Diffusion Inference

Diffusion inference methods generate data by reversing a noise diffusion process, aiming to improve the speed and quality of sample generation from complex probability distributions. Current research focuses on accelerating inference through techniques like optimized sampling algorithms (e.g., Metropolis-Adjusted Langevin Algorithm, Underdamped Langevin Dynamics), efficient attention mechanisms (e.g., sparse attention), and knowledge-based retrieval methods. These advancements are impacting diverse fields, including audio and music synthesis, image generation and editing, and scientific applications such as material science and cosmology, by enabling faster and higher-quality data generation.