Inverse-and-Edit: Effective and Fast Image Editing by Cycle Consistency
  Models
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.
685d2a3d696820ba1f28f39d
Ilia Beletskii
claimed_verified
2025-06-27T08:57:30.273Z
63889822f7d3b0df091b1232
/avatars/d79b4a5c1367c7dc6d1311e21e26b483.svg
Ilia Beletskii
user
ilushado
685d2a3d696820ba1f28f39e
Andrey Kuznetsov
claimed_verified
2025-07-11T13:13:10.716Z
643984dceb7c5616ef3f5d54
https://cdn-avatars.huggingface.co/v1/production/uploads/643984dceb7c5616ef3f5d54/10JRkblrRIEVci6UJwvPz.jpeg
Andrey Kuznetsov
user
kuznetsoffandrey
685d2a3d696820ba1f28f39f
Aibek Alanov
claimed_verified
2025-07-05T07:53:59.029Z
66680c6451545a8b46c6fd21
/avatars/03707f5ea4e2aa8dc825a9782b00ed85.svg
Aibek Alanov
user
ai-alanov
Inverse-and-Edit: Effective and Fast Image Editing by Cycle Consistency
  Models
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.
A new framework using consistency models enhances image inversion and editing efficiency, achieving top performance with fewer steps.
diffusion models high-fidelity inversion reconstruction accuracy cycle-consistency optimization state-of-the-art performance image editing tasks full-step diffusion models