  Diffusion probabilistic models (DPMs) have demonstrated a very promising
ability in high-resolution image synthesis. However, sampling from a
pre-trained DPM is time-consuming due to the multiple evaluations of the
denoising network, making it more and more important to accelerate the sampling
of DPMs. Despite recent progress in designing fast samplers, existing methods
still cannot generate satisfying images in many applications where fewer steps
(e.g., $<$10) are favored. In this paper, we develop a unified corrector (UniC)
that can be applied after any existing DPM sampler to increase the order of
accuracy without extra model evaluations, and derive a unified predictor (UniP)
that supports arbitrary order as a byproduct. Combining UniP and UniC, we
propose a unified predictor-corrector framework called UniPC for the fast
sampling of DPMs, which has a unified analytical form for any order and can
significantly improve the sampling quality over previous methods, especially in
extremely few steps. We evaluate our methods through extensive experiments
including both unconditional and conditional sampling using pixel-space and
latent-space DPMs. Our UniPC can achieve 3.87 FID on CIFAR10 (unconditional)
and 7.51 FID on ImageNet 256$\times$256 (conditional) with only 10 function
evaluations. Code is available at https://github.com/wl-zhao/UniPC.
