Fast ODE Solver

Fast ODE solvers are being actively developed to accelerate the sampling process in diffusion probabilistic models (DPMs), a class of powerful generative models used in image generation. Research focuses on improving the accuracy and efficiency of these solvers, particularly at low numbers of function evaluations, through techniques like optimized parameterizations, multistep methods, and predictor-corrector frameworks, often incorporating "empirical model statistics" derived from pre-trained models. These advancements significantly reduce the computational cost of generating high-quality samples from DPMs, impacting various applications requiring efficient generative modeling. Parallel implementations further enhance the speed and scalability of these solvers.