提出STORK方法，让扩散模型采样更快更准
STORK: Faster Diffusion And Flow Matching Sampling By Resolving Both Stiffness And Structure-Dependence
- 用稳定化泰勒正交龙格-库塔法解决采样刚性和结构依赖问题
- 在图像和视频生成中减少函数评估次数，保持生成质量
- 适用于扩散模型和流匹配模型，适合需要高效采样的研究者
扩散模型（DMs）和流匹配模型在图像与视频生成中表现优异，但采样过程需大量函数评估（NFE），导致推理成本高。因此，如何在不损失质量的前提下减少NFE成为研究热点。然而，现有无需训练的采样方法难以同时应对两个关键挑战：常微分方程（ODE）的刚性（即速度场非直线性）以及扩散模型ODE对半线性结构的依赖，后者限制了其直接应用于流匹配模型。本文提出稳定化泰勒正交龙格-库塔（STORK）方法，同时解决上述两个问题。实验表明，STORK在图像与视频生成任务中均显著提升采样质量，且能有效降低所需函数评估次数。代码已开源。
原文摘要 · Abstract (English)
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. Code is available at https://github.com/ZT220501/STORK.
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