This repository is the official implementation of OOTDiffusion
๐คฉ 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, Chengcai Chen
Xiao-i Research
Our paper is coming soon!
๐ฅ๐ฅ Our model checkpoints trained on VITON-HD (768 * 1024) have been released!
Checkpoints trained on Dress Code (768 * 1024) will be released soon. Thanks for your patience โค
๐ค 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
- 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 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
- 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
- Paper
- Gradio demo
- Inference code
- Model weights
- Training code