NaviCache: Test-Time Self-Calibration Caching for Video Generation
Abstract Overview
NaviCache is a plug-and-play, test-time self-calibration caching method for accelerating video diffusion models. The paper reframes the relationship between input feature changes and output responses as a state-space tracking problem inspired by inertial navigation systems, using an initial alignment stage, dual-state estimation, and uncertainty-aware update decisions to control computation skipping. This design is intended to avoid the calibration-data dependence and distribution-shift issues of offline calibration-based methods while improving on zero-order calibration-free heuristics. The paper also provides a theoretical analysis claiming a lower steady-state estimation error bound than zero-order heuristics, and evaluates the method on Wan 2.1, HunyuanVideo, and Open-Sora 1.2.
Novelty
The paper’s main novelty is treating video diffusion feature evolution as an inertial-navigation-style state estimation problem rather than using offline-fitted mappings or instantaneous zero-order approximations. It introduces a test-time self-calibration mechanism with initial alignment, recursive dual-state estimation, and uncertainty-triggered measurement updates for skip decisions.
Results
Across Wan 2.1, HunyuanVideo, and Open-Sora 1.2, NaviCache demonstrates a strong efficiency-quality trade-off relative to prior acceleration baselines. For example, on HunyuanVideo, NaviCache-mid achieves a 2.17× speedup with PSNR 32.65, SSIM 0.9256, and LPIPS 0.0571, slightly improving on EasyCache at a similar latency envelope; on Wan 2.1, NaviCache-slow reaches PSNR 25.10, SSIM 0.8638, and LPIPS 0.0686 at 1.86× speedup. The method requires no offline calibration and reports a negligible skip-justification overhead of 0.0109 seconds.
Key Points
- NaviCache replaces zero-order skip prediction with online state-space estimation of the output-to-input change ratio, incorporating an initial alignment phase and recursive uncertainty tracking.
- The paper provides a theoretical proof that NaviCache achieves a lower steady-state estimation error variance than zero-order heuristics, such as EasyCache, under positive process and measurement noise assumptions.
- Experiments across three video diffusion model families confirm that NaviCache preserves visual quality better than prior calibration-free baselines at similar acceleration levels while avoiding offline calibration costs.