v.0.4.0

• We checked the reproducibility of implemented GANs.
• We provide Baby, Papa, and Grandpa ImageNet datasets where images are processed using the anti-aliasing and high-quality resizer.
• StudioGAN provides a dedicatedly established Benchmark on standard datasets (CIFAR10, ImageNet, AFHQv2, and FFHQ).
• StudioGAN supports InceptionV3, ResNet50, SwAV, DINO, and Swin Transformer backbones for GAN evaluation.

v.0.3.0

• Add SOTA GANs: LGAN, TACGAN, StyleGAN2, MDGAN, MHGAN, ADCGAN, ReACGAN (our new paper).
• Add five types of differentiable augmentation: CR, DiffAugment, ADA, SimCLR, BYOL.
• Implement useful regularizations: Top-K training, Feature Matching, R1-Regularization, MaxGP
• Add Improved Precision & Recall, Density & Coverage, iFID, and CAS for reliable evaluation.
• Support Inception_V3 and SwAV backbones for GAN evaluation.
• Verify the reproducibility of StyleGAN2 and BigGAN.
• Fix bugs in FreezeD, DDP training, Mixed Precision training, and ADA.
• Support Discriminator Driven Latent Sampling, Semantic Factorization for BigGAN evaluation.
• Support Wandb logging instead of Tensorboard.

v0.2.0

Second release of StudioGAN with following features

• Fix minor bugs (slow convergence of training GAN + ADA models, tracking bn statistics during evaluation, etc.)
• Add multi-node DistributedDataParallel (DDP) training.
• Comprehensive benchmarks on CIFAR10, Tiny_ImageNet, and ImageNet datasets.
• Provide pre-trained models and log files for the future research.
• Add LARS optimizer and TSNE analysis.

v0.1.0

First StudioGAN release with following features

• Extensive GAN implementations for Pytorch: From DCGAN to ADAGAN
• Comprehensive benchmark of GANs using CIFAR10 dataset
• Better performance and lower memory consumption than original implementations
• Providing pre-trained models that are fully compatible with up-to-date PyTorch environment
• Support Multi-GPU(both DP and DDP), Mixed precision, Synchronized Batch Normalization, and Tensorboard Visualization