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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. 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× 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
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