Diffusion Transformers (DiTs) achieve remarkable performance in video synthesis, but their iterative denoising process suffers from high inference latency. To address this, caching has emerged as an effective acceleration strategy by capitalizing on inter-step redundancy during denoising. Existing dynamic caching metho...
Yu-Xiang Xiong, Rui-Yan Wang, Wen-Qiang Wang et al.· 0 citations
Video diffusion transformers (DiTs) increasingly adopt mixture-of-experts (MoE) architectures to reduce active computation, but their full expert storage remains costly. Existing one-shot pruning criteria mainly rely on static activation or routing statistics and cannot capture layer-level re-routing after expert delet...
Jia-Chang Zhang, Teng Hu, Bo-Hao Feng et al.· 0 citations
This work presents a synchronization-aware acceleration framework for efficient audio-visual generation by explicitly accounting for cross-modal dependence during acceleration, and improves inference efficiency while keeping video quality, audio quality, and audio-video synchronization.
Sheng-Chuan Gao, Teng Hu, Bo-Hao Feng et al.· 1 citation
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