STORK: Faster Diffusion and Flow Matching Sampling by Resolving both Stiffness and Structure-Dependence
Zheng Tan, Weizhen Wang, Andrea L. Bertozzi, Ernest K. Ryu
Abstract
Diffusion models (DMs) and flow-matching models have demonstrated remarkable performance in image and video generation. However, such models require a significant number of function evaluations (NFEs) during sampling, leading to costly inference. Consequently, quality-preserving fast sampling methods that require fewer NFEs have been an active area of research. However, prior training-free sampling methods fail to simultaneously address two key challenges: the stiffness of the ODE (i.e., the non-straightness of the velocity field) and dependence on the semi-linear structure of the DM ODE (which limits their direct applicability to flow-matching models). In this work, we introduce the Stabilized Taylor Orthogonal Runge–Kutta (STORK) method, addressing both design concerns. We demonstrate that STORK consistently improves the quality of diffusion and flow-matching sampling for image and video generation.
BibTeX
@inproceedings{
tan2026stork,
title={{STORK}: Faster Diffusion and Flow Matching Sampling by Resolving both Stiffness and Structure-Dependence},
author={Zheng Tan and Weizhen Wang and Andrea L. Bertozzi and Ernest K. Ryu},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=CeOIVXMl4r}
}