StudioGAN is a free, open-source PyTorch library that provides clean, unified implementations of the most influential generative adversarial networks (GANs) for image generation. Built by POSTECH’s Computer Vision Lab, it lets researchers train, compare and benchmark representative GAN architectures under one consistent codebase instead of wrestling with a dozen incompatible official repos.
StudioGAN is an open-source project on GitHub with about 3,500 stars, last updated in 2024. It re-implements landmark models โ including BigGAN, StyleGAN2, StyleGAN2-ADA and StyleGAN3 โ in a modular PyTorch framework with shared training, evaluation and data pipelines. A central goal is fair comparison: metrics such as clean FID are computed consistently across models and datasets, which makes StudioGAN a popular reference for GAN research and a reliable starting point for new generative modelling work. Configuration files drive experiments, so switching architectures or datasets requires minimal code changes.
StudioGAN is completely free and open source. You can download it from GitHub and run it on your own hardware with no license fees or usage limits.
The library also ships training scripts, pretrained checkpoints and detailed documentation for each supported model, and its standardised logging makes it easy to track runs and share results with collaborators. Because every model follows the same code structure, reading one implementation teaches you how all of them work.
StudioGAN is for machine-learning researchers and engineers who study or build on GANs and need trustworthy, comparable implementations of classic architectures for experiments, benchmarks or teaching.