PropCache: Propagation-Calibrated Step Caching for Diffusion Transformers

Step caching accelerates diffusion transformers by reusing model outputs, but which steps to reuse determines fidelity at a fixed compute budget. Existing schedulers rely on runtime triggers, candidate replay, or budget-specific search instead of pricing each reuse's terminal impact. We propose PropCache, a training-free compiler built on a propagation price. It factorizes terminal impact into a local defect and a remaining-trajectory gain, then uses one calibration to price every candidate reuse and compile exact-budget schedules for all tiers. Across six flow-matching diffusion transformers spanning 1.3B to 14B parameters, dense and dual-expert backbones, and video and image generation, PropCache establishes the strongest evaluated fidelity–compute point at every backbone's aggressive tier. It lowers Learned Perceptual Image Patch Similarity (LPIPS) by 8–29% against LeMiCa across five video models, leads LeMiCa at all nine HunyuanVideo-13B budgets by 13.5–31.8%, and reaches 4.83× end-to-end speedup. On the full 943-prompt VBench suite, its matched-latency gain is significant at p = 0.000002. Anonymized code is available at https://anonymous.4open.science/r/propcache-review-5CA2/.