title: Optimal Stepsize for Diffusion Sampling
summary: 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.
paper_authors._id: 67e6094c48742d6df7586714
paper_authors.name: Jianning Pei
paper_authors._id: 67e6094c48742d6df7586715
paper_authors.name: Han Hu
paper_authors._id: 67e6094c48742d6df7586716
paper_authors.name: Shuyang Gu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:01:17.103Z
paper_authors._id: 64c38fcf573c5a427e12cd37
paper_authors.avatarUrl: /avatars/2b9de06f29147ed2c212e920afba0eaf.svg
paper_authors.fullname: cientgu
paper_authors.type: user
paper_authors.user: cientgu
paper_title: Optimal Stepsize for Diffusion Sampling
paper_summary: 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.
paper_ai_summary: A dynamic programming framework called Optimal Stepsize Distillation improves text-to-image generation by optimizing diffusion step sizes, achieving faster generation without significant performance loss.
paper_ai_keywords: diffusion models computationally intensive sampling stepsize schedules dynamic programming knowledge distillation reference trajectories recursive error minimization global discretization bounds optimal substructure robustness ODE solvers noise schedules GenEval