Image-to-Image
Diffusers
ONNX
Safetensors
StableDiffusionXLInpaintPipeline
stable-diffusion-xl
inpainting
virtual try-on
Instructions to use yisol/IDM-VTON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yisol/IDM-VTON 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("yisol/IDM-VTON", dtype=torch.bfloat16, device_map="cuda") 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
metadata
base_model: stable-diffusion-xl-1.0-inpainting-0.1
tags:
- stable-diffusion-xl
- inpainting
- virtual try-on
license: cc-by-nc-sa-4.0
Check out more codes on our github repository!
IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild
This is an official implementation of paper 'Improving Diffusion Models for Authentic Virtual Try-on in the Wild'
🤗 Try our huggingface Demo
TODO LIST
- demo model
- inference code
- training code
Acknowledgements
For the demo, GPUs are supported from zerogpu, and auto masking generation codes are based on OOTDiffusion and DCI-VTON.
Parts of the code are based on IP-Adapter.
Citation
@article{choi2024improving,
title={Improving Diffusion Models for Virtual Try-on},
author={Choi, Yisol and Kwak, Sangkyung and Lee, Kyungmin and Choi, Hyungwon and Shin, Jinwoo},
journal={arXiv preprint arXiv:2403.05139},
year={2024}
}
License
The codes and checkpoints in this repository are under the CC BY-NC-SA 4.0 license.

