Diffusion Sampling

Diffusion sampling is a technique for generating samples from complex probability distributions, primarily used in generative modeling and increasingly in solving inverse problems and reinforcement learning. Current research focuses on accelerating the sampling process, often by employing advanced numerical methods like stochastic Runge-Kutta or optimized time steps within various model architectures, including diffusion probabilistic models (DPMs) and energy-based models. These advancements aim to improve the efficiency and scalability of diffusion-based methods, impacting fields ranging from image synthesis and manipulation to more efficient training of deep learning models and improved uncertainty quantification.