Are Image-to-Video Models Good Zero-Shot Image Editors?

• URL: http://arxiv.org/abs/2511.19435v2
• Date: Tue, 25 Nov 2025 12:53:29 GMT
• Title: Are Image-to-Video Models Good Zero-Shot Image Editors?
• Authors: Zechuan Zhang, Zhenyuan Chen, Zongxin Yang, Yi Yang,
• Abstract summary: We introduce IF-Edit, a tuning-free framework that repurposes pretrained image-to-video diffusion models for instruction-driven image editing.<n>IF-Edit addresses three key challenges: prompt misalignment, redundant temporal latents, and blurry late-stage frames.
• Score: 39.10187156757937
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large-scale video diffusion models show strong world simulation and temporal reasoning abilities, but their use as zero-shot image editors remains underexplored. We introduce IF-Edit, a tuning-free framework that repurposes pretrained image-to-video diffusion models for instruction-driven image editing. IF-Edit addresses three key challenges: prompt misalignment, redundant temporal latents, and blurry late-stage frames. It includes (1) a chain-of-thought prompt enhancement module that transforms static editing instructions into temporally grounded reasoning prompts; (2) a temporal latent dropout strategy that compresses frame latents after the expert-switch point, accelerating denoising while preserving semantic and temporal coherence; and (3) a self-consistent post-refinement step that sharpens late-stage frames using a short still-video trajectory. Experiments on four public benchmarks, covering non-rigid editing, physical and temporal reasoning, and general instruction edits, show that IF-Edit performs strongly on reasoning-centric tasks while remaining competitive on general-purpose edits. Our study provides a systematic view of video diffusion models as image editors and highlights a simple recipe for unified video-image generative reasoning.

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