Adaptive Time-Stepping Schedules for Diffusion Models

Yuzhu Chen, Fengxiang He, Shi Fu, Xinmei Tian, Dacheng Tao

Keywords: diffusion models, stepping schedule, generative model

TL;DR: We propose adaptive time-stepping schedules for diffusion models by minimizing a series of convergence bounds.

Abstract: This paper studies how to tune the stepping schedule in diffusion models, which is mostly fixed in current practice, lacking theoretical foundations and assurance of optimal performance at the chosen discretization points. In this paper, we advocate the use of adaptive time-stepping schedules and design two algorithms with an optimized sampling error bound $EB$: (1) for continuous diffusion, we treat $EB$ as the loss function to discretization points and run gradient descent to adjust them; and (2) for discrete diffusion, we propose a greedy algorithm that adjusts only one discretization point to its best position in each iteration. We conducted extensive experiments that show (1) improved generation ability in well-trained models, and (2) premature though usable generation ability in under-trained models. The code is submitted and will be released publicly.

List Of Authors: Yuzhu, Chen and Fengxiang, He and Shi, Fu and Xinmei, Tian and Dacheng, Tao

Submission Number: 242