DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model
  Statistics
Authors: name: Kaiwen Zheng, name: Cheng Lu, name: Jianfei Chen, name: Jun Zhu

Diffusion probabilistic models (DPMs) have exhibited excellent performance
for high-fidelity image generation while suffering from inefficient sampling.
Recent works accelerate the sampling procedure by proposing fast ODE solvers
that leverage the specific ODE form of DPMs. However, they highly rely on
specific parameterization during inference (such as noise/data prediction),
which might not be the optimal choice. In this work, we propose a novel
formulation towards the optimal parameterization during sampling that minimizes
the first-order discretization error of the ODE solution. Based on such
formulation, we propose DPM-Solver-v3, a new fast ODE solver for DPMs
by introducing several coefficients efficiently computed on the pretrained
model, which we call empirical model statistics. We further
incorporate multistep methods and a predictor-corrector framework, and propose
some techniques for improving sample quality at small numbers of function
evaluations (NFE) or large guidance scales. Experiments show that DPM-Solver-v3
achieves consistently better or comparable performance in both unconditional
and conditional sampling with both pixel-space and latent-space DPMs,
especially in 5sim10 NFEs. We achieve FIDs of 12.21 (5 NFE), 2.51 (10 NFE)
on unconditional CIFAR10, and MSE of 0.55 (5 NFE, 7.5 guidance scale) on Stable
Diffusion, bringing a speed-up of 15\%sim30\% compared to previous
state-of-the-art training-free methods. Code is available at
https://github.com/thu-ml/DPM-Solver-v3.