“StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation. StudioGAN aims to offer an identical playground for modern GANs so that machine learning researchers can readily compare and analyze a new idea.
- Extensive GAN implementations for PyTorch
- Comprehensive benchmark of GANs using CIFAR10, Tiny ImageNet, and ImageNet datasets
- 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 (DP, DDP, and Multinode DistributedDataParallel), Mixed Precision, Synchronized Batch Normalization, LARS, Tensorboard Visualization, and other analysis methods…”