Instructions to use yeates/OmniPaint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yeates/OmniPaint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("yeates/OmniPaint") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Update citation to ICCV 2025 proceedings
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README.md
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## Citation
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```bibtex
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@
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title={
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author={Yu, Yongsheng},
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year={2025}
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}
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```
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## Citation
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```bibtex
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@inproceedings{yu2025omnipaint,
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title={Omnipaint: Mastering object-oriented editing via disentangled insertion-removal inpainting},
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author={Yu, Yongsheng and Zeng, Ziyun and Zheng, Haitian and Luo, Jiebo},
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booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
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pages={17324--17334},
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year={2025}
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}
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```
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