Diffusion-based generative models use stochastic differential equations
(SDEs) and their equivalent ordinary differential equations (ODEs) to establish
a smooth connection between a complex data distribution and a tractable prior
distribution. In this paper, we identify several intriguing trajectory
properties in the ODE-based sampling process of diffusion models. We
characterize an implicit denoising trajectory and discuss its vital role in
forming the coupled sampling trajectory with a strong shape regularity,
regardless of the generated content. We also describe a dynamic
programming-based scheme to make the time schedule in sampling better fit the
underlying trajectory structure. This simple strategy requires minimal
modification to any given ODE-based numerical solvers and incurs negligible
computational cost, while delivering superior performance in image generation,
especially in $5\sim 10$ function evaluations.

扩散基于生成模型使用随机微分方程和其等效的常微分方程在复杂数据分布与可追踪的先验分布之间建立平滑连接。本文中，我们发现扩散模型的基于常微分方程的采样过程中存在着一些有趣的轨迹特性。我们表征了一个隐式去噪轨迹，并讨论了其在形成具有强形状规律性的耦合采样轨迹中的重要作用，无论生成的内容是什么。我们还描述了一种基于动态规划的方案，使得采样的时间安排更好地适应底层轨迹结构。这种简单的策略对于任何给定的基于常微分方程的数值求解器只需要最小的修改，并且在计算成本几乎可忽略的情况下，能够在图像生成中提供卓越的性能，特别是在 5 到 10 个函数评估中。