Core Concepts
Introducing Distilled-ODE solvers (D-ODE solvers) to enhance sampling efficiency in diffusion models by distilling knowledge from ODE solvers with smaller steps.
Abstract
The content discusses the challenges faced by diffusion models in terms of slow sampling speeds and the exploration of learning-free and learning-based sampling strategies. It introduces D-ODE solvers as a method to bridge the gap between these strategies, optimizing the sampling process. The note provides a detailed breakdown of the content, including the introduction, background, proposed method, experiments, analysis, and implementation details of D-ODE solvers.
Stats
Diffusion models require hundreds or thousands of function evaluations for sampling.
D-ODE solvers introduce a single parameter adjustment to existing ODE solvers.
D-ODE solvers optimize with smaller steps using knowledge distillation.
Quotes
"D-ODE solvers bridge the gap between learning-free and learning-based sampling."
"Our experiments showcase the efficacy of D-ODE solvers in enhancing the FID scores of state-of-the-art ODE solvers."