Optimal Stepsize for Diffusion Sampling
Authors: name: Jianning Pei, name: Han Hu, name: Shuyang Gu

Diffusion models achieve remarkable generation quality but suffer from
computational intensive sampling due to suboptimal step discretization. While
existing works focus on optimizing denoising directions, we address the
principled design of stepsize schedules. This paper proposes Optimal Stepsize
Distillation, a dynamic programming framework that extracts theoretically
optimal schedules by distilling knowledge from reference trajectories. By
reformulating stepsize optimization as recursive error minimization, our method
guarantees global discretization bounds through optimal substructure
exploitation. Crucially, the distilled schedules demonstrate strong robustness
across architectures, ODE solvers, and noise schedules. Experiments show 10x
accelerated text-to-image generation while preserving 99.4% performance on
GenEval. Our code is available at https://github.com/bebebe666/OptimalSteps.