WorldCache: Content-Aware Caching for Accelerated Video World Models

Diffusion Transformers (DiTs) power high-fidelity video worldmodels but remain computationally expensive due to sequential denois-ing and costly spatio-temporal attention. Training-free feature cachingaccelerates inference by reusing intermediate activations across denois-ing steps; however, existing methods largely rely on a Zero-Order Holdassumption i.e., reusing cached features as static snapshots when globaldrift is small. This often leads to ghosting artifacts, blur, and motion in-consistencies in dynamic scenes. We propose WorldCache, a Perception-Constrained Dynamical Caching framework that improves both whenand how to reuse features. WorldCache introduces motion-adaptive thresh-olds, saliency-weighted drift estimation, optimal approximation via blend-ing and warping, and phase-aware threshold scheduling across diffusionsteps. Our cohesive approach enables adaptive, motion-consistent featurereuse without retraining. On Cosmos-Predict2.5-2B evaluated on PAI-Bench, WorldCache achieves 2.3× inference speedup while preserving99.4% of baseline quality, substantially outperforming prior training-free caching approaches.