DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model
  Statistics
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.
65360aaa910b844786c84f3f
Kaiwen Zheng
admin_assigned
2023-10-23T08:32:21.989Z
652bf7edc3cba555d5673c6e
/avatars/78f6416c30203b30671f8423f061c657.svg
Kaiwen Zheng
user
worstcoder
65360aaa910b844786c84f40
Cheng Lu
claimed_verified
2023-10-23T08:42:12.591Z
6308ab64e3246e2be8884f4f
/avatars/7d8002caeacf27258996f31e4a2e186f.svg
LuChengTHU
user
LuChengTHU
65360aaa910b844786c84f41
Jianfei Chen
65360aaa910b844786c84f42
Jun Zhu
DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model
  Statistics
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.
A novel ODE solver for diffusion probabilistic models optimizes sampling efficiency and sample quality by minimizing discretization errors and introducing empirical model statistics, achieving faster and better results in both pixel-space and latent-space models.
diffusion probabilistic models DPMs fast ODE solvers DPM-Solver-v3 empirical model statistics multistep methods predictor-corrector framework unconditional sampling conditional sampling pixel-space latent-space FIDs MSE Stable Diffusion