StreamGVE: Training-Free Video Editing via Few-Step Streaming Video Generation

Although existing video editing methods are generally feasi-ble, they often require many costly iterations and still struggle to deliverhigh-quality yet satisfying editing results. We attribute this limitationto the prevalent data-to-data paradigm, which is less compatible withmodern generative models than noise-to-data generation. To address thisgap, we revisit video editing from a noise-to-data perspective and pro-pose Streaming-Generation-based Video Editing (StreamEdit), whichpreserves few-step sampling while seamlessly injecting source-video con-ditions. Built on pre-trained streaming generation models, StreamEditintroduces dual-branch fast sampling with a self-attention bridge andcross-attention grounding/boosting to satisfy both sampling and con-ditioning requirements. We further propose source-oriented guidance toimprove target-generation quality, and a visual prompting strategy to en-hance editing flexibility and practicality. The method is effective, robust,and generalizable across different models. Extensive experiments on di-verse video editing tasks show that StreamEdit consistently outperformsexisting approaches, even in few-step settings with minimal time cost.Code and results are available at: dsl-lab.github.io/StreamEdit/.