| title | Lightning Unified Video Editing via In-Context Sparse Attention |
|---|---|
| authors | Shitong Shao, Zikai Zhou, Haopeng Li, Yingwei Song, Wenliang Zhong, Lichen Bai, Zeke Xie |
| date | 2026-05-06 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.04569 |
| upvotes | 16 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Video editing has evolved toward In-Context Learning (ICL) paradigms, yet the resulting quadratic attention costs create a critical computational bottleneck. In this work, we propose In-context Sparse Attention (ISA), the first near-lossless empirical sparse framework tailored for ICL video editing. Our design is grounded in two key insights: first, context tokens exhibit significantly lower saliency than source tokens; second, we theoretically prove and empirically validate that Query sharpness correlates with approximation error. Motivated by these findings, ISA implements an efficient pre-selection strategy to prune redundant context, followed by a dynamic query grouping mechanism that routes high-error queries to full attention and low-error ones to a computationally efficient 0-th order Taylor sparse attention. Furthermore, we build \texttt{LIVEditor} , a novel lightning video editing model via ISA and a proposed video-editing data pipeline that curated a 1.7M high-quality dataset. Extensive experiments demonstrate that LIVEditor achieves a sim60% reduction in attention-module latency while surpassing state-of-the-art methods across EditVerseBench, IVE-Bench, and VIE-Bench, delivering near-lossless acceleration without compromising visual fidelity. |
cite
@misc{shao2026lightning,
title={Lightning Unified Video Editing via In-Context Sparse Attention},
author={Shitong Shao and Zikai Zhou and Haopeng Li and Yingwei Song and Wenliang Zhong and Lichen Bai and Zeke Xie},
year={2026},
eprint={2605.04569},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.04569}
}