PyTorch-StudioGAN

Why we included this project

Most GAN research starts with pulling together code from a stack of different repositories, each with its own quirks. StudioGAN bundles seven GAN architectures, including BigGAN, StyleGAN2 and StyleGAN3, along with pluggable conditioning methods, adversarial losses, and regularization modules that you wire together through YAML config files. That makes it handy for researchers who want to test a new idea against established baselines without rebuilding the infrastructure, and the built-in evaluation metrics (IS, FID, PRDC, IFID) plus benchmark results on CIFAR10, ImageNet, AFHQv2, and FFHQ give everyone the same measuring stick. The work is published at TPAMI and the authors provide pre-trained checkpoints on Hugging Face, so verifying that a run reproduces is fairly painless.