  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.
