Building StyleGAN in PyTorch: From Theory to Code

This article breaks down StyleGAN from first principles, starting with ProGAN and its black-box latent space. It explains mapping from z to w, AdaIN-based style modulation, noise injection, learned constant input, and style mixing. Then it walks through a full PyTorch implementation using CelebA-HQ, covering dataset setup, generator, discriminator, and training loop.