Inverse-and-Edit: Effective and Fast Image Editing by Cycle Consistency
  Models
Authors: name: Ilia Beletskii, name: Andrey Kuznetsov, name: Aibek Alanov

Recent advances in image editing with diffusion models have achieved
impressive results, offering fine-grained control over the generation process.
However, these methods are computationally intensive because of their iterative
nature. While distilled diffusion models enable faster inference, their editing
capabilities remain limited, primarily because of poor inversion quality.
High-fidelity inversion and reconstruction are essential for precise image
editing, as they preserve the structural and semantic integrity of the source
image. In this work, we propose a novel framework that enhances image inversion
using consistency models, enabling high-quality editing in just four steps. Our
method introduces a cycle-consistency optimization strategy that significantly
improves reconstruction accuracy and enables a controllable trade-off between
editability and content preservation. We achieve state-of-the-art performance
across various image editing tasks and datasets, demonstrating that our method
matches or surpasses full-step diffusion models while being substantially more
efficient. The code of our method is available on GitHub at
https://github.com/ControlGenAI/Inverse-and-Edit.