OSVE: One Step Video Editing with One Step Diffusion Models

Text-guided video editing with diffusion models is imprac-tically slow, hindered by costly multi-step sampling and inversion. Wepresent OSVE, the first framework to successfully adapt one-step Text-to-Image (T2I) models for high-quality video editing, addressing the corechallenges of inversion, editability, and temporal consistency. To by-pass slow iterative inversion, we train a learnable encoder that pre-dicts the initial noise for each frame in a single forward pass. This en-coder is trained with a novel Structure-Aware Editing (SAE) loss on acurated dataset of structurally-aligned image pairs, teaching it to pre-serve the source video’s geometry during edits. For temporal coherence,we introduce Unified-Frame Editing (UFE), a technique that concate-nates frame latents to facilitate cross-frame attention in a single gen-eration step. Furthermore, for long videos, a sliding-window strategywith an anchor frame maintains global consistency. Our extensive ex-periments demonstrate that OSVE achieves editing quality compara-ble or superior to state-of-the-art multi-step methods, while operatingapproximately 155–171 times faster. This breakthrough paves the wayfor practical, real-time video editing applications. Code is available athttps://github.com/KU-VGI/OSVE.