AI RESEARCH
Training-free Latent Inter-Frame Pruning with Attention Recovery
arXiv CS.CV
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ArXi:2603.05811v1 Announce Type: new Current video generation models suffer from high computational latency, making real-time applications prohibitively costly. In this paper, we address this limitation by exploiting the temporal redundancy inherent in video latent patches. To this end, we propose the Latent Inter-frame Pruning with Attention Recovery (LIPAR) framework, which detects and skips recomputing duplicated latent patches. Additionally, we