StyleGAN2 is a style-based Generative Adversarial Network architecture for unconditional image generation. The implementation redesigns generator normalization, revisits progressive growing techniques, and introduces a path length regularizer to improve image quality and generator invertibility. It represents a significant advancement in generative modeling, enabling synthesis of high-resolution images with improved artifact control compared to its predecessor.