A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training

Kai Wang, Mingjia Shi, YuKun Zhou, Zekai Li, Xiaojiang Peng, Zhihang Yuan, Yuzhang Shang, Hanwang Zhang, Yang You

Keywords: Diffusion Model; Efficient Training

TL;DR: Training diffusion models is always a computation-intensive task. In this paper, we introduce a novel speed-up method for diffusion model training, called SpeeD, which is based on a closer look at time steps.

Abstract: Training diffusion models is always a computation-intensive task. In this paper, we introduce a novel speed-up method for diffusion model training, called, which is based on a closer look at time steps. Our key findings are: i) Time steps can be empirically divided into acceleration, deceleration, and convergence areas based on the process increment. ii) These time steps are imbalanced, with many concentrated in the convergence area. iii) The concentrated steps provide limited benefits for diffusion training. To address this, we design an asymmetric sampling strategy that reduces the frequency of steps from the convergence area while increasing the sampling probability for steps from other areas. Additionally, we propose a weighting strategy to emphasize the importance of time steps with rapid-change process increments. As a plug-and-play and architecture-agnostic approach, SpeeD consistently achieves 3-times acceleration across various diffusion architectures, datasets, and tasks. Notably, due to its simple design, our approach significantly reduces the cost of diffusion model training with minimal overhead. Our research enables more researchers to train diffusion models at a lower cost.

Primary Area: generative models

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Submission Number: 1243