SDE Solver

Stochastic differential equation (SDE) solvers are crucial for efficiently sampling from diffusion models, a powerful class of generative models used in image generation and other applications. Current research focuses on developing faster and more accurate SDE solvers, including adaptive time-stepping methods, novel numerical schemes like exponential integrators and Gaussian mixture models, and techniques leveraging score functions to improve sampling speed and quality. These advancements aim to reduce the computational cost of generating high-quality samples, thereby improving the practicality and scalability of diffusion models for various tasks.