stylegan2 pytorch

The implementation reproduces StyleGAN2's key architectural innovations: the mapping network that transforms latent codes into disentangled style vectors, adaptive instance normalization (AdaIN) for style injection at each resolution level, and the progressive training schedule that grows the generator and discriminator from low to high resolution.

The codebase includes training scripts with mixed-precision support, gradient checkpointing for training large models within GPU memory constraints, and utilities for dataset preparation, model checkpointing, and sample generation during training.

It supports custom dataset training from local image folders, making it straightforward to fine-tune on specific image domains faces, objects, textures, medical images beyond the FFHQ (faces) dataset used in the original paper.

StyleGAN2-PyTorch is open-source and widely used by researchers studying generative models, artists creating AI-generated imagery, and engineers building face generation pipelines for avatars, synthetic data, and creative applications.

The minimal implementation style prioritizes readability over the performance optimizations in production GAN frameworks, making it a practical starting point for understanding StyleGAN2's architecture, adapting it to new domains, or using it as a baseline in generative model research.