Abstract: 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 of the ODE-based diffusion sampling. We first characterize an implicit *denoising trajectory* and discuss its vital role in forming the coupled *sampling trajectory* with strong regularity of a ``boomerang'' shape, regardless of the generated content. We 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 solvers and incurs negligible computational cost while delivering superior performance in image generation, especially in $5\sim 10$ NFEs.