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Browse files- README.md +12 -2
- requirements.txt +2 -1
- samples/unet_320x640_0.jpg +2 -2
- samples/unet_384x640_0.jpg +2 -2
- samples/unet_448x640_0.jpg +2 -2
- samples/unet_512x640_0.jpg +2 -2
- samples/unet_576x640_0.jpg +2 -2
- samples/unet_640x320_0.jpg +2 -2
- samples/unet_640x384_0.jpg +2 -2
- samples/unet_640x448_0.jpg +2 -2
- samples/unet_640x512_0.jpg +2 -2
- samples/unet_640x576_0.jpg +2 -2
- samples/unet_640x640_0.jpg +2 -2
- src/TRAIN.md +2 -2
- train.py +1 -1
README.md
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- VAE: 8x16ch, [Simple VAE](https://huggingface.co/AiArtLab/simplevae)
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The model was trained for ~3 months on
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[Gradio](https://sdxs.aiartlab.org/)
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## More examples
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-

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- VAE: 8x16ch, [Simple VAE](https://huggingface.co/AiArtLab/simplevae)
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The model was trained for ~3 months on (2-4)x RTX 5090 GPUs using over 1+ million images of various resolutions and styles, primarily anime and illustrations.
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[Gradio](https://sdxs.aiartlab.org/)
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## More examples
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## Citation
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```bibtex
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@misc{recoilme_sdxs_2025,
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title={Simple Diffusion XS},
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author={recoilme with help of AiArtLab Team},
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url={https://huggingface.co/AiArtLab/sdxs},
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year={2025}
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}
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```
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requirements.txt
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wandb>=0.19.8
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huggingface_hub>=0.29.3
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bitsandbytes>=0.45.4
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transformers
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wandb>=0.19.8
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huggingface_hub>=0.29.3
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bitsandbytes>=0.45.4
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transformers
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hf_transfer
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samples/unet_320x640_0.jpg
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src/TRAIN.md
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```
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python -m pip install --upgrade pip
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/
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pip install flash-attn --no-build-isolation # optional
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```
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Клонируйте репозиторий:
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```
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git clone https://huggingface.co/AiArtLab/sdxs
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cd sdxs/
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pip install -r requirements.txt
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```
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Подготовьте датасет:
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```
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python -m pip install --upgrade pip
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130 -U # change
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pip install flash-attn --no-build-isolation # optional
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```
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Клонируйте репозиторий:
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```
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git clone https://huggingface.co/AiArtLab/sdxs
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cd sdxs/
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pip install -r requirements.txt -U
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```
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Подготовьте датасет:
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train.py
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min_learning_rate = 1e-5
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num_epochs = 40
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# samples/save per epoch
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sample_interval_share =
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use_wandb = True
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use_comet_ml = False
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save_model = True
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min_learning_rate = 1e-5
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num_epochs = 40
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# samples/save per epoch
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sample_interval_share = 2
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use_wandb = True
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use_comet_ml = False
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save_model = True
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