This repository is the official implementation of OOTDiffusion
🤗 Try out OOTDiffusion (Thanks to ZeroGPU for providing A100 GPUs)
Or try our own demo on RTX 4090 GPUs
OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on [arXiv paper]
Yuhao Xu, Tao Gu, Weifeng Chen, Chengcai Chen
Xiao-i Research
Our model checkpoints trained on VITON-HD (half-body) and Dress Code (full-body) have been released
- 🤗 Hugging Face link
- 📢📢 We support ONNX for humanparsing now. Most environmental issues should have been addressed : )
- Please download clip-vit-large-patch14 into checkpoints folder
- We've only tested our code and models on Linux (Ubuntu 22.04)
- Clone the repository
git clone https://github.com/levihsu/OOTDiffusion- Create a conda environment and install the required packages
conda create -n ootd python==3.10
conda activate ootd
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
pip install -r requirements.txt- Half-body model
cd OOTDiffusion/run
python run_ootd.py --model_path <model-image-path> --cloth_path <cloth-image-path> --scale 2.0 --sample 4- Full-body model
Garment category must be paired: 0 = upperbody; 1 = lowerbody; 2 = dress
cd OOTDiffusion/run
python run_ootd.py --model_path <model-image-path> --cloth_path <cloth-image-path> --model_type dc --category 2 --scale 2.0 --sample 4@article{xu2024ootdiffusion,
title={OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on},
author={Xu, Yuhao and Gu, Tao and Chen, Weifeng and Chen, Chengcai},
journal={arXiv preprint arXiv:2403.01779},
year={2024}
}
- Paper
- Gradio demo
- Inference code
- Model weights
- Training code