Imagem
contrastive-unpaired-translation
CUT — contrastive unpaired image-to-image translation in PyTorch, faster and lighter than CycleGAN (ECCV 2020)
★ 2,6 mil estrelasNOASSERTION
Ver o guia →pytorch-CycleGAN-and-pix2pix é um projeto de imagem de código aberto de junyanz (junyanz/pytorch-CycleGAN-and-pix2pix no GitHub). Tem 25.256 estrelas no GitHub e 6.566 forks, é publicado sob a licença NOASSERTION e foi atualizado pela última vez no GitHub em 6 de agosto de 2025.
The classic PyTorch implementation of CycleGAN and pix2pix for image-to-image translation
Decide what you want to achieve with pytorch-CycleGAN-and-pix2pix and check the README on GitHub for requirements before installing.
Clone the repository from GitHub (or take the packaged install route in step 3 if the README offers one).
Follow the project's documented install commands — the Run section below lists the exact ones — and add any required API keys or models.
Work through the README's quickstart example end to end, then adapt it to your own project or data.
CycleGAN for unpaired data and pix2pix for paired data in one codebase.
Download and apply pre-trained CycleGAN/pix2pix models without training your own.
Documented train/test flows for both models, including multi-GPU training.
Scripts and docs for downloading the standard datasets (facades, maps, horse2zebra and more).
Guides for adapting the code to your own model and dataset.
Tips, FAQ and troubleshooting docs accumulated from years of community use.
Get the CycleGAN/pix2pix PyTorch code.
git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git
cd pytorch-CycleGAN-and-pix2pixCreate the documented conda environment (or install PyTorch plus the requirements) per the README installation section.
# install PyTorch from pytorch.org, then see the README 'Installation' for the conda env commandsDownload a pre-trained model with the provided scripts and run the test script on your images.
# see the README 'Apply a pre-trained model (CycleGAN)' section for the exact commandspytorch-CycleGAN-and-pix2pix is maintained by the junyanz organization/account on GitHub, together with open-source contributors, in the public repository junyanz/pytorch-CycleGAN-and-pix2pix.
Yes — the code is open source under the NOASSERTION license. Check the repository's LICENSE file for the exact terms before commercial use.
No API key — everything runs locally; the real cost is a GPU for training (inference with pre-trained models is lighter).
Modern diffusion tools are easier and higher fidelity for many tasks, but this repo remains the canonical, citable implementation for learning and reproducing CycleGAN/pix2pix results.
Clone the repository from GitHub, follow the install commands in the Run section (taken from the project's README), and complete the documented quickstart example before adapting it to your own use case.
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