Accelerating Diffusion Sampling with Optimized Time Steps
Shuchen Xue Zhaoqiang Liu Fei Chen Shifeng Zhang Tianyang Hu Enze Xie Zhenguo Li
Diffusion probabilistic models (DPMs) have shown remarkable performance in
high-resolution image synthesis, but their sampling efficiency is still to be
desired due to the typically large number of sampling steps. Recent
advancements in high-order numerical ODE solvers for DPMs have enabled the
generation of high-quality images with much fewer sampling steps. While this is
a significant development, most sampling methods still employ uniform time
steps, which is not optimal when using a small number of steps. To address this
issue, we propose a general framework for designing an optimization problem
that seeks more appropriate time steps for a specific numerical ODE solver for
DPMs. This optimization problem aims to minimize the distance between the
ground-truth solution to the ODE and an approximate solution corresponding to
the numerical solver. It can be efficiently solved using the constrained trust
region method, taking less than $15$ seconds. Our extensive experiments on both
unconditional and conditional sampling using pixel- and latent-space DPMs
demonstrate that, when combined with the state-of-the-art sampling method
UniPC, our optimized time steps significantly improve image generation
performance in terms of FID scores for datasets such as CIFAR-10 and ImageNet,
compared to using uniform time steps.