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", torch_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
metadata
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.
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".
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.
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.
