Temporarily remove README

README.md

@@ -1,100 +0,0 @@
-# AnimagineXL-v3-openvino
-
-This is an *unofficial* [OpenVINO](https://github.com/openvinotoolkit/openvino) variant of [cagliostrolab/animagine-xl-3.0](https://huggingface.co/cagliostrolab/animagine-xl-3.0). 
-
-The repo is provided for convenience of running the Animagine XL v3 model on Intel CPU/GPU, as loading & converting a SDXL model to openvino can be pretty slow (dozens of minutes).
-
-Table of contents:
-- [Usage](#usage)
-- [How the conversion was done](#how-the-conversion-was-done)
-- [Appendix](#appendix)
-
-
-## Usage
-
-Take CPU for example:
-
-```python
-from optimum.intel.openvino import OVStableDiffusionXLPipeline
-from diffusers import (
-    EulerAncestralDiscreteScheduler,
-    DPMSolverMultistepScheduler
-)
-
-model_id = "CodeChris/AnimagineXL-v3-openvino"
-pipe = OVStableDiffusionXLPipeline.from_pretrained(model_model)
-# Fix output image size & batch_size for faster speed
-img_w, img_h = 832, 1216  # Example
-pipe.reshape(width=img_w, height=img_h,
-             batch_size=1, num_images_per_prompt=1)
-
-## Change scheduler
-# AnimagineXL recommand Euler A:
-# pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(
-    pipe.scheduler.config,
-    use_karras_sigmas=True,
-    algorithm_type="dpmsolver++"
-)  # I prefer DPM++ 2M Karras
-# Turn off the filter
-pipe.safety_checker = None
-
-# If run on a GPU, you need:
-# pipe.to('cuda')
-```
-
-After the pipe is prepared, a txt2img task can be executed as below:
-```python
-prompt = "1girl, dress, day, masterpiece, best quality"
-negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name"
-
-images = pipe(
-    prompt,
-    negative_prompt,
-    # If reshaped, image size must equal the reshaped size
-    width=img_w, height=img_h,
-    guidance_scale=7,
-    num_inference_steps=20
-)
-img = images[0]
-img.save('sample.png')
-```
-
-For convenience, here is the recommended image sizes from the official AnimagineXL doc:
-
-```
-# Or their transpose
-896 x 1152
-832 x 1216
-768 x 1344
-640 x 1536
-1024 x 1024
-```
-
-## How the conversion was done
-
-First, install optimum:
-
-```powershell
-pip install --upgrade-strategy eager optimum[openvino,nncf]
-```
-
-Then, the repo is converted using the following command:
-
-```powershell
-optimum-cli export openvino --model 'cagliostrolab/animagine-xl-3.0' 'models/openvino/AnimagineXL-v3' --task 'stable-diffusion-xl'
-```
-
-## Appendix
-
-Push large files:
-
-```
-git lfs install
-huggingface-cli lfs-enable-largefiles .
-```
-
-Other notes:
-
-* The conversion was done using `optimum==1.16.1` and `openvino==2023.2.0`.
-* You may query `optimum-cli export openvino --help` for more usage details.

ReadMe.md

@@ -1,100 +0,0 @@
-# AnimagineXL-v3-openvino
-
-This is an *unofficial* [OpenVINO](https://github.com/openvinotoolkit/openvino) variant of [cagliostrolab/animagine-xl-3.0](https://huggingface.co/cagliostrolab/animagine-xl-3.0). 
-
-The repo is provided for convenience of running the Animagine XL v3 model on Intel CPU/GPU, as loading & converting a SDXL model to openvino can be pretty slow (dozens of minutes).
-
-Table of contents:
-- [Usage](#usage)
-- [How the conversion was done](#how-the-conversion-was-done)
-- [Appendix](#appendix)
-
-
-## Usage
-
-Take CPU for example:
-
-```python
-from optimum.intel.openvino import OVStableDiffusionXLPipeline
-from diffusers import (
-    EulerAncestralDiscreteScheduler,
-    DPMSolverMultistepScheduler
-)
-
-model_id = "CodeChris/AnimagineXL-v3-openvino"
-pipe = OVStableDiffusionXLPipeline.from_pretrained(model_model)
-# Fix output image size & batch_size for faster speed
-img_w, img_h = 832, 1216  # Example
-pipe.reshape(width=img_w, height=img_h,
-             batch_size=1, num_images_per_prompt=1)
-
-## Change scheduler
-# AnimagineXL recommand Euler A:
-# pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(
-    pipe.scheduler.config,
-    use_karras_sigmas=True,
-    algorithm_type="dpmsolver++"
-)  # I prefer DPM++ 2M Karras
-# Turn off the filter
-pipe.safety_checker = None
-
-# If run on a GPU, you need:
-# pipe.to('cuda')
-```
-
-After the pipe is prepared, a txt2img task can be executed as below:
-```python
-prompt = "1girl, dress, day, masterpiece, best quality"
-negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name"
-
-images = pipe(
-    prompt,
-    negative_prompt,
-    # If reshaped, image size must equal the reshaped size
-    width=img_w, height=img_h,
-    guidance_scale=7,
-    num_inference_steps=20
-)
-img = images[0]
-img.save('sample.png')
-```
-
-For convenience, here is the recommended image sizes from the official AnimagineXL doc:
-
-```
-# Or their transpose
-896 x 1152
-832 x 1216
-768 x 1344
-640 x 1536
-1024 x 1024
-```
-
-## How the conversion was done
-
-First, install optimum:
-
-```powershell
-pip install --upgrade-strategy eager optimum[openvino,nncf]
-```
-
-Then, the repo is converted using the following command:
-
-```powershell
-optimum-cli export openvino --model 'cagliostrolab/animagine-xl-3.0' 'models/openvino/AnimagineXL-v3' --task 'stable-diffusion-xl'
-```
-
-## Appendix
-
-Push large files:
-
-```
-git lfs install
-huggingface-cli lfs-enable-largefiles .
-```
-
-Other notes:
-
-* The conversion was done using `optimum==1.16.1` and `openvino==2023.2.0`.
-* You may query `optimum-cli export openvino --help` for more usage details.