A Complete Recipe for Diffusion Generative Models

Understand

Score-based Generative Models (SGMs) have demonstrated exceptional synthesis outcomes across various tasks.

• However, the current design landscape of the forward diffusion process remains largely untapped and often relies on physical heuristics or simplifying assumptions.
• Utilizing insights from the development of scalable Bayesian posterior samplers, we present a complete recipe for formulating forward processes in SGMs, ensuring convergence to the desired target distribution.
• Our approach reveals that several existing SGMs can be seen as specific manifestations of our framework.