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
| base_model: stable-diffusion-xl-1.0-inpainting-0.1 | |
| tags: | |
| - stable-diffusion-xl | |
| - virtual try-on | |
| license: cc-by-nc-sa-4.0 | |
| # Check out more details on our [project page](https://onemodelforall.github.io/). | |
| # One Model For All : Unified Try-On and Try-Off in Any Pose via LLM-Inspired Bidirectional Tweedie Diffusion | |
| 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). | |
| 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. | |
| - [paper](https://arxiv.org/abs/2508.04559) | |
| - [project page](https://onemodelforall.github.io/) | |
|  | |
| ## Released Weights | |
| We release two sets of weights for inference on the VITON-HD and DeepFashion-MultiModal datasets, respectively. | |
| Both support high-resolution image generation at [768, 1024]. | |
| ## License | |
| The codes and checkpoints in this repository are under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). | |