Accelerating Diffusion Sampling with Optimized Time Steps

TL;DR

This paper introduces a framework for optimizing time steps in diffusion probabilistic models to enhance sampling efficiency and image quality, especially when using fewer steps.

Contribution

It proposes a novel optimization approach for selecting non-uniform time steps tailored to specific ODE solvers in DPMs, improving sampling performance.

Findings

Optimized time steps significantly improve FID scores.

The method reduces sampling time to under 15 seconds.

Enhanced image quality on CIFAR-10 and ImageNet datasets.

Abstract

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.