Optimal Stepsize for Diffusion Sampling
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.
67e6094c48742d6df7586714
Jianning Pei
67e6094c48742d6df7586715
Han Hu
67e6094c48742d6df7586716
Shuyang Gu
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2025-03-28T09:01:17.103Z
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Optimal Stepsize for Diffusion Sampling
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.
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.
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