5X8Aje1/AI7lfwCP3X8AkKV/AJGtfwCSpX8Ak71/AJSlfwCVrX8Alm1+AJctfgCYFX4AmRl+AJrpfgCb6X4AnPl+AJ35fgCe6X4An+V+AKAdfgChJX4AoiV+AKM9fgCkJX4ApS1+AKYlfgCnXX4AqGV+AKltfgCqZX4Aq31+AKxlfgCtbX4ArmV+AK9dfgCwJX4AsS1+ALIlfgCzPX4AtCV+ALUpfgC2WXcAt9V1ALj9eQC56XUAuvl1ALvZeQC86XUAvdV1AL7RdQC/2XUAgDF2AIE9dgCCSXYAg0V2AIRBdgCFTXYAhvl0AId9dgCIoQIAiU12AIpZdgCLuXoAjEl2AI2degCOsQIAjx16AJCRVgKRKXYAkoF2AJPNdgCU2XYAlel2AJbJdgCX0VkCmKF2AJllWgKa8XYAm01aApzRdgCdYXoAnoFWAp/VdgCgBQIAoY1aAqI1VwKjCXYApCF2AKUtdgCmiVoCp5laAqi5WgKpdXYAql13ANkrAIDdKwCAESwAgDksAIBJLACAUSwAgFUsAIBhLACAfSwAgIEsAICZLACAnSwAgKUsAIC1LACAUS0AgGUtAIClLQCAuS0AgMEtAIDFLQCA1S0AgJl1CgD4LQCAJC4AgDAuAIBQLgCAXC4AgGAuAIBkLgCAgux6AINkewB8LgCAgC4AgIZ0ewCHvHsArC4AgLguAIDALgCAyC4AgNguAIDnLgCA7y4AgBsvAIAfLwCAJy8AgJJwfAArLwCAMy8AgJFMfAA7LwCASy8AgGcvAIDfLwCA8y8AgKvMfACo5HwAqdx8APcvAIB3MACAezAAgI8wAICiwHwAkzAAgJswAICjMACAzEBJAs0ASQLM/EoCzWhLAqswAIC3MACA7TAAgP0wAIARMQCAjjEAgJoxAICqMQCAsqx8ALNAfAC2MQCAwjEAgMoxAIDOMQCAtGx8ALUEfACAlQcAgZ0HAIKVBwCDqQcAhLkHAIW5BwCG2QcAh9kHAIjpBwCJ6QcAivkHAIv5BwCM6QcAjekHAI7RBwCP0QcAkLEHAJGxBwCSSQEAk0kBAJRZAQCVWQEAlkkBAJdJAQCYeQEAmXkBAJpJAQCbSQEAnFkBAJ1ZAQCeSQEAn0kBAKC5AQChuQEAoskBAKPJAQCk2QEApdkBAKbJAQCnyQEAqPkBAKn5AQCqyQEAq8kBAKzZAQCt2QEArskBAK/JAQCwuQEAsbkBALJJAQCzSQEAtFkBALVZAQC2SQEAt0kBALh5AQC5eQEAukkBALtJAQC8WQEAvVkBAL5JAQC/SQEA0jEAgNYxAIDaMQCAkjIAgNoyAIDmMgCA6jIAgO4yAIDyMgCA+jIAgP4yAIASMwCALjMAgDYzAIB2MwCAejMAgIIzAICGMwCAjjMAgJIzAIC2MwCAujMAgNYzAIDaMwCA3jMAgOIzAID2MwCAGjQAgB40AIAiNACARjQAgIY0AICKNACAqjQAgLo0AIDCNACA4jQAgAY1AIBKNQCAUjUAgGY1AIByNQCAejUAgII1AICGNQCAijUAgKI1AICmNQCAwjUAgMo1AIDSNQCA1jUAgOI1AIDqNQCA7jUAgPI1AID6NQCA/jUAgJ42AICyNgCAnoUMAOY2AIDqNgCA8jYAgIC5AwCBuQMAgskDAIPJAwCE2QMAhdkDAIbJAwCHyQMAiPkDAIn5AwCKyQMAi8kDAIzZAwCN2QMAjs0DAI/FAwCQvQMAkQEMAJJJDgCTSQ4AlFkOAJVZDgCWSQ4Al0kOAJh5DgCZeQ4AmkkOAJtJDgCcWQ4AnVkOAJ5JDgCfSQ4AoLkOAKG5DgCiyQ4Ao8kOAKTZDgCl2Q4ApskOAKfJDgCo+Q4AqfkOAKrJDgCryQ4ArNkOAK3ZDgCuyQ4Ar8kOALC5DgCxuQ4AskkOALNJDgC0WQ4AtVkOALZJDgC3SQ4AuHkOALl5DgC6SQ4Au0kOALxZDgC9WQ4AvkkOAL9JDgC8eQQAvXkEAL6JBAC/nQQAuHUEALl9BAC6aQQAu2kEALRxBAC1cQQAtnEEALdxBACwcQQAsXEEALJxBACzcQQArGkEAK1pBACucQQAr3EEAKhBBACpQQQAqkEEAKtBBACknQUApWEEAKZhBACnYQQAoJ0FAKGFBQCijQUAo4UFAJxdBQCdZQUAnm0FAJ9lBQCYXQUAmUUFAJpNBQCbRQUAlB0FAJVlBQCWbQUAl2UFAJAdBQCRBQUAkg0FAJMFBQCMMQcAjTEHAI4xBwCPMQcAiDEHAIkxBwCKMQcAizEHAIQxBwCFMQcAhjEHAIcxBwCAMQcAgTEHAIIxBwCDMQcAJjcAgC43AIA2NwCAcjcAgHY3AIB+NwCAgjcAgIY3AICyNwCAtjcAgL43AIDSNwCA1jcAgPI3AID6NwCA/jcAgCI4AIBCOACAUjgAgFY4AIBeOACAijgAgI44AICeOACAwjgAgM44AIDeOACA9jgAgP44AIACOQCABjkAgAo5AIAWOQCAGjkAgCI5AIA+OQCAQjkAgEY5AIBeOQCAYjkAgGo5AIB+OQCAgjkAgIY5AICOOQCAkjkAgJY5AICaOQCAnjkAgK45AIDGOQCAyjkAgNY5AIDaOQCA3jkAgOI5AIDqOQCA7jkAgPI5AID+OQCABjoAgA46AIASOgCAGjoAgIC5AQCBuQEAgskBAIPJAQCE2QEAhdkBAIbJAQCHyQEAiPkBAIn5AQCKyQEAi8kBAIzZAQCN2QEAjskBAI/JAQCQuQEAkbkBAJIRAACTEQAAlDEAAJUxAAAeOgCAIjoAgCo6AIAyOgCAPSMAgGUsAIBpLACAJSQAgIJgAgCZ4QAAgIAAAIGYAACC5AYAg4gEAITUGwCFlBoAhhgfALMjAICIxB4AiQAQAIqoEwCLrBEAjAAoAI20KwCOuCoAj7wpAOOwAgC+dAIAnlUAAOMUAgCCbAIAtyMAgJkNAAC+RAIAnjUAAIJoAgCZBQAAuyMAgO/MAgC+oAAAgoQAAO/YAgDj7AEA4/QBAL8jAIDjCAMAwyMAgOM4AwDHIwCA44gDAMsjAIDv4AMAzyMAgO+IAwDvPAEA78QDANMjAIDv1AMA4+wDAB43AIDXIwCA4+wDAOPsAwDj5AMA2yMAgOO4AwDvXAMA70wDAN8jAIDvSAMA7/QDAOMjAIDnIwCA7zQDAON8AwDjlAQA6yMAgO8jAIDzIwCA47QEAPcjAID7IwCA/yMAgO9sBAADJACAByQAgO9YBADvUAQACyQAgBYkAIAaJACAvQAAgOP4BADCAACAMSQAgB4kAIBtKQCA45wEAAglAIBrJQCAriUAgO9QBADaJQCABCYAgO88BAApJgCAgAlLAoYcdwC+RAIAgnQCAL5QAgA+JgCAmREBAJkNAQCPrAIAggQCAI1oAQCewQIAi3wBAJ49AQCeKQEAvggCAJfQAgCZXQEAldACAJ5VAQCT0AIAmXUBAJHQAgC+SAIAn7gCAEYmAICdtAIAnk0BAJuwAgCZXQEAmbQCAL6EAgCeqQEApowCAGImAICkgAIAmakBAGomAIChSAIAgqwCAK/kAgCCtAIAglwCAJnlAQC+CAIAgnwCAIIABACopAIAnvkBAL5wAgC1HAQAnoUBAL6oBQCyhAIAtrECAL6sBQC4KQkAuYkCALqZAgCCjAUAu+gEAIKcBQByJgCAuPAEAJ5ZBgCZbQYAnmEGAJl5BgC+fAIAnmEGAIJcAgC+QAIAmVkGAJ5dBgCCYAIAmaUGAL58AgCevQYAghwCAL4UAgCZzQYAvkwCAIJMAgCa3QYAnt0GAJ/FBgDjDAIAgrwCAJn5BgC+ZAIA7/QCAJrxBgCe6QYAn+kGAJ7ZBgCf1QYA4wQCAJklBgCaIQYAgngCAJk9BgDjBAIAgkQCAJolBgC+cAIA75wCAJ4FBgCfFQYA7+gCAJp1BgCZBQYAggQCAL5wAgDjcAIAnnUGAJ8NBgCeAQYAvnwCAOM0AgCZDQYAvmACAIJsAgDv8AIAmTUGAIKQAwDv2AIAniEGAIQmAICbxQcAmeUHAL58AgCe7QcAn8UHAOPsAwCdUAIAnNEHAIJsAgDv1AIAmc0HAIJ8AgC+cAIAmd0HAJ7dBwC+AAIA42gCAJ6tBwCZuQcA42gCAIJ8AgDjDAIAvkgCAJmpBwCCWAIA78QCAJ6ZBwC+bAIA77gCAIKUAgCejQcA77gCALsAAACZeQcAuQwAAJ5xBwC/AAAAglQCAL0EAAC+aAIAs9QDAJmxBgCxcAMAggQCALc4AACeoQYAtTQAAL5wAgCrWAMAnqEGAO9cAgCZqQYArxADAIJQAgCtFAMAmYUHAJlpBgC+WAIAnmEGAL58AgCCaAIApqACAOOQAgCZaQYA43wBAOOYAQDjrAEA49ABAOPoAQC+dAIAno0FAOMwAgDvzAIAgmgCAJnRBQDvlAIA71QBAO9wAQDvJAEA7ygBAL58AgCevQUA4wwCAIJ4AgCZrQIAvnQCAJ6lAgDjNAIAgmACAJkZAAC+YAIA7/wCAJ4NAACClAIA79QCAJAmAIDj/AIAmQkAAL5gAgCYJgCAnh0AAOMAAgCwJSoAglgCAJkNAADv9AIAvmQCAK4mAIDvwAIAnhkAAIIYAgCCOAIA43ACAJkRAACaNQAAmSkBAL50AgDsJgCAnyUAAJ4JAACZ6QEAvrQDAL7gAwCazQEA79gCAJ4RAQCC2AMA/SYAgIHEAgDjsAMAHycAgOP8AwC+/AIAhMQCAIIoAgCGEAIAKicAgIg8AgCeIQAAnw0AAHonAIDvKAMAj3QCAO8sAwCCiAIAmXUAAJoVAACSxAMAldADAJktAACa0QAAjicAgL7IAgCYaAMAm3wDAILEAwCeQQAAnykAALAnAICChAIA45ACAL4IAwC+JwCABigAgJ8ZAACe7QAA49ACAJlxAACaFQAAvhQCAO8wAgCZIQAA71gCABQoAICv7AMAggQCALFMHACwABwAniUAALJMHACeXQAAn2EAAOO8AgCZIQAA+QAAAHEpAIDvlAIAdSkAgL08HACCgB0Av8EfAHkpAIDjtB0AvnQCAJ71HwDj8B0AmQUAAH0pAIC+fAIAngkAAIJgAgCZDQAAiSkAgL5gAgDvzAIAnh0AAOklAIDv3AIA42gCAPkYAIDjPB0AIRoAgP0YAIABGQCAJRoAgCkaAIAtGgCAMRoAgDUaAIA5GgCA76QCAD0aAIDvJB0AQRoAgLHFAAAFGQCAs8UAALLdAAC1yQAAtMEAALcdAAC2wQAAuWUAALhlAAC7zQAAus0AAL3dAAC83QAAv8UAAL7JAAAJGQCADRkAgE0ZAIBhGQCAERkAgBUZAIDvFHgD7wBIA+HYTQPhOKgC41x5A+O0UAOtGQCAsRkAgLUZAIC5GQCAgMkBAIHVAQCC3QEAg20CAITdAQCFcQIAhgEEAIcdBQCIJQUAiTUFAIo9BQCLbQUAjHUFAI1lBQCObQUAj80BAJC1AQCRvQEAkrUBAJNNAwCUVQMAlV0DAJZVAwCXTQMAmHUDAJl9AwCadQMAm00DAJxVAwCdWQMAnkkDAJ9JAwCguQMAobkDAKLBAwCj3QMApMUDAKXNAwCmxQMAp/0DAKjJAwCpyQMAqtEDAKvRAwCsMQMArTEDAK4xAwCvMQMAsFEDALFRAwCyUQMAs1EDALRxAwC1cQMAtnEDALdxAwC4UQMAuVEDALpRAwC7UQMAvDEDAL0xAwC+MQMAvzEDAL0ZAIDBGQCAxRkAgMkZAIDNGQCA0RkAgNUZAIDZGQCA3RkAgOEZAIDwIAIA5RkAgOkZAIDtGQCA8RkAgPUZAICc9TYAnf02APkZAICRkAIA/RkAgKkZAIBFGQCASRkAgEUaAIC6adgASRoAgE0aAIC4sTYAubE2AFEaAIBVGgCAWRoAgF0aAIBRGQCAYRoAgGUaAIBVGQCAWRkAgF0ZAIBlGQCAaRkAgG0ZAIBxGQCAdRkAgHkZAIB9GQCAgRkAgIUZAICJGQCAjRkAgJEZAICVGQCAglgCAJkZAIBpGgCA8FgCAG0aAICdGQCAoRkAgKUZAIABGgCABRoAgJF0AwDhtDsCCRoAgOPYIgINGgCAERoAgBUaAIAZGgCAHRoAgKUqAIBVLQCAqSoAgMEqAICtKgCAljMAgO/IPwK1KgCA4ZTzAuGY0gLjlPcC4xDGAuGUtgLhkJ0C44SiAuMIhwIZGQCAHRkAgO+4swLvOIsCnSoAgOAtAIDvIJcC7+DgAoLkAgBpLQCACAIAgLrF2QAOAgCAFAIAgBoCAIAgAgCAJgIAgCwCAIAyAgCAOAIAgD4CAIBEAgCASgIAgFACAIDhgHgC8OQGAOMUagKCgAgA4aAPAuEIEwLjhA4C4xgeAlYCAIA0AwCA7zQ7Au8wHwI6AwCAQAMAgO8MEgJGAwCAJRkAgCkZAIBMAwCAUgMAgC0ZAIAxGQCAWAMAgF4DAIB2AwCAggMAgIgDAICOAwCAlAMAgJoDAIB8AwCAZAMAgDUZAIA5GQCAbQMAgFwCAIA9GQCAQRkAgHQCAIBoAgCAvAIAgHoCAICYAgCAYgIAgJICAIBuAgCApAIAgNQCAICAUQYAgV0GAIJVBgCDaQYAhHkGAIV5BgCGaQYAh2kGAIhZBgCJoQcAiqUHAIu9BwCMpQcAja0HAI6lBwDyAgCA7AIAgOACAICSCRQAkxUUAJTxBwCV8QcAlvEHAJfxBwCY0QcAmdEHAJo5FACb0QcAnIEHAJ2BBwCefQcAnx0UAJktAQCYLQEAmz0BAJo9AQCdLQEAnC0BACEZAICeVQEAkd0GAJDRBgCTJQEAkiUBAJUtAQCULQEAlx0BAJYdAQCJ8QYAiOkGAIvxBgCK+QYAjbEGAIzpBgCPqQYAjrkGAIHxBgCA7QYAg/EGAIL5BgCF0QYAhOkGAIfRBgCG2QYAua0DALitAwC7vQMAur0DAL2tAwC8rQMAv90DAL7dAwCxrQMAsK0DALO9AwCyvQMAta0DALStAwC3nQMAtp0DAKm5AQCosQEAq3UBAKqxAQCtFQEArBUBAK/dAwCu3QMAobkBAKCpAQCjiQEAorEBAKWZAQCkkQEAp4kBAKaRAQAuAwCAwgIAgM4CAIDmAgCA2gIAgAQDAICwAgCA+AIAgCIDAIAKAwCAngIAgIACAIC2AgCAyAIAgP4CAICGAgCAKAMAgKoCAIAQAwCAjAIAgBYDAIAcAwCACS0AgOsuAIDKNACAhAcAgAYFAIAVBQCAJAUAgDMFAIBCBQCASwUAgPAsOABUBQCAXQUAgGYFAICSBQCA40huA5sFAIDhTG4DpAUAgO/0AQOnBQCAqgUAgK0FAIBGOgCApkwAgNZVAIA2aACAZnEAgJZ6AID2jACAVp8AgIaoAIDtugCAJMQAgFTNAICE1gCAtN8AgDG7AIA6rgCABqUAgPkqAICJKwCAoSoAgOUqAIBBMQCAATEAgE40AIDVLACABjMAgIo3AIBiNACAHSwAgJI0AICeMwCAEjgAgFkrAICFLACA+jEAgCY5AIAdKwCArSsAgJ4xAIC8LgCAySwAgFksAIA4LgCALC4AgJGgBgDuMwCAGSsAgJ43AIB1LACAzS0AgLAFAIDh1D8D4VgaA+PcLwPjUA4D4RTyA+FA0wPjQOoD40DDA7MFAIC2BQCA73jrA+9c8gO5BQCA5QUAgO9E3gPvmCUD4bSLA+E8lwPjfKID45iLA+EwQQDhUKwD4xx/AOOIRgDoBQCA6wUAgO84ewDv4EEA7gUAgPEFAIDvzIoD7yCHA4DBGACB3RgAgikLAIMpCwCE6Q4AhekOAIYZDwCH8RgAiCUPAIntGgCK5RsAiyEdAIw5HQCN5RsAjmkQAI/VGgCQhRsAkU0PAJJFDwCTXQ8AlEUPAJVNDwCWRQ8Al30PAJhFDwCZTQ8AmkUPAJtpGwCcQQ8AnUEPAJ5BDwCfQQ8AoMEPAKHBDwCiwQ8Ao8EPAKS5CwCluQsApqkLAKfNDwCo9Q8Aqf0PAKr1DwCrzQ8ArNkPAK3ZDwCuyQ8Ar8kPALC5DwCxuQ8AsmkPALNpDwC0YQ8AtWEPALY5DwC3OQ8AuBEPALkRDwC66QEAu+kBALz5AQC9+QEAvukBAL/pAQD0BQCA9wUAgPoFAID9BQCAAAYAgCAGAIDhBACAgAUAgNMFAIAOBgCANAYAgEsGAIBoBgCAfwYAgJYGAIDdAwCA9gMAgA8EAIASBwCAQQgAgD4IAIA/BwCAOSQAgHIkAICjJACAyCQAgLkmAIDEJgCAyCYAgMwmAIDQJgCALygAgG4oAICWKACAmigAgL8oAIDHKACA4ygAgPUoAID5KACA/SgAgLrp0wAVKQCAMCkAgEspAIA9JACASiQAgFckAIBkJACAdiQAgIMkAICVJACApyQAgLckAIDMJACA1iQAgOQkAIDuJACA+yQAgAwlAIAWJQCAbyUAgHYlAIAkJQCAgBkDAIEZAwCCKQMAgykDAIQ5AwCFOQMAhikDAIcpAwCIGQMAiRkDAIppAwCLaQMAjHkDAI15AwCOaQMAj2kDAJAZAwCRGQMAkgEEAJMtAwCUNQMAlVUGAJZdBgCXVQYAmG0GAJl1BgCafQYAm3UGAJxtBgCdNQYAnj0GAJ81BgCgzQYAodUGAKLdBgCj1QYApPkDAKX5AwCm6QMAp+kDAKjZAwCp+QYAqikGAKspBgCsOQYArTkGAK7FAwCvPQMAsEUDALFNAwCyRQMAs10DALRFAwC1TQMAtkUDALd9AwC4SQMAuUkDALpZAwC7fQYAvGUGAL1tBgC+ZQYAgCUAgKkVDwCoAQ8Aq00PAKpNDwCtRQ8ArEUPAK+hDQCuqQ0AoXULAKBhCwCj7QsAoqkLAKXlCwCk5QsApzkPAKZZCAC5oQ0AuJkNALuhDQC6qQ0AvaENALy5DQAxJQCAvqkNALGhDQCw2Q0As6ENALKpDQC1oQ0AtLkNALehDQC2qQ0AOCUAgEglAIBbJQCAsiUAgLwlAICRJQCAoSUAgNAlAICB7Q0AgO0NAIP9DQCC/Q0Ahe0NAITtDQCH2Q0AhiEYAJlNDQCYTQ0Am1ENAJpdDQCdeQ0AnHUNAJ9pDQCecQ0AkYkNAJCBDQCTmQ0AkoENAJWJDQCUgQ0Al30NAJaBDQDgJACAICUAgI0lAIDMJQCA3iUAgAgmAIAtJgCAQiYAgPAlAID6JQCADCYAgBkmAIAxJgCATiYAgFgmAIB2JgCASiYAgGYmAIBuJgCAgCYAgIwmAICUJgCAoyYAgN4mAICcJgCAsiYAgKcmAIC9JgCA1CYAgOImAIABJwCAEScAgBsnAIBPJwCAkicAgOcnAIBPKQCAXSkAgGEpAIBlKQCA8CYAgC4nAIA+JwCASCcAgCMnAIBTJwCAYycAgH4nAIBwJwCAlicAgMInAIDJJwCApicAgNMnAIDdJwCAtCcAgBgoAIAKKACA6ycAgCUoAIDyJwCA/CcAgDMoAIBAKACASigAgFQoAIBeKACAcigAgH8oAICGKACAnigAgKUoAICyKACAyygAgNUoAIDnKACAASkAgA4pAIAZKQCAIykAgDQpAIA7KQCAUykAgMMDAIDmBACAhQUAgNgFAIATBgCAOQYAgFAGAIBtBgCAhAYAgJsGAIDjAwCA/AMAgBUEAIAoBACAOwQAgE4EAIBhBACAdAQAgIcEAICaBACAAAUAgA8FAIAeBQCALQUAgDwFAIBjCACAJAgAgMEGAID8BwCAHQkAgOMoEwAzCQCAKggAgC0IAIAxCACAJAcAgNwuAIDKMACA2S0AgLswAIBFMQCAJwkAgO/sEwAGCQCA3A0AgM8IAICDCACAMQcAgEwHAID8BgCACggAgJQIAIAqCQCACQkAgOANAIDsDQCA2wgAgJkIAIAVBwCAhggAgFUHAID/BgCApgcAgJEkAIDwDQCA4ggAgCcIAICcCACAWAgAgBUJAID0DQCA5QgAgBQIAICfCACA6AgAgBcIAIDJCACAoggAgOwIAIAbCACAzAgAgKYIAID3CACA/QgAgIgHAICKCACAWQcAgAMHAIA9CQCAQQkAgEkJAIA2CQCAGAkAgPgNAID0CACALQkAgAwJAIDkDQCA0ggAgI4IAIBdBwCAMAkAgA8JAIDoDQCA1QgAgJEIAIBgBwCArQgAgGMHAIDjSBIA4xQSAOP4EwDjuBMA4+wSAOOgEgDjbBIA43gSAO/ADQDv2A0A73QSAO9QEgDvqBIA79wSAO8oEwDvIBMA6QcAgMwGAIAOCACAEQgAgNgGAIDUBgCAIQgAgAcHAIBnCACADAcAgHYIAIA0BwCANwcAgKoIAIC2CACAuQgAgOPYEADjoBAA46AQAON0EQDjNBAA4wgQAOPkEADj9BAA77wQAO/gEADvzBAA7zgQAO8QEADvcBAA73AQAO9MEADjhBMA4+gTAOMwEADjEBAA42ATAONAEwDjpBMA47QTAO/IEwDvtBMA75gTAO98EwDvXBMA70wTAO8UEwDv6BAAgO08AIH1PACC/TwAg/U8AITtPACFFT0Ahh09AIcVPQCILT0AiTU9AIo9PQCLNT0AjC09AI0VPQCOHT0AjxU9AJBtPQCRdT0Akn09AJN1PQCUbT0AlRU9AJYdPQCXFT0AmC09AJk1PQCaPT0AmzU9AJwtPQCdFT0Anh09AJ8VPQCg7T0AofU9AKL9PQCj9T0ApO09AKUVPQCmHT0ApxU9AKgtPQCpNT0Aqj09AKs1PQCsLT0ArRU9AK4dPQCvFT0AsG09ALF1PQCyfT0As3U9ALRtPQC1FT0AthE9ALcRPQC4MT0AuTE9ALoxPQC7MT0AvBE9AL0RPQC+ET0AvxE9AIDxPACB/TwAgvU8AIMNPwCEFT8AhR0/AIYVPwCHDT8AiDU/AIk9PwCKNT8Aiw0/AIwVPwCNHT8AjhU/AI8NPwCQdT8AkX0/AJJ1PwCTDT8AlBU/AJUZPwCWCT8Alwk/AJg5PwCZOT8Amgk/AJsJPwCcGT8AnRk/AJ4JPwCfCT8AoPk/AKH5PwCiCT8Aowk/AKQZPwClGT8Apgk/AKcJPwCoOT8AqTk/AKoJPwCrCT8ArBk/AK0ZPwCuCT8Arwk/ALB5PwCxeT8Asgk/ALMJPwC0GT8AtRk/ALYJPwC3CT8AuDk/ALk5PwC6CT8Auwk/ALwZPwC9GT8Avgk/AL8JPwCA+TwAgfk8AIJJPQCDST0AhFk9AIVZPQCGST0Ah0k9AIh5PQCJeT0Aikk9AItJPQCMWT0AjVk9AI5JPQCPST0AkDk9AJE5PQCSAQQAk00GAJRVBgCVXQYAllUGAJdNBgCYdQYAmX0GAJp1BgCbTQYAnFUGAJ1dBgCeVQYAn00GAKC1BgChvQYAorUGAKPNBgCk1QYApd0GAKbVBgCnzQYAqPUGAKn9BgCq9QYAq80GAKzVBgCt3QYArtUGAK/NBgCwtQYAsb0GALK1BgCzTQYAtFUGALVdBgC2VQYAt00GALh1BgC5fQYAunUGALtNBgC8VQYAvV0GAL5VBgC/TQYArH0/AK2lPwCurT8Ar6U/AKh9PwCpZT8Aqm0/AKtlPwCkHT8ApUU/AKZNPwCnRT8AoB0/AKEFPwCiDT8AowU/ALydPwC9pT8Avq0/AL+lPwC4nT8AuYU/ALqNPwC7hT8AtN0/ALWlPwC2rT8At6U/ALDdPwCxxT8Ass0/ALPFPwCMZToAjW06AI5lOgCPfToAiEU6AIlNOgCKRToAi306AIRlOgCFbToAhmU6AId9OgCABToAgQ06AIIFOgCDfToAnF04AJ3lPwCe7T8An+U/AJhdOACZRTgAmk04AJtFOACUuTgAlWU4AJZtOACXZTgAkAU6AJENOgCSBToAkwE5AMAIAIDYCACA3ggAgPAIAIB2BwCAIgkAgHkHAICBBwCAVAkAgJ0HAIDLBwCAvQcAgMQGAIDcBACAewUAgM4FAIAJBgCALwYAgEYGAIBjBgCAegYAgJEGAIDXAwCA8AMAgAkEAIAiBACANQQAgEgEAIBbBACAbgQAgIEEAICUBACA+gQAgAkFAIAYBQCAJwUAgDYFAIBFBQCATgUAgFcFAIBgBQCAaQUAgJUFAICeBQCAXQgAgFYOAIBZDgCAOjoAgKwKAIAVCwCANjoAgD46AICcGQAAnRkAAJ45AACfOQAA4wwAgEI6AIB6NwCA8TAAgKI3AIBaMgCAxSoAgLksAICaMDUA7C0AgB0tAIDoLQCA1y8AgJ+ENQDSMwCAnUQpAGI1AICaNgCA1jYAgAo3AIAeOACAdjEAgAIyAICuMgCARjMAgGI2AIBGOACAcjkAgOkqAICNLACAijEAgNIyAICWNgCAwjkAgJQuAIB6MgCAhjYAgBo3AIALMACAvjUAgLSAGgC1hBkAtojmALeM5ACwABwAsZQeALIAGACznBsAvADsAL2k7wC+qO4Av6TtALgA4AC5tOMAurjiALu84QCkwAAApQAMAKbIDgCnAAgA4jYAgAcvAIAFMQCArXwDAKwAEACt5BMArugSAK9gEQCo8AoAqRwJAKr4FgCr/BQAGjIAgB4zAIAqOACAKSsAgMErAIAtLACAczAAgIIxAIDOMgCA8jMAgI42AICmNgCAyjcAgO44AICiOQCAvjkAgC40AIBuNACAvAgAgCY1AIBGNgCAejgAgE43AIChLQCAIy8AgN40AICeNQCAAjMAgDY0AICaNwCA5jgAgJ0tAIBwLgCAejEAgC4yAIBiMgCAFjUAgD41AICmOACAKSwAgJwAAACqNQCAzSsAgMkrAICaNACAKjUAgF42AICuOACAajcAgA8wAIBaNwCA0SoAgEQuAIB7LwCAMjMAgLIzAIBNLACAPjQAgDkrAIBfLwCAsSoAgO4xAICLMACAEjUAgIDpAwCB6QMAgjkvAIP9AwCE5QMAhe0DAIblAwCHfS4AiEEuAIkhAgCKeS8AiyUCAIw9AgCNJQIAjiECAI8dAgCQZQIAkW0CAJJlAgCTfQIAlGUCAJVtAgCWZQIAlx0CAJglAgCZLQIAmiUCAJs9AgCcJQIAnS0CAJ4lAgCfHQIAoOUCAKHtAgCi5QIAo/0CAKTlAgCl7QIApuUCAKdNAgCodQIAqX0CAKqpAQCrqQEArLkBAK25AQCuqQEAr6kBALDZAQCx2QEAsukBALPpAQC0eSIAtf0BALb1AQC37QEAuNUBALndAQC61QEAu60BALy1AQC9uQEAvqkBAL+pAQChLACAjS0AgP4zAIBmNgCAPjcAgLoxAIDmMQCAHzAAgB42AIA/MACArjMAgAUrAICBKwCAxSsAgFYxAID+NACA9jUAgEo3AIBaOACANSwAgOksAIAXLwCApzAAgH4yAIBCNACAljgAgHo5AIDOOQCA5jkAgOkwAICmMQCA7jcAgOMuAIC/LwCA2y8AgGswAIBuMgCAujIAgGozAICONACAMjUAgJY1AIDeNwCAbjYAgAY4AIB+OACA6SsAgBUsAID9LACAqjIAgPY2AIADLwCAcy8AgDcwAICyMQCA2jQAgCYzAIAVKwCAWS0AgKguAIB/LwCAQjMAgF4zAIBuNQCAgFEBAIEBKgCCXQEAg1UBAIRNAQCFdQEAhn0BAId1AQCITQEAiVUBAIqdKwCLWQEAjEkBAI1JAQCOuQEAj7kBAJDJAQCRyQEAktkBAJPZAQCUyQEAlckBAJb5AQCX+QEAmMkBAJnJAQCa2QEAm9kBAJzJAQCdyQEAnrkBAJ+5AQCgSQEAoZUBAKJFAQCjXQEApEUBAKVNAQCmRQEAp30BAKhFAQCpTQEAqnkPAKtBAQCsQQEArUEBAK5BAQCvQQEAsMEDALHBAwCywQMAs8EDALTBAwC1wQMAtsEDALfBAwC4wQMAucEDALrBAwC7wQMAvMEDAL3BAwC+wQMAv8kMAI41AIBiOACA4jgAgPI4AIAuOQCALSsAgII0AIBOOACAyjgAgJcvAIDxKgCAUSsAgEguAIBoLgCAlzAAgMYyAIDOMwCAejYAgBo4AIDZMACAojgAgA0sAIAlMQCAMTEAgBIyAIBKMgCATjMAgKozAIAqNACADjUAgDo5AIDrLwCAsjgAgEErAICMLgCAMjIAgOI3AIBPLwCAny8AgDkxAIC6OACA8SsAgNksAIB4LgCAwjAAgBUxAIBiMQCA9jEAgEozAIC+MwCAWjUAgPo2AIAGNwCA1jgAgF0sAIBOMgCA3SwAgMoyAIBuMwCAijYAgL44AICqOQCA0jkAgC0xAICxOSMAsBEDALMVAwCyFQMAtTUDALQ1AwC3NQMAtjUDALkVAwC4FQMAuxUDALoVAwC9dQMAvHUDAL91AwC+dQMAoZkNAKCRDQCjqQ0AopENAKW5DQCksQ0Ap6kNAKaxDQCpmQ0AqJENAKtpAwCqkQ0ArXkDAKxxAwCvaQMArnEDAJEZDQCQEQ0Aky0NAJIRDQCVPQ0AlD0NAJctDQCWLQ0AmR0NAJgdDQCbbQ0Amm0NAJ15DQCcgQ4An2kNAJ5xDQCBmQ0AgAkjAIOpDQCCkQ0AhbkNAISxDQCHqQ0AhrENAImZDQCIkQ0Ai2kNAIqRDQCNeQ0AjHENAI9pDQCOcQ0AKjIAgMY1AIDGNACA6jQAgBozAICiMgCAZjcAgA0rAIAuNgCA9SsAgOUrAIDzLgCAEzAAgPY0AIA0LgCABjIAgOUwAIDqNwCAqjgAgA8vAIBhKwCANS0AgIktAIDVMACA0SsAgCIzAIDmMwCASjQAgGY0AIBqNACAfjQAgPo4AIDuNACAkjYAgFY3AIAKOACANjgAgE45AIBSOQCAVjkAgLo5AIAuOACAxjgAgDErAIBVKwCAaSsAgCUsAIAxLACAcSwAgCUtAIBBLQCASS0AgIUtAICRLQCAdC4AgIsvAICzLwCAuy8AgJH4EADTLwCAfzAAgK8wAIDdMACAWjEAgIApAQCBKQEAgjkBAIM5AQCEKQEAhSkBAIZZAQCHWQEAiNkoAIltAQCKKSUAi2EBAIxhAQCNYQEAHjIAgDoyAICQGQEAajIAgJIVAQC+MgCA3jIAgJU1AQCWPQEAlzUBAJgNAQCZFQEAmh0BAJsVAQCcDQEAnfUBAJ7dKABSMwCAoAUBADI0AICiAQEAVjQAgFI0AIClGQEApgkBAFo0AIBeNACAdjQAgKo9AQCrNQEArC0BAK0VAQCuHQEArxUBALBtAQCxdQEAsn0BALN1AQC0bQEAtRUBALYdAQC3FQEAuC0BALk1AQC6PQEAuzUBALzZLgC9KQEAvhkBAL8ZAQC6eR4Au3keALjNAgC5eR4AvpUeAL+dHgC8QQIAvZ0eALJ9HgCzRR4AsH0eALF1HgC2XR4At0UeALRdHgC1VR4AqgUeAKsNHgCodR4AqQ0eAHo0AICeNACArBUeAK0NHgCiSR4Ao0keAKBJHgChSR4ApkkeAKf5AgCkSR4ApUkeAJqNHgCblR4AmI0eAJmFHgCeiR4An4keAJyNHgCdhR4AkgUDAJP1AACQCQMAkY05AJaxHgCXFQYAlO0AAJUBHACKvQMAi0EDAIiFAwCJnQMAjkEDAI9JAwCMyTkAjVEDAIIVAgCDHQIAgAUCAIEdAgCGzQMAh7EDAIQFAgCFxQMAs/kFALLxBQCx+QUAsOEFALeZKgC2EQMAtRkDALThBQC7NQMAujUDALklAwC4JQMAvxUDAL4VAwC9JQMAvCUDAKP9BQCi/QUAof0FAKD9BQCnnQUApp0FAKWdBQCknQUAq7kFAKqxBQCpJScAqL0FAK+ZBQCukQUArZkFAKyhBQCTAQUAkvkFAJF1OQCQ9QUAlwEFAJYZBQCVEQUAlBkFAJt5CQCaOQUAmTEFAJg5BQCfHQUAnh0FAJ0dBQCcHQUAg4kFAIKBBQCBiQUAgPEFAIeFBQCGhQUAhZUFAISBJgCLhQUAioUFAIm1BQCItQUAj4UFAI6FBQCNlQUAjJUFAM40AIA6NQCAQjUAgFY1AIB+NQCAzjUAgAI2AIBqNgCAEjcAgCo3AIBeNwCAYjcAgKY3AICqNwCAAjgAgNo4AIAeOQCANjkAgIMvAICQ6gCA5jUAgLkqAIC9KwCAfSsAgCUrAIBlKwCAkSsAgCEsAIA9LACAES0AgCEtAIA9LQCAmS0AgOQtAIDwLQCADC4AgBwuAIALLwCAEy8AgEMvAIBjLwCAky8AgKsvAICbLwCAry8AgO8vAIBHMACAUzAAgFswAICDMACACTEAgB0xAIBeMgCAVjIAgIYyAIAWNACA4jIAgBYzAIBiMwCAfjMAgKIzAIDGMwCAyjMAgOozAICAjQEAgZUBAIKdAQCDlQEAhI0BAIW1AQCGvQEAh7UBAIiNAQCJwR0AipkBAIvBHQCMhQEAjY0BAI6FAQCP/QEAkIUBAJEZHQCSkRQAk4UBAJSdAQCViTIAlk0ZAJc9GwCYsQEAmbEBAJotHACbtQEAnD0cAJ2pAQCemQEAn5kBAKDlHQChbQEAomUBAKN9AQCkZQEApW0BAKbxHQCnYQEAqKEDAKmhAwCqoQMAq6EDAKyhAwCttQEArq0DAK+lAwCwYRkAsdkDALLZAQCz7QMAtPUDALX9AwC29QMAt+0DALjFAQC50QMAumEdALvVAwC82QEAvT0XAL7FAwC/0QEA+jMAgA40AIAKNACAOjQAgLY0AIDmNACAHjUAgE41AIAyNgCAWjYAgM42AIAWNwCAIjcAgEI3AIBGNwCAUjcAgG43AIDmNwCAFjgAgEo4AIBqOACAtjgAgA45AIAqOQCAijkAgCfqAIAi6gCAVOoAgOEpAIAJKgCADSoAgNbqAIAD6wCAe+sAgBY6AIAmOgCARwgAgFIIAIBVCACASggAgE4IAIBXCQCA8Q4AgOIOAIDnDgCA9g4AgOwOAICyNACASw8AgMoPAICBDwCALw8AgFoPAIBnDwCAbw8AgJ0PAIDCDwCAuA8AgL0PAICqDwCAsQ8AgP4OAIADDwCACA8AgIBBAQCBMQMAgk0BAINFAQCEXQEAhUUBAIZNAQCHIQMAiF0fAIl9AQCKaQMAi3EBAIx1AwCNVQEAjlk6AI9ZAQCQKQEAkSkBAJI5AQCTOQEAlCkBAJUpAQCW2QEAl9kBAJjpAQCZ6QEAFQ8AgCIPAIAqDwCAMg8AgDwPAIBBDwCARg8AgFAPAIBVDwCAXQ8AgGoPAIByDwCAdw8AgHwPAICEDwCAiQ8AgJMPAICYDwCAoA8AgKUPAIDFDwCANw8AgBoPAIBiDwCAjg8AgA0PAIDdFgCA5hYAgOkWAIDvFgCA4xYAgOwWAIDgFgCAExcAgBYXAID1FgCA8hYAgPgWAICAmQcAgZkHAPsWAICDrQcAhLUHAAQXAICGsQcAh7EHAIiRBwCJkQcAipEHAIuRBwCM8QcAjfEHAI7xBwCP8QcAkJEHAJGVBwCSnQcAk5kHAJSFBwCVgQcAloEHAJeFBwCYuQcAmb0HAJq1BwCbsQcAnK0HAJ2pBwCemQcAn50HAKBhBwChZQcAom0HAKNpBwCkdQcApXEHAKZxBwCndQcAqEkHAKlNBwCqRQcAq0EHAKxdBwCtWQcArkkHAK9NBwCwMQcAsTUHALI9BwCzOQcAtCUHALUhBwC2IQcAtyUHALgZBwC5HQcAuhUHALsRBwC8DQcAvQkHAL7xAAC/9QAAgAkBAIENAQCCHQEAgxkBAITZAACF3QAAhtUAAIfRAACI8QAAifUAAIr9AACL+QAAjOkAAI3tAACO5QAAj+EAAJCdAACRmQAAkq0AAJOpAACUtQAAlbEAAJaxAACXtQAAmIkAAJmNAACahQAAm4EAAJydAACdmQAAnokAAJ+NAACgdQAAoXEAAKJ9AACjeQAApGlQAqVtUAKmYQAAp2UAAKhZAACpXQAAqlUAAKtRAACsTQAArUkAAK49AwCvOQMAsClQArEtUAIBFwCABxcAgP4WAIANFwCAChcAgBkXAIDZXFICHxcAgCUXAIAiFwCAKBcAgCsXAIA0FwCALhcAgKOhAACipQAAoZEAAKCVAACntQAAprEAAKW9AACkuQAAq40AAKqJAACpgQAAqIUAAK+FAACugQAArYkAAKyNAACz/QAAsvkAALHxAACw9QAAt5kAALadAAC1nQAAtJkAALutAAC6qQAAuaUAALilAAC/ZQEAvmEBAL1tAQC8aQEAHBcAgFcXAIBAFwCAPRcAgEgXAIBOFwCAOhcAgNksUQJLFwCAVBcAgHkWAIDhDwCAMRAAgA4QAIAiEACAHRAAgJNBAAAnEACALBAAgBMQAICXWQAAllUAAJVZAACUXQAAm3EAAJppAACZZQAAmGUAAJ9lAACeYQAAnTFTApxtAAC4gQQAuYEEALqBBAC7gQQAvIEEAFEXAIC+jQQA5g8AgLDdBQCxTQQAskUEALNdBAC0RQQAtU0EALZFBADrDwCAqKEFAKntQQCqrQUAq6UFAKy9BQCtpQUArq0FAK+lBQCgqQUAoZFBAKKpQACjoQUApKEFAKWhBQCmoQUAp6EFAP8PAIAYEACAWBAAgF0QAIBpEACAnVUFAH8QAICfWQUAjhAAgJMQAICeEACAkwUFAJQdBQCVBQUAlg0FAJcFBQC4EACAyxAAgO8QAIAhEQCAJhEAgC4RAIA9EQCATBEAgIBxBQCBcQUAgnEFAINxBQCEUQUAhVEFAIZdBQBREQCAWREAgHwRAICjEQCArxEAgM8RAIDUEQCA2REAgBMSAIAmEgCAMhIAgEoSAIDEEgCAGhMAgDMTAIA4EwCASxMAgFwTAIBuEwCAcxMAgJoTAICiEwCAtxMAgN4TAIDjEwCAPRQAgEIUAIBHFACAUxQAgF8UAIBkFACAbBQAgHgUAICSFACAlxQAgJ8UAICkFACAqRQAgK4UAICzFACAuBQAgMsUAIDQFACA7BQAgAYVAIAgFQCALBUAgEQVAIBJFQCAVhUAgHcVAICaFQCAtBUAgMAVAIDFFQCAzRUAgO4VAIAIFgCAFxYAgDQWAIA5FgCAQRYAgEYWAIBZFgCAXhYAgICtAQCBtQEAgr0BAIO1AQCErQEAhdUBAIbdAQCH1QEAiO0BAIn1AQCK/QEAi/UBAIztAQCN1QEAjt0BAI/VAQCQrQEAkbUBAJK9AQCTtQEAlK0BAJVVAwCWXQMAl1UDAJhtAwCZdQMAmn0DAJt1AwCcbQMAnVUDAJ5dAwCfVQMAoK0DAKG1AwCivQMAo7UDAKStAwCl1QMAphkOAKfZAwCobQ8AqSEOAKrhAwCr4QMArCkOAK3lAwCuGQ4ArxkOALCVAwCxnQMAsgEOALORAwC0HQ4AtQUOALa5AwC3uQMAuDkOALmNAwC6NQ4AuxEOALyBAQC9gQEAvnkBAL95AQCEFgCAkBYAgJwWAICrFgCAyBYAgM0WAIDuEQCA/xEAgHwWAICBAACAiwAAgJUAAICfAACAqQAAgLMAAID1DwCA+g8AgAQQAIB1EACAehAAgIQQAIDlEACA6hAAgBcRAIAzEQCAOBEAgEIRAIBRFQCADRYAgBIWAIAqFgCAoRYAgKYWAIC+FgCA8A8AgAkQAICJEACAHBEAgNcSAIA/FQCALxYAgGMWAIDDFgCARxEAgGQSAICfEgCAshIAgBEUAIAdFACAKRQAgI0TAICSEwCA0RMAgNYTAID9EwCAAhQAgGkSAIBuEgCAtxIAgLwSAIDCEQCAxxEAgJYRAICbEQCApD0DAKVFAwCmTQMAp0UDAKA9AwChJQMAoi0DAKMlAwCsfQMArUUDAK5NAwCvRQMAqH0DAKllAwCqbQMAq2UDALQ9AwC1xQMAts0DALfFAwCwPQMAsSUDALItAwCzJQMAvP0DAL3FAwC+zQMAv8UDALj9AwC55QMAuu0DALvlAwCEBQwAhQ0MAIYFDACHHQwAgI0MAIGpDACCGQwAg1ENAIxhDACNYQwAjmEMAI9hDACIKQwAiRUMAIodDACLFQwAlD0MAJXFAwCWzQMAl8UDAJABDACRAQwAkgEMAJMBDACc/QMAncUDAJ7NAwCfxQMAmP0DAJnlAwCa7QMAm+UDAIBpBACBaQQAgnEEAINxBACEnQQAhYUEAIaNBACHhQQAiL0EAImNBACKhQQAi50EAIyFBACNqQYAjvkEAI/5BACQiQQAkYkEAJKRBACTkQQAlLEEAJWxBACW+QYAl60EAJiVBACZwQYAmmkGAJtpBgCceQYAnXkGAJ7RBgCf/QsAoA0GAKEdCwCiGQYAo0ULAKQFBgClTQsApjUGAKe1BACoEQYAqREGAKoRBgCrNQQArC0EAK0BBACuXQQArx0GALDNBgCxbQYAsnUGALMNBgC0FQYAtR0GALYVBgC3DQYAuDUGALk9BgC6NQYAuw0GALwVBgC9HQYAvhUGAL8NBgCA9QcAgf0HAIL1BwCD9QAAhO0AAIURAwCGEQMAhxEDAIgxAwCJMQMAijEDAIsxAwCMhQcAjRUDAI4dAwCPFQMAkG0DAJGNBwCShQcAk50HAJSFBwCVjQcAloUHAJe9BwCYhQcAmY0HAJqFBwCbnQcAnIUHAJ2NBwCehQcAn4UAAKB9AAChgQMAooEDAKOBAwCkgQMApYEDAKaBAwCngQMAqBUHAKmFAwCqjQMAq4UDAKydAwCtoQMArqEDAK+hAwCwdQcAsXUHALJxBwCzhQUAtM0FALX1BQC2/QUAt8kDALj5AwC5+QMAuqEFALuhBQC8wQMAvcUDAN4RAIDjEQCAhJz7ACYTAIArEwCAYRMAgGYTAIB2EgCAghIAgJUSAICaEgCARRIAgNwSAIBXEwCASxAAgKMQAIC9EACAxBAAgJB1AACRfQAAknEAAJNxAACUAfwAlVX+AJZd/gCXVf4AmG3+AJlp/gCaef4Am3n+AJxp/gCdaf4Anln+AJ9Z/gCgpf4Aoa3+AKKl/gCjof4ApKH+AKWl/gCmrf4Ap6X+AKiZ/gCpmf4Aqun+AKvt/gCs9f4ArfH+AK7x/gCv8f4AsI3+ALGV/gCymf4As5n+ALSJ/gC1if4Atrn+ALe9/gC4hf4AuY3+ALqF/gC7nf4AvIX+AL2B/gC+gf4Av4H+AKbZCACnBQcApMEIAKWZBQCi0QgAo9EIAKCJBQChtQgArgEHAK8BBwCsMQcArTEHAKo9BwCrJQcAqD0HAKk1BwC2fQcAtwUHALR9BwC1dQcAsskFALNlBwCwcQcAsXEHAL4BBwC/AQcAvDEHAL0xBwC6IQcAuyEHALg9BwC5MQcAhjkHAIc5BwCELQcAhTkHAIINBwCDNQcAgBEHAIEFBwCOSQcAj0kHAIxNBwCN1QUAisEFAIvBBQCI1QUAiXEHAJbVBQCX2QgAlE0FAJXdBQCSUQUAk9kFAJD5BQCRoQUAnnEIAJ99CACcYQgAnWEIAJpxCACbeQUAmMUIAJl1BQD0EACA+xAAgAIRAICBEQCAuxEAgLQRAIArEgCAGBIAgB8SAIBWEgCATxIAgF0SAIDJEgCAHxMAgIcSAIB7EgCApBIAgKsSAIA9EwCAUBMAgHgTAIB/EwCAhhMAgKcTAIC8EwCAwxMAgOgTAID2EwCA7xMAgEwUAIB9FACAhBQAgAsVAIAZFQCAEhUAgPEUAIAlFQCAMRUAgHwVAICDFQCAkxUAgFsVAIBpFQCAnxUAgKYVAIBiFQCASxYAgFIWAIDzFQCA+hUAgNkVAIDgFQCAIxYAgBwWAICwFgCAbhAAgLEQAICqEACA3hAAgNcQAIAQEQCACREAgI8RAIBeEQCAgIEBAIGBAQCCgQEAg4EBAISdAQCFhQEAhokBAIeJAQCItQEAib0BAIq1AQCLjQEAjJUBAI2dAQCOlQEAj40BAIgRAIA3EgCAkv0BAJP1AQCU7QEAlZUBAJadAQCXlQEAmKkBAJmpAQCauQEAm7kBAJypAQCdrQEAnqUBAJ+dAQCgZQEAoW0BAKJlAQCjfQEApGUBAKVtAQCmZQEAp90AAKjlAACppQMAqq0DAKulAwCsvQMAraUDAK6tAwCvpQMAsN0DALHlAwCy7QMAs+UDALSpAQC1VQEAtvUDALftAwC41QMAud0DALrVAwC7rQMAvM0DAL3BAwC+vQMAv7UDANASAICOEgCARBMAgP8UAIA4FQCAlRYAgIkWAIC3FgCAuRUAgIsUAIABFgCAyhMAgMQUAIDSFQCArRUAgPgUAIC9FACAZREAgKgRAIBwFQCA0BAAgFgUAIBiEACAPhIAgOcVAIATEwCAcRQAgEIQAIA5EACAihUAgOESAID2EQCArhMAgGsWAIDqEgCA8RIAgGwRAIAEEgCApgMAgA0jAIARIwCAoAYAgMcAAIC1BgCAqyMAgK8jAIC5IQCAtSEAgOMHAIB7CQCAfwkAgEEjAICnIwCANSMAgDkjAIAdIwCAISMAgCUjAIApIwCALSMAgDEjAIDbBwCA3wcAgNEAAICATQEAgVEBAIJRAQCDTQEAhE0DAIUhAwCGRQEAh30BANcAAICiAwCAqAMAgN0HAIDTAACA1QAAgL0GAIB5AACABxQAgH0AAICHAACAkQAAgAwUAICbAACAGBQAgKUAAIAkFACArwAAgDAUAIC5AACANRQAgM8PAIBVEACAmBAAgJsQAIArEQCAVhEAgKARAIDMEQCA6BEAgOsRAIDzEQCADRIAgBASAIBzEgCAwRIAgDATAIBrEwCAlxMAgJ8TAICwpQEAsa0BALKlAQCzvQEAtKUBALWtAQC2pQEAt10BALhlAQC5bQEAumUBALt9AQC8ZQEA2xMAgDoUAIBpFACAgAW5AIHhBgCC4QYAg+EGAIThBgCoBgCAswYAgIfpBgCI2QYAifmxAIr1sQCL8bEAjO2xAI31BgCO+QYAj/0GAJDZBgCR2QYAkvWxAJwUAICUiZIClfEGAJb1BgCX9QYAmNkGAJnVsgCa3bIAm6kGAJy5BgCduQYAnqkGAJ+BBgCgoQcAoaEHAKIhsgCjpQcApIUAAKWNAACmQbMA1RQAgKiNBwCplQcAqp0HAKuVBwBOFQCAyhUAgDYQAIA+FgCAsP0HALGFBwCyjQcAaBYAgLSZBwCBFgCAtpUHALeNBwC4tQcAub0HALq1BwC7jQcAvJUHAL2dBwC+lQcAv40HAIB1BgCBlaACgpmgAoOZoAKEhaAChb2gAoaxoAKHhaACiLmgAomRoAKKnaACi5mgAoyFoAKNjQEAjoEBAI9FBgCQOQYAkT0GAJIxBgCTMQYAlC0GAJXVBgCW2QYAl90GAJjhBgCZ4QYAmu0GAJvpBgCc9QYAnf0GAJ7xBgCf9QYAoAkGAKEJBgCiBQYAowEGAKQdBgClBQYApgkGAKcNBgCoMQYAqTEGAKo9BgCrNQYArCkGAK0pBgCuJQYArx0GALBhBgCxYQYAsm0GALNpBgC0dQYAtX0GALZxBgC3dQYAuEkGALlJBgC6RQYAu0EGALxdBgC9RQYAvkkGAL9NBgCAsQUAgbEFAIK9BQCDuQUAhKUFAIWtBQCGoQUAh6UFAIiZBQCJmQUAipUFAIuRBQCMjQUAjcEFAI7NBQCPyQUAkLUFAJG9BQCSsQUAk7UFAJSpBQCVqQUAlqUFAJehBQCYnQUAmSkCAJolAgCbIQIAnD0CAJ3pAgCe5QIAn+ECAKAdAgChNQIAojkCAKM9AgCkIQIApSECAKYtAgCnKQIAqBUCAKkZAgCqFQIAqxECAKwNAgCteQIArnUCAK8V8ACwafAAsRECALIdAgCzGQIAtAUCALUhAAC2LQAAtyUAALgZAAC54QEAuu0BALvlAQC8+QEA2BQAgN0UAIC/9YYCp2kNAOIUAIDnFACAzwAAgNkAAICzAwCA4QcAgH0JAID7IgCAzNSFAszghQL/IgCAgSkAgDUkAIBuJACAjSQAgLyZBQC9mQUAvqkFAL+ZvAC4mQUAuZkFALqJBQC7iQUAtKEFALXVsQC23bEAt6kFALCxsgCxzQUAssUFALO9BQCfJACAxCQAgMMoAIDfKACA8SgAgIgmAICFKQCAaSkAgCkkAIAtJACA2WSgAoEJAIDZUKAChAkAgI0JAICKCQCAhwkAgOwhAIDvIgCA9CEAgJhlBQCZEbIA/CEAgNkwoAKUOZEClU0FAJZFBQCXXQUAkGkFAJFpBQCSWQUAk1kFAID9vACB1ZwCgmW8AIPFvACEkbwAhZ28AIalvACHjbwAiK2TAonlvACKKZACi7W8AIwRkAKNlbwAji2wAI/FnAKQ6bwAkcHIAJJBkAKT8Z0ClNW8AJXlvACW4bwAl02QAphlkAKZfZACmrm8AJupCgCcbQ8Anb0KAPMiAICfXQ8AoK0PAKElCgCibQoAo2UKAKQNCgClpQ8ApgXUAKepDwComQ8AqZkPAKopDwCrKQ8ArDkPAK05DwCuKQ8ArykPALBZDwCxndEAspXRALOF1gC0sdEAtbHRALbZ1AC32dQAuOnUALnp1AC6+dQAu/nUALzp1AC96dQAvrnUAL+51ACASdUAgUnVAIJZ1QCDWdUAhEnVAIV90ACGddAAh23QAIhV0ACJXdAAinXVAIut1QCMtdUAjb3VAI611QCPQdAAkMHQAJHB0ACSwdAAk8HQAJTB0ACVwdAAlsHQAJfB0ACYwdAAmc3QAJrF0ACb3dAAnOHVAJ3pDgCe2Q4An9kOAKDV2wChwdkAotnZAKPB2QCkxdkApc3ZAKbF2QCnGdkAqGHZAKlh2QCqydkAq8nZAKzZ2QCt2dkArs3ZAK/B2QCwCdkAsRXZALId2QCzrdoAtB3ZALWx2gC2wdwAt93dALjl3QC59d0Auv3dALut3QC8td0AvaXdAL6t3QDwIQCAgvHaAIPx2gD3IgCA5OgAgIYR2ACHEdgAhOHaAIXh2gCKKdgAiynYAK9AEwClKNoAjinYAI8p2ACMKdgAjSnYAJJh2ACTYdgA6egAgO7oAICWZdgAl23YAJR12ACVbdgAml3YAJst2ADz6ACA8FwCALEw3wCR8AIAnCnYALLQAwCiOQ0Ao1GeAqAlDQChOQ0AplUNAIS8AgCkJQ0ApV0NAKptDQCrAQQAqGENAKlRAwCuuQAAp3UAAKxhDQCtxQIA+OgAgIfMAwDwVAIAzFC6AJHYBACb9NsAkRgCAJk02wCddAQAvh0AAJ9gBQCejAUAjOwCAI2sBAD96ACAvfWKAqghvwCpLb8Aqi2/AKs9vwCsKb8ArVW/AK5RvwCvTb8AoBkIAKGlvQCiIb8AozGzAKQ9vwClJb8Apg2zAKclvwC46bMAuc3LALppswC7uQkAvH0IAL2tCQC+QQwAv50JALA5vwCxhb0Asgm/ALPtywC0Gb8AtQW/ALbtswC3Bb8AiDG9AIkxvQCKrQgAiyW9AIwJCQCNvQgAjiW+AI+JDAAC6QCAgQ0JAIKlDACDUQkAhIEIAIWBCACGmQgAh60MAJhhvQCZYb0Amm0JAJsVnQKcxQ8AnQ28AJ7BDwCfcQkAkBW+AJERnwKSNZ8Ckw2fApQJvgCVCb4AlnG9AJdxvQCCuAQAl6UHALnEAwDwWAIAkUwCAJLIAgCErAQAsD0AAAzpAIAH6QCAvQUAABHpAIDwTAIAuhEAAJEkAgCN5AQAkqwCAJasAgC4uAMAudADAJb4AgCvDQAAFukAgPB4AgCRXAIAlrACAK8FAAAb6QCAIOkAgCnpAIAy6QCAP+kAgIX4AwBM6QCAh4ADAIbAAgBZ6QCAZukAgHPpAICW6QCAuzkAAHzpAICf6QCAiekAgL8dAAC+HQAAvR0AALwhAACVwB0AlMQfAJfIGgCWABgAkSAAAJDUAQCT2B4AkgAcAJ3gEgCcABAAn+gRAJ7sEwCZ8BkAmPQbAJv4FwCaABQAnnEBAJ9xAQCABQAArOkAgM0KAICwDACAXg0AgGQNAIBqDQCAdg0AgHkNAIB8DQCAfw0AgIINAICRDQCAlw0AgJoNAICdDQCAICIAgMcNAIDWDQCA/A0AgP8NAIAODgCAEQ4AgB0OAIAYIgCAMg4AgDUOAIDXFgCAEBcAgNoWAIC4ACwAuYwvALqILgC6AwCAhpwXAMx4vACEmC0AhVwXALcDAIDKAwCAiAAoAIksFADtBACAjAUAgN8FAIAaBgCAQAYAgFcGAIB0BgCAiwYAgDgBAIA8AQCAQAEAgEQBAIBIAQCATAEAgKR9AQBQAQCAonUBAKNlAQCggQEAoYEBALxxugC9kbYAvnG6AL+ltgC48bgAuXW6ALqZzgC7dboAtGG6ALVtugC2eboAt3W6ALAZugCxEboAsgm6ALMFugCsUboArXG2AK5RugCvbboAqNG4AKldugCqRbYAq1G6AKRxlgKlYZYCpnGWAqe9ugCgzZsCofG6AKLJugCjxboAnHmaAp0tugCeDc4An4WWApgJugCZtZYCmjm6AJuJtgCUMboA+CEAgJZpugCXrZYCkHm6AJE1ugCSMboAkwG6AIxJzgCN5bYAjhmaAo+hugCIoboAiUG2AIqhugCLdbYAhAG4AIWFugCGac4Ah4W6AICxugCBvboAgqm6AIOlugCAgbkAgQ27AIIVtwCDAbsAhAG7AIUhtwCGAbsAhz27AIgJuwCJAbsAihm7AIsVuwCMcbsAjX27AI5puwCPZbsAkKG5AJEluwCSyc8AkyW7AJQhuwCVwbcAliG7AJf1twCY6c8AmUW3AJq5mwKbAbsAnLm7AJ31uwCe8bsAn8G7AKARuwChCZQCokm7AKONlwKkCbsApbWXAqY5uwCnibcAqFmbAqkNuwCqLc8Aq6WXAqwNmgKtMbsArgm7AK8FuwCw0ZcCscGXArLRlwKzHbsAtFG5ALXduwC2xbcAt9G7ALjxuwC50bcAuvG7ALvNuwC82bsAvdG7AL7JuwC/xbsAgJmkAIEliAKCqaQAgxmoAFsNAICFvaQAhp3QAIcViAKInYUCiaGkAIqZpACLlaQAjCGIAo0xiAKOIYgCj+2kAJDBpgCRTaQAklWoAJNBpACUQaQAlWGoAJZBpACXfaQAmEmkAJlBpACaWaQAm1WkAJwxpACdPaQAnimkAJ8lpACgYaYAoeWkAKIJ0ACj5aQApOGkAKUBqACm4aQApzWoAKgp0ACphagAqnmEAqvBpACseaQArTWkAK4xpACvAaQAsFGkALFJiwKyCaQAs82IArRJpAC19YgCtnmkALfJqAC4GYQCuU2kALpt0AC75YgCvE2FAr1xpAC+SaQAv0WkAIARiQKBAYkCghGJAoPdpQCEkacAhR2lAFQBAICHEaUAiDGlAIkRqQCKMaUAWAEAgFwBAICNEaUAjgmlAI8FpQCQAaUAkQ2lAJIZpQCTFaUAlLGnAGABAICW2dEAlzWlAJgRpQCZ8akAmhGlAJvFqQCc+dEAZAEAgJ6phQKfEaUAoEmlAKEFpQCiAaUAozGlAKQBpQClGYoCplmlAKediQKoOaUAqYWJAqoJpQCruakArEmFAq0dpQCuPdEAr7WJArB9hAKxQaUAsnmlALN1pQC0wYkCtdGJArbBiQK3DaUAuGGnALntpQBoAQCAu+GlALzhpQC9wakAvuGlAGwBAIC3baYAttWGArUpqgC0hdIAs7mqALJtpgCxjaoAsG2mAL8higK+5aYAvaWJAnABAIC7jaYAdAEAgLm5pgC49aYAeAEAgKZ1pgClbaYAfAEAgIABAICiTaYAhAEAgIgBAICvCaYAruXSAIwBAICsjaQAqymmAKolpgCpMaYAkAEAgJc5pgCWNaYAlQ2mAJQxhwKTmYoCkhHSAJExpgCQZYYCn62mAJ65qgCUAQCAnC2kAJthpgCarYoCmb2KApitigKHfaYAhk2mAIVJpgCEBaYAg72mAIIFhgKB+aoAgFXSAI/1qgCORaYAjcmKAox1pgCL8YoCijWmAIl1iQKIbaYAgCmnAIEhpwCCOacAgzWnAIRRpwCYAQCAhkmnAJwBAIDMSIkCzYiJAoqp0wCLRacAjEGnAI2hqwCOQacAj5WrAJDJ0wBFIwCAkpmHApMhpwCUmacAldWnAJbRpwCX4acAmPGnAJnpiAKaqacAm22LApzppwCdVYsCntmnAJ9pqwCgeYcCoS2nAKIN0wCjhYsCpC2GAqURpwCmKacApyWnAKixiwKpoYsCqrGLAqt9pwCsMaUArb2nAK6lqwCvsacAsNGnALHxqwCy0acAs+2nALT5pwC18acAtumnALflpwC4oacAua2nALq5pwC7tacAvBGlAL2VpwC+edMAv5WnAICRoACBiY8CgsmgAIMNjAKEiaAAhTWMAoa5oACHCawAiNmAAomNoACKrdQAiyWMAoyNgQKNsaAAjomgAI+FoACQUYwCkUGMApJRjAKTnaAAlNGiAJVdoACWRawAl1GgAJhxoACZUawAmnGgAJtNoACcWaAAnVGgAJ5JoACfRaAAoMGgAKHNoACi2aAAo9WgAKRxogCl9aAAphnUAKf1oACo0aAAqTGsAKrRoACrBawArDnUAK2VrACuaYACr9GgALAJoACxRaAAskGgALNxoAC0QaAAtVmPArYZoAC33YwCuHmgALnFjAK6SaAAu/msALwJgAK9XaAAvn3UAL/1jAKAvYACgYGhAIK5oQCDtaEAhAGNAoURjQKGAY0Ch82hAIihowCJLaEAijWtAIshoQCMIaEAjQGtAI4hoQCPHaEAkGmhAJFhoQCSeaEAk3WhAJQRoQCVHaEAlgmhAJcFoQCYgaMAmQWhAJrp1QCbBaEAnAGhAJ3hrQCeAaEAn9WtAKAJ1QChpa0AolmBAqPhoQCkWaEApRWhAKYRoQCnIaEAqDGhAKkpjgKqaaEAq62NAqwpoQCtlY0CrhmhAK+prQCwOYECsW2hALJN1QCzxY0CtG2AArVRoQC2aaEAt2WhALjxjQK54Y0CuvGNArs9oQC8caMAvf2hAL7lrQC/8aEAs2miALKF1gCxaaIAsO2gALe5rgC2baIAtY2uALRtogC7TaIAuvWCArkJrgC4pdYAv42iAL69ogC9uaIAvPWiAKNNogCiWa4AoUGiAKDNoACncaIApk2iAKVtrgCkTaIAq1miAKpVogCpTaIAqEWiAK8pogCuJaIArTGiAKw9ogCTla4AkiWiAJGpjgKQFaIAl5mOApYR1gCVMaIAlGWCApsZogCaFaIAmS2iAJgRgwKfYaIAnq2OAp29jgKcrY4Cg2muAIK9ogCBXa4AgL2iAIe9ogCGBYIChfmuAIRV1gCLXaIAim2iAIlpogCIJaIAj/GOAo41ogCNdY0CjG2iAIARowCBMa8AghGjAIMtowCEOaMAhTGjAIYpowCHJaMAiGGjAIltowCKeaMAi3WjAIzRoQCNVaMAjrnXAI9VowCQMaMAkdGvAJIxowCT5a8AlNnXAJV1rwCWiYMClzGjAJipowCZ5aMAmuGjAJvRowCc4aMAnfmMAp65owCffY8CoBmjAKGljwKiKaMAo5mvAKRpgwKlPaMAph3XAKeVjwKoHYICqSGjAKoZowCrFaMArKGPAq2xjwKuoY8Cr22jALBBoQCxzaMAstWvALPBowC0waMAteGvALbBowC3/aMAuMmjALnBowC62aMAu9WjALyxowC9vaMAvqmjAL+lowBnDQCA0QYAgG0NAIDIBwCAcw0AgA8HAICFDQCAlAcAgIsNAICaBwCAuA0AgH0HAIDKDQCAxQcAgAIOAIBPBwCAFA4AgFIHAIAgDgCAkB0AAOEGAIAPJACA4iUAgCguAICtLACAyS0AgKpVAACrKQAAMjcAgAErAIDGMACAsjIAgAEsAIBTLwCAmSsAgJ8wAIDtKwCAGjUAgI43AICtLQCA5SwAgGYyAIADMACALzAAgA44AIAjMACA+y8AgHI0AICAIa4AgaWsAIJJ2ACDpawAhKGsAIVBoACGoawAh3WgAIhp2ACJxaAAiv0AAIsxxgCM7QAAjdEAAI7VAACPyQAAgCmhAIFNFACCIQEAg+G4AoQ5qgCFOaoAhhG9AodRFACIEQEAidW4AorNrQCLLbsCjGEUAI3ZjQKObRQAj2UUAJB5AQCRubgCkkm9ApNFuwKUDRQAlTUUAJYZAQCXqbgCmF2qAJkBFACaIQEAmwUUAJx5vQKdhbgCnnm7Ap+JuAKggb0CoXm4AqKZCQCjlRQApFmuAKWJFACmmQEAp70UAKipAQCpvbsCqrkBAKuJFACsmRQArZkUAK6JFACviRQAsNkBALEJrgCy6QEAs9W7ArTNuwK17RQAtpW8ArfhFAC4oRQAuaEUALrBoQC7pRQAvNkBAL0ZuAK+0aoAv9GqAL9FFwC+RRcAvTUXALxBvwK7KRcAugm4ArkBuAK4PQIAt+2tALY9AgC1HRcAtB0XALMdFwCyHRcAsR0XALAtAgCvWbgCrk0CAK1pFwCsTQIAq00XAKqdrQCpQRcAqE0KAK40AIDRLACApX0XAKR9FwCjoa4Aom2CAqF9ggKgbYICnzmuAJ41rgCdDa4AnDGPApuZggKaEdoAmTGuAJhljgKXtaIAlgWuAJWJggKUNa4Ak7GCApJ1rgCRNYECkC2uAI99rgCOTa4AjUmuAIwFrgCLva4AigWOAon5ogCIVdoAh0miAIadrgCFfaIAhJ2uAIOZrgCCddoAgZmuAIAdrADMqIQCzUyGAswguQLNTLkCzECOAkYyAIDMmIUCzTyEAswQgwLNUIMCzKCDAs2MgwLMMIACzSSAAswYgALNhIACmjMAgAUsAIAxLQCAiSMAgE0jAIBXIwCAayMAgJMjAIB1IwCAnSMAgGEjAIB/IwCAzPC5As2EuQLMULgCzay7AoDNAACB1QAAgt0AAIPVAACEzQAAhfUAAIb9AACH9QAAiM0AAFcvAIDBLACA1SoAgM0qAIDdKgCAuekAgCErAICQZQAAkW0AAKiIKgA1KwCAPSsAgEUrAIBJKwCATSsAgKIAMACjzDMAoOg9AKHsPACm8DYAp/QoAKQANACl/DUAgFERAIHpiAKCXREAg1URAIQpBACF6b0Chhm4AocVvgKIfREAiUURAIppBACL2b0CjA2vAI1REQCOcQQAj1URAJBJuAKRtb0Ckkm+ApO5vQKUUbgClam9ApZJDACXRREAmKmrAJl5EQCaaQQAm00RAJx5BACdbb4CnmkEAJ9ZEQCgqREAoakRAKK5EQCjuREApIkEAKVZqwCmuQQAp4W+Aqi9vgKpnREAquW5AquREQCs8REArfERAK6RpACv9REAsOkEALEpvQKy4a8As+GvALTZuAK1mREAtukEALctvQK4BagAueW+Arq5EQC7AYgCvKURAL2tEQC+wQQAvwG9AoABuQKBDb8CglUQAINtEACEUQUAheG8AoYlrgCHeRAAiGkFAIlNEACKIbkCi928AowxvwKNwbwCjjm5Ao/BvAKQUQ0AkV0QAJKBqgCTURAAlFEFAJV1EACWUQUAl0W/AphxBQCZQRAAmkEQAJtBEACcQRAAnUEQAJ5hBQCfsaoAoKEFAKGdvwKilb8Co7UQAKTduAKlqRAAptkQAKfZEACoiaUAqe0QAKqBBQCrQbwCrJmuAK2ZrgCusbkCr/EQALDxBQCxNbwCsi2pALPNvwK0gRAAtTmJAraNEAC3hRAAuNkFALkZvAK66bkCu+W/ArytEAC9lRAAvrkFAL8JvAK5La0AuC2tALtFEwC6BboCveG/ArwlBgC/GbwCvvmqALEdEwCwabsCs20TALJtEwC1eRMAtB2mALfVvwK2FQYAqXUTAKh1EwCrhakAqlUGAK1JvAKsdQYAr2ETAK5BvAKhQRMAoGUGAKNxvAKiZQYApVUTAKRlBgCnVRMAplUTAJl1vwKYhbwCm3W/ApqNugKdiRMAnIUOAJ+FEwCeVakAkVW/ApDlBgCTzRMAkpGtAJXZEwCU/QYAl0m/Apa1ugKJmRMAiJETAIs1vwKK9QYAjdm8AozVugKPuRMAjoETAIGtEwCA7boCgxm/AoLdBgCF8bwChBGqAIcVigKGrRMAgD2sAIFhEgCCQQcAg2USAIQZuwKF5b4Chhm9AofpvgKIIbsCidm+AopFEgCLXRIAjSkAgM3pAICOzaoAj8mLApCdiwKRpYsCkrGqAJOxqgCU2akAldmpAJb5qQCX+akAmJWqAJmRiwKatYsCm42LApyJqgCdiaoAnvGpAJ/xqQCgIakAoSGpAKJ9qgCjeYsCpE2LAqV1iwKmYaoAp2GqAKgpqQCpKakAqgmpAKsJqQCsRaoArUGLAq5liwKvXYsCsDmqALE5qgCyQakAs0GpALRxqQC1cakAti2qALcpiwK4PYsCuQWLAroRqgC7EaoAvHmpAL15qQC+WakAv1mpAIKJIwBtKwCAcSsAgI0rAIC+6QCAh5kjAJEpAIB5KwCAyOkAgIu5JACpKwCAifkkAI6VIwCPiSMAsSsAgI2JJACSvSMAESsAgLkrAICR4SMAo+sAgJfFIwCU8SMA4SsAgJkpAICbkSMA+SsAgJndIwD9KwCAnwktAAksAICdjdUAogkjAJ0pAIBBLACAofUjAEUsAICnGSMApCUkAG0sAICq7SQAeSwAgKgdIwCpeSQArhUjAK8JIwCsCSQArQkkALI9IwCJLACAsDEjALFhIwC2VSMAt0UjALRxIwC1XSMAulkjALsRIwCRLACAuV0jAL6JLQCVLACAvI0tANzpAICAuSUAgX0iAIKBIgCDmSIAhK0lAIXZJQCGuSIAh5EiAIiVIgCJ8SUAljIAgIuxJQCMgSUAjYElAI6dIgCPgSIAkLkiAJHpIgCStSIAk9EiAJT5IgCV1SIAlt0iAJfNIgCY+SIAmdUiAJrRIgCbmSIAqSwAgLEsAIDh6QCAvSwAgGUAAACh/SIAogEiAKMZIgDFLACApVklAKY5IgCnESIAqBUiAKlxJQDNLACAqzElAKwBJQCtASUArh0iAK8BIgCwOSIAsWkiALI1IgCzUSIAtHkiALVVIgC2XSIAt00iALh5IgC5VSIAulEiALsZIgD1LACA4SwAgO0sAIDxLACAgI0vAIGlLwCCrS8Ag70vAISlLwCFrS8AhqUvAIfdLwCI5S8Aie0vAIrlLwD5LACAAS0AgAUtAIANLQCAFS0AgJCRLwCRkS8AkpEvAJORLwCUsS8AlbEvAJa1LwCXRTMAmE0zAJlVMwCaPTMAmxkzAJyZMwCdiTMAnlUwAJ9JMACgwTAAockwAKLZMACj1TAApM0wAKX9MACm5TAApzUwAKi1MQCpuTEAqu0xAKuxmgCs0ZYArbE6AK61OgAZLQCAsEGUALHNlgCy1ZoAs8GWALTBlgC14ZoAtsGWALf9lgC4yZYAucGWALrZlgC71ZYAvLGWAL29lgC+qZYAv6WWAMUAAAChfSAAooEgACktAICkrScALS0AgDktAICnkSAAXS0AgKnxJwCqZScAq7EnAKyBJwCtgScArp0gAK+BIACwuSAAsekgALK1IABhLQCAtPkgALXVIAC23SAAt80gAEUtAIC51SAATS0AgLuZIACpLQCAcS0AgHUtAIB5LQCAgDknAIH9IACCASAAgxkgAG0tAICFWScAhjkgAIcRIACIFSAAiXEnAIrlJwCLMScAjAEnAI0BJwCOHSAAjwEgAJA5IACRaSAAkjUgAJNRIACUeSAAlVUgAJZdIACXTSAAmHkgAJlVIACaUSAAmxkgAJyFLgCdBdYAnoEuAJ+BLgCArT8AgbU/AIK9PwCDtT8AhK0/AIW5yACG1T8Ah80/AIj1PwCJ/T8AipnIAIvxPwCMATsAjQE7AI6NyACPOQQAkEkEAJFJBACSWQQAk1UEAJRNBACV3TwAlnkEAJd1BACYWQQAmSEEAJohBACbNdQAnCEEAJ3Z5gCeJQQAnx0EAKDpBACh9QQAos0/AKP1BACkFQQApfnUAKYhyACnIcgAqNHUAKktBACqOQQAq03CAKwtBACtdcgArh0EAK95BACwKQQAsTEEALI9BACzOQQAtC0EALX9BQC2qQUAt6kFALiZBQC5mQUAunkFALtFBQC8AQUAvQEFAL4BBQC/AQUAgC0HAIE1BwCCPQcAgzUHAIQtBwCFqQcAhqUHAIdl1QCILQYAiTEGAIoxBgCLDQYAjPnJAI15BgCOWQYAj1UGAJBpyQCRNQYAkj0GAJM1BgCULQYAlcUGAJZdAwCXVQMAmG0DAJl1AwCafQMAm3UDAJxtAwCdET0AnlkDAJ9ZAwCgqQMAoakDAKK5AwCjuQMApKkDAKWpAwCm2QMAp9kDAKjpAwCp6QMAqvkDAKv9AwCs5QMAre0DAK7lAwCvbcMAsKEDALGhAwCyoQMAs6EDALShAwC1zeYAtq0DALelAwC4yeYAuZkDALppAwC7aQMAvHkDAL15AwC+aQMAv2kDAIAAAACBLQCAfS0AgJUtAIDm6QCAsS0AgLUtAIC9LQCA0S0AgPQtAIDr6QCA8OkAgAAuAIAELgCACC4AgPwtAIAQLgCAoSkAgKUpAIAYLgCAIC4AgPXpAIA8LgCAQC4AgEwuAID66QCAVC4AgFguAIA3LwCAqSkAgGwuAICILgCAhC4AgATqAICQLgCACeoAgJwuAICYLgCAoC4AgLAuAIC0LgCArSkAgMQuAIDMLgCA0C4AgNQuAICxKQCADuoAgLUpAID3LgCA+y4AgP8uAIDV6wCAGOoAgNo1AIAvLwCAuSkAgDvqAIAN6wCAPy8AgEcvAIC9KQCAWy8AgGsvAICqIfQAq7U/AKilPwCpzecArkXwAK+hPwCsSfAArTH0AKJl4gCjvT8AoLk/AKG5PwCmlT8Ap50/AKSlPwClnT8Augk8AG8vAIC4CTwAuQk8AHcvAICHLwCAxSkAgMEpAICy3T8AswU9ALBN7wCx1T8Atn3wALe55AC0HT0AtWk8AB3qAICPLwCAoy8AgKcvAIC3LwCAyy8AgMMvAIDHLwCAgrX7AM8vAICA/T8AgfU/AOMvAIDnLwCA/y8AgAcwAICavT8Am/3NAJi9PwCZtT8Anlk/AJ9ZPwCcWT8AnVk/AJKBPwCTaekAkHnkAJGxPwCWgT8Al4H0AJQh5wCVmT8AFzAAgCswAIAs6gCAJzAAgBswAIAzMACAOzAAgE8wAIAx6gCAVzAAgEoAAABLMACAQzAAgMkpAIBfMACAZzAAgG8wAIBjMACAzSkAgIcwAIA26gCAszAAgPUwAIDRMACA2SkAgNUpAIDRKQCAnSsAgKErAID5MACA4TAAgK41AIA9KgCADTEAgCExAIAZMQCAT+oAgN0pAIA1MQCAKTEAgFIxAIBZ6gCAXjEAgD0xAIBmMQCAajEAgG4xAIByMQCAfjEAgF7qAICGMQCA5SkAgJIxAIBj6gCAljEAgOkpAICiMQCArjEAgL4xAIBo6gCA/+kAgG3qAIDeMQCAcuoAgLgJAQC5CQEAuhkBALsZAQC8CQEAvQkBAL45AQC/OQEAsM3FALE1zACymQ4As5kOALSJDgC1iQ4AtjkBALc5AQCo6dkAqckOAKrZDgCrqcUArMUOAK3NDgCuxQ4Ar/kOAKA1DgChPQ4AojUOAKOxxQCk8Q4ApfEOAKbxDgCn8Q4AmGkPAJlpDwCaeQ8Am3kPAJxpDwCdaQ8Ant0OAJ/NDgCQ+eoAkXEPAJJ9DwCTdQ8AlG0PAJVpDwCWWQ8Al1kPAIh5DwCJeQ8AigkPAIsJDwCMGQ8AjRkPAI4NzACPDQ8AgHkPAIF5DwCCSQ8Ag0kPAIRZDwCFWQ8AhkkPAIdJDwCKUQIAi1ECAIj5xgCJQQIAjnECAI/txgCMQQIAjUECAIIVAgCDHQIAgAUCAIEdAgCGdQIAh30CAIQFAgCFfQIAmsUCAJvNAgCYkc8AmYXaAJ7FAgCfzQIAnNUCAJ3NAgCSDQIAkxUCAJANAgCRBQIAlg0CAJf1AgCUDQIAlQUCAKo9AgCrRQIAqD0CAKk1AgCuXQIAr0UCAKxdAgCtVQIAol3GAKMBAgCgNQIAoQ0CAKYBAgCnxdgApBECAKURAgC6OQIAuzkCALg5AgC5OQIAvtkBAL/ZAQC82QEAvdkBALI9AgCzBQIAsD0CALE1AgC2GQIAtxkCALQdAgC16cIA6jEAgPIxAIDiMQCA/jEAgA4yAIAWMgCAIjIAgCYyAIB36gCACjIAgD4yAIBCMgCA7SkAgFIyAIB86gCANjIAgHIyAICB6gCAhuoAgHYyAICKMgCAgjIAgPEpAICOMgCAnjIAgJoyAICmMgCAw+kAgLYyAICL6gCAwjIAgJXqAIDWMgCA9jIAgJrqAIAKMwCADjMAgJ/qAICk6gCAKjMAgDozAID1KQCAPjMAgPkpAIBWMwCAWjMAgGYzAIByMwCA/SkAgIozAICp6gCApjMAgK7qAIAT6gCAwjMAgLPqAIC4AAAAuOoAgL3qAIABKgCABSoAgMfqAIDC6gCAzOoAgIAB3gCB8QcAgvEHAIPxBwCEFQIAhR0CAIYVAgCHEQIAiCXeAIld3gCKOQIAizkCAIwpAgCNKQIAjhkCAI99ygCQTd4AkWECAJJhAgCT7cEAlH0CAJVlAgCWIcAAl2kCAJhZAgCZMcIAmlUCAJstAgCcNQIAnT0CAJ4xAgCfMQIAoNECAKHRAgCi0QIAo9ECAKTxAgCl8QIApvECAKfxAgCo0QIAqdECAKrRAgCr0QIArDECAK0xAgCuMQIArzECALBRAgCxUQIAslECALNRAgC0cQIAtXECALZxAgC3cQIAuFECALlRAgC6+dwAu1UCALxNAgC9NQIAvj0CAL81AgC+7QYAv/UGALztBgC95QYAuskGALvJBgC4xcsAuckGALbtBgC39QYAtO0GALXlBgCyjQYAs/UGALDR3QCxhQYArvEGAK/xBgCs5QYAreEGAKr1BgCr/QYAqMUGAKn9BgCm9QYAp/0GAKTlBgCl/QYAovUGAKP9BgCg+QYAoZ3dAJ75BgCf+QYAnPkGAJ35BgCa+QYAm/kGAJj5BgCZ+QYAlvkGAJf5BgCUcd0AlfkGAJL9BgCT5QYAkP0GAJH1BgCO/QYAj4UGAIz9BgCN9QYAiuEGAIsB3QCI8QYAifEGAIbBBgCHwQYAhPEGAIXxBgCCkccAg+EGAIDpBgCBxcAAgAAAANHqAIACNACABjQAgBI0AIARKgCAFSoAgNvqAIAmNACAGSoAgODqAIDl6gCA6uoAgJY0AIAdKgCAojQAgKY0AIDv6gCA9OoAgL40AIAhKgCA+eoAgNI0AIDWNACAJSoAgP7qAIDyNACAKSoAgAI1AID6NACACjUAgAjrAIAiNQCALSoAgC41AIA2NQCARjUAgDEqAIAS6wCAF+sAgDUqAIAc6wCAXjUAgCHrAIBqNQCAdjUAgCbrAIAr6wCAkjUAgDDrAICaNQCAQOoAgDkqAICyNQCAtjUAgEEqAIC6NQCAFC4AgDXrAIA66wCAReoAgErqAIDeNQCA9jcAgIDNAQCB1QEAgt0BAIPVAQCEzQEAhfUBAIb9AQCH9QEAiM0BAInVAQCK3QEAi/UJAIzJAQCNyQEAjgEcAI89HwCQRR8AkU0fAJJFHwCTXR8AlEUfAJVNHwCWRR8Al30fAJhBxwCZQR8AmkEfAJtBHwCcQR8AnUEfAJ5BHwCfYd8AoL0fAKHFHwCizR8Ao8UfAKTdHwClxR8Aps0fAKfFHwCo/R8AqcUfAKrNHwCrxR8ArN0fAK3FHwCuzR8Ar8UfALC9HwCxRR8Ask0fALNFHwC0/ckAtVkfALZJHwC3SR8AuHkfALl5HwC6SR8Au8XdALxVHwC9XR8AvlUfAL9NHwAKNgCABjYAgA42AIAZLACAEjYAgBY2AIAaNgCAIjYAgD/rAIAmNgCAOjYAgD42AIAqNgCAQjYAgFY2AIA2NgCASjYAgE42AIBSNgCAROsAgE7rAIBJ6wCASSoAgHI2AIB2NgCAfjYAgGLrAICCNgCAU+sAgE0qAIBRKgCAWOsAgF3rAIBVKgCAojYAgKo2AICuNgCAujYAgLY2AIDCNgCAvjYAgMY2AIDKNgCA0jYAgFkqAIDaNgCA3jYAgF0qAIDuNgCAZ+sAgP42AIACNwCAYSoAgA43AICVKQCAbOsAgHHrAIBlKgCAaSoAgDo3AIB26wCAkjcAgJY3AICuNwCAgLUBAIG9AQCCtQEAg80BAITt9ACF0QEAhtEBAIfRAQCI8QEAifEBAIrxAQCL8QEAjNEBAI3RAQCO0QEAj9EBAJB9wwCRBcMAkl35AJO9AQCUpQEAla0BAJalAQCXXQMAmGUDAJltAwCaZQMAm30DAJxlAwCdbQMAnmUDAJ85wwCgoQMAoaEDAKKhAwCjoQMApKEDAKWhAwCmoQMAp6EDAKjhAwCp4QMAquEDAKvhAwCs4QMAreEDAK7hAwCv4QMAsKEDALGhAwCyoQMAs6EDALShAwC1oQMAtqEDALehAwC4YQMAuWEDALphAwC7YQMAvGEDAL1hAwC+pcMAv6HDALo3AICA6wCA0ukAgMY3AIDCNwCAzjcAgNfpAIDaNwCAhesAgIrrAIAmOACAMjgAgDo4AICP6wCAPjgAgGY4AIByOACAdjgAgG44AICCOACAhjgAgJTrAICSOACAbSoAgJo4AICZ6wCAcSoAgNI4AICkLgCA6jgAgJ7rAICo6wCAdSoAgHkqAIASOQCAresAgH0qAICy6wCAMjkAgLfrAIBKOQCAgSoAgFo5AIBmOQCAbjkAgHY5AICFKgCAvOsAgKY5AICyOQCAiSoAgI0qAIC2OQCAwesAgJEqAIDG6wCAy+sAgNDrAICVKgCA9jkAgPo5AIACOgCACjoAgNrrAICQ1QEAkd0BAJLVAQCT7QEAlPUBAJXB+wCW8QEAl/n7AJjNAQCZ1QEAmt0BAJvVAQCcyfsAnckBAEUqAICPAAAAgNkBAIHZAQCC6QEAg+kBAIT5AQCF+QEAhukBAIfpAQCI2QEAidkBAIoJwQCLrQEAjLUBAI29AQCOtQEAj60BAKAAAAChAAAAogAAAKMAAACkAAAApQAAAKYAAACnAAAAqAAAAKkAAACqAAAAqwAAAKwAAACtAAAArgAAAK8AAACwAAAAsQAAALIAAACzAAAAtAAAALUAAAC2AAAAtwAAALgAAAC5AAAAugAAALsAAAC8AAAAvQAAAL4AAAC/AAAAACAAIMyBACDMgwAgzIQAIMyFACDMhgAgzIcAIMyIACDMiMyAACDMiMyBACDMiM2CACDMigAgzIsAIMyTACDMk8yAACDMk8yBACDMk82CACDMlAAgzJTMgAAgzJTMgQAgzJTNggAgzKcAIMyoACDMswAgzYIAIM2FACDZiwAg2YwAINmM2ZEAINmNACDZjdmRACDZjgAg2Y7ZkQAg2Y8AINmP2ZEAINmQACDZkNmRACDZkQAg2ZHZsAAg2ZIAIOOCmQAg44KaACEAISEAIT8AIgAjACQAJQAmACcAKAAoMSkAKDEwKQAoMTEpACgxMikAKDEzKQAoMTQpACgxNSkAKDE2KQAoMTcpACgxOCkAKDE5KQAoMikAKDIwKQAoMykAKDQpACg1KQAoNikAKDcpACg4KQAoOSkAKEEpAChCKQAoQykAKEQpAChFKQAoRikAKEcpAChIKQAoSSkAKEopAChLKQAoTCkAKE0pAChOKQAoTykAKFApAChRKQAoUikAKFMpAChUKQAoVSkAKFYpAChXKQAoWCkAKFkpAChaKQAoYSkAKGIpAChjKQAoZCkAKGUpAChmKQAoZykAKGgpAChpKQAoaikAKGspAChsKQAobSkAKG4pAChvKQAocCkAKHEpAChyKQAocykAKHQpACh1KQAodikAKHcpACh4KQAoeSkAKHopACjhhIApACjhhIIpACjhhIMpACjhhIUpACjhhIYpACjhhIcpACjhhIkpACjhhIspACjhhIwpACjhhI4pACjhhI8pACjhhJApACjhhJEpACjhhJIpACjkuIApACjkuIMpACjkuIkpACjkuZ0pACjkuowpACjkupQpACjku6MpACjkvIEpACjkvJEpACjlhaspACjlha0pACjlirQpACjljYEpACjljZQpACjlkI0pACjlkbwpACjlm5spACjlnJ8pACjlraYpACjml6UpACjmnIgpACjmnIkpACjmnKgpACjmoKopACjmsLQpACjngaspACjnibkpACjnm6MpACjnpL4pACjnpZ0pACjnpa0pACjoh6opACjoh7MpACjosqEpACjos4cpACjph5EpACjqsIApACjrgpgpACjri6QpACjrnbwpACjrp4gpACjrsJQpACjsgqwpACjslYQpACjsmKTsoIQpACjsmKTtm4QpACjsnpApACjso7wpACjssKgpACjsubQpACjtg4ApACjtjIwpACjtlZgpACkAKgArACwALQAuAC4uAC4uLgAvADAAMCwAMC4AMOKBhDMAMOeCuQAxADEsADEuADEwADEwLgAxMOaXpQAxMOaciAAxMOeCuQAxMQAxMS4AMTHml6UAMTHmnIgAMTHngrkAMTIAMTIuADEy5pelADEy5pyIADEy54K5ADEzADEzLgAxM+aXpQAxM+eCuQAxNAAxNC4AMTTml6UAMTTngrkAMTUAMTUuADE15pelADE154K5ADE2ADE2LgAxNuaXpQAxNueCuQAxNwAxNy4AMTfml6UAMTfngrkAMTgAMTguADE45pelADE454K5ADE5ADE5LgAxOeaXpQAxOeeCuQAx4oGEADHigYQxMAAx4oGEMgAx4oGEMwAx4oGENAAx4oGENQAx4oGENgAx4oGENwAx4oGEOAAx4oGEOQAx5pelADHmnIgAMeeCuQAyADIsADIuADIwADIwLgAyMOaXpQAyMOeCuQAyMQAyMeaXpQAyMeeCuQAyMgAyMuaXpQAyMueCuQAyMwAyM+aXpQAyM+eCuQAyNAAyNOaXpQAyNOeCuQAyNQAyNeaXpQAyNgAyNuaXpQAyNwAyN+aXpQAyOAAyOOaXpQAyOQAyOeaXpQAy4oGEMwAy4oGENQAy5pelADLmnIgAMueCuQAzADMsADMuADMwADMw5pelADMxADMx5pelADMyADMzADM0ADM1ADM2ADM3ADM4ADM5ADPigYQ0ADPigYQ1ADPigYQ4ADPml6UAM+aciAAz54K5ADQANCwANC4ANDAANDEANDIANDMANDQANDUANDYANDcANDgANDkANOKBhDUANOaXpQA05pyIADTngrkANQA1LAA1LgA1MAA14oGENgA14oGEOAA15pelADXmnIgANeeCuQA2ADYsADYuADbml6UANuaciAA254K5ADcANywANy4AN+KBhDgAN+aXpQA35pyIADfngrkAOAA4LAA4LgA45pelADjmnIgAOOeCuQA5ADksADkuADnml6UAOeaciAA554K5ADoAOjo9ADsAPAA9AD09AD09PQA+AD8APyEAPz8AQABBAEFVAEHiiJVtAEIAQnEAQwBDRABDby4AQ+KIlWtnAEQAREoARFoARHoARMW9AETFvgBFAEYARkFYAEcAR0IAR0h6AEdQYQBHeQBIAEhQAEhWAEhnAEh6AEkASUkASUlJAElKAElVAElWAElYAEoASwBLQgBLSwBLTQBMAExKAExURABMagBMwrcATQBNQgBNQwBNRABNSHoATVBhAE1WAE1XAE3OqQBOAE5KAE5qAE5vAE8AUABQSABQUE0AUFBWAFBSAFBURQBQYQBRAFIAUnMAUwBTRABTTQBTUwBTdgBUAFRFTABUSHoAVE0AVQBWAFZJAFZJSQBWSUlJAFbiiJVtAFcAV0MAV1oAV2IAWABYSQBYSUkAWQBaAFsAXABdAF4AXwBgAGEAYS5tLgBhL2MAYS9zAGHKvgBiAGJhcgBjAGMvbwBjL3UAY2FsAGNjAGNkAGNtAGNtMgBjbTMAZABkQgBkYQBkbABkbQBkbTIAZG0zAGR6AGTFvgBlAGVWAGVyZwBmAGZmAGZmaQBmZmwAZmkAZmwAZm0AZwBnYWwAaABoUGEAaGEAaQBpaQBpaWkAaWoAaW4AaXYAaXgAagBrAGtBAGtIegBrUGEAa1YAa1cAa2NhbABrZwBrbABrbQBrbTIAa20zAGt0AGvOqQBsAGxqAGxtAGxuAGxvZwBseABswrcAbQBtMgBtMwBtQQBtVgBtVwBtYgBtZwBtaWwAbWwAbW0AbW0yAG1tMwBtb2wAbXMAbeKIlXMAbeKIlXMyAG4AbkEAbkYAblYAblcAbmoAbm0AbnMAbwBvVgBwAHAubS4AcEEAcEYAcFYAcFcAcGMAcHMAcQByAHJhZAByYWTiiJVzAHJhZOKIlXMyAHMAc3IAc3QAdAB1AHYAdmkAdmlpAHZpaWkAdwB4AHhpAHhpaQB5AHoAewB8AH0AwqIAwqMAwqUAwqYAwqwAwrBDAMKwRgDCtwDDgADDgQDDggDDgwDDhADDhQDDhgDDhwDDiADDiQDDigDDiwDDjADDjQDDjgDDjwDDkQDDkgDDkwDDlADDlQDDlgDDmQDDmgDDmwDDnADDnQDDoADDoQDDogDDowDDpADDpQDDpwDDqADDqQDDqgDDqwDDrADDrQDDrgDDrwDDsADDsQDDsgDDswDDtADDtQDDtgDDuQDDugDDuwDDvADDvQDDvwDEgADEgQDEggDEgwDEhADEhQDEhgDEhwDEiADEiQDEigDEiwDEjADEjQDEjgDEjwDEkgDEkwDElADElQDElgDElwDEmADEmQDEmgDEmwDEnADEnQDEngDEnwDEoADEoQDEogDEowDEpADEpQDEpgDEpwDEqADEqQDEqgDEqwDErADErQDErgDErwDEsADEsQDEtADEtQDEtgDEtwDEuQDEugDEuwDEvADEvQDEvgDFgwDFhADFhQDFhgDFhwDFiADFiwDFjADFjQDFjgDFjwDFkADFkQDFkwDFlADFlQDFlgDFlwDFmADFmQDFmgDFmwDFnADFnQDFngDFnwDFoADFoQDFogDFowDFpADFpQDFqADFqQDFqgDFqwDFrADFrQDFrgDFrwDFsADFsQDFsgDFswDFtADFtQDFtgDFtwDFuADFuQDFugDFuwDFvADFvQDFvgDGjgDGkADGoADGoQDGqwDGrwDGsADHjQDHjgDHjwDHkADHkQDHkgDHkwDHlADHlQDHlgDHlwDHmADHmQDHmgDHmwDHnADHngDHnwDHoADHoQDHogDHowDHpgDHpwDHqADHqQDHqgDHqwDHrADHrQDHrgDHrwDHsADHtADHtQDHuADHuQDHugDHuwDHvADHvQDHvgDHvwDIgADIgQDIggDIgwDIhADIhQDIhgDIhwDIiADIiQDIigDIiwDIjADIjQDIjgDIjwDIkADIkQDIkgDIkwDIlADIlQDIlgDIlwDImADImQDImgDImwDIngDInwDIogDIpgDIpwDIqADIqQDIqgDIqwDIrADIrQDIrgDIrwDIsADIsQDIsgDIswDItwDJkADJkQDJkgDJlADJlQDJmQDJmwDJnADJnwDJoQDJowDJpQDJpgDJqADJqQDJqgDJqwDJrQDJrwDJsADJsQDJsgDJswDJtADJtQDJuADJuQDJuwDKgQDKggDKgwDKiQDKigDKiwDKjADKkADKkQDKkgDKlQDKnQDKnwDKuQDKvG4AzIAAzIEAzIjMgQDMkwDOhgDOiADOiQDOigDOjADOjgDOjwDOkADOkQDOkgDOkwDOlADOlQDOlgDOlwDOmADOmQDOmgDOmwDOnADOnQDOngDOnwDOoADOoQDOowDOpADOpQDOpgDOpwDOqADOqQDOqgDOqwDOrADOrQDOrgDOrwDOsADOsQDOsgDOswDOtADOtQDOtgDOtwDOuADOuQDOugDOuwDOvADOvEEAzrxGAM68VgDOvFcAzrxnAM68bADOvG0AzrxzAM69AM6+AM6/AM+AAM+BAM+CAM+DAM+EAM+FAM+GAM+HAM+IAM+JAM+KAM+LAM+MAM+NAM+OAM+cAM+dANCAANCBANCDANCHANCMANCNANCOANCZANC5ANC9ANGKANGMANGQANGRANGTANGXANGcANGdANGeANG2ANG3ANOBANOCANOQANORANOSANOTANOWANOXANOaANObANOcANOdANOeANOfANOiANOjANOkANOlANOmANOnANOqANOrANOsANOtANOuANOvANOwANOxANOyANOzANO0ANO1ANO4ANO5ANWl1oIA1bTVpQDVtNWrANW01a0A1bTVtgDVvtW2ANeQANeQ1rcA15DWuADXkNa8ANeQ15wA15EA15HWvADXkda/ANeSANeS1rwA15MA15PWvADXlADXlNa8ANeV1rkA15XWvADXlta8ANeY1rwA15nWtADXmda8ANea1rwA15sA15vWvADXm9a/ANecANec1rwA150A157WvADXoNa8ANeh1rwA16IA16PWvADXpNa8ANek1r8A16bWvADXp9a8ANeoANeo1rwA16nWvADXqda814EA16nWvNeCANep14EA16nXggDXqgDXqta8ANey1rcA2KEA2KIA2KMA2KQA2KUA2KYA2KbYpwDYptisANim2K0A2KbYrgDYptixANim2LIA2KbZhQDYptmGANim2YcA2KbZiADYptmJANim2YoA2KbbhgDYptuHANim24gA2KbbkADYptuVANinANin2YPYqNixANin2YTZhNmHANin2YsA2KfZtADYqADYqNisANio2K0A2KjYrdmKANio2K4A2KjYrtmKANio2LEA2KjYsgDYqNmFANio2YYA2KjZhwDYqNmJANio2YoA2KkA2KoA2KrYrADYqtis2YUA2KrYrNmJANiq2KzZigDYqtitANiq2K3YrADYqtit2YUA2KrYrgDYqtiu2YUA2KrYrtmJANiq2K7ZigDYqtixANiq2LIA2KrZhQDYqtmF2KwA2KrZhditANiq2YXYrgDYqtmF2YkA2KrZhdmKANiq2YYA2KrZhwDYqtmJANiq2YoA2KsA2KvYrADYq9ixANir2LIA2KvZhQDYq9mGANir2YcA2KvZiQDYq9mKANisANis2K0A2KzYrdmJANis2K3ZigDYrNmEINis2YTYp9mE2YcA2KzZhQDYrNmF2K0A2KzZhdmJANis2YXZigDYrNmJANis2YoA2K0A2K3YrADYrdis2YoA2K3ZhQDYrdmF2YkA2K3ZhdmKANit2YkA2K3ZigDYrgDYrtisANiu2K0A2K7ZhQDYrtmJANiu2YoA2K8A2LAA2LDZsADYsQDYsdiz2YjZhADYsdmwANix24zYp9mEANiyANizANiz2KwA2LPYrNitANiz2KzZiQDYs9itANiz2K3YrADYs9iuANiz2K7ZiQDYs9iu2YoA2LPYsQDYs9mFANiz2YXYrADYs9mF2K0A2LPZhdmFANiz2YcA2LPZiQDYs9mKANi0ANi02KwA2LTYrNmKANi02K0A2LTYrdmFANi02K3ZigDYtNiuANi02LEA2LTZhQDYtNmF2K4A2LTZhdmFANi02YcA2LTZiQDYtNmKANi1ANi12K0A2LXYrditANi12K3ZigDYtdiuANi12LEA2LXZhNi52YUA2LXZhNmJANi12YTZiSDYp9mE2YTZhyDYudmE2YrZhyDZiNiz2YTZhQDYtdmE25IA2LXZhQDYtdmF2YUA2LXZiQDYtdmKANi2ANi22KwA2LbYrQDYttit2YkA2LbYrdmKANi22K4A2LbYrtmFANi22LEA2LbZhQDYttmJANi22YoA2LcA2LfYrQDYt9mFANi32YXYrQDYt9mF2YUA2LfZhdmKANi32YkA2LfZigDYuADYuNmFANi5ANi52KwA2LnYrNmFANi52YTZitmHANi52YUA2LnZhdmFANi52YXZiQDYudmF2YoA2LnZiQDYudmKANi6ANi62KwA2LrZhQDYutmF2YUA2LrZhdmJANi62YXZigDYutmJANi62YoA2YDZiwDZgNmOANmA2Y7ZkQDZgNmPANmA2Y/ZkQDZgNmQANmA2ZDZkQDZgNmRANmA2ZIA2YEA2YHYrADZgditANmB2K4A2YHYrtmFANmB2YUA2YHZhdmKANmB2YkA2YHZigDZggDZgtitANmC2YTbkgDZgtmFANmC2YXYrQDZgtmF2YUA2YLZhdmKANmC2YkA2YLZigDZgwDZg9inANmD2KwA2YPYrQDZg9iuANmD2YQA2YPZhQDZg9mF2YUA2YPZhdmKANmD2YkA2YPZigDZhADZhNiiANmE2KMA2YTYpQDZhNinANmE2KwA2YTYrNisANmE2KzZhQDZhNis2YoA2YTYrQDZhNit2YUA2YTYrdmJANmE2K3ZigDZhNiuANmE2K7ZhQDZhNmFANmE2YXYrQDZhNmF2YoA2YTZhwDZhNmJANmE2YoA2YUA2YXYpwDZhdisANmF2KzYrQDZhdis2K4A2YXYrNmFANmF2KzZigDZhditANmF2K3YrADZhdit2YUA2YXYrdmF2K8A2YXYrdmKANmF2K4A2YXYrtisANmF2K7ZhQDZhdiu2YoA2YXZhQDZhdmF2YoA2YXZiQDZhdmKANmGANmG2KwA2YbYrNitANmG2KzZhQDZhtis2YkA2YbYrNmKANmG2K0A2YbYrdmFANmG2K3ZiQDZhtit2YoA2YbYrgDZhtixANmG2LIA2YbZhQDZhtmF2YkA2YbZhdmKANmG2YYA2YbZhwDZhtmJANmG2YoA2YcA2YfYrADZh9mFANmH2YXYrADZh9mF2YUA2YfZiQDZh9mKANmH2bAA2YgA2YjYs9mE2YUA2YjZtADZiQDZidmwANmKANmK2KwA2YrYrNmKANmK2K0A2YrYrdmKANmK2K4A2YrYsQDZitiyANmK2YUA2YrZhdmFANmK2YXZigDZitmGANmK2YcA2YrZiQDZitmKANmK2bQA2a4A2a8A2bEA2bkA2boA2bsA2b4A2b8A2oAA2oMA2oQA2oYA2ocA2ogA2owA2o0A2o4A2pEA2pgA2qEA2qQA2qYA2qkA2q0A2q8A2rEA2rMA2roA2rsA2r4A24AA24EA24IA24UA24YA24cA24fZtADbiADbiQDbiwDbjADbkADbkgDbkwDgpJXgpLwA4KSW4KS8AOCkl+CkvADgpJzgpLwA4KSh4KS8AOCkouCkvADgpKkA4KSr4KS8AOCkr+CkvADgpLEA4KS0AOCmoeCmvADgpqLgprwA4Kav4Ka8AOCniwDgp4wA4KiW4Ki8AOCol+CovADgqJzgqLwA4Kir4Ki8AOCosuCovADgqLjgqLwA4Kyh4Ky8AOCsouCsvADgrYgA4K2LAOCtjADgrpQA4K+KAOCviwDgr4wA4LGIAOCzgADgs4cA4LOIAOCzigDgs4sA4LWKAOC1iwDgtYwA4LeaAOC3nADgt50A4LeeAOC5jeC4sgDguqvgupkA4Lqr4LqhAOC7jeC6sgDgvIsA4L2A4L61AOC9guC+twDgvYzgvrcA4L2R4L63AOC9luC+twDgvZvgvrcA4L2x4L2yAOC9seC9tADgvbHgvoAA4L6Q4L61AOC+kuC+twDgvpzgvrcA4L6h4L63AOC+puC+twDgvqvgvrcA4L6y4L2x4L6AAOC+suC+gADgvrPgvbHgvoAA4L6z4L6AAOGApgDhg5wA4YSAAOGEgQDhhIIA4YSDAOGEhADhhIUA4YSGAOGEhwDhhIgA4YSJAOGEigDhhIsA4YSMAOGEjQDhhI4A4YSPAOGEkADhhJEA4YSSAOGElADhhJUA4YSaAOGEnADhhJ0A4YSeAOGEoADhhKEA4YSiAOGEowDhhKcA4YSpAOGEqwDhhKwA4YStAOGErgDhhK8A4YSyAOGEtgDhhYAA4YWHAOGFjADhhZcA4YWYAOGFmQDhhaAA4YWhAOGFogDhhaMA4YWkAOGFpQDhhaYA4YWnAOGFqADhhakA4YWqAOGFqwDhhawA4YWtAOGFrgDhha8A4YWwAOGFsQDhhbIA4YWzAOGFtADhhbUA4YaEAOGGhQDhhogA4YaRAOGGkgDhhpQA4YaeAOGGoQDhhqoA4YasAOGGrQDhhrAA4YaxAOGGsgDhhrMA4Ya0AOGGtQDhh4cA4YeIAOGHjADhh44A4YeTAOGHlwDhh5kA4YedAOGHnwDhh7EA4YeyAOGshgDhrIgA4ayKAOGsjADhrI4A4aySAOGsuwDhrL0A4a2AAOGtgQDhrYMA4bSCAOG0lgDhtJcA4bScAOG0nQDhtKUA4bW7AOG2hQDhuIAA4biBAOG4ggDhuIMA4biEAOG4hQDhuIYA4biHAOG4iADhuIkA4biKAOG4iwDhuIwA4biNAOG4jgDhuI8A4biQAOG4kQDhuJIA4biTAOG4lADhuJUA4biWAOG4lwDhuJgA4biZAOG4mgDhuJsA4bicAOG4nQDhuJ4A4bifAOG4oADhuKEA4biiAOG4owDhuKQA4bilAOG4pgDhuKcA4bioAOG4qQDhuKoA4birAOG4rADhuK0A4biuAOG4rwDhuLAA4bixAOG4sgDhuLMA4bi0AOG4tQDhuLYA4bi3AOG4uADhuLkA4bi6AOG4uwDhuLwA4bi9AOG4vgDhuL8A4bmAAOG5gQDhuYIA4bmDAOG5hADhuYUA4bmGAOG5hwDhuYgA4bmJAOG5igDhuYsA4bmMAOG5jQDhuY4A4bmPAOG5kADhuZEA4bmSAOG5kwDhuZQA4bmVAOG5lgDhuZcA4bmYAOG5mQDhuZoA4bmbAOG5nADhuZ0A4bmeAOG5nwDhuaAA4bmhAOG5ogDhuaMA4bmkAOG5pQDhuaYA4bmnAOG5qADhuakA4bmqAOG5qwDhuawA4bmtAOG5rgDhua8A4bmwAOG5sQDhubIA4bmzAOG5tADhubUA4bm2AOG5twDhubgA4bm5AOG5ugDhubsA4bm8AOG5vQDhub4A4bm/AOG6gADhuoEA4bqCAOG6gwDhuoQA4bqFAOG6hgDhuocA4bqIAOG6iQDhuooA4bqLAOG6jADhuo0A4bqOAOG6jwDhupAA4bqRAOG6kgDhupMA4bqUAOG6lQDhupYA4bqXAOG6mADhupkA4bqgAOG6oQDhuqIA4bqjAOG6pADhuqUA4bqmAOG6pwDhuqgA4bqpAOG6qgDhuqsA4bqsAOG6rQDhuq4A4bqvAOG6sADhurEA4bqyAOG6swDhurQA4bq1AOG6tgDhurcA4bq4AOG6uQDhuroA4bq7AOG6vADhur0A4bq+AOG6vwDhu4AA4buBAOG7ggDhu4MA4buEAOG7hQDhu4YA4buHAOG7iADhu4kA4buKAOG7iwDhu4wA4buNAOG7jgDhu48A4buQAOG7kQDhu5IA4buTAOG7lADhu5UA4buWAOG7lwDhu5gA4buZAOG7mgDhu5sA4bucAOG7nQDhu54A4bufAOG7oADhu6EA4buiAOG7owDhu6QA4bulAOG7pgDhu6cA4buoAOG7qQDhu6oA4burAOG7rADhu60A4buuAOG7rwDhu7AA4buxAOG7sgDhu7MA4bu0AOG7tQDhu7YA4bu3AOG7uADhu7kA4byAAOG8gQDhvIIA4byDAOG8hADhvIUA4byGAOG8hwDhvIgA4byJAOG8igDhvIsA4byMAOG8jQDhvI4A4byPAOG8kADhvJEA4bySAOG8kwDhvJQA4byVAOG8mADhvJkA4byaAOG8mwDhvJwA4bydAOG8oADhvKEA4byiAOG8owDhvKQA4bylAOG8pgDhvKcA4byoAOG8qQDhvKoA4byrAOG8rADhvK0A4byuAOG8rwDhvLAA4byxAOG8sgDhvLMA4by0AOG8tQDhvLYA4by3AOG8uADhvLkA4by6AOG8uwDhvLwA4by9AOG8vgDhvL8A4b2AAOG9gQDhvYIA4b2DAOG9hADhvYUA4b2IAOG9iQDhvYoA4b2LAOG9jADhvY0A4b2QAOG9kQDhvZIA4b2TAOG9lADhvZUA4b2WAOG9lwDhvZkA4b2bAOG9nQDhvZ8A4b2gAOG9oQDhvaIA4b2jAOG9pADhvaUA4b2mAOG9pwDhvagA4b2pAOG9qgDhvasA4b2sAOG9rQDhva4A4b2vAOG9sADhvbIA4b20AOG9tgDhvbgA4b26AOG9vADhvoAA4b6BAOG+ggDhvoMA4b6EAOG+hQDhvoYA4b6HAOG+iADhvokA4b6KAOG+iwDhvowA4b6NAOG+jgDhvo8A4b6QAOG+kQDhvpIA4b6TAOG+lADhvpUA4b6WAOG+lwDhvpgA4b6ZAOG+mgDhvpsA4b6cAOG+nQDhvp4A4b6fAOG+oADhvqEA4b6iAOG+owDhvqQA4b6lAOG+pgDhvqcA4b6oAOG+qQDhvqoA4b6rAOG+rADhvq0A4b6uAOG+rwDhvrAA4b6xAOG+sgDhvrMA4b60AOG+tgDhvrcA4b64AOG+uQDhvroA4b68AOG/ggDhv4MA4b+EAOG/hgDhv4cA4b+IAOG/igDhv4wA4b+QAOG/kQDhv5IA4b+WAOG/lwDhv5gA4b+ZAOG/mgDhv6AA4b+hAOG/ogDhv6QA4b+lAOG/pgDhv6cA4b+oAOG/qQDhv6oA4b+sAOG/sgDhv7MA4b+0AOG/tgDhv7cA4b+4AOG/ugDhv7wA4oCQAOKAkwDigJQA4oCy4oCyAOKAsuKAsuKAsgDigLLigLLigLLigLIA4oC14oC1AOKAteKAteKAtQDigqkA4oaQAOKGkQDihpIA4oaTAOKGmgDihpsA4oauAOKHjQDih44A4oePAOKIggDiiIQA4oiHAOKIiQDiiIwA4oiRAOKIkgDiiKQA4oimAOKIq+KIqwDiiKviiKviiKsA4oir4oir4oir4oirAOKIruKIrgDiiK7iiK7iiK4A4omBAOKJhADiiYcA4omJAOKJoADiiaIA4omtAOKJrgDiia8A4omwAOKJsQDiibQA4om1AOKJuADiibkA4oqAAOKKgQDiioQA4oqFAOKKiADiiokA4oqsAOKKrQDiiq4A4oqvAOKLoADii6EA4ouiAOKLowDii6oA4ourAOKLrADii60A4pSCAOKWoADil4sA4qaFAOKmhgDiq53MuADitaEA44CBAOOAggDjgIgA44CJAOOAigDjgIsA44CMAOOAjQDjgI4A44CPAOOAkADjgJEA44CSAOOAlADjgJRT44CVAOOAlOS4ieOAlQDjgJTkuozjgJUA44CU5Yud44CVAOOAlOWuieOAlQDjgJTmiZPjgJUA44CU5pWX44CVAOOAlOacrOOAlQDjgJTngrnjgJUA44CU55uX44CVAOOAlQDjgJYA44CXAOOBjADjgY4A44GQAOOBkgDjgZQA44GWAOOBmADjgZoA44GcAOOBngDjgaAA44GiAOOBpQDjgacA44GpAOOBsADjgbEA44GzAOOBtADjgbYA44G3AOOBuQDjgboA44G744GLAOOBvADjgb0A44KI44KKAOOClADjgpkA44KaAOOCngDjgqEA44KiAOOCouODkeODvOODiADjgqLjg6vjg5XjgqEA44Ki44Oz44Oa44KiAOOCouODvOODqwDjgqMA44KkAOOCpOODi+ODs+OCsADjgqTjg7Pjg4EA44KlAOOCpgDjgqbjgqnjg7MA44KnAOOCqADjgqjjgrnjgq/jg7zjg4kA44Ko44O844Kr44O8AOOCqQDjgqoA44Kq44Oz44K5AOOCquODvOODoADjgqsA44Kr44Kk44OqAOOCq+ODqeODg+ODiADjgqvjg63jg6rjg7wA44KsAOOCrOODreODswDjgqzjg7Pjg54A44KtAOOCreODpeODquODvADjgq3jg60A44Kt44Ot44Kw44Op44OgAOOCreODreODoeODvOODiOODqwDjgq3jg63jg6/jg4Pjg4gA44KuAOOCruOCrADjgq7jg4vjg7wA44Ku44Or44OA44O8AOOCrwDjgq/jg6vjgrzjgqTjg60A44Kv44Ot44O844ONAOOCsADjgrDjg6njg6AA44Kw44Op44Og44OI44OzAOOCsQDjgrHjg7zjgrkA44KyAOOCswDjgrPjgrMA44Kz44OIAOOCs+ODq+ODigDjgrPjg7zjg50A44K0AOOCtQDjgrXjgqTjgq/jg6sA44K144Oz44OB44O844OgAOOCtgDjgrcA44K344Oq44Oz44KwAOOCuADjgrkA44K6AOOCuwDjgrvjg7Pjg4EA44K744Oz44OIAOOCvADjgr0A44K+AOOCvwDjg4AA44OA44O844K5AOODgQDjg4IA44ODAOODhADjg4UA44OGAOODhwDjg4fjgrcA44OIAOODiOODswDjg4kA44OJ44OrAOODigDjg4rjg44A44OLAOODjADjg40A44OOAOODjuODg+ODiADjg48A44OP44Kk44OEAOODkADjg5Djg7zjg6zjg6sA44ORAOODkeODvOOCu+ODs+ODiADjg5Hjg7zjg4QA44OSAOODkwDjg5Pjg6sA44OUAOODlOOCouOCueODiOODqwDjg5Tjgq/jg6sA44OU44KzAOODlQDjg5XjgqHjg6njg4Pjg4kA44OV44Kj44O844OIAOODleODqeODswDjg5YA44OW44OD44K344Kn44OrAOODlwDjg5gA44OY44Kv44K/44O844OrAOODmOODq+ODhADjg5kA44OZ44O844K/AOODmgDjg5rjgr0A44Oa44OL44OSAOODmuODs+OCuQDjg5rjg7zjgrgA44ObAOODm+ODswDjg5vjg7zjg6sA44Ob44O844OzAOODnADjg5zjg6vjg4gA44OdAOODneOCpOODs+ODiADjg53jg7Pjg4kA44OeAOODnuOCpOOCr+ODrQDjg57jgqTjg6sA44Oe44OD44OPAOODnuODq+OCrwDjg57jg7Pjgrfjg6fjg7MA44OfAOODn+OCr+ODreODswDjg5/jg6oA44Of44Oq44OQ44O844OrAOODoADjg6EA44Oh44KsAOODoeOCrOODiOODswDjg6Hjg7zjg4jjg6sA44OiAOODowDjg6QA44Ok44O844OJAOODpOODvOODqwDjg6UA44OmAOODpuOCouODswDjg6cA44OoAOODqQDjg6oA44Oq44OD44OI44OrAOODquODqQDjg6sA44Or44OU44O8AOODq+ODvOODluODqwDjg6wA44Os44OgAOODrOODs+ODiOOCsuODswDjg60A44OvAOODr+ODg+ODiADjg7AA44OxAOODsgDjg7MA44O0AOODtwDjg7gA44O5AOODugDjg7sA44O8AOODvgDjkp4A45K5AOOSuwDjk58A45SVAOObrgDjm7wA456BAOOgrwDjoaIA46G8AOOjhwDjo6MA46ScAOOkugDjqK4A46msAOOrpADjrIgA46yZAOOtiQDjrp0A47CYAOOxjgDjtLMA47aWAOO6rADjurgA47ybAOO/vADkgIgA5ICYAOSAuQDkgYYA5IKWAOSDowDkhK8A5IiCAOSIpwDkiqAA5IyBAOSMtADkjZkA5I+VAOSPmQDkkIsA5JGrAOSUqwDklZ0A5JWhAOSVqwDkl5cA5Je5AOSYtQDkmr4A5JuHAOSmlQDkp6YA5KmuAOSptgDkqrIA5KyzAOSvjgDks44A5LOtAOSzuADktZYA5LiAAOS4gQDkuIMA5LiJAOS4igDkuIsA5LiNAOS4mQDkuKYA5LioAOS4rQDkuLIA5Li2AOS4uADkuLkA5Li9AOS4vwDkuYEA5LmZAOS5nQDkuoIA5LqFAOS6hgDkuowA5LqUAOS6oADkuqQA5LquAOS6ugDku4AA5LuMAOS7pADkvIEA5LyRAOS9oADkvoAA5L6GAOS+iwDkvq4A5L67AOS+vwDlgIIA5YCrAOWBugDlgpkA5YOPAOWDmgDlg6cA5YSqAOWEvwDlhYAA5YWFAOWFjQDlhZQA5YWkAOWFpQDlhacA5YWoAOWFqQDlhasA5YWtAOWFtwDlhoAA5YaCAOWGjQDlhpIA5YaVAOWGlgDlhpcA5YaZAOWGpADlhqsA5YasAOWGtQDlhrcA5YeJAOWHjADlh5wA5YeeAOWHoADlh7UA5YiAAOWIgwDliIcA5YiXAOWInQDliKkA5Yi6AOWIuwDliYYA5YmNAOWJsgDlibcA5YqJAOWKmwDliqMA5YqzAOWKtADli4cA5YuJAOWLkgDli54A5YukAOWLtQDli7kA5Yu6AOWMhQDljIYA5YyVAOWMlwDljJoA5Yy4AOWMuwDljL8A5Y2BAOWNhADljYUA5Y2JAOWNkQDljZQA5Y2aAOWNnADljakA5Y2wAOWNswDljbUA5Y29AOWNvwDljoIA5Y62AOWPgwDlj4gA5Y+KAOWPjADlj58A5Y+jAOWPpQDlj6sA5Y+vAOWPsQDlj7MA5ZCGAOWQiADlkI0A5ZCPAOWQnQDlkLgA5ZC5AOWRggDlkYgA5ZGoAOWSngDlkqIA5ZK9AOWTtgDllJAA5ZWPAOWVkwDllZUA5ZWjAOWWhADllocA5ZaZAOWWnQDllqsA5ZazAOWWtgDll4AA5ZeCAOWXogDlmIYA5ZmRAOWZqADlmbQA5ZuXAOWbmwDlm7kA5ZyWAOWclwDlnJ8A5ZywAOWeiwDln44A5Z+0AOWgjQDloLEA5aCyAOWhgADloZoA5aGeAOWiqADloqwA5aKzAOWjmADlo58A5aOrAOWjrgDlo7AA5aOyAOWjtwDlpIIA5aSGAOWkigDlpJUA5aSaAOWknADlpKIA5aSnAOWkp+atowDlpKkA5aWEAOWliADlpZEA5aWUAOWlogDlpbMA5aeYAOWnrADlqJsA5ainAOWpogDlqaYA5aq1AOWsiADlrKgA5ay+AOWtkADlrZcA5a2mAOWugADlroUA5a6XAOWvgwDlr5gA5a+nAOWvrgDlr7MA5a+4AOWvvwDlsIYA5bCPAOWwogDlsLgA5bC/AOWxoADlsaIA5bGkAOWxpQDlsa4A5bGxAOWyjQDls4AA5bSZAOW1gwDltZAA5bWrAOW1rgDltbwA5bayAOW2ugDlt5sA5behAOW3ogDlt6UA5bemAOW3sQDlt70A5be+AOW4qADluL0A5bmpAOW5sgDlubPmiJAA5bm0AOW5ugDlubwA5bm/AOW6pgDlurAA5bqzAOW6tgDlu4kA5buKAOW7kgDlu5MA5buZAOW7rADlu7QA5bu+AOW8hADlvIsA5byTAOW8ogDlvZAA5b2TAOW9oQDlvaIA5b2pAOW9qwDlvbMA5b6LAOW+jADlvpcA5b6aAOW+qQDlvq0A5b+DAOW/jQDlv5cA5b+1AOW/uQDmgJIA5oCcAOaBtQDmgoEA5oKUAOaDhwDmg5gA5oOhAOaEiADmhYQA5oWIAOaFjADmhY4A5oWgAOaFqADmhboA5oaOAOaGkADmhqQA5oavAOaGsgDmh54A5oeyAOaHtgDmiIAA5oiIAOaIkADmiJsA5oiuAOaItADmiLYA5omLAOaJkwDmiZ0A5oqVAOaKsQDmi4kA5ouPAOaLkwDmi5QA5ou8AOaLvgDmjIcA5oy9AOaNkADmjZUA5o2oAOaNuwDmjoMA5o6gAOaOqQDmj4QA5o+FAOaPpADmkJwA5pCiAOaRkgDmkakA5pG3AOaRvgDmkpoA5pKdAOaThADmlK8A5pS0AOaVjwDmlZYA5pWsAOaVuADmlocA5paXAOaWmQDmlqQA5pawAOaWuQDml4UA5pegAOaXogDml6MA5pelAOaYjuayuwDmmJMA5pigAOaYreWSjADmmYkA5pm0AOaaiADmmpEA5pqcAOaatADmm4YA5puwAOabtADmm7gA5pyAAOaciADmnIkA5pyXAOacmwDmnKEA5pyoAOadjgDmnZMA5p2WAOadngDmnbsA5p6FAOaelwDmn7MA5p+6AOaglwDmoJ8A5qCqAOagquW8j+S8muekvgDmoZIA5qKBAOaihQDmoo4A5qKoAOaklADmpYIA5qajAOanqgDmqIIA5qiTAOaqqADmq5MA5qubAOashADmrKAA5qyhAOatlADmraIA5q2jAOatsgDmrbcA5q25AOaunwDmrq4A5q6zAOauugDmrrsA5q+LAOavjQDmr5QA5q+bAOawjwDmsJQA5rC0AOaxjgDmsacA5rKIAOayvwDms4wA5rONAOazpQDms6gA5rSWAOa0mwDmtJ4A5rS0AOa0vgDmtYEA5rWpAOa1qgDmtbcA5rW4AOa2hQDmt4sA5reaAOa3qgDmt7kA5riaAOa4rwDmua4A5rqAAOa6nADmuroA5ruHAOa7iwDmu5EA5rubAOa8jwDmvJQA5ryiAOa8owDmva4A5r+GAOa/qwDmv74A54CbAOeAngDngLkA54GKAOeBqwDngbAA54G3AOeBvQDngpkA54KtAOeDiADng5kA54ShAOeFhQDnhYkA54WuAOeGnADnh44A54eQAOeIkADniJsA54ioAOeIqgDniKsA54i1AOeItgDniLsA54i/AOeJhwDniZAA54mZAOeJmwDniaIA54m5AOeKgADnipUA54qsAOeKrwDni4AA54u8AOeMqgDnjbUA5426AOeOhADnjocA546JAOeOiwDnjqUA546yAOePngDnkIYA55CJAOeQogDnkYcA55GcAOeRqQDnkbEA55KFAOeSiQDnkpgA55OKAOeTnADnk6YA55SGAOeUmADnlJ8A55SkAOeUqADnlLAA55SyAOeUswDnlLcA55S7AOeUvgDnlZkA55WlAOeVsADnlosA55aSAOeXogDnmJAA55idAOeYnwDnmYIA55mpAOeZtgDnmb0A55quAOeavwDnm4oA55ubAOebowDnm6cA55uuAOebtADnnIEA55yeAOecnwDnnYAA552KAOeeiwDnnqcA55+bAOefogDnn7MA56GOAOehqwDnoowA56KRAOejigDno4wA56O7AOekqgDnpLoA56S8AOekvgDnpYgA56WJAOelkADnpZYA56WdAOelngDnpaUA56W/AOemgQDnpo0A56aOAOemjwDnpq4A56a4AOemvgDnp4oA56eYAOenqwDnqJwA56mAAOepigDnqY8A56m0AOepugDnqoEA56qxAOeriwDnq64A56u5AOesoADnro8A56+AAOevhgDnr4kA57C+AOexoADnsbMA57G7AOeykgDnsr4A57OSAOezlgDns6MA57OnAOezqADns7gA57SAAOe0kADntKIA57SvAOe1ggDntZsA57WjAOe2oADntr4A57eHAOe3tADnuIIA57iJAOe4twDnuYEA57mFAOe8tgDnvL4A572RAOe9sgDnvbkA5726AOe+hQDnvooA576VAOe+mgDnvr0A57+6AOiAgQDogIUA6ICMAOiAkgDogLMA6IGGAOiBoADoga8A6IGwAOiBvgDogb8A6IKJAOiCiwDogq0A6IKyAOiEgwDohL4A6IeYAOiHowDoh6gA6IeqAOiHrQDoh7MA6Ie8AOiIgQDoiIQA6IiMAOiImADoiJsA6IifAOiJrgDoia8A6ImyAOiJuADoibkA6IqLAOiKkQDoip0A6IqxAOiKswDoir0A6IulAOiLpgDojJ0A6IyjAOiMtgDojZIA6I2TAOiNowDojq0A6I69AOiPiQDoj4oA6I+MAOiPnADoj6cA6I+vAOiPsQDokL0A6JGJAOiRlwDok64A6JOxAOiTswDok7wA6JSWAOiVpADol40A6Je6AOiYhgDomJIA6JitAOiYvwDomY0A6JmQAOiZnADomacA6JmpAOiZqwDomogA6JqpAOibogDonI4A6JyoAOidqwDonbkA6J6GAOieugDon6EA6KCBAOignwDooYAA6KGMAOihoADooaMA6KOCAOijjwDoo5cA6KOeAOijoQDoo7gA6KO6AOikkADopYEA6KWkAOilvgDopoYA6KaLAOimlgDop5IA6KejAOiogADoqqAA6KqqAOiqvwDoq4sA6KuSAOirlgDoq60A6Ku4AOirvgDorIEA6Ky5AOitmADoroAA6K6KAOiwtwDosYYA6LGIAOixlQDosbgA6LKdAOiyoQDosqkA6LKrAOizgQDos4IA6LOHAOiziADos5MA6LSIAOi0mwDotaQA6LWwAOi1twDotrMA6La8AOi3iwDot68A6LewAOi6qwDou4oA6LuUAOi8pgDovKoA6Ly4AOi8uwDovaIA6L6bAOi+ngDovrAA6L61AOi+tgDpgKMA6YC4AOmBigDpgakA6YGyAOmBvADpgo8A6YKRAOmClADpg44A6YOeAOmDsQDpg70A6YSRAOmEmwDphYkA6YWqAOmGmQDphrQA6YeGAOmHjADph48A6YeRAOmItADpiLgA6Ym2AOmJvADpi5cA6YuYAOmMhADpjYoA6Y+5AOmQlQDplbcA6ZaAAOmWiwDplq0A6Za3AOmYnADpmK4A6ZmLAOmZjQDpmbUA6Zm4AOmZvADpmoYA6ZqjAOmatgDpmrcA6Zq4AOmauQDpm4MA6ZuiAOmbowDpm6gA6Zu2AOmbtwDpnKMA6ZyyAOmdiADpnZEA6Z2WAOmdngDpnaIA6Z2pAOmfiwDpn5sA6Z+gAOmfrQDpn7MA6Z+/AOmggQDpoIUA6aCLAOmgmADpoKkA6aC7AOmhngDpoqgA6aObAOmjnwDpo6IA6aOvAOmjvADppKgA6aSpAOmmlgDpppkA6aanAOmmrADpp4IA6aexAOmnvgDpqaoA6aqoAOmrmADpq58A6aySAOmspQDprK8A6ayyAOmsvADprZoA6a2vAOmxgADpsZcA6bOlAOmzvQDptacA6ba0AOm3ugDpuJ4A6bm1AOm5vwDpupcA6bqfAOm6pQDpursA6buDAOm7jQDpu44A6buRAOm7uQDpu70A6bu+AOm8hQDpvI4A6byPAOm8kwDpvJYA6bygAOm8uwDpvYMA6b2KAOm9kgDpvo0A6b6OAOm+nADpvp8A6b6gAOqcpwDqna8A6qy3AOqtkgDqsIAA6rCBAOqwggDqsIMA6rCEAOqwhQDqsIYA6rCHAOqwiADqsIkA6rCKAOqwiwDqsIwA6rCNAOqwjgDqsI8A6rCQAOqwkQDqsJIA6rCTAOqwlADqsJUA6rCWAOqwlwDqsJgA6rCZAOqwmgDqsJsA6rCcAOqwnQDqsJ4A6rCfAOqwoADqsKEA6rCiAOqwowDqsKQA6rClAOqwpgDqsKcA6rCoAOqwqQDqsKoA6rCrAOqwrADqsK0A6rCuAOqwrwDqsLAA6rCxAOqwsgDqsLMA6rC0AOqwtQDqsLYA6rC3AOqwuADqsLkA6rC6AOqwuwDqsLwA6rC9AOqwvgDqsL8A6rGAAOqxgQDqsYIA6rGDAOqxhADqsYUA6rGGAOqxhwDqsYgA6rGJAOqxigDqsYsA6rGMAOqxjQDqsY4A6rGPAOqxkADqsZEA6rGSAOqxkwDqsZQA6rGVAOqxlgDqsZcA6rGYAOqxmQDqsZoA6rGbAOqxnADqsZ0A6rGeAOqxnwDqsaAA6rGhAOqxogDqsaMA6rGkAOqxpQDqsaYA6rGnAOqxqADqsakA6rGqAOqxqwDqsawA6rGtAOqxrgDqsa8A6rGwAOqxsQDqsbIA6rGzAOqxtADqsbUA6rG2AOqxtwDqsbgA6rG5AOqxugDqsbsA6rG8AOqxvQDqsb4A6rG/AOqygADqsoEA6rKCAOqygwDqsoQA6rKFAOqyhgDqsocA6rKIAOqyiQDqsooA6rKLAOqyjADqso0A6rKOAOqyjwDqspAA6rKRAOqykgDqspMA6rKUAOqylQDqspYA6rKXAOqymADqspkA6rKaAOqymwDqspwA6rKdAOqyngDqsp8A6rKgAOqyoQDqsqIA6rKjAOqypADqsqUA6rKmAOqypwDqsqgA6rKpAOqyqgDqsqsA6rKsAOqyrQDqsq4A6rKvAOqysADqsrEA6rKyAOqyswDqsrQA6rK1AOqytgDqsrcA6rK4AOqyuQDqsroA6rK7AOqyvADqsr0A6rK+AOqyvwDqs4AA6rOBAOqzggDqs4MA6rOEAOqzhQDqs4YA6rOHAOqziADqs4kA6rOKAOqziwDqs4wA6rONAOqzjgDqs48A6rOQAOqzkQDqs5IA6rOTAOqzlADqs5UA6rOWAOqzlwDqs5gA6rOZAOqzmgDqs5sA6rOcAOqznQDqs54A6rOfAOqzoADqs6EA6rOiAOqzowDqs6QA6rOlAOqzpgDqs6cA6rOoAOqzqQDqs6oA6rOrAOqzrADqs60A6rOuAOqzrwDqs7AA6rOxAOqzsgDqs7MA6rO0AOqztQDqs7YA6rO3AOqzuADqs7kA6rO6AOqzuwDqs7wA6rO9AOqzvgDqs78A6rSAAOq0gQDqtIIA6rSDAOq0hADqtIUA6rSGAOq0hwDqtIgA6rSJAOq0igDqtIsA6rSMAOq0jQDqtI4A6rSPAOq0kADqtJEA6rSSAOq0kwDqtJQA6rSVAOq0lgDqtJcA6rSYAOq0mQDqtJoA6rSbAOq0nADqtJ0A6rSeAOq0nwDqtKAA6rShAOq0ogDqtKMA6rSkAOq0pQDqtKYA6rSnAOq0qADqtKkA6rSqAOq0qwDqtKwA6rStAOq0rgDqtK8A6rSwAOq0sQDqtLIA6rSzAOq0tADqtLUA6rS2AOq0twDqtLgA6rS5AOq0ugDqtLsA6rS8AOq0vQDqtL4A6rS/AOq1gADqtYEA6rWCAOq1gwDqtYQA6rWFAOq1hgDqtYcA6rWIAOq1iQDqtYoA6rWLAOq1jADqtY0A6rWOAOq1jwDqtZAA6rWRAOq1kgDqtZMA6rWUAOq1lQDqtZYA6rWXAOq1mADqtZkA6rWaAOq1mwDqtZwA6rWdAOq1ngDqtZ8A6rWgAOq1oQDqtaIA6rWjAOq1pADqtaUA6rWmAOq1pwDqtagA6rWpAOq1qgDqtasA6rWsAOq1rQDqta4A6rWvAOq1sADqtbEA6rWyAOq1swDqtbQA6rW1AOq1tgDqtbcA6rW4AOq1uQDqtboA6rW7AOq1vADqtb0A6rW+AOq1vwDqtoAA6raBAOq2ggDqtoMA6raEAOq2hQDqtoYA6raHAOq2iADqtokA6raKAOq2iwDqtowA6raNAOq2jgDqto8A6raQAOq2kQDqtpIA6raTAOq2lADqtpUA6raWAOq2lwDqtpgA6raZAOq2mgDqtpsA6racAOq2nQDqtp4A6rafAOq2oADqtqEA6raiAOq2owDqtqQA6ralAOq2pgDqtqcA6raoAOq2qQDqtqoA6rarAOq2rADqtq0A6rauAOq2rwDqtrAA6raxAOq2sgDqtrMA6ra0AOq2tQDqtrYA6ra3AOq2uADqtrkA6ra6AOq2uwDqtrwA6ra9AOq2vgDqtr8A6reAAOq3gQDqt4IA6reDAOq3hADqt4UA6reGAOq3hwDqt4gA6reJAOq3igDqt4sA6reMAOq3jQDqt44A6rePAOq3kADqt5EA6reSAOq3kwDqt5QA6reVAOq3lgDqt5cA6reYAOq3mQDqt5oA6rebAOq3nADqt50A6reeAOq3nwDqt6AA6rehAOq3ogDqt6MA6rekAOq3pQDqt6YA6renAOq3qADqt6kA6reqAOq3qwDqt6wA6retAOq3rgDqt68A6rewAOq3sQDqt7IA6rezAOq3tADqt7UA6re2AOq3twDqt7gA6re5AOq3ugDqt7sA6re8AOq3vQDqt74A6re/AOq4gADquIEA6riCAOq4gwDquIQA6riFAOq4hgDquIcA6riIAOq4iQDquIoA6riLAOq4jADquI0A6riOAOq4jwDquJAA6riRAOq4kgDquJMA6riUAOq4lQDquJYA6riXAOq4mADquJkA6riaAOq4mwDquJwA6ridAOq4ngDquJ8A6rigAOq4oQDquKIA6rijAOq4pADquKUA6rimAOq4pwDquKgA6ripAOq4qgDquKsA6risAOq4rQDquK4A6rivAOq4sADquLEA6riyAOq4swDquLQA6ri1AOq4tgDquLcA6ri4AOq4uQDquLoA6ri7AOq4vADquL0A6ri+AOq4vwDquYAA6rmBAOq5ggDquYMA6rmEAOq5hQDquYYA6rmHAOq5iADquYkA6rmKAOq5iwDquYwA6rmNAOq5jgDquY8A6rmQAOq5kQDquZIA6rmTAOq5lADquZUA6rmWAOq5lwDquZgA6rmZAOq5mgDquZsA6rmcAOq5nQDquZ4A6rmfAOq5oADquaEA6rmiAOq5owDquaQA6rmlAOq5pgDquacA6rmoAOq5qQDquaoA6rmrAOq5rADqua0A6rmuAOq5rwDqubAA6rmxAOq5sgDqubMA6rm0AOq5tQDqubYA6rm3AOq5uADqubkA6rm6AOq5uwDqubwA6rm9AOq5vgDqub8A6rqAAOq6gQDquoIA6rqDAOq6hADquoUA6rqGAOq6hwDquogA6rqJAOq6igDquosA6rqMAOq6jQDquo4A6rqPAOq6kADqupEA6rqSAOq6kwDqupQA6rqVAOq6lgDqupcA6rqYAOq6mQDqupoA6rqbAOq6nADqup0A6rqeAOq6nwDquqAA6rqhAOq6ogDquqMA6rqkAOq6pQDquqYA6rqnAOq6qADquqkA6rqqAOq6qwDquqwA6rqtAOq6rgDquq8A6rqwAOq6sQDqurIA6rqzAOq6tADqurUA6rq2AOq6twDqurgA6rq5AOq6ugDqursA6rq8AOq6vQDqur4A6rq/AOq7gADqu4EA6ruCAOq7gwDqu4QA6ruFAOq7hgDqu4cA6ruIAOq7iQDqu4oA6ruLAOq7jADqu40A6ruOAOq7jwDqu5AA6ruRAOq7kgDqu5MA6ruUAOq7lQDqu5YA6ruXAOq7mADqu5kA6ruaAOq7mwDqu5wA6rudAOq7ngDqu58A6rugAOq7oQDqu6IA6rujAOq7pADqu6UA6rumAOq7pwDqu6gA6rupAOq7qgDqu6sA6rusAOq7rQDqu64A6ruvAOq7sADqu7EA6ruyAOq7swDqu7QA6ru1AOq7tgDqu7cA6ru4AOq7uQDqu7oA6ru7AOq7vADqu70A6ru+AOq7vwDqvIAA6ryBAOq8ggDqvIMA6ryEAOq8hQDqvIYA6ryHAOq8iADqvIkA6ryKAOq8iwDqvIwA6ryNAOq8jgDqvI8A6ryQAOq8kQDqvJIA6ryTAOq8lADqvJUA6ryWAOq8lwDqvJgA6ryZAOq8mgDqvJsA6rycAOq8nQDqvJ4A6ryfAOq8oADqvKEA6ryiAOq8owDqvKQA6rylAOq8pgDqvKcA6ryoAOq8qQDqvKoA6ryrAOq8rADqvK0A6ryuAOq8rwDqvLAA6ryxAOq8sgDqvLMA6ry0AOq8tQDqvLYA6ry3AOq8uADqvLkA6ry6AOq8uwDqvLwA6ry9AOq8vgDqvL8A6r2AAOq9gQDqvYIA6r2DAOq9hADqvYUA6r2GAOq9hwDqvYgA6r2JAOq9igDqvYsA6r2MAOq9jQDqvY4A6r2PAOq9kADqvZEA6r2SAOq9kwDqvZQA6r2VAOq9lgDqvZcA6r2YAOq9mQDqvZoA6r2bAOq9nADqvZ0A6r2eAOq9nwDqvaAA6r2hAOq9ogDqvaMA6r2kAOq9pQDqvaYA6r2nAOq9qADqvakA6r2qAOq9qwDqvawA6r2tAOq9rgDqva8A6r2wAOq9sQDqvbIA6r2zAOq9tADqvbUA6r22AOq9twDqvbgA6r25AOq9ugDqvbsA6r28AOq9vQDqvb4A6r2/AOq+gADqvoEA6r6CAOq+gwDqvoQA6r6FAOq+hgDqvocA6r6IAOq+iQDqvooA6r6LAOq+jADqvo0A6r6OAOq+jwDqvpAA6r6RAOq+kgDqvpMA6r6UAOq+lQDqvpYA6r6XAOq+mADqvpkA6r6aAOq+mwDqvpwA6r6dAOq+ngDqvp8A6r6gAOq+oQDqvqIA6r6jAOq+pADqvqUA6r6mAOq+pwDqvqgA6r6pAOq+qgDqvqsA6r6sAOq+rQDqvq4A6r6vAOq+sADqvrEA6r6yAOq+swDqvrQA6r61AOq+tgDqvrcA6r64AOq+uQDqvroA6r67AOq+vADqvr0A6r6+AOq+vwDqv4AA6r+BAOq/ggDqv4MA6r+EAOq/hQDqv4YA6r+HAOq/iADqv4kA6r+KAOq/iwDqv4wA6r+NAOq/jgDqv48A6r+QAOq/kQDqv5IA6r+TAOq/lADqv5UA6r+WAOq/lwDqv5gA6r+ZAOq/mgDqv5sA6r+cAOq/nQDqv54A6r+fAOq/oADqv6EA6r+iAOq/owDqv6QA6r+lAOq/pgDqv6cA6r+oAOq/qQDqv6oA6r+rAOq/rADqv60A6r+uAOq/rwDqv7AA6r+xAOq/sgDqv7MA6r+0AOq/tQDqv7YA6r+3AOq/uADqv7kA6r+6AOq/uwDqv7wA6r+9AOq/vgDqv78A64CAAOuAgQDrgIIA64CDAOuAhADrgIUA64CGAOuAhwDrgIgA64CJAOuAigDrgIsA64CMAOuAjQDrgI4A64CPAOuAkADrgJEA64CSAOuAkwDrgJQA64CVAOuAlgDrgJcA64CYAOuAmQDrgJoA64CbAOuAnADrgJ0A64CeAOuAnwDrgKAA64ChAOuAogDrgKMA64CkAOuApQDrgKYA64CnAOuAqADrgKkA64CqAOuAqwDrgKwA64CtAOuArgDrgK8A64CwAOuAsQDrgLIA64CzAOuAtADrgLUA64C2AOuAtwDrgLgA64C5AOuAugDrgLsA64C8AOuAvQDrgL4A64C/AOuBgADrgYEA64GCAOuBgwDrgYQA64GFAOuBhgDrgYcA64GIAOuBiQDrgYoA64GLAOuBjADrgY0A64GOAOuBjwDrgZAA64GRAOuBkgDrgZMA64GUAOuBlQDrgZYA64GXAOuBmADrgZkA64GaAOuBmwDrgZwA64GdAOuBngDrgZ8A64GgAOuBoQDrgaIA64GjAOuBpADrgaUA64GmAOuBpwDrgagA64GpAOuBqgDrgasA64GsAOuBrQDrga4A64GvAOuBsADrgbEA64GyAOuBswDrgbQA64G1AOuBtgDrgbcA64G4AOuBuQDrgboA64G7AOuBvADrgb0A64G+AOuBvwDrgoAA64KBAOuCggDrgoMA64KEAOuChQDrgoYA64KHAOuCiADrgokA64KKAOuCiwDrgowA64KNAOuCjgDrgo8A64KQAOuCkQDrgpIA64KTAOuClADrgpUA64KWAOuClwDrgpgA64KZAOuCmgDrgpsA64KcAOuCnQDrgp4A64KfAOuCoADrgqEA64KiAOuCowDrgqQA64KlAOuCpgDrgqcA64KoAOuCqQDrgqoA64KrAOuCrADrgq0A64KuAOuCrwDrgrAA64KxAOuCsgDrgrMA64K0AOuCtQDrgrYA64K3AOuCuADrgrkA64K6AOuCuwDrgrwA64K9AOuCvgDrgr8A64OAAOuDgQDrg4IA64ODAOuDhADrg4UA64OGAOuDhwDrg4gA64OJAOuDigDrg4sA64OMAOuDjQDrg44A64OPAOuDkADrg5EA64OSAOuDkwDrg5QA64OVAOuDlgDrg5cA64OYAOuDmQDrg5oA64ObAOuDnADrg50A64OeAOuDnwDrg6AA64OhAOuDogDrg6MA64OkAOuDpQDrg6YA64OnAOuDqADrg6kA64OqAOuDqwDrg6wA64OtAOuDrgDrg68A64OwAOuDsQDrg7IA64OzAOuDtADrg7UA64O2AOuDtwDrg7gA64O5AOuDugDrg7sA64O8AOuDvQDrg74A64O/AOuEgADrhIEA64SCAOuEgwDrhIQA64SFAOuEhgDrhIcA64SIAOuEiQDrhIoA64SLAOuEjADrhI0A64SOAOuEjwDrhJAA64SRAOuEkgDrhJMA64SUAOuElQDrhJYA64SXAOuEmADrhJkA64SaAOuEmwDrhJwA64SdAOuEngDrhJ8A64SgAOuEoQDrhKIA64SjAOuEpADrhKUA64SmAOuEpwDrhKgA64SpAOuEqgDrhKsA64SsAOuErQDrhK4A64SvAOuEsADrhLEA64SyAOuEswDrhLQA64S1AOuEtgDrhLcA64S4AOuEuQDrhLoA64S7AOuEvADrhL0A64S+AOuEvwDrhYAA64WBAOuFggDrhYMA64WEAOuFhQDrhYYA64WHAOuFiADrhYkA64WKAOuFiwDrhYwA64WNAOuFjgDrhY8A64WQAOuFkQDrhZIA64WTAOuFlADrhZUA64WWAOuFlwDrhZgA64WZAOuFmgDrhZsA64WcAOuFnQDrhZ4A64WfAOuFoADrhaEA64WiAOuFowDrhaQA64WlAOuFpgDrhacA64WoAOuFqQDrhaoA64WrAOuFrADrha0A64WuAOuFrwDrhbAA64WxAOuFsgDrhbMA64W0AOuFtQDrhbYA64W3AOuFuADrhbkA64W6AOuFuwDrhbwA64W9AOuFvgDrhb8A64aAAOuGgQDrhoIA64aDAOuGhADrhoUA64aGAOuGhwDrhogA64aJAOuGigDrhosA64aMAOuGjQDrho4A64aPAOuGkADrhpEA64aSAOuGkwDrhpQA64aVAOuGlgDrhpcA64aYAOuGmQDrhpoA64abAOuGnADrhp0A64aeAOuGnwDrhqAA64ahAOuGogDrhqMA64akAOuGpQDrhqYA64anAOuGqADrhqkA64aqAOuGqwDrhqwA64atAOuGrgDrhq8A64awAOuGsQDrhrIA64azAOuGtADrhrUA64a2AOuGtwDrhrgA64a5AOuGugDrhrsA64a8AOuGvQDrhr4A64a/AOuHgADrh4EA64eCAOuHgwDrh4QA64eFAOuHhgDrh4cA64eIAOuHiQDrh4oA64eLAOuHjADrh40A64eOAOuHjwDrh5AA64eRAOuHkgDrh5MA64eUAOuHlQDrh5YA64eXAOuHmADrh5kA64eaAOuHmwDrh5wA64edAOuHngDrh58A64egAOuHoQDrh6IA64ejAOuHpADrh6UA64emAOuHpwDrh6gA64epAOuHqgDrh6sA64esAOuHrQDrh64A64evAOuHsADrh7EA64eyAOuHswDrh7QA64e1AOuHtgDrh7cA64e4AOuHuQDrh7oA64e7AOuHvADrh70A64e+AOuHvwDriIAA64iBAOuIggDriIMA64iEAOuIhQDriIYA64iHAOuIiADriIkA64iKAOuIiwDriIwA64iNAOuIjgDriI8A64iQAOuIkQDriJIA64iTAOuIlADriJUA64iWAOuIlwDriJgA64iZAOuImgDriJsA64icAOuInQDriJ4A64ifAOuIoADriKEA64iiAOuIowDriKQA64ilAOuIpgDriKcA64ioAOuIqQDriKoA64irAOuIrADriK0A64iuAOuIrwDriLAA64ixAOuIsgDriLMA64i0AOuItQDriLYA64i3AOuIuADriLkA64i6AOuIuwDriLwA64i9AOuIvgDriL8A64mAAOuJgQDriYIA64mDAOuJhADriYUA64mGAOuJhwDriYgA64mJAOuJigDriYsA64mMAOuJjQDriY4A64mPAOuJkADriZEA64mSAOuJkwDriZQA64mVAOuJlgDriZcA64mYAOuJmQDriZoA64mbAOuJnADriZ0A64meAOuJnwDriaAA64mhAOuJogDriaMA64mkAOuJpQDriaYA64mnAOuJqADriakA64mqAOuJqwDriawA64mtAOuJrgDria8A64mwAOuJsQDribIA64mzAOuJtADribUA64m2AOuJtwDribgA64m5AOuJugDribsA64m8AOuJvQDrib4A64m/AOuKgADrioEA64qCAOuKgwDrioQA64qFAOuKhgDriocA64qIAOuKiQDriooA64qLAOuKjADrio0A64qOAOuKjwDripAA64qRAOuKkgDripMA64qUAOuKlQDripYA64qXAOuKmADripkA64qaAOuKmwDripwA64qdAOuKngDrip8A64qgAOuKoQDriqIA64qjAOuKpADriqUA64qmAOuKpwDriqgA64qpAOuKqgDriqsA64qsAOuKrQDriq4A64qvAOuKsADrirEA64qyAOuKswDrirQA64q1AOuKtgDrircA64q4AOuKuQDriroA64q7AOuKvADrir0A64q+AOuKvwDri4AA64uBAOuLggDri4MA64uEAOuLhQDri4YA64uHAOuLiADri4kA64uKAOuLiwDri4wA64uNAOuLjgDri48A64uQAOuLkQDri5IA64uTAOuLlADri5UA64uWAOuLlwDri5gA64uZAOuLmgDri5sA64ucAOuLnQDri54A64ufAOuLoADri6EA64uiAOuLowDri6QA64ulAOuLpgDri6cA64uoAOuLqQDri6oA64urAOuLrADri60A64uuAOuLrwDri7AA64uxAOuLsgDri7MA64u0AOuLtQDri7YA64u3AOuLuADri7kA64u6AOuLuwDri7wA64u9AOuLvgDri78A64yAAOuMgQDrjIIA64yDAOuMhADrjIUA64yGAOuMhwDrjIgA64yJAOuMigDrjIsA64yMAOuMjQDrjI4A64yPAOuMkADrjJEA64ySAOuMkwDrjJQA64yVAOuMlgDrjJcA64yYAOuMmQDrjJoA64ybAOuMnADrjJ0A64yeAOuMnwDrjKAA64yhAOuMogDrjKMA64ykAOuMpQDrjKYA64ynAOuMqADrjKkA64yqAOuMqwDrjKwA64ytAOuMrgDrjK8A64ywAOuMsQDrjLIA64yzAOuMtADrjLUA64y2AOuMtwDrjLgA64y5AOuMugDrjLsA64y8AOuMvQDrjL4A64y/AOuNgADrjYEA642CAOuNgwDrjYQA642FAOuNhgDrjYcA642IAOuNiQDrjYoA642LAOuNjADrjY0A642OAOuNjwDrjZAA642RAOuNkgDrjZMA642UAOuNlQDrjZYA642XAOuNmADrjZkA642aAOuNmwDrjZwA642dAOuNngDrjZ8A642gAOuNoQDrjaIA642jAOuNpADrjaUA642mAOuNpwDrjagA642pAOuNqgDrjasA642sAOuNrQDrja4A642vAOuNsADrjbEA642yAOuNswDrjbQA6421AOuNtgDrjbcA6424AOuNuQDrjboA6427AOuNvADrjb0A642+AOuNvwDrjoAA646BAOuOggDrjoMA646EAOuOhQDrjoYA646HAOuOiADrjokA646KAOuOiwDrjowA646NAOuOjgDrjo8A646QAOuOkQDrjpIA646TAOuOlADrjpUA646WAOuOlwDrjpgA646ZAOuOmgDrjpsA646cAOuOnQDrjp4A646fAOuOoADrjqEA646iAOuOowDrjqQA646lAOuOpgDrjqcA646oAOuOqQDrjqoA646rAOuOrADrjq0A646uAOuOrwDrjrAA646xAOuOsgDrjrMA6460AOuOtQDrjrYA6463AOuOuADrjrkA6466AOuOuwDrjrwA6469AOuOvgDrjr8A64+AAOuPgQDrj4IA64+DAOuPhADrj4UA64+GAOuPhwDrj4gA64+JAOuPigDrj4sA64+MAOuPjQDrj44A64+PAOuPkADrj5EA64+SAOuPkwDrj5QA64+VAOuPlgDrj5cA64+YAOuPmQDrj5oA64+bAOuPnADrj50A64+eAOuPnwDrj6AA64+hAOuPogDrj6MA64+kAOuPpQDrj6YA64+nAOuPqADrj6kA64+qAOuPqwDrj6wA64+tAOuPrgDrj68A64+wAOuPsQDrj7IA64+zAOuPtADrj7UA64+2AOuPtwDrj7gA64+5AOuPugDrj7sA64+8AOuPvQDrj74A64+/AOuQgADrkIEA65CCAOuQgwDrkIQA65CFAOuQhgDrkIcA65CIAOuQiQDrkIoA65CLAOuQjADrkI0A65COAOuQjwDrkJAA65CRAOuQkgDrkJMA65CUAOuQlQDrkJYA65CXAOuQmADrkJkA65CaAOuQmwDrkJwA65CdAOuQngDrkJ8A65CgAOuQoQDrkKIA65CjAOuQpADrkKUA65CmAOuQpwDrkKgA65CpAOuQqgDrkKsA65CsAOuQrQDrkK4A65CvAOuQsADrkLEA65CyAOuQswDrkLQA65C1AOuQtgDrkLcA65C4AOuQuQDrkLoA65C7AOuQvADrkL0A65C+AOuQvwDrkYAA65GBAOuRggDrkYMA65GEAOuRhQDrkYYA65GHAOuRiADrkYkA65GKAOuRiwDrkYwA65GNAOuRjgDrkY8A65GQAOuRkQDrkZIA65GTAOuRlADrkZUA65GWAOuRlwDrkZgA65GZAOuRmgDrkZsA65GcAOuRnQDrkZ4A65GfAOuRoADrkaEA65GiAOuRowDrkaQA65GlAOuRpgDrkacA65GoAOuRqQDrkaoA65GrAOuRrADrka0A65GuAOuRrwDrkbAA65GxAOuRsgDrkbMA65G0AOuRtQDrkbYA65G3AOuRuADrkbkA65G6AOuRuwDrkbwA65G9AOuRvgDrkb8A65KAAOuSgQDrkoIA65KDAOuShADrkoUA65KGAOuShwDrkogA65KJAOuSigDrkosA65KMAOuSjQDrko4A65KPAOuSkADrkpEA65KSAOuSkwDrkpQA65KVAOuSlgDrkpcA65KYAOuSmQDrkpoA65KbAOuSnADrkp0A65KeAOuSnwDrkqAA65KhAOuSogDrkqMA65KkAOuSpQDrkqYA65KnAOuSqADrkqkA65KqAOuSqwDrkqwA65KtAOuSrgDrkq8A65KwAOuSsQDrkrIA65KzAOuStADrkrUA65K2AOuStwDrkrgA65K5AOuSugDrkrsA65K8AOuSvQDrkr4A65K/AOuTgADrk4EA65OCAOuTgwDrk4QA65OFAOuThgDrk4cA65OIAOuTiQDrk4oA65OLAOuTjADrk40A65OOAOuTjwDrk5AA65ORAOuTkgDrk5MA65OUAOuTlQDrk5YA65OXAOuTmADrk5kA65OaAOuTmwDrk5wA65OdAOuTngDrk58A65OgAOuToQDrk6IA65OjAOuTpADrk6UA65OmAOuTpwDrk6gA65OpAOuTqgDrk6sA65OsAOuTrQDrk64A65OvAOuTsADrk7EA65OyAOuTswDrk7QA65O1AOuTtgDrk7cA65O4AOuTuQDrk7oA65O7AOuTvADrk70A65O+AOuTvwDrlIAA65SBAOuUggDrlIMA65SEAOuUhQDrlIYA65SHAOuUiADrlIkA65SKAOuUiwDrlIwA65SNAOuUjgDrlI8A65SQAOuUkQDrlJIA65STAOuUlADrlJUA65SWAOuUlwDrlJgA65SZAOuUmgDrlJsA65ScAOuUnQDrlJ4A65SfAOuUoADrlKEA65SiAOuUowDrlKQA65SlAOuUpgDrlKcA65SoAOuUqQDrlKoA65SrAOuUrADrlK0A65SuAOuUrwDrlLAA65SxAOuUsgDrlLMA65S0AOuUtQDrlLYA65S3AOuUuADrlLkA65S6AOuUuwDrlLwA65S9AOuUvgDrlL8A65WAAOuVgQDrlYIA65WDAOuVhADrlYUA65WGAOuVhwDrlYgA65WJAOuVigDrlYsA65WMAOuVjQDrlY4A65WPAOuVkADrlZEA65WSAOuVkwDrlZQA65WVAOuVlgDrlZcA65WYAOuVmQDrlZoA65WbAOuVnADrlZ0A65WeAOuVnwDrlaAA65WhAOuVogDrlaMA65WkAOuVpQDrlaYA65WnAOuVqADrlakA65WqAOuVqwDrlawA65WtAOuVrgDrla8A65WwAOuVsQDrlbIA65WzAOuVtADrlbUA65W2AOuVtwDrlbgA65W5AOuVugDrlbsA65W8AOuVvQDrlb4A65W/AOuWgADrloEA65aCAOuWgwDrloQA65aFAOuWhgDrlocA65aIAOuWiQDrlooA65aLAOuWjADrlo0A65aOAOuWjwDrlpAA65aRAOuWkgDrlpMA65aUAOuWlQDrlpYA65aXAOuWmADrlpkA65aaAOuWmwDrlpwA65adAOuWngDrlp8A65agAOuWoQDrlqIA65ajAOuWpADrlqUA65amAOuWpwDrlqgA65apAOuWqgDrlqsA65asAOuWrQDrlq4A65avAOuWsADrlrEA65ayAOuWswDrlrQA65a1AOuWtgDrlrcA65a4AOuWuQDrlroA65a7AOuWvADrlr0A65a+AOuWvwDrl4AA65eBAOuXggDrl4MA65eEAOuXhQDrl4YA65eHAOuXiADrl4kA65eKAOuXiwDrl4wA65eNAOuXjgDrl48A65eQAOuXkQDrl5IA65eTAOuXlADrl5UA65eWAOuXlwDrl5gA65eZAOuXmgDrl5sA65ecAOuXnQDrl54A65efAOuXoADrl6EA65eiAOuXowDrl6QA65elAOuXpgDrl6cA65eoAOuXqQDrl6oA65erAOuXrADrl60A65euAOuXrwDrl7AA65exAOuXsgDrl7MA65e0AOuXtQDrl7YA65e3AOuXuADrl7kA65e6AOuXuwDrl7wA65e9AOuXvgDrl78A65iAAOuYgQDrmIIA65iDAOuYhADrmIUA65iGAOuYhwDrmIgA65iJAOuYigDrmIsA65iMAOuYjQDrmI4A65iPAOuYkADrmJEA65iSAOuYkwDrmJQA65iVAOuYlgDrmJcA65iYAOuYmQDrmJoA65ibAOuYnADrmJ0A65ieAOuYnwDrmKAA65ihAOuYogDrmKMA65ikAOuYpQDrmKYA65inAOuYqADrmKkA65iqAOuYqwDrmKwA65itAOuYrgDrmK8A65iwAOuYsQDrmLIA65izAOuYtADrmLUA65i2AOuYtwDrmLgA65i5AOuYugDrmLsA65i8AOuYvQDrmL4A65i/AOuZgADrmYEA65mCAOuZgwDrmYQA65mFAOuZhgDrmYcA65mIAOuZiQDrmYoA65mLAOuZjADrmY0A65mOAOuZjwDrmZAA65mRAOuZkgDrmZMA65mUAOuZlQDrmZYA65mXAOuZmADrmZkA65maAOuZmwDrmZwA65mdAOuZngDrmZ8A65mgAOuZoQDrmaIA65mjAOuZpADrmaUA65mmAOuZpwDrmagA65mpAOuZqgDrmasA65msAOuZrQDrma4A65mvAOuZsADrmbEA65myAOuZswDrmbQA65m1AOuZtgDrmbcA65m4AOuZuQDrmboA65m7AOuZvADrmb0A65m+AOuZvwDrmoAA65qBAOuaggDrmoMA65qEAOuahQDrmoYA65qHAOuaiADrmokA65qKAOuaiwDrmowA65qNAOuajgDrmo8A65qQAOuakQDrmpIA65qTAOualADrmpUA65qWAOualwDrmpgA65qZAOuamgDrmpsA65qcAOuanQDrmp4A65qfAOuaoADrmqEA65qiAOuaowDrmqQA65qlAOuapgDrmqcA65qoAOuaqQDrmqoA65qrAOuarADrmq0A65quAOuarwDrmrAA65qxAOuasgDrmrMA65q0AOuatQDrmrYA65q3AOuauADrmrkA65q6AOuauwDrmrwA65q9AOuavgDrmr8A65uAAOubgQDrm4IA65uDAOubhADrm4UA65uGAOubhwDrm4gA65uJAOubigDrm4sA65uMAOubjQDrm44A65uPAOubkADrm5EA65uSAOubkwDrm5QA65uVAOublgDrm5cA65uYAOubmQDrm5oA65ubAOubnADrm50A65ueAOubnwDrm6AA65uhAOubogDrm6MA65ukAOubpQDrm6YA65unAOubqADrm6kA65uqAOubqwDrm6wA65utAOubrgDrm68A65uwAOubsQDrm7IA65uzAOubtADrm7UA65u2AOubtwDrm7gA65u5AOubugDrm7sA65u8AOubvQDrm74A65u/AOucgADrnIEA65yCAOucgwDrnIQA65yFAOuchgDrnIcA65yIAOuciQDrnIoA65yLAOucjADrnI0A65yOAOucjwDrnJAA65yRAOuckgDrnJMA65yUAOuclQDrnJYA65yXAOucmADrnJkA65yaAOucmwDrnJwA65ydAOucngDrnJ8A65ygAOucoQDrnKIA65yjAOucpADrnKUA65ymAOucpwDrnKgA65ypAOucqgDrnKsA65ysAOucrQDrnK4A65yvAOucsADrnLEA65yyAOucswDrnLQA65y1AOuctgDrnLcA65y4AOucuQDrnLoA65y7AOucvADrnL0A65y+AOucvwDrnYAA652BAOudggDrnYMA652EAOudhQDrnYYA652HAOudiADrnYkA652KAOudiwDrnYwA652NAOudjgDrnY8A652QAOudkQDrnZIA652TAOudlADrnZUA652WAOudlwDrnZgA652ZAOudmgDrnZsA652cAOudnQDrnZ4A652fAOudoADrnaEA652iAOudowDrnaQA652lAOudpgDrnacA652oAOudqQDrnaoA652rAOudrADrna0A652uAOudrwDrnbAA652xAOudsgDrnbMA6520AOudtQDrnbYA6523AOuduADrnbkA6526AOuduwDrnbwA6529AOudvgDrnb8A656AAOuegQDrnoIA656DAOuehADrnoUA656GAOuehwDrnogA656JAOueigDrnosA656MAOuejQDrno4A656PAOuekADrnpEA656SAOuekwDrnpQA656VAOuelgDrnpcA656YAOuemQDrnpoA656bAOuenADrnp0A656eAOuenwDrnqAA656hAOueogDrnqMA656kAOuepQDrnqYA656nAOueqADrnqkA656qAOueqwDrnqwA656tAOuergDrnq8A656wAOuesQDrnrIA656zAOuetADrnrUA6562AOuetwDrnrgA6565AOueugDrnrsA6568AOuevQDrnr4A656/AOufgADrn4EA65+CAOufgwDrn4QA65+FAOufhgDrn4cA65+IAOufiQDrn4oA65+LAOufjADrn40A65+OAOufjwDrn5AA65+RAOufkgDrn5MA65+UAOuflQDrn5YA65+XAOufmADrn5kA65+aAOufmwDrn5wA65+dAOufngDrn58A65+gAOufoQDrn6IA65+jAOufpADrn6UA65+mAOufpwDrn6gA65+pAOufqgDrn6sA65+sAOufrQDrn64A65+vAOufsADrn7EA65+yAOufswDrn7QA65+1AOuftgDrn7cA65+4AOufuQDrn7oA65+7AOufvADrn70A65++AOufvwDroIAA66CBAOugggDroIMA66CEAOughQDroIYA66CHAOugiADroIkA66CKAOugiwDroIwA66CNAOugjgDroI8A66CQAOugkQDroJIA66CTAOuglADroJUA66CWAOuglwDroJgA66CZAOugmgDroJsA66CcAOugnQDroJ4A66CfAOugoADroKEA66CiAOugowDroKQA66ClAOugpgDroKcA66CoAOugqQDroKoA66CrAOugrADroK0A66CuAOugrwDroLAA66CxAOugsgDroLMA66C0AOugtQDroLYA66C3AOuguADroLkA66C6AOuguwDroLwA66C9AOugvgDroL8A66GAAOuhgQDroYIA66GDAOuhhADroYUA66GGAOuhhwDroYgA66GJAOuhigDroYsA66GMAOuhjQDroY4A66GPAOuhkADroZEA66GSAOuhkwDroZQA66GVAOuhlgDroZcA66GYAOuhmQDroZoA66GbAOuhnADroZ0A66GeAOuhnwDroaAA66GhAOuhogDroaMA66GkAOuhpQDroaYA66GnAOuhqADroakA66GqAOuhqwDroawA66GtAOuhrgDroa8A66GwAOuhsQDrobIA66GzAOuhtADrobUA66G2AOuhtwDrobgA66G5AOuhugDrobsA66G8AOuhvQDrob4A66G/AOuigADrooEA66KCAOuigwDrooQA66KFAOuihgDroocA66KIAOuiiQDroooA66KLAOuijADroo0A66KOAOuijwDropAA66KRAOuikgDropMA66KUAOuilQDropYA66KXAOuimADropkA66KaAOuimwDropwA66KdAOuingDrop8A66KgAOuioQDroqIA66KjAOuipADroqUA66KmAOuipwDroqgA66KpAOuiqgDroqsA66KsAOuirQDroq4A66KvAOuisADrorEA66KyAOuiswDrorQA66K1AOuitgDrorcA66K4AOuiuQDroroA66K7AOuivADror0A66K+AOuivwDro4AA66OBAOujggDro4MA66OEAOujhQDro4YA66OHAOujiADro4kA66OKAOujiwDro4wA66ONAOujjgDro48A66OQAOujkQDro5IA66OTAOujlADro5UA66OWAOujlwDro5gA66OZAOujmgDro5sA66OcAOujnQDro54A66OfAOujoADro6EA66OiAOujowDro6QA66OlAOujpgDro6cA66OoAOujqQDro6oA66OrAOujrADro60A66OuAOujrwDro7AA66OxAOujsgDro7MA66O0AOujtQDro7YA66O3AOujuADro7kA66O6AOujuwDro7wA66O9AOujvgDro78A66SAAOukgQDrpIIA66SDAOukhADrpIUA66SGAOukhwDrpIgA66SJAOukigDrpIsA66SMAOukjQDrpI4A66SPAOukkADrpJEA66SSAOukkwDrpJQA66SVAOuklgDrpJcA66SYAOukmQDrpJoA66SbAOuknADrpJ0A66SeAOuknwDrpKAA66ShAOukogDrpKMA66SkAOukpQDrpKYA66SnAOukqADrpKkA66SqAOukqwDrpKwA66StAOukrgDrpK8A66SwAOuksQDrpLIA66SzAOuktADrpLUA66S2AOuktwDrpLgA66S5AOukugDrpLsA66S8AOukvQDrpL4A66S/AOulgADrpYEA66WCAOulgwDrpYQA66WFAOulhgDrpYcA66WIAOuliQDrpYoA66WLAOuljADrpY0A66WOAOuljwDrpZAA66WRAOulkgDrpZMA66WUAOullQDrpZYA66WXAOulmADrpZkA66WaAOulmwDrpZwA66WdAOulngDrpZ8A66WgAOuloQDrpaIA66WjAOulpADrpaUA66WmAOulpwDrpagA66WpAOulqgDrpasA66WsAOulrQDrpa4A66WvAOulsADrpbEA66WyAOulswDrpbQA66W1AOultgDrpbcA66W4AOuluQDrpboA66W7AOulvADrpb0A66W+AOulvwDrpoAA66aBAOumggDrpoMA66aEAOumhQDrpoYA66aHAOumiADrpokA66aKAOumiwDrpowA66aNAOumjgDrpo8A66aQAOumkQDrppIA66aTAOumlADrppUA66aWAOumlwDrppgA66aZAOummgDrppsA66acAOumnQDrpp4A66afAOumoADrpqEA66aiAOumowDrpqQA66alAOumpgDrpqcA66aoAOumqQDrpqoA66arAOumrADrpq0A66auAOumrwDrprAA66axAOumsgDrprMA66a0AOumtQDrprYA66a3AOumuADrprkA66a6AOumuwDrprwA66a9AOumvgDrpr8A66eAAOungQDrp4IA66eDAOunhADrp4UA66eGAOunhwDrp4gA66eJAOunigDrp4sA66eMAOunjQDrp44A66ePAOunkADrp5EA66eSAOunkwDrp5QA66eVAOunlgDrp5cA66eYAOunmQDrp5oA66ebAOunnADrp50A66eeAOunnwDrp6AA66ehAOunogDrp6MA66ekAOunpQDrp6YA66enAOunqADrp6kA66eqAOunqwDrp6wA66etAOunrgDrp68A66ewAOunsQDrp7IA66ezAOuntADrp7UA66e2AOuntwDrp7gA66e5AOunugDrp7sA66e8AOunvQDrp74A66e/AOuogADrqIEA66iCAOuogwDrqIQA66iFAOuohgDrqIcA66iIAOuoiQDrqIoA66iLAOuojADrqI0A66iOAOuojwDrqJAA66iRAOuokgDrqJMA66iUAOuolQDrqJYA66iXAOuomADrqJkA66iaAOuomwDrqJwA66idAOuongDrqJ8A66igAOuooQDrqKIA66ijAOuopADrqKUA66imAOuopwDrqKgA66ipAOuoqgDrqKsA66isAOuorQDrqK4A66ivAOuosADrqLEA66iyAOuoswDrqLQA66i1AOuotgDrqLcA66i4AOuouQDrqLoA66i7AOuovADrqL0A66i+AOuovwDrqYAA66mBAOupggDrqYMA66mEAOuphQDrqYYA66mHAOupiADrqYkA66mKAOupiwDrqYwA66mNAOupjgDrqY8A66mQAOupkQDrqZIA66mTAOuplADrqZUA66mWAOuplwDrqZgA66mZAOupmgDrqZsA66mcAOupnQDrqZ4A66mfAOupoADrqaEA66miAOupowDrqaQA66mlAOuppgDrqacA66moAOupqQDrqaoA66mrAOuprADrqa0A66muAOuprwDrqbAA66mxAOupsgDrqbMA66m0AOuptQDrqbYA66m3AOupuADrqbkA66m6AOupuwDrqbwA66m9AOupvgDrqb8A66qAAOuqgQDrqoIA66qDAOuqhADrqoUA66qGAOuqhwDrqogA66qJAOuqigDrqosA66qMAOuqjQDrqo4A66qPAOuqkADrqpEA66qSAOuqkwDrqpQA66qVAOuqlgDrqpcA66qYAOuqmQDrqpoA66qbAOuqnADrqp0A66qeAOuqnwDrqqAA66qhAOuqogDrqqMA66qkAOuqpQDrqqYA66qnAOuqqADrqqkA66qqAOuqqwDrqqwA66qtAOuqrgDrqq8A66qwAOuqsQDrqrIA66qzAOuqtADrqrUA66q2AOuqtwDrqrgA66q5AOuqugDrqrsA66q8AOuqvQDrqr4A66q/AOurgADrq4EA66uCAOurgwDrq4QA66uFAOurhgDrq4cA66uIAOuriQDrq4oA66uLAOurjADrq40A66uOAOurjwDrq5AA66uRAOurkgDrq5MA66uUAOurlQDrq5YA66uXAOurmADrq5kA66uaAOurmwDrq5wA66udAOurngDrq58A66ugAOuroQDrq6IA66ujAOurpADrq6UA66umAOurpwDrq6gA66upAOurqgDrq6sA66usAOurrQDrq64A66uvAOursADrq7EA66uyAOurswDrq7QA66u1AOurtgDrq7cA66u4AOuruQDrq7oA66u7AOurvADrq70A66u+AOurvwDrrIAA66yBAOusggDrrIMA66yEAOushQDrrIYA66yHAOusiADrrIkA66yKAOusiwDrrIwA66yNAOusjgDrrI8A66yQAOuskQDrrJIA66yTAOuslADrrJUA66yWAOuslwDrrJgA66yZAOusmgDrrJsA66ycAOusnQDrrJ4A66yfAOusoADrrKEA66yiAOusowDrrKQA66ylAOuspgDrrKcA66yoAOusqQDrrKoA66yrAOusrADrrK0A66yuAOusrwDrrLAA66yxAOussgDrrLMA66y0AOustQDrrLYA66y3AOusuADrrLkA66y6AOusuwDrrLwA66y9AOusvgDrrL8A662AAOutgQDrrYIA662DAOuthADrrYUA662GAOuthwDrrYgA662JAOutigDrrYsA662MAOutjQDrrY4A662PAOutkADrrZEA662SAOutkwDrrZQA662VAOutlgDrrZcA662YAOutmQDrrZoA662bAOutnADrrZ0A662eAOutnwDrraAA662hAOutogDrraMA662kAOutpQDrraYA662nAOutqADrrakA662qAOutqwDrrawA662tAOutrgDrra8A662wAOutsQDrrbIA662zAOuttADrrbUA6622AOuttwDrrbgA6625AOutugDrrbsA6628AOutvQDrrb4A662/AOuugADrroEA666CAOuugwDrroQA666FAOuuhgDrrocA666IAOuuiQDrrooA666LAOuujADrro0A666OAOuujwDrrpAA666RAOuukgDrrpMA666UAOuulQDrrpYA666XAOuumADrrpkA666aAOuumwDrrpwA666dAOuungDrrp8A666gAOuuoQDrrqIA666jAOuupADrrqUA666mAOuupwDrrqgA666pAOuuqgDrrqsA666sAOuurQDrrq4A666vAOuusADrrrEA666yAOuuswDrrrQA6661AOuutgDrrrcA6664AOuuuQDrrroA6667AOuuvADrrr0A666+AOuuvwDrr4AA66+BAOuvggDrr4MA66+EAOuvhQDrr4YA66+HAOuviADrr4kA66+KAOuviwDrr4wA66+NAOuvjgDrr48A66+QAOuvkQDrr5IA66+TAOuvlADrr5UA66+WAOuvlwDrr5gA66+ZAOuvmgDrr5sA66+cAOuvnQDrr54A66+fAOuvoADrr6EA66+iAOuvowDrr6QA66+lAOuvpgDrr6cA66+oAOuvqQDrr6oA66+rAOuvrADrr60A66+uAOuvrwDrr7AA66+xAOuvsgDrr7MA66+0AOuvtQDrr7YA66+3AOuvuADrr7kA66+6AOuvuwDrr7wA66+9AOuvvgDrr78A67CAAOuwgQDrsIIA67CDAOuwhADrsIUA67CGAOuwhwDrsIgA67CJAOuwigDrsIsA67CMAOuwjQDrsI4A67CPAOuwkADrsJEA67CSAOuwkwDrsJQA67CVAOuwlgDrsJcA67CYAOuwmQDrsJoA67CbAOuwnADrsJ0A67CeAOuwnwDrsKAA67ChAOuwogDrsKMA67CkAOuwpQDrsKYA67CnAOuwqADrsKkA67CqAOuwqwDrsKwA67CtAOuwrgDrsK8A67CwAOuwsQDrsLIA67CzAOuwtADrsLUA67C2AOuwtwDrsLgA67C5AOuwugDrsLsA67C8AOuwvQDrsL4A67C/AOuxgADrsYEA67GCAOuxgwDrsYQA67GFAOuxhgDrsYcA67GIAOuxiQDrsYoA67GLAOuxjADrsY0A67GOAOuxjwDrsZAA67GRAOuxkgDrsZMA67GUAOuxlQDrsZYA67GXAOuxmADrsZkA67GaAOuxmwDrsZwA67GdAOuxngDrsZ8A67GgAOuxoQDrsaIA67GjAOuxpADrsaUA67GmAOuxpwDrsagA67GpAOuxqgDrsasA67GsAOuxrQDrsa4A67GvAOuxsADrsbEA67GyAOuxswDrsbQA67G1AOuxtgDrsbcA67G4AOuxuQDrsboA67G7AOuxvADrsb0A67G+AOuxvwDrsoAA67KBAOuyggDrsoMA67KEAOuyhQDrsoYA67KHAOuyiADrsokA67KKAOuyiwDrsowA67KNAOuyjgDrso8A67KQAOuykQDrspIA67KTAOuylADrspUA67KWAOuylwDrspgA67KZAOuymgDrspsA67KcAOuynQDrsp4A67KfAOuyoADrsqEA67KiAOuyowDrsqQA67KlAOuypgDrsqcA67KoAOuyqQDrsqoA67KrAOuyrADrsq0A67KuAOuyrwDrsrAA67KxAOuysgDrsrMA67K0AOuytQDrsrYA67K3AOuyuADrsrkA67K6AOuyuwDrsrwA67K9AOuyvgDrsr8A67OAAOuzgQDrs4IA67ODAOuzhADrs4UA67OGAOuzhwDrs4gA67OJAOuzigDrs4sA67OMAOuzjQDrs44A67OPAOuzkADrs5EA67OSAOuzkwDrs5QA67OVAOuzlgDrs5cA67OYAOuzmQDrs5oA67ObAOuznADrs50A67OeAOuznwDrs6AA67OhAOuzogDrs6MA67OkAOuzpQDrs6YA67OnAOuzqADrs6kA67OqAOuzqwDrs6wA67OtAOuzrgDrs68A67OwAOuzsQDrs7IA67OzAOuztADrs7UA67O2AOuztwDrs7gA67O5AOuzugDrs7sA67O8AOuzvQDrs74A67O/AOu0gADrtIEA67SCAOu0gwDrtIQA67SFAOu0hgDrtIcA67SIAOu0iQDrtIoA67SLAOu0jADrtI0A67SOAOu0jwDrtJAA67SRAOu0kgDrtJMA67SUAOu0lQDrtJYA67SXAOu0mADrtJkA67SaAOu0mwDrtJwA67SdAOu0ngDrtJ8A67SgAOu0oQDrtKIA67SjAOu0pADrtKUA67SmAOu0pwDrtKgA67SpAOu0qgDrtKsA67SsAOu0rQDrtK4A67SvAOu0sADrtLEA67SyAOu0swDrtLQA67S1AOu0tgDrtLcA67S4AOu0uQDrtLoA67S7AOu0vADrtL0A67S+AOu0vwDrtYAA67WBAOu1ggDrtYMA67WEAOu1hQDrtYYA67WHAOu1iADrtYkA67WKAOu1iwDrtYwA67WNAOu1jgDrtY8A67WQAOu1kQDrtZIA67WTAOu1lADrtZUA67WWAOu1lwDrtZgA67WZAOu1mgDrtZsA67WcAOu1nQDrtZ4A67WfAOu1oADrtaEA67WiAOu1owDrtaQA67WlAOu1pgDrtacA67WoAOu1qQDrtaoA67WrAOu1rADrta0A67WuAOu1rwDrtbAA67WxAOu1sgDrtbMA67W0AOu1tQDrtbYA67W3AOu1uADrtbkA67W6AOu1uwDrtbwA67W9AOu1vgDrtb8A67aAAOu2gQDrtoIA67aDAOu2hADrtoUA67aGAOu2hwDrtogA67aJAOu2igDrtosA67aMAOu2jQDrto4A67aPAOu2kADrtpEA67aSAOu2kwDrtpQA67aVAOu2lgDrtpcA67aYAOu2mQDrtpoA67abAOu2nADrtp0A67aeAOu2nwDrtqAA67ahAOu2ogDrtqMA67akAOu2pQDrtqYA67anAOu2qADrtqkA67aqAOu2qwDrtqwA67atAOu2rgDrtq8A67awAOu2sQDrtrIA67azAOu2tADrtrUA67a2AOu2twDrtrgA67a5AOu2ugDrtrsA67a8AOu2vQDrtr4A67a/AOu3gADrt4EA67eCAOu3gwDrt4QA67eFAOu3hgDrt4cA67eIAOu3iQDrt4oA67eLAOu3jADrt40A67eOAOu3jwDrt5AA67eRAOu3kgDrt5MA67eUAOu3lQDrt5YA67eXAOu3mADrt5kA67eaAOu3mwDrt5wA67edAOu3ngDrt58A67egAOu3oQDrt6IA67ejAOu3pADrt6UA67emAOu3pwDrt6gA67epAOu3qgDrt6sA67esAOu3rQDrt64A67evAOu3sADrt7EA67eyAOu3swDrt7QA67e1AOu3tgDrt7cA67e4AOu3uQDrt7oA67e7AOu3vADrt70A67e+AOu3vwDruIAA67iBAOu4ggDruIMA67iEAOu4hQDruIYA67iHAOu4iADruIkA67iKAOu4iwDruIwA67iNAOu4jgDruI8A67iQAOu4kQDruJIA67iTAOu4lADruJUA67iWAOu4lwDruJgA67iZAOu4mgDruJsA67icAOu4nQDruJ4A67ifAOu4oADruKEA67iiAOu4owDruKQA67ilAOu4pgDruKcA67ioAOu4qQDruKoA67irAOu4rADruK0A67iuAOu4rwDruLAA67ixAOu4sgDruLMA67i0AOu4tQDruLYA67i3AOu4uADruLkA67i6AOu4uwDruLwA67i9AOu4vgDruL8A67mAAOu5gQDruYIA67mDAOu5hADruYUA67mGAOu5hwDruYgA67mJAOu5igDruYsA67mMAOu5jQDruY4A67mPAOu5kADruZEA67mSAOu5kwDruZQA67mVAOu5lgDruZcA67mYAOu5mQDruZoA67mbAOu5nADruZ0A67meAOu5nwDruaAA67mhAOu5ogDruaMA67mkAOu5pQDruaYA67mnAOu5qADruakA67mqAOu5qwDruawA67mtAOu5rgDrua8A67mwAOu5sQDrubIA67mzAOu5tADrubUA67m2AOu5twDrubgA67m5AOu5ugDrubsA67m8AOu5vQDrub4A67m/AOu6gADruoEA67qCAOu6gwDruoQA67qFAOu6hgDruocA67qIAOu6iQDruooA67qLAOu6jADruo0A67qOAOu6jwDrupAA67qRAOu6kgDrupMA67qUAOu6lQDrupYA67qXAOu6mADrupkA67qaAOu6mwDrupwA67qdAOu6ngDrup8A67qgAOu6oQDruqIA67qjAOu6pADruqUA67qmAOu6pwDruqgA67qpAOu6qgDruqsA67qsAOu6rQDruq4A67qvAOu6sADrurEA67qyAOu6swDrurQA67q1AOu6tgDrurcA67q4AOu6uQDruroA67q7AOu6vADrur0A67q+AOu6vwDru4AA67uBAOu7ggDru4MA67uEAOu7hQDru4YA67uHAOu7iADru4kA67uKAOu7iwDru4wA67uNAOu7jgDru48A67uQAOu7kQDru5IA67uTAOu7lADru5UA67uWAOu7lwDru5gA67uZAOu7mgDru5sA67ucAOu7nQDru54A67ufAOu7oADru6EA67uiAOu7owDru6QA67ulAOu7pgDru6cA67uoAOu7qQDru6oA67urAOu7rADru60A67uuAOu7rwDru7AA67uxAOu7sgDru7MA67u0AOu7tQDru7YA67u3AOu7uADru7kA67u6AOu7uwDru7wA67u9AOu7vgDru78A67yAAOu8gQDrvIIA67yDAOu8hADrvIUA67yGAOu8hwDrvIgA67yJAOu8igDrvIsA67yMAOu8jQDrvI4A67yPAOu8kADrvJEA67ySAOu8kwDrvJQA67yVAOu8lgDrvJcA67yYAOu8mQDrvJoA67ybAOu8nADrvJ0A67yeAOu8nwDrvKAA67yhAOu8ogDrvKMA67ykAOu8pQDrvKYA67ynAOu8qADrvKkA67yqAOu8qwDrvKwA67ytAOu8rgDrvK8A67ywAOu8sQDrvLIA67yzAOu8tADrvLUA67y2AOu8twDrvLgA67y5AOu8ugDrvLsA67y8AOu8vQDrvL4A67y/AOu9gADrvYEA672CAOu9gwDrvYQA672FAOu9hgDrvYcA672IAOu9iQDrvYoA672LAOu9jADrvY0A672OAOu9jwDrvZAA672RAOu9kgDrvZMA672UAOu9lQDrvZYA672XAOu9mADrvZkA672aAOu9mwDrvZwA672dAOu9ngDrvZ8A672gAOu9oQDrvaIA672jAOu9pADrvaUA672mAOu9pwDrvagA672pAOu9qgDrvasA672sAOu9rQDrva4A672vAOu9sADrvbEA672yAOu9swDrvbQA6721AOu9tgDrvbcA6724AOu9uQDrvboA6727AOu9vADrvb0A672+AOu9vwDrvoAA676BAOu+ggDrvoMA676EAOu+hQDrvoYA676HAOu+iADrvokA676KAOu+iwDrvowA676NAOu+jgDrvo8A676QAOu+kQDrvpIA676TAOu+lADrvpUA676WAOu+lwDrvpgA676ZAOu+mgDrvpsA676cAOu+nQDrvp4A676fAOu+oADrvqEA676iAOu+owDrvqQA676lAOu+pgDrvqcA676oAOu+qQDrvqoA676rAOu+rADrvq0A676uAOu+rwDrvrAA676xAOu+sgDrvrMA6760AOu+tQDrvrYA6763AOu+uADrvrkA6766AOu+uwDrvrwA6769AOu+vgDrvr8A67+AAOu/gQDrv4IA67+DAOu/hADrv4UA67+GAOu/hwDrv4gA67+JAOu/igDrv4sA67+MAOu/jQDrv44A67+PAOu/kADrv5EA67+SAOu/kwDrv5QA67+VAOu/lgDrv5cA67+YAOu/mQDrv5oA67+bAOu/nADrv50A67+eAOu/nwDrv6AA67+hAOu/ogDrv6MA67+kAOu/pQDrv6YA67+nAOu/qADrv6kA67+qAOu/qwDrv6wA67+tAOu/rgDrv68A67+wAOu/sQDrv7IA67+zAOu/tADrv7UA67+2AOu/twDrv7gA67+5AOu/ugDrv7sA67+8AOu/vQDrv74A67+/AOyAgADsgIEA7ICCAOyAgwDsgIQA7ICFAOyAhgDsgIcA7ICIAOyAiQDsgIoA7ICLAOyAjADsgI0A7ICOAOyAjwDsgJAA7ICRAOyAkgDsgJMA7ICUAOyAlQDsgJYA7ICXAOyAmADsgJkA7ICaAOyAmwDsgJwA7ICdAOyAngDsgJ8A7ICgAOyAoQDsgKIA7ICjAOyApADsgKUA7ICmAOyApwDsgKgA7ICpAOyAqgDsgKsA7ICsAOyArQDsgK4A7ICvAOyAsADsgLEA7ICyAOyAswDsgLQA7IC1AOyAtgDsgLcA7IC4AOyAuQDsgLoA7IC7AOyAvADsgL0A7IC+AOyAvwDsgYAA7IGBAOyBggDsgYMA7IGEAOyBhQDsgYYA7IGHAOyBiADsgYkA7IGKAOyBiwDsgYwA7IGNAOyBjgDsgY8A7IGQAOyBkQDsgZIA7IGTAOyBlADsgZUA7IGWAOyBlwDsgZgA7IGZAOyBmgDsgZsA7IGcAOyBnQDsgZ4A7IGfAOyBoADsgaEA7IGiAOyBowDsgaQA7IGlAOyBpgDsgacA7IGoAOyBqQDsgaoA7IGrAOyBrADsga0A7IGuAOyBrwDsgbAA7IGxAOyBsgDsgbMA7IG0AOyBtQDsgbYA7IG3AOyBuADsgbkA7IG6AOyBuwDsgbwA7IG9AOyBvgDsgb8A7IKAAOyCgQDsgoIA7IKDAOyChADsgoUA7IKGAOyChwDsgogA7IKJAOyCigDsgosA7IKMAOyCjQDsgo4A7IKPAOyCkADsgpEA7IKSAOyCkwDsgpQA7IKVAOyClgDsgpcA7IKYAOyCmQDsgpoA7IKbAOyCnADsgp0A7IKeAOyCnwDsgqAA7IKhAOyCogDsgqMA7IKkAOyCpQDsgqYA7IKnAOyCqADsgqkA7IKqAOyCqwDsgqwA7IKtAOyCrgDsgq8A7IKwAOyCsQDsgrIA7IKzAOyCtADsgrUA7IK2AOyCtwDsgrgA7IK5AOyCugDsgrsA7IK8AOyCvQDsgr4A7IK/AOyDgADsg4EA7IOCAOyDgwDsg4QA7IOFAOyDhgDsg4cA7IOIAOyDiQDsg4oA7IOLAOyDjADsg40A7IOOAOyDjwDsg5AA7IORAOyDkgDsg5MA7IOUAOyDlQDsg5YA7IOXAOyDmADsg5kA7IOaAOyDmwDsg5wA7IOdAOyDngDsg58A7IOgAOyDoQDsg6IA7IOjAOyDpADsg6UA7IOmAOyDpwDsg6gA7IOpAOyDqgDsg6sA7IOsAOyDrQDsg64A7IOvAOyDsADsg7EA7IOyAOyDswDsg7QA7IO1AOyDtgDsg7cA7IO4AOyDuQDsg7oA7IO7AOyDvADsg70A7IO+AOyDvwDshIAA7ISBAOyEggDshIMA7ISEAOyEhQDshIYA7ISHAOyEiADshIkA7ISKAOyEiwDshIwA7ISNAOyEjgDshI8A7ISQAOyEkQDshJIA7ISTAOyElADshJUA7ISWAOyElwDshJgA7ISZAOyEmgDshJsA7IScAOyEnQDshJ4A7ISfAOyEoADshKEA7ISiAOyEowDshKQA7ISlAOyEpgDshKcA7ISoAOyEqQDshKoA7ISrAOyErADshK0A7ISuAOyErwDshLAA7ISxAOyEsgDshLMA7IS0AOyEtQDshLYA7IS3AOyEuADshLkA7IS6AOyEuwDshLwA7IS9AOyEvgDshL8A7IWAAOyFgQDshYIA7IWDAOyFhADshYUA7IWGAOyFhwDshYgA7IWJAOyFigDshYsA7IWMAOyFjQDshY4A7IWPAOyFkADshZEA7IWSAOyFkwDshZQA7IWVAOyFlgDshZcA7IWYAOyFmQDshZoA7IWbAOyFnADshZ0A7IWeAOyFnwDshaAA7IWhAOyFogDshaMA7IWkAOyFpQDshaYA7IWnAOyFqADshakA7IWqAOyFqwDshawA7IWtAOyFrgDsha8A7IWwAOyFsQDshbIA7IWzAOyFtADshbUA7IW2AOyFtwDshbgA7IW5AOyFugDshbsA7IW8AOyFvQDshb4A7IW/AOyGgADshoEA7IaCAOyGgwDshoQA7IaFAOyGhgDshocA7IaIAOyGiQDshooA7IaLAOyGjADsho0A7IaOAOyGjwDshpAA7IaRAOyGkgDshpMA7IaUAOyGlQDshpYA7IaXAOyGmADshpkA7IaaAOyGmwDshpwA7IadAOyGngDshp8A7IagAOyGoQDshqIA7IajAOyGpADshqUA7IamAOyGpwDshqgA7IapAOyGqgDshqsA7IasAOyGrQDshq4A7IavAOyGsADshrEA7IayAOyGswDshrQA7Ia1AOyGtgDshrcA7Ia4AOyGuQDshroA7Ia7AOyGvADshr0A7Ia+AOyGvwDsh4AA7IeBAOyHggDsh4MA7IeEAOyHhQDsh4YA7IeHAOyHiADsh4kA7IeKAOyHiwDsh4wA7IeNAOyHjgDsh48A7IeQAOyHkQDsh5IA7IeTAOyHlADsh5UA7IeWAOyHlwDsh5gA7IeZAOyHmgDsh5sA7IecAOyHnQDsh54A7IefAOyHoADsh6EA7IeiAOyHowDsh6QA7IelAOyHpgDsh6cA7IeoAOyHqQDsh6oA7IerAOyHrADsh60A7IeuAOyHrwDsh7AA7IexAOyHsgDsh7MA7Ie0AOyHtQDsh7YA7Ie3AOyHuADsh7kA7Ie6AOyHuwDsh7wA7Ie9AOyHvgDsh78A7IiAAOyIgQDsiIIA7IiDAOyIhADsiIUA7IiGAOyIhwDsiIgA7IiJAOyIigDsiIsA7IiMAOyIjQDsiI4A7IiPAOyIkADsiJEA7IiSAOyIkwDsiJQA7IiVAOyIlgDsiJcA7IiYAOyImQDsiJoA7IibAOyInADsiJ0A7IieAOyInwDsiKAA7IihAOyIogDsiKMA7IikAOyIpQDsiKYA7IinAOyIqADsiKkA7IiqAOyIqwDsiKwA7IitAOyIrgDsiK8A7IiwAOyIsQDsiLIA7IizAOyItADsiLUA7Ii2AOyItwDsiLgA7Ii5AOyIugDsiLsA7Ii8AOyIvQDsiL4A7Ii/AOyJgADsiYEA7ImCAOyJgwDsiYQA7ImFAOyJhgDsiYcA7ImIAOyJiQDsiYoA7ImLAOyJjADsiY0A7ImOAOyJjwDsiZAA7ImRAOyJkgDsiZMA7ImUAOyJlQDsiZYA7ImXAOyJmADsiZkA7ImaAOyJmwDsiZwA7ImdAOyJngDsiZ8A7ImgAOyJoQDsiaIA7ImjAOyJpADsiaUA7ImmAOyJpwDsiagA7ImpAOyJqgDsiasA7ImsAOyJrQDsia4A7ImvAOyJsADsibEA7ImyAOyJswDsibQA7Im1AOyJtgDsibcA7Im4AOyJuQDsiboA7Im7AOyJvADsib0A7Im+AOyJvwDsioAA7IqBAOyKggDsioMA7IqEAOyKhQDsioYA7IqHAOyKiADsiokA7IqKAOyKiwDsiowA7IqNAOyKjgDsio8A7IqQAOyKkQDsipIA7IqTAOyKlADsipUA7IqWAOyKlwDsipgA7IqZAOyKmgDsipsA7IqcAOyKnQDsip4A7IqfAOyKoADsiqEA7IqiAOyKowDsiqQA7IqlAOyKpgDsiqcA7IqoAOyKqQDsiqoA7IqrAOyKrADsiq0A7IquAOyKrwDsirAA7IqxAOyKsgDsirMA7Iq0AOyKtQDsirYA7Iq3AOyKuADsirkA7Iq6AOyKuwDsirwA7Iq9AOyKvgDsir8A7IuAAOyLgQDsi4IA7IuDAOyLhADsi4UA7IuGAOyLhwDsi4gA7IuJAOyLigDsi4sA7IuMAOyLjQDsi44A7IuPAOyLkADsi5EA7IuSAOyLkwDsi5QA7IuVAOyLlgDsi5cA7IuYAOyLmQDsi5oA7IubAOyLnADsi50A7IueAOyLnwDsi6AA7IuhAOyLogDsi6MA7IukAOyLpQDsi6YA7IunAOyLqADsi6kA7IuqAOyLqwDsi6wA7IutAOyLrgDsi68A7IuwAOyLsQDsi7IA7IuzAOyLtADsi7UA7Iu2AOyLtwDsi7gA7Iu5AOyLugDsi7sA7Iu8AOyLvQDsi74A7Iu/AOyMgADsjIEA7IyCAOyMgwDsjIQA7IyFAOyMhgDsjIcA7IyIAOyMiQDsjIoA7IyLAOyMjADsjI0A7IyOAOyMjwDsjJAA7IyRAOyMkgDsjJMA7IyUAOyMlQDsjJYA7IyXAOyMmADsjJkA7IyaAOyMmwDsjJwA7IydAOyMngDsjJ8A7IygAOyMoQDsjKIA7IyjAOyMpADsjKUA7IymAOyMpwDsjKgA7IypAOyMqgDsjKsA7IysAOyMrQDsjK4A7IyvAOyMsADsjLEA7IyyAOyMswDsjLQA7Iy1AOyMtgDsjLcA7Iy4AOyMuQDsjLoA7Iy7AOyMvADsjL0A7Iy+AOyMvwDsjYAA7I2BAOyNggDsjYMA7I2EAOyNhQDsjYYA7I2HAOyNiADsjYkA7I2KAOyNiwDsjYwA7I2NAOyNjgDsjY8A7I2QAOyNkQDsjZIA7I2TAOyNlADsjZUA7I2WAOyNlwDsjZgA7I2ZAOyNmgDsjZsA7I2cAOyNnQDsjZ4A7I2fAOyNoADsjaEA7I2iAOyNowDsjaQA7I2lAOyNpgDsjacA7I2oAOyNqQDsjaoA7I2rAOyNrADsja0A7I2uAOyNrwDsjbAA7I2xAOyNsgDsjbMA7I20AOyNtQDsjbYA7I23AOyNuADsjbkA7I26AOyNuwDsjbwA7I29AOyNvgDsjb8A7I6AAOyOgQDsjoIA7I6DAOyOhADsjoUA7I6GAOyOhwDsjogA7I6JAOyOigDsjosA7I6MAOyOjQDsjo4A7I6PAOyOkADsjpEA7I6SAOyOkwDsjpQA7I6VAOyOlgDsjpcA7I6YAOyOmQDsjpoA7I6bAOyOnADsjp0A7I6eAOyOnwDsjqAA7I6hAOyOogDsjqMA7I6kAOyOpQDsjqYA7I6nAOyOqADsjqkA7I6qAOyOqwDsjqwA7I6tAOyOrgDsjq8A7I6wAOyOsQDsjrIA7I6zAOyOtADsjrUA7I62AOyOtwDsjrgA7I65AOyOugDsjrsA7I68AOyOvQDsjr4A7I6/AOyPgADsj4EA7I+CAOyPgwDsj4QA7I+FAOyPhgDsj4cA7I+IAOyPiQDsj4oA7I+LAOyPjADsj40A7I+OAOyPjwDsj5AA7I+RAOyPkgDsj5MA7I+UAOyPlQDsj5YA7I+XAOyPmADsj5kA7I+aAOyPmwDsj5wA7I+dAOyPngDsj58A7I+gAOyPoQDsj6IA7I+jAOyPpADsj6UA7I+mAOyPpwDsj6gA7I+pAOyPqgDsj6sA7I+sAOyPrQDsj64A7I+vAOyPsADsj7EA7I+yAOyPswDsj7QA7I+1AOyPtgDsj7cA7I+4AOyPuQDsj7oA7I+7AOyPvADsj70A7I++AOyPvwDskIAA7JCBAOyQggDskIMA7JCEAOyQhQDskIYA7JCHAOyQiADskIkA7JCKAOyQiwDskIwA7JCNAOyQjgDskI8A7JCQAOyQkQDskJIA7JCTAOyQlADskJUA7JCWAOyQlwDskJgA7JCZAOyQmgDskJsA7JCcAOyQnQDskJ4A7JCfAOyQoADskKEA7JCiAOyQowDskKQA7JClAOyQpgDskKcA7JCoAOyQqQDskKoA7JCrAOyQrADskK0A7JCuAOyQrwDskLAA7JCxAOyQsgDskLMA7JC0AOyQtQDskLYA7JC3AOyQuADskLkA7JC6AOyQuwDskLwA7JC9AOyQvgDskL8A7JGAAOyRgQDskYIA7JGDAOyRhADskYUA7JGGAOyRhwDskYgA7JGJAOyRigDskYsA7JGMAOyRjQDskY4A7JGPAOyRkADskZEA7JGSAOyRkwDskZQA7JGVAOyRlgDskZcA7JGYAOyRmQDskZoA7JGbAOyRnADskZ0A7JGeAOyRnwDskaAA7JGhAOyRogDskaMA7JGkAOyRpQDskaYA7JGnAOyRqADskakA7JGqAOyRqwDskawA7JGtAOyRrgDska8A7JGwAOyRsQDskbIA7JGzAOyRtADskbUA7JG2AOyRtwDskbgA7JG5AOyRugDskbsA7JG8AOyRvQDskb4A7JG/AOySgADskoEA7JKCAOySgwDskoQA7JKFAOyShgDskocA7JKIAOySiQDskooA7JKLAOySjADsko0A7JKOAOySjwDskpAA7JKRAOySkgDskpMA7JKUAOySlQDskpYA7JKXAOySmADskpkA7JKaAOySmwDskpwA7JKdAOySngDskp8A7JKgAOySoQDskqIA7JKjAOySpADskqUA7JKmAOySpwDskqgA7JKpAOySqgDskqsA7JKsAOySrQDskq4A7JKvAOySsADskrEA7JKyAOySswDskrQA7JK1AOyStgDskrcA7JK4AOySuQDskroA7JK7AOySvADskr0A7JK+AOySvwDsk4AA7JOBAOyTggDsk4MA7JOEAOyThQDsk4YA7JOHAOyTiADsk4kA7JOKAOyTiwDsk4wA7JONAOyTjgDsk48A7JOQAOyTkQDsk5IA7JOTAOyTlADsk5UA7JOWAOyTlwDsk5gA7JOZAOyTmgDsk5sA7JOcAOyTnQDsk54A7JOfAOyToADsk6EA7JOiAOyTowDsk6QA7JOlAOyTpgDsk6cA7JOoAOyTqQDsk6oA7JOrAOyTrADsk60A7JOuAOyTrwDsk7AA7JOxAOyTsgDsk7MA7JO0AOyTtQDsk7YA7JO3AOyTuADsk7kA7JO6AOyTuwDsk7wA7JO9AOyTvgDsk78A7JSAAOyUgQDslIIA7JSDAOyUhADslIUA7JSGAOyUhwDslIgA7JSJAOyUigDslIsA7JSMAOyUjQDslI4A7JSPAOyUkADslJEA7JSSAOyUkwDslJQA7JSVAOyUlgDslJcA7JSYAOyUmQDslJoA7JSbAOyUnADslJ0A7JSeAOyUnwDslKAA7JShAOyUogDslKMA7JSkAOyUpQDslKYA7JSnAOyUqADslKkA7JSqAOyUqwDslKwA7JStAOyUrgDslK8A7JSwAOyUsQDslLIA7JSzAOyUtADslLUA7JS2AOyUtwDslLgA7JS5AOyUugDslLsA7JS8AOyUvQDslL4A7JS/AOyVgADslYEA7JWCAOyVgwDslYQA7JWFAOyVhgDslYcA7JWIAOyViQDslYoA7JWLAOyVjADslY0A7JWOAOyVjwDslZAA7JWRAOyVkgDslZMA7JWUAOyVlQDslZYA7JWXAOyVmADslZkA7JWaAOyVmwDslZwA7JWdAOyVngDslZ8A7JWgAOyVoQDslaIA7JWjAOyVpADslaUA7JWmAOyVpwDslagA7JWpAOyVqgDslasA7JWsAOyVrQDsla4A7JWvAOyVsADslbEA7JWyAOyVswDslbQA7JW1AOyVtgDslbcA7JW4AOyVuQDslboA7JW7AOyVvADslb0A7JW+AOyVvwDsloAA7JaBAOyWggDsloMA7JaEAOyWhQDsloYA7JaHAOyWiADslokA7JaKAOyWiwDslowA7JaNAOyWjgDslo8A7JaQAOyWkQDslpIA7JaTAOyWlADslpUA7JaWAOyWlwDslpgA7JaZAOyWmgDslpsA7JacAOyWnQDslp4A7JafAOyWoADslqEA7JaiAOyWowDslqQA7JalAOyWpgDslqcA7JaoAOyWqQDslqoA7JarAOyWrADslq0A7JauAOyWrwDslrAA7JaxAOyWsgDslrMA7Ja0AOyWtQDslrYA7Ja3AOyWuADslrkA7Ja6AOyWuwDslrwA7Ja9AOyWvgDslr8A7JeAAOyXgQDsl4IA7JeDAOyXhADsl4UA7JeGAOyXhwDsl4gA7JeJAOyXigDsl4sA7JeMAOyXjQDsl44A7JePAOyXkADsl5EA7JeSAOyXkwDsl5QA7JeVAOyXlgDsl5cA7JeYAOyXmQDsl5oA7JebAOyXnADsl50A7JeeAOyXnwDsl6AA7JehAOyXogDsl6MA7JekAOyXpQDsl6YA7JenAOyXqADsl6kA7JeqAOyXqwDsl6wA7JetAOyXrgDsl68A7JewAOyXsQDsl7IA7JezAOyXtADsl7UA7Je2AOyXtwDsl7gA7Je5AOyXugDsl7sA7Je8AOyXvQDsl74A7Je/AOyYgADsmIEA7JiCAOyYgwDsmIQA7JiFAOyYhgDsmIcA7JiIAOyYiQDsmIoA7JiLAOyYjADsmI0A7JiOAOyYjwDsmJAA7JiRAOyYkgDsmJMA7JiUAOyYlQDsmJYA7JiXAOyYmADsmJkA7JiaAOyYmwDsmJwA7JidAOyYngDsmJ8A7JigAOyYoQDsmKIA7JijAOyYpADsmKUA7JimAOyYpwDsmKgA7JipAOyYqgDsmKsA7JisAOyYrQDsmK4A7JivAOyYsADsmLEA7JiyAOyYswDsmLQA7Ji1AOyYtgDsmLcA7Ji4AOyYuQDsmLoA7Ji7AOyYvADsmL0A7Ji+AOyYvwDsmYAA7JmBAOyZggDsmYMA7JmEAOyZhQDsmYYA7JmHAOyZiADsmYkA7JmKAOyZiwDsmYwA7JmNAOyZjgDsmY8A7JmQAOyZkQDsmZIA7JmTAOyZlADsmZUA7JmWAOyZlwDsmZgA7JmZAOyZmgDsmZsA7JmcAOyZnQDsmZ4A7JmfAOyZoADsmaEA7JmiAOyZowDsmaQA7JmlAOyZpgDsmacA7JmoAOyZqQDsmaoA7JmrAOyZrADsma0A7JmuAOyZrwDsmbAA7JmxAOyZsgDsmbMA7Jm0AOyZtQDsmbYA7Jm3AOyZuADsmbkA7Jm6AOyZuwDsmbwA7Jm9AOyZvgDsmb8A7JqAAOyagQDsmoIA7JqDAOyahADsmoUA7JqGAOyahwDsmogA7JqJAOyaigDsmosA7JqMAOyajQDsmo4A7JqPAOyakADsmpEA7JqSAOyakwDsmpQA7JqVAOyalgDsmpcA7JqYAOyamQDsmpoA7JqbAOyanADsmp0A7JqeAOyanwDsmqAA7JqhAOyaogDsmqMA7JqkAOyapQDsmqYA7JqnAOyaqADsmqkA7JqqAOyaqwDsmqwA7JqtAOyargDsmq8A7JqwAOyasQDsmrIA7JqzAOyatADsmrUA7Jq2AOyatwDsmrgA7Jq5AOyaugDsmrsA7Jq8AOyavQDsmr4A7Jq/AOybgADsm4EA7JuCAOybgwDsm4QA7JuFAOybhgDsm4cA7JuIAOybiQDsm4oA7JuLAOybjADsm40A7JuOAOybjwDsm5AA7JuRAOybkgDsm5MA7JuUAOyblQDsm5YA7JuXAOybmADsm5kA7JuaAOybmwDsm5wA7JudAOybngDsm58A7JugAOyboQDsm6IA7JujAOybpADsm6UA7JumAOybpwDsm6gA7JupAOybqgDsm6sA7JusAOybrQDsm64A7JuvAOybsADsm7EA7JuyAOybswDsm7QA7Ju1AOybtgDsm7cA7Ju4AOybuQDsm7oA7Ju7AOybvADsm70A7Ju+AOybvwDsnIAA7JyBAOycggDsnIMA7JyEAOychQDsnIYA7JyHAOyciADsnIkA7JyKAOyciwDsnIwA7JyNAOycjgDsnI8A7JyQAOyckQDsnJIA7JyTAOyclADsnJUA7JyWAOyclwDsnJgA7JyZAOycmgDsnJsA7JycAOycnQDsnJ4A7JyfAOycoADsnKEA7JyiAOycowDsnKQA7JylAOycpgDsnKcA7JyoAOycqQDsnKoA7JyrAOycrADsnK0A7JyuAOycrwDsnLAA7JyxAOycsgDsnLMA7Jy0AOyctQDsnLYA7Jy3AOycuADsnLkA7Jy6AOycuwDsnLwA7Jy9AOycvgDsnL8A7J2AAOydgQDsnYIA7J2DAOydhADsnYUA7J2GAOydhwDsnYgA7J2JAOydigDsnYsA7J2MAOydjQDsnY4A7J2PAOydkADsnZEA7J2SAOydkwDsnZQA7J2VAOydlgDsnZcA7J2YAOydmQDsnZoA7J2bAOydnADsnZ0A7J2eAOydnwDsnaAA7J2hAOydogDsnaMA7J2kAOydpQDsnaYA7J2nAOydqADsnakA7J2qAOydqwDsnawA7J2tAOydrgDsna8A7J2wAOydsQDsnbIA7J2zAOydtADsnbUA7J22AOydtwDsnbgA7J25AOydugDsnbsA7J28AOydvQDsnb4A7J2/AOyegADsnoEA7J6CAOyegwDsnoQA7J6FAOyehgDsnocA7J6IAOyeiQDsnooA7J6LAOyejADsno0A7J6OAOyejwDsnpAA7J6RAOyekgDsnpMA7J6UAOyelQDsnpYA7J6XAOyemADsnpkA7J6aAOyemwDsnpwA7J6dAOyengDsnp8A7J6gAOyeoQDsnqIA7J6jAOyepADsnqUA7J6mAOyepwDsnqgA7J6pAOyeqgDsnqsA7J6sAOyerQDsnq4A7J6vAOyesADsnrEA7J6yAOyeswDsnrQA7J61AOyetgDsnrcA7J64AOyeuQDsnroA7J67AOyevADsnr0A7J6+AOyevwDsn4AA7J+BAOyfggDsn4MA7J+EAOyfhQDsn4YA7J+HAOyfiADsn4kA7J+KAOyfiwDsn4wA7J+NAOyfjgDsn48A7J+QAOyfkQDsn5IA7J+TAOyflADsn5UA7J+WAOyflwDsn5gA7J+ZAOyfmgDsn5sA7J+cAOyfnQDsn54A7J+fAOyfoADsn6EA7J+iAOyfowDsn6QA7J+lAOyfpgDsn6cA7J+oAOyfqQDsn6oA7J+rAOyfrADsn60A7J+uAOyfrwDsn7AA7J+xAOyfsgDsn7MA7J+0AOyftQDsn7YA7J+3AOyfuADsn7kA7J+6AOyfuwDsn7wA7J+9AOyfvgDsn78A7KCAAOyggQDsoIIA7KCDAOyghADsoIUA7KCGAOyghwDsoIgA7KCJAOygigDsoIsA7KCMAOygjQDsoI4A7KCPAOygkADsoJEA7KCSAOygkwDsoJQA7KCVAOyglgDsoJcA7KCYAOygmQDsoJoA7KCbAOygnADsoJ0A7KCeAOygnwDsoKAA7KChAOygogDsoKMA7KCkAOygpQDsoKYA7KCnAOygqADsoKkA7KCqAOygqwDsoKwA7KCtAOygrgDsoK8A7KCwAOygsQDsoLIA7KCzAOygtADsoLUA7KC2AOygtwDsoLgA7KC5AOygugDsoLsA7KC8AOygvQDsoL4A7KC/AOyhgADsoYEA7KGCAOyhgwDsoYQA7KGFAOyhhgDsoYcA7KGIAOyhiQDsoYoA7KGLAOyhjADsoY0A7KGOAOyhjwDsoZAA7KGRAOyhkgDsoZMA7KGUAOyhlQDsoZYA7KGXAOyhmADsoZkA7KGaAOyhmwDsoZwA7KGdAOyhngDsoZ8A7KGgAOyhoQDsoaIA7KGjAOyhpADsoaUA7KGmAOyhpwDsoagA7KGpAOyhqgDsoasA7KGsAOyhrQDsoa4A7KGvAOyhsADsobEA7KGyAOyhswDsobQA7KG1AOyhtgDsobcA7KG4AOyhuQDsoboA7KG7AOyhvADsob0A7KG+AOyhvwDsooAA7KKBAOyiggDsooMA7KKEAOyihQDsooYA7KKHAOyiiADsookA7KKKAOyiiwDsoowA7KKNAOyijgDsoo8A7KKQAOyikQDsopIA7KKTAOyilADsopUA7KKWAOyilwDsopgA7KKZAOyimgDsopsA7KKcAOyinQDsop4A7KKfAOyioADsoqEA7KKiAOyiowDsoqQA7KKlAOyipgDsoqcA7KKoAOyiqQDsoqoA7KKrAOyirADsoq0A7KKuAOyirwDsorAA7KKxAOyisgDsorMA7KK0AOyitQDsorYA7KK3AOyiuADsorkA7KK6AOyiuwDsorwA7KK9AOyivgDsor8A7KOAAOyjgQDso4IA7KODAOyjhADso4UA7KOGAOyjhwDso4gA7KOJAOyjigDso4sA7KOMAOyjjQDso44A7KOPAOyjkADso5EA7KOSAOyjkwDso5QA7KOVAOyjlgDso5cA7KOYAOyjmQDso5oA7KObAOyjnADso50A7KOeAOyjnwDso6AA7KOhAOyjogDso6MA7KOkAOyjpQDso6YA7KOnAOyjqADso6kA7KOqAOyjqwDso6wA7KOtAOyjrgDso68A7KOwAOyjsQDso7IA7KOzAOyjtADso7UA7KO2AOyjtwDso7gA7KO5AOyjugDso7sA7KO8AOyjvOydmADso70A7KO+AOyjvwDspIAA7KSBAOykggDspIMA7KSEAOykhQDspIYA7KSHAOykiADspIkA7KSKAOykiwDspIwA7KSNAOykjgDspI8A7KSQAOykkQDspJIA7KSTAOyklADspJUA7KSWAOyklwDspJgA7KSZAOykmgDspJsA7KScAOyknQDspJ4A7KSfAOykoADspKEA7KSiAOykowDspKQA7KSlAOykpgDspKcA7KSoAOykqQDspKoA7KSrAOykrADspK0A7KSuAOykrwDspLAA7KSxAOyksgDspLMA7KS0AOyktQDspLYA7KS3AOykuADspLkA7KS6AOykuwDspLwA7KS9AOykvgDspL8A7KWAAOylgQDspYIA7KWDAOylhADspYUA7KWGAOylhwDspYgA7KWJAOyligDspYsA7KWMAOyljQDspY4A7KWPAOylkADspZEA7KWSAOylkwDspZQA7KWVAOyllgDspZcA7KWYAOylmQDspZoA7KWbAOylnADspZ0A7KWeAOylnwDspaAA7KWhAOylogDspaMA7KWkAOylpQDspaYA7KWnAOylqADspakA7KWqAOylqwDspawA7KWtAOylrgDspa8A7KWwAOylsQDspbIA7KWzAOyltADspbUA7KW2AOyltwDspbgA7KW5AOylugDspbsA7KW8AOylvQDspb4A7KW/AOymgADspoEA7KaCAOymgwDspoQA7KaFAOymhgDspocA7KaIAOymiQDspooA7KaLAOymjADspo0A7KaOAOymjwDsppAA7KaRAOymkgDsppMA7KaUAOymlQDsppYA7KaXAOymmADsppkA7KaaAOymmwDsppwA7KadAOymngDspp8A7KagAOymoQDspqIA7KajAOympADspqUA7KamAOympwDspqgA7KapAOymqgDspqsA7KasAOymrQDspq4A7KavAOymsADsprEA7KayAOymswDsprQA7Ka1AOymtgDsprcA7Ka4AOymuQDsproA7Ka7AOymvADspr0A7Ka+AOymvwDsp4AA7KeBAOynggDsp4MA7KeEAOynhQDsp4YA7KeHAOyniADsp4kA7KeKAOyniwDsp4wA7KeNAOynjgDsp48A7KeQAOynkQDsp5IA7KeTAOynlADsp5UA7KeWAOynlwDsp5gA7KeZAOynmgDsp5sA7KecAOynnQDsp54A7KefAOynoADsp6EA7KeiAOynowDsp6QA7KelAOynpgDsp6cA7KeoAOynqQDsp6oA7KerAOynrADsp60A7KeuAOynrwDsp7AA7KexAOynsgDsp7MA7Ke0AOyntQDsp7YA7Ke3AOynuADsp7kA7Ke6AOynuwDsp7wA7Ke9AOynvgDsp78A7KiAAOyogQDsqIIA7KiDAOyohADsqIUA7KiGAOyohwDsqIgA7KiJAOyoigDsqIsA7KiMAOyojQDsqI4A7KiPAOyokADsqJEA7KiSAOyokwDsqJQA7KiVAOyolgDsqJcA7KiYAOyomQDsqJoA7KibAOyonADsqJ0A7KieAOyonwDsqKAA7KihAOyoogDsqKMA7KikAOyopQDsqKYA7KinAOyoqADsqKkA7KiqAOyoqwDsqKwA7KitAOyorgDsqK8A7KiwAOyosQDsqLIA7KizAOyotADsqLUA7Ki2AOyotwDsqLgA7Ki5AOyougDsqLsA7Ki8AOyovQDsqL4A7Ki/AOypgADsqYEA7KmCAOypgwDsqYQA7KmFAOyphgDsqYcA7KmIAOypiQDsqYoA7KmLAOypjADsqY0A7KmOAOypjwDsqZAA7KmRAOypkgDsqZMA7KmUAOyplQDsqZYA7KmXAOypmADsqZkA7KmaAOypmwDsqZwA7KmdAOypngDsqZ8A7KmgAOypoQDsqaIA7KmjAOyppADsqaUA7KmmAOyppwDsqagA7KmpAOypqgDsqasA7KmsAOyprQDsqa4A7KmvAOypsADsqbEA7KmyAOypswDsqbQA7Km1AOyptgDsqbcA7Km4AOypuQDsqboA7Km7AOypvADsqb0A7Km+AOypvwDsqoAA7KqBAOyqggDsqoMA7KqEAOyqhQDsqoYA7KqHAOyqiADsqokA7KqKAOyqiwDsqowA7KqNAOyqjgDsqo8A7KqQAOyqkQDsqpIA7KqTAOyqlADsqpUA7KqWAOyqlwDsqpgA7KqZAOyqmgDsqpsA7KqcAOyqnQDsqp4A7KqfAOyqoADsqqEA7KqiAOyqowDsqqQA7KqlAOyqpgDsqqcA7KqoAOyqqQDsqqoA7KqrAOyqrADsqq0A7KquAOyqrwDsqrAA7KqxAOyqsgDsqrMA7Kq0AOyqtQDsqrYA7Kq3AOyquADsqrkA7Kq6AOyquwDsqrwA7Kq9AOyqvgDsqr8A7KuAAOyrgQDsq4IA7KuDAOyrhADsq4UA7KuGAOyrhwDsq4gA7KuJAOyrigDsq4sA7KuMAOyrjQDsq44A7KuPAOyrkADsq5EA7KuSAOyrkwDsq5QA7KuVAOyrlgDsq5cA7KuYAOyrmQDsq5oA7KubAOyrnADsq50A7KueAOyrnwDsq6AA7KuhAOyrogDsq6MA7KukAOyrpQDsq6YA7KunAOyrqADsq6kA7KuqAOyrqwDsq6wA7KutAOyrrgDsq68A7KuwAOyrsQDsq7IA7KuzAOyrtADsq7UA7Ku2AOyrtwDsq7gA7Ku5AOyrugDsq7sA7Ku8AOyrvQDsq74A7Ku/AOysgADsrIEA7KyCAOysgwDsrIQA7KyFAOyshgDsrIcA7KyIAOysiQDsrIoA7KyLAOysjADsrI0A7KyOAOysjwDsrJAA7KyRAOyskgDsrJMA7KyUAOyslQDsrJYA7KyXAOysmADsrJkA7KyaAOysmwDsrJwA7KydAOysngDsrJ8A7KygAOysoQDsrKIA7KyjAOyspADsrKUA7KymAOyspwDsrKgA7KypAOysqgDsrKsA7KysAOysrQDsrK4A7KyvAOyssADsrLEA7KyyAOysswDsrLQA7Ky1AOystgDsrLcA7Ky4AOysuQDsrLoA7Ky7AOysvADsrL0A7Ky+AOysvwDsrYAA7K2BAOytggDsrYMA7K2EAOythQDsrYYA7K2HAOytiADsrYkA7K2KAOytiwDsrYwA7K2NAOytjgDsrY8A7K2QAOytkQDsrZIA7K2TAOytlADsrZUA7K2WAOytlwDsrZgA7K2ZAOytmgDsrZsA7K2cAOytnQDsrZ4A7K2fAOytoADsraEA7K2iAOytowDsraQA7K2lAOytpgDsracA7K2oAOytqQDsraoA7K2rAOytrADsra0A7K2uAOytrwDsrbAA7K2xAOytsgDsrbMA7K20AOyttQDsrbYA7K23AOytuADsrbkA7K26AOytuwDsrbwA7K29AOytvgDsrb8A7K6AAOyugQDsroIA7K6DAOyuhADsroUA7K6GAOyuhwDsrogA7K6JAOyuigDsrosA7K6MAOyujQDsro4A7K6PAOyukADsrpEA7K6SAOyukwDsrpQA7K6VAOyulgDsrpcA7K6YAOyumQDsrpoA7K6bAOyunADsrp0A7K6eAOyunwDsrqAA7K6hAOyuogDsrqMA7K6kAOyupQDsrqYA7K6nAOyuqADsrqkA7K6qAOyuqwDsrqwA7K6tAOyurgDsrq8A7K6wAOyusQDsrrIA7K6zAOyutADsrrUA7K62AOyutwDsrrgA7K65AOyuugDsrrsA7K68AOyuvQDsrr4A7K6/AOyvgADsr4EA7K+CAOyvgwDsr4QA7K+FAOyvhgDsr4cA7K+IAOyviQDsr4oA7K+LAOyvjADsr40A7K+OAOyvjwDsr5AA7K+RAOyvkgDsr5MA7K+UAOyvlQDsr5YA7K+XAOyvmADsr5kA7K+aAOyvmwDsr5wA7K+dAOyvngDsr58A7K+gAOyvoQDsr6IA7K+jAOyvpADsr6UA7K+mAOyvpwDsr6gA7K+pAOyvqgDsr6sA7K+sAOyvrQDsr64A7K+vAOyvsADsr7EA7K+yAOyvswDsr7QA7K+1AOyvtgDsr7cA7K+4AOyvuQDsr7oA7K+7AOyvvADsr70A7K++AOyvvwDssIAA7LCBAOywggDssIMA7LCEAOywhQDssIYA7LCHAOywiADssIkA7LCKAOywiwDssIwA7LCNAOywjgDssI8A7LCQAOywkQDssJIA7LCTAOywlADssJUA7LCWAOywlwDssJgA7LCZAOywmgDssJsA7LCcAOywnQDssJ4A7LCfAOywoADssKEA7LCiAOywowDssKQA7LClAOywpgDssKcA7LCoAOywqQDssKoA7LCrAOywrADssK0A7LCuAOywrwDssLAA7LCxAOywsgDssLMA7LC0AOywtQDssLYA7LC3AOywuADssLjqs6AA7LC5AOywugDssLsA7LC8AOywvQDssL4A7LC/AOyxgADssYEA7LGCAOyxgwDssYQA7LGFAOyxhgDssYcA7LGIAOyxiQDssYoA7LGLAOyxjADssY0A7LGOAOyxjwDssZAA7LGRAOyxkgDssZMA7LGUAOyxlQDssZYA7LGXAOyxmADssZkA7LGaAOyxmwDssZwA7LGdAOyxngDssZ8A7LGgAOyxoQDssaIA7LGjAOyxpADssaUA7LGmAOyxpwDssagA7LGpAOyxqgDssasA7LGsAOyxrQDssa4A7LGvAOyxsADssbEA7LGyAOyxswDssbQA7LG1AOyxtgDssbcA7LG4AOyxuQDssboA7LG7AOyxvADssb0A7LG+AOyxvwDssoAA7LKBAOyyggDssoMA7LKEAOyyhQDssoYA7LKHAOyyiADssokA7LKKAOyyiwDssowA7LKNAOyyjgDsso8A7LKQAOyykQDsspIA7LKTAOyylADsspUA7LKWAOyylwDsspgA7LKZAOyymgDsspsA7LKcAOyynQDssp4A7LKfAOyyoADssqEA7LKiAOyyowDssqQA7LKlAOyypgDssqcA7LKoAOyyqQDssqoA7LKrAOyyrADssq0A7LKuAOyyrwDssrAA7LKxAOyysgDssrMA7LK0AOyytQDssrYA7LK3AOyyuADssrkA7LK6AOyyuwDssrwA7LK9AOyyvgDssr8A7LOAAOyzgQDss4IA7LODAOyzhADss4UA7LOGAOyzhwDss4gA7LOJAOyzigDss4sA7LOMAOyzjQDss44A7LOPAOyzkADss5EA7LOSAOyzkwDss5QA7LOVAOyzlgDss5cA7LOYAOyzmQDss5oA7LObAOyznADss50A7LOeAOyznwDss6AA7LOhAOyzogDss6MA7LOkAOyzpQDss6YA7LOnAOyzqADss6kA7LOqAOyzqwDss6wA7LOtAOyzrgDss68A7LOwAOyzsQDss7IA7LOzAOyztADss7UA7LO2AOyztwDss7gA7LO5AOyzugDss7sA7LO8AOyzvQDss74A7LO/AOy0gADstIEA7LSCAOy0gwDstIQA7LSFAOy0hgDstIcA7LSIAOy0iQDstIoA7LSLAOy0jADstI0A7LSOAOy0jwDstJAA7LSRAOy0kgDstJMA7LSUAOy0lQDstJYA7LSXAOy0mADstJkA7LSaAOy0mwDstJwA7LSdAOy0ngDstJ8A7LSgAOy0oQDstKIA7LSjAOy0pADstKUA7LSmAOy0pwDstKgA7LSpAOy0qgDstKsA7LSsAOy0rQDstK4A7LSvAOy0sADstLEA7LSyAOy0swDstLQA7LS1AOy0tgDstLcA7LS4AOy0uQDstLoA7LS7AOy0vADstL0A7LS+AOy0vwDstYAA7LWBAOy1ggDstYMA7LWEAOy1hQDstYYA7LWHAOy1iADstYkA7LWKAOy1iwDstYwA7LWNAOy1jgDstY8A7LWQAOy1kQDstZIA7LWTAOy1lADstZUA7LWWAOy1lwDstZgA7LWZAOy1mgDstZsA7LWcAOy1nQDstZ4A7LWfAOy1oADstaEA7LWiAOy1owDstaQA7LWlAOy1pgDstacA7LWoAOy1qQDstaoA7LWrAOy1rADsta0A7LWuAOy1rwDstbAA7LWxAOy1sgDstbMA7LW0AOy1tQDstbYA7LW3AOy1uADstbkA7LW6AOy1uwDstbwA7LW9AOy1vgDstb8A7LaAAOy2gQDstoIA7LaDAOy2hADstoUA7LaGAOy2hwDstogA7LaJAOy2igDstosA7LaMAOy2jQDsto4A7LaPAOy2kADstpEA7LaSAOy2kwDstpQA7LaVAOy2lgDstpcA7LaYAOy2mQDstpoA7LabAOy2nADstp0A7LaeAOy2nwDstqAA7LahAOy2ogDstqMA7LakAOy2pQDstqYA7LanAOy2qADstqkA7LaqAOy2qwDstqwA7LatAOy2rgDstq8A7LawAOy2sQDstrIA7LazAOy2tADstrUA7La2AOy2twDstrgA7La5AOy2ugDstrsA7La8AOy2vQDstr4A7La/AOy3gADst4EA7LeCAOy3gwDst4QA7LeFAOy3hgDst4cA7LeIAOy3iQDst4oA7LeLAOy3jADst40A7LeOAOy3jwDst5AA7LeRAOy3kgDst5MA7LeUAOy3lQDst5YA7LeXAOy3mADst5kA7LeaAOy3mwDst5wA7LedAOy3ngDst58A7LegAOy3oQDst6IA7LejAOy3pADst6UA7LemAOy3pwDst6gA7LepAOy3qgDst6sA7LesAOy3rQDst64A7LevAOy3sADst7EA7LeyAOy3swDst7QA7Le1AOy3tgDst7cA7Le4AOy3uQDst7oA7Le7AOy3vADst70A7Le+AOy3vwDsuIAA7LiBAOy4ggDsuIMA7LiEAOy4hQDsuIYA7LiHAOy4iADsuIkA7LiKAOy4iwDsuIwA7LiNAOy4jgDsuI8A7LiQAOy4kQDsuJIA7LiTAOy4lADsuJUA7LiWAOy4lwDsuJgA7LiZAOy4mgDsuJsA7LicAOy4nQDsuJ4A7LifAOy4oADsuKEA7LiiAOy4owDsuKQA7LilAOy4pgDsuKcA7LioAOy4qQDsuKoA7LirAOy4rADsuK0A7LiuAOy4rwDsuLAA7LixAOy4sgDsuLMA7Li0AOy4tQDsuLYA7Li3AOy4uADsuLkA7Li6AOy4uwDsuLwA7Li9AOy4vgDsuL8A7LmAAOy5gQDsuYIA7LmDAOy5hADsuYUA7LmGAOy5hwDsuYgA7LmJAOy5igDsuYsA7LmMAOy5jQDsuY4A7LmPAOy5kADsuZEA7LmSAOy5kwDsuZQA7LmVAOy5lgDsuZcA7LmYAOy5mQDsuZoA7LmbAOy5nADsuZ0A7LmeAOy5nwDsuaAA7LmhAOy5ogDsuaMA7LmkAOy5pQDsuaYA7LmnAOy5qADsuakA7LmqAOy5qwDsuawA7LmtAOy5rgDsua8A7LmwAOy5sQDsubIA7LmzAOy5tADsubUA7Lm2AOy5twDsubgA7Lm5AOy5ugDsubsA7Lm8AOy5vQDsub4A7Lm/AOy6gADsuoEA7LqCAOy6gwDsuoQA7LqFAOy6hgDsuocA7LqIAOy6iQDsuooA7LqLAOy6jADsuo0A7LqOAOy6jwDsupAA7LqRAOy6kgDsupMA7LqUAOy6lQDsupYA7LqXAOy6mADsupkA7LqaAOy6mwDsupwA7LqdAOy6ngDsup8A7LqgAOy6oQDsuqIA7LqjAOy6pADsuqUA7LqmAOy6pwDsuqgA7LqpAOy6qgDsuqsA7LqsAOy6rQDsuq4A7LqvAOy6sADsurEA7LqyAOy6swDsurQA7Lq1AOy6tgDsurcA7Lq4AOy6uQDsuroA7Lq7AOy6vADsur0A7Lq+AOy6vwDsu4AA7LuBAOy7ggDsu4MA7LuEAOy7hQDsu4YA7LuHAOy7iADsu4kA7LuKAOy7iwDsu4wA7LuNAOy7jgDsu48A7LuQAOy7kQDsu5IA7LuTAOy7lADsu5UA7LuWAOy7lwDsu5gA7LuZAOy7mgDsu5sA7LucAOy7nQDsu54A7LufAOy7oADsu6EA7LuiAOy7owDsu6QA7LulAOy7pgDsu6cA7LuoAOy7qQDsu6oA7LurAOy7rADsu60A7LuuAOy7rwDsu7AA7LuxAOy7sgDsu7MA7Lu0AOy7tQDsu7YA7Lu3AOy7uADsu7kA7Lu6AOy7uwDsu7wA7Lu9AOy7vgDsu78A7LyAAOy8gQDsvIIA7LyDAOy8hADsvIUA7LyGAOy8hwDsvIgA7LyJAOy8igDsvIsA7LyMAOy8jQDsvI4A7LyPAOy8kADsvJEA7LySAOy8kwDsvJQA7LyVAOy8lgDsvJcA7LyYAOy8mQDsvJoA7LybAOy8nADsvJ0A7LyeAOy8nwDsvKAA7LyhAOy8ogDsvKMA7LykAOy8pQDsvKYA7LynAOy8qADsvKkA7LyqAOy8qwDsvKwA7LytAOy8rgDsvK8A7LywAOy8sQDsvLIA7LyzAOy8tADsvLUA7Ly2AOy8twDsvLgA7Ly5AOy8ugDsvLsA7Ly8AOy8vQDsvL4A7Ly/AOy9gADsvYEA7L2CAOy9gwDsvYQA7L2FAOy9hgDsvYcA7L2IAOy9iQDsvYoA7L2LAOy9jADsvY0A7L2OAOy9jwDsvZAA7L2RAOy9kgDsvZMA7L2UAOy9lQDsvZYA7L2XAOy9mADsvZkA7L2aAOy9mwDsvZwA7L2dAOy9ngDsvZ8A7L2gAOy9oQDsvaIA7L2jAOy9pADsvaUA7L2mAOy9pwDsvagA7L2pAOy9qgDsvasA7L2sAOy9rQDsva4A7L2vAOy9sADsvbEA7L2yAOy9swDsvbQA7L21AOy9tgDsvbcA7L24AOy9uQDsvboA7L27AOy9vADsvb0A7L2+AOy9vwDsvoAA7L6BAOy+ggDsvoMA7L6EAOy+hQDsvoYA7L6HAOy+iADsvokA7L6KAOy+iwDsvowA7L6NAOy+jgDsvo8A7L6QAOy+kQDsvpIA7L6TAOy+lADsvpUA7L6WAOy+lwDsvpgA7L6ZAOy+mgDsvpsA7L6cAOy+nQDsvp4A7L6fAOy+oADsvqEA7L6iAOy+owDsvqQA7L6lAOy+pgDsvqcA7L6oAOy+qQDsvqoA7L6rAOy+rADsvq0A7L6uAOy+rwDsvrAA7L6xAOy+sgDsvrMA7L60AOy+tQDsvrYA7L63AOy+uADsvrkA7L66AOy+uwDsvrwA7L69AOy+vgDsvr8A7L+AAOy/gQDsv4IA7L+DAOy/hADsv4UA7L+GAOy/hwDsv4gA7L+JAOy/igDsv4sA7L+MAOy/jQDsv44A7L+PAOy/kADsv5EA7L+SAOy/kwDsv5QA7L+VAOy/lgDsv5cA7L+YAOy/mQDsv5oA7L+bAOy/nADsv50A7L+eAOy/nwDsv6AA7L+hAOy/ogDsv6MA7L+kAOy/pQDsv6YA7L+nAOy/qADsv6kA7L+qAOy/qwDsv6wA7L+tAOy/rgDsv68A7L+wAOy/sQDsv7IA7L+zAOy/tADsv7UA7L+2AOy/twDsv7gA7L+5AOy/ugDsv7sA7L+8AOy/vQDsv74A7L+/AO2AgADtgIEA7YCCAO2AgwDtgIQA7YCFAO2AhgDtgIcA7YCIAO2AiQDtgIoA7YCLAO2AjADtgI0A7YCOAO2AjwDtgJAA7YCRAO2AkgDtgJMA7YCUAO2AlQDtgJYA7YCXAO2AmADtgJkA7YCaAO2AmwDtgJwA7YCdAO2AngDtgJ8A7YCgAO2AoQDtgKIA7YCjAO2ApADtgKUA7YCmAO2ApwDtgKgA7YCpAO2AqgDtgKsA7YCsAO2ArQDtgK4A7YCvAO2AsADtgLEA7YCyAO2AswDtgLQA7YC1AO2AtgDtgLcA7YC4AO2AuQDtgLoA7YC7AO2AvADtgL0A7YC+AO2AvwDtgYAA7YGBAO2BggDtgYMA7YGEAO2BhQDtgYYA7YGHAO2BiADtgYkA7YGKAO2BiwDtgYwA7YGNAO2BjgDtgY8A7YGQAO2BkQDtgZIA7YGTAO2BlADtgZUA7YGWAO2BlwDtgZgA7YGZAO2BmgDtgZsA7YGcAO2BnQDtgZ4A7YGfAO2BoADtgaEA7YGiAO2BowDtgaQA7YGlAO2BpgDtgacA7YGoAO2BqQDtgaoA7YGrAO2BrADtga0A7YGuAO2BrwDtgbAA7YGxAO2BsgDtgbMA7YG0AO2BtQDtgbYA7YG3AO2BuADtgbkA7YG6AO2BuwDtgbwA7YG9AO2BvgDtgb8A7YKAAO2CgQDtgoIA7YKDAO2ChADtgoUA7YKGAO2ChwDtgogA7YKJAO2CigDtgosA7YKMAO2CjQDtgo4A7YKPAO2CkADtgpEA7YKSAO2CkwDtgpQA7YKVAO2ClgDtgpcA7YKYAO2CmQDtgpoA7YKbAO2CnADtgp0A7YKeAO2CnwDtgqAA7YKhAO2CogDtgqMA7YKkAO2CpQDtgqYA7YKnAO2CqADtgqkA7YKqAO2CqwDtgqwA7YKtAO2CrgDtgq8A7YKwAO2CsQDtgrIA7YKzAO2CtADtgrUA7YK2AO2CtwDtgrgA7YK5AO2CugDtgrsA7YK8AO2CvQDtgr4A7YK/AO2DgADtg4EA7YOCAO2DgwDtg4QA7YOFAO2DhgDtg4cA7YOIAO2DiQDtg4oA7YOLAO2DjADtg40A7YOOAO2DjwDtg5AA7YORAO2DkgDtg5MA7YOUAO2DlQDtg5YA7YOXAO2DmADtg5kA7YOaAO2DmwDtg5wA7YOdAO2DngDtg58A7YOgAO2DoQDtg6IA7YOjAO2DpADtg6UA7YOmAO2DpwDtg6gA7YOpAO2DqgDtg6sA7YOsAO2DrQDtg64A7YOvAO2DsADtg7EA7YOyAO2DswDtg7QA7YO1AO2DtgDtg7cA7YO4AO2DuQDtg7oA7YO7AO2DvADtg70A7YO+AO2DvwDthIAA7YSBAO2EggDthIMA7YSEAO2EhQDthIYA7YSHAO2EiADthIkA7YSKAO2EiwDthIwA7YSNAO2EjgDthI8A7YSQAO2EkQDthJIA7YSTAO2ElADthJUA7YSWAO2ElwDthJgA7YSZAO2EmgDthJsA7YScAO2EnQDthJ4A7YSfAO2EoADthKEA7YSiAO2EowDthKQA7YSlAO2EpgDthKcA7YSoAO2EqQDthKoA7YSrAO2ErADthK0A7YSuAO2ErwDthLAA7YSxAO2EsgDthLMA7YS0AO2EtQDthLYA7YS3AO2EuADthLkA7YS6AO2EuwDthLwA7YS9AO2EvgDthL8A7YWAAO2FgQDthYIA7YWDAO2FhADthYUA7YWGAO2FhwDthYgA7YWJAO2FigDthYsA7YWMAO2FjQDthY4A7YWPAO2FkADthZEA7YWSAO2FkwDthZQA7YWVAO2FlgDthZcA7YWYAO2FmQDthZoA7YWbAO2FnADthZ0A7YWeAO2FnwDthaAA7YWhAO2FogDthaMA7YWkAO2FpQDthaYA7YWnAO2FqADthakA7YWqAO2FqwDthawA7YWtAO2FrgDtha8A7YWwAO2FsQDthbIA7YWzAO2FtADthbUA7YW2AO2FtwDthbgA7YW5AO2FugDthbsA7YW8AO2FvQDthb4A7YW/AO2GgADthoEA7YaCAO2GgwDthoQA7YaFAO2GhgDthocA7YaIAO2GiQDthooA7YaLAO2GjADtho0A7YaOAO2GjwDthpAA7YaRAO2GkgDthpMA7YaUAO2GlQDthpYA7YaXAO2GmADthpkA7YaaAO2GmwDthpwA7YadAO2GngDthp8A7YagAO2GoQDthqIA7YajAO2GpADthqUA7YamAO2GpwDthqgA7YapAO2GqgDthqsA7YasAO2GrQDthq4A7YavAO2GsADthrEA7YayAO2GswDthrQA7Ya1AO2GtgDthrcA7Ya4AO2GuQDthroA7Ya7AO2GvADthr0A7Ya+AO2GvwDth4AA7YeBAO2HggDth4MA7YeEAO2HhQDth4YA7YeHAO2HiADth4kA7YeKAO2HiwDth4wA7YeNAO2HjgDth48A7YeQAO2HkQDth5IA7YeTAO2HlADth5UA7YeWAO2HlwDth5gA7YeZAO2HmgDth5sA7YecAO2HnQDth54A7YefAO2HoADth6EA7YeiAO2HowDth6QA7YelAO2HpgDth6cA7YeoAO2HqQDth6oA7YerAO2HrADth60A7YeuAO2HrwDth7AA7YexAO2HsgDth7MA7Ye0AO2HtQDth7YA7Ye3AO2HuADth7kA7Ye6AO2HuwDth7wA7Ye9AO2HvgDth78A7YiAAO2IgQDtiIIA7YiDAO2IhADtiIUA7YiGAO2IhwDtiIgA7YiJAO2IigDtiIsA7YiMAO2IjQDtiI4A7YiPAO2IkADtiJEA7YiSAO2IkwDtiJQA7YiVAO2IlgDtiJcA7YiYAO2ImQDtiJoA7YibAO2InADtiJ0A7YieAO2InwDtiKAA7YihAO2IogDtiKMA7YikAO2IpQDtiKYA7YinAO2IqADtiKkA7YiqAO2IqwDtiKwA7YitAO2IrgDtiK8A7YiwAO2IsQDtiLIA7YizAO2ItADtiLUA7Yi2AO2ItwDtiLgA7Yi5AO2IugDtiLsA7Yi8AO2IvQDtiL4A7Yi/AO2JgADtiYEA7YmCAO2JgwDtiYQA7YmFAO2JhgDtiYcA7YmIAO2JiQDtiYoA7YmLAO2JjADtiY0A7YmOAO2JjwDtiZAA7YmRAO2JkgDtiZMA7YmUAO2JlQDtiZYA7YmXAO2JmADtiZkA7YmaAO2JmwDtiZwA7YmdAO2JngDtiZ8A7YmgAO2JoQDtiaIA7YmjAO2JpADtiaUA7YmmAO2JpwDtiagA7YmpAO2JqgDtiasA7YmsAO2JrQDtia4A7YmvAO2JsADtibEA7YmyAO2JswDtibQA7Ym1AO2JtgDtibcA7Ym4AO2JuQDtiboA7Ym7AO2JvADtib0A7Ym+AO2JvwDtioAA7YqBAO2KggDtioMA7YqEAO2KhQDtioYA7YqHAO2KiADtiokA7YqKAO2KiwDtiowA7YqNAO2KjgDtio8A7YqQAO2KkQDtipIA7YqTAO2KlADtipUA7YqWAO2KlwDtipgA7YqZAO2KmgDtipsA7YqcAO2KnQDtip4A7YqfAO2KoADtiqEA7YqiAO2KowDtiqQA7YqlAO2KpgDtiqcA7YqoAO2KqQDtiqoA7YqrAO2KrADtiq0A7YquAO2KrwDtirAA7YqxAO2KsgDtirMA7Yq0AO2KtQDtirYA7Yq3AO2KuADtirkA7Yq6AO2KuwDtirwA7Yq9AO2KvgDtir8A7YuAAO2LgQDti4IA7YuDAO2LhADti4UA7YuGAO2LhwDti4gA7YuJAO2LigDti4sA7YuMAO2LjQDti44A7YuPAO2LkADti5EA7YuSAO2LkwDti5QA7YuVAO2LlgDti5cA7YuYAO2LmQDti5oA7YubAO2LnADti50A7YueAO2LnwDti6AA7YuhAO2LogDti6MA7YukAO2LpQDti6YA7YunAO2LqADti6kA7YuqAO2LqwDti6wA7YutAO2LrgDti68A7YuwAO2LsQDti7IA7YuzAO2LtADti7UA7Yu2AO2LtwDti7gA7Yu5AO2LugDti7sA7Yu8AO2LvQDti74A7Yu/AO2MgADtjIEA7YyCAO2MgwDtjIQA7YyFAO2MhgDtjIcA7YyIAO2MiQDtjIoA7YyLAO2MjADtjI0A7YyOAO2MjwDtjJAA7YyRAO2MkgDtjJMA7YyUAO2MlQDtjJYA7YyXAO2MmADtjJkA7YyaAO2MmwDtjJwA7YydAO2MngDtjJ8A7YygAO2MoQDtjKIA7YyjAO2MpADtjKUA7YymAO2MpwDtjKgA7YypAO2MqgDtjKsA7YysAO2MrQDtjK4A7YyvAO2MsADtjLEA7YyyAO2MswDtjLQA7Yy1AO2MtgDtjLcA7Yy4AO2MuQDtjLoA7Yy7AO2MvADtjL0A7Yy+AO2MvwDtjYAA7Y2BAO2NggDtjYMA7Y2EAO2NhQDtjYYA7Y2HAO2NiADtjYkA7Y2KAO2NiwDtjYwA7Y2NAO2NjgDtjY8A7Y2QAO2NkQDtjZIA7Y2TAO2NlADtjZUA7Y2WAO2NlwDtjZgA7Y2ZAO2NmgDtjZsA7Y2cAO2NnQDtjZ4A7Y2fAO2NoADtjaEA7Y2iAO2NowDtjaQA7Y2lAO2NpgDtjacA7Y2oAO2NqQDtjaoA7Y2rAO2NrADtja0A7Y2uAO2NrwDtjbAA7Y2xAO2NsgDtjbMA7Y20AO2NtQDtjbY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a/modules/cond/Activation.py +++ b/modules/cond/Activation.py @@ -1,31 +1,31 @@ -import torch -import torch.nn as nn -from modules.cond import cast - - -class GEGLU(nn.Module): - """#### Class representing the GEGLU activation function. - - GEGLU is a gated activation function that is a combination of GELU and ReLU, - used to fire the neurons in the network. - - #### Args: - - `dim_in` (int): The input dimension. - - `dim_out` (int): The output dimension. - """ - - def __init__(self, dim_in: int, dim_out: int): - super().__init__() - self.proj = cast.manual_cast.Linear(dim_in, dim_out * 2) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """#### Forward pass for the GEGLU activation function. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - x, gate = self.proj(x).chunk(2, dim=-1) - return x * torch.nn.functional.gelu(gate) +import torch +import torch.nn as nn +from modules.cond import cast + + +class GEGLU(nn.Module): + """#### Class representing the GEGLU activation function. + + GEGLU is a gated activation function that is a combination of GELU and ReLU, + used to fire the neurons in the network. + + #### Args: + - `dim_in` (int): The input dimension. + - `dim_out` (int): The output dimension. + """ + + def __init__(self, dim_in: int, dim_out: int): + super().__init__() + self.proj = cast.manual_cast.Linear(dim_in, dim_out * 2) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """#### Forward pass for the GEGLU activation function. + + #### Args: + - `x` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + x, gate = self.proj(x).chunk(2, dim=-1) + return x * torch.nn.functional.gelu(gate) diff --git a/modules/cond/cast.py b/modules/cond/cast.py index 2b9a1f55e12357ebdf71a7da107110095ba9aac5..e7ccfe1b64e784c4e77239848970a16894dedfa7 100644 --- a/modules/cond/cast.py +++ b/modules/cond/cast.py @@ -1,525 +1,525 @@ -from modules.Device import Device -import torch - -def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False): - """#### Cast a weight tensor to a specified dtype and device. - - #### Args: - - `weight` (torch.Tensor): The weight tensor. - - `dtype` (torch.dtype): The data type. - - `device` (torch.device): The device. - - `non_blocking` (bool): Whether to use non-blocking transfer. - - `copy` (bool): Whether to copy the tensor. - - #### Returns: - - `torch.Tensor`: The casted weight tensor. - """ - if device is None or weight.device == device: - if not copy: - if dtype is None or weight.dtype == dtype: - return weight - return weight.to(dtype=dtype, copy=copy) - - r = torch.empty_like(weight, dtype=dtype, device=device) - r.copy_(weight, non_blocking=non_blocking) - return r - - -def cast_to_input(weight, input, non_blocking=False, copy=True): - """#### Cast a weight tensor to match the input tensor. - - #### Args: - - `weight` (torch.Tensor): The weight tensor. - - `input` (torch.Tensor): The input tensor. - - `non_blocking` (bool): Whether to use non-blocking transfer. - - `copy` (bool): Whether to copy the tensor. - - #### Returns: - - `torch.Tensor`: The casted weight tensor. - """ - return cast_to( - weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy - ) - -def cast_bias_weight(s: torch.nn.Module, input: torch.Tensor= None, dtype:torch.dtype = None, device:torch.device = None, bias_dtype:torch.dtype = None) -> tuple: - """#### Cast the bias and weight of a module to match the input tensor. - - #### Args: - - `s` (torch.nn.Module): The module. - - `input` (torch.Tensor): The input tensor. - - #### Returns: - - `tuple`: The cast weight and bias. - """ - if input is not None: - if dtype is None: - dtype = input.dtype - if bias_dtype is None: - bias_dtype = dtype - if device is None: - device = input.device - - bias = None - non_blocking = Device.device_supports_non_blocking(device) - if s.bias is not None: - has_function = s.bias_function is not None - bias = cast_to( - s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function - ) - if has_function: - bias = s.bias_function(bias) - - has_function = s.weight_function is not None - weight = cast_to( - s.weight, dtype, device, non_blocking=non_blocking, copy=has_function - ) - if has_function: - weight = s.weight_function(weight) - return weight, bias - -class CastWeightBiasOp: - """#### Class representing a cast weight and bias operation.""" - - comfy_cast_weights: bool = False - weight_function: callable = None - bias_function: callable = None - - -class disable_weight_init: - """#### Class representing a module with disabled weight initialization.""" - - class Linear(torch.nn.Linear, CastWeightBiasOp): - """#### Linear layer with disabled weight initialization.""" - def reset_parameters(self): - """#### Reset the parameters of the Linear layer.""" - return None - - def forward_comfy_cast_weights(self, input): - """#### Forward pass with comfy cast weights. - - #### Args: - - `input` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.linear(input, weight, bias) - - def forward(self, *args, **kwargs): - """#### Forward pass for the Linear layer. - - #### Args: - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if self.comfy_cast_weights: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class Conv1d(torch.nn.Conv1d, CastWeightBiasOp): - """#### Conv1d layer with disabled weight initialization.""" - def reset_parameters(self): - """#### Reset the parameters of the Conv1d layer.""" - return None - - def forward_comfy_cast_weights(self, input): - """#### Forward pass with comfy cast weights. - - #### Args: - - `input` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) - - def forward(self, *args, **kwargs): - """#### Forward pass for the Conv1d layer. - - #### Args: - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if self.comfy_cast_weights: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class Conv2d(torch.nn.Conv2d, CastWeightBiasOp): - """#### Conv2d layer with disabled weight initialization.""" - - def reset_parameters(self) -> None: - """#### Reset the parameters of the Conv2d layer.""" - return None - - def forward_cast_weights(self, input: torch.Tensor) -> torch.Tensor: - """#### Forward pass with comfy cast weights. - - #### Args: - - `input` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) - - def forward(self, *args, **kwargs) -> torch.Tensor: - """#### Forward pass for the Conv2d layer. - - #### Args: - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if self.comfy_cast_weights: - return self.forward_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class Conv3d(torch.nn.Conv3d, CastWeightBiasOp): - """#### Conv3d layer with disabled weight initialization.""" - def reset_parameters(self): - """#### Reset the parameters of the Conv3d layer.""" - return None - - def forward_comfy_cast_weights(self, input): - """#### Forward pass with comfy cast weights. - - #### Args: - - `input` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) - - def forward(self, *args, **kwargs): - """#### Forward pass for the Conv3d layer. - - #### Args: - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if self.comfy_cast_weights: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp): - """#### GroupNorm layer with disabled weight initialization.""" - - def reset_parameters(self) -> None: - """#### Reset the parameters of the GroupNorm layer.""" - return None - - def forward_comfy_cast_weights(self, input): - """#### Forward pass with comfy cast weights. - - #### Args: - - `input` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.group_norm( - input, self.num_groups, weight, bias, self.eps - ) - - def forward(self, *args, **kwargs): - """#### Forward pass for the GroupNorm layer. - - #### Args: - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if self.comfy_cast_weights: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp): - """#### LayerNorm layer with disabled weight initialization.""" - - def reset_parameters(self) -> None: - """#### Reset the parameters of the LayerNorm layer.""" - return None - - def forward_cast_weights(self, input: torch.Tensor) -> torch.Tensor: - """#### Forward pass with cast weights. - - #### Args: - - `input` (torch.Tensor): The input tensor. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if self.weight is not None: - weight, bias = cast_bias_weight(self, input) - else: - weight = None - bias = None - return torch.nn.functional.layer_norm( - input, self.normalized_shape, weight, bias, self.eps - ) - - def forward(self, *args, **kwargs) -> torch.Tensor: - """#### Forward pass for the LayerNorm layer. - - #### Args: - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if self.comfy_cast_weights: - return self.forward_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp): - """#### ConvTranspose2d layer with disabled weight initialization.""" - def reset_parameters(self): - """#### Reset the parameters of the ConvTranspose2d layer.""" - return None - - def forward_comfy_cast_weights(self, input, output_size=None): - """#### Forward pass with comfy cast weights. - - #### Args: - - `input` (torch.Tensor): The input tensor. - - `output_size` (torch.Size): The output size. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - num_spatial_dims = 2 - output_padding = self._output_padding( - input, - output_size, - self.stride, - self.padding, - self.kernel_size, - num_spatial_dims, - self.dilation, - ) - - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.conv_transpose2d( - input, - weight, - bias, - self.stride, - self.padding, - output_padding, - self.groups, - self.dilation, - ) - - def forward(self, *args, **kwargs): - """#### Forward pass for the ConvTranspose2d layer. - - #### Args: - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if self.comfy_cast_weights: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp): - """#### ConvTranspose1d layer with disabled weight initialization.""" - def reset_parameters(self): - """#### Reset the parameters of the ConvTranspose1d layer.""" - return None - - def forward_comfy_cast_weights(self, input, output_size=None): - """#### Forward pass with comfy cast weights. - - #### Args: - - `input` (torch.Tensor): The input tensor. - - `output_size` (torch.Size): The output size. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - num_spatial_dims = 1 - output_padding = self._output_padding( - input, - output_size, - self.stride, - self.padding, - self.kernel_size, - num_spatial_dims, - self.dilation, - ) - - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.conv_transpose1d( - input, - weight, - bias, - self.stride, - self.padding, - output_padding, - self.groups, - self.dilation, - ) - - def forward(self, *args, **kwargs): - """#### Forward pass for the ConvTranspose1d layer. - - #### Args: - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if self.comfy_cast_weights: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class Embedding(torch.nn.Embedding, CastWeightBiasOp): - """#### Embedding layer with disabled weight initialization.""" - def reset_parameters(self): - """#### Reset the parameters of the Embedding layer.""" - self.bias = None - return None - - def forward_comfy_cast_weights(self, input, out_dtype=None): - """#### Forward pass with comfy cast weights. - - #### Args: - - `input` (torch.Tensor): The input tensor. - - `out_dtype` (torch.dtype): The output data type. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - output_dtype = out_dtype - if ( - self.weight.dtype == torch.float16 - or self.weight.dtype == torch.bfloat16 - ): - out_dtype = None - weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype) - return torch.nn.functional.embedding( - input, - weight, - self.padding_idx, - self.max_norm, - self.norm_type, - self.scale_grad_by_freq, - self.sparse, - ).to(dtype=output_dtype) - - def forward(self, *args, **kwargs): - """#### Forward pass for the Embedding layer. - - #### Args: - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - if self.comfy_cast_weights: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - if "out_dtype" in kwargs: - kwargs.pop("out_dtype") - return super().forward(*args, **kwargs) - - @classmethod - def conv_nd(s, dims: int, *args, **kwargs) -> torch.nn.Conv2d: - """#### Create a Conv2d layer with the specified dimensions. - - #### Args: - - `dims` (int): The number of dimensions. - - `*args`: Variable length argument list. - - `**kwargs`: Arbitrary keyword arguments. - - #### Returns: - - `torch.nn.Conv2d`: The Conv2d layer. - """ - if dims == 2: - return s.Conv2d(*args, **kwargs) - elif dims == 3: - return s.Conv3d(*args, **kwargs) - else: - raise ValueError(f"unsupported dimensions: {dims}") - - -class manual_cast(disable_weight_init): - """#### Class representing a module with manual casting.""" - - class Linear(disable_weight_init.Linear): - """#### Linear layer with manual casting.""" - - comfy_cast_weights: bool = True - - class Conv1d(disable_weight_init.Conv1d): - """#### Conv1d layer with manual casting.""" - - comfy_cast_weights = True - - class Conv2d(disable_weight_init.Conv2d): - """#### Conv2d layer with manual casting.""" - - comfy_cast_weights: bool = True - - class Conv3d(disable_weight_init.Conv3d): - """#### Conv3d layer with manual casting.""" - - comfy_cast_weights = True - - class GroupNorm(disable_weight_init.GroupNorm): - """#### GroupNorm layer with manual casting.""" - - comfy_cast_weights: bool = True - - class LayerNorm(disable_weight_init.LayerNorm): - """#### LayerNorm layer with manual casting.""" - - comfy_cast_weights: bool = True - - class ConvTranspose2d(disable_weight_init.ConvTranspose2d): - """#### ConvTranspose2d layer with manual casting.""" - - comfy_cast_weights = True - - class ConvTranspose1d(disable_weight_init.ConvTranspose1d): - """#### ConvTranspose1d layer with manual casting.""" - - comfy_cast_weights = True - - class Embedding(disable_weight_init.Embedding): - """#### Embedding layer with manual casting.""" - - comfy_cast_weights = True +from modules.Device import Device +import torch + +def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False): + """#### Cast a weight tensor to a specified dtype and device. + + #### Args: + - `weight` (torch.Tensor): The weight tensor. + - `dtype` (torch.dtype): The data type. + - `device` (torch.device): The device. + - `non_blocking` (bool): Whether to use non-blocking transfer. + - `copy` (bool): Whether to copy the tensor. + + #### Returns: + - `torch.Tensor`: The casted weight tensor. + """ + if device is None or weight.device == device: + if not copy: + if dtype is None or weight.dtype == dtype: + return weight + return weight.to(dtype=dtype, copy=copy) + + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight, non_blocking=non_blocking) + return r + + +def cast_to_input(weight, input, non_blocking=False, copy=True): + """#### Cast a weight tensor to match the input tensor. + + #### Args: + - `weight` (torch.Tensor): The weight tensor. + - `input` (torch.Tensor): The input tensor. + - `non_blocking` (bool): Whether to use non-blocking transfer. + - `copy` (bool): Whether to copy the tensor. + + #### Returns: + - `torch.Tensor`: The casted weight tensor. + """ + return cast_to( + weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy + ) + +def cast_bias_weight(s: torch.nn.Module, input: torch.Tensor= None, dtype:torch.dtype = None, device:torch.device = None, bias_dtype:torch.dtype = None) -> tuple: + """#### Cast the bias and weight of a module to match the input tensor. + + #### Args: + - `s` (torch.nn.Module): The module. + - `input` (torch.Tensor): The input tensor. + + #### Returns: + - `tuple`: The cast weight and bias. + """ + if input is not None: + if dtype is None: + dtype = input.dtype + if bias_dtype is None: + bias_dtype = dtype + if device is None: + device = input.device + + bias = None + non_blocking = Device.device_supports_non_blocking(device) + if s.bias is not None: + has_function = s.bias_function is not None + bias = cast_to( + s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function + ) + if has_function: + bias = s.bias_function(bias) + + has_function = s.weight_function is not None + weight = cast_to( + s.weight, dtype, device, non_blocking=non_blocking, copy=has_function + ) + if has_function: + weight = s.weight_function(weight) + return weight, bias + +class CastWeightBiasOp: + """#### Class representing a cast weight and bias operation.""" + + comfy_cast_weights: bool = False + weight_function: callable = None + bias_function: callable = None + + +class disable_weight_init: + """#### Class representing a module with disabled weight initialization.""" + + class Linear(torch.nn.Linear, CastWeightBiasOp): + """#### Linear layer with disabled weight initialization.""" + def reset_parameters(self): + """#### Reset the parameters of the Linear layer.""" + return None + + def forward_comfy_cast_weights(self, input): + """#### Forward pass with comfy cast weights. + + #### Args: + - `input` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.linear(input, weight, bias) + + def forward(self, *args, **kwargs): + """#### Forward pass for the Linear layer. + + #### Args: + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv1d(torch.nn.Conv1d, CastWeightBiasOp): + """#### Conv1d layer with disabled weight initialization.""" + def reset_parameters(self): + """#### Reset the parameters of the Conv1d layer.""" + return None + + def forward_comfy_cast_weights(self, input): + """#### Forward pass with comfy cast weights. + + #### Args: + - `input` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + """#### Forward pass for the Conv1d layer. + + #### Args: + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv2d(torch.nn.Conv2d, CastWeightBiasOp): + """#### Conv2d layer with disabled weight initialization.""" + + def reset_parameters(self) -> None: + """#### Reset the parameters of the Conv2d layer.""" + return None + + def forward_cast_weights(self, input: torch.Tensor) -> torch.Tensor: + """#### Forward pass with comfy cast weights. + + #### Args: + - `input` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs) -> torch.Tensor: + """#### Forward pass for the Conv2d layer. + + #### Args: + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if self.comfy_cast_weights: + return self.forward_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv3d(torch.nn.Conv3d, CastWeightBiasOp): + """#### Conv3d layer with disabled weight initialization.""" + def reset_parameters(self): + """#### Reset the parameters of the Conv3d layer.""" + return None + + def forward_comfy_cast_weights(self, input): + """#### Forward pass with comfy cast weights. + + #### Args: + - `input` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + """#### Forward pass for the Conv3d layer. + + #### Args: + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp): + """#### GroupNorm layer with disabled weight initialization.""" + + def reset_parameters(self) -> None: + """#### Reset the parameters of the GroupNorm layer.""" + return None + + def forward_comfy_cast_weights(self, input): + """#### Forward pass with comfy cast weights. + + #### Args: + - `input` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.group_norm( + input, self.num_groups, weight, bias, self.eps + ) + + def forward(self, *args, **kwargs): + """#### Forward pass for the GroupNorm layer. + + #### Args: + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp): + """#### LayerNorm layer with disabled weight initialization.""" + + def reset_parameters(self) -> None: + """#### Reset the parameters of the LayerNorm layer.""" + return None + + def forward_cast_weights(self, input: torch.Tensor) -> torch.Tensor: + """#### Forward pass with cast weights. + + #### Args: + - `input` (torch.Tensor): The input tensor. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if self.weight is not None: + weight, bias = cast_bias_weight(self, input) + else: + weight = None + bias = None + return torch.nn.functional.layer_norm( + input, self.normalized_shape, weight, bias, self.eps + ) + + def forward(self, *args, **kwargs) -> torch.Tensor: + """#### Forward pass for the LayerNorm layer. + + #### Args: + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if self.comfy_cast_weights: + return self.forward_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp): + """#### ConvTranspose2d layer with disabled weight initialization.""" + def reset_parameters(self): + """#### Reset the parameters of the ConvTranspose2d layer.""" + return None + + def forward_comfy_cast_weights(self, input, output_size=None): + """#### Forward pass with comfy cast weights. + + #### Args: + - `input` (torch.Tensor): The input tensor. + - `output_size` (torch.Size): The output size. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + num_spatial_dims = 2 + output_padding = self._output_padding( + input, + output_size, + self.stride, + self.padding, + self.kernel_size, + num_spatial_dims, + self.dilation, + ) + + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.conv_transpose2d( + input, + weight, + bias, + self.stride, + self.padding, + output_padding, + self.groups, + self.dilation, + ) + + def forward(self, *args, **kwargs): + """#### Forward pass for the ConvTranspose2d layer. + + #### Args: + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp): + """#### ConvTranspose1d layer with disabled weight initialization.""" + def reset_parameters(self): + """#### Reset the parameters of the ConvTranspose1d layer.""" + return None + + def forward_comfy_cast_weights(self, input, output_size=None): + """#### Forward pass with comfy cast weights. + + #### Args: + - `input` (torch.Tensor): The input tensor. + - `output_size` (torch.Size): The output size. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + num_spatial_dims = 1 + output_padding = self._output_padding( + input, + output_size, + self.stride, + self.padding, + self.kernel_size, + num_spatial_dims, + self.dilation, + ) + + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.conv_transpose1d( + input, + weight, + bias, + self.stride, + self.padding, + output_padding, + self.groups, + self.dilation, + ) + + def forward(self, *args, **kwargs): + """#### Forward pass for the ConvTranspose1d layer. + + #### Args: + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Embedding(torch.nn.Embedding, CastWeightBiasOp): + """#### Embedding layer with disabled weight initialization.""" + def reset_parameters(self): + """#### Reset the parameters of the Embedding layer.""" + self.bias = None + return None + + def forward_comfy_cast_weights(self, input, out_dtype=None): + """#### Forward pass with comfy cast weights. + + #### Args: + - `input` (torch.Tensor): The input tensor. + - `out_dtype` (torch.dtype): The output data type. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + output_dtype = out_dtype + if ( + self.weight.dtype == torch.float16 + or self.weight.dtype == torch.bfloat16 + ): + out_dtype = None + weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype) + return torch.nn.functional.embedding( + input, + weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ).to(dtype=output_dtype) + + def forward(self, *args, **kwargs): + """#### Forward pass for the Embedding layer. + + #### Args: + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + if "out_dtype" in kwargs: + kwargs.pop("out_dtype") + return super().forward(*args, **kwargs) + + @classmethod + def conv_nd(s, dims: int, *args, **kwargs) -> torch.nn.Conv2d: + """#### Create a Conv2d layer with the specified dimensions. + + #### Args: + - `dims` (int): The number of dimensions. + - `*args`: Variable length argument list. + - `**kwargs`: Arbitrary keyword arguments. + + #### Returns: + - `torch.nn.Conv2d`: The Conv2d layer. + """ + if dims == 2: + return s.Conv2d(*args, **kwargs) + elif dims == 3: + return s.Conv3d(*args, **kwargs) + else: + raise ValueError(f"unsupported dimensions: {dims}") + + +class manual_cast(disable_weight_init): + """#### Class representing a module with manual casting.""" + + class Linear(disable_weight_init.Linear): + """#### Linear layer with manual casting.""" + + comfy_cast_weights: bool = True + + class Conv1d(disable_weight_init.Conv1d): + """#### Conv1d layer with manual casting.""" + + comfy_cast_weights = True + + class Conv2d(disable_weight_init.Conv2d): + """#### Conv2d layer with manual casting.""" + + comfy_cast_weights: bool = True + + class Conv3d(disable_weight_init.Conv3d): + """#### Conv3d layer with manual casting.""" + + comfy_cast_weights = True + + class GroupNorm(disable_weight_init.GroupNorm): + """#### GroupNorm layer with manual casting.""" + + comfy_cast_weights: bool = True + + class LayerNorm(disable_weight_init.LayerNorm): + """#### LayerNorm layer with manual casting.""" + + comfy_cast_weights: bool = True + + class ConvTranspose2d(disable_weight_init.ConvTranspose2d): + """#### ConvTranspose2d layer with manual casting.""" + + comfy_cast_weights = True + + class ConvTranspose1d(disable_weight_init.ConvTranspose1d): + """#### ConvTranspose1d layer with manual casting.""" + + comfy_cast_weights = True + + class Embedding(disable_weight_init.Embedding): + """#### Embedding layer with manual casting.""" + + comfy_cast_weights = True diff --git a/modules/cond/cond.py b/modules/cond/cond.py index 9c3a9723173186e413ebeb5eb8d5cf608717bf55..3990d96e4ef6b4c9877749e533e0a248b4d3ee17 100644 --- a/modules/cond/cond.py +++ b/modules/cond/cond.py @@ -1,445 +1,445 @@ -import torch -from modules.Utilities import util -from modules.Device import Device -from modules.cond import cond_util -from modules.sample import ksampler_util - - -class CONDRegular: - """#### Class representing a regular condition.""" - - def __init__(self, cond: torch.Tensor): - """#### Initialize the CONDRegular class. - - #### Args: - - `cond` (torch.Tensor): The condition tensor. - """ - self.cond = cond - - def _copy_with(self, cond: torch.Tensor) -> "CONDRegular": - """#### Copy the condition with a new condition. - - #### Args: - - `cond` (torch.Tensor): The new condition. - - #### Returns: - - `CONDRegular`: The copied condition. - """ - return self.__class__(cond) - - def process_cond( - self, batch_size: int, device: torch.device, **kwargs - ) -> "CONDRegular": - """#### Process the condition. - - #### Args: - - `batch_size` (int): The batch size. - - `device` (torch.device): The device. - - #### Returns: - - `CONDRegular`: The processed condition. - """ - return self._copy_with( - util.repeat_to_batch_size(self.cond, batch_size).to(device) - ) - - def can_concat(self, other: "CONDRegular") -> bool: - """#### Check if conditions can be concatenated. - - #### Args: - - `other` (CONDRegular): The other condition. - - #### Returns: - - `bool`: True if conditions can be concatenated, False otherwise. - """ - if self.cond.shape != other.cond.shape: - return False - return True - - def concat(self, others: list) -> torch.Tensor: - """#### Concatenate conditions. - - #### Args: - - `others` (list): The list of other conditions. - - #### Returns: - - `torch.Tensor`: The concatenated conditions. - """ - conds = [self.cond] - for x in others: - conds.append(x.cond) - return torch.cat(conds) - - -class CONDCrossAttn(CONDRegular): - """#### Class representing a cross-attention condition.""" - - def can_concat(self, other: "CONDRegular") -> bool: - """#### Check if conditions can be concatenated. - - #### Args: - - `other` (CONDRegular): The other condition. - - #### Returns: - - `bool`: True if conditions can be concatenated, False otherwise. - """ - s1 = self.cond.shape - s2 = other.cond.shape - if s1 != s2: - if s1[0] != s2[0] or s1[2] != s2[2]: # these 2 cases should not happen - return False - - mult_min = torch.lcm(s1[1], s2[1]) - diff = mult_min // min(s1[1], s2[1]) - if ( - diff > 4 - ): # arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much - return False - return True - - def concat(self, others: list) -> torch.Tensor: - """#### Concatenate cross-attention conditions. - - #### Args: - - `others` (list): The list of other conditions. - - #### Returns: - - `torch.Tensor`: The concatenated conditions. - """ - conds = [self.cond] - crossattn_max_len = self.cond.shape[1] - for x in others: - c = x.cond - crossattn_max_len = util.lcm(crossattn_max_len, c.shape[1]) - conds.append(c) - - out = [] - for c in conds: - if c.shape[1] < crossattn_max_len: - c = c.repeat( - 1, crossattn_max_len // c.shape[1], 1 - ) # padding with repeat doesn't change result, but avoids an error on tensor shape - out.append(c) - return torch.cat(out) - - -def convert_cond(cond: list) -> list: - """#### Convert conditions to cross-attention conditions. - - #### Args: - - `cond` (list): The list of conditions. - - #### Returns: - - `list`: The converted conditions. - """ - out = [] - for c in cond: - temp = c[1].copy() - model_conds = temp.get("model_conds", {}) - if c[0] is not None: - model_conds["c_crossattn"] = CONDCrossAttn(c[0]) - temp["cross_attn"] = c[0] - temp["model_conds"] = model_conds - out.append(temp) - return out - - -def calc_cond_batch( - model: object, - conds: list, - x_in: torch.Tensor, - timestep: torch.Tensor, - model_options: dict, -) -> list: - """#### Calculate the condition batch. - - #### Args: - - `model` (object): The model. - - `conds` (list): The list of conditions. - - `x_in` (torch.Tensor): The input tensor. - - `timestep` (torch.Tensor): The timestep tensor. - - `model_options` (dict): The model options. - - #### Returns: - - `list`: The calculated condition batch. - """ - out_conds = [] - out_counts = [] - to_run = [] - - for i in range(len(conds)): - out_conds.append(torch.zeros_like(x_in)) - out_counts.append(torch.ones_like(x_in) * 1e-37) - - cond = conds[i] - if cond is not None: - for x in cond: - p = ksampler_util.get_area_and_mult(x, x_in, timestep) - if p is None: - continue - - to_run += [(p, i)] - - while len(to_run) > 0: - first = to_run[0] - first_shape = first[0][0].shape - to_batch_temp = [] - for x in range(len(to_run)): - if cond_util.can_concat_cond(to_run[x][0], first[0]): - to_batch_temp += [x] - - to_batch_temp.reverse() - to_batch = to_batch_temp[:1] - - free_memory = Device.get_free_memory(x_in.device) - for i in range(1, len(to_batch_temp) + 1): - batch_amount = to_batch_temp[: len(to_batch_temp) // i] - input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] - if model.memory_required(input_shape) * 1.5 < free_memory: - to_batch = batch_amount - break - - input_x = [] - mult = [] - c = [] - cond_or_uncond = [] - area = [] - control = None - patches = None - for x in to_batch: - o = to_run.pop(x) - p = o[0] - input_x.append(p.input_x) - mult.append(p.mult) - c.append(p.conditioning) - area.append(p.area) - cond_or_uncond.append(o[1]) - control = p.control - patches = p.patches - - batch_chunks = len(cond_or_uncond) - input_x = torch.cat(input_x) - c = cond_util.cond_cat(c) - timestep_ = torch.cat([timestep] * batch_chunks) - - if control is not None: - c["control"] = control.get_control( - input_x, timestep_, c, len(cond_or_uncond) - ) - - transformer_options = {} - if "transformer_options" in model_options: - transformer_options = model_options["transformer_options"].copy() - - if patches is not None: - if "patches" in transformer_options: - cur_patches = transformer_options["patches"].copy() - for p in patches: - if p in cur_patches: - cur_patches[p] = cur_patches[p] + patches[p] - else: - cur_patches[p] = patches[p] - transformer_options["patches"] = cur_patches - else: - transformer_options["patches"] = patches - - transformer_options["cond_or_uncond"] = cond_or_uncond[:] - transformer_options["sigmas"] = timestep - - c["transformer_options"] = transformer_options - - if "model_function_wrapper" in model_options: - output = model_options["model_function_wrapper"]( - model.apply_model, - { - "input": input_x, - "timestep": timestep_, - "c": c, - "cond_or_uncond": cond_or_uncond, - }, - ).chunk(batch_chunks) - else: - output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) - - for o in range(batch_chunks): - cond_index = cond_or_uncond[o] - a = area[o] - if a is None: - out_conds[cond_index] += output[o] * mult[o] - out_counts[cond_index] += mult[o] - else: - out_c = out_conds[cond_index] - out_cts = out_counts[cond_index] - dims = len(a) // 2 - for i in range(dims): - out_c = out_c.narrow(i + 2, a[i + dims], a[i]) - out_cts = out_cts.narrow(i + 2, a[i + dims], a[i]) - out_c += output[o] * mult[o] - out_cts += mult[o] - - for i in range(len(out_conds)): - out_conds[i] /= out_counts[i] - - return out_conds - - -def encode_model_conds( - model_function: callable, - conds: list, - noise: torch.Tensor, - device: torch.device, - prompt_type: str, - **kwargs, -) -> list: - """#### Encode model conditions. - - #### Args: - - `model_function` (callable): The model function. - - `conds` (list): The list of conditions. - - `noise` (torch.Tensor): The noise tensor. - - `device` (torch.device): The device. - - `prompt_type` (str): The prompt type. - - `**kwargs`: Additional keyword arguments. - - #### Returns: - - `list`: The encoded model conditions. - """ - for t in range(len(conds)): - x = conds[t] - params = x.copy() - params["device"] = device - params["noise"] = noise - default_width = None - if len(noise.shape) >= 4: # TODO: 8 multiple should be set by the model - default_width = noise.shape[3] * 8 - params["width"] = params.get("width", default_width) - params["height"] = params.get("height", noise.shape[2] * 8) - params["prompt_type"] = params.get("prompt_type", prompt_type) - for k in kwargs: - if k not in params: - params[k] = kwargs[k] - - out = model_function(**params) - x = x.copy() - model_conds = x["model_conds"].copy() - for k in out: - model_conds[k] = out[k] - x["model_conds"] = model_conds - conds[t] = x - return conds - -def resolve_areas_and_cond_masks_multidim(conditions: list, dims: tuple, device: torch.device) -> None: - """#### Resolve areas and condition masks for multidimensional conditions. - - #### Args: - - `conditions` (list): The list of conditions. - - `dims` (tuple): The dimensions. - - `device` (torch.device): The device. - """ - # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. - # While we're doing this, we can also resolve the mask device and scaling for performance reasons - for i in range(len(conditions)): - c = conditions[i] - if "area" in c: - area = c["area"] - if area[0] == "percentage": - modified = c.copy() - a = area[1:] - a_len = len(a) // 2 - area = () - for d in range(len(dims)): - area += (max(1, round(a[d] * dims[d])),) - for d in range(len(dims)): - area += (round(a[d + a_len] * dims[d]),) - - modified["area"] = area - c = modified - conditions[i] = c - - if "mask" in c: - mask = c["mask"] - mask = mask.to(device=device) - modified = c.copy() - if len(mask.shape) == len(dims): - mask = mask.unsqueeze(0) - if mask.shape[1:] != dims: - mask = torch.nn.functional.interpolate( - mask.unsqueeze(1), size=dims, mode="bilinear", align_corners=False - ).squeeze(1) - - modified["mask"] = mask - conditions[i] = modified - -def process_conds( - model: object, - noise: torch.Tensor, - conds: dict, - device: torch.device, - latent_image: torch.Tensor = None, - denoise_mask: torch.Tensor = None, - seed: int = None, -) -> dict: - """#### Process conditions. - - #### Args: - - `model` (object): The model. - - `noise` (torch.Tensor): The noise tensor. - - `conds` (dict): The conditions. - - `device` (torch.device): The device. - - `latent_image` (torch.Tensor, optional): The latent image tensor. Defaults to None. - - `denoise_mask` (torch.Tensor, optional): The denoise mask tensor. Defaults to None. - - `seed` (int, optional): The seed. Defaults to None. - - #### Returns: - - `dict`: The processed conditions. - """ - for k in conds: - conds[k] = conds[k][:] - resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device) - - for k in conds: - ksampler_util.calculate_start_end_timesteps(model, conds[k]) - - if hasattr(model, "extra_conds"): - for k in conds: - conds[k] = encode_model_conds( - model.extra_conds, - conds[k], - noise, - device, - k, - latent_image=latent_image, - denoise_mask=denoise_mask, - seed=seed, - ) - - # make sure each cond area has an opposite one with the same area - for k in conds: - for c in conds[k]: - for kk in conds: - if k != kk: - cond_util.create_cond_with_same_area_if_none(conds[kk], c) - - for k in conds: - ksampler_util.pre_run_control(model, conds[k]) - - if "positive" in conds: - positive = conds["positive"] - for k in conds: - if k != "positive": - ksampler_util.apply_empty_x_to_equal_area( - list( - filter( - lambda c: c.get("control_apply_to_uncond", False) is True, - positive, - ) - ), - conds[k], - "control", - lambda cond_cnets, x: cond_cnets[x], - ) - ksampler_util.apply_empty_x_to_equal_area( - positive, conds[k], "gligen", lambda cond_cnets, x: cond_cnets[x] - ) - +import torch +from modules.Utilities import util +from modules.Device import Device +from modules.cond import cond_util +from modules.sample import ksampler_util + + +class CONDRegular: + """#### Class representing a regular condition.""" + + def __init__(self, cond: torch.Tensor): + """#### Initialize the CONDRegular class. + + #### Args: + - `cond` (torch.Tensor): The condition tensor. + """ + self.cond = cond + + def _copy_with(self, cond: torch.Tensor) -> "CONDRegular": + """#### Copy the condition with a new condition. + + #### Args: + - `cond` (torch.Tensor): The new condition. + + #### Returns: + - `CONDRegular`: The copied condition. + """ + return self.__class__(cond) + + def process_cond( + self, batch_size: int, device: torch.device, **kwargs + ) -> "CONDRegular": + """#### Process the condition. + + #### Args: + - `batch_size` (int): The batch size. + - `device` (torch.device): The device. + + #### Returns: + - `CONDRegular`: The processed condition. + """ + return self._copy_with( + util.repeat_to_batch_size(self.cond, batch_size).to(device) + ) + + def can_concat(self, other: "CONDRegular") -> bool: + """#### Check if conditions can be concatenated. + + #### Args: + - `other` (CONDRegular): The other condition. + + #### Returns: + - `bool`: True if conditions can be concatenated, False otherwise. + """ + if self.cond.shape != other.cond.shape: + return False + return True + + def concat(self, others: list) -> torch.Tensor: + """#### Concatenate conditions. + + #### Args: + - `others` (list): The list of other conditions. + + #### Returns: + - `torch.Tensor`: The concatenated conditions. + """ + conds = [self.cond] + for x in others: + conds.append(x.cond) + return torch.cat(conds) + + +class CONDCrossAttn(CONDRegular): + """#### Class representing a cross-attention condition.""" + + def can_concat(self, other: "CONDRegular") -> bool: + """#### Check if conditions can be concatenated. + + #### Args: + - `other` (CONDRegular): The other condition. + + #### Returns: + - `bool`: True if conditions can be concatenated, False otherwise. + """ + s1 = self.cond.shape + s2 = other.cond.shape + if s1 != s2: + if s1[0] != s2[0] or s1[2] != s2[2]: # these 2 cases should not happen + return False + + mult_min = torch.lcm(s1[1], s2[1]) + diff = mult_min // min(s1[1], s2[1]) + if ( + diff > 4 + ): # arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much + return False + return True + + def concat(self, others: list) -> torch.Tensor: + """#### Concatenate cross-attention conditions. + + #### Args: + - `others` (list): The list of other conditions. + + #### Returns: + - `torch.Tensor`: The concatenated conditions. + """ + conds = [self.cond] + crossattn_max_len = self.cond.shape[1] + for x in others: + c = x.cond + crossattn_max_len = util.lcm(crossattn_max_len, c.shape[1]) + conds.append(c) + + out = [] + for c in conds: + if c.shape[1] < crossattn_max_len: + c = c.repeat( + 1, crossattn_max_len // c.shape[1], 1 + ) # padding with repeat doesn't change result, but avoids an error on tensor shape + out.append(c) + return torch.cat(out) + + +def convert_cond(cond: list) -> list: + """#### Convert conditions to cross-attention conditions. + + #### Args: + - `cond` (list): The list of conditions. + + #### Returns: + - `list`: The converted conditions. + """ + out = [] + for c in cond: + temp = c[1].copy() + model_conds = temp.get("model_conds", {}) + if c[0] is not None: + model_conds["c_crossattn"] = CONDCrossAttn(c[0]) + temp["cross_attn"] = c[0] + temp["model_conds"] = model_conds + out.append(temp) + return out + + +def calc_cond_batch( + model: object, + conds: list, + x_in: torch.Tensor, + timestep: torch.Tensor, + model_options: dict, +) -> list: + """#### Calculate the condition batch. + + #### Args: + - `model` (object): The model. + - `conds` (list): The list of conditions. + - `x_in` (torch.Tensor): The input tensor. + - `timestep` (torch.Tensor): The timestep tensor. + - `model_options` (dict): The model options. + + #### Returns: + - `list`: The calculated condition batch. + """ + out_conds = [] + out_counts = [] + to_run = [] + + for i in range(len(conds)): + out_conds.append(torch.zeros_like(x_in)) + out_counts.append(torch.ones_like(x_in) * 1e-37) + + cond = conds[i] + if cond is not None: + for x in cond: + p = ksampler_util.get_area_and_mult(x, x_in, timestep) + if p is None: + continue + + to_run += [(p, i)] + + while len(to_run) > 0: + first = to_run[0] + first_shape = first[0][0].shape + to_batch_temp = [] + for x in range(len(to_run)): + if cond_util.can_concat_cond(to_run[x][0], first[0]): + to_batch_temp += [x] + + to_batch_temp.reverse() + to_batch = to_batch_temp[:1] + + free_memory = Device.get_free_memory(x_in.device) + for i in range(1, len(to_batch_temp) + 1): + batch_amount = to_batch_temp[: len(to_batch_temp) // i] + input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] + if model.memory_required(input_shape) * 1.5 < free_memory: + to_batch = batch_amount + break + + input_x = [] + mult = [] + c = [] + cond_or_uncond = [] + area = [] + control = None + patches = None + for x in to_batch: + o = to_run.pop(x) + p = o[0] + input_x.append(p.input_x) + mult.append(p.mult) + c.append(p.conditioning) + area.append(p.area) + cond_or_uncond.append(o[1]) + control = p.control + patches = p.patches + + batch_chunks = len(cond_or_uncond) + input_x = torch.cat(input_x) + c = cond_util.cond_cat(c) + timestep_ = torch.cat([timestep] * batch_chunks) + + if control is not None: + c["control"] = control.get_control( + input_x, timestep_, c, len(cond_or_uncond) + ) + + transformer_options = {} + if "transformer_options" in model_options: + transformer_options = model_options["transformer_options"].copy() + + if patches is not None: + if "patches" in transformer_options: + cur_patches = transformer_options["patches"].copy() + for p in patches: + if p in cur_patches: + cur_patches[p] = cur_patches[p] + patches[p] + else: + cur_patches[p] = patches[p] + transformer_options["patches"] = cur_patches + else: + transformer_options["patches"] = patches + + transformer_options["cond_or_uncond"] = cond_or_uncond[:] + transformer_options["sigmas"] = timestep + + c["transformer_options"] = transformer_options + + if "model_function_wrapper" in model_options: + output = model_options["model_function_wrapper"]( + model.apply_model, + { + "input": input_x, + "timestep": timestep_, + "c": c, + "cond_or_uncond": cond_or_uncond, + }, + ).chunk(batch_chunks) + else: + output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) + + for o in range(batch_chunks): + cond_index = cond_or_uncond[o] + a = area[o] + if a is None: + out_conds[cond_index] += output[o] * mult[o] + out_counts[cond_index] += mult[o] + else: + out_c = out_conds[cond_index] + out_cts = out_counts[cond_index] + dims = len(a) // 2 + for i in range(dims): + out_c = out_c.narrow(i + 2, a[i + dims], a[i]) + out_cts = out_cts.narrow(i + 2, a[i + dims], a[i]) + out_c += output[o] * mult[o] + out_cts += mult[o] + + for i in range(len(out_conds)): + out_conds[i] /= out_counts[i] + + return out_conds + + +def encode_model_conds( + model_function: callable, + conds: list, + noise: torch.Tensor, + device: torch.device, + prompt_type: str, + **kwargs, +) -> list: + """#### Encode model conditions. + + #### Args: + - `model_function` (callable): The model function. + - `conds` (list): The list of conditions. + - `noise` (torch.Tensor): The noise tensor. + - `device` (torch.device): The device. + - `prompt_type` (str): The prompt type. + - `**kwargs`: Additional keyword arguments. + + #### Returns: + - `list`: The encoded model conditions. + """ + for t in range(len(conds)): + x = conds[t] + params = x.copy() + params["device"] = device + params["noise"] = noise + default_width = None + if len(noise.shape) >= 4: # TODO: 8 multiple should be set by the model + default_width = noise.shape[3] * 8 + params["width"] = params.get("width", default_width) + params["height"] = params.get("height", noise.shape[2] * 8) + params["prompt_type"] = params.get("prompt_type", prompt_type) + for k in kwargs: + if k not in params: + params[k] = kwargs[k] + + out = model_function(**params) + x = x.copy() + model_conds = x["model_conds"].copy() + for k in out: + model_conds[k] = out[k] + x["model_conds"] = model_conds + conds[t] = x + return conds + +def resolve_areas_and_cond_masks_multidim(conditions: list, dims: tuple, device: torch.device) -> None: + """#### Resolve areas and condition masks for multidimensional conditions. + + #### Args: + - `conditions` (list): The list of conditions. + - `dims` (tuple): The dimensions. + - `device` (torch.device): The device. + """ + # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. + # While we're doing this, we can also resolve the mask device and scaling for performance reasons + for i in range(len(conditions)): + c = conditions[i] + if "area" in c: + area = c["area"] + if area[0] == "percentage": + modified = c.copy() + a = area[1:] + a_len = len(a) // 2 + area = () + for d in range(len(dims)): + area += (max(1, round(a[d] * dims[d])),) + for d in range(len(dims)): + area += (round(a[d + a_len] * dims[d]),) + + modified["area"] = area + c = modified + conditions[i] = c + + if "mask" in c: + mask = c["mask"] + mask = mask.to(device=device) + modified = c.copy() + if len(mask.shape) == len(dims): + mask = mask.unsqueeze(0) + if mask.shape[1:] != dims: + mask = torch.nn.functional.interpolate( + mask.unsqueeze(1), size=dims, mode="bilinear", align_corners=False + ).squeeze(1) + + modified["mask"] = mask + conditions[i] = modified + +def process_conds( + model: object, + noise: torch.Tensor, + conds: dict, + device: torch.device, + latent_image: torch.Tensor = None, + denoise_mask: torch.Tensor = None, + seed: int = None, +) -> dict: + """#### Process conditions. + + #### Args: + - `model` (object): The model. + - `noise` (torch.Tensor): The noise tensor. + - `conds` (dict): The conditions. + - `device` (torch.device): The device. + - `latent_image` (torch.Tensor, optional): The latent image tensor. Defaults to None. + - `denoise_mask` (torch.Tensor, optional): The denoise mask tensor. Defaults to None. + - `seed` (int, optional): The seed. Defaults to None. + + #### Returns: + - `dict`: The processed conditions. + """ + for k in conds: + conds[k] = conds[k][:] + resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device) + + for k in conds: + ksampler_util.calculate_start_end_timesteps(model, conds[k]) + + if hasattr(model, "extra_conds"): + for k in conds: + conds[k] = encode_model_conds( + model.extra_conds, + conds[k], + noise, + device, + k, + latent_image=latent_image, + denoise_mask=denoise_mask, + seed=seed, + ) + + # make sure each cond area has an opposite one with the same area + for k in conds: + for c in conds[k]: + for kk in conds: + if k != kk: + cond_util.create_cond_with_same_area_if_none(conds[kk], c) + + for k in conds: + ksampler_util.pre_run_control(model, conds[k]) + + if "positive" in conds: + positive = conds["positive"] + for k in conds: + if k != "positive": + ksampler_util.apply_empty_x_to_equal_area( + list( + filter( + lambda c: c.get("control_apply_to_uncond", False) is True, + positive, + ) + ), + conds[k], + "control", + lambda cond_cnets, x: cond_cnets[x], + ) + ksampler_util.apply_empty_x_to_equal_area( + positive, conds[k], "gligen", lambda cond_cnets, x: cond_cnets[x] + ) + return conds \ No newline at end of file diff --git a/modules/cond/cond_util.py b/modules/cond/cond_util.py index 1a3b267cbdfa705c7fc769cc5b0cae0fb18178bc..ed123084b0efc11842e221e9fc5f5e153f77e5ba 100644 --- a/modules/cond/cond_util.py +++ b/modules/cond/cond_util.py @@ -1,223 +1,223 @@ -from modules.Device import Device -import torch -from typing import List, Tuple, Any - - -def get_models_from_cond(cond: dict, model_type: str) -> List[object]: - """#### Get models from a condition. - - #### Args: - - `cond` (dict): The condition. - - `model_type` (str): The model type. - - #### Returns: - - `List[object]`: The list of models. - """ - models = [] - for c in cond: - if model_type in c: - models += [c[model_type]] - return models - - -def get_additional_models(conds: dict, dtype: torch.dtype) -> Tuple[List[object], int]: - """#### Load additional models in conditioning. - - #### Args: - - `conds` (dict): The conditions. - - `dtype` (torch.dtype): The data type. - - #### Returns: - - `Tuple[List[object], int]`: The list of models and the inference memory. - """ - cnets = [] - gligen = [] - - for k in conds: - cnets += get_models_from_cond(conds[k], "control") - gligen += get_models_from_cond(conds[k], "gligen") - - control_nets = set(cnets) - - inference_memory = 0 - control_models = [] - for m in control_nets: - control_models += m.get_models() - inference_memory += m.inference_memory_requirements(dtype) - - gligen = [x[1] for x in gligen] - models = control_models + gligen - return models, inference_memory - - -def prepare_sampling( - model: object, noise_shape: Tuple[int], conds: dict, flux_enabled: bool = False -) -> Tuple[object, dict, List[object]]: - """#### Prepare the model for sampling. - - #### Args: - - `model` (object): The model. - - `noise_shape` (Tuple[int]): The shape of the noise. - - `conds` (dict): The conditions. - - `flux_enabled` (bool, optional): Whether flux is enabled. Defaults to False. - - #### Returns: - - `Tuple[object, dict, List[object]]`: The prepared model, conditions, and additional models. - """ - real_model = None - models, inference_memory = get_additional_models(conds, model.model_dtype()) - memory_required = ( - model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) - + inference_memory - ) - minimum_memory_required = ( - model.memory_required([noise_shape[0]] + list(noise_shape[1:])) - + inference_memory - ) - Device.load_models_gpu( - [model] + models, - memory_required=memory_required, - minimum_memory_required=minimum_memory_required, - flux_enabled=flux_enabled, - ) - real_model = model.model - - return real_model, conds, models - -def cleanup_additional_models(models: List[object]) -> None: - """#### Clean up additional models. - - #### Args: - - `models` (List[object]): The list of models. - """ - for m in models: - if hasattr(m, "cleanup"): - m.cleanup() - -def cleanup_models(conds: dict, models: List[object]) -> None: - """#### Clean up the models after sampling. - - #### Args: - - `conds` (dict): The conditions. - - `models` (List[object]): The list of models. - """ - cleanup_additional_models(models) - - control_cleanup = [] - for k in conds: - control_cleanup += get_models_from_cond(conds[k], "control") - - cleanup_additional_models(set(control_cleanup)) - - -def cond_equal_size(c1: Any, c2: Any) -> bool: - """#### Check if two conditions have equal size. - - #### Args: - - `c1` (Any): The first condition. - - `c2` (Any): The second condition. - - #### Returns: - - `bool`: Whether the conditions have equal size. - """ - if c1 is c2: - return True - if c1.keys() != c2.keys(): - return False - return True - - -def can_concat_cond(c1: Any, c2: Any) -> bool: - """#### Check if two conditions can be concatenated. - - #### Args: - - `c1` (Any): The first condition. - - `c2` (Any): The second condition. - - #### Returns: - - `bool`: Whether the conditions can be concatenated. - """ - if c1.input_x.shape != c2.input_x.shape: - return False - - def objects_concatable(obj1, obj2): - """#### Check if two objects can be concatenated.""" - if (obj1 is None) != (obj2 is None): - return False - if obj1 is not None: - if obj1 is not obj2: - return False - return True - - if not objects_concatable(c1.control, c2.control): - return False - - if not objects_concatable(c1.patches, c2.patches): - return False - - return cond_equal_size(c1.conditioning, c2.conditioning) - - -def cond_cat(c_list: List[dict]) -> dict: - """#### Concatenate a list of conditions. - - #### Args: - - `c_list` (List[dict]): The list of conditions. - - #### Returns: - - `dict`: The concatenated conditions. - """ - temp = {} - for x in c_list: - for k in x: - cur = temp.get(k, []) - cur.append(x[k]) - temp[k] = cur - - out = {} - for k in temp: - conds = temp[k] - out[k] = conds[0].concat(conds[1:]) - - return out - - -def create_cond_with_same_area_if_none(conds: List[dict], c: dict) -> None: - """#### Create a condition with the same area if none exists. - - #### Args: - - `conds` (List[dict]): The list of conditions. - - `c` (dict): The condition. - """ - if "area" not in c: - return - - c_area = c["area"] - smallest = None - for x in conds: - if "area" in x: - a = x["area"] - if c_area[2] >= a[2] and c_area[3] >= a[3]: - if a[0] + a[2] >= c_area[0] + c_area[2]: - if a[1] + a[3] >= c_area[1] + c_area[3]: - if smallest is None: - smallest = x - elif "area" not in smallest: - smallest = x - else: - if smallest["area"][0] * smallest["area"][1] > a[0] * a[1]: - smallest = x - else: - if smallest is None: - smallest = x - if smallest is None: - return - if "area" in smallest: - if smallest["area"] == c_area: - return - - out = c.copy() - out["model_conds"] = smallest[ - "model_conds" - ].copy() - conds += [out] +from modules.Device import Device +import torch +from typing import List, Tuple, Any + + +def get_models_from_cond(cond: dict, model_type: str) -> List[object]: + """#### Get models from a condition. + + #### Args: + - `cond` (dict): The condition. + - `model_type` (str): The model type. + + #### Returns: + - `List[object]`: The list of models. + """ + models = [] + for c in cond: + if model_type in c: + models += [c[model_type]] + return models + + +def get_additional_models(conds: dict, dtype: torch.dtype) -> Tuple[List[object], int]: + """#### Load additional models in conditioning. + + #### Args: + - `conds` (dict): The conditions. + - `dtype` (torch.dtype): The data type. + + #### Returns: + - `Tuple[List[object], int]`: The list of models and the inference memory. + """ + cnets = [] + gligen = [] + + for k in conds: + cnets += get_models_from_cond(conds[k], "control") + gligen += get_models_from_cond(conds[k], "gligen") + + control_nets = set(cnets) + + inference_memory = 0 + control_models = [] + for m in control_nets: + control_models += m.get_models() + inference_memory += m.inference_memory_requirements(dtype) + + gligen = [x[1] for x in gligen] + models = control_models + gligen + return models, inference_memory + + +def prepare_sampling( + model: object, noise_shape: Tuple[int], conds: dict, flux_enabled: bool = False +) -> Tuple[object, dict, List[object]]: + """#### Prepare the model for sampling. + + #### Args: + - `model` (object): The model. + - `noise_shape` (Tuple[int]): The shape of the noise. + - `conds` (dict): The conditions. + - `flux_enabled` (bool, optional): Whether flux is enabled. Defaults to False. + + #### Returns: + - `Tuple[object, dict, List[object]]`: The prepared model, conditions, and additional models. + """ + real_model = None + models, inference_memory = get_additional_models(conds, model.model_dtype()) + memory_required = ( + model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + + inference_memory + ) + minimum_memory_required = ( + model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + + inference_memory + ) + Device.load_models_gpu( + [model] + models, + memory_required=memory_required, + minimum_memory_required=minimum_memory_required, + flux_enabled=flux_enabled, + ) + real_model = model.model + + return real_model, conds, models + +def cleanup_additional_models(models: List[object]) -> None: + """#### Clean up additional models. + + #### Args: + - `models` (List[object]): The list of models. + """ + for m in models: + if hasattr(m, "cleanup"): + m.cleanup() + +def cleanup_models(conds: dict, models: List[object]) -> None: + """#### Clean up the models after sampling. + + #### Args: + - `conds` (dict): The conditions. + - `models` (List[object]): The list of models. + """ + cleanup_additional_models(models) + + control_cleanup = [] + for k in conds: + control_cleanup += get_models_from_cond(conds[k], "control") + + cleanup_additional_models(set(control_cleanup)) + + +def cond_equal_size(c1: Any, c2: Any) -> bool: + """#### Check if two conditions have equal size. + + #### Args: + - `c1` (Any): The first condition. + - `c2` (Any): The second condition. + + #### Returns: + - `bool`: Whether the conditions have equal size. + """ + if c1 is c2: + return True + if c1.keys() != c2.keys(): + return False + return True + + +def can_concat_cond(c1: Any, c2: Any) -> bool: + """#### Check if two conditions can be concatenated. + + #### Args: + - `c1` (Any): The first condition. + - `c2` (Any): The second condition. + + #### Returns: + - `bool`: Whether the conditions can be concatenated. + """ + if c1.input_x.shape != c2.input_x.shape: + return False + + def objects_concatable(obj1, obj2): + """#### Check if two objects can be concatenated.""" + if (obj1 is None) != (obj2 is None): + return False + if obj1 is not None: + if obj1 is not obj2: + return False + return True + + if not objects_concatable(c1.control, c2.control): + return False + + if not objects_concatable(c1.patches, c2.patches): + return False + + return cond_equal_size(c1.conditioning, c2.conditioning) + + +def cond_cat(c_list: List[dict]) -> dict: + """#### Concatenate a list of conditions. + + #### Args: + - `c_list` (List[dict]): The list of conditions. + + #### Returns: + - `dict`: The concatenated conditions. + """ + temp = {} + for x in c_list: + for k in x: + cur = temp.get(k, []) + cur.append(x[k]) + temp[k] = cur + + out = {} + for k in temp: + conds = temp[k] + out[k] = conds[0].concat(conds[1:]) + + return out + + +def create_cond_with_same_area_if_none(conds: List[dict], c: dict) -> None: + """#### Create a condition with the same area if none exists. + + #### Args: + - `conds` (List[dict]): The list of conditions. + - `c` (dict): The condition. + """ + if "area" not in c: + return + + c_area = c["area"] + smallest = None + for x in conds: + if "area" in x: + a = x["area"] + if c_area[2] >= a[2] and c_area[3] >= a[3]: + if a[0] + a[2] >= c_area[0] + c_area[2]: + if a[1] + a[3] >= c_area[1] + c_area[3]: + if smallest is None: + smallest = x + elif "area" not in smallest: + smallest = x + else: + if smallest["area"][0] * smallest["area"][1] > a[0] * a[1]: + smallest = x + else: + if smallest is None: + smallest = x + if smallest is None: + return + if "area" in smallest: + if smallest["area"] == c_area: + return + + out = c.copy() + out["model_conds"] = smallest[ + "model_conds" + ].copy() + conds += [out] diff --git a/modules/hidiffusion/msw_msa_attention.py b/modules/hidiffusion/msw_msa_attention.py index 9ec187e8c7aee3c5a998a7e266e2ff3f67b941bc..27a27556156ad82ba3f2820bcc2b8ec3a29f4198 100644 --- a/modules/hidiffusion/msw_msa_attention.py +++ b/modules/hidiffusion/msw_msa_attention.py @@ -1,821 +1,821 @@ -from __future__ import annotations - -import itertools -import math -from time import time -from typing import Any, NamedTuple -from modules.Model import ModelPatcher - -import torch - -from . import utils -from .utils import ( - IntegratedNode, - ModelType, - StrEnum, - TimeMode, - block_to_num, - check_time, - convert_time, - get_sigma, - guess_model_type, - logger, - parse_blocks, - rescale_size, - scale_samples, -) - -F = torch.nn.functional - -SCALE_METHODS = () -REVERSE_SCALE_METHODS = () - - -# Taken from https://github.com/blepping/comfyui_jankhidiffusion - - -def init_integrations(_integrations) -> None: - """#### Initialize integrations. - - #### Args: - - `_integrations` (Any): The integrations object. - """ - global scale_samples, SCALE_METHODS, REVERSE_SCALE_METHODS # noqa: PLW0603 - SCALE_METHODS = ("disabled", "skip", *utils.UPSCALE_METHODS) - REVERSE_SCALE_METHODS = utils.UPSCALE_METHODS - scale_samples = utils.scale_samples - - -utils.MODULES.register_init_handler(init_integrations) - -DEFAULT_WARN_INTERVAL = 60 - - -class Preset(NamedTuple): - """#### Class representing a preset configuration. - - #### Args: - - `input_blocks` (str): The input blocks. - - `middle_blocks` (str): The middle blocks. - - `output_blocks` (str): The output blocks. - - `time_mode` (TimeMode): The time mode. - - `start_time` (float): The start time. - - `end_time` (float): The end time. - - `scale_mode` (str): The scale mode. - - `reverse_scale_mode` (str): The reverse scale mode. - """ - input_blocks: str = "" - middle_blocks: str = "" - output_blocks: str = "" - time_mode: TimeMode = TimeMode.PERCENT - start_time: float = 0.2 - end_time: float = 1.0 - scale_mode: str = "nearest-exact" - reverse_scale_mode: str = "nearest-exact" - - @property - def as_dict(self): - """#### Convert the preset to a dictionary. - - #### Returns: - - `Dict[str, Any]`: The preset as a dictionary. - """ - return {k: getattr(self, k) for k in self._fields} - - @property - def pretty_blocks(self): - """#### Get a pretty string representation of the blocks. - - #### Returns: - - `str`: The pretty string representation of the blocks. - """ - blocks = (self.input_blocks, self.middle_blocks, self.output_blocks) - return " / ".join(b or "none" for b in blocks) - - -SIMPLE_PRESETS = { - ModelType.SD15: Preset(input_blocks="1,2", output_blocks="11,10,9"), - ModelType.SDXL: Preset(input_blocks="4,5", output_blocks="3,4,5"), -} - - -class WindowSize(NamedTuple): - """#### Class representing the window size. - - #### Args: - - `height` (int): The height of the window. - - `width` (int): The width of the window. - """ - height: int - width: int - - @property - def sum(self): - """#### Get the sum of the height and width. - - #### Returns: - - `int`: The sum of the height and width. - """ - return self.height * self.width - - def __neg__(self): - """#### Negate the window size. - - #### Returns: - - `WindowSize`: The negated window size. - """ - return self.__class__(-self.height, -self.width) - - -class ShiftSize(WindowSize): - """#### Class representing the shift size.""" - pass - - -class LastShiftMode(StrEnum): - """#### Enum for the last shift mode.""" - GLOBAL = "global" - BLOCK = "block" - BOTH = "both" - IGNORE = "ignore" - - -class LastShiftStrategy(StrEnum): - """#### Enum for the last shift strategy.""" - INCREMENT = "increment" - DECREMENT = "decrement" - RETRY = "retry" - - -class Config(NamedTuple): - """#### Class representing the configuration. - - #### Args: - - `start_sigma` (float): The start sigma. - - `end_sigma` (float): The end sigma. - - `use_blocks` (set): The blocks to use. - - `scale_mode` (str): The scale mode. - - `reverse_scale_mode` (str): The reverse scale mode. - - `silent` (bool): Whether to disable log warnings. - - `last_shift_mode` (LastShiftMode): The last shift mode. - - `last_shift_strategy` (LastShiftStrategy): The last shift strategy. - - `pre_window_multiplier` (float): The pre-window multiplier. - - `post_window_multiplier` (float): The post-window multiplier. - - `pre_window_reverse_multiplier` (float): The pre-window reverse multiplier. - - `post_window_reverse_multiplier` (float): The post-window reverse multiplier. - - `force_apply_attn2` (bool): Whether to force apply attention 2. - - `rescale_search_tolerance` (int): The rescale search tolerance. - - `verbose` (int): The verbosity level. - """ - start_sigma: float - end_sigma: float - use_blocks: set - scale_mode: str = "nearest-exact" - reverse_scale_mode: str = "nearest-exact" - # Allows disabling the log warning for incompatible sizes. - silent: bool = False - # Mode for trying to avoid using the same window size consecutively. - last_shift_mode: LastShiftMode = LastShiftMode.GLOBAL - # Strategy to use when avoiding a duplicate window size. - last_shift_strategy: LastShiftStrategy = LastShiftStrategy.INCREMENT - # Allows multiplying the tensor going into/out of the window or window reverse effect. - pre_window_multiplier: float = 1.0 - post_window_multiplier: float = 1.0 - pre_window_reverse_multiplier: float = 1.0 - post_window_reverse_multiplier: float = 1.0 - force_apply_attn2: bool = False - rescale_search_tolerance: int = 1 - verbose: int = 0 - - @classmethod - def build( - cls, - *, - ms: object, - input_blocks: str | list[int], - middle_blocks: str | list[int], - output_blocks: str | list[int], - time_mode: str | TimeMode, - start_time: float, - end_time: float, - **kwargs: dict, - ) -> object: - """#### Build a configuration object. - - #### Args: - - `ms` (object): The model sampling object. - - `input_blocks` (str | List[int]): The input blocks. - - `middle_blocks` (str | List[int]): The middle blocks. - - `output_blocks` (str | List[int]): The output blocks. - - `time_mode` (str | TimeMode): The time mode. - - `start_time` (float): The start time. - - `end_time` (float): The end time. - - `kwargs` (Dict[str, Any]): Additional keyword arguments. - - #### Returns: - - `Config`: The configuration object. - """ - time_mode: TimeMode = TimeMode(time_mode) - start_sigma, end_sigma = convert_time(ms, time_mode, start_time, end_time) - input_blocks, middle_blocks, output_blocks = itertools.starmap( - parse_blocks, - ( - ("input", input_blocks), - ("middle", middle_blocks), - ("output", output_blocks), - ), - ) - return cls.__new__( - cls, - start_sigma=start_sigma, - end_sigma=end_sigma, - use_blocks=input_blocks | middle_blocks | output_blocks, - **kwargs, - ) - - @staticmethod - def maybe_multiply( - t: torch.Tensor, - multiplier: float = 1.0, - post: bool = False, - ) -> torch.Tensor: - """#### Multiply a tensor by a multiplier. - - #### Args: - - `t` (torch.Tensor): The input tensor. - - `multiplier` (float, optional): The multiplier. Defaults to 1.0. - - `post` (bool, optional): Whether to multiply in-place. Defaults to False. - - #### Returns: - - `torch.Tensor`: The multiplied tensor. - """ - if multiplier == 1.0: - return t - return t.mul_(multiplier) if post else t * multiplier - - -class State: - """#### Class representing the state. - - #### Args: - - `config` (Config): The configuration object. - """ - __slots__ = ( - "config", - "last_block", - "last_shift", - "last_shifts", - "last_sigma", - "last_warned", - "window_args", - ) - - def __init__(self, config): - self.config = config - self.last_warned = None - self.reset() - - def reset(self): - """#### Reset the state.""" - self.window_args = None - self.last_sigma = None - self.last_block = None - self.last_shift = None - self.last_shifts = {} - - @property - def pretty_last_block(self) -> str: - """#### Get a pretty string representation of the last block. - - #### Returns: - - `str`: The pretty string representation of the last block. - """ - if self.last_block is None: - return "unknown" - bt, bnum = self.last_block - attstr = "" if not self.config.force_apply_attn2 else "attn2." - btstr = ("in", "mid", "out")[bt] - return f"{attstr}{btstr}.{bnum}" - - def maybe_warning(self, s): - """#### Log a warning if necessary. - - #### Args: - - `s` (str): The warning message. - """ - if self.config.silent: - return - now = time() - if ( - self.config.verbose >= 2 - or self.last_warned is None - or now - self.last_warned >= DEFAULT_WARN_INTERVAL - ): - logger.warning( - f"** jankhidiffusion: MSW-MSA attention({self.pretty_last_block}): {s}", - ) - self.last_warned = now - - def __repr__(self): - """#### Get a string representation of the state. - - #### Returns: - - `str`: The string representation of the state. - """ - return f"" - - -class ApplyMSWMSAAttention(metaclass=IntegratedNode): - """#### Class for applying MSW-MSA attention.""" - RETURN_TYPES = ("MODEL",) - OUTPUT_TOOLTIPS = ("Model patched with the MSW-MSA attention effect.",) - FUNCTION = "patch" - CATEGORY = "model_patches/unet" - DESCRIPTION = "This node applies an attention patch which _may_ slightly improve quality especially when generating at high resolutions. It is a large performance increase on SD1.x, may improve performance on SDXL. This is the advanced version of the node with more parameters, use ApplyMSWMSAAttentionSimple if this seems too complex. NOTE: Only supports SD1.x, SD2.x and SDXL." - - @classmethod - def INPUT_TYPES(cls): - """#### Get the input types for the class. - - #### Returns: - - `Dict[str, Any]`: The input types. - """ - return { - "required": { - "input_blocks": ( - "STRING", - { - "default": "1,2", - "tooltip": "Comma-separated list of input blocks to patch. Default is for SD1.x, you can try 4,5 for SDXL", - }, - ), - "middle_blocks": ( - "STRING", - { - "default": "", - "tooltip": "Comma-separated list of middle blocks to patch. Generally not recommended.", - }, - ), - "output_blocks": ( - "STRING", - { - "default": "9,10,11", - "tooltip": "Comma-separated list of output blocks to patch. Default is for SD1.x, you can try 3,4,5 for SDXL", - }, - ), - "time_mode": ( - tuple(str(val) for val in TimeMode), - { - "default": "percent", - "tooltip": "Time mode controls how to interpret the values in start_time and end_time.", - }, - ), - "start_time": ( - "FLOAT", - { - "default": 0.0, - "min": 0.0, - "max": 999.0, - "round": False, - "step": 0.01, - "tooltip": "Time the MSW-MSA attention effect starts applying - value is inclusive.", - }, - ), - "end_time": ( - "FLOAT", - { - "default": 1.0, - "min": 0.0, - "max": 999.0, - "round": False, - "step": 0.01, - "tooltip": "Time the MSW-MSA attention effect ends - value is inclusive.", - }, - ), - "model": ( - "MODEL", - { - "tooltip": "Model to patch with the MSW-MSA attention effect.", - }, - ), - }, - "optional": { - "yaml_parameters": ( - "STRING", - { - "tooltip": "Allows specifying custom parameters via YAML. You can also override any of the normal parameters by key. This input can be converted into a multiline text widget. See main README for possible options. Note: When specifying paramaters this way, there is very little error checking.", - "dynamicPrompts": False, - "multiline": True, - "defaultInput": True, - }, - ), - }, - } - - # reference: https://github.com/microsoft/Swin-Transformer - # Window functions adapted from https://github.com/megvii-research/HiDiffusion - @staticmethod - def window_partition( - x: torch.Tensor, - state: State, - window_index: int, - ) -> torch.Tensor: - """#### Partition a tensor into windows. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `state` (State): The state object. - - `window_index` (int): The window index. - - #### Returns: - - `torch.Tensor`: The partitioned tensor. - """ - config = state.config - scale_mode = config.scale_mode - x = config.maybe_multiply(x, config.pre_window_multiplier) - window_size, shift_size, height, width = state.window_args[window_index] - do_rescale = (height % 2 + width % 2) != 0 - if do_rescale: - if scale_mode == "skip": - state.maybe_warning( - "Incompatible latent size - skipping MSW-MSA attention.", - ) - return x - if scale_mode == "disabled": - state.maybe_warning( - "Incompatible latent size - trying to proceed anyway. This may result in an error.", - ) - do_rescale = False - else: - state.maybe_warning( - "Incompatible latent size - applying scaling workaround. Note: This may reduce quality - use resolutions that are multiples of 64 when possible.", - ) - batch, _features, channels = x.shape - wheight, wwidth = window_size - x = x.view(batch, height, width, channels) - if do_rescale: - x = ( - scale_samples( - x.permute(0, 3, 1, 2).contiguous(), - wwidth * 2, - wheight * 2, - mode=scale_mode, - sigma=state.last_sigma, - ) - .permute(0, 2, 3, 1) - .contiguous() - ) - if shift_size.sum > 0: - x = torch.roll(x, shifts=-shift_size, dims=(1, 2)) - x = x.view(batch, 2, wheight, 2, wwidth, channels) - windows = ( - x.permute(0, 1, 3, 2, 4, 5) - .contiguous() - .view(-1, window_size.height, window_size.width, channels) - ) - return config.maybe_multiply( - windows.view(-1, window_size.sum, channels), - config.post_window_multiplier, - ) - - @staticmethod - def window_reverse( - windows: torch.Tensor, - state: State, - window_index: int = 0, - ) -> torch.Tensor: - """#### Reverse the window partitioning of a tensor. - - #### Args: - - `windows` (torch.Tensor): The input windows tensor. - - `state` (State): The state object. - - `window_index` (int, optional): The window index. Defaults to 0. - - #### Returns: - - `torch.Tensor`: The reversed tensor. - """ - config = state.config - windows = config.maybe_multiply(windows, config.pre_window_reverse_multiplier) - window_size, shift_size, height, width = state.window_args[window_index] - do_rescale = (height % 2 + width % 2) != 0 - if do_rescale: - if config.scale_mode == "skip": - return windows - if config.scale_mode == "disabled": - do_rescale = False - batch, _features, channels = windows.shape - wheight, wwidth = window_size - windows = windows.view(-1, wheight, wwidth, channels) - batch = int(windows.shape[0] / 4) - x = windows.view(batch, 2, 2, wheight, wwidth, -1) - x = ( - x.permute(0, 1, 3, 2, 4, 5) - .contiguous() - .view(batch, wheight * 2, wwidth * 2, -1) - ) - if shift_size.sum > 0: - x = torch.roll(x, shifts=shift_size, dims=(1, 2)) - if do_rescale: - x = ( - scale_samples( - x.permute(0, 3, 1, 2).contiguous(), - width, - height, - mode=config.reverse_scale_mode, - sigma=state.last_sigma, - ) - .permute(0, 2, 3, 1) - .contiguous() - ) - return config.maybe_multiply( - x.view(batch, height * width, channels), - config.post_window_reverse_multiplier, - ) - - @staticmethod - def get_window_args( - config: Config, - n: torch.Tensor, - orig_shape: tuple, - shift: int, - ) -> tuple[WindowSize, ShiftSize, int, int]: - """#### Get window arguments for MSW-MSA attention. - - #### Args: - - `config` (Config): The configuration object. - - `n` (torch.Tensor): The input tensor. - - `orig_shape` (tuple): The original shape of the tensor. - - `shift` (int): The shift value. - - #### Returns: - - `tuple[WindowSize, ShiftSize, int, int]`: The window size, shift size, height, and width. - """ - _batch, features, _channels = n.shape - orig_height, orig_width = orig_shape[-2:] - - width, height = rescale_size( - orig_width, - orig_height, - features, - tolerance=config.rescale_search_tolerance, - ) - # if (height, width) != (orig_height, orig_width): - # print( - # f"\nRESC: features={features}, orig={(orig_height, orig_width)}, new={(height, width)}", - # ) - wheight, wwidth = math.ceil(height / 2), math.ceil(width / 2) - - if shift == 0: - shift_size = ShiftSize(0, 0) - elif shift == 1: - shift_size = ShiftSize(wheight // 4, wwidth // 4) - elif shift == 2: - shift_size = ShiftSize(wheight // 4 * 2, wwidth // 4 * 2) - else: - shift_size = ShiftSize(wheight // 4 * 3, wwidth // 4 * 3) - return (WindowSize(wheight, wwidth), shift_size, height, width) - - @staticmethod - def get_shift( - curr_block: tuple, - state: State, - *, - shift_count=4, - ) -> int: - """#### Get the shift value for MSW-MSA attention. - - #### Args: - - `curr_block` (tuple): The current block. - - `state` (State): The state object. - - `shift_count` (int, optional): The shift count. Defaults to 4. - - #### Returns: - - `int`: The shift value. - """ - mode = state.config.last_shift_mode - strat = state.config.last_shift_strategy - shift = int(torch.rand(1, device="cpu").item() * shift_count) - block_last_shift = state.last_shifts.get(curr_block) - last_shift = state.last_shift - if mode == LastShiftMode.BOTH: - avoid = {block_last_shift, last_shift} - elif mode == LastShiftMode.BLOCK: - avoid = {block_last_shift} - elif mode == LastShiftMode.GLOBAL: - avoid = {last_shift} - else: - avoid = {} - if shift in avoid: - if strat == LastShiftStrategy.DECREMENT: - while shift in avoid: - shift -= 1 - if shift < 0: - shift = shift_count - 1 - elif strat == LastShiftStrategy.RETRY: - while shift in avoid: - shift = int(torch.rand(1, device="cpu").item() * shift_count) - else: - # Increment - while shift in avoid: - shift = (shift + 1) % shift_count - return shift - - @classmethod - def patch( - cls, - *, - model: ModelPatcher.ModelPatcher, - yaml_parameters: str | None = None, - **kwargs: dict[str, Any], - ) -> tuple[ModelPatcher.ModelPatcher]: - """#### Patch the model with MSW-MSA attention. - - #### Args: - - `model` (ModelPatcher.ModelPatcher): The model patcher. - - `yaml_parameters` (str | None, optional): The YAML parameters. Defaults to None. - - `kwargs` (dict[str, Any]): Additional keyword arguments. - - #### Returns: - - `tuple[ModelPatcher.ModelPatcher]`: The patched model. - """ - if yaml_parameters: - import yaml # noqa: PLC0415 - - extra_params = yaml.safe_load(yaml_parameters) - if extra_params is None: - pass - elif not isinstance(extra_params, dict): - raise ValueError( - "MSWMSAAttention: yaml_parameters must either be null or an object", - ) - else: - kwargs |= extra_params - config = Config.build( - ms=model.get_model_object("model_sampling"), - **kwargs, - ) - if not config.use_blocks: - return (model,) - if config.verbose: - logger.info( - f"** jankhidiffusion: MSW-MSA Attention: Using config: {config}", - ) - - model = model.clone() - state = State(config) - - def attn_patch( - q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - extra_options: dict, - ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """#### Apply attention patch. - - #### Args: - - `q` (torch.Tensor): The query tensor. - - `k` (torch.Tensor): The key tensor. - - `v` (torch.Tensor): The value tensor. - - `extra_options` (dict): Additional options. - - #### Returns: - - `tuple[torch.Tensor, torch.Tensor, torch.Tensor]`: The patched tensors. - """ - state.window_args = None - sigma = get_sigma(extra_options) - block = extra_options.get("block", ("missing", 0)) - curr_block = block_to_num(*block) - if state.last_sigma is not None and sigma > state.last_sigma: - # logging.warning( - # f"Doing reset: block={block}, sigma={sigma}, state={state}", - # ) - state.reset() - state.last_block = curr_block - state.last_sigma = sigma - if block not in config.use_blocks or not check_time( - sigma, - config.start_sigma, - config.end_sigma, - ): - return q, k, v - orig_shape = extra_options["original_shape"] - # MSW-MSA - shift = cls.get_shift(curr_block, state) - state.last_shifts[curr_block] = state.last_shift = shift - try: - # get_window_args() can fail with ValueError in rescale_size() for some weird resolutions/aspect ratios - # so we catch it here and skip MSW-MSA attention in that case. - state.window_args = tuple( - cls.get_window_args(config, x, orig_shape, shift) - if x is not None - else None - for x in (q, k, v) - ) - attn_parts = (q,) if q is not None and q is k and q is v else (q, k, v) - result = tuple( - cls.window_partition(tensor, state, idx) - if tensor is not None - else None - for idx, tensor in enumerate(attn_parts) - ) - except (RuntimeError, ValueError) as exc: - logger.warning( - f"** jankhidiffusion: Exception applying MSW-MSA attention: Incompatible model patches or bad resolution. Try using resolutions that are multiples of 64 or set scale/reverse_scale modes to something other than disabled. Original exception: {exc}", - ) - state.window_args = None - return q, k, v - return result * 3 if len(result) == 1 else result - - def attn_output_patch(n: torch.Tensor, extra_options: dict) -> torch.Tensor: - """#### Apply attention output patch. - - #### Args: - - `n` (torch.Tensor): The input tensor. - - `extra_options` (dict): Additional options. - - #### Returns: - - `torch.Tensor`: The patched tensor. - """ - if state.window_args is None or state.last_block != block_to_num( - *extra_options.get("block", ("missing", 0)), - ): - state.window_args = None - return n - result = cls.window_reverse(n, state) - state.window_args = None - return result - - if not config.force_apply_attn2: - model.set_model_attn1_patch(attn_patch) - model.set_model_attn1_output_patch(attn_output_patch) - else: - model.set_model_attn2_patch(attn_patch) - model.set_model_attn2_output_patch(attn_output_patch) - return (model,) - - -class ApplyMSWMSAAttentionSimple(metaclass=IntegratedNode): - """Class representing a simplified version of MSW-MSA Attention.""" - RETURN_TYPES = ("MODEL",) - OUTPUT_TOOLTIPS = ("Model patched with the MSW-MSA attention effect.",) - FUNCTION = "go" - CATEGORY = "model_patches/unet" - DESCRIPTION = "This node applies an attention patch which _may_ slightly improve quality especially when generating at high resolutions. It is a large performance increase on SD1.x, may improve performance on SDXL. This is the simplified version of the node with less parameters. Use ApplyMSWMSAAttention if you require more control. NOTE: Only supports SD1.x, SD2.x and SDXL." - - @classmethod - def INPUT_TYPES(cls) -> dict: - """#### Get input types for the class. - - #### Returns: - - `dict`: The input types. - """ - return { - "required": { - "model_type": ( - ("auto", "SD15", "SDXL"), - { - "tooltip": "Model type being patched. Generally safe to leave on auto. Choose SD15 for SD 1.4, SD 2.x.", - }, - ), - "model": ( - "MODEL", - { - "tooltip": "Model to patch with the MSW-MSA attention effect.", - }, - ), - }, - } - - @classmethod - def go( - cls, - model_type: str | ModelType, - model: ModelPatcher.ModelPatcher, - ) -> tuple[ModelPatcher.ModelPatcher]: - """#### Apply the MSW-MSA attention patch. - - #### Args: - - `model_type` (str | ModelType): The model type. - - `model` (ModelPatcher.ModelPatcher): The model patcher. - - #### Returns: - - `tuple[ModelPatcher.ModelPatcher]`: The patched model. - """ - if model_type == "auto": - guessed_model_type = guess_model_type(model) - if guessed_model_type not in SIMPLE_PRESETS: - raise RuntimeError("Unable to guess model type") - model_type = guessed_model_type - else: - model_type = ModelType(model_type) - preset = SIMPLE_PRESETS.get(model_type) - if preset is None: - errstr = f"Unknown model type {model_type!s}" - raise ValueError(errstr) - logger.info( - f"** ApplyMSWMSAAttentionSimple: Using preset {model_type!s}: in/mid/out blocks [{preset.pretty_blocks}], start/end percent {preset.start_time:.2}/{preset.end_time:.2}", - ) - return ApplyMSWMSAAttention.patch(model=model, **preset.as_dict) - - +from __future__ import annotations + +import itertools +import math +from time import time +from typing import Any, NamedTuple +from modules.Model import ModelPatcher + +import torch + +from . import utils +from .utils import ( + IntegratedNode, + ModelType, + StrEnum, + TimeMode, + block_to_num, + check_time, + convert_time, + get_sigma, + guess_model_type, + logger, + parse_blocks, + rescale_size, + scale_samples, +) + +F = torch.nn.functional + +SCALE_METHODS = () +REVERSE_SCALE_METHODS = () + + +# Taken from https://github.com/blepping/comfyui_jankhidiffusion + + +def init_integrations(_integrations) -> None: + """#### Initialize integrations. + + #### Args: + - `_integrations` (Any): The integrations object. + """ + global scale_samples, SCALE_METHODS, REVERSE_SCALE_METHODS # noqa: PLW0603 + SCALE_METHODS = ("disabled", "skip", *utils.UPSCALE_METHODS) + REVERSE_SCALE_METHODS = utils.UPSCALE_METHODS + scale_samples = utils.scale_samples + + +utils.MODULES.register_init_handler(init_integrations) + +DEFAULT_WARN_INTERVAL = 60 + + +class Preset(NamedTuple): + """#### Class representing a preset configuration. + + #### Args: + - `input_blocks` (str): The input blocks. + - `middle_blocks` (str): The middle blocks. + - `output_blocks` (str): The output blocks. + - `time_mode` (TimeMode): The time mode. + - `start_time` (float): The start time. + - `end_time` (float): The end time. + - `scale_mode` (str): The scale mode. + - `reverse_scale_mode` (str): The reverse scale mode. + """ + input_blocks: str = "" + middle_blocks: str = "" + output_blocks: str = "" + time_mode: TimeMode = TimeMode.PERCENT + start_time: float = 0.2 + end_time: float = 1.0 + scale_mode: str = "nearest-exact" + reverse_scale_mode: str = "nearest-exact" + + @property + def as_dict(self): + """#### Convert the preset to a dictionary. + + #### Returns: + - `Dict[str, Any]`: The preset as a dictionary. + """ + return {k: getattr(self, k) for k in self._fields} + + @property + def pretty_blocks(self): + """#### Get a pretty string representation of the blocks. + + #### Returns: + - `str`: The pretty string representation of the blocks. + """ + blocks = (self.input_blocks, self.middle_blocks, self.output_blocks) + return " / ".join(b or "none" for b in blocks) + + +SIMPLE_PRESETS = { + ModelType.SD15: Preset(input_blocks="1,2", output_blocks="11,10,9"), + ModelType.SDXL: Preset(input_blocks="4,5", output_blocks="3,4,5"), +} + + +class WindowSize(NamedTuple): + """#### Class representing the window size. + + #### Args: + - `height` (int): The height of the window. + - `width` (int): The width of the window. + """ + height: int + width: int + + @property + def sum(self): + """#### Get the sum of the height and width. + + #### Returns: + - `int`: The sum of the height and width. + """ + return self.height * self.width + + def __neg__(self): + """#### Negate the window size. + + #### Returns: + - `WindowSize`: The negated window size. + """ + return self.__class__(-self.height, -self.width) + + +class ShiftSize(WindowSize): + """#### Class representing the shift size.""" + pass + + +class LastShiftMode(StrEnum): + """#### Enum for the last shift mode.""" + GLOBAL = "global" + BLOCK = "block" + BOTH = "both" + IGNORE = "ignore" + + +class LastShiftStrategy(StrEnum): + """#### Enum for the last shift strategy.""" + INCREMENT = "increment" + DECREMENT = "decrement" + RETRY = "retry" + + +class Config(NamedTuple): + """#### Class representing the configuration. + + #### Args: + - `start_sigma` (float): The start sigma. + - `end_sigma` (float): The end sigma. + - `use_blocks` (set): The blocks to use. + - `scale_mode` (str): The scale mode. + - `reverse_scale_mode` (str): The reverse scale mode. + - `silent` (bool): Whether to disable log warnings. + - `last_shift_mode` (LastShiftMode): The last shift mode. + - `last_shift_strategy` (LastShiftStrategy): The last shift strategy. + - `pre_window_multiplier` (float): The pre-window multiplier. + - `post_window_multiplier` (float): The post-window multiplier. + - `pre_window_reverse_multiplier` (float): The pre-window reverse multiplier. + - `post_window_reverse_multiplier` (float): The post-window reverse multiplier. + - `force_apply_attn2` (bool): Whether to force apply attention 2. + - `rescale_search_tolerance` (int): The rescale search tolerance. + - `verbose` (int): The verbosity level. + """ + start_sigma: float + end_sigma: float + use_blocks: set + scale_mode: str = "nearest-exact" + reverse_scale_mode: str = "nearest-exact" + # Allows disabling the log warning for incompatible sizes. + silent: bool = False + # Mode for trying to avoid using the same window size consecutively. + last_shift_mode: LastShiftMode = LastShiftMode.GLOBAL + # Strategy to use when avoiding a duplicate window size. + last_shift_strategy: LastShiftStrategy = LastShiftStrategy.INCREMENT + # Allows multiplying the tensor going into/out of the window or window reverse effect. + pre_window_multiplier: float = 1.0 + post_window_multiplier: float = 1.0 + pre_window_reverse_multiplier: float = 1.0 + post_window_reverse_multiplier: float = 1.0 + force_apply_attn2: bool = False + rescale_search_tolerance: int = 1 + verbose: int = 0 + + @classmethod + def build( + cls, + *, + ms: object, + input_blocks: str | list[int], + middle_blocks: str | list[int], + output_blocks: str | list[int], + time_mode: str | TimeMode, + start_time: float, + end_time: float, + **kwargs: dict, + ) -> object: + """#### Build a configuration object. + + #### Args: + - `ms` (object): The model sampling object. + - `input_blocks` (str | List[int]): The input blocks. + - `middle_blocks` (str | List[int]): The middle blocks. + - `output_blocks` (str | List[int]): The output blocks. + - `time_mode` (str | TimeMode): The time mode. + - `start_time` (float): The start time. + - `end_time` (float): The end time. + - `kwargs` (Dict[str, Any]): Additional keyword arguments. + + #### Returns: + - `Config`: The configuration object. + """ + time_mode: TimeMode = TimeMode(time_mode) + start_sigma, end_sigma = convert_time(ms, time_mode, start_time, end_time) + input_blocks, middle_blocks, output_blocks = itertools.starmap( + parse_blocks, + ( + ("input", input_blocks), + ("middle", middle_blocks), + ("output", output_blocks), + ), + ) + return cls.__new__( + cls, + start_sigma=start_sigma, + end_sigma=end_sigma, + use_blocks=input_blocks | middle_blocks | output_blocks, + **kwargs, + ) + + @staticmethod + def maybe_multiply( + t: torch.Tensor, + multiplier: float = 1.0, + post: bool = False, + ) -> torch.Tensor: + """#### Multiply a tensor by a multiplier. + + #### Args: + - `t` (torch.Tensor): The input tensor. + - `multiplier` (float, optional): The multiplier. Defaults to 1.0. + - `post` (bool, optional): Whether to multiply in-place. Defaults to False. + + #### Returns: + - `torch.Tensor`: The multiplied tensor. + """ + if multiplier == 1.0: + return t + return t.mul_(multiplier) if post else t * multiplier + + +class State: + """#### Class representing the state. + + #### Args: + - `config` (Config): The configuration object. + """ + __slots__ = ( + "config", + "last_block", + "last_shift", + "last_shifts", + "last_sigma", + "last_warned", + "window_args", + ) + + def __init__(self, config): + self.config = config + self.last_warned = None + self.reset() + + def reset(self): + """#### Reset the state.""" + self.window_args = None + self.last_sigma = None + self.last_block = None + self.last_shift = None + self.last_shifts = {} + + @property + def pretty_last_block(self) -> str: + """#### Get a pretty string representation of the last block. + + #### Returns: + - `str`: The pretty string representation of the last block. + """ + if self.last_block is None: + return "unknown" + bt, bnum = self.last_block + attstr = "" if not self.config.force_apply_attn2 else "attn2." + btstr = ("in", "mid", "out")[bt] + return f"{attstr}{btstr}.{bnum}" + + def maybe_warning(self, s): + """#### Log a warning if necessary. + + #### Args: + - `s` (str): The warning message. + """ + if self.config.silent: + return + now = time() + if ( + self.config.verbose >= 2 + or self.last_warned is None + or now - self.last_warned >= DEFAULT_WARN_INTERVAL + ): + logger.warning( + f"** jankhidiffusion: MSW-MSA attention({self.pretty_last_block}): {s}", + ) + self.last_warned = now + + def __repr__(self): + """#### Get a string representation of the state. + + #### Returns: + - `str`: The string representation of the state. + """ + return f"" + + +class ApplyMSWMSAAttention(metaclass=IntegratedNode): + """#### Class for applying MSW-MSA attention.""" + RETURN_TYPES = ("MODEL",) + OUTPUT_TOOLTIPS = ("Model patched with the MSW-MSA attention effect.",) + FUNCTION = "patch" + CATEGORY = "model_patches/unet" + DESCRIPTION = "This node applies an attention patch which _may_ slightly improve quality especially when generating at high resolutions. It is a large performance increase on SD1.x, may improve performance on SDXL. This is the advanced version of the node with more parameters, use ApplyMSWMSAAttentionSimple if this seems too complex. NOTE: Only supports SD1.x, SD2.x and SDXL." + + @classmethod + def INPUT_TYPES(cls): + """#### Get the input types for the class. + + #### Returns: + - `Dict[str, Any]`: The input types. + """ + return { + "required": { + "input_blocks": ( + "STRING", + { + "default": "1,2", + "tooltip": "Comma-separated list of input blocks to patch. Default is for SD1.x, you can try 4,5 for SDXL", + }, + ), + "middle_blocks": ( + "STRING", + { + "default": "", + "tooltip": "Comma-separated list of middle blocks to patch. Generally not recommended.", + }, + ), + "output_blocks": ( + "STRING", + { + "default": "9,10,11", + "tooltip": "Comma-separated list of output blocks to patch. Default is for SD1.x, you can try 3,4,5 for SDXL", + }, + ), + "time_mode": ( + tuple(str(val) for val in TimeMode), + { + "default": "percent", + "tooltip": "Time mode controls how to interpret the values in start_time and end_time.", + }, + ), + "start_time": ( + "FLOAT", + { + "default": 0.0, + "min": 0.0, + "max": 999.0, + "round": False, + "step": 0.01, + "tooltip": "Time the MSW-MSA attention effect starts applying - value is inclusive.", + }, + ), + "end_time": ( + "FLOAT", + { + "default": 1.0, + "min": 0.0, + "max": 999.0, + "round": False, + "step": 0.01, + "tooltip": "Time the MSW-MSA attention effect ends - value is inclusive.", + }, + ), + "model": ( + "MODEL", + { + "tooltip": "Model to patch with the MSW-MSA attention effect.", + }, + ), + }, + "optional": { + "yaml_parameters": ( + "STRING", + { + "tooltip": "Allows specifying custom parameters via YAML. You can also override any of the normal parameters by key. This input can be converted into a multiline text widget. See main README for possible options. Note: When specifying paramaters this way, there is very little error checking.", + "dynamicPrompts": False, + "multiline": True, + "defaultInput": True, + }, + ), + }, + } + + # reference: https://github.com/microsoft/Swin-Transformer + # Window functions adapted from https://github.com/megvii-research/HiDiffusion + @staticmethod + def window_partition( + x: torch.Tensor, + state: State, + window_index: int, + ) -> torch.Tensor: + """#### Partition a tensor into windows. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `state` (State): The state object. + - `window_index` (int): The window index. + + #### Returns: + - `torch.Tensor`: The partitioned tensor. + """ + config = state.config + scale_mode = config.scale_mode + x = config.maybe_multiply(x, config.pre_window_multiplier) + window_size, shift_size, height, width = state.window_args[window_index] + do_rescale = (height % 2 + width % 2) != 0 + if do_rescale: + if scale_mode == "skip": + state.maybe_warning( + "Incompatible latent size - skipping MSW-MSA attention.", + ) + return x + if scale_mode == "disabled": + state.maybe_warning( + "Incompatible latent size - trying to proceed anyway. This may result in an error.", + ) + do_rescale = False + else: + state.maybe_warning( + "Incompatible latent size - applying scaling workaround. Note: This may reduce quality - use resolutions that are multiples of 64 when possible.", + ) + batch, _features, channels = x.shape + wheight, wwidth = window_size + x = x.view(batch, height, width, channels) + if do_rescale: + x = ( + scale_samples( + x.permute(0, 3, 1, 2).contiguous(), + wwidth * 2, + wheight * 2, + mode=scale_mode, + sigma=state.last_sigma, + ) + .permute(0, 2, 3, 1) + .contiguous() + ) + if shift_size.sum > 0: + x = torch.roll(x, shifts=-shift_size, dims=(1, 2)) + x = x.view(batch, 2, wheight, 2, wwidth, channels) + windows = ( + x.permute(0, 1, 3, 2, 4, 5) + .contiguous() + .view(-1, window_size.height, window_size.width, channels) + ) + return config.maybe_multiply( + windows.view(-1, window_size.sum, channels), + config.post_window_multiplier, + ) + + @staticmethod + def window_reverse( + windows: torch.Tensor, + state: State, + window_index: int = 0, + ) -> torch.Tensor: + """#### Reverse the window partitioning of a tensor. + + #### Args: + - `windows` (torch.Tensor): The input windows tensor. + - `state` (State): The state object. + - `window_index` (int, optional): The window index. Defaults to 0. + + #### Returns: + - `torch.Tensor`: The reversed tensor. + """ + config = state.config + windows = config.maybe_multiply(windows, config.pre_window_reverse_multiplier) + window_size, shift_size, height, width = state.window_args[window_index] + do_rescale = (height % 2 + width % 2) != 0 + if do_rescale: + if config.scale_mode == "skip": + return windows + if config.scale_mode == "disabled": + do_rescale = False + batch, _features, channels = windows.shape + wheight, wwidth = window_size + windows = windows.view(-1, wheight, wwidth, channels) + batch = int(windows.shape[0] / 4) + x = windows.view(batch, 2, 2, wheight, wwidth, -1) + x = ( + x.permute(0, 1, 3, 2, 4, 5) + .contiguous() + .view(batch, wheight * 2, wwidth * 2, -1) + ) + if shift_size.sum > 0: + x = torch.roll(x, shifts=shift_size, dims=(1, 2)) + if do_rescale: + x = ( + scale_samples( + x.permute(0, 3, 1, 2).contiguous(), + width, + height, + mode=config.reverse_scale_mode, + sigma=state.last_sigma, + ) + .permute(0, 2, 3, 1) + .contiguous() + ) + return config.maybe_multiply( + x.view(batch, height * width, channels), + config.post_window_reverse_multiplier, + ) + + @staticmethod + def get_window_args( + config: Config, + n: torch.Tensor, + orig_shape: tuple, + shift: int, + ) -> tuple[WindowSize, ShiftSize, int, int]: + """#### Get window arguments for MSW-MSA attention. + + #### Args: + - `config` (Config): The configuration object. + - `n` (torch.Tensor): The input tensor. + - `orig_shape` (tuple): The original shape of the tensor. + - `shift` (int): The shift value. + + #### Returns: + - `tuple[WindowSize, ShiftSize, int, int]`: The window size, shift size, height, and width. + """ + _batch, features, _channels = n.shape + orig_height, orig_width = orig_shape[-2:] + + width, height = rescale_size( + orig_width, + orig_height, + features, + tolerance=config.rescale_search_tolerance, + ) + # if (height, width) != (orig_height, orig_width): + # print( + # f"\nRESC: features={features}, orig={(orig_height, orig_width)}, new={(height, width)}", + # ) + wheight, wwidth = math.ceil(height / 2), math.ceil(width / 2) + + if shift == 0: + shift_size = ShiftSize(0, 0) + elif shift == 1: + shift_size = ShiftSize(wheight // 4, wwidth // 4) + elif shift == 2: + shift_size = ShiftSize(wheight // 4 * 2, wwidth // 4 * 2) + else: + shift_size = ShiftSize(wheight // 4 * 3, wwidth // 4 * 3) + return (WindowSize(wheight, wwidth), shift_size, height, width) + + @staticmethod + def get_shift( + curr_block: tuple, + state: State, + *, + shift_count=4, + ) -> int: + """#### Get the shift value for MSW-MSA attention. + + #### Args: + - `curr_block` (tuple): The current block. + - `state` (State): The state object. + - `shift_count` (int, optional): The shift count. Defaults to 4. + + #### Returns: + - `int`: The shift value. + """ + mode = state.config.last_shift_mode + strat = state.config.last_shift_strategy + shift = int(torch.rand(1, device="cpu").item() * shift_count) + block_last_shift = state.last_shifts.get(curr_block) + last_shift = state.last_shift + if mode == LastShiftMode.BOTH: + avoid = {block_last_shift, last_shift} + elif mode == LastShiftMode.BLOCK: + avoid = {block_last_shift} + elif mode == LastShiftMode.GLOBAL: + avoid = {last_shift} + else: + avoid = {} + if shift in avoid: + if strat == LastShiftStrategy.DECREMENT: + while shift in avoid: + shift -= 1 + if shift < 0: + shift = shift_count - 1 + elif strat == LastShiftStrategy.RETRY: + while shift in avoid: + shift = int(torch.rand(1, device="cpu").item() * shift_count) + else: + # Increment + while shift in avoid: + shift = (shift + 1) % shift_count + return shift + + @classmethod + def patch( + cls, + *, + model: ModelPatcher.ModelPatcher, + yaml_parameters: str | None = None, + **kwargs: dict[str, Any], + ) -> tuple[ModelPatcher.ModelPatcher]: + """#### Patch the model with MSW-MSA attention. + + #### Args: + - `model` (ModelPatcher.ModelPatcher): The model patcher. + - `yaml_parameters` (str | None, optional): The YAML parameters. Defaults to None. + - `kwargs` (dict[str, Any]): Additional keyword arguments. + + #### Returns: + - `tuple[ModelPatcher.ModelPatcher]`: The patched model. + """ + if yaml_parameters: + import yaml # noqa: PLC0415 + + extra_params = yaml.safe_load(yaml_parameters) + if extra_params is None: + pass + elif not isinstance(extra_params, dict): + raise ValueError( + "MSWMSAAttention: yaml_parameters must either be null or an object", + ) + else: + kwargs |= extra_params + config = Config.build( + ms=model.get_model_object("model_sampling"), + **kwargs, + ) + if not config.use_blocks: + return (model,) + if config.verbose: + logger.info( + f"** jankhidiffusion: MSW-MSA Attention: Using config: {config}", + ) + + model = model.clone() + state = State(config) + + def attn_patch( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + extra_options: dict, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """#### Apply attention patch. + + #### Args: + - `q` (torch.Tensor): The query tensor. + - `k` (torch.Tensor): The key tensor. + - `v` (torch.Tensor): The value tensor. + - `extra_options` (dict): Additional options. + + #### Returns: + - `tuple[torch.Tensor, torch.Tensor, torch.Tensor]`: The patched tensors. + """ + state.window_args = None + sigma = get_sigma(extra_options) + block = extra_options.get("block", ("missing", 0)) + curr_block = block_to_num(*block) + if state.last_sigma is not None and sigma > state.last_sigma: + # logging.warning( + # f"Doing reset: block={block}, sigma={sigma}, state={state}", + # ) + state.reset() + state.last_block = curr_block + state.last_sigma = sigma + if block not in config.use_blocks or not check_time( + sigma, + config.start_sigma, + config.end_sigma, + ): + return q, k, v + orig_shape = extra_options["original_shape"] + # MSW-MSA + shift = cls.get_shift(curr_block, state) + state.last_shifts[curr_block] = state.last_shift = shift + try: + # get_window_args() can fail with ValueError in rescale_size() for some weird resolutions/aspect ratios + # so we catch it here and skip MSW-MSA attention in that case. + state.window_args = tuple( + cls.get_window_args(config, x, orig_shape, shift) + if x is not None + else None + for x in (q, k, v) + ) + attn_parts = (q,) if q is not None and q is k and q is v else (q, k, v) + result = tuple( + cls.window_partition(tensor, state, idx) + if tensor is not None + else None + for idx, tensor in enumerate(attn_parts) + ) + except (RuntimeError, ValueError) as exc: + logger.warning( + f"** jankhidiffusion: Exception applying MSW-MSA attention: Incompatible model patches or bad resolution. Try using resolutions that are multiples of 64 or set scale/reverse_scale modes to something other than disabled. Original exception: {exc}", + ) + state.window_args = None + return q, k, v + return result * 3 if len(result) == 1 else result + + def attn_output_patch(n: torch.Tensor, extra_options: dict) -> torch.Tensor: + """#### Apply attention output patch. + + #### Args: + - `n` (torch.Tensor): The input tensor. + - `extra_options` (dict): Additional options. + + #### Returns: + - `torch.Tensor`: The patched tensor. + """ + if state.window_args is None or state.last_block != block_to_num( + *extra_options.get("block", ("missing", 0)), + ): + state.window_args = None + return n + result = cls.window_reverse(n, state) + state.window_args = None + return result + + if not config.force_apply_attn2: + model.set_model_attn1_patch(attn_patch) + model.set_model_attn1_output_patch(attn_output_patch) + else: + model.set_model_attn2_patch(attn_patch) + model.set_model_attn2_output_patch(attn_output_patch) + return (model,) + + +class ApplyMSWMSAAttentionSimple(metaclass=IntegratedNode): + """Class representing a simplified version of MSW-MSA Attention.""" + RETURN_TYPES = ("MODEL",) + OUTPUT_TOOLTIPS = ("Model patched with the MSW-MSA attention effect.",) + FUNCTION = "go" + CATEGORY = "model_patches/unet" + DESCRIPTION = "This node applies an attention patch which _may_ slightly improve quality especially when generating at high resolutions. It is a large performance increase on SD1.x, may improve performance on SDXL. This is the simplified version of the node with less parameters. Use ApplyMSWMSAAttention if you require more control. NOTE: Only supports SD1.x, SD2.x and SDXL." + + @classmethod + def INPUT_TYPES(cls) -> dict: + """#### Get input types for the class. + + #### Returns: + - `dict`: The input types. + """ + return { + "required": { + "model_type": ( + ("auto", "SD15", "SDXL"), + { + "tooltip": "Model type being patched. Generally safe to leave on auto. Choose SD15 for SD 1.4, SD 2.x.", + }, + ), + "model": ( + "MODEL", + { + "tooltip": "Model to patch with the MSW-MSA attention effect.", + }, + ), + }, + } + + @classmethod + def go( + cls, + model_type: str | ModelType, + model: ModelPatcher.ModelPatcher, + ) -> tuple[ModelPatcher.ModelPatcher]: + """#### Apply the MSW-MSA attention patch. + + #### Args: + - `model_type` (str | ModelType): The model type. + - `model` (ModelPatcher.ModelPatcher): The model patcher. + + #### Returns: + - `tuple[ModelPatcher.ModelPatcher]`: The patched model. + """ + if model_type == "auto": + guessed_model_type = guess_model_type(model) + if guessed_model_type not in SIMPLE_PRESETS: + raise RuntimeError("Unable to guess model type") + model_type = guessed_model_type + else: + model_type = ModelType(model_type) + preset = SIMPLE_PRESETS.get(model_type) + if preset is None: + errstr = f"Unknown model type {model_type!s}" + raise ValueError(errstr) + logger.info( + f"** ApplyMSWMSAAttentionSimple: Using preset {model_type!s}: in/mid/out blocks [{preset.pretty_blocks}], start/end percent {preset.start_time:.2}/{preset.end_time:.2}", + ) + return ApplyMSWMSAAttention.patch(model=model, **preset.as_dict) + + __all__ = ("ApplyMSWMSAAttention", "ApplyMSWMSAAttentionSimple") \ No newline at end of file diff --git a/modules/hidiffusion/utils.py b/modules/hidiffusion/utils.py index c2a69103d566e4e4d6a0f51f6d5ffae8039770ef..e6130042146e35c8b979d1d3109cce0779d3a27f 100644 --- a/modules/hidiffusion/utils.py +++ b/modules/hidiffusion/utils.py @@ -1,521 +1,521 @@ -from __future__ import annotations - -import contextlib -import importlib -import itertools -import logging -import math -import sys -from functools import partial -from typing import TYPE_CHECKING, Callable, NamedTuple -from modules.Utilities import Latent, upscale - -import torch.nn.functional as torchf - -if TYPE_CHECKING: - from collections.abc import Sequence - from types import ModuleType - -try: - from enum import StrEnum -except ImportError: - # Compatibility workaround for pre-3.11 Python versions. - from enum import Enum - - class StrEnum(str, Enum): - @staticmethod - def _generate_next_value_(name: str, *_unused: list) -> str: - return name.lower() - - def __str__(self) -> str: - return str(self.value) - - -logger = logging.getLogger(__name__) - -UPSCALE_METHODS = ("bicubic", "bislerp", "bilinear", "nearest-exact", "nearest", "area") - - -class TimeMode(StrEnum): - PERCENT = "percent" - TIMESTEP = "timestep" - SIGMA = "sigma" - - -class ModelType(StrEnum): - SD15 = "SD15" - SDXL = "SDXL" - - -def parse_blocks(name: str, val: str | Sequence[int]) -> set[tuple[str, int]]: - """#### Parse block definitions. - - #### Args: - - `name` (str): The name of the block. - - `val` (Union[str, Sequence[int]]): The block values. - - #### Returns: - - `set[tuple[str, int]]`: The parsed blocks. - """ - if isinstance(val, (tuple, list)): - # Handle a sequence passed in via YAML parameters. - if not all(isinstance(item, int) and item >= 0 for item in val): - raise ValueError( - "Bad blocks definition, must be comma separated string or sequence of positive int", - ) - return {(name, item) for item in val} - vals = (rawval.strip() for rawval in val.split(",")) - return {(name, int(val.strip())) for val in vals if val} - - -def convert_time( - ms: object, - time_mode: TimeMode, - start_time: float, - end_time: float, -) -> tuple[float, float]: - """#### Convert time based on the mode. - - #### Args: - - `ms` (Any): The time object. - - `time_mode` (TimeMode): The time mode. - - `start_time` (float): The start time. - - `end_time` (float): The end time. - - #### Returns: - - `Tuple[float, float]`: The converted start and end times. - """ - if time_mode == TimeMode.SIGMA: - return (start_time, end_time) - if time_mode == TimeMode.TIMESTEP: - start_time = 1.0 - (start_time / 999.0) - end_time = 1.0 - (end_time / 999.0) - else: - if start_time > 1.0 or start_time < 0.0: - raise ValueError( - "invalid value for start percent", - ) - if end_time > 1.0 or end_time < 0.0: - raise ValueError( - "invalid value for end percent", - ) - return ( - round(ms.percent_to_sigma(start_time), 4), - round(ms.percent_to_sigma(end_time), 4), - ) - raise ValueError("invalid time mode") - - -def get_sigma(options: dict, key: str = "sigmas") -> float | None: - """#### Get the sigma value from options. - - #### Args: - - `options` (dict): The options dictionary. - - `key` (str, optional): The key to look for. Defaults to "sigmas". - - #### Returns: - - `Optional[float]`: The sigma value if found, otherwise None. - """ - if not isinstance(options, dict): - return None - sigmas = options.get(key) - if sigmas is None: - return None - if isinstance(sigmas, float): - return sigmas - return sigmas.detach().cpu().max().item() - - -def check_time(time_arg: dict | float, start_sigma: float, end_sigma: float) -> bool: - """#### Check if the time is within the sigma range. - - #### Args: - - `time_arg` (Union[dict, float]): The time argument. - - `start_sigma` (float): The start sigma. - - `end_sigma` (float): The end sigma. - - #### Returns: - - `bool`: Whether the time is within the range. - """ - sigma = get_sigma(time_arg) if not isinstance(time_arg, float) else time_arg - if sigma is None: - return False - return sigma <= start_sigma and sigma >= end_sigma - - -__block_to_num_map = {"input": 0, "middle": 1, "output": 2} - - -def block_to_num(block_type: str, block_id: int) -> tuple[int, int]: - """#### Convert block type and id to numerical representation. - - #### Args: - - `block_type` (str): The block type. - - `block_id` (int): The block id. - - #### Returns: - - `Tuple[int, int]`: The numerical representation of the block. - """ - type_id = __block_to_num_map.get(block_type) - if type_id is None: - errstr = f"Got unexpected block type {block_type}!" - raise ValueError(errstr) - return (type_id, block_id) - - -# Naive and totally inaccurate way to factorize target_res into rescaled integer width/height -def rescale_size( - width: int, - height: int, - target_res: int, - *, - tolerance=1, -) -> tuple[int, int]: - """#### Rescale size to fit target resolution. - - #### Args: - - `width` (int): The width. - - `height` (int): The height. - - `target_res` (int): The target resolution. - - `tolerance` (int, optional): The tolerance. Defaults to 1. - - #### Returns: - - `Tuple[int, int]`: The rescaled width and height. - """ - tolerance = min(target_res, tolerance) - - def get_neighbors(num: float): - if num < 1: - return None - numi = int(num) - return tuple( - numi + adj - for adj in sorted( - range( - -min(numi - 1, tolerance), - tolerance + 1 + math.ceil(num - numi), - ), - key=abs, - ) - ) - - scale = math.sqrt(height * width / target_res) - height_scaled, width_scaled = height / scale, width / scale - height_rounded = get_neighbors(height_scaled) - width_rounded = get_neighbors(width_scaled) - for h, w in itertools.zip_longest(height_rounded, width_rounded): - h_adj = target_res / w if w is not None else 0.1 - if h_adj % 1 == 0: - return (w, int(h_adj)) - if h is None: - continue - w_adj = target_res / h - if w_adj % 1 == 0: - return (int(w_adj), h) - msg = f"Can't rescale {width} and {height} to fit {target_res}" - raise ValueError(msg) - - -def guess_model_type(model: object) -> ModelType | None: - """#### Guess the model type. - - #### Args: - - `model` (object): The model object. - - #### Returns: - - `Optional[ModelType]`: The guessed model type. - """ - latent_format = model.get_model_object("latent_format") - if isinstance(latent_format, Latent.SD15): - return ModelType.SD15 - return None - - -def sigma_to_pct(ms, sigma): - """#### Convert sigma to percentage. - - #### Args: - - `ms` (Any): The time object. - - `sigma` (float): The sigma value. - - #### Returns: - - `float`: The percentage. - """ - return (1.0 - (ms.timestep(sigma).detach().cpu() / 999.0)).clamp(0.0, 1.0).item() - - -def fade_scale( - pct, - start_pct=0.0, - end_pct=1.0, - fade_start=1.0, - fade_cap=0.0, -): - """#### Calculate the fade scale. - - #### Args: - - `pct` (float): The percentage. - - `start_pct` (float, optional): The start percentage. Defaults to 0.0. - - `end_pct` (float, optional): The end percentage. Defaults to 1.0. - - `fade_start` (float, optional): The fade start. Defaults to 1.0. - - `fade_cap` (float, optional): The fade cap. Defaults to 0.0. - - #### Returns: - - `float`: The fade scale. - """ - if not (start_pct <= pct <= end_pct) or start_pct > end_pct: - return 0.0 - if pct < fade_start: - return 1.0 - scaling_pct = 1.0 - ((pct - fade_start) / (end_pct - fade_start)) - return max(fade_cap, scaling_pct) - - -def scale_samples( - samples, - width, - height, - mode="bicubic", - sigma=None, # noqa: ARG001 -): - """#### Scale samples to the specified width and height. - - #### Args: - - `samples` (torch.Tensor): The input samples. - - `width` (int): The target width. - - `height` (int): The target height. - - `mode` (str, optional): The scaling mode. Defaults to "bicubic". - - `sigma` (Optional[float], optional): The sigma value. Defaults to None. - - #### Returns: - - `torch.Tensor`: The scaled samples. - """ - if mode == "bislerp": - return upscale.bislerp(samples, width, height) - return torchf.interpolate(samples, size=(height, width), mode=mode) - - -class Integrations: - """#### Class for managing integrations.""" - class Integration(NamedTuple): - key: str - module_name: str - handler: Callable | None = None - - def __init__(self): - """#### Initialize the Integrations class.""" - self.initialized = False - self.modules = {} - self.init_handlers = [] - self.handlers = [] - - def __getitem__(self, key): - """#### Get a module by key. - - #### Args: - - `key` (str): The key. - - #### Returns: - - `ModuleType`: The module. - """ - return self.modules[key] - - def __contains__(self, key): - """#### Check if a module is in the integrations. - - #### Args: - - `key` (str): The key. - - #### Returns: - - `bool`: Whether the module is in the integrations. - """ - return key in self.modules - - def __getattr__(self, key): - """#### Get a module by attribute. - - #### Args: - - `key` (str): The key. - - #### Returns: - - `Optional[ModuleType]`: The module if found, otherwise None. - """ - return self.modules.get(key) - - @staticmethod - def get_custom_node(name: str) -> ModuleType | None: - """#### Get a custom node by name. - - #### Args: - - `name` (str): The name of the custom node. - - #### Returns: - - `Optional[ModuleType]`: The custom node if found, otherwise None. - """ - module_key = f"custom_nodes.{name}" - with contextlib.suppress(StopIteration): - spec = importlib.util.find_spec(module_key) - if spec is None: - return None - return next( - v - for v in sys.modules.copy().values() - if hasattr(v, "__spec__") - and v.__spec__ is not None - and v.__spec__.origin == spec.origin - ) - return None - - def register_init_handler(self, handler): - """#### Register an initialization handler. - - #### Args: - - `handler` (Callable): The handler. - """ - self.init_handlers.append(handler) - - def register_integration(self, key: str, module_name: str, handler=None) -> None: - """#### Register an integration. - - #### Args: - - `key` (str): The key. - - `module_name` (str): The module name. - - `handler` (Optional[Callable], optional): The handler. Defaults to None. - """ - if self.initialized: - raise ValueError( - "Internal error: Cannot register integration after initialization", - ) - if any(item[0] == key or item[1] == module_name for item in self.handlers): - errstr = ( - f"Module {module_name} ({key}) already in integration handlers list!" - ) - raise ValueError(errstr) - self.handlers.append(self.Integration(key, module_name, handler)) - - def initialize(self) -> None: - """#### Initialize the integrations.""" - if self.initialized: - return - self.initialized = True - for ih in self.handlers: - module = self.get_custom_node(ih.module_name) - if module is None: - continue - if ih.handler is not None: - module = ih.handler(module) - if module is not None: - self.modules[ih.key] = module - - for init_handler in self.init_handlers: - init_handler(self) - - -class JHDIntegrations(Integrations): - """#### Class for managing JHD integrations.""" - def __init__(self, *args: list, **kwargs: dict): - """#### Initialize the JHDIntegrations class.""" - super().__init__(*args, **kwargs) - self.register_integration("bleh", "ComfyUI-bleh", self.bleh_integration) - self.register_integration("freeu_advanced", "FreeU_Advanced") - - @classmethod - def bleh_integration(cls, bleh: ModuleType) -> ModuleType | None: - """#### Integrate with BLEH. - - #### Args: - - `bleh` (ModuleType): The BLEH module. - - #### Returns: - - `Optional[ModuleType]`: The integrated BLEH module if successful, otherwise None. - """ - bleh_version = getattr(bleh, "BLEH_VERSION", -1) - if bleh_version < 0: - return None - return bleh - - -MODULES = JHDIntegrations() - - -class IntegratedNode(type): - """#### Metaclass for integrated nodes.""" - @staticmethod - def wrap_INPUT_TYPES(orig_method: Callable, *args: list, **kwargs: dict) -> dict: - """#### Wrap the INPUT_TYPES method to initialize modules. - - #### Args: - - `orig_method` (Callable): The original method. - - `args` (list): The arguments. - - `kwargs` (dict): The keyword arguments. - - #### Returns: - - `dict`: The result of the original method. - """ - MODULES.initialize() - return orig_method(*args, **kwargs) - - def __new__(cls: type, name: str, bases: tuple, attrs: dict) -> object: - """#### Create a new instance of the class. - - #### Args: - - `name` (str): The name of the class. - - `bases` (tuple): The base classes. - - `attrs` (dict): The attributes. - - #### Returns: - - `object`: The new instance. - """ - obj = type.__new__(cls, name, bases, attrs) - if hasattr(obj, "INPUT_TYPES"): - obj.INPUT_TYPES = partial(cls.wrap_INPUT_TYPES, obj.INPUT_TYPES) - return obj - - -def init_integrations(integrations) -> None: - """#### Initialize integrations. - - #### Args: - - `integrations` (Integrations): The integrations object. - """ - global scale_samples, UPSCALE_METHODS # noqa: PLW0603 - ext_bleh = integrations.bleh - if ext_bleh is None: - return - bleh_latentutils = getattr(ext_bleh.py, "latent_utils", None) - if bleh_latentutils is None: - return - bleh_version = getattr(ext_bleh, "BLEH_VERSION", -1) - UPSCALE_METHODS = bleh_latentutils.UPSCALE_METHODS - if bleh_version >= 0: - scale_samples = bleh_latentutils.scale_samples - return - - def scale_samples_wrapped(*args: list, sigma=None, **kwargs: dict): # noqa: ARG001 - """#### Wrap the scale_samples method. - - #### Args: - - `args` (list): The arguments. - - `sigma` (Optional[float], optional): The sigma value. Defaults to None. - - `kwargs` (dict): The keyword arguments. - - #### Returns: - - `Any`: The result of the scale_samples method. - """ - return bleh_latentutils.scale_samples(*args, **kwargs) - - scale_samples = scale_samples_wrapped - - -MODULES.register_init_handler(init_integrations) - -__all__ = ( - "UPSCALE_METHODS", - "check_time", - "convert_time", - "get_sigma", - "guess_model_type", - "parse_blocks", - "rescale_size", - "scale_samples", +from __future__ import annotations + +import contextlib +import importlib +import itertools +import logging +import math +import sys +from functools import partial +from typing import TYPE_CHECKING, Callable, NamedTuple +from modules.Utilities import Latent, upscale + +import torch.nn.functional as torchf + +if TYPE_CHECKING: + from collections.abc import Sequence + from types import ModuleType + +try: + from enum import StrEnum +except ImportError: + # Compatibility workaround for pre-3.11 Python versions. + from enum import Enum + + class StrEnum(str, Enum): + @staticmethod + def _generate_next_value_(name: str, *_unused: list) -> str: + return name.lower() + + def __str__(self) -> str: + return str(self.value) + + +logger = logging.getLogger(__name__) + +UPSCALE_METHODS = ("bicubic", "bislerp", "bilinear", "nearest-exact", "nearest", "area") + + +class TimeMode(StrEnum): + PERCENT = "percent" + TIMESTEP = "timestep" + SIGMA = "sigma" + + +class ModelType(StrEnum): + SD15 = "SD15" + SDXL = "SDXL" + + +def parse_blocks(name: str, val: str | Sequence[int]) -> set[tuple[str, int]]: + """#### Parse block definitions. + + #### Args: + - `name` (str): The name of the block. + - `val` (Union[str, Sequence[int]]): The block values. + + #### Returns: + - `set[tuple[str, int]]`: The parsed blocks. + """ + if isinstance(val, (tuple, list)): + # Handle a sequence passed in via YAML parameters. + if not all(isinstance(item, int) and item >= 0 for item in val): + raise ValueError( + "Bad blocks definition, must be comma separated string or sequence of positive int", + ) + return {(name, item) for item in val} + vals = (rawval.strip() for rawval in val.split(",")) + return {(name, int(val.strip())) for val in vals if val} + + +def convert_time( + ms: object, + time_mode: TimeMode, + start_time: float, + end_time: float, +) -> tuple[float, float]: + """#### Convert time based on the mode. + + #### Args: + - `ms` (Any): The time object. + - `time_mode` (TimeMode): The time mode. + - `start_time` (float): The start time. + - `end_time` (float): The end time. + + #### Returns: + - `Tuple[float, float]`: The converted start and end times. + """ + if time_mode == TimeMode.SIGMA: + return (start_time, end_time) + if time_mode == TimeMode.TIMESTEP: + start_time = 1.0 - (start_time / 999.0) + end_time = 1.0 - (end_time / 999.0) + else: + if start_time > 1.0 or start_time < 0.0: + raise ValueError( + "invalid value for start percent", + ) + if end_time > 1.0 or end_time < 0.0: + raise ValueError( + "invalid value for end percent", + ) + return ( + round(ms.percent_to_sigma(start_time), 4), + round(ms.percent_to_sigma(end_time), 4), + ) + raise ValueError("invalid time mode") + + +def get_sigma(options: dict, key: str = "sigmas") -> float | None: + """#### Get the sigma value from options. + + #### Args: + - `options` (dict): The options dictionary. + - `key` (str, optional): The key to look for. Defaults to "sigmas". + + #### Returns: + - `Optional[float]`: The sigma value if found, otherwise None. + """ + if not isinstance(options, dict): + return None + sigmas = options.get(key) + if sigmas is None: + return None + if isinstance(sigmas, float): + return sigmas + return sigmas.detach().cpu().max().item() + + +def check_time(time_arg: dict | float, start_sigma: float, end_sigma: float) -> bool: + """#### Check if the time is within the sigma range. + + #### Args: + - `time_arg` (Union[dict, float]): The time argument. + - `start_sigma` (float): The start sigma. + - `end_sigma` (float): The end sigma. + + #### Returns: + - `bool`: Whether the time is within the range. + """ + sigma = get_sigma(time_arg) if not isinstance(time_arg, float) else time_arg + if sigma is None: + return False + return sigma <= start_sigma and sigma >= end_sigma + + +__block_to_num_map = {"input": 0, "middle": 1, "output": 2} + + +def block_to_num(block_type: str, block_id: int) -> tuple[int, int]: + """#### Convert block type and id to numerical representation. + + #### Args: + - `block_type` (str): The block type. + - `block_id` (int): The block id. + + #### Returns: + - `Tuple[int, int]`: The numerical representation of the block. + """ + type_id = __block_to_num_map.get(block_type) + if type_id is None: + errstr = f"Got unexpected block type {block_type}!" + raise ValueError(errstr) + return (type_id, block_id) + + +# Naive and totally inaccurate way to factorize target_res into rescaled integer width/height +def rescale_size( + width: int, + height: int, + target_res: int, + *, + tolerance=1, +) -> tuple[int, int]: + """#### Rescale size to fit target resolution. + + #### Args: + - `width` (int): The width. + - `height` (int): The height. + - `target_res` (int): The target resolution. + - `tolerance` (int, optional): The tolerance. Defaults to 1. + + #### Returns: + - `Tuple[int, int]`: The rescaled width and height. + """ + tolerance = min(target_res, tolerance) + + def get_neighbors(num: float): + if num < 1: + return None + numi = int(num) + return tuple( + numi + adj + for adj in sorted( + range( + -min(numi - 1, tolerance), + tolerance + 1 + math.ceil(num - numi), + ), + key=abs, + ) + ) + + scale = math.sqrt(height * width / target_res) + height_scaled, width_scaled = height / scale, width / scale + height_rounded = get_neighbors(height_scaled) + width_rounded = get_neighbors(width_scaled) + for h, w in itertools.zip_longest(height_rounded, width_rounded): + h_adj = target_res / w if w is not None else 0.1 + if h_adj % 1 == 0: + return (w, int(h_adj)) + if h is None: + continue + w_adj = target_res / h + if w_adj % 1 == 0: + return (int(w_adj), h) + msg = f"Can't rescale {width} and {height} to fit {target_res}" + raise ValueError(msg) + + +def guess_model_type(model: object) -> ModelType | None: + """#### Guess the model type. + + #### Args: + - `model` (object): The model object. + + #### Returns: + - `Optional[ModelType]`: The guessed model type. + """ + latent_format = model.get_model_object("latent_format") + if isinstance(latent_format, Latent.SD15): + return ModelType.SD15 + return None + + +def sigma_to_pct(ms, sigma): + """#### Convert sigma to percentage. + + #### Args: + - `ms` (Any): The time object. + - `sigma` (float): The sigma value. + + #### Returns: + - `float`: The percentage. + """ + return (1.0 - (ms.timestep(sigma).detach().cpu() / 999.0)).clamp(0.0, 1.0).item() + + +def fade_scale( + pct, + start_pct=0.0, + end_pct=1.0, + fade_start=1.0, + fade_cap=0.0, +): + """#### Calculate the fade scale. + + #### Args: + - `pct` (float): The percentage. + - `start_pct` (float, optional): The start percentage. Defaults to 0.0. + - `end_pct` (float, optional): The end percentage. Defaults to 1.0. + - `fade_start` (float, optional): The fade start. Defaults to 1.0. + - `fade_cap` (float, optional): The fade cap. Defaults to 0.0. + + #### Returns: + - `float`: The fade scale. + """ + if not (start_pct <= pct <= end_pct) or start_pct > end_pct: + return 0.0 + if pct < fade_start: + return 1.0 + scaling_pct = 1.0 - ((pct - fade_start) / (end_pct - fade_start)) + return max(fade_cap, scaling_pct) + + +def scale_samples( + samples, + width, + height, + mode="bicubic", + sigma=None, # noqa: ARG001 +): + """#### Scale samples to the specified width and height. + + #### Args: + - `samples` (torch.Tensor): The input samples. + - `width` (int): The target width. + - `height` (int): The target height. + - `mode` (str, optional): The scaling mode. Defaults to "bicubic". + - `sigma` (Optional[float], optional): The sigma value. Defaults to None. + + #### Returns: + - `torch.Tensor`: The scaled samples. + """ + if mode == "bislerp": + return upscale.bislerp(samples, width, height) + return torchf.interpolate(samples, size=(height, width), mode=mode) + + +class Integrations: + """#### Class for managing integrations.""" + class Integration(NamedTuple): + key: str + module_name: str + handler: Callable | None = None + + def __init__(self): + """#### Initialize the Integrations class.""" + self.initialized = False + self.modules = {} + self.init_handlers = [] + self.handlers = [] + + def __getitem__(self, key): + """#### Get a module by key. + + #### Args: + - `key` (str): The key. + + #### Returns: + - `ModuleType`: The module. + """ + return self.modules[key] + + def __contains__(self, key): + """#### Check if a module is in the integrations. + + #### Args: + - `key` (str): The key. + + #### Returns: + - `bool`: Whether the module is in the integrations. + """ + return key in self.modules + + def __getattr__(self, key): + """#### Get a module by attribute. + + #### Args: + - `key` (str): The key. + + #### Returns: + - `Optional[ModuleType]`: The module if found, otherwise None. + """ + return self.modules.get(key) + + @staticmethod + def get_custom_node(name: str) -> ModuleType | None: + """#### Get a custom node by name. + + #### Args: + - `name` (str): The name of the custom node. + + #### Returns: + - `Optional[ModuleType]`: The custom node if found, otherwise None. + """ + module_key = f"custom_nodes.{name}" + with contextlib.suppress(StopIteration): + spec = importlib.util.find_spec(module_key) + if spec is None: + return None + return next( + v + for v in sys.modules.copy().values() + if hasattr(v, "__spec__") + and v.__spec__ is not None + and v.__spec__.origin == spec.origin + ) + return None + + def register_init_handler(self, handler): + """#### Register an initialization handler. + + #### Args: + - `handler` (Callable): The handler. + """ + self.init_handlers.append(handler) + + def register_integration(self, key: str, module_name: str, handler=None) -> None: + """#### Register an integration. + + #### Args: + - `key` (str): The key. + - `module_name` (str): The module name. + - `handler` (Optional[Callable], optional): The handler. Defaults to None. + """ + if self.initialized: + raise ValueError( + "Internal error: Cannot register integration after initialization", + ) + if any(item[0] == key or item[1] == module_name for item in self.handlers): + errstr = ( + f"Module {module_name} ({key}) already in integration handlers list!" + ) + raise ValueError(errstr) + self.handlers.append(self.Integration(key, module_name, handler)) + + def initialize(self) -> None: + """#### Initialize the integrations.""" + if self.initialized: + return + self.initialized = True + for ih in self.handlers: + module = self.get_custom_node(ih.module_name) + if module is None: + continue + if ih.handler is not None: + module = ih.handler(module) + if module is not None: + self.modules[ih.key] = module + + for init_handler in self.init_handlers: + init_handler(self) + + +class JHDIntegrations(Integrations): + """#### Class for managing JHD integrations.""" + def __init__(self, *args: list, **kwargs: dict): + """#### Initialize the JHDIntegrations class.""" + super().__init__(*args, **kwargs) + self.register_integration("bleh", "ComfyUI-bleh", self.bleh_integration) + self.register_integration("freeu_advanced", "FreeU_Advanced") + + @classmethod + def bleh_integration(cls, bleh: ModuleType) -> ModuleType | None: + """#### Integrate with BLEH. + + #### Args: + - `bleh` (ModuleType): The BLEH module. + + #### Returns: + - `Optional[ModuleType]`: The integrated BLEH module if successful, otherwise None. + """ + bleh_version = getattr(bleh, "BLEH_VERSION", -1) + if bleh_version < 0: + return None + return bleh + + +MODULES = JHDIntegrations() + + +class IntegratedNode(type): + """#### Metaclass for integrated nodes.""" + @staticmethod + def wrap_INPUT_TYPES(orig_method: Callable, *args: list, **kwargs: dict) -> dict: + """#### Wrap the INPUT_TYPES method to initialize modules. + + #### Args: + - `orig_method` (Callable): The original method. + - `args` (list): The arguments. + - `kwargs` (dict): The keyword arguments. + + #### Returns: + - `dict`: The result of the original method. + """ + MODULES.initialize() + return orig_method(*args, **kwargs) + + def __new__(cls: type, name: str, bases: tuple, attrs: dict) -> object: + """#### Create a new instance of the class. + + #### Args: + - `name` (str): The name of the class. + - `bases` (tuple): The base classes. + - `attrs` (dict): The attributes. + + #### Returns: + - `object`: The new instance. + """ + obj = type.__new__(cls, name, bases, attrs) + if hasattr(obj, "INPUT_TYPES"): + obj.INPUT_TYPES = partial(cls.wrap_INPUT_TYPES, obj.INPUT_TYPES) + return obj + + +def init_integrations(integrations) -> None: + """#### Initialize integrations. + + #### Args: + - `integrations` (Integrations): The integrations object. + """ + global scale_samples, UPSCALE_METHODS # noqa: PLW0603 + ext_bleh = integrations.bleh + if ext_bleh is None: + return + bleh_latentutils = getattr(ext_bleh.py, "latent_utils", None) + if bleh_latentutils is None: + return + bleh_version = getattr(ext_bleh, "BLEH_VERSION", -1) + UPSCALE_METHODS = bleh_latentutils.UPSCALE_METHODS + if bleh_version >= 0: + scale_samples = bleh_latentutils.scale_samples + return + + def scale_samples_wrapped(*args: list, sigma=None, **kwargs: dict): # noqa: ARG001 + """#### Wrap the scale_samples method. + + #### Args: + - `args` (list): The arguments. + - `sigma` (Optional[float], optional): The sigma value. Defaults to None. + - `kwargs` (dict): The keyword arguments. + + #### Returns: + - `Any`: The result of the scale_samples method. + """ + return bleh_latentutils.scale_samples(*args, **kwargs) + + scale_samples = scale_samples_wrapped + + +MODULES.register_init_handler(init_integrations) + +__all__ = ( + "UPSCALE_METHODS", + "check_time", + "convert_time", + "get_sigma", + "guess_model_type", + "parse_blocks", + "rescale_size", + "scale_samples", ) \ No newline at end of file diff --git a/modules/sample/CFG.py b/modules/sample/CFG.py index 54c867a68c1742452d6df40af97b5c27fb5b71be..610dc1240f59970302c71ea76294cd2d79f2f853 100644 --- a/modules/sample/CFG.py +++ b/modules/sample/CFG.py @@ -1,318 +1,318 @@ -import math -import torch -from modules.cond import cond, cond_util - - -def cfg_function( - model: torch.nn.Module, - cond_pred: torch.Tensor, - uncond_pred: torch.Tensor, - cond_scale: float, - x: torch.Tensor, - timestep: int, - model_options: dict = {}, - cond: torch.Tensor = None, - uncond: torch.Tensor = None, -) -> torch.Tensor: - """#### Apply classifier-free guidance (CFG) to the model predictions. - - #### Args: - - `model` (torch.nn.Module): The model. - - `cond_pred` (torch.Tensor): The conditioned prediction. - - `uncond_pred` (torch.Tensor): The unconditioned prediction. - - `cond_scale` (float): The CFG scale. - - `x` (torch.Tensor): The input tensor. - - `timestep` (int): The current timestep. - - `model_options` (dict, optional): Additional model options. Defaults to {}. - - `cond` (torch.Tensor, optional): The conditioned tensor. Defaults to None. - - `uncond` (torch.Tensor, optional): The unconditioned tensor. Defaults to None. - - #### Returns: - - `torch.Tensor`: The CFG result. - """ - if "sampler_cfg_function" in model_options: - args = { - "cond": x - cond_pred, - "uncond": x - uncond_pred, - "cond_scale": cond_scale, - "timestep": timestep, - "input": x, - "sigma": timestep, - "cond_denoised": cond_pred, - "uncond_denoised": uncond_pred, - "model": model, - "model_options": model_options, - } - cfg_result = x - model_options["sampler_cfg_function"](args) - else: - cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale - - for fn in model_options.get("sampler_post_cfg_function", []): - args = { - "denoised": cfg_result, - "cond": cond, - "uncond": uncond, - "model": model, - "uncond_denoised": uncond_pred, - "cond_denoised": cond_pred, - "sigma": timestep, - "model_options": model_options, - "input": x, - } - cfg_result = fn(args) - - return cfg_result - - -def sampling_function( - model: torch.nn.Module, - x: torch.Tensor, - timestep: int, - uncond: torch.Tensor, - condo: torch.Tensor, - cond_scale: float, - model_options: dict = {}, - seed: int = None, -) -> torch.Tensor: - """#### Perform sampling with CFG. - - #### Args: - - `model` (torch.nn.Module): The model. - - `x` (torch.Tensor): The input tensor. - - `timestep` (int): The current timestep. - - `uncond` (torch.Tensor): The unconditioned tensor. - - `condo` (torch.Tensor): The conditioned tensor. - - `cond_scale` (float): The CFG scale. - - `model_options` (dict, optional): Additional model options. Defaults to {}. - - `seed` (int, optional): The random seed. Defaults to None. - - #### Returns: - - `torch.Tensor`: The sampled tensor. - """ - if ( - math.isclose(cond_scale, 1.0) - and model_options.get("disable_cfg1_optimization", False) is False - ): - uncond_ = None - else: - uncond_ = uncond - - conds = [condo, uncond_] - out = cond.calc_cond_batch(model, conds, x, timestep, model_options) - - for fn in model_options.get("sampler_pre_cfg_function", []): - args = { - "conds": conds, - "conds_out": out, - "cond_scale": cond_scale, - "timestep": timestep, - "input": x, - "sigma": timestep, - "model": model, - "model_options": model_options, - } - out = fn(args) - - return cfg_function( - model, - out[0], - out[1], - cond_scale, - x, - timestep, - model_options=model_options, - cond=condo, - uncond=uncond_, - ) - - -class CFGGuider: - """#### Class for guiding the sampling process with CFG.""" - def __init__(self, model_patcher, flux=False): - """#### Initialize the CFGGuider. - - #### Args: - - `model_patcher` (object): The model patcher. - """ - self.model_patcher = model_patcher - self.model_options = model_patcher.model_options - self.original_conds = {} - self.cfg = 1.0 - self.flux = flux - - def set_conds(self, positive, negative): - """#### Set the conditions for CFG. - - #### Args: - - `positive` (torch.Tensor): The positive condition. - - `negative` (torch.Tensor): The negative condition. - """ - self.inner_set_conds({"positive": positive, "negative": negative}) - - def set_cfg(self, cfg): - """#### Set the CFG scale. - - #### Args: - - `cfg` (float): The CFG scale. - """ - self.cfg = cfg - - def inner_set_conds(self, conds): - """#### Set the internal conditions. - - #### Args: - - `conds` (dict): The conditions. - """ - for k in conds: - self.original_conds[k] = cond.convert_cond(conds[k]) - - def __call__(self, *args, **kwargs): - """#### Call the CFGGuider to predict noise. - - #### Returns: - - `torch.Tensor`: The predicted noise. - """ - return self.predict_noise(*args, **kwargs) - - def predict_noise(self, x, timestep, model_options={}, seed=None): - """#### Predict noise using CFG. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `timestep` (int): The current timestep. - - `model_options` (dict, optional): Additional model options. Defaults to {}. - - `seed` (int, optional): The random seed. Defaults to None. - - #### Returns: - - `torch.Tensor`: The predicted noise. - """ - return sampling_function( - self.inner_model, - x, - timestep, - self.conds.get("negative", None), - self.conds.get("positive", None), - self.cfg, - model_options=model_options, - seed=seed, - ) - - def inner_sample( - self, - noise, - latent_image, - device, - sampler, - sigmas, - denoise_mask, - callback, - disable_pbar, - seed, - pipeline=False, - ): - """#### Perform the inner sampling process. - - #### Args: - - `noise` (torch.Tensor): The noise tensor. - - `latent_image` (torch.Tensor): The latent image tensor. - - `device` (torch.device): The device to use. - - `sampler` (object): The sampler object. - - `sigmas` (torch.Tensor): The sigmas tensor. - - `denoise_mask` (torch.Tensor): The denoise mask tensor. - - `callback` (callable): The callback function. - - `disable_pbar` (bool): Whether to disable the progress bar. - - `seed` (int): The random seed. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - #### Returns: - - `torch.Tensor`: The sampled tensor. - """ - if ( - latent_image is not None and torch.count_nonzero(latent_image) > 0 - ): # Don't shift the empty latent image. - latent_image = self.inner_model.process_latent_in(latent_image) - - self.conds = cond.process_conds( - self.inner_model, - noise, - self.conds, - device, - latent_image, - denoise_mask, - seed, - ) - - extra_args = {"model_options": self.model_options, "seed": seed} - - samples = sampler.sample( - self, - sigmas, - extra_args, - callback, - noise, - latent_image, - denoise_mask, - disable_pbar, - pipeline=pipeline, - ) - return self.inner_model.process_latent_out(samples.to(torch.float32)) - - def sample( - self, - noise, - latent_image, - sampler, - sigmas, - denoise_mask=None, - callback=None, - disable_pbar=False, - seed=None, - pipeline=False, - ): - """#### Perform the sampling process with CFG. - - #### Args: - - `noise` (torch.Tensor): The noise tensor. - - `latent_image` (torch.Tensor): The latent image tensor. - - `sampler` (object): The sampler object. - - `sigmas` (torch.Tensor): The sigmas tensor. - - `denoise_mask` (torch.Tensor, optional): The denoise mask tensor. Defaults to None. - - `callback` (callable, optional): The callback function. Defaults to None. - - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. - - `seed` (int, optional): The random seed. Defaults to None. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - #### Returns: - - `torch.Tensor`: The sampled tensor. - """ - self.conds = {} - for k in self.original_conds: - self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) - - self.inner_model, self.conds, self.loaded_models = cond_util.prepare_sampling( - self.model_patcher, noise.shape, self.conds, flux_enabled=self.flux - ) - device = self.model_patcher.load_device - - noise = noise.to(device) - latent_image = latent_image.to(device) - sigmas = sigmas.to(device) - - output = self.inner_sample( - noise, - latent_image, - device, - sampler, - sigmas, - denoise_mask, - callback, - disable_pbar, - seed, - pipeline=pipeline, - ) - - cond_util.cleanup_models(self.conds, self.loaded_models) - del self.inner_model - del self.conds - del self.loaded_models +import math +import torch +from modules.cond import cond, cond_util + + +def cfg_function( + model: torch.nn.Module, + cond_pred: torch.Tensor, + uncond_pred: torch.Tensor, + cond_scale: float, + x: torch.Tensor, + timestep: int, + model_options: dict = {}, + cond: torch.Tensor = None, + uncond: torch.Tensor = None, +) -> torch.Tensor: + """#### Apply classifier-free guidance (CFG) to the model predictions. + + #### Args: + - `model` (torch.nn.Module): The model. + - `cond_pred` (torch.Tensor): The conditioned prediction. + - `uncond_pred` (torch.Tensor): The unconditioned prediction. + - `cond_scale` (float): The CFG scale. + - `x` (torch.Tensor): The input tensor. + - `timestep` (int): The current timestep. + - `model_options` (dict, optional): Additional model options. Defaults to {}. + - `cond` (torch.Tensor, optional): The conditioned tensor. Defaults to None. + - `uncond` (torch.Tensor, optional): The unconditioned tensor. Defaults to None. + + #### Returns: + - `torch.Tensor`: The CFG result. + """ + if "sampler_cfg_function" in model_options: + args = { + "cond": x - cond_pred, + "uncond": x - uncond_pred, + "cond_scale": cond_scale, + "timestep": timestep, + "input": x, + "sigma": timestep, + "cond_denoised": cond_pred, + "uncond_denoised": uncond_pred, + "model": model, + "model_options": model_options, + } + cfg_result = x - model_options["sampler_cfg_function"](args) + else: + cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale + + for fn in model_options.get("sampler_post_cfg_function", []): + args = { + "denoised": cfg_result, + "cond": cond, + "uncond": uncond, + "model": model, + "uncond_denoised": uncond_pred, + "cond_denoised": cond_pred, + "sigma": timestep, + "model_options": model_options, + "input": x, + } + cfg_result = fn(args) + + return cfg_result + + +def sampling_function( + model: torch.nn.Module, + x: torch.Tensor, + timestep: int, + uncond: torch.Tensor, + condo: torch.Tensor, + cond_scale: float, + model_options: dict = {}, + seed: int = None, +) -> torch.Tensor: + """#### Perform sampling with CFG. + + #### Args: + - `model` (torch.nn.Module): The model. + - `x` (torch.Tensor): The input tensor. + - `timestep` (int): The current timestep. + - `uncond` (torch.Tensor): The unconditioned tensor. + - `condo` (torch.Tensor): The conditioned tensor. + - `cond_scale` (float): The CFG scale. + - `model_options` (dict, optional): Additional model options. Defaults to {}. + - `seed` (int, optional): The random seed. Defaults to None. + + #### Returns: + - `torch.Tensor`: The sampled tensor. + """ + if ( + math.isclose(cond_scale, 1.0) + and model_options.get("disable_cfg1_optimization", False) is False + ): + uncond_ = None + else: + uncond_ = uncond + + conds = [condo, uncond_] + out = cond.calc_cond_batch(model, conds, x, timestep, model_options) + + for fn in model_options.get("sampler_pre_cfg_function", []): + args = { + "conds": conds, + "conds_out": out, + "cond_scale": cond_scale, + "timestep": timestep, + "input": x, + "sigma": timestep, + "model": model, + "model_options": model_options, + } + out = fn(args) + + return cfg_function( + model, + out[0], + out[1], + cond_scale, + x, + timestep, + model_options=model_options, + cond=condo, + uncond=uncond_, + ) + + +class CFGGuider: + """#### Class for guiding the sampling process with CFG.""" + def __init__(self, model_patcher, flux=False): + """#### Initialize the CFGGuider. + + #### Args: + - `model_patcher` (object): The model patcher. + """ + self.model_patcher = model_patcher + self.model_options = model_patcher.model_options + self.original_conds = {} + self.cfg = 1.0 + self.flux = flux + + def set_conds(self, positive, negative): + """#### Set the conditions for CFG. + + #### Args: + - `positive` (torch.Tensor): The positive condition. + - `negative` (torch.Tensor): The negative condition. + """ + self.inner_set_conds({"positive": positive, "negative": negative}) + + def set_cfg(self, cfg): + """#### Set the CFG scale. + + #### Args: + - `cfg` (float): The CFG scale. + """ + self.cfg = cfg + + def inner_set_conds(self, conds): + """#### Set the internal conditions. + + #### Args: + - `conds` (dict): The conditions. + """ + for k in conds: + self.original_conds[k] = cond.convert_cond(conds[k]) + + def __call__(self, *args, **kwargs): + """#### Call the CFGGuider to predict noise. + + #### Returns: + - `torch.Tensor`: The predicted noise. + """ + return self.predict_noise(*args, **kwargs) + + def predict_noise(self, x, timestep, model_options={}, seed=None): + """#### Predict noise using CFG. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `timestep` (int): The current timestep. + - `model_options` (dict, optional): Additional model options. Defaults to {}. + - `seed` (int, optional): The random seed. Defaults to None. + + #### Returns: + - `torch.Tensor`: The predicted noise. + """ + return sampling_function( + self.inner_model, + x, + timestep, + self.conds.get("negative", None), + self.conds.get("positive", None), + self.cfg, + model_options=model_options, + seed=seed, + ) + + def inner_sample( + self, + noise, + latent_image, + device, + sampler, + sigmas, + denoise_mask, + callback, + disable_pbar, + seed, + pipeline=False, + ): + """#### Perform the inner sampling process. + + #### Args: + - `noise` (torch.Tensor): The noise tensor. + - `latent_image` (torch.Tensor): The latent image tensor. + - `device` (torch.device): The device to use. + - `sampler` (object): The sampler object. + - `sigmas` (torch.Tensor): The sigmas tensor. + - `denoise_mask` (torch.Tensor): The denoise mask tensor. + - `callback` (callable): The callback function. + - `disable_pbar` (bool): Whether to disable the progress bar. + - `seed` (int): The random seed. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + + #### Returns: + - `torch.Tensor`: The sampled tensor. + """ + if ( + latent_image is not None and torch.count_nonzero(latent_image) > 0 + ): # Don't shift the empty latent image. + latent_image = self.inner_model.process_latent_in(latent_image) + + self.conds = cond.process_conds( + self.inner_model, + noise, + self.conds, + device, + latent_image, + denoise_mask, + seed, + ) + + extra_args = {"model_options": self.model_options, "seed": seed} + + samples = sampler.sample( + self, + sigmas, + extra_args, + callback, + noise, + latent_image, + denoise_mask, + disable_pbar, + pipeline=pipeline, + ) + return self.inner_model.process_latent_out(samples.to(torch.float32)) + + def sample( + self, + noise, + latent_image, + sampler, + sigmas, + denoise_mask=None, + callback=None, + disable_pbar=False, + seed=None, + pipeline=False, + ): + """#### Perform the sampling process with CFG. + + #### Args: + - `noise` (torch.Tensor): The noise tensor. + - `latent_image` (torch.Tensor): The latent image tensor. + - `sampler` (object): The sampler object. + - `sigmas` (torch.Tensor): The sigmas tensor. + - `denoise_mask` (torch.Tensor, optional): The denoise mask tensor. Defaults to None. + - `callback` (callable, optional): The callback function. Defaults to None. + - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. + - `seed` (int, optional): The random seed. Defaults to None. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + + #### Returns: + - `torch.Tensor`: The sampled tensor. + """ + self.conds = {} + for k in self.original_conds: + self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) + + self.inner_model, self.conds, self.loaded_models = cond_util.prepare_sampling( + self.model_patcher, noise.shape, self.conds, flux_enabled=self.flux + ) + device = self.model_patcher.load_device + + noise = noise.to(device) + latent_image = latent_image.to(device) + sigmas = sigmas.to(device) + + output = self.inner_sample( + noise, + latent_image, + device, + sampler, + sigmas, + denoise_mask, + callback, + disable_pbar, + seed, + pipeline=pipeline, + ) + + cond_util.cleanup_models(self.conds, self.loaded_models) + del self.inner_model + del self.conds + del self.loaded_models return output \ No newline at end of file diff --git a/modules/sample/ksampler_util.py b/modules/sample/ksampler_util.py index 75e4e2c21bb6e1e36b6fc1ca927386905ddae42e..708523f50314cbd60165963187549125a06bbee9 100644 --- a/modules/sample/ksampler_util.py +++ b/modules/sample/ksampler_util.py @@ -1,261 +1,261 @@ -import collections -import logging -import numpy as np -import scipy -import torch -from modules.sample import sampling_util - - -def calculate_start_end_timesteps(model: torch.nn.Module, conds: list) -> None: - """#### Calculate the start and end timesteps for a model. - - #### Args: - - `model` (torch.nn.Module): The input model. - - `conds` (list): The list of conditions. - """ - s = model.model_sampling - for t in range(len(conds)): - x = conds[t] - - timestep_start = None - timestep_end = None - if "start_percent" in x: - timestep_start = s.percent_to_sigma(x["start_percent"]) - if "end_percent" in x: - timestep_end = s.percent_to_sigma(x["end_percent"]) - - if (timestep_start is not None) or (timestep_end is not None): - n = x.copy() - if timestep_start is not None: - n["timestep_start"] = timestep_start - if timestep_end is not None: - n["timestep_end"] = timestep_end - conds[t] = n - - -def pre_run_control(model: torch.nn.Module, conds: list) -> None: - """#### Pre-run control for a model. - - #### Args: - - `model` (torch.nn.Module): The input model. - - `conds` (list): The list of conditions. - """ - s = model.model_sampling - for t in range(len(conds)): - x = conds[t] - - def percent_to_timestep_function(a): - return s.percent_to_sigma(a) - if "control" in x: - x["control"].pre_run(model, percent_to_timestep_function) - - -def apply_empty_x_to_equal_area( - conds: list, uncond: list, name: str, uncond_fill_func: callable -) -> None: - """#### Apply empty x to equal area. - - #### Args: - - `conds` (list): The list of conditions. - - `uncond` (list): The list of unconditional conditions. - - `name` (str): The name. - - `uncond_fill_func` (callable): The unconditional fill function. - """ - cond_cnets = [] - cond_other = [] - uncond_cnets = [] - uncond_other = [] - for t in range(len(conds)): - x = conds[t] - if "area" not in x: - if name in x and x[name] is not None: - cond_cnets.append(x[name]) - else: - cond_other.append((x, t)) - for t in range(len(uncond)): - x = uncond[t] - if "area" not in x: - if name in x and x[name] is not None: - uncond_cnets.append(x[name]) - else: - uncond_other.append((x, t)) - - if len(uncond_cnets) > 0: - return - - for x in range(len(cond_cnets)): - temp = uncond_other[x % len(uncond_other)] - o = temp[0] - if name in o and o[name] is not None: - n = o.copy() - n[name] = uncond_fill_func(cond_cnets, x) - uncond += [n] - else: - n = o.copy() - n[name] = uncond_fill_func(cond_cnets, x) - uncond[temp[1]] = n - - -def get_area_and_mult( - conds: dict, x_in: torch.Tensor, timestep_in: int -) -> collections.namedtuple: - """#### Get the area and multiplier. - - #### Args: - - `conds` (dict): The conditions. - - `x_in` (torch.Tensor): The input tensor. - - `timestep_in` (int): The timestep. - - #### Returns: - - `collections.namedtuple`: The area and multiplier. - """ - area = (x_in.shape[2], x_in.shape[3], 0, 0) - strength = 1.0 - - input_x = x_in[:, :, area[2] : area[0] + area[2], area[3] : area[1] + area[3]] - mask = torch.ones_like(input_x) - mult = mask * strength - - conditioning = {} - model_conds = conds["model_conds"] - for c in model_conds: - conditioning[c] = model_conds[c].process_cond( - batch_size=x_in.shape[0], device=x_in.device, area=area - ) - - control = conds.get("control", None) - patches = None - cond_obj = collections.namedtuple( - "cond_obj", ["input_x", "mult", "conditioning", "area", "control", "patches"] - ) - return cond_obj(input_x, mult, conditioning, area, control, patches) - - -def normal_scheduler( - model_sampling: torch.nn.Module, steps: int, sgm: bool = False, floor: bool = False -) -> torch.FloatTensor: - """#### Create a normal scheduler. - - #### Args: - - `model_sampling` (torch.nn.Module): The model sampling module. - - `steps` (int): The number of steps. - - `sgm` (bool, optional): Whether to use SGM. Defaults to False. - - `floor` (bool, optional): Whether to floor the values. Defaults to False. - - #### Returns: - - `torch.FloatTensor`: The scheduler. - """ - s = model_sampling - start = s.timestep(s.sigma_max) - end = s.timestep(s.sigma_min) - - timesteps = torch.linspace(start, end, steps) - - sigs = [] - for x in range(len(timesteps)): - ts = timesteps[x] - sigs.append(s.sigma(ts)) - sigs += [0.0] - return torch.FloatTensor(sigs) - -def simple_scheduler(model_sampling: torch.nn.Module, steps: int) -> torch.FloatTensor: - """#### Create a simple scheduler. - - #### Args: - - `model_sampling` (torch.nn.Module): The model sampling module. - - `steps` (int): The number of steps. - - #### Returns: - - `torch.FloatTensor`: The scheduler. - """ - s = model_sampling - sigs = [] - ss = len(s.sigmas) / steps - for x in range(steps): - sigs += [float(s.sigmas[-(1 + int(x * ss))])] - sigs += [0.0] - return torch.FloatTensor(sigs) - -# Implemented based on: https://arxiv.org/abs/2407.12173 -def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6): - total_timesteps = (len(model_sampling.sigmas) - 1) - ts = 1 - np.linspace(0, 1, steps, endpoint=False) - ts = np.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps) - - sigs = [] - last_t = -1 - for t in ts: - if t != last_t: - sigs += [float(model_sampling.sigmas[int(t)])] - last_t = t - sigs += [0.0] - return torch.FloatTensor(sigs) - -def calculate_sigmas( - model_sampling: torch.nn.Module, scheduler_name: str, steps: int -) -> torch.Tensor: - """#### Calculate the sigmas for a model. - - #### Args: - - `model_sampling` (torch.nn.Module): The model sampling module. - - `scheduler_name` (str): The scheduler name. - - `steps` (int): The number of steps. - - #### Returns: - - `torch.Tensor`: The calculated sigmas. - """ - if scheduler_name == "karras": - sigmas = sampling_util.get_sigmas_karras( - n=steps, - sigma_min=float(model_sampling.sigma_min), - sigma_max=float(model_sampling.sigma_max), - ) - elif scheduler_name == "normal": - sigmas = normal_scheduler(model_sampling, steps) - elif scheduler_name == "simple": - sigmas = simple_scheduler(model_sampling, steps) - elif scheduler_name == "beta": - sigmas = beta_scheduler(model_sampling, steps) - else: - logging.error("error invalid scheduler {}".format(scheduler_name)) - return sigmas - - -def prepare_noise( - latent_image: torch.Tensor, seed: int, noise_inds: list = None -) -> torch.Tensor: - """#### Prepare noise for a latent image. - - #### Args: - - `latent_image` (torch.Tensor): The latent image tensor. - - `seed` (int): The seed for random noise. - - `noise_inds` (list, optional): The noise indices. Defaults to None. - - #### Returns: - - `torch.Tensor`: The prepared noise tensor. - """ - generator = torch.manual_seed(seed) - if noise_inds is None: - return torch.randn( - latent_image.size(), - dtype=latent_image.dtype, - layout=latent_image.layout, - generator=generator, - device="cpu", - ) - - unique_inds, inverse = np.unique(noise_inds, return_inverse=True) - noises = [] - for i in range(unique_inds[-1] + 1): - noise = torch.randn( - [1] + list(latent_image.size())[1:], - dtype=latent_image.dtype, - layout=latent_image.layout, - generator=generator, - device="cpu", - ) - if i in unique_inds: - noises.append(noise) - noises = [noises[i] for i in inverse] - noises = torch.cat(noises, axis=0) - return noises +import collections +import logging +import numpy as np +import scipy +import torch +from modules.sample import sampling_util + + +def calculate_start_end_timesteps(model: torch.nn.Module, conds: list) -> None: + """#### Calculate the start and end timesteps for a model. + + #### Args: + - `model` (torch.nn.Module): The input model. + - `conds` (list): The list of conditions. + """ + s = model.model_sampling + for t in range(len(conds)): + x = conds[t] + + timestep_start = None + timestep_end = None + if "start_percent" in x: + timestep_start = s.percent_to_sigma(x["start_percent"]) + if "end_percent" in x: + timestep_end = s.percent_to_sigma(x["end_percent"]) + + if (timestep_start is not None) or (timestep_end is not None): + n = x.copy() + if timestep_start is not None: + n["timestep_start"] = timestep_start + if timestep_end is not None: + n["timestep_end"] = timestep_end + conds[t] = n + + +def pre_run_control(model: torch.nn.Module, conds: list) -> None: + """#### Pre-run control for a model. + + #### Args: + - `model` (torch.nn.Module): The input model. + - `conds` (list): The list of conditions. + """ + s = model.model_sampling + for t in range(len(conds)): + x = conds[t] + + def percent_to_timestep_function(a): + return s.percent_to_sigma(a) + if "control" in x: + x["control"].pre_run(model, percent_to_timestep_function) + + +def apply_empty_x_to_equal_area( + conds: list, uncond: list, name: str, uncond_fill_func: callable +) -> None: + """#### Apply empty x to equal area. + + #### Args: + - `conds` (list): The list of conditions. + - `uncond` (list): The list of unconditional conditions. + - `name` (str): The name. + - `uncond_fill_func` (callable): The unconditional fill function. + """ + cond_cnets = [] + cond_other = [] + uncond_cnets = [] + uncond_other = [] + for t in range(len(conds)): + x = conds[t] + if "area" not in x: + if name in x and x[name] is not None: + cond_cnets.append(x[name]) + else: + cond_other.append((x, t)) + for t in range(len(uncond)): + x = uncond[t] + if "area" not in x: + if name in x and x[name] is not None: + uncond_cnets.append(x[name]) + else: + uncond_other.append((x, t)) + + if len(uncond_cnets) > 0: + return + + for x in range(len(cond_cnets)): + temp = uncond_other[x % len(uncond_other)] + o = temp[0] + if name in o and o[name] is not None: + n = o.copy() + n[name] = uncond_fill_func(cond_cnets, x) + uncond += [n] + else: + n = o.copy() + n[name] = uncond_fill_func(cond_cnets, x) + uncond[temp[1]] = n + + +def get_area_and_mult( + conds: dict, x_in: torch.Tensor, timestep_in: int +) -> collections.namedtuple: + """#### Get the area and multiplier. + + #### Args: + - `conds` (dict): The conditions. + - `x_in` (torch.Tensor): The input tensor. + - `timestep_in` (int): The timestep. + + #### Returns: + - `collections.namedtuple`: The area and multiplier. + """ + area = (x_in.shape[2], x_in.shape[3], 0, 0) + strength = 1.0 + + input_x = x_in[:, :, area[2] : area[0] + area[2], area[3] : area[1] + area[3]] + mask = torch.ones_like(input_x) + mult = mask * strength + + conditioning = {} + model_conds = conds["model_conds"] + for c in model_conds: + conditioning[c] = model_conds[c].process_cond( + batch_size=x_in.shape[0], device=x_in.device, area=area + ) + + control = conds.get("control", None) + patches = None + cond_obj = collections.namedtuple( + "cond_obj", ["input_x", "mult", "conditioning", "area", "control", "patches"] + ) + return cond_obj(input_x, mult, conditioning, area, control, patches) + + +def normal_scheduler( + model_sampling: torch.nn.Module, steps: int, sgm: bool = False, floor: bool = False +) -> torch.FloatTensor: + """#### Create a normal scheduler. + + #### Args: + - `model_sampling` (torch.nn.Module): The model sampling module. + - `steps` (int): The number of steps. + - `sgm` (bool, optional): Whether to use SGM. Defaults to False. + - `floor` (bool, optional): Whether to floor the values. Defaults to False. + + #### Returns: + - `torch.FloatTensor`: The scheduler. + """ + s = model_sampling + start = s.timestep(s.sigma_max) + end = s.timestep(s.sigma_min) + + timesteps = torch.linspace(start, end, steps) + + sigs = [] + for x in range(len(timesteps)): + ts = timesteps[x] + sigs.append(s.sigma(ts)) + sigs += [0.0] + return torch.FloatTensor(sigs) + +def simple_scheduler(model_sampling: torch.nn.Module, steps: int) -> torch.FloatTensor: + """#### Create a simple scheduler. + + #### Args: + - `model_sampling` (torch.nn.Module): The model sampling module. + - `steps` (int): The number of steps. + + #### Returns: + - `torch.FloatTensor`: The scheduler. + """ + s = model_sampling + sigs = [] + ss = len(s.sigmas) / steps + for x in range(steps): + sigs += [float(s.sigmas[-(1 + int(x * ss))])] + sigs += [0.0] + return torch.FloatTensor(sigs) + +# Implemented based on: https://arxiv.org/abs/2407.12173 +def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6): + total_timesteps = (len(model_sampling.sigmas) - 1) + ts = 1 - np.linspace(0, 1, steps, endpoint=False) + ts = np.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps) + + sigs = [] + last_t = -1 + for t in ts: + if t != last_t: + sigs += [float(model_sampling.sigmas[int(t)])] + last_t = t + sigs += [0.0] + return torch.FloatTensor(sigs) + +def calculate_sigmas( + model_sampling: torch.nn.Module, scheduler_name: str, steps: int +) -> torch.Tensor: + """#### Calculate the sigmas for a model. + + #### Args: + - `model_sampling` (torch.nn.Module): The model sampling module. + - `scheduler_name` (str): The scheduler name. + - `steps` (int): The number of steps. + + #### Returns: + - `torch.Tensor`: The calculated sigmas. + """ + if scheduler_name == "karras": + sigmas = sampling_util.get_sigmas_karras( + n=steps, + sigma_min=float(model_sampling.sigma_min), + sigma_max=float(model_sampling.sigma_max), + ) + elif scheduler_name == "normal": + sigmas = normal_scheduler(model_sampling, steps) + elif scheduler_name == "simple": + sigmas = simple_scheduler(model_sampling, steps) + elif scheduler_name == "beta": + sigmas = beta_scheduler(model_sampling, steps) + else: + logging.error("error invalid scheduler {}".format(scheduler_name)) + return sigmas + + +def prepare_noise( + latent_image: torch.Tensor, seed: int, noise_inds: list = None +) -> torch.Tensor: + """#### Prepare noise for a latent image. + + #### Args: + - `latent_image` (torch.Tensor): The latent image tensor. + - `seed` (int): The seed for random noise. + - `noise_inds` (list, optional): The noise indices. Defaults to None. + + #### Returns: + - `torch.Tensor`: The prepared noise tensor. + """ + generator = torch.manual_seed(seed) + if noise_inds is None: + return torch.randn( + latent_image.size(), + dtype=latent_image.dtype, + layout=latent_image.layout, + generator=generator, + device="cpu", + ) + + unique_inds, inverse = np.unique(noise_inds, return_inverse=True) + noises = [] + for i in range(unique_inds[-1] + 1): + noise = torch.randn( + [1] + list(latent_image.size())[1:], + dtype=latent_image.dtype, + layout=latent_image.layout, + generator=generator, + device="cpu", + ) + if i in unique_inds: + noises.append(noise) + noises = [noises[i] for i in inverse] + noises = torch.cat(noises, axis=0) + return noises diff --git a/modules/sample/samplers.py b/modules/sample/samplers.py index 22c051e5d22a6d1ab8e6d974afadb01d6e0c4298..605924923ba1c23c553170a35e745f8ae372987c 100644 --- a/modules/sample/samplers.py +++ b/modules/sample/samplers.py @@ -1,285 +1,634 @@ -import threading -import torch -from tqdm.auto import trange -from modules.Utilities import util - - -from modules.sample import sampling_util - -disable_gui = False - - -@torch.no_grad() -def sample_euler_ancestral( - model, - x, - sigmas, - extra_args=None, - callback=None, - disable=None, - eta=1.0, - s_noise=1.0, - noise_sampler=None, - pipeline=False, -): - """#### Perform ancestral sampling using the Euler method. - - #### Args: - - `model` (torch.nn.Module): The model to use for denoising. - - `x` (torch.Tensor): The input tensor to be denoised. - - `sigmas` (list or torch.Tensor): A list or tensor of sigma values for the noise schedule. - - `extra_args` (dict, optional): Additional arguments to pass to the model. Defaults to None. - - `callback` (callable, optional): A callback function to be called at each iteration. Defaults to None. - - `disable` (bool, optional): If True, disables the progress bar. Defaults to None. - - `eta` (float, optional): The eta parameter for the ancestral step. Defaults to 1.0. - - `s_noise` (float, optional): The noise scaling factor. Defaults to 1.0. - - `noise_sampler` (callable, optional): A function to sample noise. Defaults to None. - - #### Returns: - - `torch.Tensor`: The denoised tensor after ancestral sampling. - """ - global disable_gui - disable_gui = True if pipeline is True else False - if disable_gui is False: - from modules.AutoEncoders import taesd - from modules.user import app_instance - extra_args = {} if extra_args is None else extra_args - noise_sampler = sampling_util.default_noise_sampler(x) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - for i in trange(len(sigmas) - 1, disable=disable): - # Move interrupt check outside pipeline condition - if not pipeline and hasattr(app_instance.app, 'interrupt_flag') and app_instance.app.interrupt_flag is True: - return x - - if pipeline is False: - try: - app_instance.app.title(f"LightDiffusion - {i}it") - app_instance.app.progress.set(((i)/(len(sigmas)-1))) - except: - pass - - # Rest of sampling code remains the same - denoised = model(x, sigmas[i] * s_in, **extra_args) - sigma_down, sigma_up = sampling_util.get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) - d = util.to_d(x, sigmas[i], denoised) - dt = sigma_down - sigmas[i] - x = x + d * dt - if sigmas[i + 1] > 0: - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - - if pipeline is False: - if app_instance.app.previewer_var.get() is True and i % 5 == 0: - threading.Thread(target=taesd.taesd_preview, args=(x,)).start() - - return x - -@torch.no_grad() -def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2, pipeline=False, seed=None): - """DPM-Solver++ (stochastic).""" - global disable_gui - disable_gui = True if pipeline is True else False - if disable_gui is False: - from modules.AutoEncoders import taesd - from modules.user import app_instance - if len(sigmas) <= 1: - return x - - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - noise_sampler = sampling_util.BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - sigma_fn = lambda t: t.neg().exp() - t_fn = lambda sigma: sigma.log().neg() - - for i in trange(len(sigmas) - 1, disable=disable): - # Move interrupt check outside pipeline condition - if not pipeline and hasattr(app_instance.app, 'interrupt_flag') and app_instance.app.interrupt_flag is True: - return x - - if pipeline is False: - try: - app_instance.app.title(f"LightDiffusion - {i}it") - app_instance.app.progress.set(((i)/(len(sigmas)-1))) - except: - pass - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Euler method - d = util.to_d(x, sigmas[i], denoised) - dt = sigmas[i + 1] - sigmas[i] - x = x + d * dt - else: - # DPM-Solver++ - t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) - h = t_next - t - s = t + h * r - fac = 1 / (2 * r) - - # Step 1 - sd, su = sampling_util.get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) - s_ = t_fn(sd) - x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised - x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su - denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) - - # Step 2 - sd, su = sampling_util.get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta) - t_next_ = t_fn(sd) - denoised_d = (1 - fac) * denoised + fac * denoised_2 - x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d - x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su - if pipeline is False: - if app_instance.app.previewer_var.get() is True and i % 5 == 0: - threading.Thread(target=taesd.taesd_preview, args=(x,)).start() - return x - -@torch.no_grad() -def sample_dpmpp_2m( - model, - x, - sigmas, - extra_args=None, - callback=None, - disable=None, - pipeline=False, -): - """ - #### Samples from a model using the DPM-Solver++(2M) SDE method. - - #### Args: - - `model` (torch.nn.Module): The model to sample from. - - `x` (torch.Tensor): The initial input tensor. - - `sigmas` (torch.Tensor): A tensor of sigma values for the SDE. - - `extra_args` (dict, optional): Additional arguments for the model. Default is None. - - `callback` (callable, optional): A callback function to be called at each step. Default is None. - - `disable` (bool, optional): If True, disables the progress bar. Default is None. - - `pipeline` (bool, optional): If True, disables the progress bar. Default is False. - - #### Returns: - - `torch.Tensor`: The final sampled tensor. - """ - global disable_gui - disable_gui = True if pipeline is True else False - if disable_gui is False: - from modules.AutoEncoders import taesd - from modules.user import app_instance - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - def sigma_fn(t): - return t.neg().exp() - def t_fn(sigma): - return sigma.log().neg() - old_denoised = None - - for i in trange(len(sigmas) - 1, disable=disable): - if not pipeline and hasattr(app_instance.app, 'interrupt_flag') and app_instance.app.interrupt_flag is True: - return x - - if pipeline is False: - app_instance.app.progress.set(((i)/(len(sigmas)-1))) - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) - h = t_next - t - if old_denoised is None or sigmas[i + 1] == 0: - x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised - else: - h_last = t - t_fn(sigmas[i - 1]) - r = h_last / h - denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised - x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d - old_denoised = denoised - if pipeline is False: - if app_instance.app.previewer_var.get() is True and i % 5 == 0: - threading.Thread(target=taesd.taesd_preview, args=(x,)).start() - else: - pass - return x - - -@torch.no_grad() -def sample_euler( - model: torch.nn.Module, - x: torch.Tensor, - sigmas: torch.Tensor, - extra_args: dict = None, - callback: callable = None, - disable: bool = None, - s_churn: float = 0.0, - s_tmin: float = 0.0, - s_tmax: float = float("inf"), - s_noise: float = 1.0, - pipeline: bool = False, -): - """#### Implements Algorithm 2 (Euler steps) from Karras et al. (2022). - - #### Args: - - `model` (torch.nn.Module): The model to use for denoising. - - `x` (torch.Tensor): The input tensor to be denoised. - - `sigmas` (list or torch.Tensor): A list or tensor of sigma values for the noise schedule. - - `extra_args` (dict, optional): Additional arguments to pass to the model. Defaults to None. - - `callback` (callable, optional): A callback function to be called at each iteration. Defaults to None. - - `disable` (bool, optional): If True, disables the progress bar. Defaults to None. - - `s_churn` (float, optional): The churn rate. Defaults to 0.0. - - `s_tmin` (float, optional): The minimum sigma value for churn. Defaults to 0.0. - - `s_tmax` (float, optional): The maximum sigma value for churn. Defaults to float("inf"). - - `s_noise` (float, optional): The noise scaling factor. Defaults to 1.0. - - `pipeline` (bool, optional): If True, disables the progress bar. Defaults to False. - - #### Returns: - - `torch.Tensor`: The denoised tensor after Euler sampling. - """ - global disable_gui - disable_gui = True if pipeline is True else False - if disable_gui is False: - from modules.AutoEncoders import taesd - from modules.user import app_instance - - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - - for i in trange(len(sigmas) - 1, disable=disable): - if not pipeline and hasattr(app_instance.app, 'interrupt_flag') and app_instance.app.interrupt_flag is True: - return x - - if pipeline is False: - app_instance.app.progress.set(((i)/(len(sigmas)-1))) - if s_churn > 0: - gamma = ( - min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) - if s_tmin <= sigmas[i] <= s_tmax - else 0.0 - ) - sigma_hat = sigmas[i] * (gamma + 1) - else: - gamma = 0 - sigma_hat = sigmas[i] - - if gamma > 0: - eps = torch.randn_like(x) * s_noise - x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = util.to_d(x, sigma_hat, denoised) - if callback is not None: - callback( - { - "x": x, - "i": i, - "sigma": sigmas[i], - "sigma_hat": sigma_hat, - "denoised": denoised, - } - ) - dt = sigmas[i + 1] - sigma_hat - # Euler method - x = x + d * dt - if pipeline is False: - if app_instance.app.previewer_var.get() is True and i % 5 == 0: - threading.Thread(target=taesd.taesd_preview, args=(x, True)).start() - else: - pass - return x +import threading +import torch +from tqdm.auto import trange +from modules.Utilities import util + + +from modules.sample import sampling_util + +disable_gui = False + + +@torch.no_grad() +def sample_euler_ancestral( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + eta=1.0, + s_noise=1.0, + noise_sampler=None, + pipeline=False, +): + # Pre-calculate common values + device = x.device + global disable_gui + disable_gui = pipeline + + if not disable_gui: + from modules.AutoEncoders import taesd + from modules.user import app_instance + + # Pre-allocate tensors and init noise sampler + s_in = torch.ones((x.shape[0],), device=device) + noise_sampler = ( + sampling_util.default_noise_sampler(x) + if noise_sampler is None + else noise_sampler + ) + + for i in trange(len(sigmas) - 1, disable=disable): + if ( + not pipeline + and hasattr(app_instance.app, "interrupt_flag") + and app_instance.app.interrupt_flag + ): + return x + + if not pipeline: + app_instance.app.progress.set(i / (len(sigmas) - 1)) + + # Combined model inference and step calculation + denoised = model(x, sigmas[i] * s_in, **(extra_args or {})) + sigma_down, sigma_up = sampling_util.get_ancestral_step( + sigmas[i], sigmas[i + 1], eta=eta + ) + + # Fused update step + x = x + util.to_d(x, sigmas[i], denoised) * (sigma_down - sigmas[i]) + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + + if callback is not None: + callback({"x": x, "i": i, "sigma": sigmas[i], "denoised": denoised}) + + if not pipeline and app_instance.app.previewer_var.get() and i % 5 == 0: + threading.Thread(target=taesd.taesd_preview, args=(x,)).start() + + return x + + +@torch.no_grad() +def sample_euler( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + s_churn=0.0, + s_tmin=0.0, + s_tmax=float("inf"), + s_noise=1.0, + pipeline=False, +): + # Pre-calculate common values + device = x.device + global disable_gui + disable_gui = pipeline + + if not disable_gui: + from modules.AutoEncoders import taesd + from modules.user import app_instance + + # Pre-allocate tensors and cache parameters + s_in = torch.ones((x.shape[0],), device=device) + gamma_max = min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_churn > 0 else 0 + + for i in trange(len(sigmas) - 1, disable=disable): + if ( + not pipeline + and hasattr(app_instance.app, "interrupt_flag") + and app_instance.app.interrupt_flag + ): + return x + + if not pipeline: + app_instance.app.progress.set(i / (len(sigmas) - 1)) + + # Combined sigma calculation and update + sigma_hat = ( + sigmas[i] * (1 + (gamma_max if s_tmin <= sigmas[i] <= s_tmax else 0)) + if gamma_max > 0 + else sigmas[i] + ) + + if gamma_max > 0 and sigma_hat > sigmas[i]: + x = ( + x + + torch.randn_like(x) * s_noise * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5 + ) + + # Fused model inference and update step + denoised = model(x, sigma_hat * s_in, **(extra_args or {})) + x = x + util.to_d(x, sigma_hat, denoised) * (sigmas[i + 1] - sigma_hat) + + if callback is not None: + callback( + { + "x": x, + "i": i, + "sigma": sigmas[i], + "sigma_hat": sigma_hat, + "denoised": denoised, + } + ) + + if not pipeline and app_instance.app.previewer_var.get() and i % 5 == 0: + threading.Thread(target=taesd.taesd_preview, args=(x, True)).start() + + return x + + +@torch.no_grad() +def sample_dpmpp_sde( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + eta=1.0, + s_noise=1.0, + noise_sampler=None, + r=1 / 2, + pipeline=False, + seed=None, +): + # Pre-calculate common values + device = x.device + global disable_gui + disable_gui = pipeline + + if not disable_gui: + from modules.AutoEncoders import taesd + from modules.user import app_instance + + # Early return check + if len(sigmas) <= 1: + return x + + # Pre-allocate tensors and values + s_in = torch.ones((x.shape[0],), device=device) + n_steps = len(sigmas) - 1 + extra_args = {} if extra_args is None else extra_args + + # Define helper functions + def sigma_fn(t): + return (-t).exp() + + def t_fn(sigma): + return -sigma.log() + + # Initialize noise sampler + if noise_sampler is None: + noise_sampler = sampling_util.BrownianTreeNoiseSampler( + x, sigmas[sigmas > 0].min(), sigmas.max(), seed=seed, cpu=True + ) + + for i in trange(n_steps, disable=disable): + if ( + not pipeline + and hasattr(app_instance.app, "interrupt_flag") + and app_instance.app.interrupt_flag + ): + return x + + if not pipeline: + app_instance.app.progress.set(i / n_steps) + + # Model inference + denoised = model(x, sigmas[i] * s_in, **extra_args) + + if callback is not None: + callback({"x": x, "i": i, "sigma": sigmas[i], "denoised": denoised}) + + if sigmas[i + 1] == 0: + # Single fused Euler step + x = x + util.to_d(x, sigmas[i], denoised) * (sigmas[i + 1] - sigmas[i]) + else: + # Fused DPM-Solver++ steps + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + s = t + (t_next - t) * r + + # Step 1 - Combined calculations + sd, su = sampling_util.get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) + s_ = t_fn(sd) + x_2 = ( + (sigma_fn(s_) / sigma_fn(t)) * x + - (t - s_).expm1() * denoised + + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su + ) + + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + + # Step 2 - Combined calculations + sd, su = sampling_util.get_ancestral_step( + sigma_fn(t), sigma_fn(t_next), eta + ) + t_next_ = t_fn(sd) + + # Final update in single calculation + x = ( + (sigma_fn(t_next_) / sigma_fn(t)) * x + - (t - t_next_).expm1() + * ((1 - 1 / (2 * r)) * denoised + (1 / (2 * r)) * denoised_2) + + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su + ) + + # Preview updates + if not pipeline and app_instance.app.previewer_var.get() and i % 5 == 0: + threading.Thread(target=taesd.taesd_preview, args=(x,)).start() + + return x + + +@torch.no_grad() +def sample_dpmpp_2m( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + pipeline=False, +): + """DPM-Solver++(2M) sampler with optimizations""" + # Pre-calculate common values and setup + device = x.device + global disable_gui + disable_gui = pipeline + + if not disable_gui: + from modules.AutoEncoders import taesd + from modules.user import app_instance + + # Pre-allocate tensors and transform sigmas + s_in = torch.ones((x.shape[0],), device=device) + t_steps = -torch.log(sigmas) # Fused calculation + + # Pre-calculate all needed values in one go + sigma_steps = torch.exp(-t_steps) # Fused calculation + ratios = sigma_steps[1:] / sigma_steps[:-1] + h_steps = t_steps[1:] - t_steps[:-1] + + old_denoised = None + extra_args = {} if extra_args is None else extra_args + + for i in trange(len(sigmas) - 1, disable=disable): + if ( + not pipeline + and hasattr(app_instance.app, "interrupt_flag") + and app_instance.app.interrupt_flag + ): + return x + + if not pipeline: + app_instance.app.progress.set(i / (len(sigmas) - 1)) + + # Fused model inference and update calculations + denoised = model(x, sigmas[i] * s_in, **extra_args) + + if callback is not None: + callback( + { + "x": x, + "i": i, + "sigma": sigmas[i], + "sigma_hat": sigmas[i], + "denoised": denoised, + } + ) + + # Combined update step + x = ratios[i] * x - (-h_steps[i]).expm1() * ( + denoised + if old_denoised is None or sigmas[i + 1] == 0 + else (1 + h_steps[i - 1] / (2 * h_steps[i])) * denoised + - (h_steps[i - 1] / (2 * h_steps[i])) * old_denoised + ) + + old_denoised = denoised + + # Preview updates + if not pipeline and app_instance.app.previewer_var.get() and i % 5 == 0: + threading.Thread(target=taesd.taesd_preview, args=(x,)).start() + + return x + + +@torch.no_grad() +def sample_dpmpp_2m_cfgpp( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + pipeline=False, + # CFG++ parameters + cfg_scale=7.5, + cfg_x0_scale=1.0, + cfg_s_scale=1.0, + cfg_min=1.0, +): + """DPM-Solver++(2M) sampler with CFG++ optimizations""" + # Pre-calculate common values and setup + device = x.device + global disable_gui + disable_gui = pipeline + + if not disable_gui: + from modules.AutoEncoders import taesd + from modules.user import app_instance + + # Pre-allocate tensors and transform sigmas + s_in = torch.ones((x.shape[0],), device=device) + t_steps = -torch.log(sigmas) # Fused calculation + n_steps = len(sigmas) - 1 + + # Pre-calculate all needed values in one go + sigma_steps = torch.exp(-t_steps) # Fused calculation + ratios = sigma_steps[1:] / sigma_steps[:-1] + h_steps = t_steps[1:] - t_steps[:-1] + + # CFG++ scheduling + def get_cfg_scale(step): + # Linear scheduling from cfg_scale to cfg_min + progress = step / n_steps + return cfg_scale + (cfg_min - cfg_scale) * progress + + old_denoised = None + old_uncond_denoised = None + extra_args = {} if extra_args is None else extra_args + + for i in trange(len(sigmas) - 1, disable=disable): + if ( + not pipeline + and hasattr(app_instance.app, "interrupt_flag") + and app_instance.app.interrupt_flag + ): + return x + + if not pipeline: + app_instance.app.progress.set(i / (len(sigmas) - 1)) + + # Get current CFG scale + current_cfg = get_cfg_scale(i) + + def post_cfg_function(args): + nonlocal old_uncond_denoised + old_uncond_denoised = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = set_model_options_post_cfg_function( + model_options, post_cfg_function, disable_cfg1_optimization=True + ) + + # Fused model inference and update calculations + denoised = model(x, sigmas[i] * s_in, **extra_args) + uncond_denoised = extra_args.get("model_options", {}).get( + "sampler_post_cfg_function", [] + )[-1]({"denoised": denoised, "uncond_denoised": None}) + + if callback is not None: + callback( + { + "x": x, + "i": i, + "sigma": sigmas[i], + "sigma_hat": sigmas[i], + "denoised": denoised, + "cfg_scale": current_cfg, + } + ) + + # CFG++ update step + if old_uncond_denoised is None or sigmas[i + 1] == 0: + # First step or last step - regular update + cfg_denoised = uncond_denoised + (denoised - uncond_denoised) * current_cfg + else: + # CFG++ combination with momentum + x0_coeff = cfg_x0_scale * current_cfg + s_coeff = cfg_s_scale * current_cfg + + # Momentum terms + h_ratio = h_steps[i - 1] / (2 * h_steps[i]) + momentum = (1 + h_ratio) * denoised - h_ratio * old_denoised + uncond_momentum = ( + 1 + h_ratio + ) * uncond_denoised - h_ratio * old_uncond_denoised + + # Combined update + cfg_denoised = uncond_momentum + (momentum - uncond_momentum) * x0_coeff + + # Apply update + x = ratios[i] * x - (-h_steps[i]).expm1() * cfg_denoised + + old_denoised = denoised + old_uncond_denoised = uncond_denoised + + # Preview updates + if not pipeline and app_instance.app.previewer_var.get() and i % 5 == 0: + threading.Thread(target=taesd.taesd_preview, args=(x,)).start() + + return x + + +def set_model_options_post_cfg_function( + model_options, post_cfg_function, disable_cfg1_optimization=False +): + model_options["sampler_post_cfg_function"] = model_options.get( + "sampler_post_cfg_function", [] + ) + [post_cfg_function] + if disable_cfg1_optimization: + model_options["disable_cfg1_optimization"] = True + return model_options + + +@torch.no_grad() +def sample_dpmpp_sde_cfgpp( + model, + x, + sigmas, + extra_args=None, + callback=None, + disable=None, + eta=1.0, + s_noise=1.0, + noise_sampler=None, + r=1 / 2, + pipeline=False, + seed=None, + # CFG++ parameters + cfg_scale=7.5, + cfg_x0_scale=1.0, + cfg_s_scale=1.0, + cfg_min=1.0, +): + """DPM-Solver++ (SDE) with CFG++ optimizations""" + # Pre-calculate common values + device = x.device + global disable_gui + disable_gui = pipeline + + if not disable_gui: + from modules.AutoEncoders import taesd + from modules.user import app_instance + + # Early return check + if len(sigmas) <= 1: + return x + + # Pre-allocate tensors and values + s_in = torch.ones((x.shape[0],), device=device) + n_steps = len(sigmas) - 1 + extra_args = {} if extra_args is None else extra_args + + # CFG++ scheduling + def get_cfg_scale(step): + progress = step / n_steps + return cfg_scale + (cfg_min - cfg_scale) * progress + + # Helper functions + def sigma_fn(t): + return (-t).exp() + + def t_fn(sigma): + return -sigma.log() + + # Initialize noise sampler + if noise_sampler is None: + noise_sampler = sampling_util.BrownianTreeNoiseSampler( + x, sigmas[sigmas > 0].min(), sigmas.max(), seed=seed, cpu=True + ) + + # Track previous predictions + old_denoised = None + old_uncond_denoised = None + + def post_cfg_function(args): + nonlocal old_uncond_denoised + old_uncond_denoised = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = set_model_options_post_cfg_function( + model_options, post_cfg_function, disable_cfg1_optimization=True + ) + + for i in trange(n_steps, disable=disable): + if ( + not pipeline + and hasattr(app_instance.app, "interrupt_flag") + and app_instance.app.interrupt_flag + ): + return x + + if not pipeline: + app_instance.app.progress.set(i / n_steps) + + # Get current CFG scale + current_cfg = get_cfg_scale(i) + + # Model inference + denoised = model(x, sigmas[i] * s_in, **extra_args) + uncond_denoised = extra_args.get("model_options", {}).get( + "sampler_post_cfg_function", [] + )[-1]({"denoised": denoised, "uncond_denoised": None}) + + if callback is not None: + callback( + { + "x": x, + "i": i, + "sigma": sigmas[i], + "denoised": denoised, + "cfg_scale": current_cfg, + } + ) + + if sigmas[i + 1] == 0: + # Final step - regular CFG + cfg_denoised = uncond_denoised + (denoised - uncond_denoised) * current_cfg + x = x + util.to_d(x, sigmas[i], cfg_denoised) * (sigmas[i + 1] - sigmas[i]) + else: + # Two-step update with CFG++ + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + s = t + (t_next - t) * r + + # Step 1 with CFG++ + sd, su = sampling_util.get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) + s_ = t_fn(sd) + + if old_uncond_denoised is None: + # First step - regular CFG + cfg_denoised = ( + uncond_denoised + (denoised - uncond_denoised) * current_cfg + ) + else: + # CFG++ with momentum + x0_coeff = cfg_x0_scale * current_cfg + s_coeff = cfg_s_scale * current_cfg + + # Calculate momentum terms + h_ratio = (t - s_) / (2 * (t - t_next)) + momentum = (1 + h_ratio) * denoised - h_ratio * old_denoised + uncond_momentum = ( + 1 + h_ratio + ) * uncond_denoised - h_ratio * old_uncond_denoised + + # Combine with CFG++ scaling + cfg_denoised = uncond_momentum + (momentum - uncond_momentum) * x0_coeff + + x_2 = ( + (sigma_fn(s_) / sigma_fn(t)) * x + - (t - s_).expm1() * cfg_denoised + + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su + ) + + # Step 2 inference + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + uncond_denoised_2 = extra_args.get("model_options", {}).get( + "sampler_post_cfg_function", [] + )[-1]({"denoised": denoised_2, "uncond_denoised": None}) + + # Step 2 CFG++ combination + if old_uncond_denoised is None: + cfg_denoised_2 = ( + uncond_denoised_2 + (denoised_2 - uncond_denoised_2) * current_cfg + ) + else: + momentum_2 = (1 + h_ratio) * denoised_2 - h_ratio * denoised + uncond_momentum_2 = ( + 1 + h_ratio + ) * uncond_denoised_2 - h_ratio * uncond_denoised + cfg_denoised_2 = ( + uncond_momentum_2 + (momentum_2 - uncond_momentum_2) * x0_coeff + ) + + # Final ancestral step + sd, su = sampling_util.get_ancestral_step( + sigma_fn(t), sigma_fn(t_next), eta + ) + t_next_ = t_fn(sd) + + # Combined update with both predictions + x = ( + (sigma_fn(t_next_) / sigma_fn(t)) * x + - (t - t_next_).expm1() + * ((1 - 1 / (2 * r)) * cfg_denoised + (1 / (2 * r)) * cfg_denoised_2) + + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su + ) + + old_denoised = denoised + old_uncond_denoised = uncond_denoised + + # Preview updates + if not pipeline and app_instance.app.previewer_var.get() and i % 5 == 0: + threading.Thread(target=taesd.taesd_preview, args=(x,)).start() + + return x diff --git a/modules/sample/sampling.py b/modules/sample/sampling.py index 4d427ff513ceffa7b6a903e7e90201af9fdbea7c..1e20b24c7141cd8c6c87d8c5f2fe5dcd3b25cf8b 100644 --- a/modules/sample/sampling.py +++ b/modules/sample/sampling.py @@ -1,1094 +1,1189 @@ -from enum import Enum -import threading -import torch.nn as nn - -import math -import torch - -from modules.Utilities import Latent -from modules.Device import Device -from modules.sample import ksampler_util, samplers, sampling_util -from modules.sample import CFG - - -class TimestepBlock1(nn.Module): - """#### A block for timestep embedding.""" - pass - - -class TimestepEmbedSequential1(nn.Sequential, TimestepBlock1): - """#### A sequential block for timestep embedding.""" - pass - - -class EPS: - """#### Class for EPS calculations.""" - - def calculate_input(self, sigma: torch.Tensor, noise: torch.Tensor) -> torch.Tensor: - """#### Calculate the input for EPS. - - #### Args: - - `sigma` (torch.Tensor): The sigma value. - - `noise` (torch.Tensor): The noise tensor. - - #### Returns: - - `torch.Tensor`: The calculated input tensor. - """ - sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) - return noise / (sigma**2 + self.sigma_data**2) ** 0.5 - - def calculate_denoised(self, sigma: torch.Tensor, model_output: torch.Tensor, model_input: torch.Tensor) -> torch.Tensor: - """#### Calculate the denoised tensor. - - #### Args: - - `sigma` (torch.Tensor): The sigma value. - - `model_output` (torch.Tensor): The model output tensor. - - `model_input` (torch.Tensor): The model input tensor. - - #### Returns: - - `torch.Tensor`: The denoised tensor. - """ - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) - return model_input - model_output * sigma - - def noise_scaling(self, sigma: torch.Tensor, noise: torch.Tensor, latent_image: torch.Tensor, max_denoise: bool = False) -> torch.Tensor: - """#### Scale the noise. - - #### Args: - - `sigma` (torch.Tensor): The sigma value. - - `noise` (torch.Tensor): The noise tensor. - - `latent_image` (torch.Tensor): The latent image tensor. - - `max_denoise` (bool, optional): Whether to apply maximum denoising. Defaults to False. - - #### Returns: - - `torch.Tensor`: The scaled noise tensor. - """ - if max_denoise: - noise = noise * torch.sqrt(1.0 + sigma**2.0) - else: - noise = noise * sigma - - noise += latent_image - return noise - - def inverse_noise_scaling(self, sigma: torch.Tensor, latent: torch.Tensor) -> torch.Tensor: - """#### Inverse the noise scaling. - - #### Args: - - `sigma` (torch.Tensor): The sigma value. - - `latent` (torch.Tensor): The latent tensor. - - #### Returns: - - `torch.Tensor`: The inversely scaled noise tensor. - """ - return latent - - -class CONST: - def calculate_input(self, sigma: torch.Tensor, noise: torch.Tensor) -> torch.Tensor: - """#### Calculate the input for CONST. - - #### Args: - - `sigma` (torch.Tensor): The sigma value. - - `noise` (torch.Tensor): The noise tensor. - - #### Returns: - - `torch.Tensor`: The calculated input tensor. - """ - return noise - - def calculate_denoised(self, sigma: torch.Tensor, model_output: torch.Tensor, model_input: torch.Tensor) -> torch.Tensor: - """#### Calculate the denoised tensor. - - #### Args: - - `sigma` (torch.Tensor): The sigma value. - - `model_output` (torch.Tensor): The model output tensor. - - `model_input` (torch.Tensor): The model input tensor. - - #### Returns: - - `torch.Tensor`: The denoised tensor. - """ - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) - return model_input - model_output * sigma - - def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): - """#### Scale the noise. - - #### Args: - - `sigma` (torch.Tensor): The sigma value. - - `noise` (torch.Tensor): The noise tensor. - - `latent_image` (torch.Tensor): The latent image tensor. - - `max_denoise` (bool, optional): Whether to apply maximum denoising. Defaults to False. - - #### Returns: - - `torch.Tensor`: The scaled noise tensor. - """ - return sigma * noise + (1.0 - sigma) * latent_image - - def inverse_noise_scaling(self, sigma: torch.Tensor, latent: torch.Tensor) -> torch.Tensor: - """#### Inverse the noise scaling. - - #### Args: - - `sigma` (torch.Tensor): The sigma value. - - `latent` (torch.Tensor): The latent tensor. - - #### Returns: - - `torch.Tensor`: The inversely scaled noise tensor. - """ - return latent / (1.0 - sigma) - - -def flux_time_shift(mu: float, sigma: float, t) -> float: - """#### Calculate the flux time shift. - - #### Args: - - `mu` (float): The mu value. - - `sigma` (float): The sigma value. - - `t` (float): The t value. - - #### Returns: - - `float`: The calculated flux time shift. - """ - return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) - - -class ModelSamplingFlux(torch.nn.Module): - def __init__(self, model_config=None): - super().__init__() - if model_config is not None: - sampling_settings = model_config.sampling_settings - else: - sampling_settings = {} - - self.set_parameters(shift=sampling_settings.get("shift", 1.15)) - - def set_parameters(self, shift=1.15, timesteps=10000): - """#### Set the parameters for the model. - - #### Args: - - `shift` (float, optional): The shift value. Defaults to 1.15. - - `timesteps` (int, optional): The number of timesteps. Defaults to 10000. - """ - self.shift = shift - ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps)) - self.register_buffer("sigmas", ts) - - @property - def sigma_max(self): - """#### Get the maximum sigma value.""" - return self.sigmas[-1] - - def timestep(self, sigma: torch.Tensor)-> torch.Tensor: - """#### Convert sigma to timestep. - - #### Args: - - `sigma` (torch.Tensor): The sigma value. - - #### Returns: - - `torch.Tensor`: The timestep value. - """ - return sigma - - def sigma(self, timestep: torch.Tensor) -> torch.Tensor: - """#### Convert timestep to sigma. - - #### Args: - - `timestep` (torch.Tensor): The timestep value. - - #### Returns: - - `torch.Tensor`: The sigma value. - """ - return flux_time_shift(self.shift, 1.0, timestep) - - -class ModelSamplingDiscrete(torch.nn.Module): - """#### Class for discrete model sampling.""" - - def __init__(self, model_config: dict = None): - """#### Initialize the ModelSamplingDiscrete class. - - #### Args: - - `model_config` (dict, optional): The model configuration. Defaults to None. - """ - super().__init__() - sampling_settings = model_config.sampling_settings - beta_schedule = sampling_settings.get("beta_schedule", "linear") - linear_start = sampling_settings.get("linear_start", 0.00085) - linear_end = sampling_settings.get("linear_end", 0.012) - - self._register_schedule( - given_betas=None, - beta_schedule=beta_schedule, - timesteps=1000, - linear_start=linear_start, - linear_end=linear_end, - cosine_s=8e-3, - ) - self.sigma_data = 1.0 - - def _register_schedule( - self, - given_betas: torch.Tensor = None, - beta_schedule: str = "linear", - timesteps: int = 1000, - linear_start: float = 1e-4, - linear_end: float = 2e-2, - cosine_s: float = 8e-3, - ): - """#### Register the schedule for the model. - - #### Args: - - `given_betas` (torch.Tensor, optional): The given betas. Defaults to None. - - `beta_schedule` (str, optional): The beta schedule. Defaults to "linear". - - `timesteps` (int, optional): The number of timesteps. Defaults to 1000. - - `linear_start` (float, optional): The linear start value. Defaults to 1e-4. - - `linear_end` (float, optional): The linear end value. Defaults to 2e-2. - - `cosine_s` (float, optional): The cosine s value. Defaults to 8e-3. - """ - betas = sampling_util.make_beta_schedule( - beta_schedule, - timesteps, - linear_start=linear_start, - linear_end=linear_end, - cosine_s=cosine_s, - ) - alphas = 1.0 - betas - alphas_cumprod = torch.cumprod(alphas, dim=0) - - (timesteps,) = betas.shape - self.num_timesteps = int(timesteps) - self.linear_start = linear_start - self.linear_end = linear_end - sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 - self.set_sigmas(sigmas) - - def set_sigmas(self, sigmas: torch.Tensor): - """#### Set the sigmas for the model. - - #### Args: - - `sigmas` (torch.Tensor): The sigmas tensor. - """ - self.register_buffer("sigmas", sigmas.float()) - self.register_buffer("log_sigmas", sigmas.log().float()) - - @property - def sigma_min(self) -> torch.Tensor: - """#### Get the minimum sigma value. - - #### Returns: - - `torch.Tensor`: The minimum sigma value. - """ - return self.sigmas[0] - - @property - def sigma_max(self) -> torch.Tensor: - """#### Get the maximum sigma value. - - #### Returns: - - `torch.Tensor`: The maximum sigma value. - """ - return self.sigmas[-1] - - def timestep(self, sigma: torch.Tensor) -> torch.Tensor: - """#### Convert sigma to timestep. - - #### Args: - - `sigma` (torch.Tensor): The sigma value. - - #### Returns: - - `torch.Tensor`: The timestep value. - """ - log_sigma = sigma.log() - dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] - return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) - - def sigma(self, timestep: torch.Tensor) -> torch.Tensor: - """#### Convert timestep to sigma. - - #### Args: - - `timestep` (torch.Tensor): The timestep value. - - #### Returns: - - `torch.Tensor`: The sigma value. - """ - t = torch.clamp( - timestep.float().to(self.log_sigmas.device), - min=0, - max=(len(self.sigmas) - 1), - ) - low_idx = t.floor().long() - high_idx = t.ceil().long() - w = t.frac() - log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] - return log_sigma.exp().to(timestep.device) - - def percent_to_sigma(self, percent: float) -> float: - """#### Convert percent to sigma. - - #### Args: - - `percent` (float): The percent value. - - #### Returns: - - `float`: The sigma value. - """ - if percent <= 0.0: - return 999999999.9 - if percent >= 1.0: - return 0.0 - percent = 1.0 - percent - return self.sigma(torch.tensor(percent * 999.0)).item() - - -class InterruptProcessingException(Exception): - """#### Exception class for interrupting processing.""" - pass - - -interrupt_processing_mutex = threading.RLock() - -interrupt_processing = False - - -class KSamplerX0Inpaint: - """#### Class for KSampler X0 Inpainting.""" - - def __init__(self, model: torch.nn.Module, sigmas: torch.Tensor): - """#### Initialize the KSamplerX0Inpaint class. - - #### Args: - - `model` (torch.nn.Module): The model. - - `sigmas` (torch.Tensor): The sigmas tensor. - """ - self.inner_model = model - self.sigmas = sigmas - - def __call__(self, x: torch.Tensor, sigma: torch.Tensor, denoise_mask: torch.Tensor, model_options: dict = {}, seed: int = None) -> torch.Tensor: - """#### Call the KSamplerX0Inpaint class. - - #### Args: - - `x` (torch.Tensor): The input tensor. - - `sigma` (torch.Tensor): The sigma value. - - `denoise_mask` (torch.Tensor): The denoise mask tensor. - - `model_options` (dict, optional): The model options. Defaults to {}. - - `seed` (int, optional): The seed value. Defaults to None. - - #### Returns: - - `torch.Tensor`: The output tensor. - """ - out = self.inner_model(x, sigma, model_options=model_options, seed=seed) - return out - - -class Sampler: - """#### Class for sampling.""" - - def max_denoise(self, model_wrap: torch.nn.Module, sigmas: torch.Tensor) -> bool: - """#### Check if maximum denoising is required. - - #### Args: - - `model_wrap` (torch.nn.Module): The model wrapper. - - `sigmas` (torch.Tensor): The sigmas tensor. - - #### Returns: - - `bool`: Whether maximum denoising is required. - """ - max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max) - sigma = float(sigmas[0]) - return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma - - -class KSAMPLER(Sampler): - """#### Class for KSAMPLER.""" - - def __init__(self, sampler_function: callable, extra_options: dict = {}, inpaint_options: dict = {}): - """#### Initialize the KSAMPLER class. - - #### Args: - - `sampler_function` (callable): The sampler function. - - `extra_options` (dict, optional): The extra options. Defaults to {}. - - `inpaint_options` (dict, optional): The inpaint options. Defaults to {}. - """ - self.sampler_function = sampler_function - self.extra_options = extra_options - self.inpaint_options = inpaint_options - - def sample( - self, - model_wrap: torch.nn.Module, - sigmas: torch.Tensor, - extra_args: dict, - callback: callable, - noise: torch.Tensor, - latent_image: torch.Tensor = None, - denoise_mask: torch.Tensor = None, - disable_pbar: bool = False, - pipeline: bool = False, - ) -> torch.Tensor: - """#### Sample using the KSAMPLER. - - #### Args: - - `model_wrap` (torch.nn.Module): The model wrapper. - - `sigmas` (torch.Tensor): The sigmas tensor. - - `extra_args` (dict): The extra arguments. - - `callback` (callable): The callback function. - - `noise` (torch.Tensor): The noise tensor. - - `latent_image` (torch.Tensor, optional): The latent image tensor. Defaults to None. - - `denoise_mask` (torch.Tensor, optional): The denoise mask tensor. Defaults to None. - - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - #### Returns: - - `torch.Tensor`: The sampled tensor. - """ - extra_args["denoise_mask"] = denoise_mask - model_k = KSamplerX0Inpaint(model_wrap, sigmas) - model_k.latent_image = latent_image - model_k.noise = noise - - noise = model_wrap.inner_model.model_sampling.noise_scaling( - sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas) - ) - - k_callback = None - - samples = self.sampler_function( - model_k, - noise, - sigmas, - extra_args=extra_args, - callback=k_callback, - disable=disable_pbar, - pipeline=pipeline, - **self.extra_options, - ) - samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling( - sigmas[-1], samples - ) - return samples - - -def ksampler(sampler_name: str, pipeline: bool = False, extra_options: dict = {}, inpaint_options: dict = {}) -> KSAMPLER: - """#### Get a KSAMPLER. - - #### Args: - - `sampler_name` (str): The sampler name. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - `extra_options` (dict, optional): The extra options. Defaults to {}. - - `inpaint_options` (dict, optional): The inpaint options. Defaults to {}. - - #### Returns: - - `KSAMPLER`: The KSAMPLER object. - """ - if sampler_name == "dpmpp_2m": - - def dpmpp_2m_function( - model: torch.nn.Module, - noise: torch.Tensor, - sigmas: torch.Tensor, - extra_args: dict, - callback: callable, - disable: bool, - pipeline: bool, - **extra_options, - ) -> torch.Tensor: - sigma_min = sigmas[-1] - if sigma_min == 0: - sigma_min = sigmas[-2] - return samplers.sample_dpmpp_2m( - model, - noise, - sigmas, - extra_args=extra_args, - callback=callback, - disable=disable, - pipeline=pipeline, - **extra_options, - ) - - sampler_function = dpmpp_2m_function - elif sampler_name == "dpmpp_sde": - def dpmpp_sde_function( - model: torch.nn.Module, - noise: torch.Tensor, - sigmas: torch.Tensor, - extra_args: dict, - callback: callable, - disable: bool, - pipeline: bool, - **extra_options, - ) -> torch.Tensor: - return samplers.sample_dpmpp_sde( - model, - noise, - sigmas, - extra_args=extra_args, - callback=callback, - disable=disable, - pipeline=pipeline, - **extra_options, - ) - sampler_function = dpmpp_sde_function - - elif sampler_name == "euler_ancestral": - - def euler_ancestral_function( - model: torch.nn.Module, - noise: torch.Tensor, - sigmas: torch.Tensor, - extra_args: dict, - callback: callable, - disable: bool, - pipeline: bool, - ) -> torch.Tensor: - return samplers.sample_euler_ancestral( - model, - noise, - sigmas, - extra_args=extra_args, - callback=callback, - disable=disable, - pipeline=pipeline, - **extra_options, - ) - - sampler_function = euler_ancestral_function - - elif sampler_name == "euler": - - def euler_function(model, noise, sigmas, extra_args, callback, disable, pipeline=False): - return samplers.sample_euler( - model, - noise, - sigmas, - extra_args=extra_args, - callback=callback, - disable=disable, - pipeline=pipeline, - **extra_options, - ) - - sampler_function = euler_function - - return KSAMPLER(sampler_function, extra_options, inpaint_options) - - -def sample( - model: torch.nn.Module, - noise: torch.Tensor, - positive: torch.Tensor, - negative: torch.Tensor, - cfg: float, - device: torch.device, - sampler: KSAMPLER, - sigmas: torch.Tensor, - model_options: dict = {}, - latent_image: torch.Tensor = None, - denoise_mask: torch.Tensor = None, - callback: callable = None, - disable_pbar: bool = False, - seed: int = None, - pipeline: bool = False, - flux: bool = False, -) -> torch.Tensor: - """#### Sample using the given parameters. - - #### Args: - - `model` (torch.nn.Module): The model. - - `noise` (torch.Tensor): The noise tensor. - - `positive` (torch.Tensor): The positive tensor. - - `negative` (torch.Tensor): The negative tensor. - - `cfg` (float): The CFG value. - - `device` (torch.device): The device. - - `sampler` (KSAMPLER): The KSAMPLER object. - - `sigmas` (torch.Tensor): The sigmas tensor. - - `model_options` (dict, optional): The model options. Defaults to {}. - - `latent_image` (torch.Tensor, optional): The latent image tensor. Defaults to None. - - `denoise_mask` (torch.Tensor, optional): The denoise mask tensor. Defaults to None. - - `callback` (callable, optional): The callback function. Defaults to None. - - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. - - `seed` (int, optional): The seed value. Defaults to None. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - #### Returns: - - `torch.Tensor`: The sampled tensor. - """ - cfg_guider = CFG.CFGGuider(model, flux=flux) - cfg_guider.set_conds(positive, negative) - cfg_guider.set_cfg(cfg) - return cfg_guider.sample( - noise, - latent_image, - sampler, - sigmas, - denoise_mask, - callback, - disable_pbar, - seed, - pipeline=pipeline, - ) - - -def sampler_object(name: str, pipeline: bool = False) -> KSAMPLER: - """#### Get a sampler object. - - #### Args: - - `name` (str): The sampler name. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - #### Returns: - - `KSAMPLER`: The KSAMPLER object. - """ - sampler = ksampler(name, pipeline=pipeline) - return sampler - - -class KSampler1: - """#### Class for KSampler1.""" - - def __init__( - self, - model: torch.nn.Module, - steps: int, - device, - sampler: str = None, - scheduler: str = None, - denoise: float = None, - model_options: dict = {}, - pipeline: bool = False, - ): - """#### Initialize the KSampler1 class. - - #### Args: - - `model` (torch.nn.Module): The model. - - `steps` (int): The number of steps. - - `device` (torch.device): The device. - - `sampler` (str, optional): The sampler name. Defaults to None. - - `scheduler` (str, optional): The scheduler name. Defaults to None. - - `denoise` (float, optional): The denoise factor. Defaults to None. - - `model_options` (dict, optional): The model options. Defaults to {}. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - """ - self.model = model - self.device = device - self.scheduler = scheduler - self.sampler = sampler - self.set_steps(steps, denoise) - self.denoise = denoise - self.model_options = model_options - self.pipeline = pipeline - - def calculate_sigmas(self, steps: int) -> torch.Tensor: - """#### Calculate the sigmas for the given steps. - - #### Args: - - `steps` (int): The number of steps. - - #### Returns: - - `torch.Tensor`: The calculated sigmas. - """ - sigmas = ksampler_util.calculate_sigmas( - self.model.get_model_object("model_sampling"), self.scheduler, steps - ) - return sigmas - - def set_steps(self, steps: int, denoise: float = None): - """#### Set the steps and calculate the sigmas. - - #### Args: - - `steps` (int): The number of steps. - - `denoise` (float, optional): The denoise factor. Defaults to None. - """ - self.steps = steps - if denoise is None or denoise > 0.9999: - self.sigmas = self.calculate_sigmas(steps).to(self.device) - else: - if denoise <= 0.0: - self.sigmas = torch.FloatTensor([]) - else: - new_steps = int(steps / denoise) - sigmas = self.calculate_sigmas(new_steps).to(self.device) - self.sigmas = sigmas[-(steps + 1) :] - - def sample( - self, - noise: torch.Tensor, - positive: torch.Tensor, - negative: torch.Tensor, - cfg: float, - latent_image: torch.Tensor = None, - start_step: int = None, - last_step: int = None, - force_full_denoise: bool = False, - denoise_mask: torch.Tensor = None, - sigmas: torch.Tensor = None, - callback: callable = None, - disable_pbar: bool = False, - seed: int = None, - pipeline: bool = False, - flux: bool = False, - ) -> torch.Tensor: - """#### Sample using the KSampler1. - - #### Args: - - `noise` (torch.Tensor): The noise tensor. - - `positive` (torch.Tensor): The positive tensor. - - `negative` (torch.Tensor): The negative tensor. - - `cfg` (float): The CFG value. - - `latent_image` (torch.Tensor, optional): The latent image tensor. Defaults to None. - - `start_step` (int, optional): The start step. Defaults to None. - - `last_step` (int, optional): The last step. Defaults to None. - - `force_full_denoise` (bool, optional): Whether to force full denoise. Defaults to False. - - `denoise_mask` (torch.Tensor, optional): The denoise mask tensor. Defaults to None. - - `sigmas` (torch.Tensor, optional): The sigmas tensor. Defaults to None. - - `callback` (callable, optional): The callback function. Defaults to None. - - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. - - `seed` (int, optional): The seed value. Defaults to None. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - #### Returns: - - `torch.Tensor`: The sampled tensor. - """ - if sigmas is None: - sigmas = self.sigmas - - if last_step is not None and last_step < (len(sigmas) - 1): - sigmas = sigmas[: last_step + 1] - if force_full_denoise: - sigmas[-1] = 0 - - if start_step is not None: - if start_step < (len(sigmas) - 1): - sigmas = sigmas[start_step:] - else: - if latent_image is not None: - return latent_image - else: - return torch.zeros_like(noise) - - sampler = sampler_object(self.sampler, pipeline=pipeline) - - return sample( - self.model, - noise, - positive, - negative, - cfg, - self.device, - sampler, - sigmas, - self.model_options, - latent_image=latent_image, - denoise_mask=denoise_mask, - callback=callback, - disable_pbar=disable_pbar, - seed=seed, - pipeline=pipeline, - flux=flux - ) - - -def sample1( - model: torch.nn.Module, - noise: torch.Tensor, - steps: int, - cfg: float, - sampler_name: str, - scheduler: str, - positive: torch.Tensor, - negative: torch.Tensor, - latent_image: torch.Tensor, - denoise: float = 1.0, - disable_noise: bool = False, - start_step: int = None, - last_step: int = None, - force_full_denoise: bool = False, - noise_mask: torch.Tensor = None, - sigmas: torch.Tensor = None, - callback: callable = None, - disable_pbar: bool = False, - seed: int = None, - pipeline: bool = False, - flux: bool = False, -) -> torch.Tensor: - """#### Sample using the given parameters. - - #### Args: - - `model` (torch.nn.Module): The model. - - `noise` (torch.Tensor): The noise tensor. - - `steps` (int): The number of steps. - - `cfg` (float): The CFG value. - - `sampler_name` (str): The sampler name. - - `scheduler` (str): The scheduler name. - - `positive` (torch.Tensor): The positive tensor. - - `negative` (torch.Tensor): The negative tensor. - - `latent_image` (torch.Tensor): The latent image tensor. - - `denoise` (float, optional): The denoise factor. Defaults to 1.0. - - `disable_noise` (bool, optional): Whether to disable noise. Defaults to False. - - `start_step` (int, optional): The start step. Defaults to None. - - `last_step` (int, optional): The last step. Defaults to None. - - `force_full_denoise` (bool, optional): Whether to force full denoise. Defaults to False. - - `noise_mask` (torch.Tensor, optional): The noise mask tensor. Defaults to None. - - `sigmas` (torch.Tensor, optional): The sigmas tensor. Defaults to None. - - `callback` (callable, optional): The callback function. Defaults to None. - - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. - - `seed` (int, optional): The seed value. Defaults to None. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - #### Returns: - - `torch.Tensor`: The sampled tensor. - """ - sampler = KSampler1( - model, - steps=steps, - device=model.load_device, - sampler=sampler_name, - scheduler=scheduler, - denoise=denoise, - model_options=model.model_options, - pipeline=pipeline, - ) - - samples = sampler.sample( - noise, - positive, - negative, - cfg=cfg, - latent_image=latent_image, - start_step=start_step, - last_step=last_step, - force_full_denoise=force_full_denoise, - denoise_mask=noise_mask, - sigmas=sigmas, - callback=callback, - disable_pbar=disable_pbar, - seed=seed, - pipeline=pipeline, - flux=flux - ) - samples = samples.to(Device.intermediate_device()) - return samples - - -def common_ksampler( - model: torch.nn.Module, - seed: int, - steps: int, - cfg: float, - sampler_name: str, - scheduler: str, - positive: torch.Tensor, - negative: torch.Tensor, - latent: dict, - denoise: float = 1.0, - disable_noise: bool = False, - start_step: int = None, - last_step: int = None, - force_full_denoise: bool = False, - pipeline: bool = False, - flux: bool = False, -) -> tuple: - """#### Common ksampler function. - - #### Args: - - `model` (torch.nn.Module): The model. - - `seed` (int): The seed value. - - `steps` (int): The number of steps. - - `cfg` (float): The CFG value. - - `sampler_name` (str): The sampler name. - - `scheduler` (str): The scheduler name. - - `positive` (torch.Tensor): The positive tensor. - - `negative` (torch.Tensor): The negative tensor. - - `latent` (dict): The latent dictionary. - - `denoise` (float, optional): The denoise factor. Defaults to 1.0. - - `disable_noise` (bool, optional): Whether to disable noise. Defaults to False. - - `start_step` (int, optional): The start step. Defaults to None. - - `last_step` (int, optional): The last step. Defaults to None. - - `force_full_denoise` (bool, optional): Whether to force full denoise. Defaults to False. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - #### Returns: - - `tuple`: The output tuple containing the latent dictionary and samples. - """ - latent_image = latent["samples"] - latent_image = Latent.fix_empty_latent_channels(model, latent_image) - - if disable_noise: - noise = torch.zeros( - latent_image.size(), - dtype=latent_image.dtype, - layout=latent_image.layout, - device="cpu", - ) - else: - batch_inds = latent["batch_index"] if "batch_index" in latent else None - noise = ksampler_util.prepare_noise(latent_image, seed, batch_inds) - - noise_mask = None - if "noise_mask" in latent: - noise_mask = latent["noise_mask"] - samples = sample1( - model, - noise, - steps, - cfg, - sampler_name, - scheduler, - positive, - negative, - latent_image, - denoise=denoise, - disable_noise=disable_noise, - start_step=start_step, - last_step=last_step, - force_full_denoise=force_full_denoise, - noise_mask=noise_mask, - seed=seed, - pipeline=pipeline, - flux=flux - ) - out = latent.copy() - out["samples"] = samples - return (out,) - - -class KSampler2: - """#### Class for KSampler2.""" - - def sample( - self, - model: torch.nn.Module, - seed: int, - steps: int, - cfg: float, - sampler_name: str, - scheduler: str, - positive: torch.Tensor, - negative: torch.Tensor, - latent_image: torch.Tensor, - denoise: float = 1.0, - pipeline: bool = False, - flux: bool = False, - ) -> tuple: - """#### Sample using the KSampler2. - - #### Args: - - `model` (torch.nn.Module): The model. - - `seed` (int): The seed value. - - `steps` (int): The number of steps. - - `cfg` (float): The CFG value. - - `sampler_name` (str): The sampler name. - - `scheduler` (str): The scheduler name. - - `positive` (torch.Tensor): The positive tensor. - - `negative` (torch.Tensor): The negative tensor. - - `latent_image` (torch.Tensor): The latent image tensor. - - `denoise` (float, optional): The denoise factor. Defaults to 1.0. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - #### Returns: - - `tuple`: The output tuple containing the latent dictionary and samples. - """ - return common_ksampler( - model, - seed, - steps, - cfg, - sampler_name, - scheduler, - positive, - negative, - latent_image, - denoise=denoise, - pipeline=pipeline, - flux=flux - ) - - -class ModelType(Enum): - """#### Enum for Model Types.""" - EPS = 1 - FLUX = 8 - - -def model_sampling(model_config: dict, model_type: ModelType, flux: bool = False) -> torch.nn.Module: - """#### Create a model sampling instance. - - #### Args: - - `model_config` (dict): The model configuration. - - `model_type` (ModelType): The model type. - - #### Returns: - - `torch.nn.Module`: The model sampling instance. - """ - if not flux: - s = ModelSamplingDiscrete - if model_type == ModelType.EPS: - c = EPS - - class ModelSampling(s, c): - pass - - return ModelSampling(model_config) - else: - c = CONST - s = ModelSamplingFlux - - class ModelSampling(s, c): - pass - - return ModelSampling(model_config) - - -def sample_custom( - model: torch.nn.Module, - noise: torch.Tensor, - cfg: float, - sampler: KSAMPLER, - sigmas: torch.Tensor, - positive: torch.Tensor, - negative: torch.Tensor, - latent_image: torch.Tensor, - noise_mask: torch.Tensor = None, - callback: callable = None, - disable_pbar: bool = False, - seed: int = None, - pipeline: bool = False, -) -> torch.Tensor: - """#### Custom sampling function. - - #### Args: - - `model` (torch.nn.Module): The model. - - `noise` (torch.Tensor): The noise tensor. - - `cfg` (float): The CFG value. - - `sampler` (KSAMPLER): The KSAMPLER object. - - `sigmas` (torch.Tensor): The sigmas tensor. - - `positive` (torch.Tensor): The positive tensor. - - `negative` (torch.Tensor): The negative tensor. - - `latent_image` (torch.Tensor): The latent image tensor. - - `noise_mask` (torch.Tensor, optional): The noise mask tensor. Defaults to None. - - `callback` (callable, optional): The callback function. Defaults to None. - - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. - - `seed` (int, optional): The seed value. Defaults to None. - - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. - - #### Returns: - - `torch.Tensor`: The sampled tensor. - """ - samples = sample( - model, - noise, - positive, - negative, - cfg, - model.load_device, - sampler, - sigmas, - model_options=model.model_options, - latent_image=latent_image, - denoise_mask=noise_mask, - callback=callback, - disable_pbar=disable_pbar, - seed=seed, - pipeline=pipeline, - ) - samples = samples.to(Device.intermediate_device()) - return samples +from enum import Enum +import threading +import torch.nn as nn + +import math +import torch + +from modules.Utilities import Latent +from modules.Device import Device +from modules.sample import ksampler_util, samplers, sampling_util +from modules.sample import CFG + + +class TimestepBlock1(nn.Module): + """#### A block for timestep embedding.""" + + pass + + +class TimestepEmbedSequential1(nn.Sequential, TimestepBlock1): + """#### A sequential block for timestep embedding.""" + + pass + + +class EPS: + """#### Class for EPS calculations.""" + + def calculate_input(self, sigma: torch.Tensor, noise: torch.Tensor) -> torch.Tensor: + """#### Calculate the input for EPS. + + #### Args: + - `sigma` (torch.Tensor): The sigma value. + - `noise` (torch.Tensor): The noise tensor. + + #### Returns: + - `torch.Tensor`: The calculated input tensor. + """ + sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) + return noise / (sigma**2 + self.sigma_data**2) ** 0.5 + + def calculate_denoised( + self, sigma: torch.Tensor, model_output: torch.Tensor, model_input: torch.Tensor + ) -> torch.Tensor: + """#### Calculate the denoised tensor. + + #### Args: + - `sigma` (torch.Tensor): The sigma value. + - `model_output` (torch.Tensor): The model output tensor. + - `model_input` (torch.Tensor): The model input tensor. + + #### Returns: + - `torch.Tensor`: The denoised tensor. + """ + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input - model_output * sigma + + def noise_scaling( + self, + sigma: torch.Tensor, + noise: torch.Tensor, + latent_image: torch.Tensor, + max_denoise: bool = False, + ) -> torch.Tensor: + """#### Scale the noise. + + #### Args: + - `sigma` (torch.Tensor): The sigma value. + - `noise` (torch.Tensor): The noise tensor. + - `latent_image` (torch.Tensor): The latent image tensor. + - `max_denoise` (bool, optional): Whether to apply maximum denoising. Defaults to False. + + #### Returns: + - `torch.Tensor`: The scaled noise tensor. + """ + if max_denoise: + noise = noise * torch.sqrt(1.0 + sigma**2.0) + else: + noise = noise * sigma + + noise += latent_image + return noise + + def inverse_noise_scaling( + self, sigma: torch.Tensor, latent: torch.Tensor + ) -> torch.Tensor: + """#### Inverse the noise scaling. + + #### Args: + - `sigma` (torch.Tensor): The sigma value. + - `latent` (torch.Tensor): The latent tensor. + + #### Returns: + - `torch.Tensor`: The inversely scaled noise tensor. + """ + return latent + + +class CONST: + def calculate_input(self, sigma: torch.Tensor, noise: torch.Tensor) -> torch.Tensor: + """#### Calculate the input for CONST. + + #### Args: + - `sigma` (torch.Tensor): The sigma value. + - `noise` (torch.Tensor): The noise tensor. + + #### Returns: + - `torch.Tensor`: The calculated input tensor. + """ + return noise + + def calculate_denoised( + self, sigma: torch.Tensor, model_output: torch.Tensor, model_input: torch.Tensor + ) -> torch.Tensor: + """#### Calculate the denoised tensor. + + #### Args: + - `sigma` (torch.Tensor): The sigma value. + - `model_output` (torch.Tensor): The model output tensor. + - `model_input` (torch.Tensor): The model input tensor. + + #### Returns: + - `torch.Tensor`: The denoised tensor. + """ + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input - model_output * sigma + + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + """#### Scale the noise. + + #### Args: + - `sigma` (torch.Tensor): The sigma value. + - `noise` (torch.Tensor): The noise tensor. + - `latent_image` (torch.Tensor): The latent image tensor. + - `max_denoise` (bool, optional): Whether to apply maximum denoising. Defaults to False. + + #### Returns: + - `torch.Tensor`: The scaled noise tensor. + """ + return sigma * noise + (1.0 - sigma) * latent_image + + def inverse_noise_scaling( + self, sigma: torch.Tensor, latent: torch.Tensor + ) -> torch.Tensor: + """#### Inverse the noise scaling. + + #### Args: + - `sigma` (torch.Tensor): The sigma value. + - `latent` (torch.Tensor): The latent tensor. + + #### Returns: + - `torch.Tensor`: The inversely scaled noise tensor. + """ + return latent / (1.0 - sigma) + + +def flux_time_shift(mu: float, sigma: float, t) -> float: + """#### Calculate the flux time shift. + + #### Args: + - `mu` (float): The mu value. + - `sigma` (float): The sigma value. + - `t` (float): The t value. + + #### Returns: + - `float`: The calculated flux time shift. + """ + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + + +class ModelSamplingFlux(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + self.set_parameters(shift=sampling_settings.get("shift", 1.15)) + + def set_parameters(self, shift=1.15, timesteps=10000): + """#### Set the parameters for the model. + + #### Args: + - `shift` (float, optional): The shift value. Defaults to 1.15. + - `timesteps` (int, optional): The number of timesteps. Defaults to 10000. + """ + self.shift = shift + ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps)) + self.register_buffer("sigmas", ts) + + @property + def sigma_max(self): + """#### Get the maximum sigma value.""" + return self.sigmas[-1] + + def timestep(self, sigma: torch.Tensor) -> torch.Tensor: + """#### Convert sigma to timestep. + + #### Args: + - `sigma` (torch.Tensor): The sigma value. + + #### Returns: + - `torch.Tensor`: The timestep value. + """ + return sigma + + def sigma(self, timestep: torch.Tensor) -> torch.Tensor: + """#### Convert timestep to sigma. + + #### Args: + - `timestep` (torch.Tensor): The timestep value. + + #### Returns: + - `torch.Tensor`: The sigma value. + """ + return flux_time_shift(self.shift, 1.0, timestep) + + +class ModelSamplingDiscrete(torch.nn.Module): + """#### Class for discrete model sampling.""" + + def __init__(self, model_config: dict = None): + """#### Initialize the ModelSamplingDiscrete class. + + #### Args: + - `model_config` (dict, optional): The model configuration. Defaults to None. + """ + super().__init__() + sampling_settings = model_config.sampling_settings + beta_schedule = sampling_settings.get("beta_schedule", "linear") + linear_start = sampling_settings.get("linear_start", 0.00085) + linear_end = sampling_settings.get("linear_end", 0.012) + + self._register_schedule( + given_betas=None, + beta_schedule=beta_schedule, + timesteps=1000, + linear_start=linear_start, + linear_end=linear_end, + cosine_s=8e-3, + ) + self.sigma_data = 1.0 + + def _register_schedule( + self, + given_betas: torch.Tensor = None, + beta_schedule: str = "linear", + timesteps: int = 1000, + linear_start: float = 1e-4, + linear_end: float = 2e-2, + cosine_s: float = 8e-3, + ): + """#### Register the schedule for the model. + + #### Args: + - `given_betas` (torch.Tensor, optional): The given betas. Defaults to None. + - `beta_schedule` (str, optional): The beta schedule. Defaults to "linear". + - `timesteps` (int, optional): The number of timesteps. Defaults to 1000. + - `linear_start` (float, optional): The linear start value. Defaults to 1e-4. + - `linear_end` (float, optional): The linear end value. Defaults to 2e-2. + - `cosine_s` (float, optional): The cosine s value. Defaults to 8e-3. + """ + betas = sampling_util.make_beta_schedule( + beta_schedule, + timesteps, + linear_start=linear_start, + linear_end=linear_end, + cosine_s=cosine_s, + ) + alphas = 1.0 - betas + alphas_cumprod = torch.cumprod(alphas, dim=0) + + (timesteps,) = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 + self.set_sigmas(sigmas) + + def set_sigmas(self, sigmas: torch.Tensor): + """#### Set the sigmas for the model. + + #### Args: + - `sigmas` (torch.Tensor): The sigmas tensor. + """ + self.register_buffer("sigmas", sigmas.float()) + self.register_buffer("log_sigmas", sigmas.log().float()) + + @property + def sigma_min(self) -> torch.Tensor: + """#### Get the minimum sigma value. + + #### Returns: + - `torch.Tensor`: The minimum sigma value. + """ + return self.sigmas[0] + + @property + def sigma_max(self) -> torch.Tensor: + """#### Get the maximum sigma value. + + #### Returns: + - `torch.Tensor`: The maximum sigma value. + """ + return self.sigmas[-1] + + def timestep(self, sigma: torch.Tensor) -> torch.Tensor: + """#### Convert sigma to timestep. + + #### Args: + - `sigma` (torch.Tensor): The sigma value. + + #### Returns: + - `torch.Tensor`: The timestep value. + """ + log_sigma = sigma.log() + dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] + return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) + + def sigma(self, timestep: torch.Tensor) -> torch.Tensor: + """#### Convert timestep to sigma. + + #### Args: + - `timestep` (torch.Tensor): The timestep value. + + #### Returns: + - `torch.Tensor`: The sigma value. + """ + t = torch.clamp( + timestep.float().to(self.log_sigmas.device), + min=0, + max=(len(self.sigmas) - 1), + ) + low_idx = t.floor().long() + high_idx = t.ceil().long() + w = t.frac() + log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] + return log_sigma.exp().to(timestep.device) + + def percent_to_sigma(self, percent: float) -> float: + """#### Convert percent to sigma. + + #### Args: + - `percent` (float): The percent value. + + #### Returns: + - `float`: The sigma value. + """ + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + percent = 1.0 - percent + return self.sigma(torch.tensor(percent * 999.0)).item() + + +class InterruptProcessingException(Exception): + """#### Exception class for interrupting processing.""" + + pass + + +interrupt_processing_mutex = threading.RLock() + +interrupt_processing = False + + +class KSamplerX0Inpaint: + """#### Class for KSampler X0 Inpainting.""" + + def __init__(self, model: torch.nn.Module, sigmas: torch.Tensor): + """#### Initialize the KSamplerX0Inpaint class. + + #### Args: + - `model` (torch.nn.Module): The model. + - `sigmas` (torch.Tensor): The sigmas tensor. + """ + self.inner_model = model + self.sigmas = sigmas + + def __call__( + self, + x: torch.Tensor, + sigma: torch.Tensor, + denoise_mask: torch.Tensor, + model_options: dict = {}, + seed: int = None, + ) -> torch.Tensor: + """#### Call the KSamplerX0Inpaint class. + + #### Args: + - `x` (torch.Tensor): The input tensor. + - `sigma` (torch.Tensor): The sigma value. + - `denoise_mask` (torch.Tensor): The denoise mask tensor. + - `model_options` (dict, optional): The model options. Defaults to {}. + - `seed` (int, optional): The seed value. Defaults to None. + + #### Returns: + - `torch.Tensor`: The output tensor. + """ + out = self.inner_model(x, sigma, model_options=model_options, seed=seed) + return out + + +class Sampler: + """#### Class for sampling.""" + + def max_denoise(self, model_wrap: torch.nn.Module, sigmas: torch.Tensor) -> bool: + """#### Check if maximum denoising is required. + + #### Args: + - `model_wrap` (torch.nn.Module): The model wrapper. + - `sigmas` (torch.Tensor): The sigmas tensor. + + #### Returns: + - `bool`: Whether maximum denoising is required. + """ + max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max) + sigma = float(sigmas[0]) + return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma + + +class KSAMPLER(Sampler): + """#### Class for KSAMPLER.""" + + def __init__( + self, + sampler_function: callable, + extra_options: dict = {}, + inpaint_options: dict = {}, + ): + """#### Initialize the KSAMPLER class. + + #### Args: + - `sampler_function` (callable): The sampler function. + - `extra_options` (dict, optional): The extra options. Defaults to {}. + - `inpaint_options` (dict, optional): The inpaint options. Defaults to {}. + """ + self.sampler_function = sampler_function + self.extra_options = extra_options + self.inpaint_options = inpaint_options + + def sample( + self, + model_wrap: torch.nn.Module, + sigmas: torch.Tensor, + extra_args: dict, + callback: callable, + noise: torch.Tensor, + latent_image: torch.Tensor = None, + denoise_mask: torch.Tensor = None, + disable_pbar: bool = False, + pipeline: bool = False, + ) -> torch.Tensor: + """#### Sample using the KSAMPLER. + + #### Args: + - `model_wrap` (torch.nn.Module): The model wrapper. + - `sigmas` (torch.Tensor): The sigmas tensor. + - `extra_args` (dict): The extra arguments. + - `callback` (callable): The callback function. + - `noise` (torch.Tensor): The noise tensor. + - `latent_image` (torch.Tensor, optional): The latent image tensor. Defaults to None. + - `denoise_mask` (torch.Tensor, optional): The denoise mask tensor. Defaults to None. + - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + + #### Returns: + - `torch.Tensor`: The sampled tensor. + """ + extra_args["denoise_mask"] = denoise_mask + model_k = KSamplerX0Inpaint(model_wrap, sigmas) + model_k.latent_image = latent_image + model_k.noise = noise + + noise = model_wrap.inner_model.model_sampling.noise_scaling( + sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas) + ) + + k_callback = None + + samples = self.sampler_function( + model_k, + noise, + sigmas, + extra_args=extra_args, + callback=k_callback, + disable=disable_pbar, + pipeline=pipeline, + **self.extra_options, + ) + samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling( + sigmas[-1], samples + ) + return samples + + +def ksampler( + sampler_name: str, + pipeline: bool = False, + extra_options: dict = {}, + inpaint_options: dict = {}, +) -> KSAMPLER: + """#### Get a KSAMPLER. + + #### Args: + - `sampler_name` (str): The sampler name. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + - `extra_options` (dict, optional): The extra options. Defaults to {}. + - `inpaint_options` (dict, optional): The inpaint options. Defaults to {}. + + #### Returns: + - `KSAMPLER`: The KSAMPLER object. + """ + if sampler_name == "dpmpp_2m": + + def dpmpp_2m_function( + model: torch.nn.Module, + noise: torch.Tensor, + sigmas: torch.Tensor, + extra_args: dict, + callback: callable, + disable: bool, + pipeline: bool, + **extra_options, + ) -> torch.Tensor: + sigma_min = sigmas[-1] + if sigma_min == 0: + sigma_min = sigmas[-2] + return samplers.sample_dpmpp_2m( + model, + noise, + sigmas, + extra_args=extra_args, + callback=callback, + disable=disable, + pipeline=pipeline, + **extra_options, + ) + + sampler_function = dpmpp_2m_function + + elif sampler_name == "dpmpp_2m_cfgpp": + + def dpmpp_2m_dy_function( + model: torch.nn.Module, + noise: torch.Tensor, + sigmas: torch.Tensor, + extra_args: dict, + callback: callable, + disable: bool, + pipeline: bool, + **extra_options, + ) -> torch.Tensor: + sigma_min = sigmas[-1] + if sigma_min == 0: + sigma_min = sigmas[-2] + return samplers.sample_dpmpp_2m_cfgpp( + model, + noise, + sigmas, + extra_args=extra_args, + callback=callback, + disable=disable, + pipeline=pipeline, + **extra_options, + ) + + sampler_function = dpmpp_2m_dy_function + + elif sampler_name == "dpmpp_sde": + + def dpmpp_sde_function( + model: torch.nn.Module, + noise: torch.Tensor, + sigmas: torch.Tensor, + extra_args: dict, + callback: callable, + disable: bool, + pipeline: bool, + **extra_options, + ) -> torch.Tensor: + return samplers.sample_dpmpp_sde( + model, + noise, + sigmas, + extra_args=extra_args, + callback=callback, + disable=disable, + pipeline=pipeline, + **extra_options, + ) + + sampler_function = dpmpp_sde_function + + elif sampler_name == "euler_ancestral": + + def euler_ancestral_function( + model: torch.nn.Module, + noise: torch.Tensor, + sigmas: torch.Tensor, + extra_args: dict, + callback: callable, + disable: bool, + pipeline: bool, + ) -> torch.Tensor: + return samplers.sample_euler_ancestral( + model, + noise, + sigmas, + extra_args=extra_args, + callback=callback, + disable=disable, + pipeline=pipeline, + **extra_options, + ) + + sampler_function = euler_ancestral_function + + elif sampler_name == "dpmpp_sde_cfgpp": + + def dpmpp_sde_dy_function( + model: torch.nn.Module, + noise: torch.Tensor, + sigmas: torch.Tensor, + extra_args: dict, + callback: callable, + disable: bool, + pipeline: bool, + **extra_options, + ) -> torch.Tensor: + return samplers.sample_dpmpp_sde_cfgpp( + model, + noise, + sigmas, + extra_args=extra_args, + callback=callback, + disable=disable, + pipeline=pipeline, + **extra_options, + ) + + sampler_function = dpmpp_sde_dy_function + + elif sampler_name == "euler": + + def euler_function( + model, noise, sigmas, extra_args, callback, disable, pipeline=False + ): + return samplers.sample_euler( + model, + noise, + sigmas, + extra_args=extra_args, + callback=callback, + disable=disable, + pipeline=pipeline, + **extra_options, + ) + + sampler_function = euler_function + + return KSAMPLER(sampler_function, extra_options, inpaint_options) + + +def sample( + model: torch.nn.Module, + noise: torch.Tensor, + positive: torch.Tensor, + negative: torch.Tensor, + cfg: float, + device: torch.device, + sampler: KSAMPLER, + sigmas: torch.Tensor, + model_options: dict = {}, + latent_image: torch.Tensor = None, + denoise_mask: torch.Tensor = None, + callback: callable = None, + disable_pbar: bool = False, + seed: int = None, + pipeline: bool = False, + flux: bool = False, +) -> torch.Tensor: + """#### Sample using the given parameters. + + #### Args: + - `model` (torch.nn.Module): The model. + - `noise` (torch.Tensor): The noise tensor. + - `positive` (torch.Tensor): The positive tensor. + - `negative` (torch.Tensor): The negative tensor. + - `cfg` (float): The CFG value. + - `device` (torch.device): The device. + - `sampler` (KSAMPLER): The KSAMPLER object. + - `sigmas` (torch.Tensor): The sigmas tensor. + - `model_options` (dict, optional): The model options. Defaults to {}. + - `latent_image` (torch.Tensor, optional): The latent image tensor. Defaults to None. + - `denoise_mask` (torch.Tensor, optional): The denoise mask tensor. Defaults to None. + - `callback` (callable, optional): The callback function. Defaults to None. + - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. + - `seed` (int, optional): The seed value. Defaults to None. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + + #### Returns: + - `torch.Tensor`: The sampled tensor. + """ + cfg_guider = CFG.CFGGuider(model, flux=flux) + cfg_guider.set_conds(positive, negative) + cfg_guider.set_cfg(cfg) + return cfg_guider.sample( + noise, + latent_image, + sampler, + sigmas, + denoise_mask, + callback, + disable_pbar, + seed, + pipeline=pipeline, + ) + + +def sampler_object(name: str, pipeline: bool = False) -> KSAMPLER: + """#### Get a sampler object. + + #### Args: + - `name` (str): The sampler name. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + + #### Returns: + - `KSAMPLER`: The KSAMPLER object. + """ + sampler = ksampler(name, pipeline=pipeline) + return sampler + + +class KSampler1: + """#### Class for KSampler1.""" + + def __init__( + self, + model: torch.nn.Module, + steps: int, + device, + sampler: str = None, + scheduler: str = None, + denoise: float = None, + model_options: dict = {}, + pipeline: bool = False, + ): + """#### Initialize the KSampler1 class. + + #### Args: + - `model` (torch.nn.Module): The model. + - `steps` (int): The number of steps. + - `device` (torch.device): The device. + - `sampler` (str, optional): The sampler name. Defaults to None. + - `scheduler` (str, optional): The scheduler name. Defaults to None. + - `denoise` (float, optional): The denoise factor. Defaults to None. + - `model_options` (dict, optional): The model options. Defaults to {}. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + """ + self.model = model + self.device = device + self.scheduler = scheduler + self.sampler = sampler + self.set_steps(steps, denoise) + self.denoise = denoise + self.model_options = model_options + self.pipeline = pipeline + + def calculate_sigmas(self, steps: int) -> torch.Tensor: + """#### Calculate the sigmas for the given steps. + + #### Args: + - `steps` (int): The number of steps. + + #### Returns: + - `torch.Tensor`: The calculated sigmas. + """ + sigmas = ksampler_util.calculate_sigmas( + self.model.get_model_object("model_sampling"), self.scheduler, steps + ) + return sigmas + + def set_steps(self, steps: int, denoise: float = None): + """#### Set the steps and calculate the sigmas. + + #### Args: + - `steps` (int): The number of steps. + - `denoise` (float, optional): The denoise factor. Defaults to None. + """ + self.steps = steps + if denoise is None or denoise > 0.9999: + self.sigmas = self.calculate_sigmas(steps).to(self.device) + else: + if denoise <= 0.0: + self.sigmas = torch.FloatTensor([]) + else: + new_steps = int(steps / denoise) + sigmas = self.calculate_sigmas(new_steps).to(self.device) + self.sigmas = sigmas[-(steps + 1) :] + + def sample( + self, + noise: torch.Tensor, + positive: torch.Tensor, + negative: torch.Tensor, + cfg: float, + latent_image: torch.Tensor = None, + start_step: int = None, + last_step: int = None, + force_full_denoise: bool = False, + denoise_mask: torch.Tensor = None, + sigmas: torch.Tensor = None, + callback: callable = None, + disable_pbar: bool = False, + seed: int = None, + pipeline: bool = False, + flux: bool = False, + ) -> torch.Tensor: + """#### Sample using the KSampler1. + + #### Args: + - `noise` (torch.Tensor): The noise tensor. + - `positive` (torch.Tensor): The positive tensor. + - `negative` (torch.Tensor): The negative tensor. + - `cfg` (float): The CFG value. + - `latent_image` (torch.Tensor, optional): The latent image tensor. Defaults to None. + - `start_step` (int, optional): The start step. Defaults to None. + - `last_step` (int, optional): The last step. Defaults to None. + - `force_full_denoise` (bool, optional): Whether to force full denoise. Defaults to False. + - `denoise_mask` (torch.Tensor, optional): The denoise mask tensor. Defaults to None. + - `sigmas` (torch.Tensor, optional): The sigmas tensor. Defaults to None. + - `callback` (callable, optional): The callback function. Defaults to None. + - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. + - `seed` (int, optional): The seed value. Defaults to None. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + + #### Returns: + - `torch.Tensor`: The sampled tensor. + """ + if sigmas is None: + sigmas = self.sigmas + + if last_step is not None and last_step < (len(sigmas) - 1): + sigmas = sigmas[: last_step + 1] + if force_full_denoise: + sigmas[-1] = 0 + + if start_step is not None: + if start_step < (len(sigmas) - 1): + sigmas = sigmas[start_step:] + else: + if latent_image is not None: + return latent_image + else: + return torch.zeros_like(noise) + + sampler = sampler_object(self.sampler, pipeline=pipeline) + + return sample( + self.model, + noise, + positive, + negative, + cfg, + self.device, + sampler, + sigmas, + self.model_options, + latent_image=latent_image, + denoise_mask=denoise_mask, + callback=callback, + disable_pbar=disable_pbar, + seed=seed, + pipeline=pipeline, + flux=flux, + ) + + +def sample1( + model: torch.nn.Module, + noise: torch.Tensor, + steps: int, + cfg: float, + sampler_name: str, + scheduler: str, + positive: torch.Tensor, + negative: torch.Tensor, + latent_image: torch.Tensor, + denoise: float = 1.0, + disable_noise: bool = False, + start_step: int = None, + last_step: int = None, + force_full_denoise: bool = False, + noise_mask: torch.Tensor = None, + sigmas: torch.Tensor = None, + callback: callable = None, + disable_pbar: bool = False, + seed: int = None, + pipeline: bool = False, + flux: bool = False, +) -> torch.Tensor: + """#### Sample using the given parameters. + + #### Args: + - `model` (torch.nn.Module): The model. + - `noise` (torch.Tensor): The noise tensor. + - `steps` (int): The number of steps. + - `cfg` (float): The CFG value. + - `sampler_name` (str): The sampler name. + - `scheduler` (str): The scheduler name. + - `positive` (torch.Tensor): The positive tensor. + - `negative` (torch.Tensor): The negative tensor. + - `latent_image` (torch.Tensor): The latent image tensor. + - `denoise` (float, optional): The denoise factor. Defaults to 1.0. + - `disable_noise` (bool, optional): Whether to disable noise. Defaults to False. + - `start_step` (int, optional): The start step. Defaults to None. + - `last_step` (int, optional): The last step. Defaults to None. + - `force_full_denoise` (bool, optional): Whether to force full denoise. Defaults to False. + - `noise_mask` (torch.Tensor, optional): The noise mask tensor. Defaults to None. + - `sigmas` (torch.Tensor, optional): The sigmas tensor. Defaults to None. + - `callback` (callable, optional): The callback function. Defaults to None. + - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. + - `seed` (int, optional): The seed value. Defaults to None. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + + #### Returns: + - `torch.Tensor`: The sampled tensor. + """ + sampler = KSampler1( + model, + steps=steps, + device=model.load_device, + sampler=sampler_name, + scheduler=scheduler, + denoise=denoise, + model_options=model.model_options, + pipeline=pipeline, + ) + + samples = sampler.sample( + noise, + positive, + negative, + cfg=cfg, + latent_image=latent_image, + start_step=start_step, + last_step=last_step, + force_full_denoise=force_full_denoise, + denoise_mask=noise_mask, + sigmas=sigmas, + callback=callback, + disable_pbar=disable_pbar, + seed=seed, + pipeline=pipeline, + flux=flux, + ) + samples = samples.to(Device.intermediate_device()) + return samples + + +def common_ksampler( + model: torch.nn.Module, + seed: int, + steps: int, + cfg: float, + sampler_name: str, + scheduler: str, + positive: torch.Tensor, + negative: torch.Tensor, + latent: dict, + denoise: float = 1.0, + disable_noise: bool = False, + start_step: int = None, + last_step: int = None, + force_full_denoise: bool = False, + pipeline: bool = False, + flux: bool = False, +) -> tuple: + """#### Common ksampler function. + + #### Args: + - `model` (torch.nn.Module): The model. + - `seed` (int): The seed value. + - `steps` (int): The number of steps. + - `cfg` (float): The CFG value. + - `sampler_name` (str): The sampler name. + - `scheduler` (str): The scheduler name. + - `positive` (torch.Tensor): The positive tensor. + - `negative` (torch.Tensor): The negative tensor. + - `latent` (dict): The latent dictionary. + - `denoise` (float, optional): The denoise factor. Defaults to 1.0. + - `disable_noise` (bool, optional): Whether to disable noise. Defaults to False. + - `start_step` (int, optional): The start step. Defaults to None. + - `last_step` (int, optional): The last step. Defaults to None. + - `force_full_denoise` (bool, optional): Whether to force full denoise. Defaults to False. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + + #### Returns: + - `tuple`: The output tuple containing the latent dictionary and samples. + """ + latent_image = latent["samples"] + latent_image = Latent.fix_empty_latent_channels(model, latent_image) + + if disable_noise: + noise = torch.zeros( + latent_image.size(), + dtype=latent_image.dtype, + layout=latent_image.layout, + device="cpu", + ) + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = ksampler_util.prepare_noise(latent_image, seed, batch_inds) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + samples = sample1( + model, + noise, + steps, + cfg, + sampler_name, + scheduler, + positive, + negative, + latent_image, + denoise=denoise, + disable_noise=disable_noise, + start_step=start_step, + last_step=last_step, + force_full_denoise=force_full_denoise, + noise_mask=noise_mask, + seed=seed, + pipeline=pipeline, + flux=flux, + ) + out = latent.copy() + out["samples"] = samples + return (out,) + + +class KSampler2: + """#### Class for KSampler2.""" + + def sample( + self, + model: torch.nn.Module, + seed: int, + steps: int, + cfg: float, + sampler_name: str, + scheduler: str, + positive: torch.Tensor, + negative: torch.Tensor, + latent_image: torch.Tensor, + denoise: float = 1.0, + pipeline: bool = False, + flux: bool = False, + ) -> tuple: + """#### Sample using the KSampler2. + + #### Args: + - `model` (torch.nn.Module): The model. + - `seed` (int): The seed value. + - `steps` (int): The number of steps. + - `cfg` (float): The CFG value. + - `sampler_name` (str): The sampler name. + - `scheduler` (str): The scheduler name. + - `positive` (torch.Tensor): The positive tensor. + - `negative` (torch.Tensor): The negative tensor. + - `latent_image` (torch.Tensor): The latent image tensor. + - `denoise` (float, optional): The denoise factor. Defaults to 1.0. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + + #### Returns: + - `tuple`: The output tuple containing the latent dictionary and samples. + """ + return common_ksampler( + model, + seed, + steps, + cfg, + sampler_name, + scheduler, + positive, + negative, + latent_image, + denoise=denoise, + pipeline=pipeline, + flux=flux, + ) + + +class ModelType(Enum): + """#### Enum for Model Types.""" + + EPS = 1 + FLUX = 8 + + +def model_sampling( + model_config: dict, model_type: ModelType, flux: bool = False +) -> torch.nn.Module: + """#### Create a model sampling instance. + + #### Args: + - `model_config` (dict): The model configuration. + - `model_type` (ModelType): The model type. + + #### Returns: + - `torch.nn.Module`: The model sampling instance. + """ + if not flux: + s = ModelSamplingDiscrete + if model_type == ModelType.EPS: + c = EPS + + class ModelSampling(s, c): + pass + + return ModelSampling(model_config) + else: + c = CONST + s = ModelSamplingFlux + + class ModelSampling(s, c): + pass + + return ModelSampling(model_config) + + +def sample_custom( + model: torch.nn.Module, + noise: torch.Tensor, + cfg: float, + sampler: KSAMPLER, + sigmas: torch.Tensor, + positive: torch.Tensor, + negative: torch.Tensor, + latent_image: torch.Tensor, + noise_mask: torch.Tensor = None, + callback: callable = None, + disable_pbar: bool = False, + seed: int = None, + pipeline: bool = False, +) -> torch.Tensor: + """#### Custom sampling function. + + #### Args: + - `model` (torch.nn.Module): The model. + - `noise` (torch.Tensor): The noise tensor. + - `cfg` (float): The CFG value. + - `sampler` (KSAMPLER): The KSAMPLER object. + - `sigmas` (torch.Tensor): The sigmas tensor. + - `positive` (torch.Tensor): The positive tensor. + - `negative` (torch.Tensor): The negative tensor. + - `latent_image` (torch.Tensor): The latent image tensor. + - `noise_mask` (torch.Tensor, optional): The noise mask tensor. Defaults to None. + - `callback` (callable, optional): The callback function. Defaults to None. + - `disable_pbar` (bool, optional): Whether to disable the progress bar. Defaults to False. + - `seed` (int, optional): The seed value. Defaults to None. + - `pipeline` (bool, optional): Whether to use the pipeline. Defaults to False. + + #### Returns: + - `torch.Tensor`: The sampled tensor. + """ + samples = sample( + model, + noise, + positive, + negative, + cfg, + model.load_device, + sampler, + sigmas, + model_options=model.model_options, + latent_image=latent_image, + denoise_mask=noise_mask, + callback=callback, + disable_pbar=disable_pbar, + seed=seed, + pipeline=pipeline, + ) + samples = samples.to(Device.intermediate_device()) + return samples diff --git a/modules/sample/sampling_util.py b/modules/sample/sampling_util.py index deb3cbed0a382d1f0ae2f2842c0ee2bd680ae041..d69fd0353c4b3458e5610a925266b6760099682b 100644 --- a/modules/sample/sampling_util.py +++ b/modules/sample/sampling_util.py @@ -1,287 +1,287 @@ -import logging -import math -import threading -import torch -import torchsde -from torch import nn - -from modules.Utilities import util - - -disable_gui = False - -logging_level = logging.INFO - -logging.basicConfig(format="%(message)s", level=logging_level) - - -def make_beta_schedule( - schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3 -): - """#### Create a beta schedule. - - #### Args: - - `schedule` (str): The schedule type. - - `n_timestep` (int): The number of timesteps. - - `linear_start` (float, optional): The linear start value. Defaults to 1e-4. - - `linear_end` (float, optional): The linear end value. Defaults to 2e-2. - - `cosine_s` (float, optional): The cosine s value. Defaults to 8e-3. - - #### Returns: - - `list`: The beta schedule. - """ - betas = ( - torch.linspace( - linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64 - ) - ** 2 - ) - return betas - - -def checkpoint(func, inputs, params, flag): - """#### Create a checkpoint. - - #### Args: - - `func` (callable): The function to checkpoint. - - `inputs` (list): The inputs to the function. - - `params` (list): The parameters of the function. - - `flag` (bool): The checkpoint flag. - - #### Returns: - - `any`: The checkpointed output. - """ - return func(*inputs) - -def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): - """#### Create a timestep embedding. - - #### Args: - - `timesteps` (torch.Tensor): The timesteps. - - `dim` (int): The embedding dimension. - - `max_period` (int, optional): The maximum period. Defaults to 10000. - - `repeat_only` (bool, optional): Whether to repeat only. Defaults to False. - - #### Returns: - - `torch.Tensor`: The timestep embedding. - """ - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) - * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) - / half - ) - args = timesteps[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - return embedding - -def timestep_embedding_flux(t: torch.Tensor, dim, max_period=10000, time_factor: float = 1000.0): - """#### Create a timestep embedding. - - #### Args: - - `timesteps` (torch.Tensor): The timesteps. - - `dim` (int): The embedding dimension. - - `max_period` (int, optional): The maximum period. Defaults to 10000. - - `repeat_only` (bool, optional): Whether to repeat only. Defaults to False. - - #### Returns: - - `torch.Tensor`: The timestep embedding. - """ - t = time_factor * t - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) - * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) - / half - ) - - args = t[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) - if torch.is_floating_point(t): - embedding = embedding.to(t) - return embedding - -def get_sigmas_karras(n, sigma_min, sigma_max, rho=7.0, device="cpu"): - """#### Get the sigmas for Karras sampling. - - constructs the noise schedule of Karras et al. (2022). - - #### Args: - - `n` (int): The number of sigmas. - - `sigma_min` (float): The minimum sigma value. - - `sigma_max` (float): The maximum sigma value. - - `rho` (float, optional): The rho value. Defaults to 7.0. - - `device` (str, optional): The device to use. Defaults to "cpu". - - #### Returns: - - `torch.Tensor`: The sigmas. - """ - ramp = torch.linspace(0, 1, n, device=device) - min_inv_rho = sigma_min ** (1 / rho) - max_inv_rho = sigma_max ** (1 / rho) - sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho - return util.append_zero(sigmas).to(device) - - -def get_ancestral_step(sigma_from, sigma_to, eta=1.0): - """ - #### Calculate the ancestral step in a diffusion process. - - This function computes the values of `sigma_down` and `sigma_up` based on the - input parameters `sigma_from`, `sigma_to`, and `eta`. These values are used - in the context of diffusion models to determine the next step in the process. - - #### Parameters: - - `sigma_from` (float): The starting value of sigma. - - `sigma_to` (float): The target value of sigma. - - `eta` (float, optional): A scaling factor for the step size. Default is 1.0. - - #### Returns: - - `tuple`: A tuple containing `sigma_down` and `sigma_up`: - - `sigma_down` (float): The computed value of sigma for the downward step. - - `sigma_up` (float): The computed value of sigma for the upward step. - """ - sigma_up = min( - sigma_to, - eta * (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5, - ) - sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5 - return sigma_down, sigma_up - - -def default_noise_sampler(x): - """ - #### Returns a noise sampling function that generates random noise with the same shape as the input tensor `x`. - - #### Args: - - `x` (torch.Tensor): The input tensor whose shape will be used to generate random noise. - - #### Returns: - - `function`: A function that takes two arguments, `sigma` and `sigma_next`, and returns a tensor of random noise - with the same shape as `x`. - """ - return lambda sigma, sigma_next: torch.randn_like(x) - - -class BatchedBrownianTree: - """#### A class to represent a batched Brownian tree for stochastic differential equations. - - #### Attributes: - - `cpu_tree` : bool - Indicates if the tree is on CPU. - - `sign` : int - Sign indicating the order of t0 and t1. - - `batched` : bool - Indicates if the tree is batched. - - `trees` : list - List of BrownianTree instances. - - #### Methods: - - `__init__(x, t0, t1, seed=None, **kwargs)`: - Initializes the BatchedBrownianTree with given parameters. - - `sort(a, b)`: - Static method to sort two values and return them along with a sign. - - `__call__(t0, t1)`: - Calls the Brownian tree with given time points t0 and t1. - """ - - def __init__(self, x, t0, t1, seed=None, **kwargs): - self.cpu_tree = True - if "cpu" in kwargs: - self.cpu_tree = kwargs.pop("cpu") - t0, t1, self.sign = self.sort(t0, t1) - w0 = kwargs.get("w0", torch.zeros_like(x)) - if seed is None: - seed = torch.randint(0, 2**63 - 1, []).item() - self.batched = True - seed = [seed] - self.batched = False - self.trees = [ - torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) - for s in seed - ] - - @staticmethod - def sort(a, b): - """#### Sort two values and return them along with a sign. - - #### Args: - - `a` (float): The first value. - - `b` (float): The second value. - - #### Returns: - - `tuple`: A tuple containing the sorted values and a sign: - """ - return (a, b, 1) if a < b else (b, a, -1) - - def __call__(self, t0, t1): - """#### Call the Brownian tree with given time points t0 and t1. - - #### Args: - - `t0` (torch.Tensor): The starting time point. - - `t1` (torch.Tensor): The target time point. - - #### Returns: - - `torch.Tensor`: The Brownian tree values. - """ - t0, t1, sign = self.sort(t0, t1) - w = torch.stack( - [ - tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) - for tree in self.trees - ] - ) * (self.sign * sign) - return w if self.batched else w[0] - - -class BrownianTreeNoiseSampler: - """#### A class to sample noise using a Brownian tree approach. - - #### Attributes: - - `transform` (callable): A function to transform the sigma values. - - `tree` (BatchedBrownianTree): An instance of the BatchedBrownianTree class. - - #### Methods: - - `__init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False)`: - Initializes the BrownianTreeNoiseSampler with the given parameters. - - `__call__(self, sigma, sigma_next)`: - Samples noise between the given sigma values. - """ - - def __init__( - self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False - ): - """#### Initializes the BrownianTreeNoiseSampler with the given parameters. - - #### Args: - - `x` (Tensor): The initial tensor. - - `sigma_min` (float): The minimum sigma value. - - `sigma_max` (float): The maximum sigma value. - - `seed` (int, optional): The seed for random number generation. Defaults to None. - - `transform` (callable, optional): A function to transform the sigma values. Defaults to identity function. - - `cpu` (bool, optional): Whether to use CPU for computations. Defaults to False. - """ - self.transform = transform - t0, t1 = ( - self.transform(torch.as_tensor(sigma_min)), - self.transform(torch.as_tensor(sigma_max)), - ) - self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) - - def __call__(self, sigma, sigma_next): - """#### Samples noise between the given sigma values. - - #### Args: - - `sigma` (float): The current sigma value. - - `sigma_next` (float): The next sigma value. - - #### Returns: - - `Tensor`: The sampled noise. - """ - t0, t1 = ( - self.transform(torch.as_tensor(sigma)), - self.transform(torch.as_tensor(sigma_next)), - ) - return self.tree(t0, t1) / (t1 - t0).abs().sqrt() +import logging +import math +import threading +import torch +import torchsde +from torch import nn + +from modules.Utilities import util + + +disable_gui = False + +logging_level = logging.INFO + +logging.basicConfig(format="%(message)s", level=logging_level) + + +def make_beta_schedule( + schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3 +): + """#### Create a beta schedule. + + #### Args: + - `schedule` (str): The schedule type. + - `n_timestep` (int): The number of timesteps. + - `linear_start` (float, optional): The linear start value. Defaults to 1e-4. + - `linear_end` (float, optional): The linear end value. Defaults to 2e-2. + - `cosine_s` (float, optional): The cosine s value. Defaults to 8e-3. + + #### Returns: + - `list`: The beta schedule. + """ + betas = ( + torch.linspace( + linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64 + ) + ** 2 + ) + return betas + + +def checkpoint(func, inputs, params, flag): + """#### Create a checkpoint. + + #### Args: + - `func` (callable): The function to checkpoint. + - `inputs` (list): The inputs to the function. + - `params` (list): The parameters of the function. + - `flag` (bool): The checkpoint flag. + + #### Returns: + - `any`: The checkpointed output. + """ + return func(*inputs) + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """#### Create a timestep embedding. + + #### Args: + - `timesteps` (torch.Tensor): The timesteps. + - `dim` (int): The embedding dimension. + - `max_period` (int, optional): The maximum period. Defaults to 10000. + - `repeat_only` (bool, optional): Whether to repeat only. Defaults to False. + + #### Returns: + - `torch.Tensor`: The timestep embedding. + """ + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) + / half + ) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + return embedding + +def timestep_embedding_flux(t: torch.Tensor, dim, max_period=10000, time_factor: float = 1000.0): + """#### Create a timestep embedding. + + #### Args: + - `timesteps` (torch.Tensor): The timesteps. + - `dim` (int): The embedding dimension. + - `max_period` (int, optional): The maximum period. Defaults to 10000. + - `repeat_only` (bool, optional): Whether to repeat only. Defaults to False. + + #### Returns: + - `torch.Tensor`: The timestep embedding. + """ + t = time_factor * t + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) + / half + ) + + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + if torch.is_floating_point(t): + embedding = embedding.to(t) + return embedding + +def get_sigmas_karras(n, sigma_min, sigma_max, rho=7.0, device="cpu"): + """#### Get the sigmas for Karras sampling. + + constructs the noise schedule of Karras et al. (2022). + + #### Args: + - `n` (int): The number of sigmas. + - `sigma_min` (float): The minimum sigma value. + - `sigma_max` (float): The maximum sigma value. + - `rho` (float, optional): The rho value. Defaults to 7.0. + - `device` (str, optional): The device to use. Defaults to "cpu". + + #### Returns: + - `torch.Tensor`: The sigmas. + """ + ramp = torch.linspace(0, 1, n, device=device) + min_inv_rho = sigma_min ** (1 / rho) + max_inv_rho = sigma_max ** (1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return util.append_zero(sigmas).to(device) + + +def get_ancestral_step(sigma_from, sigma_to, eta=1.0): + """ + #### Calculate the ancestral step in a diffusion process. + + This function computes the values of `sigma_down` and `sigma_up` based on the + input parameters `sigma_from`, `sigma_to`, and `eta`. These values are used + in the context of diffusion models to determine the next step in the process. + + #### Parameters: + - `sigma_from` (float): The starting value of sigma. + - `sigma_to` (float): The target value of sigma. + - `eta` (float, optional): A scaling factor for the step size. Default is 1.0. + + #### Returns: + - `tuple`: A tuple containing `sigma_down` and `sigma_up`: + - `sigma_down` (float): The computed value of sigma for the downward step. + - `sigma_up` (float): The computed value of sigma for the upward step. + """ + sigma_up = min( + sigma_to, + eta * (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5, + ) + sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5 + return sigma_down, sigma_up + + +def default_noise_sampler(x): + """ + #### Returns a noise sampling function that generates random noise with the same shape as the input tensor `x`. + + #### Args: + - `x` (torch.Tensor): The input tensor whose shape will be used to generate random noise. + + #### Returns: + - `function`: A function that takes two arguments, `sigma` and `sigma_next`, and returns a tensor of random noise + with the same shape as `x`. + """ + return lambda sigma, sigma_next: torch.randn_like(x) + + +class BatchedBrownianTree: + """#### A class to represent a batched Brownian tree for stochastic differential equations. + + #### Attributes: + - `cpu_tree` : bool + Indicates if the tree is on CPU. + - `sign` : int + Sign indicating the order of t0 and t1. + - `batched` : bool + Indicates if the tree is batched. + - `trees` : list + List of BrownianTree instances. + + #### Methods: + - `__init__(x, t0, t1, seed=None, **kwargs)`: + Initializes the BatchedBrownianTree with given parameters. + - `sort(a, b)`: + Static method to sort two values and return them along with a sign. + - `__call__(t0, t1)`: + Calls the Brownian tree with given time points t0 and t1. + """ + + def __init__(self, x, t0, t1, seed=None, **kwargs): + self.cpu_tree = True + if "cpu" in kwargs: + self.cpu_tree = kwargs.pop("cpu") + t0, t1, self.sign = self.sort(t0, t1) + w0 = kwargs.get("w0", torch.zeros_like(x)) + if seed is None: + seed = torch.randint(0, 2**63 - 1, []).item() + self.batched = True + seed = [seed] + self.batched = False + self.trees = [ + torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) + for s in seed + ] + + @staticmethod + def sort(a, b): + """#### Sort two values and return them along with a sign. + + #### Args: + - `a` (float): The first value. + - `b` (float): The second value. + + #### Returns: + - `tuple`: A tuple containing the sorted values and a sign: + """ + return (a, b, 1) if a < b else (b, a, -1) + + def __call__(self, t0, t1): + """#### Call the Brownian tree with given time points t0 and t1. + + #### Args: + - `t0` (torch.Tensor): The starting time point. + - `t1` (torch.Tensor): The target time point. + + #### Returns: + - `torch.Tensor`: The Brownian tree values. + """ + t0, t1, sign = self.sort(t0, t1) + w = torch.stack( + [ + tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) + for tree in self.trees + ] + ) * (self.sign * sign) + return w if self.batched else w[0] + + +class BrownianTreeNoiseSampler: + """#### A class to sample noise using a Brownian tree approach. + + #### Attributes: + - `transform` (callable): A function to transform the sigma values. + - `tree` (BatchedBrownianTree): An instance of the BatchedBrownianTree class. + + #### Methods: + - `__init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False)`: + Initializes the BrownianTreeNoiseSampler with the given parameters. + - `__call__(self, sigma, sigma_next)`: + Samples noise between the given sigma values. + """ + + def __init__( + self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False + ): + """#### Initializes the BrownianTreeNoiseSampler with the given parameters. + + #### Args: + - `x` (Tensor): The initial tensor. + - `sigma_min` (float): The minimum sigma value. + - `sigma_max` (float): The maximum sigma value. + - `seed` (int, optional): The seed for random number generation. Defaults to None. + - `transform` (callable, optional): A function to transform the sigma values. Defaults to identity function. + - `cpu` (bool, optional): Whether to use CPU for computations. Defaults to False. + """ + self.transform = transform + t0, t1 = ( + self.transform(torch.as_tensor(sigma_min)), + self.transform(torch.as_tensor(sigma_max)), + ) + self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) + + def __call__(self, sigma, sigma_next): + """#### Samples noise between the given sigma values. + + #### Args: + - `sigma` (float): The current sigma value. + - `sigma_next` (float): The next sigma value. + + #### Returns: + - `Tensor`: The sampled noise. + """ + t0, t1 = ( + self.transform(torch.as_tensor(sigma)), + self.transform(torch.as_tensor(sigma_next)), + ) + return self.tree(t0, t1) / (t1 - t0).abs().sqrt() diff --git a/modules/tests/test.py b/modules/tests/test.py index 84faa43ef0238918f7994d42336eee5532a2b75b..8671f47515165e400c05d40c5f41d1f212a11e44 100644 --- a/modules/tests/test.py +++ b/modules/tests/test.py @@ -1,86 +1,86 @@ -import unittest -import os -from modules.user.pipeline import pipeline - -class TestPipeline(unittest.TestCase): - def setUp(self): - self.test_prompt = "a cute cat, high quality, detailed" - self.test_img_path = "../_internal/Flux_00001.png" # Make sure this test image exists - - def test_basic_generation_small(self): - pipeline(self.test_prompt, 128, 128, number=1) - # Check if output files exist - - def test_basic_generation_medium(self): - pipeline(self.test_prompt, 512, 512, number=1) - - def test_basic_generation_large(self): - pipeline(self.test_prompt, 1024, 1024, number=1) - - def test_hires_fix(self): - pipeline(self.test_prompt, 512, 512, number=1, hires_fix=True) - - def test_adetailer(self): - pipeline( - "a portrait of a person, high quality", - 512, - 512, - number=1, - adetailer=True - ) - - def test_enhance_prompt(self): - pipeline( - self.test_prompt, - 512, - 512, - number=1, - enhance_prompt=True - ) - - def test_img2img(self): - # Skip if test image doesn't exist - if not os.path.exists(self.test_img_path): - self.skipTest("Test image not found") - - pipeline( - self.test_img_path, - 512, - 512, - number=1, - img2img=True - ) - - def test_stable_fast(self): - resolutions = [(128, 128), (512, 512), (1024, 1024)] - for w, h in resolutions: - pipeline( - self.test_prompt, - w, - h, - number=1, - stable_fast=True - ) - - def test_reuse_seed(self): - pipeline( - self.test_prompt, - 512, - 512, - number=2, - reuse_seed=True - ) - - def test_flux_mode(self): - resolutions = [(128, 128), (512, 512), (1024, 1024)] - for w, h in resolutions: - pipeline( - self.test_prompt, - w, - h, - number=1, - flux_enabled=True - ) - -if __name__ == '__main__': +import unittest +import os +from modules.user.pipeline import pipeline + +class TestPipeline(unittest.TestCase): + def setUp(self): + self.test_prompt = "a cute cat, high quality, detailed" + self.test_img_path = "../_internal/Flux_00001.png" # Make sure this test image exists + + def test_basic_generation_small(self): + pipeline(self.test_prompt, 128, 128, number=1) + # Check if output files exist + + def test_basic_generation_medium(self): + pipeline(self.test_prompt, 512, 512, number=1) + + def test_basic_generation_large(self): + pipeline(self.test_prompt, 1024, 1024, number=1) + + def test_hires_fix(self): + pipeline(self.test_prompt, 512, 512, number=1, hires_fix=True) + + def test_adetailer(self): + pipeline( + "a portrait of a person, high quality", + 512, + 512, + number=1, + adetailer=True + ) + + def test_enhance_prompt(self): + pipeline( + self.test_prompt, + 512, + 512, + number=1, + enhance_prompt=True + ) + + def test_img2img(self): + # Skip if test image doesn't exist + if not os.path.exists(self.test_img_path): + self.skipTest("Test image not found") + + pipeline( + self.test_img_path, + 512, + 512, + number=1, + img2img=True + ) + + def test_stable_fast(self): + resolutions = [(128, 128), (512, 512), (1024, 1024)] + for w, h in resolutions: + pipeline( + self.test_prompt, + w, + h, + number=1, + stable_fast=True + ) + + def test_reuse_seed(self): + pipeline( + self.test_prompt, + 512, + 512, + number=2, + reuse_seed=True + ) + + def test_flux_mode(self): + resolutions = [(128, 128), (512, 512), (1024, 1024)] + for w, h in resolutions: + pipeline( + self.test_prompt, + w, + h, + number=1, + flux_enabled=True + ) + +if __name__ == '__main__': unittest.main() \ No newline at end of file diff --git a/modules/user/GUI.py b/modules/user/GUI.py index 8b80d062d66c2a0063ab099b4c03e040b10cdfb3..d9df3ff30ed291b4da2de0ce26e9cf0565a05d2a 100644 --- a/modules/user/GUI.py +++ b/modules/user/GUI.py @@ -1,1332 +1,1390 @@ -import os -import queue -import sys -import random -import threading -import tkinter as tk -from tkinter import filedialog -from typing import Union -from PIL import Image, ImageTk -import numpy as np -import customtkinter as ctk -import glob -import time - -import torch - -# Add the directory containing LightDiffusion.py to the Python path -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) - -from modules.AutoDetailer import SAM, ADetailer, bbox, SEGS -from modules.AutoEncoders import VariationalAE -from modules.clip import Clip -from modules.sample import sampling - -from modules.Utilities import util -from modules.UltimateSDUpscale import USDU_upscaler, UltimateSDUpscale - -from modules.FileManaging import Downloader, ImageSaver, Loader -from modules.Model import LoRas -from modules.Utilities import Enhancer, Latent, upscale -from modules.Quantize import Quantizer -from modules.WaveSpeed import fbcache_nodes, misc_nodes -from modules.hidiffusion import msw_msa_attention - -Downloader.CheckAndDownload() - -files = glob.glob("./_internal/checkpoints/*.safetensors") -loras = glob.glob("./_internal/loras/*.safetensors") -loras += glob.glob("./_internal/loras/*.pt") - - -def debounce(wait): - """Decorator to debounce resize events""" - - def decorator(fn): - last_call = [0] - - def debounced(*args, **kwargs): - current_time = time.time() - if current_time - last_call[0] >= wait: - fn(*args, **kwargs) - last_call[0] = current_time - - return debounced - - return decorator - - -class App(tk.Tk): - """Main application class for the LightDiffusion GUI.""" - - def __init__(self): - """Initialize the App class.""" - super().__init__() - self.title("LightDiffusion") - self.geometry("800x700") - - # Configure main window grid - self.grid_columnconfigure(1, weight=1) - self.grid_rowconfigure(0, weight=1) - - file_names = [os.path.basename(file) for file in files] - lora_names = [os.path.basename(lora) for lora in loras] - - selected_file = tk.StringVar() - selected_lora = tk.StringVar() - if file_names: - selected_file.set(file_names[0]) - if lora_names: - selected_lora.set(lora_names[0]) - - # Create main sidebar frame with padding and grid - self.sidebar = tk.Frame(self, bg="#FBFBFB", padx=10, pady=10) - self.sidebar.grid(row=0, column=0, sticky="nsew") - self.sidebar.grid_columnconfigure(0, weight=1) - - # Configure sidebar grid rows - for i in range(8): - self.sidebar.grid_rowconfigure(i, weight=1) - - # Text input frames with expansion - self.prompt_frame = tk.Frame(self.sidebar, bg="#FBFBFB") - self.prompt_frame.grid(row=0, column=0, sticky="nsew", pady=(0, 5)) - self.prompt_frame.grid_columnconfigure(0, weight=1) - self.prompt_frame.grid_rowconfigure(0, weight=2) - self.prompt_frame.grid_rowconfigure(1, weight=1) - - # Prompt textbox with expansion - self.prompt_entry = ctk.CTkTextbox( - self.prompt_frame, - height=150, - fg_color="#E8F9FF", - text_color="black", - border_color="gray", - border_width=2, - ) - self.prompt_entry.grid(row=0, column=0, sticky="nsew") - - # Negative prompt textbox with expansion - self.neg = ctk.CTkTextbox( - self.prompt_frame, - height=75, - fg_color="#E8F9FF", - text_color="black", - border_color="gray", - border_width=2, - ) - self.neg.grid(row=1, column=0, sticky="nsew", pady=(5, 0)) - - # Add model dropdown with error handling for empty lists - model_values = (file_names if file_names else ["No models found"]) + ["flux"] - - # Model dropdown and Flux checkbox - self.dropdown = ctk.CTkOptionMenu( - self.sidebar, - values=model_values, - fg_color="#F5EFFF", - text_color="black", - command=self.on_model_selected, - ) - self.dropdown.grid(row=2, column=0, sticky="ew") - - # LoRA selection - self.lora_selection = ctk.CTkOptionMenu( - self.sidebar, values=lora_names, fg_color="#F5EFFF", text_color="black" - ) - self.lora_selection.grid(row=3, column=0, sticky="ew", pady=5) - - # Display frame with expansion - self.display = tk.Frame(self, bg="#FBFBFB") - self.display.grid(row=0, column=1, sticky="nsew", padx=10, pady=10) - self.display.grid_columnconfigure(0, weight=1) - self.img = None - - # Add row configuration for both image and checkbox - self.display.grid_rowconfigure(0, weight=1) # For image - self.display.grid_rowconfigure(1, weight=0) # For checkbox - - # Image label with expansion - self.image_label = tk.Label(self.display, bg="#FBFBFB") - self.image_label.grid(row=0, column=0, sticky="nsew") - - # Previewer checkbox - changed from pack to grid - self.previewer_var = tk.BooleanVar() - self.previewer_checkbox = ctk.CTkCheckBox( - self.display, - text="Previewer", - variable=self.previewer_var, - command=self.print_previewer, - text_color="black", - ) - self.previewer_checkbox.grid(row=1, column=0, pady=10) - - # Progress Bar - self.progress = ctk.CTkProgressBar(self.display, fg_color="#FBFBFB") - self.progress.grid(row=2, column=0, sticky="ew", pady=10, padx=10) - self.progress.set(0) - - # Make sliders frame expand - self.sliders_frame = tk.Frame(self.sidebar, bg="#FBFBFB") - self.sliders_frame.grid(row=4, column=0, sticky="nsew", pady=5) - self.sliders_frame.grid_columnconfigure(1, weight=1) - - # Configure slider weights - for i in range(3): - self.sliders_frame.grid_rowconfigure(i, weight=1) - - # Make checkbox frame expand - self.checkbox_frame = tk.Frame(self.sidebar, bg="#FBFBFB") - self.checkbox_frame.grid(row=5, column=0, sticky="nsew", pady=10) - self.checkbox_frame.grid_columnconfigure(0, weight=1) - self.checkbox_frame.grid_columnconfigure(1, weight=1) - - # Make button frame expand - self.button_frame = tk.Frame(self.sidebar, bg="#FBFBFB") - self.button_frame.grid(row=7, column=0, sticky="nsew", pady=10) - self.button_frame.grid_columnconfigure(0, weight=1) - self.button_frame.grid_columnconfigure(1, weight=1) - - # Width slider - tk.Label(self.sliders_frame, text="Width:", bg="#FBFBFB").grid( - row=0, column=0, padx=(0, 5) - ) - self.width_slider = ctk.CTkSlider( - self.sliders_frame, from_=1, to=2048, number_of_steps=32, fg_color="#F5EFFF" - ) - self.width_slider.grid(row=0, column=1, sticky="ew") - self.width_label = ctk.CTkLabel(self.sliders_frame, text="") - self.width_label.grid(row=0, column=2, padx=(5, 0)) - - # Height slider - tk.Label(self.sliders_frame, text="Height:", bg="#FBFBFB").grid( - row=1, column=0, padx=(0, 5) - ) - self.height_slider = ctk.CTkSlider( - self.sliders_frame, from_=1, to=2048, number_of_steps=32, fg_color="#F5EFFF" - ) - self.height_slider.grid(row=1, column=1, sticky="ew") - self.height_label = ctk.CTkLabel(self.sliders_frame, text="") - self.height_label.grid(row=1, column=2, padx=(5, 0)) - - # CFG slider - tk.Label(self.sliders_frame, text="CFG:", bg="#FBFBFB").grid( - row=2, column=0, padx=(0, 5) - ) - self.cfg_slider = ctk.CTkSlider( - self.sliders_frame, from_=1, to=15, number_of_steps=14, fg_color="#F5EFFF" - ) - self.cfg_slider.grid(row=2, column=1, sticky="ew") - self.cfg_label = ctk.CTkLabel(self.sliders_frame, text="") - self.cfg_label.grid(row=2, column=2, padx=(5, 0)) - - # Batch size slider - tk.Label(self.sliders_frame, text="Batch Size:", bg="#FBFBFB").grid( - row=3, column=0, padx=(0, 5) - ) - self.batch_slider = ctk.CTkSlider( - self.sliders_frame, from_=1, to=10, number_of_steps=9, fg_color="#F5EFFF" - ) - self.batch_slider.grid(row=3, column=1, sticky="ew") - self.batch_label = ctk.CTkLabel(self.sliders_frame, text="") - self.batch_label.grid(row=3, column=2, padx=(5, 0)) - - # Configure grid columns and rows to distribute space evenly - self.checkbox_frame.grid_columnconfigure(0, weight=1) - self.checkbox_frame.grid_columnconfigure(1, weight=1) - self.checkbox_frame.grid_rowconfigure(0, weight=1) - self.checkbox_frame.grid_rowconfigure(1, weight=1) - self.checkbox_frame.grid_rowconfigure(2, weight=1) - - # checkbox for hiresfix - self.hires_fix_var = tk.BooleanVar() - self.hires_fix_checkbox = ctk.CTkCheckBox( - self.checkbox_frame, - text="Hires Fix", - variable=self.hires_fix_var, - command=self.print_hires_fix, - text_color="black", - ) - self.hires_fix_checkbox.grid( - row=0, column=0, padx=(75, 5), pady=5, sticky="nsew" - ) - - # checkbox for Adetailer - self.adetailer_var = tk.BooleanVar() - self.adetailer_checkbox = ctk.CTkCheckBox( - self.checkbox_frame, - text="Adetailer", - variable=self.adetailer_var, - command=self.print_adetailer, - text_color="black", - ) - self.adetailer_checkbox.grid(row=0, column=1, padx=5, pady=5, sticky="nsew") - - # checkbox to enable stable-fast optimization - self.stable_fast_var = tk.BooleanVar() - self.stable_fast_checkbox = ctk.CTkCheckBox( - self.checkbox_frame, - text="Stable Fast", - variable=self.stable_fast_var, - text_color="black", - ) - self.stable_fast_checkbox.grid( - row=1, column=0, padx=(75, 5), pady=5, sticky="nsew" - ) - - # checkbox to enable prompt enhancer - self.enhancer_var = tk.BooleanVar() - self.enhancer_checkbox = ctk.CTkCheckBox( - self.checkbox_frame, - text="Prompt enhancer", - variable=self.enhancer_var, - text_color="black", - ) - self.enhancer_checkbox.grid(row=1, column=1, padx=5, pady=5, sticky="nsew") - - self.prioritize_speed_var = tk.BooleanVar() - self.prioritize_speed_checkbox = ctk.CTkCheckBox( - self.checkbox_frame, - text="Prioritize Speed", - variable=self.prioritize_speed_var, - text_color="black", - ) - self.prioritize_speed_checkbox.grid(row=2, column=0, padx=(75, 5), pady=5, sticky="nsew") - - # Button to launch the generation - self.generate_button = ctk.CTkButton( - self.sidebar, - text="Generate", - command=self.generate_image, - fg_color="#C4D9FF", - text_color="black", - border_color="gray", - border_width=2, - ) - self.generate_button.grid( - row=6, column=0, pady=10, sticky="ew" - ) # Changed from pack to grid - - self.ckpt = None - - # load the checkpoint on an another thread - threading.Thread(target=self._prep, daemon=True).start() - - # img2img button - self.img2img_button = ctk.CTkButton( - self.button_frame, - text="img2img", - command=self.img2img, - fg_color="#F5EFFF", - text_color="black", - border_color="gray", - border_width=2, - ) - self.img2img_button.grid(row=0, column=0, padx=5, sticky="ew") - - # interrupt button - self.generation_threads = [] - self.interrupt_flag = False - self.interrupt_button = ctk.CTkButton( - self.button_frame, - text="Interrupt", - command=self.interrupt_generation, - fg_color="#F5EFFF", - text_color="black", - border_color="gray", - border_width=2, - ) - self.interrupt_button.grid(row=0, column=1, padx=5, sticky="ew") - - prompt, neg, width, height, cfg = util.load_parameters_from_file() - self.prompt_entry.insert(tk.END, prompt) - self.neg.insert(tk.END, neg) - self.width_slider.set(width) - self.height_slider.set(height) - self.cfg_slider.set(cfg) - self.batch_slider.set(1) - - self.width_slider.bind("", lambda event: self.update_labels()) - self.height_slider.bind("", lambda event: self.update_labels()) - self.cfg_slider.bind("", lambda event: self.update_labels()) - self.batch_slider.bind("", lambda event: self.update_labels()) - self.update_labels() - self.prompt_entry.bind( - "", - lambda event: util.write_parameters_to_file( - self.prompt_entry.get("1.0", tk.END), - self.neg.get("1.0", tk.END), - self.width_slider.get(), - self.height_slider.get(), - self.cfg_slider.get(), - ), - ) - self.neg.bind( - "", - lambda event: util.write_parameters_to_file( - self.prompt_entry.get("1.0", tk.END), - self.neg.get("1.0", tk.END), - self.width_slider.get(), - self.height_slider.get(), - self.cfg_slider.get(), - ), - ) - self.width_slider.bind( - "", - lambda event: util.write_parameters_to_file( - self.prompt_entry.get("1.0", tk.END), - self.neg.get("1.0", tk.END), - self.width_slider.get(), - self.height_slider.get(), - self.cfg_slider.get(), - ), - ) - self.height_slider.bind( - "", - lambda event: util.write_parameters_to_file( - self.prompt_entry.get("1.0", tk.END), - self.neg.get("1.0", tk.END), - self.width_slider.get(), - self.height_slider.get(), - self.cfg_slider.get(), - ), - ) - self.cfg_slider.bind( - "", - lambda event: util.write_parameters_to_file( - self.prompt_entry.get("1.0", tk.END), - self.neg.get("1.0", tk.END), - self.width_slider.get(), - self.height_slider.get(), - self.cfg_slider.get(), - ), - ) - # Add resize handling variables - self._resize_queue = queue.Queue() - self._resize_thread = None - self._resize_event = threading.Event() - self._resize_lock = threading.Lock() - self._resize_running = True - self._last_resize_time = 0 - self._resize_delay = 0.1 - self._image_cache = {} - self._current_image = None - - # Start resize worker thread - self._start_resize_worker() - - # Bind resize event - self.bind("", self._queue_resize) - - # Bind cleanup - self.protocol("WM_DELETE_WINDOW", self._cleanup) - self.display_most_recent_image_flag = False - self.display_most_recent_image() - self.is_generating = False - self.sampler = "dpmpp_sde" if not self.prioritize_speed_var.get() else "dpmpp_2m" - - def _img2img(self, file_path: str) -> None: - """Perform img2img on the selected image. - - Args: - file_path (str): The path to the selected image. - """ - self.is_generating = True - self.img2img_button.configure(state="disabled") - self.display_most_recent_image_flag = False - prompt = self.prompt_entry.get("1.0", tk.END) - neg = self.neg.get("1.0", tk.END) - img = Image.open(file_path) - img_array = np.array(img) - img_tensor = torch.from_numpy(img_array).float().to("cpu") / 255.0 - img_tensor = img_tensor.unsqueeze(0) - self.interrupt_flag = False - self.sampler = "dpmpp_sde" if not self.prioritize_speed_var.get() else "dpmpp_2m" - with torch.inference_mode(): - ( - checkpointloadersimple_241, - cliptextencode, - emptylatentimage, - ksampler_instance, - vaedecode, - saveimage, - latentupscale, - upscalemodelloader, - ultimatesdupscale, - ) = self._prep() - try: - loraloader = LoRas.LoraLoader() - loraloader_274 = loraloader.load_lora( - lora_name="add_detail.safetensors", - strength_model=2, - strength_clip=2, - model=checkpointloadersimple_241[0], - clip=checkpointloadersimple_241[1], - ) - except: - loraloader_274 = checkpointloadersimple_241 - - if self.stable_fast_var.get() is True: - from modules.StableFast import StableFast - - try: - app.title("LigtDiffusion - Generating StableFast model") - except: - pass - applystablefast = StableFast.ApplyStableFastUnet() - applystablefast_158 = applystablefast.apply_stable_fast( - enable_cuda_graph=False, - model=loraloader_274[0], - ) - else: - applystablefast_158 = loraloader_274 - fb_cache = fbcache_nodes.ApplyFBCacheOnModel() - applystablefast_158 = fb_cache.patch(applystablefast_158, "diffusion_model", 0.120) - clipsetlastlayer = Clip.CLIPSetLastLayer() - clipsetlastlayer_257 = clipsetlastlayer.set_last_layer( - stop_at_clip_layer=-2, clip=loraloader_274[1] - ) - - cliptextencode_242 = cliptextencode.encode( - text=prompt, - clip=clipsetlastlayer_257[0], - ) - cliptextencode_243 = cliptextencode.encode( - text=neg, - clip=clipsetlastlayer_257[0], - ) - upscalemodelloader_244 = upscalemodelloader.load_model( - "RealESRGAN_x4plus.pth" - ) - try: - app.title("LightDiffusion - Upscaling") - except: - pass - ultimatesdupscale_250 = ultimatesdupscale.upscale( - upscale_by=2, - seed=random.randint(1, 2**64), - steps=8, - cfg=6, - sampler_name=self.sampler, - scheduler="karras", - denoise=0.3, - mode_type="Linear", - tile_width=512, - tile_height=512, - mask_blur=16, - tile_padding=32, - seam_fix_mode="Half Tile", - seam_fix_denoise=0.2, - seam_fix_width=64, - seam_fix_mask_blur=16, - seam_fix_padding=32, - force_uniform_tiles="enable", - image=img_tensor, - model=applystablefast_158[0], - positive=cliptextencode_242[0], - negative=cliptextencode_243[0], - vae=checkpointloadersimple_241[2], - upscale_model=upscalemodelloader_244[0], - ) - saveimage.save_images( - filename_prefix="LD-i2i", - images=ultimatesdupscale_250[0], - ) - for image in ultimatesdupscale_250[0]: - i = 255.0 * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - self.update_image(img) - global generated - generated = img - self.display_most_recent_image_flag = True - try: - app.title("LightDiffusion") - except: - pass - self.is_generating = False - self.img2img_button.configure(state="normal") - - def img2img(self) -> None: - """Open the file selector and run img2img on the selected image.""" - if self.is_generating: - return - file_path = filedialog.askopenfilename() - if file_path: - threading.Thread( - target=self._img2img, args=(file_path,), daemon=True - ).start() - - def print_hires_fix(self) -> None: - """Print the status of the hires fix checkbox.""" - if self.hires_fix_var.get() is True: - print("Hires fix is ON") - else: - print("Hires fix is OFF") - - def print_adetailer(self) -> None: - """Print the status of the adetailer checkbox.""" - if self.adetailer_var.get() is True: - print("Adetailer is ON") - else: - print("Adetailer is OFF") - - def print_previewer(self) -> None: - """Print the status of the previewer checkbox.""" - if self.previewer_var.get() is True: - print("Previewer is ON") - else: - print("Previewer is OFF") - - def generate_image(self) -> None: - """Start the image generation process.""" - if self.is_generating: - return - - if self.dropdown.get() == "flux": - self.generate_thread = threading.Thread( - target=self._generate_image_flux, daemon=True - ).start() - else: - self.generate_thread = threading.Thread( - target=self._generate_image, daemon=True - ).start() - - def _prep(self) -> tuple: - """Prepare the necessary components for image generation. - - Returns: - tuple: The prepared components. - """ - if self.dropdown.get() != self.ckpt and self.dropdown.get() != "flux": - self.ckpt = self.dropdown.get() - with torch.inference_mode(): - self.checkpointloadersimple = Loader.CheckpointLoaderSimple() - self.checkpointloadersimple_241 = ( - self.checkpointloadersimple.load_checkpoint( - ckpt_name="./_internal/checkpoints/" + self.ckpt - ) - ) - self.cliptextencode = Clip.CLIPTextEncode() - self.emptylatentimage = Latent.EmptyLatentImage() - self.ksampler_instance = sampling.KSampler2() - self.vaedecode = VariationalAE.VAEDecode() - self.saveimage = ImageSaver.SaveImage() - self.latent_upscale = upscale.LatentUpscale() - self.upscalemodelloader = USDU_upscaler.UpscaleModelLoader() - self.ultimatesdupscale = UltimateSDUpscale.UltimateSDUpscale() - return ( - self.checkpointloadersimple_241, - self.cliptextencode, - self.emptylatentimage, - self.ksampler_instance, - self.vaedecode, - self.saveimage, - self.latent_upscale, - self.upscalemodelloader, - self.ultimatesdupscale, - ) - - def _generate_image(self) -> None: - """Generate image with proper interrupt handling.""" - self.is_generating = True - self.generate_button.configure(state="disabled") - - current_thread = threading.current_thread() - self.generation_threads.append(current_thread) - images = [] - self.interrupt_flag = False - self.sampler = "dpmpp_sde" if not self.prioritize_speed_var.get() else "dpmpp_2m" - try: - # Disable generate button during generation - self.generate_button.configure(state="disabled") - self.display_most_recent_image_flag = False - self.progress.set(0) - - # Early interrupt check - if self.interrupt_flag: - return - - # Get generation parameters - prompt = self.prompt_entry.get("1.0", tk.END) - neg = self.neg.get("1.0", tk.END) - w = int(self.width_slider.get()) - h = int(self.height_slider.get()) - cfg = int(self.cfg_slider.get()) - - try: - if self.enhancer_var.get() is True: - prompt = Enhancer.enhance_prompt(prompt) - while prompt is None: - pass - except: - pass - - # Main generation with proper interrupt handling - with torch.inference_mode(): - components = self._prep() - if self.interrupt_flag: - return - ( - checkpointloadersimple_241, - cliptextencode, - emptylatentimage, - ksampler_instance, - vaedecode, - saveimage, - latentupscale, - upscalemodelloader, - ultimatesdupscale, - ) = self._prep() - try: - loraloader = LoRas.LoraLoader() - loraloader_274 = loraloader.load_lora( - lora_name=self.lora_selection.get().replace( - "./_internal/loras/", "" - ), - strength_model=0.7, - strength_clip=0.7, - model=checkpointloadersimple_241[0], - clip=checkpointloadersimple_241[1], - ) - print( - "loading", - self.lora_selection.get().replace("./_internal/loras/", ""), - ) - except: - loraloader_274 = checkpointloadersimple_241 - try: - cliptextencode_124 = cliptextencode.encode( - text="royal, detailed, magnificient, beautiful, seducing", - clip=loraloader_274[1], - ) - - ultralyticsdetectorprovider = bbox.UltralyticsDetectorProvider() - ultralyticsdetectorprovider_151 = ultralyticsdetectorprovider.doit( - # model_name="face_yolov8m.pt" - model_name="person_yolov8m-seg.pt" - ) - - bboxdetectorsegs = bbox.BboxDetectorForEach() - samdetectorcombined = SAM.SAMDetectorCombined() - impactsegsandmask = SEGS.SegsBitwiseAndMask() - detailerforeachdebug = ADetailer.DetailerForEachTest() - except: - pass - clipsetlastlayer = Clip.CLIPSetLastLayer() - clipsetlastlayer_257 = clipsetlastlayer.set_last_layer( - stop_at_clip_layer=-2, clip=loraloader_274[1] - ) - self.progress.set(0.2) - if self.stable_fast_var.get() is True: - from modules.StableFast import StableFast - - try: - self.title("LightDiffusion - Generating StableFast model") - except: - pass - applystablefast = StableFast.ApplyStableFastUnet() - applystablefast_158 = applystablefast.apply_stable_fast( - enable_cuda_graph=False, - model=loraloader_274[0], - ) - else: - applystablefast_158 = loraloader_274 - fb_cache = fbcache_nodes.ApplyFBCacheOnModel() - applystablefast_158 = fb_cache.patch(applystablefast_158, "diffusion_model", 0.120) - hidiffoptimizer = msw_msa_attention.ApplyMSWMSAAttentionSimple() - cliptextencode_242 = cliptextencode.encode( - text=prompt, - clip=clipsetlastlayer_257[0], - ) - cliptextencode_243 = cliptextencode.encode( - text=neg, - clip=clipsetlastlayer_257[0], - ) - emptylatentimage_244 = emptylatentimage.generate( - width=w, height=h, batch_size=int(self.batch_slider.get()) - ) - ksampler_239 = ksampler_instance.sample( - seed=random.randint(1, 2**64), - steps=20, - cfg=cfg, - sampler_name=self.sampler, - scheduler="karras", - denoise=1, - model=hidiffoptimizer.go( - model_type="auto", model=applystablefast_158[0] - )[0], - positive=cliptextencode_242[0], - negative=cliptextencode_243[0], - latent_image=emptylatentimage_244[0], - ) - self.progress.set(0.4) - if self.hires_fix_var.get() is True: - latentupscale_254 = latentupscale.upscale( - width=w * 2, - height=h * 2, - samples=ksampler_239[0], - ) - ksampler_253 = ksampler_instance.sample( - seed=random.randint(1, 2**64), - steps=10, - cfg=8, - sampler_name="euler_ancestral", - scheduler="normal", - denoise=0.45, - model=hidiffoptimizer.go( - model_type="auto", model=applystablefast_158[0] - )[0], - positive=cliptextencode_242[0], - negative=cliptextencode_243[0], - latent_image=latentupscale_254[0], - ) - vaedecode_240 = vaedecode.decode( - samples=ksampler_253[0], - vae=checkpointloadersimple_241[2], - ) - saveimage.save_images( - filename_prefix="LD-HiresFix", images=vaedecode_240[0] - ) - for image in vaedecode_240[0]: - i = 255.0 * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - images.append(img) - else: - vaedecode_240 = vaedecode.decode( - samples=ksampler_239[0], - vae=checkpointloadersimple_241[2], - ) - saveimage.save_images(filename_prefix="LD", images=vaedecode_240[0]) - for image in vaedecode_240[0]: - i = 255.0 * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - images.append(img) - if self.interrupt_flag: - return - - self.progress.set(0.6) - if self.adetailer_var.get() is True: - samloader = SAM.SAMLoader() - samloader_87 = samloader.load_model( - model_name="sam_vit_b_01ec64.pth", device_mode="AUTO" - ) - bboxdetectorsegs_132 = bboxdetectorsegs.doit( - threshold=0.5, - dilation=10, - crop_factor=2, - drop_size=10, - labels="all", - bbox_detector=ultralyticsdetectorprovider_151[0], - image=vaedecode_240[0], - ) - samdetectorcombined_139 = samdetectorcombined.doit( - detection_hint="center-1", - dilation=0, - threshold=0.93, - bbox_expansion=0, - mask_hint_threshold=0.7, - mask_hint_use_negative="False", - sam_model=samloader_87[0], - segs=bboxdetectorsegs_132, - image=vaedecode_240[0], - ) - if samdetectorcombined_139[0] is None: - return - impactsegsandmask_152 = impactsegsandmask.doit( - segs=bboxdetectorsegs_132, - mask=samdetectorcombined_139[0], - ) - detailerforeachdebug_145 = detailerforeachdebug.doit( - guide_size=512, - guide_size_for=False, - max_size=768, - seed=random.randint(1, 2**64), - steps=20, - cfg=6.5, - sampler_name=self.sampler, - scheduler="karras", - denoise=0.5, - feather=5, - noise_mask=True, - force_inpaint=True, - wildcard="", - cycle=1, - inpaint_model=False, - noise_mask_feather=20, - image=vaedecode_240[0], - segs=impactsegsandmask_152[0], - model=applystablefast_158[0], - clip=checkpointloadersimple_241[1], - vae=checkpointloadersimple_241[2], - positive=cliptextencode_124[0], - negative=cliptextencode_243[0], - ) - saveimage.save_images( - filename_prefix="LD-refined", - images=detailerforeachdebug_145[0], - ) - ultralyticsdetectorprovider = bbox.UltralyticsDetectorProvider() - ultralyticsdetectorprovider_151 = ultralyticsdetectorprovider.doit( - model_name="face_yolov9c.pt" - ) - bboxdetectorsegs_132 = bboxdetectorsegs.doit( - threshold=0.5, - dilation=10, - crop_factor=2, - drop_size=10, - labels="all", - bbox_detector=ultralyticsdetectorprovider_151[0], - image=detailerforeachdebug_145[0], - ) - samdetectorcombined_139 = samdetectorcombined.doit( - detection_hint="center-1", - dilation=0, - threshold=0.93, - bbox_expansion=0, - mask_hint_threshold=0.7, - mask_hint_use_negative="False", - sam_model=samloader_87[0], - segs=bboxdetectorsegs_132, - image=detailerforeachdebug_145[0], - ) - impactsegsandmask_152 = impactsegsandmask.doit( - segs=bboxdetectorsegs_132, - mask=samdetectorcombined_139[0], - ) - detailerforeachdebug_145 = detailerforeachdebug.doit( - guide_size=512, - guide_size_for=False, - max_size=768, - seed=random.randint(1, 2**64), - steps=20, - cfg=6.5, - sampler_name=self.sampler, - scheduler="karras", - denoise=0.5, - feather=5, - noise_mask=True, - force_inpaint=True, - wildcard="", - cycle=1, - inpaint_model=False, - noise_mask_feather=20, - image=detailerforeachdebug_145[0], - segs=impactsegsandmask_152[0], - model=applystablefast_158[0], - clip=checkpointloadersimple_241[1], - vae=checkpointloadersimple_241[2], - positive=cliptextencode_124[0], - negative=cliptextencode_243[0], - ) - saveimage.save_images( - filename_prefix="lD-2ndrefined", - images=detailerforeachdebug_145[0], - ) - for image in detailerforeachdebug_145[0]: - i = 255.0 * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - images.append(img) - - self.progress.set(0.8) - - if not self.interrupt_flag: - self.progress.set(1.0) - self.img = img - self.update_image(images) - self.display_most_recent_image_flag = True - - except Exception as e: - print(f"Generation error: {e}") - self.title(f"LightDiffusion - Error: {str(e)}") - - finally: - # Reset state when done - self.is_generating = False - self.generate_button.configure(state="normal") - if current_thread in self.generation_threads: - self.generation_threads.remove(current_thread) - self.progress.set(0) - - # Clear CUDA cache - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - def _generate_image_flux(self) -> None: - """Generate an image using the Flux model.""" - self.is_generating = True - self.generate_button.configure(state="disabled") - # Add current thread to list at start - current_thread = threading.current_thread() - self.generation_threads.append(current_thread) - self.display_most_recent_image_flag = False - w = int(self.width_slider.get()) - h = int(self.height_slider.get()) - prompt = self.prompt_entry.get("1.0", tk.END) - try: - if self.enhancer_var.get() is True: - prompt = Enhancer.enhance_prompt(prompt) - while prompt is None: - pass - self.interrupt_flag = False - Downloader.CheckAndDownloadFlux() - with torch.inference_mode(): - dualcliploadergguf = Quantizer.DualCLIPLoaderGGUF() - emptylatentimage = Latent.EmptyLatentImage() - vaeloader = VariationalAE.VAELoader() - unetloadergguf = Quantizer.UnetLoaderGGUF() - cliptextencodeflux = Quantizer.CLIPTextEncodeFlux() - conditioningzeroout = Quantizer.ConditioningZeroOut() - ksampler = sampling.KSampler2() - vaedecode = VariationalAE.VAEDecode() - saveimage = ImageSaver.SaveImage() - unetloadergguf_10 = unetloadergguf.load_unet( - unet_name="flux1-dev-Q8_0.gguf" - ) - vaeloader_11 = vaeloader.load_vae(vae_name="ae.safetensors") - dualcliploadergguf_19 = dualcliploadergguf.load_clip( - clip_name1="clip_l.safetensors", - clip_name2="t5-v1_1-xxl-encoder-Q8_0.gguf", - type="flux", - ) - emptylatentimage_5 = emptylatentimage.generate( - width=w, height=h, batch_size=int(self.batch_slider.get()) - ) - cliptextencodeflux_15 = cliptextencodeflux.encode( - clip_l=prompt, - t5xxl=prompt, - guidance=2.5, - clip=dualcliploadergguf_19[0], - flux_enabled=True, - ) - conditioningzeroout_16 = conditioningzeroout.zero_out( - conditioning=cliptextencodeflux_15[0] - ) - fb_cache = fbcache_nodes.ApplyFBCacheOnModel() - unetloadergguf_10 = fb_cache.patch(unetloadergguf_10, "diffusion_model", 0.120) - # try: - # import triton - # compiler = misc_nodes.EnhancedCompileModel() - # unetloadergguf_10 = compiler.patch(unetloadergguf_10, True, "diffusion_model", "torch.compile", False, False, None, None, False, "inductor") - # except ImportError: - # print("Triton not found, skipping compilation") - ksampler_3 = ksampler.sample( - seed=random.randint(1, 2**64), - steps=20, - cfg=1, - sampler_name="euler", - scheduler="beta", - denoise=1, - model=unetloadergguf_10[0], - positive=cliptextencodeflux_15[0], - negative=conditioningzeroout_16[0], - latent_image=emptylatentimage_5[0], - flux=True, - ) - vaedecode_8 = vaedecode.decode( - samples=ksampler_3[0], - vae=vaeloader_11[0], - flux=True, - ) - saveimage.save_images(filename_prefix="Flux", images=vaedecode_8[0]) - for image in vaedecode_8[0]: - i = 255.0 * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - self.img = img - self.update_image(img) - self.display_most_recent_image_flag = True - finally: - # Reset state when done - self.is_generating = False - self.generate_button.configure(state="normal") - if current_thread in self.generation_threads: - self.generation_threads.remove(current_thread) - - def on_model_selected(self, *args): - """Handle model selection changes""" - if self.dropdown.get() == "flux": - # Disable incompatible controls - self.adetailer_checkbox._state = tk.DISABLED - self.hires_fix_checkbox._state = tk.DISABLED - self.stable_fast_checkbox._state = tk.DISABLED - self.lora_selection._state = tk.DISABLED - self.cfg_slider._state = tk.DISABLED - else: - # Enable controls - self.adetailer_checkbox._state = tk.NORMAL - self.hires_fix_checkbox._state = tk.NORMAL - self.stable_fast_checkbox._state = tk.NORMAL - self.lora_selection._state = tk.NORMAL - self.cfg_slider._state = tk.NORMAL - - def update_labels(self) -> None: - """Update the labels for the sliders.""" - self.width_label.configure(text=f"{int(self.width_slider.get())}") - self.height_label.configure(text=f"{int(self.height_slider.get())}") - self.cfg_label.configure(text=f"{int(self.cfg_slider.get())}") - self.batch_label.configure(text=f"{int(self.batch_slider.get())}") - - def create_image_grid(self, images: list[Image.Image]) -> Image.Image: - """Create a grid of images. - - Args: - images (list[Image.Image]): List of images to arrange in grid - - Returns: - Image.Image: Combined grid image - """ - # Calculate grid dimensions - n = len(images) - if n <= 1: - return images[0] - - cols = int(np.ceil(np.sqrt(n))) - rows = int(np.ceil(n / cols)) - - # Get max dimensions - w_max = max(img.width for img in images) - h_max = max(img.height for img in images) - - # Create output image - grid = Image.new("RGB", (w_max * cols, h_max * rows)) - - # Paste images into grid - for idx, img in enumerate(images): - i = idx // cols - j = idx % cols - grid.paste(img, (j * w_max, i * h_max)) - - return grid - - def update_image(self, images: Union[Image.Image, list[Image.Image]]) -> None: - """Update the displayed image(s). - - Args: - images: Single image or list of images to display - """ - # Convert single image to list - if isinstance(images, Image.Image): - images = [images] - - # Create grid of all images - grid_img = self.create_image_grid(images) - - # Calculate the aspect ratio of the grid - aspect_ratio = grid_img.width / grid_img.height - - # Determine the new dimensions while maintaining the aspect ratio - label_width = int(4 * self.winfo_width() / 7) - label_height = int(4 * self.winfo_height() / 7) - - if label_width / aspect_ratio <= label_height: - new_width = label_width - new_height = int(label_width / aspect_ratio) - else: - new_height = label_height - new_width = int(label_height * aspect_ratio) - - # Resize the grid image - try: - grid_img = grid_img.resize((new_width, new_height), Image.LANCZOS) - except RecursionError: - pass - self.img = grid_img - if self.display_most_recent_image_flag is False: - self._update_image_label(grid_img) - - def _update_image_label(self, img: Image.Image) -> None: - """Update the image label with the provided image. - - Args: - img (Image.Image): The image to display. - """ - # Convert the PIL image to a Tkinter PhotoImage - tk_image = ImageTk.PhotoImage(img) - # Update the image label with the Tkinter PhotoImage - self.image_label.config(image=tk_image) - # Keep a reference to the image to prevent it from being garbage collected - self.image_label.image = tk_image - - def display_most_recent_image(self) -> None: - """Display the most recent image(s) from the output directory.""" - # Get a list of all image files in the output directory - image_files = glob.glob("./_internal/output/*") - - # If there are no image files, return - if not image_files: - return - - # Sort files by modification time in descending order - image_files.sort(key=os.path.getmtime, reverse=True) - - # Get most recent timestamp - latest_time = os.path.getmtime(image_files[0]) - - # Get all images from same batch (within 1 second of most recent) - batch_images = [] - for file in image_files: - if abs(os.path.getmtime(file) - latest_time) < 1.0: - try: - img = Image.open(file) - batch_images.append(img) - except: - continue - - if not batch_images: - return - - # Display single image or grid of batch - if len(batch_images) == 1: - self.update_image(batch_images[0]) - else: - self.update_image(batch_images) - - def _start_resize_worker(self): - """Start the resize worker thread""" - self._resize_thread = threading.Thread(target=self._resize_worker, daemon=True) - self._resize_thread.start() - - def _resize_worker(self): - """Worker thread for handling resize operations""" - while self._resize_running: - try: - # Wait for resize event or timeout - if self._resize_queue.qsize() > 0: - event = self._resize_queue.get(timeout=0.1) - self._do_resize(event) - self._resize_queue.task_done() - else: - self._resize_event.wait(timeout=0.1) - self._resize_event.clear() - except queue.Empty: - continue - - def _queue_resize(self, event): - """Queue a resize event""" - current_time = time.time() - - # Debounce resize events - if current_time - self._last_resize_time > self._resize_delay: - self._last_resize_time = current_time - self._resize_queue.put(event) - self._resize_event.set() - - def _do_resize(self, event): - """Handle resize operation in worker thread""" - width = self.winfo_width() - height = self.winfo_height() - - # Update UI components in main thread - self.after(0, lambda: self._update_components(width, height)) - - # Update image if exists - if hasattr(self, "img"): - self._update_image_threaded(self.img) - - def _update_components(self, width, height): - """Update UI components sizes""" - # Update component sizes based on window dimensions - width = self.winfo_width() - height = self.winfo_height() - - # Scale text boxes - prompt_height = int(height * 0.25) - neg_height = int(height * 0.15) - self.prompt_entry.configure(height=prompt_height) - self.neg.configure(height=neg_height) - - def _update_image_threaded(self, img): - """Thread-safe image update with caching""" - if img is None: - return - - with self._resize_lock: - # Calculate dimensions - aspect_ratio = img.width / img.height - label_width = int(4 * self.winfo_width() / 7) - label_height = int(4 * self.winfo_height() / 7) - - if label_width / aspect_ratio <= label_height: - new_width = label_width - new_height = int(label_width / aspect_ratio) - else: - new_height = label_height - new_width = int(label_height * aspect_ratio) - - # Check cache - cache_key = (new_width, new_height) - if cache_key in self._image_cache: - resized_img = self._image_cache[cache_key] - else: - try: - resized_img = img.resize((new_width, new_height), Image.LANCZOS) - self._image_cache[cache_key] = resized_img - - # Limit cache size - if len(self._image_cache) > 5: - self._image_cache.pop(next(iter(self._image_cache))) - except: - return - if not self.display_most_recent_image_flag: - # Update image in main thread - self.after(0, lambda: self._update_image_label_safe(resized_img)) - - def _update_image_label_safe(self, img): - """Thread-safe image label update""" - if not self.display_most_recent_image_flag: - self._current_image = ImageTk.PhotoImage(img) - self.image_label.configure(image=self._current_image) - - def _cleanup(self): - """Clean up threads before closing""" - self._resize_running = False - self._resize_event.set() - if self._resize_thread: - self._resize_thread.join(timeout=1.0) - self.destroy() - - def interrupt_generation(self) -> None: - """Interrupt ongoing image generation process.""" - if not self.is_generating: - return - - # Set interrupt flag first - self.interrupt_flag = True - - # Clear CUDA cache and release memory - if torch.cuda.is_available(): - torch.cuda.empty_cache() - torch.cuda.synchronize() - - # Stop and cleanup threads - for thread in self.generation_threads[:]: - if thread and thread.is_alive(): - thread.join(timeout=1.0) - if thread in self.generation_threads: - self.generation_threads.remove(thread) - - # Reset UI state - self.progress.set(0) - self.title("LightDiffusion") - self.generate_button.configure(state="normal") - self.display_most_recent_image_flag = True - - # Clear any pending resize tasks - with self._resize_lock: - self._resize_queue.queue.clear() - - # Reset model state if needed - if hasattr(self, "checkpointloadersimple_241"): - del self.checkpointloadersimple_241 - self.ckpt = None - - # Always reset flags - self.generation_threads.clear() - # Reset generation state - self.is_generating = False - self.generate_button.configure(state="normal") - - -if __name__ == "__main__": - from modules.user.app_instance import app - - app.mainloop() +import os +import queue +import sys +import random +import threading +import tkinter as tk +from tkinter import filedialog +from typing import Union +from PIL import Image, ImageTk +import numpy as np +import customtkinter as ctk +import glob +import time + +import torch + +# Add the directory containing LightDiffusion.py to the Python path +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) + +from modules.AutoDetailer import SAM, ADetailer, bbox, SEGS +from modules.AutoEncoders import VariationalAE +from modules.clip import Clip +from modules.sample import sampling + +from modules.Utilities import util +from modules.UltimateSDUpscale import USDU_upscaler, UltimateSDUpscale + +from modules.FileManaging import Downloader, ImageSaver, Loader +from modules.Model import LoRas +from modules.Utilities import Enhancer, Latent, upscale +from modules.Quantize import Quantizer +from modules.WaveSpeed import fbcache_nodes +from modules.hidiffusion import msw_msa_attention +from modules.AutoHDR import ahdr + +Downloader.CheckAndDownload() + +files = glob.glob("./_internal/checkpoints/*.safetensors") +loras = glob.glob("./_internal/loras/*.safetensors") +loras += glob.glob("./_internal/loras/*.pt") + + +def debounce(wait): + """Decorator to debounce resize events""" + + def decorator(fn): + last_call = [0] + + def debounced(*args, **kwargs): + current_time = time.time() + if current_time - last_call[0] >= wait: + fn(*args, **kwargs) + last_call[0] = current_time + + return debounced + + return decorator + + +class App(tk.Tk): + """Main application class for the LightDiffusion GUI.""" + + def __init__(self): + """Initialize the App class.""" + super().__init__() + self.title("LightDiffusion") + self.geometry("900x750") + + # Configure main window grid + self.grid_columnconfigure(1, weight=1) + self.grid_rowconfigure(0, weight=1) + + file_names = [os.path.basename(file) for file in files] + lora_names = [os.path.basename(lora) for lora in loras] + + selected_file = tk.StringVar() + selected_lora = tk.StringVar() + if file_names: + selected_file.set(file_names[0]) + if lora_names: + selected_lora.set(lora_names[0]) + + # Create main sidebar frame with padding and grid + self.sidebar = tk.Frame(self, bg="#FBFBFB", padx=10, pady=10) + self.sidebar.grid(row=0, column=0, sticky="nsew") + self.sidebar.grid_columnconfigure(0, weight=1) + + # Configure sidebar grid rows + for i in range(8): + self.sidebar.grid_rowconfigure(i, weight=1) + + # Text input frames with expansion + self.prompt_frame = tk.Frame(self.sidebar, bg="#FBFBFB") + self.prompt_frame.grid(row=0, column=0, sticky="nsew", pady=(0, 5)) + self.prompt_frame.grid_columnconfigure(0, weight=1) + self.prompt_frame.grid_rowconfigure(0, weight=2) + self.prompt_frame.grid_rowconfigure(1, weight=1) + + # Prompt textbox with expansion + self.prompt_entry = ctk.CTkTextbox( + self.prompt_frame, + height=150, + fg_color="#E8F9FF", + text_color="black", + border_color="gray", + border_width=2, + ) + self.prompt_entry.grid(row=0, column=0, sticky="nsew") + + # Negative prompt textbox with expansion + self.neg = ctk.CTkTextbox( + self.prompt_frame, + height=75, + fg_color="#E8F9FF", + text_color="black", + border_color="gray", + border_width=2, + ) + self.neg.grid(row=1, column=0, sticky="nsew", pady=(5, 0)) + + # Add model dropdown with error handling for empty lists + model_values = (file_names if file_names else ["No models found"]) + ["flux"] + + # Model dropdown and Flux checkbox + self.dropdown = ctk.CTkOptionMenu( + self.sidebar, + values=model_values, + fg_color="#F5EFFF", + text_color="black", + command=self.on_model_selected, + ) + self.dropdown.grid(row=2, column=0, sticky="ew") + + # LoRA selection + self.lora_selection = ctk.CTkOptionMenu( + self.sidebar, values=lora_names, fg_color="#F5EFFF", text_color="black" + ) + self.lora_selection.grid(row=3, column=0, sticky="ew", pady=5) + + # Display frame with expansion + self.display = tk.Frame(self, bg="#FBFBFB") + self.display.grid(row=0, column=1, sticky="nsew", padx=10, pady=10) + self.display.grid_columnconfigure(0, weight=1) + self.img = None + + # Add row configuration for both image and checkbox + self.display.grid_rowconfigure(0, weight=1) # For image + self.display.grid_rowconfigure(1, weight=0) # For checkbox + + # Image label with expansion + self.image_label = tk.Label(self.display, bg="#FBFBFB") + self.image_label.grid(row=0, column=0, sticky="nsew") + + # Previewer checkbox - changed from pack to grid + self.previewer_var = tk.BooleanVar() + self.previewer_checkbox = ctk.CTkCheckBox( + self.display, + text="Previewer", + variable=self.previewer_var, + command=self.print_previewer, + text_color="black", + ) + self.previewer_checkbox.grid(row=1, column=0, pady=10) + + # Progress Bar + self.progress = ctk.CTkProgressBar(self.display, fg_color="#FBFBFB") + self.progress.grid(row=2, column=0, sticky="ew", pady=10, padx=10) + self.progress.set(0) + + # Make sliders frame expand + self.sliders_frame = tk.Frame(self.sidebar, bg="#FBFBFB") + self.sliders_frame.grid(row=4, column=0, sticky="nsew", pady=5) + self.sliders_frame.grid_columnconfigure(1, weight=1) + + # Configure slider weights + for i in range(3): + self.sliders_frame.grid_rowconfigure(i, weight=1) + + # Make checkbox frame expand + self.checkbox_frame = tk.Frame(self.sidebar, bg="#FBFBFB") + self.checkbox_frame.grid(row=5, column=0, sticky="nsew", pady=10) + self.checkbox_frame.grid_columnconfigure(0, weight=1) + self.checkbox_frame.grid_columnconfigure(1, weight=1) + + # Make button frame expand + self.button_frame = tk.Frame(self.sidebar, bg="#FBFBFB") + self.button_frame.grid(row=7, column=0, sticky="nsew", pady=10) + self.button_frame.grid_columnconfigure(0, weight=1) + self.button_frame.grid_columnconfigure(1, weight=1) + + # Width slider + tk.Label(self.sliders_frame, text="Width:", bg="#FBFBFB").grid( + row=0, column=0, padx=(0, 5) + ) + self.width_slider = ctk.CTkSlider( + self.sliders_frame, from_=1, to=2048, number_of_steps=32, fg_color="#F5EFFF" + ) + self.width_slider.grid(row=0, column=1, sticky="ew") + self.width_label = ctk.CTkLabel(self.sliders_frame, text="") + self.width_label.grid(row=0, column=2, padx=(5, 0)) + + # Height slider + tk.Label(self.sliders_frame, text="Height:", bg="#FBFBFB").grid( + row=1, column=0, padx=(0, 5) + ) + self.height_slider = ctk.CTkSlider( + self.sliders_frame, from_=1, to=2048, number_of_steps=32, fg_color="#F5EFFF" + ) + self.height_slider.grid(row=1, column=1, sticky="ew") + self.height_label = ctk.CTkLabel(self.sliders_frame, text="") + self.height_label.grid(row=1, column=2, padx=(5, 0)) + + # CFG slider + tk.Label(self.sliders_frame, text="CFG:", bg="#FBFBFB").grid( + row=2, column=0, padx=(0, 5) + ) + self.cfg_slider = ctk.CTkSlider( + self.sliders_frame, from_=1, to=15, number_of_steps=14, fg_color="#F5EFFF" + ) + self.cfg_slider.grid(row=2, column=1, sticky="ew") + self.cfg_label = ctk.CTkLabel(self.sliders_frame, text="") + self.cfg_label.grid(row=2, column=2, padx=(5, 0)) + + # Batch size slider + tk.Label(self.sliders_frame, text="Batch Size:", bg="#FBFBFB").grid( + row=3, column=0, padx=(0, 5) + ) + self.batch_slider = ctk.CTkSlider( + self.sliders_frame, from_=1, to=10, number_of_steps=9, fg_color="#F5EFFF" + ) + self.batch_slider.grid(row=3, column=1, sticky="ew") + self.batch_label = ctk.CTkLabel(self.sliders_frame, text="") + self.batch_label.grid(row=3, column=2, padx=(5, 0)) + + # Configure grid columns and rows to distribute space evenly + self.checkbox_frame.grid_columnconfigure(0, weight=1) + self.checkbox_frame.grid_columnconfigure(1, weight=1) + self.checkbox_frame.grid_rowconfigure(0, weight=1) + self.checkbox_frame.grid_rowconfigure(1, weight=1) + self.checkbox_frame.grid_rowconfigure(2, weight=1) + + # checkbox for hiresfix + self.hires_fix_var = tk.BooleanVar() + self.hires_fix_checkbox = ctk.CTkCheckBox( + self.checkbox_frame, + text="Hires Fix", + variable=self.hires_fix_var, + command=self.print_hires_fix, + text_color="black", + ) + self.hires_fix_checkbox.grid( + row=0, column=0, padx=(75, 5), pady=5, sticky="nsew" + ) + + # checkbox for Adetailer + self.adetailer_var = tk.BooleanVar() + self.adetailer_checkbox = ctk.CTkCheckBox( + self.checkbox_frame, + text="Adetailer", + variable=self.adetailer_var, + command=self.print_adetailer, + text_color="black", + ) + self.adetailer_checkbox.grid(row=0, column=1, padx=5, pady=5, sticky="nsew") + + # checkbox to enable stable-fast optimization + self.stable_fast_var = tk.BooleanVar() + self.stable_fast_checkbox = ctk.CTkCheckBox( + self.checkbox_frame, + text="Stable Fast", + variable=self.stable_fast_var, + text_color="black", + ) + self.stable_fast_checkbox.grid( + row=1, column=0, padx=(75, 5), pady=5, sticky="nsew" + ) + + # checkbox to enable prompt enhancer + self.enhancer_var = tk.BooleanVar() + self.enhancer_checkbox = ctk.CTkCheckBox( + self.checkbox_frame, + text="Prompt enhancer", + variable=self.enhancer_var, + text_color="black", + ) + self.enhancer_checkbox.grid(row=1, column=1, padx=5, pady=5, sticky="nsew") + + self.prioritize_speed_var = tk.BooleanVar() + self.prioritize_speed_checkbox = ctk.CTkCheckBox( + self.checkbox_frame, + text="Prioritize Speed", + variable=self.prioritize_speed_var, + text_color="black", + ) + self.prioritize_speed_checkbox.grid( + row=2, column=0, padx=(75, 5), pady=5, sticky="nsew" + ) + + # Button to launch the generation + self.generate_button = ctk.CTkButton( + self.sidebar, + text="Generate", + command=self.generate_image, + fg_color="#C4D9FF", + text_color="black", + border_color="gray", + border_width=2, + ) + self.generate_button.grid( + row=6, column=0, pady=10, sticky="ew" + ) # Changed from pack to grid + + self.ckpt = None + + # load the checkpoint on an another thread + threading.Thread(target=self._prep, daemon=True).start() + + # img2img button + self.img2img_button = ctk.CTkButton( + self.button_frame, + text="img2img", + command=self.img2img, + fg_color="#F5EFFF", + text_color="black", + border_color="gray", + border_width=2, + ) + self.img2img_button.grid(row=0, column=0, padx=5, sticky="ew") + + # interrupt button + self.generation_threads = [] + self.interrupt_flag = False + self.interrupt_button = ctk.CTkButton( + self.button_frame, + text="Interrupt", + command=self.interrupt_generation, + fg_color="#F5EFFF", + text_color="black", + border_color="gray", + border_width=2, + ) + self.interrupt_button.grid(row=0, column=1, padx=5, sticky="ew") + + prompt, neg, width, height, cfg = util.load_parameters_from_file() + self.prompt_entry.insert(tk.END, prompt) + self.neg.insert(tk.END, neg) + self.width_slider.set(width) + self.height_slider.set(height) + self.cfg_slider.set(cfg) + self.batch_slider.set(1) + + self.width_slider.bind("", lambda event: self.update_labels()) + self.height_slider.bind("", lambda event: self.update_labels()) + self.cfg_slider.bind("", lambda event: self.update_labels()) + self.batch_slider.bind("", lambda event: self.update_labels()) + self.update_labels() + self.prompt_entry.bind( + "", + lambda event: util.write_parameters_to_file( + self.prompt_entry.get("1.0", tk.END), + self.neg.get("1.0", tk.END), + self.width_slider.get(), + self.height_slider.get(), + self.cfg_slider.get(), + ), + ) + self.neg.bind( + "", + lambda event: util.write_parameters_to_file( + self.prompt_entry.get("1.0", tk.END), + self.neg.get("1.0", tk.END), + self.width_slider.get(), + self.height_slider.get(), + self.cfg_slider.get(), + ), + ) + self.width_slider.bind( + "", + lambda event: util.write_parameters_to_file( + self.prompt_entry.get("1.0", tk.END), + self.neg.get("1.0", tk.END), + self.width_slider.get(), + self.height_slider.get(), + self.cfg_slider.get(), + ), + ) + self.height_slider.bind( + "", + lambda event: util.write_parameters_to_file( + self.prompt_entry.get("1.0", tk.END), + self.neg.get("1.0", tk.END), + self.width_slider.get(), + self.height_slider.get(), + self.cfg_slider.get(), + ), + ) + self.cfg_slider.bind( + "", + lambda event: util.write_parameters_to_file( + self.prompt_entry.get("1.0", tk.END), + self.neg.get("1.0", tk.END), + self.width_slider.get(), + self.height_slider.get(), + self.cfg_slider.get(), + ), + ) + # Add resize handling variables + self._resize_queue = queue.Queue() + self._resize_thread = None + self._resize_event = threading.Event() + self._resize_lock = threading.Lock() + self._resize_running = True + self._last_resize_time = 0 + self._resize_delay = 0.1 + self._image_cache = {} + self._current_image = None + + # Start resize worker thread + self._start_resize_worker() + + # Bind resize event + self.bind("", self._queue_resize) + + # Bind cleanup + self.protocol("WM_DELETE_WINDOW", self._cleanup) + self.display_most_recent_image_flag = False + self.display_most_recent_image() + self.is_generating = False + self.sampler = ( + "dpmpp_sde_cfgpp" + if not self.prioritize_speed_var.get() + else "dpmpp_2m_cfgpp" + ) + + def _img2img(self, file_path: str) -> None: + """Perform img2img on the selected image. + + Args: + file_path (str): The path to the selected image. + """ + self.is_generating = True + self.img2img_button.configure(state="disabled") + self.display_most_recent_image_flag = False + prompt = self.prompt_entry.get("1.0", tk.END) + neg = self.neg.get("1.0", tk.END) + img = Image.open(file_path) + img_array = np.array(img) + img_tensor = torch.from_numpy(img_array).float().to("cpu") / 255.0 + img_tensor = img_tensor.unsqueeze(0) + self.interrupt_flag = False + self.sampler = ( + "dpmpp_sde" if not self.prioritize_speed_var.get() else "dpmpp_2m" + ) + with torch.inference_mode(): + ( + checkpointloadersimple_241, + cliptextencode, + emptylatentimage, + ksampler_instance, + vaedecode, + latentupscale, + upscalemodelloader, + ultimatesdupscale, + ) = self._prep() + try: + loraloader = LoRas.LoraLoader() + loraloader_274 = loraloader.load_lora( + lora_name="add_detail.safetensors", + strength_model=2, + strength_clip=2, + model=checkpointloadersimple_241[0], + clip=checkpointloadersimple_241[1], + ) + except: + loraloader_274 = checkpointloadersimple_241 + + if self.stable_fast_var.get() is True: + from modules.StableFast import StableFast + + try: + app.title("LigtDiffusion - Generating StableFast model") + except: + pass + applystablefast = StableFast.ApplyStableFastUnet() + applystablefast_158 = applystablefast.apply_stable_fast( + enable_cuda_graph=False, + model=loraloader_274[0], + ) + else: + applystablefast_158 = loraloader_274 + fb_cache = fbcache_nodes.ApplyFBCacheOnModel() + applystablefast_158 = fb_cache.patch( + applystablefast_158, "diffusion_model", 0.120 + ) + clipsetlastlayer = Clip.CLIPSetLastLayer() + clipsetlastlayer_257 = clipsetlastlayer.set_last_layer( + stop_at_clip_layer=-2, clip=loraloader_274[1] + ) + + cliptextencode_242 = cliptextencode.encode( + text=prompt, + clip=clipsetlastlayer_257[0], + ) + cliptextencode_243 = cliptextencode.encode( + text=neg, + clip=clipsetlastlayer_257[0], + ) + upscalemodelloader_244 = upscalemodelloader.load_model( + "RealESRGAN_x4plus.pth" + ) + try: + app.title("LightDiffusion - Upscaling") + except: + pass + ultimatesdupscale_250 = ultimatesdupscale.upscale( + upscale_by=2, + seed=random.randint(1, 2**64), + steps=8, + cfg=6, + sampler_name=self.sampler, + scheduler="karras", + denoise=0.3, + mode_type="Linear", + tile_width=512, + tile_height=512, + mask_blur=16, + tile_padding=32, + seam_fix_mode="Half Tile", + seam_fix_denoise=0.2, + seam_fix_width=64, + seam_fix_mask_blur=16, + seam_fix_padding=32, + force_uniform_tiles="enable", + image=img_tensor, + model=applystablefast_158[0], + positive=cliptextencode_242[0], + negative=cliptextencode_243[0], + vae=checkpointloadersimple_241[2], + upscale_model=upscalemodelloader_244[0], + ) + self.update_from_decode(ultimatesdupscale_250[0], "LD-I2I") + self.update_image(img) + global generated + generated = img + self.display_most_recent_image_flag = True + try: + app.title("LightDiffusion") + except: + pass + self.is_generating = False + self.img2img_button.configure(state="normal") + + def img2img(self) -> None: + """Open the file selector and run img2img on the selected image.""" + if self.is_generating: + return + file_path = filedialog.askopenfilename() + if file_path: + threading.Thread( + target=self._img2img, args=(file_path,), daemon=True + ).start() + + def print_hires_fix(self) -> None: + """Print the status of the hires fix checkbox.""" + if self.hires_fix_var.get() is True: + print("Hires fix is ON") + else: + print("Hires fix is OFF") + + def print_adetailer(self) -> None: + """Print the status of the adetailer checkbox.""" + if self.adetailer_var.get() is True: + print("Adetailer is ON") + else: + print("Adetailer is OFF") + + def print_previewer(self) -> None: + """Print the status of the previewer checkbox.""" + if self.previewer_var.get() is True: + print("Previewer is ON") + else: + print("Previewer is OFF") + + def generate_image(self) -> None: + """Start the image generation process.""" + if self.is_generating: + return + + if self.dropdown.get() == "flux": + self.generate_thread = threading.Thread( + target=self._generate_image_flux, daemon=True + ).start() + else: + self.generate_thread = threading.Thread( + target=self._generate_image, daemon=True + ).start() + + def _prep(self) -> tuple: + """Prepare the necessary components for image generation. + + Returns: + tuple: The prepared components. + """ + if self.dropdown.get() != self.ckpt and self.dropdown.get() != "flux": + self.ckpt = self.dropdown.get() + with torch.inference_mode(): + self.checkpointloadersimple = Loader.CheckpointLoaderSimple() + self.checkpointloadersimple_241 = ( + self.checkpointloadersimple.load_checkpoint( + ckpt_name="./_internal/checkpoints/" + self.ckpt + ) + ) + self.cliptextencode = Clip.CLIPTextEncode() + self.emptylatentimage = Latent.EmptyLatentImage() + self.ksampler_instance = sampling.KSampler2() + self.vaedecode = VariationalAE.VAEDecode() + self.latent_upscale = upscale.LatentUpscale() + self.upscalemodelloader = USDU_upscaler.UpscaleModelLoader() + self.ultimatesdupscale = UltimateSDUpscale.UltimateSDUpscale() + return ( + self.checkpointloadersimple_241, + self.cliptextencode, + self.emptylatentimage, + self.ksampler_instance, + self.vaedecode, + self.latent_upscale, + self.upscalemodelloader, + self.ultimatesdupscale, + ) + + def _generate_image(self) -> None: + """Generate image with proper interrupt handling.""" + self.is_generating = True + self.generate_button.configure(state="disabled") + + current_thread = threading.current_thread() + self.generation_threads.append(current_thread) + self.interrupt_flag = False + self.sampler = ( + "dpmpp_sde" if not self.prioritize_speed_var.get() else "dpmpp_2m" + ) + try: + # Disable generate button during generation + self.generate_button.configure(state="disabled") + self.display_most_recent_image_flag = False + self.progress.set(0) + + # Early interrupt check + if self.interrupt_flag: + return + + # Get generation parameters + prompt = self.prompt_entry.get("1.0", tk.END) + neg = self.neg.get("1.0", tk.END) + w = int(self.width_slider.get()) + h = int(self.height_slider.get()) + cfg = int(self.cfg_slider.get()) + + try: + if self.enhancer_var.get() is True: + prompt = Enhancer.enhance_prompt(prompt) + while prompt is None: + pass + except: + pass + + # Main generation with proper interrupt handling + with torch.inference_mode(): + components = self._prep() + if self.interrupt_flag: + return + ( + checkpointloadersimple_241, + cliptextencode, + emptylatentimage, + ksampler_instance, + vaedecode, + latentupscale, + upscalemodelloader, + ultimatesdupscale, + ) = self._prep() + + try: + loraloader = LoRas.LoraLoader() + loraloader_274 = loraloader.load_lora( + lora_name=self.lora_selection.get().replace( + "./_internal/loras/", "" + ), + strength_model=0.7, + strength_clip=0.7, + model=checkpointloadersimple_241[0], + clip=checkpointloadersimple_241[1], + ) + print( + "loading", + self.lora_selection.get().replace("./_internal/loras/", ""), + ) + except: + loraloader_274 = checkpointloadersimple_241 + try: + cliptextencode_124 = cliptextencode.encode( + text="royal, detailed, magnificient, beautiful, seducing", + clip=loraloader_274[1], + ) + + ultralyticsdetectorprovider = bbox.UltralyticsDetectorProvider() + ultralyticsdetectorprovider_151 = ultralyticsdetectorprovider.doit( + # model_name="face_yolov8m.pt" + model_name="person_yolov8m-seg.pt" + ) + + bboxdetectorsegs = bbox.BboxDetectorForEach() + samdetectorcombined = SAM.SAMDetectorCombined() + impactsegsandmask = SEGS.SegsBitwiseAndMask() + detailerforeachdebug = ADetailer.DetailerForEachTest() + except: + pass + clipsetlastlayer = Clip.CLIPSetLastLayer() + clipsetlastlayer_257 = clipsetlastlayer.set_last_layer( + stop_at_clip_layer=-2, clip=loraloader_274[1] + ) + self.progress.set(0.2) + if self.stable_fast_var.get() is True: + from modules.StableFast import StableFast + + try: + self.title("LightDiffusion - Generating StableFast model") + except: + pass + applystablefast = StableFast.ApplyStableFastUnet() + applystablefast_158 = applystablefast.apply_stable_fast( + enable_cuda_graph=False, + model=loraloader_274[0], + ) + else: + applystablefast_158 = loraloader_274 + fb_cache = fbcache_nodes.ApplyFBCacheOnModel() + applystablefast_158 = fb_cache.patch( + applystablefast_158, "diffusion_model", 0.120 + ) + hidiffoptimizer = msw_msa_attention.ApplyMSWMSAAttentionSimple() + cliptextencode_242 = cliptextencode.encode( + text=prompt, + clip=clipsetlastlayer_257[0], + ) + cliptextencode_243 = cliptextencode.encode( + text=neg, + clip=clipsetlastlayer_257[0], + ) + emptylatentimage_244 = emptylatentimage.generate( + width=w, height=h, batch_size=int(self.batch_slider.get()) + ) + ksampler_239 = ksampler_instance.sample( + seed=random.randint(1, 2**64), + steps=20, + cfg=cfg, + sampler_name=self.sampler, + scheduler="karras", + denoise=1, + model=hidiffoptimizer.go( + model_type="auto", model=applystablefast_158[0] + )[0], + positive=cliptextencode_242[0], + negative=cliptextencode_243[0], + latent_image=emptylatentimage_244[0], + ) + self.progress.set(0.4) + if self.hires_fix_var.get() is True: + latentupscale_254 = latentupscale.upscale( + width=w * 2, + height=h * 2, + samples=ksampler_239[0], + ) + ksampler_253 = ksampler_instance.sample( + seed=random.randint(1, 2**64), + steps=10, + cfg=8, + sampler_name="euler_ancestral", + scheduler="normal", + denoise=0.45, + model=hidiffoptimizer.go( + model_type="auto", model=applystablefast_158[0] + )[0], + positive=cliptextencode_242[0], + negative=cliptextencode_243[0], + latent_image=latentupscale_254[0], + ) + vaedecode_240 = vaedecode.decode( + samples=ksampler_253[0], + vae=checkpointloadersimple_241[2], + ) + self.update_from_decode(vaedecode_240[0], "LD-HF") + else: + vaedecode_240 = vaedecode.decode( + samples=ksampler_239[0], + vae=checkpointloadersimple_241[2], + ) + self.update_from_decode(vaedecode_240[0], "LD") + if self.interrupt_flag: + return + + self.progress.set(0.6) + if self.adetailer_var.get() is True: + samloader = SAM.SAMLoader() + samloader_87 = samloader.load_model( + model_name="sam_vit_b_01ec64.pth", device_mode="AUTO" + ) + bboxdetectorsegs_132 = bboxdetectorsegs.doit( + threshold=0.5, + dilation=10, + crop_factor=2, + drop_size=10, + labels="all", + bbox_detector=ultralyticsdetectorprovider_151[0], + image=vaedecode_240[0], + ) + samdetectorcombined_139 = samdetectorcombined.doit( + detection_hint="center-1", + dilation=0, + threshold=0.93, + bbox_expansion=0, + mask_hint_threshold=0.7, + mask_hint_use_negative="False", + sam_model=samloader_87[0], + segs=bboxdetectorsegs_132, + image=vaedecode_240[0], + ) + if samdetectorcombined_139[0] is None: + return + impactsegsandmask_152 = impactsegsandmask.doit( + segs=bboxdetectorsegs_132, + mask=samdetectorcombined_139[0], + ) + detailerforeachdebug_145 = detailerforeachdebug.doit( + guide_size=512, + guide_size_for=False, + max_size=768, + seed=random.randint(1, 2**64), + steps=20, + cfg=6.5, + sampler_name=self.sampler, + scheduler="karras", + denoise=0.5, + feather=5, + noise_mask=True, + force_inpaint=True, + wildcard="", + cycle=1, + inpaint_model=False, + noise_mask_feather=20, + image=vaedecode_240[0], + segs=impactsegsandmask_152[0], + model=applystablefast_158[0], + clip=checkpointloadersimple_241[1], + vae=checkpointloadersimple_241[2], + positive=cliptextencode_124[0], + negative=cliptextencode_243[0], + ) + self.update_from_decode(detailerforeachdebug_145[0], "LD-body") + ultralyticsdetectorprovider = bbox.UltralyticsDetectorProvider() + ultralyticsdetectorprovider_151 = ultralyticsdetectorprovider.doit( + model_name="face_yolov9c.pt" + ) + bboxdetectorsegs_132 = bboxdetectorsegs.doit( + threshold=0.5, + dilation=10, + crop_factor=2, + drop_size=10, + labels="all", + bbox_detector=ultralyticsdetectorprovider_151[0], + image=detailerforeachdebug_145[0], + ) + samdetectorcombined_139 = samdetectorcombined.doit( + detection_hint="center-1", + dilation=0, + threshold=0.93, + bbox_expansion=0, + mask_hint_threshold=0.7, + mask_hint_use_negative="False", + sam_model=samloader_87[0], + segs=bboxdetectorsegs_132, + image=detailerforeachdebug_145[0], + ) + impactsegsandmask_152 = impactsegsandmask.doit( + segs=bboxdetectorsegs_132, + mask=samdetectorcombined_139[0], + ) + detailerforeachdebug_145 = detailerforeachdebug.doit( + guide_size=512, + guide_size_for=False, + max_size=768, + seed=random.randint(1, 2**64), + steps=20, + cfg=6.5, + sampler_name=self.sampler, + scheduler="karras", + denoise=0.5, + feather=5, + noise_mask=True, + force_inpaint=True, + wildcard="", + cycle=1, + inpaint_model=False, + noise_mask_feather=20, + image=detailerforeachdebug_145[0], + segs=impactsegsandmask_152[0], + model=applystablefast_158[0], + clip=checkpointloadersimple_241[1], + vae=checkpointloadersimple_241[2], + positive=cliptextencode_124[0], + negative=cliptextencode_243[0], + ) + self.update_from_decode(detailerforeachdebug_145[0], "LD-head") + + self.progress.set(0.8) + + except Exception as e: + print(f"Generation error: {e}") + self.title(f"LightDiffusion - Error: {str(e)}") + + finally: + # Reset state when done + self.is_generating = False + self.generate_button.configure(state="normal") + if current_thread in self.generation_threads: + self.generation_threads.remove(current_thread) + self.progress.set(0) + + # Clear CUDA cache + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + def _generate_image_flux(self) -> None: + """Generate an image using the Flux model.""" + self.is_generating = True + self.generate_button.configure(state="disabled") + # Add current thread to list at start + current_thread = threading.current_thread() + self.generation_threads.append(current_thread) + self.display_most_recent_image_flag = False + w = int(self.width_slider.get()) + h = int(self.height_slider.get()) + prompt = self.prompt_entry.get("1.0", tk.END) + try: + if self.enhancer_var.get() is True: + prompt = Enhancer.enhance_prompt(prompt) + while prompt is None: + pass + self.interrupt_flag = False + Downloader.CheckAndDownloadFlux() + with torch.inference_mode(): + dualcliploadergguf = Quantizer.DualCLIPLoaderGGUF() + emptylatentimage = Latent.EmptyLatentImage() + vaeloader = VariationalAE.VAELoader() + unetloadergguf = Quantizer.UnetLoaderGGUF() + cliptextencodeflux = Quantizer.CLIPTextEncodeFlux() + conditioningzeroout = Quantizer.ConditioningZeroOut() + ksampler = sampling.KSampler2() + vaedecode = VariationalAE.VAEDecode() + unetloadergguf_10 = unetloadergguf.load_unet( + unet_name="flux1-dev-Q8_0.gguf" + ) + vaeloader_11 = vaeloader.load_vae(vae_name="ae.safetensors") + dualcliploadergguf_19 = dualcliploadergguf.load_clip( + clip_name1="clip_l.safetensors", + clip_name2="t5-v1_1-xxl-encoder-Q8_0.gguf", + type="flux", + ) + emptylatentimage_5 = emptylatentimage.generate( + width=w, height=h, batch_size=int(self.batch_slider.get()) + ) + cliptextencodeflux_15 = cliptextencodeflux.encode( + clip_l=prompt, + t5xxl=prompt, + guidance=3.0, + clip=dualcliploadergguf_19[0], + flux_enabled=True, + ) + conditioningzeroout_16 = conditioningzeroout.zero_out( + conditioning=cliptextencodeflux_15[0] + ) + fb_cache = fbcache_nodes.ApplyFBCacheOnModel() + unetloadergguf_10 = fb_cache.patch( + unetloadergguf_10, "diffusion_model", 0.120 + ) + # try: + # import triton + # compiler = misc_nodes.EnhancedCompileModel() + # unetloadergguf_10 = compiler.patch(unetloadergguf_10, True, "diffusion_model", "torch.compile", False, False, None, None, False, "inductor") + # except ImportError: + # print("Triton not found, skipping compilation") + ksampler_3 = ksampler.sample( + seed=random.randint(1, 2**64), + steps=20, + cfg=1, + sampler_name="euler", + scheduler="beta", + denoise=1, + model=unetloadergguf_10[0], + positive=cliptextencodeflux_15[0], + negative=conditioningzeroout_16[0], + latent_image=emptylatentimage_5[0], + flux=True, + ) + vaedecode_8 = vaedecode.decode( + samples=ksampler_3[0], + vae=vaeloader_11[0], + flux=True, + ) + self.update_from_decode(vaedecode_8[0], "LD-Flux") + finally: + # Reset state when done + self.is_generating = False + self.generate_button.configure(state="normal") + if current_thread in self.generation_threads: + self.generation_threads.remove(current_thread) + + def on_model_selected(self, *args): + """Handle model selection changes""" + if self.dropdown.get() == "flux": + # Disable incompatible controls + self.adetailer_checkbox._state = tk.DISABLED + self.hires_fix_checkbox._state = tk.DISABLED + self.stable_fast_checkbox._state = tk.DISABLED + self.lora_selection._state = tk.DISABLED + self.cfg_slider._state = tk.DISABLED + else: + # Enable controls + self.adetailer_checkbox._state = tk.NORMAL + self.hires_fix_checkbox._state = tk.NORMAL + self.stable_fast_checkbox._state = tk.NORMAL + self.lora_selection._state = tk.NORMAL + self.cfg_slider._state = tk.NORMAL + + def _handle_decoded_image(self, decoded, prefix: str) -> None: + """Handle decoded image processing with HDR effects. + + Args: + decoded: Decoded tensor image + prefix: Prefix for saved files + """ + try: + # Initialize components + saveimage = ImageSaver.SaveImage() + hdr = ahdr.HDREffects() + images = [] + + # Apply HDR effects + if isinstance(decoded, tuple): + # Handle tuple return + tensor_image = decoded[0] + else: + tensor_image = decoded + + # Apply HDR as batch process + processed = hdr.apply_hdr2(tensor_image) + + # Save images with prefix + saveimage.save_images( + filename_prefix=prefix, + images=processed[0] if isinstance(processed, tuple) else processed, + ) + + # Convert processed tensors to PIL images + for img_tensor in ( + processed[0] if isinstance(processed, tuple) else [processed] + ): + # Convert to numpy and scale + img_array = 255.0 * img_tensor.cpu().numpy() + + # Handle different dimensions + if img_array.ndim == 4: + img_array = np.squeeze(img_array) + img_array = img_array.reshape( + -1, img_array.shape[-2], img_array.shape[-1] + ) + + # Convert to PIL image + img = Image.fromarray(np.clip(img_array, 0, 255).astype(np.uint8)) + images.append(img) + + # Update display if not interrupted + if not self.interrupt_flag: + self.progress.set(1.0) + if images: + self.img = images[0] + self.update_image(images) + self.display_most_recent_image_flag = True + + except Exception as e: + print(f"Image processing error: {e}") + self.title(f"LightDiffusion - Error: {str(e)}") + + def update_from_decode(self, decoded: Image.Image, prefix: str) -> None: + """Update the image from the decode function. + + Args: + decoded (Image.Image): The decoded image tensor/tuple + prefix (str): Prefix for saved files + """ + try: + # Handle image processing in separate function + self._handle_decoded_image(decoded, prefix) + + except Exception as e: + print(f"Decode error: {e}") + self.title(f"LightDiffusion - Error: {str(e)}") + + finally: + # Ensure cleanup + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def update_labels(self) -> None: + """Update the labels for the sliders.""" + self.width_label.configure(text=f"{int(self.width_slider.get())}") + self.height_label.configure(text=f"{int(self.height_slider.get())}") + self.cfg_label.configure(text=f"{int(self.cfg_slider.get())}") + self.batch_label.configure(text=f"{int(self.batch_slider.get())}") + + def create_image_grid(self, images: list[Image.Image]) -> Image.Image: + """Create a grid of images. + + Args: + images (list[Image.Image]): List of images to arrange in grid + + Returns: + Image.Image: Combined grid image + """ + # Calculate grid dimensions + n = len(images) + if n <= 1: + return images[0] + + cols = int(np.ceil(np.sqrt(n))) + rows = int(np.ceil(n / cols)) + + # Get max dimensions + w_max = max(img.width for img in images) + h_max = max(img.height for img in images) + + # Create output image + grid = Image.new("RGB", (w_max * cols, h_max * rows)) + + # Paste images into grid + for idx, img in enumerate(images): + i = idx // cols + j = idx % cols + grid.paste(img, (j * w_max, i * h_max)) + + return grid + + def update_image(self, images: Union[Image.Image, list[Image.Image]]) -> None: + """Update the displayed image(s). + + Args: + images: Single image or list of images to display + """ + # Convert single image to list + if isinstance(images, Image.Image): + images = [images] + + # Create grid of all images + grid_img = self.create_image_grid(images) + + # Calculate the aspect ratio of the grid + aspect_ratio = grid_img.width / grid_img.height + + # Determine the new dimensions while maintaining the aspect ratio + label_width = int(4 * self.winfo_width() / 7) + label_height = int(4 * self.winfo_height() / 7) + + if label_width / aspect_ratio <= label_height: + new_width = label_width + new_height = int(label_width / aspect_ratio) + else: + new_height = label_height + new_width = int(label_height * aspect_ratio) + + # Resize the grid image + try: + grid_img = grid_img.resize((new_width, new_height), Image.LANCZOS) + except RecursionError: + pass + self.img = grid_img + if self.display_most_recent_image_flag is False: + self._update_image_label(grid_img) + + def _update_image_label(self, img: Image.Image) -> None: + """Update the image label with the provided image. + + Args: + img (Image.Image): The image to display. + """ + # Convert the PIL image to a Tkinter PhotoImage + tk_image = ImageTk.PhotoImage(img) + # Update the image label with the Tkinter PhotoImage + self.image_label.config(image=tk_image) + # Keep a reference to the image to prevent it from being garbage collected + self.image_label.image = tk_image + + def display_most_recent_image(self) -> None: + """Display the most recent image(s) from the output directory.""" + # Get a list of all image files in the output directory + image_files = glob.glob("./_internal/output/Classic/*") + image_files += glob.glob("./_internal/output/Adetailer/*") + image_files += glob.glob("./_internal/output/Flux/*") + image_files += glob.glob("./_internal/output/HiresFix/*") + image_files += glob.glob("./_internal/output/Img2Img/*") + + # If there are no image files, return + if not image_files: + return + + # Sort files by modification time in descending order + image_files.sort(key=os.path.getmtime, reverse=True) + + # Get most recent timestamp + latest_time = os.path.getmtime(image_files[0]) + + # Get all images from same batch (within 1 second of most recent) + batch_images = [] + for file in image_files: + if abs(os.path.getmtime(file) - latest_time) < 1.0: + try: + img = Image.open(file) + batch_images.append(img) + except: + continue + + if not batch_images: + return + + # Display single image or grid of batch + if len(batch_images) == 1: + self.update_image(batch_images[0]) + else: + self.update_image(batch_images) + + def _start_resize_worker(self): + """Start the resize worker thread""" + self._resize_thread = threading.Thread(target=self._resize_worker, daemon=True) + self._resize_thread.start() + + def _resize_worker(self): + """Worker thread for handling resize operations""" + while self._resize_running: + try: + # Wait for resize event or timeout + if self._resize_queue.qsize() > 0: + event = self._resize_queue.get(timeout=0.1) + self._do_resize(event) + self._resize_queue.task_done() + else: + self._resize_event.wait(timeout=0.1) + self._resize_event.clear() + except queue.Empty: + continue + + def _queue_resize(self, event): + """Queue a resize event""" + current_time = time.time() + + # Debounce resize events + if current_time - self._last_resize_time > self._resize_delay: + self._last_resize_time = current_time + self._resize_queue.put(event) + self._resize_event.set() + + def _do_resize(self, event): + """Handle resize operation in worker thread""" + width = self.winfo_width() + height = self.winfo_height() + + # Update UI components in main thread + self.after(0, lambda: self._update_components(width, height)) + + # Update image if exists + if hasattr(self, "img"): + self._update_image_threaded(self.img) + + def _update_components(self, width, height): + """Update UI components sizes""" + # Update component sizes based on window dimensions + width = self.winfo_width() + height = self.winfo_height() + + # Scale text boxes + prompt_height = int(height * 0.25) + neg_height = int(height * 0.15) + self.prompt_entry.configure(height=prompt_height) + self.neg.configure(height=neg_height) + + def _update_image_threaded(self, img): + """Thread-safe image update with caching""" + if img is None: + return + + with self._resize_lock: + # Calculate dimensions + aspect_ratio = img.width / img.height + label_width = int(4 * self.winfo_width() / 7) + label_height = int(4 * self.winfo_height() / 7) + + if label_width / aspect_ratio <= label_height: + new_width = label_width + new_height = int(label_width / aspect_ratio) + else: + new_height = label_height + new_width = int(label_height * aspect_ratio) + + # Check cache + cache_key = (new_width, new_height) + if cache_key in self._image_cache: + resized_img = self._image_cache[cache_key] + else: + try: + resized_img = img.resize((new_width, new_height), Image.LANCZOS) + self._image_cache[cache_key] = resized_img + + # Limit cache size + if len(self._image_cache) > 5: + self._image_cache.pop(next(iter(self._image_cache))) + except: + return + if not self.display_most_recent_image_flag: + # Update image in main thread + self.after(0, lambda: self._update_image_label_safe(resized_img)) + + def _update_image_label_safe(self, img): + """Thread-safe image label update""" + if not self.display_most_recent_image_flag: + self._current_image = ImageTk.PhotoImage(img) + self.image_label.configure(image=self._current_image) + + def _cleanup(self): + """Clean up threads before closing""" + self._resize_running = False + self._resize_event.set() + if self._resize_thread: + self._resize_thread.join(timeout=1.0) + self.destroy() + + def interrupt_generation(self) -> None: + """Interrupt ongoing image generation process.""" + if not self.is_generating: + return + + # Set interrupt flag first + self.interrupt_flag = True + + # Clear CUDA cache and release memory + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + # Stop and cleanup threads + for thread in self.generation_threads[:]: + if thread and thread.is_alive(): + thread.join(timeout=1.0) + if thread in self.generation_threads: + self.generation_threads.remove(thread) + + # Reset UI state + self.progress.set(0) + self.title("LightDiffusion") + self.generate_button.configure(state="normal") + self.display_most_recent_image_flag = True + + # Clear any pending resize tasks + with self._resize_lock: + self._resize_queue.queue.clear() + + # Reset model state if needed + if hasattr(self, "checkpointloadersimple_241"): + del self.checkpointloadersimple_241 + self.ckpt = None + + # Always reset flags + self.generation_threads.clear() + # Reset generation state + self.is_generating = False + self.generate_button.configure(state="normal") + + +if __name__ == "__main__": + from modules.user.app_instance import app + + app.mainloop() diff --git a/modules/user/app_instance.py b/modules/user/app_instance.py index a44b44c11836bd7baf25ea47b5a8c2e02847f5d9..7d33aaac335d6c31679dad823a2f080dff3e4c58 100644 --- a/modules/user/app_instance.py +++ b/modules/user/app_instance.py @@ -1,3 +1,3 @@ -from modules.user.GUI import App - -app = App() +from modules.user.GUI import App + +app = App() diff --git a/modules/user/pipeline.py b/modules/user/pipeline.py index 01f34bd0f865053236f2ea48fec135bd2628ca05..d1e8c8c67b86596fd7b5c19cdbacccaebc03d6b4 100644 --- a/modules/user/pipeline.py +++ b/modules/user/pipeline.py @@ -1,513 +1,546 @@ -import argparse -import os -import random -import sys - -import numpy as np -import torch -from PIL import Image - -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) - -from modules.AutoDetailer import SAM, SEGS, ADetailer, bbox -from modules.AutoEncoders import VariationalAE -from modules.clip import Clip -from modules.Device import Device -from modules.FileManaging import Downloader, ImageSaver, Loader -from modules.hidiffusion import msw_msa_attention -from modules.Model import LoRas -from modules.Quantize import Quantizer -from modules.sample import sampling -from modules.UltimateSDUpscale import UltimateSDUpscale, USDU_upscaler -from modules.Utilities import Enhancer, Latent, upscale -from modules.WaveSpeed import fbcache_nodes - -last_seed = 0 - -Downloader.CheckAndDownload() - -def pipeline( - prompt: str, - w: int, - h: int, - number: int = 1, - batch: int = 1, - hires_fix: bool = False, - adetailer: bool = False, - enhance_prompt: bool = False, - img2img: bool = False, - stable_fast: bool = False, - reuse_seed: bool = False, - flux_enabled: bool = False, - prio_speed: bool = False, -) -> None: - """#### Run the LightDiffusion pipeline. - - #### Args: - - `prompt` (str): The prompt for the pipeline. - - `w` (int): The width of the generated image. - - `h` (int): The height of the generated image. - - `hires_fix` (bool, optional): Enable high-resolution fix. Defaults to False. - - `adetailer` (bool, optional): Enable automatic face and body enhancing. Defaults to False. - - `enhance_prompt` (bool, optional): Enable Ollama prompt enhancement. Defaults to False. - - `img2img` (bool, optional): Use LightDiffusion in Image to Image mode, the prompt input becomes the path to the input image. Defaults to False. - - `stable_fast` (bool, optional): Enable Stable-Fast speedup offering a 70% speed improvement in return of a compilation time. Defaults to False. - - `reuse_seed` (bool, optional): Reuse the last used seed, if False the seed will be kept random. Default to False. - - `flux_enabled` (bool, optional): Enable the flux mode. Defaults to False. - - `prio_speed` (bool, optional): Prioritize speed over quality. Defaults to False. - """ - global last_seed - if reuse_seed: - seed = last_seed - else: - seed = random.randint(1, 2**64) - last_seed = seed - if enhance_prompt: - try: - prompt = Enhancer.enhance_prompt(prompt) - except: - pass - sampler_name = "dpmpp_sde" if not prio_speed else "dpmpp_2m" - ckpt = "./_internal/checkpoints/Meina V10 - baked VAE.safetensors" - with torch.inference_mode(): - if not flux_enabled: - checkpointloadersimple = Loader.CheckpointLoaderSimple() - checkpointloadersimple_241 = checkpointloadersimple.load_checkpoint( - ckpt_name=ckpt - ) - hidiffoptimizer = msw_msa_attention.ApplyMSWMSAAttentionSimple() - cliptextencode = Clip.CLIPTextEncode() - emptylatentimage = Latent.EmptyLatentImage() - ksampler_instance = sampling.KSampler2() - vaedecode = VariationalAE.VAEDecode() - saveimage = ImageSaver.SaveImage() - latent_upscale = upscale.LatentUpscale() - for _ in range(number): - if img2img: - img = Image.open(prompt) - img_array = np.array(img) - img_tensor = torch.from_numpy(img_array).float().to("cpu") / 255.0 - img_tensor = img_tensor.unsqueeze(0) - with torch.inference_mode(): - ultimatesdupscale = UltimateSDUpscale.UltimateSDUpscale() - try: - loraloader = LoRas.LoraLoader() - loraloader_274 = loraloader.load_lora( - lora_name="add_detail.safetensors", - strength_model=2, - strength_clip=2, - model=checkpointloadersimple_241[0], - clip=checkpointloadersimple_241[1], - ) - except: - loraloader_274 = checkpointloadersimple_241 - - if stable_fast is True: - from modules.StableFast import StableFast - - applystablefast = StableFast.ApplyStableFastUnet() - applystablefast_158 = applystablefast.apply_stable_fast( - enable_cuda_graph=False, - model=loraloader_274[0], - ) - else: - applystablefast_158 = loraloader_274 - - clipsetlastlayer = Clip.CLIPSetLastLayer() - clipsetlastlayer_257 = clipsetlastlayer.set_last_layer( - stop_at_clip_layer=-2, clip=loraloader_274[1] - ) - - cliptextencode_242 = cliptextencode.encode( - text=prompt, - clip=clipsetlastlayer_257[0], - ) - cliptextencode_243 = cliptextencode.encode( - text="(worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic), (embedding:EasyNegative), (embedding:badhandv4), (embedding:lr), (embedding:ng_deepnegative_v1_75t)", - clip=clipsetlastlayer_257[0], - ) - upscalemodelloader = USDU_upscaler.UpscaleModelLoader() - upscalemodelloader_244 = upscalemodelloader.load_model( - "RealESRGAN_x4plus.pth" - ) - ultimatesdupscale_250 = ultimatesdupscale.upscale( - upscale_by=2, - seed=random.randint(1, 2**64), - steps=8, - cfg=6, - sampler_name=sampler_name, - scheduler="karras", - denoise=0.3, - mode_type="Linear", - tile_width=512, - tile_height=512, - mask_blur=16, - tile_padding=32, - seam_fix_mode="Half Tile", - seam_fix_denoise=0.2, - seam_fix_width=64, - seam_fix_mask_blur=16, - seam_fix_padding=32, - force_uniform_tiles="enable", - image=img_tensor, - model=applystablefast_158[0], - positive=cliptextencode_242[0], - negative=cliptextencode_243[0], - vae=checkpointloadersimple_241[2], - upscale_model=upscalemodelloader_244[0], - pipeline=True, - ) - saveimage.save_images( - filename_prefix="LD-i2i", - images=ultimatesdupscale_250[0], - ) - elif flux_enabled: - Downloader.CheckAndDownloadFlux() - with torch.inference_mode(): - dualcliploadergguf = Quantizer.DualCLIPLoaderGGUF() - emptylatentimage = Latent.EmptyLatentImage() - vaeloader = VariationalAE.VAELoader() - unetloadergguf = Quantizer.UnetLoaderGGUF() - cliptextencodeflux = Quantizer.CLIPTextEncodeFlux() - conditioningzeroout = Quantizer.ConditioningZeroOut() - ksampler = sampling.KSampler2() - unetloadergguf_10 = unetloadergguf.load_unet( - unet_name="flux1-dev-Q8_0.gguf" - ) - fb_cache = fbcache_nodes.ApplyFBCacheOnModel() - unetloadergguf_10 = fb_cache.patch(unetloadergguf_10, "diffusion_model", 0.120) - vaeloader_11 = vaeloader.load_vae(vae_name="ae.safetensors") - dualcliploadergguf_19 = dualcliploadergguf.load_clip( - clip_name1="clip_l.safetensors", - clip_name2="t5-v1_1-xxl-encoder-Q8_0.gguf", - type="flux", - ) - emptylatentimage_5 = emptylatentimage.generate( - width=w, height=h, batch_size=batch - ) - cliptextencodeflux_15 = cliptextencodeflux.encode( - clip_l=prompt, - t5xxl=prompt, - guidance=2.5, - clip=dualcliploadergguf_19[0], - flux_enabled=True, - ) - conditioningzeroout_16 = conditioningzeroout.zero_out( - conditioning=cliptextencodeflux_15[0] - ) - ksampler_3 = ksampler.sample( - seed=random.randint(1, 2**64), - steps=20, - cfg=1, - sampler_name="euler", - scheduler="beta", - denoise=1, - model=unetloadergguf_10[0], - positive=cliptextencodeflux_15[0], - negative=conditioningzeroout_16[0], - latent_image=emptylatentimage_5[0], - pipeline=True, - flux=True, - ) - - vaedecode_8 = vaedecode.decode( - samples=ksampler_3[0], - vae=vaeloader_11[0], - flux=True, - ) - - saveimage.save_images( - filename_prefix="Flux", images=vaedecode_8[0] - ) - else: - while prompt is None: - pass - with torch.inference_mode(): - try: - loraloader = LoRas.LoraLoader() - loraloader_274 = loraloader.load_lora( - lora_name="add_detail.safetensors", - strength_model=0.7, - strength_clip=0.7, - model=checkpointloadersimple_241[0], - clip=checkpointloadersimple_241[1], - ) - print("loading add_detail.safetensors") - except: - loraloader_274 = checkpointloadersimple_241 - clipsetlastlayer = Clip.CLIPSetLastLayer() - clipsetlastlayer_257 = clipsetlastlayer.set_last_layer( - stop_at_clip_layer=-2, clip=loraloader_274[1] - ) - applystablefast_158 = loraloader_274 - cliptextencode_242 = cliptextencode.encode( - text=prompt, - clip=clipsetlastlayer_257[0], - ) - cliptextencode_243 = cliptextencode.encode( - text="(worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic), (embedding:EasyNegative), (embedding:badhandv4), (embedding:lr), (embedding:ng_deepnegative_v1_75t)", - clip=clipsetlastlayer_257[0], - ) - emptylatentimage_244 = emptylatentimage.generate( - width=w, height=h, batch_size=batch - ) - if stable_fast is True: - from modules.StableFast import StableFast - - try: - self.title("LightDiffusion - Generating StableFast model") - except: - pass - applystablefast = StableFast.ApplyStableFastUnet() - applystablefast_158 = applystablefast.apply_stable_fast( - enable_cuda_graph=False, - model=loraloader_274[0], - ) - else: - applystablefast_158 = loraloader_274 - fb_cache = fbcache_nodes.ApplyFBCacheOnModel() - applystablefast_158 = fb_cache.patch(applystablefast_158, "diffusion_model", 0.120) - - ksampler_239 = ksampler_instance.sample( - seed=seed, - steps=20, - cfg=7, - sampler_name=sampler_name, - scheduler="karras", - denoise=1, - pipeline=True, - model=hidiffoptimizer.go( - model_type="auto", model=applystablefast_158[0] - )[0], - positive=cliptextencode_242[0], - negative=cliptextencode_243[0], - latent_image=emptylatentimage_244[0], - ) - if hires_fix: - latentupscale_254 = latent_upscale.upscale( - width=w * 2, - height=h * 2, - samples=ksampler_239[0], - ) - ksampler_253 = ksampler_instance.sample( - seed=random.randint(1, 2**64), - steps=10, - cfg=8, - sampler_name="euler_ancestral", - scheduler="normal", - denoise=0.45, - model=hidiffoptimizer.go( - model_type="auto", model=applystablefast_158[0] - )[0], - positive=cliptextencode_242[0], - negative=cliptextencode_243[0], - latent_image=latentupscale_254[0], - pipeline=True, - ) - else: - ksampler_253 = ksampler_239 - - vaedecode_240 = vaedecode.decode( - samples=ksampler_253[0], - vae=checkpointloadersimple_241[2], - ) - - if adetailer: - with torch.inference_mode(): - samloader = SAM.SAMLoader() - samloader_87 = samloader.load_model( - model_name="sam_vit_b_01ec64.pth", device_mode="AUTO" - ) - cliptextencode_124 = cliptextencode.encode( - text="royal, detailed, magnificient, beautiful, seducing", - clip=loraloader_274[1], - ) - ultralyticsdetectorprovider = bbox.UltralyticsDetectorProvider() - ultralyticsdetectorprovider_151 = ultralyticsdetectorprovider.doit( - # model_name="face_yolov8m.pt" - model_name="person_yolov8m-seg.pt" - ) - bboxdetectorsegs = bbox.BboxDetectorForEach() - samdetectorcombined = SAM.SAMDetectorCombined() - impactsegsandmask = SEGS.SegsBitwiseAndMask() - detailerforeachdebug = ADetailer.DetailerForEachTest() - bboxdetectorsegs_132 = bboxdetectorsegs.doit( - threshold=0.5, - dilation=10, - crop_factor=2, - drop_size=10, - labels="all", - bbox_detector=ultralyticsdetectorprovider_151[0], - image=vaedecode_240[0], - ) - samdetectorcombined_139 = samdetectorcombined.doit( - detection_hint="center-1", - dilation=0, - threshold=0.93, - bbox_expansion=0, - mask_hint_threshold=0.7, - mask_hint_use_negative="False", - sam_model=samloader_87[0], - segs=bboxdetectorsegs_132, - image=vaedecode_240[0], - ) - if samdetectorcombined_139 is None: - return - impactsegsandmask_152 = impactsegsandmask.doit( - segs=bboxdetectorsegs_132, - mask=samdetectorcombined_139[0], - ) - detailerforeachdebug_145 = detailerforeachdebug.doit( - guide_size=512, - guide_size_for=False, - max_size=768, - seed=random.randint(1, 2**64), - steps=20, - cfg=6.5, - sampler_name=sampler_name, - scheduler="karras", - denoise=0.5, - feather=5, - noise_mask=True, - force_inpaint=True, - wildcard="", - cycle=1, - inpaint_model=False, - noise_mask_feather=20, - image=vaedecode_240[0], - segs=impactsegsandmask_152[0], - model=applystablefast_158[0], - clip=checkpointloadersimple_241[1], - vae=checkpointloadersimple_241[2], - positive=cliptextencode_124[0], - negative=cliptextencode_243[0], - pipeline=True, - ) - saveimage.save_images( - filename_prefix="LD-refined", - images=detailerforeachdebug_145[0], - ) - ultralyticsdetectorprovider = bbox.UltralyticsDetectorProvider() - ultralyticsdetectorprovider_151 = ultralyticsdetectorprovider.doit( - model_name="face_yolov9c.pt" - ) - bboxdetectorsegs_132 = bboxdetectorsegs.doit( - threshold=0.5, - dilation=10, - crop_factor=2, - drop_size=10, - labels="all", - bbox_detector=ultralyticsdetectorprovider_151[0], - image=detailerforeachdebug_145[0], - ) - samdetectorcombined_139 = samdetectorcombined.doit( - detection_hint="center-1", - dilation=0, - threshold=0.93, - bbox_expansion=0, - mask_hint_threshold=0.7, - mask_hint_use_negative="False", - sam_model=samloader_87[0], - segs=bboxdetectorsegs_132, - image=detailerforeachdebug_145[0], - ) - impactsegsandmask_152 = impactsegsandmask.doit( - segs=bboxdetectorsegs_132, - mask=samdetectorcombined_139[0], - ) - detailerforeachdebug_145 = detailerforeachdebug.doit( - guide_size=512, - guide_size_for=False, - max_size=768, - seed=random.randint(1, 2**64), - steps=20, - cfg=6.5, - sampler_name=sampler_name, - scheduler="karras", - denoise=0.5, - feather=5, - noise_mask=True, - force_inpaint=True, - wildcard="", - cycle=1, - inpaint_model=False, - noise_mask_feather=20, - image=detailerforeachdebug_145[0], - segs=impactsegsandmask_152[0], - model=applystablefast_158[0], - clip=checkpointloadersimple_241[1], - vae=checkpointloadersimple_241[2], - positive=cliptextencode_124[0], - negative=cliptextencode_243[0], - pipeline=True, - ) - saveimage.save_images( - filename_prefix="lD-2ndrefined", - images=detailerforeachdebug_145[0], - ) - else: - saveimage.save_images(filename_prefix="LD", images=vaedecode_240[0]) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Run the LightDiffusion pipeline.") - parser.add_argument("prompt", type=str, help="The prompt for the pipeline.") - parser.add_argument("width", type=int, help="The width of the generated image.") - parser.add_argument("height", type=int, help="The height of the generated image.") - parser.add_argument("number", type=int, help="The number of images to generate.") - parser.add_argument("batch", type=int, help="The batch size. aka the number of images to generate at once.") - parser.add_argument( - "--hires-fix", action="store_true", help="Enable high-resolution fix." - ) - parser.add_argument( - "--adetailer", - action="store_true", - help="Enable automatic face and body enhancin.g", - ) - parser.add_argument( - "--enhance-prompt", - action="store_true", - help="Enable Ollama prompt enhancement. Make sure to have ollama with Ollama installed.", - ) - parser.add_argument( - "--img2img", - action="store_true", - help="Enable image-to-image mode. This will use the image as the prompt.", - ) - parser.add_argument( - "--stable-fast", - action="store_true", - help="Enable StableFast mode. This will compile the model for faster inference.", - ) - parser.add_argument( - "--reuse-seed", - action="store_true", - help="Enable to reuse last used seed for sampling, default for False is a random seed at every use.", - ) - parser.add_argument( - "--flux", - action="store_true", - help="Enable the flux mode.", - ) - parser.add_argument( - "--prio-speed", - action="store_true", - help="Prioritize speed over quality.", - ) - args = parser.parse_args() - - pipeline( - args.prompt, - args.width, - args.height, - args.number, - args.batch, - args.hires_fix, - args.adetailer, - args.enhance_prompt, - args.img2img, - args.stable_fast, - args.reuse_seed, - args.flux, - args.prio_speed, - ) +import argparse +import os +import random +import sys + +import numpy as np +import torch +from PIL import Image + +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) + +from modules.AutoDetailer import SAM, SEGS, ADetailer, bbox +from modules.AutoEncoders import VariationalAE +from modules.clip import Clip +from modules.FileManaging import Downloader, ImageSaver, Loader +from modules.hidiffusion import msw_msa_attention +from modules.Model import LoRas +from modules.Quantize import Quantizer +from modules.sample import sampling +from modules.UltimateSDUpscale import UltimateSDUpscale, USDU_upscaler +from modules.Utilities import Enhancer, Latent, upscale +from modules.WaveSpeed import fbcache_nodes +from modules.AutoHDR import ahdr + +with open(os.path.join("./_internal/", "last_seed.txt"), "r") as f: + last_seed = int(f.read()) + +Downloader.CheckAndDownload() + + +def pipeline( + prompt: str, + w: int, + h: int, + number: int = 1, + batch: int = 1, + hires_fix: bool = False, + adetailer: bool = False, + enhance_prompt: bool = False, + img2img: bool = False, + stable_fast: bool = False, + reuse_seed: bool = False, + flux_enabled: bool = False, + prio_speed: bool = False, + autohdr: bool = False, +) -> None: + """#### Run the LightDiffusion pipeline. + + #### Args: + - `prompt` (str): The prompt for the pipeline. + - `w` (int): The width of the generated image. + - `h` (int): The height of the generated image. + - `hires_fix` (bool, optional): Enable high-resolution fix. Defaults to False. + - `adetailer` (bool, optional): Enable automatic face and body enhancing. Defaults to False. + - `enhance_prompt` (bool, optional): Enable Ollama prompt enhancement. Defaults to False. + - `img2img` (bool, optional): Use LightDiffusion in Image to Image mode, the prompt input becomes the path to the input image. Defaults to False. + - `stable_fast` (bool, optional): Enable Stable-Fast speedup offering a 70% speed improvement in return of a compilation time. Defaults to False. + - `reuse_seed` (bool, optional): Reuse the last used seed, if False the seed will be kept random. Default to False. + - `flux_enabled` (bool, optional): Enable the flux mode. Defaults to False. + - `prio_speed` (bool, optional): Prioritize speed over quality. Defaults to False. + - `autohdr` (bool, optional): Enable the AutoHDR mode. Defaults to False. + """ + global last_seed + if reuse_seed: + seed = last_seed + + else: + seed = random.randint(1, 2**64) + last_seed = seed + with open(os.path.join("./_internal/", "last_seed.txt"), "w") as f: + f.write(str(seed)) + if enhance_prompt: + try: + prompt = Enhancer.enhance_prompt(prompt) + except: + pass + + sampler_name = "dpmpp_sde_cfgpp" if not prio_speed else "dpmpp_2m_cfgpp" + ckpt = "./_internal/checkpoints/Meina V10 - baked VAE.safetensors" + with torch.inference_mode(): + if not flux_enabled: + checkpointloadersimple = Loader.CheckpointLoaderSimple() + checkpointloadersimple_241 = checkpointloadersimple.load_checkpoint( + ckpt_name=ckpt + ) + hidiffoptimizer = msw_msa_attention.ApplyMSWMSAAttentionSimple() + cliptextencode = Clip.CLIPTextEncode() + emptylatentimage = Latent.EmptyLatentImage() + ksampler_instance = sampling.KSampler2() + vaedecode = VariationalAE.VAEDecode() + saveimage = ImageSaver.SaveImage() + latent_upscale = upscale.LatentUpscale() + hdr = ahdr.HDREffects() + for _ in range(number): + if img2img: + img = Image.open(prompt) + img_array = np.array(img) + img_tensor = torch.from_numpy(img_array).float().to("cpu") / 255.0 + img_tensor = img_tensor.unsqueeze(0) + with torch.inference_mode(): + ultimatesdupscale = UltimateSDUpscale.UltimateSDUpscale() + try: + loraloader = LoRas.LoraLoader() + loraloader_274 = loraloader.load_lora( + lora_name="add_detail.safetensors", + strength_model=2, + strength_clip=2, + model=checkpointloadersimple_241[0], + clip=checkpointloadersimple_241[1], + ) + except: + loraloader_274 = checkpointloadersimple_241 + + if stable_fast is True: + from modules.StableFast import StableFast + + applystablefast = StableFast.ApplyStableFastUnet() + applystablefast_158 = applystablefast.apply_stable_fast( + enable_cuda_graph=False, + model=loraloader_274[0], + ) + else: + applystablefast_158 = loraloader_274 + + clipsetlastlayer = Clip.CLIPSetLastLayer() + clipsetlastlayer_257 = clipsetlastlayer.set_last_layer( + stop_at_clip_layer=-2, clip=loraloader_274[1] + ) + + cliptextencode_242 = cliptextencode.encode( + text=prompt, + clip=clipsetlastlayer_257[0], + ) + cliptextencode_243 = cliptextencode.encode( + text="(worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic), (embedding:EasyNegative), (embedding:badhandv4), (embedding:lr), (embedding:ng_deepnegative_v1_75t)", + clip=clipsetlastlayer_257[0], + ) + upscalemodelloader = USDU_upscaler.UpscaleModelLoader() + upscalemodelloader_244 = upscalemodelloader.load_model( + "RealESRGAN_x4plus.pth" + ) + ultimatesdupscale_250 = ultimatesdupscale.upscale( + upscale_by=2, + seed=random.randint(1, 2**64), + steps=8, + cfg=6, + sampler_name=sampler_name, + scheduler="karras", + denoise=0.3, + mode_type="Linear", + tile_width=512, + tile_height=512, + mask_blur=16, + tile_padding=32, + seam_fix_mode="Half Tile", + seam_fix_denoise=0.2, + seam_fix_width=64, + seam_fix_mask_blur=16, + seam_fix_padding=32, + force_uniform_tiles="enable", + image=img_tensor, + model=applystablefast_158[0], + positive=cliptextencode_242[0], + negative=cliptextencode_243[0], + vae=checkpointloadersimple_241[2], + upscale_model=upscalemodelloader_244[0], + pipeline=True, + ) + saveimage.save_images( + filename_prefix="LD-I2I", + images=hdr.apply_hdr2(ultimatesdupscale_250[0]) + if autohdr + else ultimatesdupscale_250[0], + ) + elif flux_enabled: + Downloader.CheckAndDownloadFlux() + with torch.inference_mode(): + dualcliploadergguf = Quantizer.DualCLIPLoaderGGUF() + emptylatentimage = Latent.EmptyLatentImage() + vaeloader = VariationalAE.VAELoader() + unetloadergguf = Quantizer.UnetLoaderGGUF() + cliptextencodeflux = Quantizer.CLIPTextEncodeFlux() + conditioningzeroout = Quantizer.ConditioningZeroOut() + ksampler = sampling.KSampler2() + unetloadergguf_10 = unetloadergguf.load_unet( + unet_name="flux1-dev-Q8_0.gguf" + ) + fb_cache = fbcache_nodes.ApplyFBCacheOnModel() + unetloadergguf_10 = fb_cache.patch( + unetloadergguf_10, "diffusion_model", 0.120 + ) + vaeloader_11 = vaeloader.load_vae(vae_name="ae.safetensors") + dualcliploadergguf_19 = dualcliploadergguf.load_clip( + clip_name1="clip_l.safetensors", + clip_name2="t5-v1_1-xxl-encoder-Q8_0.gguf", + type="flux", + ) + emptylatentimage_5 = emptylatentimage.generate( + width=w, height=h, batch_size=batch + ) + cliptextencodeflux_15 = cliptextencodeflux.encode( + clip_l=prompt, + t5xxl=prompt, + guidance=3.0, + clip=dualcliploadergguf_19[0], + flux_enabled=True, + ) + conditioningzeroout_16 = conditioningzeroout.zero_out( + conditioning=cliptextencodeflux_15[0] + ) + ksampler_3 = ksampler.sample( + seed=random.randint(1, 2**64), + steps=20, + cfg=1, + sampler_name="euler", + scheduler="beta", + denoise=1, + model=unetloadergguf_10[0], + positive=cliptextencodeflux_15[0], + negative=conditioningzeroout_16[0], + latent_image=emptylatentimage_5[0], + pipeline=True, + flux=True, + ) + + vaedecode_8 = vaedecode.decode( + samples=ksampler_3[0], + vae=vaeloader_11[0], + flux=True, + ) + + saveimage.save_images( + filename_prefix="LD-Flux", + images=hdr.apply_hdr2(vaedecode_8[0]) + if autohdr + else vaedecode_8[0], + ) + else: + while prompt is None: + pass + with torch.inference_mode(): + try: + loraloader = LoRas.LoraLoader() + loraloader_274 = loraloader.load_lora( + lora_name="add_detail.safetensors", + strength_model=0.7, + strength_clip=0.7, + model=checkpointloadersimple_241[0], + clip=checkpointloadersimple_241[1], + ) + print("loading add_detail.safetensors") + except: + loraloader_274 = checkpointloadersimple_241 + clipsetlastlayer = Clip.CLIPSetLastLayer() + clipsetlastlayer_257 = clipsetlastlayer.set_last_layer( + stop_at_clip_layer=-2, clip=loraloader_274[1] + ) + applystablefast_158 = loraloader_274 + cliptextencode_242 = cliptextencode.encode( + text=prompt, + clip=clipsetlastlayer_257[0], + ) + cliptextencode_243 = cliptextencode.encode( + text="(worst quality, low quality:1.4), (zombie, sketch, interlocked fingers, comic), (embedding:EasyNegative), (embedding:badhandv4), (embedding:lr), (embedding:ng_deepnegative_v1_75t)", + clip=clipsetlastlayer_257[0], + ) + emptylatentimage_244 = emptylatentimage.generate( + width=w, height=h, batch_size=batch + ) + if stable_fast is True: + from modules.StableFast import StableFast + + applystablefast = StableFast.ApplyStableFastUnet() + applystablefast_158 = applystablefast.apply_stable_fast( + enable_cuda_graph=False, + model=loraloader_274[0], + ) + else: + applystablefast_158 = loraloader_274 + fb_cache = fbcache_nodes.ApplyFBCacheOnModel() + applystablefast_158 = fb_cache.patch( + applystablefast_158, "diffusion_model", 0.120 + ) + + ksampler_239 = ksampler_instance.sample( + seed=seed, + steps=20, + cfg=7, + sampler_name=sampler_name, + scheduler="karras", + denoise=1, + pipeline=True, + model=hidiffoptimizer.go( + model_type="auto", model=applystablefast_158[0] + )[0], + positive=cliptextencode_242[0], + negative=cliptextencode_243[0], + latent_image=emptylatentimage_244[0], + ) + if hires_fix: + latentupscale_254 = latent_upscale.upscale( + width=w * 2, + height=h * 2, + samples=ksampler_239[0], + ) + ksampler_253 = ksampler_instance.sample( + seed=random.randint(1, 2**64), + steps=10, + cfg=8, + sampler_name="euler_ancestral", + scheduler="normal", + denoise=0.45, + model=hidiffoptimizer.go( + model_type="auto", model=applystablefast_158[0] + )[0], + positive=cliptextencode_242[0], + negative=cliptextencode_243[0], + latent_image=latentupscale_254[0], + pipeline=True, + ) + else: + ksampler_253 = ksampler_239 + + vaedecode_240 = vaedecode.decode( + samples=ksampler_253[0], + vae=checkpointloadersimple_241[2], + ) + + if adetailer: + with torch.inference_mode(): + samloader = SAM.SAMLoader() + samloader_87 = samloader.load_model( + model_name="sam_vit_b_01ec64.pth", device_mode="AUTO" + ) + cliptextencode_124 = cliptextencode.encode( + text="royal, detailed, magnificient, beautiful, seducing", + clip=loraloader_274[1], + ) + ultralyticsdetectorprovider = bbox.UltralyticsDetectorProvider() + ultralyticsdetectorprovider_151 = ultralyticsdetectorprovider.doit( + # model_name="face_yolov8m.pt" + model_name="person_yolov8m-seg.pt" + ) + bboxdetectorsegs = bbox.BboxDetectorForEach() + samdetectorcombined = SAM.SAMDetectorCombined() + impactsegsandmask = SEGS.SegsBitwiseAndMask() + detailerforeachdebug = ADetailer.DetailerForEachTest() + bboxdetectorsegs_132 = bboxdetectorsegs.doit( + threshold=0.5, + dilation=10, + crop_factor=2, + drop_size=10, + labels="all", + bbox_detector=ultralyticsdetectorprovider_151[0], + image=vaedecode_240[0], + ) + samdetectorcombined_139 = samdetectorcombined.doit( + detection_hint="center-1", + dilation=0, + threshold=0.93, + bbox_expansion=0, + mask_hint_threshold=0.7, + mask_hint_use_negative="False", + sam_model=samloader_87[0], + segs=bboxdetectorsegs_132, + image=vaedecode_240[0], + ) + if samdetectorcombined_139 is None: + return + impactsegsandmask_152 = impactsegsandmask.doit( + segs=bboxdetectorsegs_132, + mask=samdetectorcombined_139[0], + ) + detailerforeachdebug_145 = detailerforeachdebug.doit( + guide_size=512, + guide_size_for=False, + max_size=768, + seed=random.randint(1, 2**64), + steps=20, + cfg=6.5, + sampler_name=sampler_name, + scheduler="karras", + denoise=0.5, + feather=5, + noise_mask=True, + force_inpaint=True, + wildcard="", + cycle=1, + inpaint_model=False, + noise_mask_feather=20, + image=vaedecode_240[0], + segs=impactsegsandmask_152[0], + model=applystablefast_158[0], + clip=checkpointloadersimple_241[1], + vae=checkpointloadersimple_241[2], + positive=cliptextencode_124[0], + negative=cliptextencode_243[0], + pipeline=True, + ) + saveimage.save_images( + filename_prefix="LD-body", + images=hdr.apply_hdr2(detailerforeachdebug_145[0]) + if autohdr + else detailerforeachdebug_145[0], + ) + ultralyticsdetectorprovider = bbox.UltralyticsDetectorProvider() + ultralyticsdetectorprovider_151 = ultralyticsdetectorprovider.doit( + model_name="face_yolov9c.pt" + ) + bboxdetectorsegs_132 = bboxdetectorsegs.doit( + threshold=0.5, + dilation=10, + crop_factor=2, + drop_size=10, + labels="all", + bbox_detector=ultralyticsdetectorprovider_151[0], + image=detailerforeachdebug_145[0], + ) + samdetectorcombined_139 = samdetectorcombined.doit( + detection_hint="center-1", + dilation=0, + threshold=0.93, + bbox_expansion=0, + mask_hint_threshold=0.7, + mask_hint_use_negative="False", + sam_model=samloader_87[0], + segs=bboxdetectorsegs_132, + image=detailerforeachdebug_145[0], + ) + impactsegsandmask_152 = impactsegsandmask.doit( + segs=bboxdetectorsegs_132, + mask=samdetectorcombined_139[0], + ) + detailerforeachdebug_145 = detailerforeachdebug.doit( + guide_size=512, + guide_size_for=False, + max_size=768, + seed=random.randint(1, 2**64), + steps=20, + cfg=6.5, + sampler_name=sampler_name, + scheduler="karras", + denoise=0.5, + feather=5, + noise_mask=True, + force_inpaint=True, + wildcard="", + cycle=1, + inpaint_model=False, + noise_mask_feather=20, + image=detailerforeachdebug_145[0], + segs=impactsegsandmask_152[0], + model=applystablefast_158[0], + clip=checkpointloadersimple_241[1], + vae=checkpointloadersimple_241[2], + positive=cliptextencode_124[0], + negative=cliptextencode_243[0], + pipeline=True, + ) + saveimage.save_images( + filename_prefix="LD-head", + images=hdr.apply_hdr2(detailerforeachdebug_145[0]) + if autohdr + else detailerforeachdebug_145[0], + ) + else: + saveimage.save_images( + filename_prefix="LD-HF" if hires_fix else "LD", + images=hdr.apply_hdr2(vaedecode_240[0]) + if autohdr + else vaedecode_240[0], + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Run the LightDiffusion pipeline.") + parser.add_argument("prompt", type=str, help="The prompt for the pipeline.") + parser.add_argument("width", type=int, help="The width of the generated image.") + parser.add_argument("height", type=int, help="The height of the generated image.") + parser.add_argument("number", type=int, help="The number of images to generate.") + parser.add_argument( + "batch", + type=int, + help="The batch size. aka the number of images to generate at once.", + ) + parser.add_argument( + "--hires-fix", action="store_true", help="Enable high-resolution fix." + ) + parser.add_argument( + "--adetailer", + action="store_true", + help="Enable automatic face and body enhancin.g", + ) + parser.add_argument( + "--enhance-prompt", + action="store_true", + help="Enable Ollama prompt enhancement. Make sure to have ollama with Ollama installed.", + ) + parser.add_argument( + "--img2img", + action="store_true", + help="Enable image-to-image mode. This will use the prompt as path to the image.", + ) + parser.add_argument( + "--stable-fast", + action="store_true", + help="Enable StableFast mode. This will compile the model for faster inference.", + ) + parser.add_argument( + "--reuse-seed", + action="store_true", + help="Enable to reuse last used seed for sampling, default for False is a random seed at every use.", + ) + parser.add_argument( + "--flux", + action="store_true", + help="Enable the flux mode.", + ) + parser.add_argument( + "--prio-speed", + action="store_true", + help="Prioritize speed over quality.", + ) + parser.add_argument( + "--autohdr", + action="store_true", + help="Enable the AutoHDR mode.", + ) + args = parser.parse_args() + + pipeline( + args.prompt, + args.width, + args.height, + args.number, + args.batch, + args.hires_fix, + args.adetailer, + args.enhance_prompt, + args.img2img, + args.stable_fast, + args.reuse_seed, + args.flux, + args.prio_speed, + args.autohdr, + ) diff --git a/pipeline.bat b/pipeline.bat index a3a24a5b2095659fcfb64e8baaab05e83829e08e..a56271cb951bd6de067f573a2c35db34fa459a0c 100644 --- a/pipeline.bat +++ b/pipeline.bat @@ -1,55 +1,57 @@ -@echo off -SET VENV_DIR=.venv - -REM Check if .venv exists -IF NOT EXIST %VENV_DIR% ( - echo Creating virtual environment... - python -m venv %VENV_DIR% -) - -REM Activate the virtual environment -CALL %VENV_DIR%\Scripts\activate - -REM Upgrade pip -echo Upgrading pip... -python -m pip install --upgrade pip - -REM Install specific packages -echo Installing required packages... -pip install uv - -REM Check GPU type -SET TORCH_URL=https://download.pytorch.org/whl/cpu -nvidia-smi >nul 2>&1 -IF %ERRORLEVEL% EQU 0 ( - echo NVIDIA GPU detected - SET TORCH_URL=https://download.pytorch.org/whl/cu124 - uv pip install xformers torch torchvision --index-url %TORCH_URL% -) ELSE ( - echo No compatible GPU detected, using CPU - uv pip install torch torchvision --index-url %TORCH_URL% -) - -REM Install additional requirements -IF EXIST requirements.txt ( - echo Installing additional requirements... - uv pip install -r requirements.txt -) ELSE ( - echo requirements.txt not found, skipping... -) - -REM Check for enhance-prompt argument -echo Checking for enhance-prompt argument... -echo %* | findstr /i /c:"--enhance-prompt" >nul -IF %ERRORLEVEL% EQU 0 ( - echo Installing ollama with winget... - winget install --id ollama.ollama - ollama pull deepseek-r1 -) - -REM Launch the script -echo Launching LightDiffusion... -python .\modules\user\pipeline.py %* - -REM Deactivate the virtual environment -deactivate +@echo off +SET VENV_DIR=.venv + +REM Check if .venv exists +IF NOT EXIST %VENV_DIR% ( + echo Creating virtual environment... + python -m venv %VENV_DIR% +) + +REM Activate the virtual environment +CALL %VENV_DIR%\Scripts\activate + +REM Upgrade pip +echo Upgrading pip... +python -m pip install --upgrade pip + +REM Install specific packages +echo Installing required packages... +pip install uv + +REM Check for NVIDIA GPU +FOR /F "delims=" %%i IN ('nvidia-smi 2^>^&1') DO ( + SET GPU_CHECK=%%i +) +IF NOT ERRORLEVEL 1 ( + echo NVIDIA GPU detected, installing GPU dependencies... + uv pip install xformers torch torchvision --index-url https://download.pytorch.org/whl/cu126 +) ELSE ( + echo No NVIDIA GPU detected, installing CPU dependencies... + uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu +) + +uv pip install "numpy>=1.24.3" + +REM Install additional requirements +IF EXIST requirements.txt ( + echo Installing additional requirements... + uv pip install -r requirements.txt +) ELSE ( + echo requirements.txt not found, skipping... +) + +REM Check for enhance-prompt argument +echo Checking for enhance-prompt argument... +echo %* | findstr /i /c:"--enhance-prompt" >nul +IF %ERRORLEVEL% EQU 0 ( + echo Installing ollama with winget... + winget install --id ollama.ollama + ollama pull deepseek-r1 +) + +REM Launch the script +echo Launching LightDiffusion... +python .\modules\user\pipeline.py %* + +REM Deactivate the virtual environment +deactivate diff --git a/pipeline.sh b/pipeline.sh index 214a582b1daf9991f24d8a15d1f948836a8b6d93..88a519f39c8ef39df1bc2f146bdbb04e4ee9203d 100644 --- a/pipeline.sh +++ b/pipeline.sh @@ -1,68 +1,69 @@ -#!/bin/bash - -VENV_DIR=.venv -# for WSL2 Ubuntu install -# sudo apt install software-properties-common -# sudo add-apt-repository ppa:deadsnakes/ppa -sudo apt-get install python3.10 python3.10-venv python3.10-full python3-pip - -# Check if .venv exists -if [ ! -d "$VENV_DIR" ]; then - echo "Creating virtual environment..." - python3.10 -m venv $VENV_DIR -fi - -# Activate the virtual environment -source $VENV_DIR/bin/activate - -# Upgrade pip -echo "Upgrading pip..." -pip install --upgrade pip -pip3 install uv - -# Check GPU type -TORCH_URL="https://download.pytorch.org/whl/cpu" -if command -v nvidia-smi &> /dev/null; then - echo "NVIDIA GPU detected" - TORCH_URL="https://download.pytorch.org/whl/cu124" - uv pip install --index-url $TORCH_URL \ - torch==2.2.2 torchvision "xformers>=0.0.22" "triton>=2.1.0" \ - stable_fast-1.0.5+torch222cu124-cp310-cp310-manylinux2014_x86_64.whl -elif command -v rocminfo &> /dev/null; then - echo "AMD GPU detected" - TORCH_URL="https://download.pytorch.org/whl/rocm5.7" - uv pip install --index-url $TORCH_URL \ - torch==2.2.2 torchvision "triton>=2.1.0" -else - echo "No compatible GPU detected, using CPU" - uv pip install --index-url $TORCH_URL \ - torch==2.2.2+cpu torchvision -fi - - -# Install tkinter -echo "Installing tkinter..." -sudo apt-get install python3.10-tk - -# Install additional requirements -if [ -f requirements.txt ]; then - echo "Installing additional requirements..." - uv pip install -r requirements.txt -else - echo "requirements.txt not found, skipping..." -fi - -REM Check for enhance-prompt argument -echo Checking for enhance-prompt argument... -if [[ " $* " == *" --enhance-prompt "* ]]; then - echo "Installing ollama..." - curl -fsSL https://ollama.com/install.sh | sh - ollama pull deepseek-r1 -fi - -# Launch the script -echo "Launching LightDiffusion..." -python3.10 "./modules/user/pipeline.py" "$@" - -# Deactivate the virtual environment -deactivate +#!/bin/bash + +VENV_DIR=.venv +# for WSL2 Ubuntu install +# sudo apt install software-properties-common +# sudo add-apt-repository ppa:deadsnakes/ppa +sudo apt-get install python3.10 python3.10-venv python3.10-full python3-pip + +# Check if .venv exists +if [ ! -d "$VENV_DIR" ]; then + echo "Creating virtual environment..." + python3.10 -m venv $VENV_DIR +fi + +# Activate the virtual environment +source $VENV_DIR/bin/activate + +# Upgrade pip +echo "Upgrading pip..." +pip install --upgrade pip +pip3 install uv + +# Check GPU type +TORCH_URL="https://download.pytorch.org/whl/cpu" +if command -v nvidia-smi &> /dev/null; then + echo "NVIDIA GPU detected" + TORCH_URL="https://download.pytorch.org/whl/cu121" + uv pip install --index-url $TORCH_URL \ + torch==2.2.2 torchvision "xformers>=0.0.22" "triton>=2.1.0" \ + stable_fast-1.0.5+torch222cu121-cp310-cp310-manylinux2014_x86_64.whl +elif command -v rocminfo &> /dev/null; then + echo "AMD GPU detected" + TORCH_URL="https://download.pytorch.org/whl/rocm5.7" + uv pip install --index-url $TORCH_URL \ + torch==2.2.2 torchvision "triton>=2.1.0" +else + echo "No compatible GPU detected, using CPU" + uv pip install --index-url $TORCH_URL \ + torch==2.2.2+cpu torchvision +fi + +uv pip install "numpy<2.0.0" + +# Install tkinter +echo "Installing tkinter..." +sudo apt-get install python3.10-tk + +# Install additional requirements +if [ -f requirements.txt ]; then + echo "Installing additional requirements..." + uv pip install -r requirements.txt +else + echo "requirements.txt not found, skipping..." +fi + +REM Check for enhance-prompt argument +echo Checking for enhance-prompt argument... +if [[ " $* " == *" --enhance-prompt "* ]]; then + echo "Installing ollama..." + curl -fsSL https://ollama.com/install.sh | sh + ollama pull deepseek-r1 +fi + +# Launch the script +echo "Launching LightDiffusion..." +python3.10 "./modules/user/pipeline.py" "$@" + +# Deactivate the virtual environment +deactivate diff --git a/run.bat b/run.bat index 622443aaec5377955a22905c32a2b493fc4e1d5c..0562233af0bead446e1e881b001dfd3b29d13fa2 100644 --- a/run.bat +++ b/run.bat @@ -1,46 +1,48 @@ -@echo off -SET VENV_DIR=.venv - -REM Check if .venv exists -IF NOT EXIST %VENV_DIR% ( - echo Creating virtual environment... - python -m venv %VENV_DIR% -) - -REM Activate the virtual environment -CALL %VENV_DIR%\Scripts\activate - -REM Upgrade pip -echo Upgrading pip... -python -m pip install --upgrade pip - -REM Install specific packages -echo Installing required packages... -pip install uv - -REM Check GPU type -SET TORCH_URL=https://download.pytorch.org/whl/cpu -nvidia-smi >nul 2>&1 -IF %ERRORLEVEL% EQU 0 ( - echo NVIDIA GPU detected - SET TORCH_URL=https://download.pytorch.org/whl/cu124 - uv pip install xformers torch torchvision --index-url %TORCH_URL% -) ELSE ( - echo No compatible GPU detected, using CPU - uv pip install torch torchvision --index-url %TORCH_URL% -) - -REM Install additional requirements -IF EXIST requirements.txt ( - echo Installing additional requirements... - uv pip install -r requirements.txt -) ELSE ( - echo requirements.txt not found, skipping... -) - -REM Launch the script -echo Launching LightDiffusion... -python .\modules\user\GUI.py - -REM Deactivate the virtual environment -deactivate +@echo off +SET VENV_DIR=.venv + +REM Check if .venv exists +IF NOT EXIST %VENV_DIR% ( + echo Creating virtual environment... + python -m venv %VENV_DIR% +) + +REM Activate the virtual environment +CALL %VENV_DIR%\Scripts\activate + +REM Upgrade pip +echo Upgrading pip... +python -m pip install --upgrade pip + +REM Install specific packages +echo Installing required packages... +pip install uv + +REM Check for NVIDIA GPU +FOR /F "delims=" %%i IN ('nvidia-smi 2^>^&1') DO ( + SET GPU_CHECK=%%i +) +IF NOT ERRORLEVEL 1 ( + echo NVIDIA GPU detected, installing GPU dependencies... + uv pip install xformers torch torchvision --index-url https://download.pytorch.org/whl/cu126 +) ELSE ( + echo No NVIDIA GPU detected, installing CPU dependencies... + uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu +) + +uv pip install "numpy>=1.24.3" + +REM Install additional requirements +IF EXIST requirements.txt ( + echo Installing additional requirements... + uv pip install -r requirements.txt +) ELSE ( + echo requirements.txt not found, skipping... +) + +REM Launch the script +echo Launching LightDiffusion... +python .\modules\user\GUI.py + +REM Deactivate the virtual environment +deactivate diff --git a/run.sh b/run.sh index 84aa8c3653cc44ab75f0fcd4241053ec8d3a6c45..4cd3abffd99312645706f53737fa7841c53b7afb 100644 --- a/run.sh +++ b/run.sh @@ -1,59 +1,61 @@ -#!/bin/bash - -VENV_DIR=.venv -# for WSL2 Ubuntu install -# sudo apt install software-properties-common -# sudo add-apt-repository ppa:deadsnakes/ppa -sudo apt-get install python3.10 python3.10-venv python3.10-full python3-pip - -# Check if .venv exists -if [ ! -d "$VENV_DIR" ]; then - echo "Creating virtual environment..." - python3.10 -m venv $VENV_DIR -fi - -# Activate the virtual environment -source $VENV_DIR/bin/activate - -# Upgrade pip -echo "Upgrading pip..." -pip install --upgrade pip -pip3 install uv - -# Check GPU type -TORCH_URL="https://download.pytorch.org/whl/cpu" -if command -v nvidia-smi &> /dev/null; then - echo "NVIDIA GPU detected" - TORCH_URL="https://download.pytorch.org/whl/cu124" - uv pip install --index-url $TORCH_URL \ - torch==2.2.2 torchvision "xformers>=0.0.22" "triton>=2.1.0" \ - stable_fast-1.0.5+torch222cu124-cp310-cp310-manylinux2014_x86_64.whl -elif command -v rocminfo &> /dev/null; then - echo "AMD GPU detected" - TORCH_URL="https://download.pytorch.org/whl/rocm5.7" - uv pip install --index-url $TORCH_URL \ - torch==2.2.2 torchvision "triton>=2.1.0" -else - echo "No compatible GPU detected, using CPU" - uv pip install --index-url $TORCH_URL \ - torch==2.2.2+cpu torchvision -fi - -# Install tkinter -echo "Installing tkinter..." -sudo apt-get install python3.10-tk - -# Install additional requirements -if [ -f requirements.txt ]; then - echo "Installing additional requirements..." - uv pip install -r requirements.txt -else - echo "requirements.txt not found, skipping..." -fi - -# Launch the script -echo "Launching LightDiffusion..." -python3.10 ./modules/user/GUI.py - -# Deactivate the virtual environment -deactivate +#!/bin/bash + +VENV_DIR=.venv +# for WSL2 Ubuntu install +# sudo apt install software-properties-common +# sudo add-apt-repository ppa:deadsnakes/ppa +sudo apt-get install python3.10 python3.10-venv python3.10-full python3-pip + +# Check if .venv exists +if [ ! -d "$VENV_DIR" ]; then + echo "Creating virtual environment..." + python3.10 -m venv $VENV_DIR +fi + +# Activate the virtual environment +source $VENV_DIR/bin/activate + +# Upgrade pip +echo "Upgrading pip..." +pip install --upgrade pip +pip3 install uv + +# Check GPU type +TORCH_URL="https://download.pytorch.org/whl/cpu" +if command -v nvidia-smi &> /dev/null; then + echo "NVIDIA GPU detected" + TORCH_URL="https://download.pytorch.org/whl/cu121" + uv pip install --index-url $TORCH_URL \ + torch==2.2.2 torchvision "xformers>=0.0.22" "triton>=2.1.0" \ + stable_fast-1.0.5+torch222cu121-cp310-cp310-manylinux2014_x86_64.whl +elif command -v rocminfo &> /dev/null; then + echo "AMD GPU detected" + TORCH_URL="https://download.pytorch.org/whl/rocm5.7" + uv pip install --index-url $TORCH_URL \ + torch==2.2.2 torchvision "triton>=2.1.0" +else + echo "No compatible GPU detected, using CPU" + uv pip install --index-url $TORCH_URL \ + torch==2.2.2+cpu torchvision +fi + +uv pip install "numpy<2.0.0" + +# Install tkinter +echo "Installing tkinter..." +sudo apt-get install python3.10-tk + +# Install additional requirements +if [ -f requirements.txt ]; then + echo "Installing additional requirements..." + uv pip install -r requirements.txt +else + echo "requirements.txt not found, skipping..." +fi + +# Launch the script +echo "Launching LightDiffusion..." +python3.10 ./modules/user/GUI.py + +# Deactivate the virtual environment +deactivate diff --git a/run_web.bat b/run_web.bat new file mode 100644 index 0000000000000000000000000000000000000000..6675ee21a5c274b704b087e9db80ab70c2ffda2d --- /dev/null +++ b/run_web.bat @@ -0,0 +1,48 @@ +@echo off +SET VENV_DIR=.venv + +REM Check if .venv exists +IF NOT EXIST %VENV_DIR% ( + echo Creating virtual environment... + python -m venv %VENV_DIR% +) + +REM Activate the virtual environment +CALL %VENV_DIR%\Scripts\activate + +REM Upgrade pip +echo Upgrading pip... +python -m pip install --upgrade pip + +REM Install specific packages +echo Installing required packages... +pip install uv + +REM Check for NVIDIA GPU +FOR /F "delims=" %%i IN ('nvidia-smi 2^>^&1') DO ( + SET GPU_CHECK=%%i +) +IF NOT ERRORLEVEL 1 ( + echo NVIDIA GPU detected, installing GPU dependencies... + uv pip install xformers torch torchvision --index-url https://download.pytorch.org/whl/cu126 +) ELSE ( + echo No NVIDIA GPU detected, installing CPU dependencies... + uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu +) + +uv pip install "numpy>=1.24.3" + +REM Install additional requirements +IF EXIST requirements.txt ( + echo Installing additional requirements... + uv pip install -r requirements.txt +) ELSE ( + echo requirements.txt not found, skipping... +) + +REM Launch the script +echo Launching LightDiffusion... +python app.py + +REM Deactivate the virtual environment +deactivate diff --git a/run_web.sh b/run_web.sh index dbdedd2cbfb8c6e29fd1945b8f8d3c263ded6324..2a3334cd28185d076460bfaca12aa0887b913887 100644 --- a/run_web.sh +++ b/run_web.sh @@ -1,59 +1,61 @@ -#!/bin/bash - -VENV_DIR=.venv -# for WSL2 Ubuntu install -# sudo apt install software-properties-common -# sudo add-apt-repository ppa:deadsnakes/ppa -sudo apt-get install python3.10 python3.10-venv python3.10-full python3-pip - -# Check if .venv exists -if [ ! -d "$VENV_DIR" ]; then - echo "Creating virtual environment..." - python3.10 -m venv $VENV_DIR -fi - -# Activate the virtual environment -source $VENV_DIR/bin/activate - -# Upgrade pip -echo "Upgrading pip..." -pip install --upgrade pip -pip3 install uv - -# Check GPU type -TORCH_URL="https://download.pytorch.org/whl/cpu" -if command -v nvidia-smi &> /dev/null; then - echo "NVIDIA GPU detected" - TORCH_URL="https://download.pytorch.org/whl/cu124" - uv pip install --index-url $TORCH_URL \ - torch==2.2.2 torchvision "xformers>=0.0.22" "triton>=2.1.0" \ - stable_fast-1.0.5+torch222cu124-cp310-cp310-manylinux2014_x86_64.whl -elif command -v rocminfo &> /dev/null; then - echo "AMD GPU detected" - TORCH_URL="https://download.pytorch.org/whl/rocm5.7" - uv pip install --index-url $TORCH_URL \ - torch==2.2.2 torchvision "triton>=2.1.0" -else - echo "No compatible GPU detected, using CPU" - uv pip install --index-url $TORCH_URL \ - torch==2.2.2+cpu torchvision -fi - -# Install tkinter -echo "Installing tkinter..." -sudo apt-get install python3.10-tk - -# Install additional requirements -if [ -f requirements.txt ]; then - echo "Installing additional requirements..." - uv pip install -r requirements.txt -else - echo "requirements.txt not found, skipping..." -fi - -# Launch the script -echo "Launching LightDiffusion..." -python3.10 app.py - -# Deactivate the virtual environment -deactivate +#!/bin/bash + +VENV_DIR=.venv +# for WSL2 Ubuntu install +# sudo apt install software-properties-common +# sudo add-apt-repository ppa:deadsnakes/ppa +sudo apt-get install python3.10 python3.10-venv python3.10-full python3-pip + +# Check if .venv exists +if [ ! -d "$VENV_DIR" ]; then + echo "Creating virtual environment..." + python3.10 -m venv $VENV_DIR +fi + +# Activate the virtual environment +source $VENV_DIR/bin/activate + +# Upgrade pip +echo "Upgrading pip..." +pip install --upgrade pip +pip3 install uv + +# Check GPU type +TORCH_URL="https://download.pytorch.org/whl/cpu" +if command -v nvidia-smi &> /dev/null; then + echo "NVIDIA GPU detected" + TORCH_URL="https://download.pytorch.org/whl/cu121" + uv pip install --index-url $TORCH_URL \ + torch==2.2.2 torchvision "xformers>=0.0.22" "triton>=2.1.0" \ + stable_fast-1.0.5+torch222cu121-cp310-cp310-manylinux2014_x86_64.whl +elif command -v rocminfo &> /dev/null; then + echo "AMD GPU detected" + TORCH_URL="https://download.pytorch.org/whl/rocm5.7" + uv pip install --index-url $TORCH_URL \ + torch==2.2.2 torchvision "triton>=2.1.0" +else + echo "No compatible GPU detected, using CPU" + uv pip install --index-url $TORCH_URL \ + torch==2.2.2+cpu torchvision +fi + +uv pip install "numpy<2.0.0" + +# Install tkinter +echo "Installing tkinter..." +sudo apt-get install python3.10-tk + +# Install additional requirements +if [ -f requirements.txt ]; then + echo "Installing additional requirements..." + uv pip install -r requirements.txt +else + echo "requirements.txt not found, skipping..." +fi + +# Launch the script +echo "Launching LightDiffusion..." +python3.10 app.py + +# Deactivate the virtual environment +deactivate