Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. However, sampling from a pre-trained DPM usually requires hundreds of model evaluations, which is computationally expensive. Despite recent progress in designing high-order solvers for DPMs, there still exists room for further speedup, especially in extremely few steps (e.g., 5~10 steps). Inspired by the predictor-corrector for ODE solvers, 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. 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 256x256 (conditional) with only 10 function evaluations.
A Unified Predictor-Corrector Solver
We propose a unified predictor-corrector solver of DPMs called UniPC, consisting of UniP and UniC. Our UniPC is unified in mainly two aspects: 1) the predictor (UniP) and the corrector (UniC) share the same analytical form; 2) UniP supports arbitrary order and UniC can be applied after off-the-shelf fast samplers of DPMs to increase the order of accuracy.
We show UniPC outperforms previous methods in both unconditional and conditional sampling tasks.