OSVE: One Step Video Editing with One Step Diffusion Models

📰 ArXiv cs.AI

Learn to edit videos using one-step diffusion models with OSVE, a framework that adapts text-to-image models for high-quality video editing

advanced Published 23 Jul 2026

Action Steps

Train a learnable encoder to predict initial noise for each frame
Apply one-step diffusion models for text-guided video editing
Configure the OSVE framework to address inversion, editability, and temporal consistency
Test OSVE on various video editing tasks to evaluate its performance
Compare OSVE with traditional multi-step sampling and inversion methods

Who Needs to Know This

Video editors, computer vision engineers, and AI researchers can benefit from OSVE to improve video editing efficiency and quality

Key Insight

💡 OSVE adapts one-step text-to-image models for high-quality video editing, bypassing slow iterative inversion

Key Takeaways

Learn to edit videos using one-step diffusion models with OSVE, a framework that adapts text-to-image models for high-quality video editing

Full Article

Title: OSVE: One Step Video Editing with One Step Diffusion Models

Abstract:
arXiv:2607.19895v1 Announce Type: cross Abstract: 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

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