Diffusion Model Architecture

Diffusion model architectures are neural network designs enabling the generation of diverse data, such as images, videos, and even 3D shapes, by reversing a noise diffusion process. Current research emphasizes improving efficiency and scalability, exploring architectures like U-Nets, transformers, and Mamba models, as well as optimizing training strategies to reduce computational costs and enhance generation quality. These advancements have significant implications for various fields, including computer vision, speech synthesis, and even medical imaging, by providing powerful tools for data generation and analysis.