StarGAN: A Python repository from bicsi

PyTorch implementation of StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. StarGAN can flexibly translate an input image to any desired target domain using only a single generator and a discriminator. The demo video for StarGAN can be found here.

Authors

Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sung Kim, and Jaegul Choo Korea Universitiy, Clova AI Research (NAVER), The College of New Jersey, HKUST

Results

Facial Attribute Transfer on CelebA

The images are generated by StarGAN trained on the CelebA dataset.

Model Description

See original repo.

Prerequisites

Python 3.5+
PyTorch 0.2.0
TensorFlow 1.3+ (optional for tensorboard)

Getting Started

1. Clone the repository

$ git clone https://github.com/ericpts/StarGAN.git
$ cd StarGAN/

2. Download the dataset

$ python3 init.py

3. Train StarGAN

$ python main.py --mode='train' --dataset='CelebA' --c_dim=5 --image_size=128 \
                 --sample_path='stargan_celebA/samples' --log_path='stargan_celebA/logs' \
                 --model_save_path='stargan_celebA/models' --result_path='stargan_celebA/results'

4. Test StarGAN

(i) Facial attribute transfer on CelebA

$ python main.py --mode='test' --dataset='CelebA' --c_dim=5 --image_size=128 --test_model='20_1000' \
                 --sample_path='stargan_celebA/samples' --log_path='stargan_celebA/logs' \
                 --model_save_path='stargan_celebA/models' --result_path='stargan_celebA/results'

(ii) Facial expression synthesis on RaFD

$ python main.py --mode='test' --dataset='RaFD' --c_dim=8 --image_size=128 \
                 --test_model='200_200' --rafd_image_path='data/RaFD/test' \
                 --sample_path='stargan_rafd/samples' --log_path='stargan_rafd/logs' \
                 --model_save_path='stargan_rafd/models' --result_path='stargan_rafd/results'

(iii) Facial expression synthesis on CelebA

$ python main.py --mode='test' --dataset='Both' --image_size=256 --test_model='200000' \
                 --sample_path='stargan_both/samples' --log_path='stargan_both/logs' \
                 --model_save_path='stargan_both/models' --result_path='stargan_both/results'

Citation

If this work is useful for your research, please cite our arXiv paper.

@article{choi2017stargan,
 title = {StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation},
 author = {Choi, Yunjey and Choi, Minje and Kim, Munyoung and Ha, Jung-Woo and Kim, Sunghun and Choo, Jaegul},
 journal= {arXiv preprint arXiv:1711.09020},
 Year = {2017}
}

Acknowledgement

This work was mainly done while the first author did a research internship at Clova AI Research, NAVER (CLAIR). We also thank all the researchers at CLAIR, especially Donghyun Kwak, for insightful discussions.

bicsi/StarGAN