Linear Multistep Solver Distillation for Fast Sampling of Diffusion Models
Keywords: Diffusion Probabilistic Model, Diffusion Sampler, Solver Schedule
TL;DR: We provide a solver distillation framework for diffusion models and search for solver schedules based on it.
Abstract: Sampling from diffusion models can be seen as solving the corresponding
probability flow ordinary differential equation (ODE).
The solving process requires a significant number of function
evaluations (NFE), making it time-consuming.
Recently, several solver search frameworks have attempted to find
better-performing model-specific solvers. However, predicting the impact of
intermediate solving strategies on final sample quality remains challenging,
rendering the search process inefficient.
In this paper, we propose a novel method for designing
solving strategies. We first introduce a unified prediction formula
for linear multistep solvers. Subsequently, we present a solver distillation
framework, which enables a student solver to mimic the sampling trajectory
generated by a teacher solver with more steps. We utilize the mean Euclidean
distance between the student and teacher sampling trajectories as a metric,
facilitating rapid adjustment and optimization of intermediate solving strategies.
The design space of our framework encompasses multiple aspects,
including prediction coefficients, time step schedules, and time scaling
factors.
Our framework has the ability to complete a solver search
for Stable-Diffusion in under 12 total GPU hours.
Compared to previous reinforcement learning-based
search frameworks,
our approach achieves over a 10$\times$ increase in search efficiency.
With just 5 NFE, we achieve FID scores of 3.23 on CIFAR10, 7.16 on ImageNet-64,
5.44 on LSUN-Bedroom, and 12.52 on MS-COCO, resulting in a 2$\times$ sampling acceleration ratio
compared to handcrafted solvers.
Primary Area: generative models
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 165
Loading