OTCache: Optimal Transport for Geometry-Aware Caching in Diffusion Models

We propose OTCache, a training-free framework for ac-celerating diffusion sampling via caching schedule prediction. Existinggraph-based caching methods reduce redundant computation by opti-mizing shortest-path objectives, but rely on an additive independenceassumption, which often breaks down in the low NFE regime. To addressthis issue, OTCache models caching schedules across inference budgetsas a smooth evolution in policy space, inspired by Optimal Transport(OT). The framework consists of three stages: (1) obtaining a high-fidelity reference schedule using a graph-based caching method un-der a conservative budget; (2) performing a lightweight anchor searchunder an extreme low-budget setting via Optuna optimization with anend-to-end perceptual objective; and (3) predicting schedules for tar-get budgets via quantile interpolation between the reference and an-chor policies using continuous warping representations. Experiments onFLUX.1 [dev], Qwen-Image, and HunyuanVideo show that OTCacheachieves 4.5×, 4.7×, and 3.66× acceleration, respectively, while consis-tently improving generation fidelity over state-of-the-art caching base-lines. This work provides a new perspective on accelerating diffusionmodels through Optimal-Transport-inspired schedule modeling. Code:https://github.com/UnicomAI/OTCache