  Generative diffusion models have emerged as a powerful tool for high-quality
image synthesis, yet their iterative nature demands significant computational
resources. This paper proposes an efficient time step sampling method based on
an image spectral analysis of the diffusion process, aimed at optimizing the
denoising process. Instead of the traditional uniform distribution-based time
step sampling, we introduce a Beta distribution-like sampling technique that
prioritizes critical steps in the early and late stages of the process. Our
hypothesis is that certain steps exhibit significant changes in image content,
while others contribute minimally. We validated our approach using Fourier
transforms to measure frequency response changes at each step, revealing
substantial low-frequency changes early on and high-frequency adjustments
later. Experiments with ADM and Stable Diffusion demonstrated that our Beta
Sampling method consistently outperforms uniform sampling, achieving better FID
and IS scores, and offers competitive efficiency relative to state-of-the-art
methods like AutoDiffusion. This work provides a practical framework for
enhancing diffusion model efficiency by focusing computational resources on the
most impactful steps, with potential for further optimization and broader
application.
