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This paper introduces OSVE, a novel framework that adapts one-step Text-to-Image diffusion models for efficient video editing by addressing challenges in inversion, editability, and temporal consistency. By training a learnable encoder with a Structure-Aware Editing loss, OSVE predicts initial noise for video frames in a single pass, significantly speeding up the editing process. Experimental results show that OSVE achieves editing quality on par with or better than traditional multi-step methods while being approximately 155–171 times faster, enabling practical real-time applications.
Achieving video editing speeds 155–171 times faster than traditional methods without sacrificing quality could revolutionize real-time content creation.
Text-guided video editing with diffusion models is impractically slow, hindered by costly multi-step sampling and inversion. We present OSVE, the first framework to successfully adapt one-step Text-to-Image (T2I) models for high-quality video editing, addressing the core challenges of inversion, editability, and temporal consistency. To bypass slow iterative inversion, we train a learnable encoder that predicts the initial noise for each frame in a single forward pass. This encoder is trained with a novel Structure-Aware Editing (SAE) loss on a curated dataset of structurally-aligned image pairs, teaching it to preserve the source video's geometry during edits. For temporal coherence, we introduce Unified-Frame Editing (UFE), a technique that concatenates frame latents to facilitate cross-frame attention in a single generation step. Furthermore, for long videos, a sliding-window strategy with an anchor frame maintains global consistency. Our extensive experiments demonstrate that OSVE achieves editing quality comparable or superior to state-of-the-art multi-step methods, while operating approximately 155--171 times faster. This breakthrough paves the way for practical, real-time video editing applications. Code is available at https://github.com/KU-VGI/OSVE.
