- Linux or macOS
- Python 3
- CPU or NVIDIA GPU + CUDA CuDNN
- Clone this repo:
git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
cd pytorch-CycleGAN-and-pix2pix- Install PyTorch and 0.4+ and other dependencies (e.g., torchvision, visdom and dominate).
- For pip users, please type the command
pip install -r requirements.txt. - For Conda users, you can create a new Conda environment using
conda env create -f environment.yml. - For Docker users, we provide the pre-built Docker image and Dockerfile. Please refer to our Docker page.
- For Repl users, please click
.
- For pip users, please type the command
Download pix2pix/CycleGAN datasets and create your own datasets.
If you use this code for your research, please cite our papers.
@inproceedings{CycleGAN2017,
title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
year={2017}
}
@inproceedings{isola2017image,
title={Image-to-Image Translation with Conditional Adversarial Networks},
author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on},
year={2017}
}