Instructions to use liujx233/OneModelForAll with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use liujx233/OneModelForAll with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("liujx233/OneModelForAll", 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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README.md
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This repository provides the open-source weights for the paper ["One Model For All: Unified Try-On and Try-Off in Any Pose via LLM-Inspired Bidirectional Tweedie Diffusion"](https://arxiv.org/abs/2508.04559).
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Our project supports virtual try-on, virtual try-off, and arbitrary-pose try-on in a unified framework, enabling flexible outfit transfer from only a single portrait and a target garment.
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- [paper](https://arxiv.org/abs/2508.04559)
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- [project page](https://onemodelforall.github.io/)
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## Released Weights
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This repository provides the open-source weights for the paper ["One Model For All: Unified Try-On and Try-Off in Any Pose via LLM-Inspired Bidirectional Tweedie Diffusion"](https://arxiv.org/abs/2508.04559).
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Our project supports virtual try-on, virtual try-off, and arbitrary-pose try-on in a unified framework, enabling flexible outfit transfer from only a single portrait and a target garment.
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- [paper](https://arxiv.org/abs/2508.04559)
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- [project page](https://onemodelforall.github.io/)
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## Released Weights
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