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.

翻译：扩散概率模型在高分辨率图像合成中展现出极具前景的能力。然而，从预训练的扩散模型中采样因需多次评估去噪网络而耗时，这使得加速扩散模型采样愈发重要。尽管最近在快速采样器设计方面取得了进展，现有方法在需要极少步数（例如<10步）的众多应用中仍无法生成令人满意的图像。本文开发了一种统一校正器（UniC），可在任意现有扩散模型采样器后应用，以在不增加额外模型评估的情况下提高精度阶数，并作为副产品推导出支持任意阶数的统一预测器（UniP）。结合UniP与UniC，我们提出了一种名为UniPC的统一预测-校正框架，用于扩散模型的快速采样。该框架对任意阶数具有统一解析形式，能显著提升采样质量（尤其在极步数场景下）优于先前方法。我们通过大量实验评估方法，包括使用像素空间和潜在空间扩散模型进行无条件采样与条件采样。在仅需10次函数评估的情况下，我们的UniPC在CIFAR10（无条件）上达到3.87 FID，在ImageNet 256×256（条件）上达到7.51 FID。代码见https://github.com/wl-zhao/UniPC。