Kavli Affiliate: Feng Wang
| First 5 Authors: Zehong Ma, Longhui Wei, Feng Wang, Shiliang Zhang, Qi Tian
| Summary:
Existing acceleration techniques for video diffusion models often rely on
uniform heuristics or time-embedding variants to skip timesteps and reuse
cached features. These approaches typically require extensive calibration with
curated prompts and risk inconsistent outputs due to prompt-specific
overfitting. In this paper, we introduce a novel and robust discovery: a
unified magnitude law observed across different models and prompts.
Specifically, the magnitude ratio of successive residual outputs decreases
monotonically and steadily in most timesteps while rapidly in the last several
steps. Leveraging this insight, we introduce a Magnitude-aware Cache (MagCache)
that adaptively skips unimportant timesteps using an error modeling mechanism
and adaptive caching strategy. Unlike existing methods requiring dozens of
curated samples for calibration, MagCache only requires a single sample for
calibration. Experimental results show that MagCache achieves 2.1x and 2.68x
speedups on Open-Sora and Wan 2.1, respectively, while preserving superior
visual fidelity. It significantly outperforms existing methods in LPIPS, SSIM,
and PSNR, under comparable computational budgets.
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