EditVid一个框架就能做多种视频编辑，无需训练，效果比现有方法好一半以上。
EditVid是一个无需训练的统一视频编辑框架，结合稀疏因果记忆实现局部连贯性，基于对应关系的注意力注入保持长程身份一致性，软潜在混合实现编辑局部性。该框架支持指令引导和参考引导的编辑，包括风格转换、属性修改、对象插入、部分编辑和主体替换。在FiVE基准上，EditVid达到78.16的FiVE-Acc分数，比最强的无需训练基线高出19.21分。用户研究显示，EditVid比7种竞争方法获得51.8%的整体偏好。
One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing
Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We introduce EditVid, a training-free framework combining sparse causal memory for local coherence, correspondence-based post-attention token injection for long-range identity preservation, and soft latent blending for edit locality. The same framework supports instruction-guided and reference-guided edits, including style transfer, attribute modification, object insertion, part-level editing, and subject replacement. On FiVE, EditVid achieves 78.16 FiVE-Acc, compared with 58.95 for the strongest evaluated training-free baseline, while obtaining competitive results on IVEBench. A user study further shows a 51.8\% overall preference for EditVid over 7 competing methods.