ainisa20 / OOTDiffusion

Official implementation of OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on

OOTDiffusion

This repository is the official implementation of OOTDiffusion

Try our OOTDiffusion

🤩 Please give me a star if you find it interesting!

OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on
Yuhao Xu, Tao Gu, Weifeng Chen, and Chengcai Chen
Xiao-i Research

An early version of our paper is available now! [arXiv]

🥳🥳 Our model checkpoints trained on VITON-HD (half-body) and Dress Code (full-body) have been released!

• 🤗 Hugging Face Link
• We use checkpoints of humanparsing and openpose in preprocess. Please refer to their guidance if you encounter relevant environmental issues
• Please download clip-vit-large-patch14 into checkpoints folder
• We've only tested our code and models on Linux (Ubuntu 22.04)

   

Installation

1. Clone the repository

git clone https://github.com/levihsu/OOTDiffusion

2. 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 numpy==1.24.4 scipy==1.10.1 scikit-image==0.21.0 opencv-python==4.7.0.72 pillow==9.4.0 diffusers==0.24.0 transformers==4.36.2 accelerate==0.26.1 matplotlib==3.7.4 tqdm==4.64.1 gradio==4.16.0 config==0.5.1 einops==0.7.0 ninja==1.10.2

Inference

1. 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

2. 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

Citation

@misc{xu2024ootdiffusion,
      title={OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on}, 
      author={Yuhao Xu and Tao Gu and Weifeng Chen and Chengcai Chen},
      year={2024},
      eprint={2403.01779},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

TODO List

• Paper
• Gradio demo
• Inference code
• Model weights
• Training code