Generative image editing has recently seen substantial progress with the emergence of image diffusion models, though critical challenges remain. Models tend to lack fidelity, altering essential aspects of original images and still struggling to accurately follow complex edit instructions.Simultaneously, video generation has made remarkable strides, with models that effectively function as consistent and continuous world simulators. In this paper, we propose merging these two fields by utilizing image-to-video models for image editing. We reformulate image editing as a temporal process, using pretrained video models to create smooth transitions from the original image to the desired edit. This approach traverses the image manifold continuously, ensuring consistent edits while preserving the original image's key aspects.Our approach achieves state-of-the-art results on text-based image editing benchmarks, demonstrating significant improvements in both edit accuracy and image preservation.