  We present a novel task called online video editing, which is designed to
edit \textbf{streaming} frames while maintaining temporal consistency. Unlike
existing offline video editing assuming all frames are pre-established and
accessible, online video editing is tailored to real-life applications such as
live streaming and online chat, requiring (1) fast continual step inference,
(2) long-term temporal modeling, and (3) zero-shot video editing capability. To
solve these issues, we propose Streaming Video Diffusion (SVDiff), which
incorporates the compact spatial-aware temporal recurrence into off-the-shelf
Stable Diffusion and is trained with the segment-level scheme on large-scale
long videos. This simple yet effective setup allows us to obtain a single model
that is capable of executing a broad range of videos and editing each streaming
frame with temporal coherence. Our experiments indicate that our model can edit
long, high-quality videos with remarkable results, achieving a real-time
inference speed of 15.2 FPS at a resolution of 512x512.
