A research-quality implementation of StyleGAN 2 in PyTorch, reproducing the architecture and training methodology from the original paper. The model uses style-based generator architecture with mapping networks and adaptive instance normalization to produce high-fidelity images across multiple resolutions. It supports distributed training on multiple GPUs and includes tools for converting official checkpoints and generating samples.