Denoising diffusion probabilistic models (DDPMs) are a class of powerful
generative models. The past few years have witnessed the great success of DDPMs
in generating high-fidelity samples. A significant limitation of the DDPMs is
the slow sampling procedure. DDPMs generally need hundreds or thousands of
sequential function evaluations (steps) of neural networks to generate a
sample. This paper aims to develop a fast sampling method for DDPMs requiring
much fewer steps while retaining high sample quality. The inference process of
DDPMs approximates solving the corresponding diffusion ordinary differential
equations (diffusion ODEs) in the continuous limit. This work analyzes how the
backward error affects the diffusion ODEs and the sample quality in DDPMs. We
propose fast sampling through the \textbf{Restricting Backward Error schedule
(RBE schedule)} based on dynamically moderating the long-time backward error.
Our method accelerates DDPMs without any further training. Our experiments show
that sampling with an RBE schedule generates high-quality samples within only 8
to 20 function evaluations on various benchmark datasets. We achieved 12.01 FID
in 8 function evaluations on the ImageNet $128\times128$, and a $20\times$
speedup compared with previous baseline samplers.

本研究提出了一种基于 “限制后向误差进度表”（RBE 进度表）的快速采样方法，使 Denoising Diffusion Probabilistic Models（DDPM）拥有更快的采样速度而不需要进一步训练，并在各种基准数据集上实现了高品质图像的生成。