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--- |
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language: |
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- en |
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tags: |
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- text-to-image |
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- stable-diffusion |
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- safetensors |
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- stable-diffusion-xl |
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- animagine-xl |
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base_model: cagliostrolab/animagine-xl-3.0 |
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--- |
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# AnimagineXL-v3-openvino |
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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). |
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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). |
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Table of contents: |
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- [Usage](#usage) |
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- [How the conversion was done](#how-the-conversion-was-done) |
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- [Appendix](#appendix) |
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## Usage |
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Take CPU for example: |
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```python |
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from optimum.intel.openvino import OVStableDiffusionXLPipeline |
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from diffusers import ( |
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EulerAncestralDiscreteScheduler, |
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DPMSolverMultistepScheduler |
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) |
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model_id = "CodeChris/AnimagineXL-v3-openvino" |
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pipe = OVStableDiffusionXLPipeline.from_pretrained(model_model) |
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# Fix output image size & batch_size for faster speed |
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img_w, img_h = 832, 1216 # Example |
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pipe.reshape(width=img_w, height=img_h, |
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batch_size=1, num_images_per_prompt=1) |
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## Change scheduler |
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# AnimagineXL recommand Euler A: |
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# pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) |
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pipe.scheduler = DPMSolverMultistepScheduler.from_config( |
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pipe.scheduler.config, |
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use_karras_sigmas=True, |
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algorithm_type="dpmsolver++" |
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) # I prefer DPM++ 2M Karras |
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# Turn off the filter |
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pipe.safety_checker = None |
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# If run on a GPU, you need: |
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# pipe.to('cuda') |
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``` |
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After the pipe is prepared, a txt2img task can be executed as below: |
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```python |
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prompt = "1girl, dress, day, masterpiece, best quality" |
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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" |
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images = pipe( |
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prompt, |
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negative_prompt, |
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# If reshaped, image size must equal the reshaped size |
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width=img_w, height=img_h, |
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guidance_scale=7, |
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num_inference_steps=20 |
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) |
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img = images[0] |
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img.save('sample.png') |
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``` |
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For convenience, here is the recommended image sizes from the official AnimagineXL doc: |
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``` |
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# Or their transpose |
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896 x 1152 |
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832 x 1216 |
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768 x 1344 |
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640 x 1536 |
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1024 x 1024 |
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``` |
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## How the conversion was done |
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First, install optimum: |
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```powershell |
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pip install --upgrade-strategy eager optimum[openvino,nncf] |
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``` |
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Then, the repo is converted using the following command: |
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```powershell |
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optimum-cli export openvino --model 'cagliostrolab/animagine-xl-3.0' 'models/openvino/AnimagineXL-v3' --task 'stable-diffusion-xl' |
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``` |
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## Appendix |
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Push large files **without** git commit the latest changes: |
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``` |
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git lfs install |
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huggingface-cli lfs-enable-largefiles . |
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huggingface-cli upload --commit-message 'Upload model files' 'CodeChris/AnimagineXL-v3-openvino' . |
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``` |
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Other notes: |
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* The conversion was done using `optimum==1.16.1` and `openvino==2023.2.0`. |
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* You may query `optimum-cli export openvino --help` for more usage details. |
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