Instructions to use levihsu/OOTDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use levihsu/OOTDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("levihsu/OOTDiffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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license: cc-by-nc-sa-4.0
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license: cc-by-nc-sa-4.0
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# OOTDiffusion
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[Our OOTDiffusion GitHub repository](https://github.com/levihsu/OOTDiffusion)
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[Try our OOTDiffusion](https://ootd.ibot.cn/)
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Please give me a star if you find it interesting!
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> **OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on**<br>
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> [Yuhao Xu](https://scholar.google.com/citations?user=FF7JVLsAAAAJ&hl=zh-CN), [Tao Gu](https://github.com/T-Gu), [Weifeng Chen](https://github.com/chenshine1), Chengcai Chen<br>
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> Xiao-i Research
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