VITON-HD — Official PyTorch Implementation with Docker

VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization
Seunghwan Choi*¹, Sunghyun Park*¹, Minsoo Lee*¹, Jaegul Choo^1
1KAIST
In CVPR 2021. (* indicates equal contribution)

Paper: https://arxiv.org/abs/2103.16874
Project page: https://psh01087.github.io/VITON-HD

How to run

1. Clone this repository:

git clone https://github.com/shadow2496/VITON-HD.git
cd ./VITON-HD/

2. Pre-trained model and data for testing

We cannot share the training code or the collected dataset due to the commercial issue. Instead, we provide pre-trained networks and sample images from the test dataset. Please download *.pkl and dataset-related files from the VITON-HD Google Drive folder and unzip *.zip files. test.py assumes that the downloaded files are placed in ./checkpoints/ and ./datasets/ directories.

3a. Installation (Docker)

**Note: make sure that CUDA, driver...(https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) and nvidia-docker2 (https://github.com/NVIDIA/nvidia-docker) are installed.

Build image: "docker build -t <image-name>:<tag> ."

docker build -t viton-hd-docker:v1 .

Run container with mount volume: "docker run --gpus=all --shm-size 4G -v <full-path-to-local-VITON-HD>:<path-in-image> -itd viton-hd-docker:v1 bash"

docker run --gpus=all --shm-size 4G -v /home/cr7/VITON-HD:/mnt -itd viton-hd-docker:v1 bash

Run test:

cd /mnt
python test.py --name [NAME]

3b. Installation (local)

Install PyTorch and other dependencies:

conda create -y -n [ENV] python=3.8
conda activate [ENV]
conda install -y pytorch=[>=1.6.0] torchvision cudatoolkit=[>=9.2] -c pytorch
pip install opencv-python torchgeometry

4. Testing

To generate virtual try-on images, run:

CUDA_VISIBLE_DEVICES=[GPU_ID] python test.py --name [NAME]

The results are saved in the ./results/ directory. You can change the location by specifying the --save_dir argument. To synthesize virtual try-on images with different pairs of a person and a clothing item, edit ./datasets/test_pairs.txt and run the same command.

License

All material is made available under Creative Commons BY-NC 4.0 license by NeStyle Inc. You can use, redistribute, and adapt the material for non-commercial purposes, as long as you give appropriate credit by citing our paper and indicate any changes that you've made.

Citation

If you find this work useful for your research, please cite our paper:

@inproceedings{choi2021viton,
  title={VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization},
  author={Choi, Seunghwan and Park, Sunghyun and Lee, Minsoo and Choo, Jaegul},
  booktitle={Proc. of the IEEE conference on computer vision and pattern recognition (CVPR)},
  year={2021}
}