On the Trajectory Regularity of ODE-based Diffusion Sampling

Code

• github.com/zju-pi/diff-sampler
  OfficialIn paperpytorch★ 360
• github.com/zhyzhouu/amed-solver
  pytorch★ 360

Abstract

Diffusion-based generative models use stochastic differential equations (SDEs) and their equivalent ordinary differential equations (ODEs) to establish a smooth connection between a complex data distribution and a tractable prior distribution. In this paper, we identify several intriguing trajectory properties in the ODE-based sampling process of diffusion models. We characterize an implicit denoising trajectory and discuss its vital role in forming the coupled sampling trajectory with a strong shape regularity, regardless of the generated content. We also describe a dynamic programming-based scheme to make the time schedule in sampling better fit the underlying trajectory structure. This simple strategy requires minimal modification to any given ODE-based numerical solvers and incurs negligible computational cost, while delivering superior performance in image generation, especially in 5 10 function evaluations.

Tasks

Reproductions