Upload folder using huggingface_hub

.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

.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

.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

.gradio/certificate.pem

@@ -1,31 +1,31 @@
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-emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
------END CERTIFICATE-----
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
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+-----END CERTIFICATE-----

.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"
 }

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'(?<![a-zA-Z0-9_\.])({name}\.)(?![)\]])'
-            # Pattern 2: matches module name inside brackets while preserving them  
-            pattern2 = rf'(\[|\()({name}\.)([^\]\)]+?)(\]|\))'
-            pattern3 = 'cond_util\.'
-            patterns.extend([
-                (pattern1, ''),  # Remove module name and dot outside brackets
-                (pattern2, r'\1\3\4'),  # Keep brackets, remove only module name
-                (pattern3, '')
-            ])
-            seen.add(name)
-    return patterns
-
-def remove_file_names(line):
-    patterns = get_file_patterns()
-    result = line
-    for pattern, replacement in patterns:
-        result = re.sub(pattern, replacement, result)
-    return result
-
-try:
-    with open("./compiled.py", "w") as output_file:
-        for file_path in files_ordered:
-            try:
-                with open(file_path, "r") as input_file:
-                    for line in input_file:
-                        if not line.lstrip().startswith("from modules."):
-                            # Apply the file name removal before writing
-                            modified_line = remove_file_names(line)
-                            output_file.write(modified_line)
-                    output_file.write("\n\n")
-                print(f"Processed: {file_path}")
-            except FileNotFoundError:
-                print(f"Error: Could not find file {file_path}")
-            except Exception as e:
-                print(f"Error processing {file_path}: {str(e)}")
-except Exception as e:
+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'(?<![a-zA-Z0-9_\.])({name}\.)(?![)\]])'
+            # Pattern 2: matches module name inside brackets while preserving them  
+            pattern2 = rf'(\[|\()({name}\.)([^\]\)]+?)(\]|\))'
+            pattern3 = 'cond_util\.'
+            patterns.extend([
+                (pattern1, ''),  # Remove module name and dot outside brackets
+                (pattern2, r'\1\3\4'),  # Keep brackets, remove only module name
+                (pattern3, '')
+            ])
+            seen.add(name)
+    return patterns
+
+def remove_file_names(line):
+    patterns = get_file_patterns()
+    result = line
+    for pattern, replacement in patterns:
+        result = re.sub(pattern, replacement, result)
+    return result
+
+try:
+    with open("./compiled.py", "w") as output_file:
+        for file_path in files_ordered:
+            try:
+                with open(file_path, "r") as input_file:
+                    for line in input_file:
+                        if not line.lstrip().startswith("from modules."):
+                            # Apply the file name removal before writing
+                            modified_line = remove_file_names(line)
+                            output_file.write(modified_line)
+                    output_file.write("\n\n")
+                print(f"Processed: {file_path}")
+            except FileNotFoundError:
+                print(f"Error: Could not find file {file_path}")
+            except Exception as e:
+                print(f"Error processing {file_path}: {str(e)}")
+except Exception as e:
     print(f"Error creating compiled.py: {str(e)}")

LICENSE

@@ -1,674 +1,674 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- 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.  By contrast,
-the GNU General Public License is intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users.  We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors.  You can apply it to
-your programs, too.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
-  To protect your rights, we need to prevent others from denying you
-these rights or asking you to surrender the rights.  Therefore, you have
-certain responsibilities if you distribute copies of the software, or if
-you modify it: responsibilities to respect the freedom of others.
-
-  For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must pass on to the recipients the same
-freedoms that you received.  You must make sure that they, too, receive
-or can get the source code.  And you must show them these terms so they
-know their rights.
-
-  Developers that use the GNU GPL protect your rights with two steps:
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+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  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
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,144 +1,144 @@
----
-title: LightDiffusion-Next
-app_file: app.py
-sdk: gradio
-sdk_version: 5.14.0
----
-<div align="center">
-
-# 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)&nbsp;
-
-**LightDiffusion-Next**  is the fastest AI-powered image generation GUI/CLI, combining speed, precision, and flexibility in one cohesive tool.
-</br>
-</br>
-  <a href="https://github.com/LightDiffusion/LightDiffusion-Next">
-    <img src="./HomeImage.png" alt="Logo">
-
-  </a>
-</br>
-</div>
-
-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 <prompt> <width> <height> <num_images> <batch_size>
-```
-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
+---
+<div align="center">
+
+# 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)&nbsp;
+
+**LightDiffusion-Next**  is the fastest AI-powered image generation GUI/CLI, combining speed, precision, and flexibility in one cohesive tool.
+</br>
+</br>
+  <a href="https://github.com/LightDiffusion/LightDiffusion-Next">
+    <img src="./HomeImage.png" alt="Logo">
+
+  </a>
+</br>
+</div>
+
+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 <prompt> <width> <height> <num_images> <batch_size>
+```
+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!

_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
+}

_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
+  }
+}

_internal/sd1_tokenizer/tokenizer_config.json

@@ -1,34 +1,34 @@
-{
-  "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
-  }
-}
+{
+  "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
+  }
+}

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
-        )
+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,
+        )

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)

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

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

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,
+        )

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

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], []),
+        )

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)

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

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)

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

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,)
+
+

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

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)

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]

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/",
+        )

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}}

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]

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)

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

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)

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

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

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,)

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
 ]

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

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)

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,)

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]

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()

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

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,)