  Generative Adversarial Network (GAN) is one of the state-of-the-art
generative models for realistic image synthesis. While training and evaluating
GAN becomes increasingly important, the current GAN research ecosystem does not
provide reliable benchmarks for which the evaluation is conducted consistently
and fairly. Furthermore, because there are few validated GAN implementations,
researchers devote considerable time to reproducing baselines. We study the
taxonomy of GAN approaches and present a new open-source library named
StudioGAN. StudioGAN supports 7 GAN architectures, 9 conditioning methods, 4
adversarial losses, 12 regularization modules, 3 differentiable augmentations,
7 evaluation metrics, and 5 evaluation backbones. With our training and
evaluation protocol, we present a large-scale benchmark using various datasets
(CIFAR10, ImageNet, AFHQv2, FFHQ, and Baby/Papa/Granpa-ImageNet) and 3
different evaluation backbones (InceptionV3, SwAV, and Swin Transformer).
Unlike other benchmarks used in the GAN community, we train representative
GANs, including BigGAN and StyleGAN series in a unified training pipeline and
quantify generation performance with 7 evaluation metrics. The benchmark
evaluates other cutting-edge generative models (e.g., StyleGAN-XL, ADM,
MaskGIT, and RQ-Transformer). StudioGAN provides GAN implementations, training,
and evaluation scripts with the pre-trained weights. StudioGAN is available at
https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.
