Torch implementation for learning an image-to-image translation (i.e. pix2pix) without input-output pairs, for example:

New: Please check out contrastive-unpaired-translation (CUT), our new unpaired image-to-image translation model that enables fast and memory-efficient training.

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Jun-Yan Zhu*, Taesung Park*, Phillip Isola, Alexei A. Efros
Berkeley AI Research Lab, UC Berkeley
In ICCV 2017. (* equal contributions)

This package includes CycleGAN, pix2pix, as well as other methods like BiGAN/ALI and Apple's paper S+U learning.
The code was written by Jun-Yan Zhu and Taesung Park.
Update: Please check out PyTorch implementation for CycleGAN and pix2pix. The PyTorch version is under active development and can produce results comparable or better than this Torch version.

[Tensorflow] (by Harry Yang), [Tensorflow] (by Archit Rathore), [Tensorflow] (by Van Huy), [Tensorflow] (by Xiaowei Hu), [Tensorflow-simple] (by Zhenliang He), [TensorLayer] (by luoxier), [Chainer] (by Yanghua Jin), [Minimal PyTorch] (by yunjey), [Mxnet] (by Ldpe2G), [lasagne/Keras] (by tjwei), [Keras] (by Simon Karlsson)

• Linux or OSX
• NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN may work with minimal modification, but untested)
• For MAC users, you need the Linux/GNU commands gfind and gwc, which can be installed with brew install findutils coreutils.

• Install torch and dependencies from https://github.com/torch/distro
• Install torch packages nngraph, class, display

luarocks install nngraph
luarocks install class
luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec

• Clone this repo:

git clone https://github.com/junyanz/CycleGAN