arXiv Computer Vision By Lifan Jiang, Yibo Zhao, Senlin cheng, Bolei Xu, Xiaobo Li, Can Ye, Dehai Zhao, Deng Cai, Boxi Wu

CAVC: Convergence-Adaptive Audio-Visual Cache for Modality-Asymmetric Diffusion Generation

Read the original on arXiv Computer Vision →

The paper introduces CAVC, a training‑free, modality‑asymmetric caching framework that speeds up diffusion‑based audio‑visual generation by selectively caching audio denoiser outputs while continuing full video refinement. By detecting when audio denoiser outputs become cache‑tolerant, CAVC bypasses audio denoising and cross‑modal attention in the middle of the sampling trajectory, then restores a few joint steps at the end to correct drift and improve synchronization. Experiments on public benchmarks show 1.10–1.35× wall‑clock acceleration, better visual dynamics and temporal statistics, and competitive audio fidelity and alignment.

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