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.
VRWKV-Editor: Reducing quadratic complexity in transformer-based video editing
International Artificial Intelligence Center of Morocco University Mohammed VI Polytechnic Rabat, Morocco（摩洛哥国际人工智能中心摩洛哥穆莱·伊沙克·穆莱·阿卜杜勒阿齐兹 polytechnic 大学拉巴特分校）；University Mohammed VI Polytechnic Rabat, Morocco（摩洛哥穆莱·伊沙克·穆莱·阿卜杜勒阿齐兹 polytechnic 大学拉巴特分校）；Sorbonne University, LIP6 - UMR 7606 CNRS, France（索邦大学，LIP6 - UMR 7606 CNRS，法国）
ETH Zurich（苏黎世联邦理工学院）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；University of Oxford（牛津大学）；Facebook（脸书公司）；Dartmouth College（达特茅斯学院）；University of Tübingen（蒂宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
