Hierarchical diffusion exemplifies the iterative refinement used in StyleGAN, contributing to its remarkable image generation capabilities. It involves a multi-scale training approach, starting with coarse feature generation and gradually refining them at finer scales. This hierarchical process enables the network to capture both global and local details, resulting in highly detailed and realistic images.
