AI RESEARCH
Temporal Aware Pruning for Efficient Diffusion-based Video Generation
arXiv CS.CV
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ArXi:2605.17837v1 Announce Type: new Video diffusion models have recently enabled high-quality video generation with ViT-based architectures, but remain computationally intensive because generation requires attention computation over long spatiotemporal sequences. Token pruning has proven effective for ViTs and VLMs. However, most prior pruning methods are attention-based and operate per frame, failing to ensure the vital temporal coherence across frames in video generation tasks.