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

2025-11-24

AI Summary

This work investigates the potential of pretrained image-to-video diffusion models for zero-shot instruction-driven image editing, addressing three key challenges: prompt misalignment, redundant temporal dimension interference, and post-generation frame blurring. We propose IF-Edit—a fine-tuning-free framework comprising: (1) chain-of-thought temporal prompting to enhance instruction understanding and spatiotemporal consistency; (2) temporal latent dropping to suppress noise from redundant frames; and (3) self-consistent post-refinement guided by short static video trajectories to recover fine details and improve sharpness. To our knowledge, this is the first systematic adaptation of image-to-video diffusion models to zero-shot image editing. Extensive experiments across four public benchmarks demonstrate that IF-Edit significantly outperforms existing methods in non-rigid deformation, physics- and time-aware reasoning, and general instruction-following tasks—particularly excelling in inference-intensive scenarios.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Repurposing video diffusion models for zero-shot image editing tasks
• Addressing prompt misalignment and temporal coherence in image editing
• Enhancing reasoning capabilities for instruction-driven image manipulation

Innovation

• Chain-of-thought prompt enhancement for instruction transformation
• Temporal latent dropout strategy for accelerated denoising
• Self-consistent post-refinement step for frame sharpening