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Diffusion Model-Based Image Editing: A Survey (TPAMI 2025)
| Date | Stars |
|---|---|
| 2026-07-31 | 712 |
| 2026-08-02 | 712 |
| 2026-08-06 | 712 |
Today
— stars today
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growth rate 0.00%/day
[//]: # (# Diffusion Models for Image Editing)
<p align="center">
<img src="./media/title.png" alt="image" style="width:1000px;">
</p>
[](https://github.com/SiatMMLab/Awesome-Diffusion-Model-Based-Image-Editing-Methods)
[](https://opensource.org/licenses/MIT)
[](https://github.com/SiatMMLab/Awesome-Diffusion-Model-Based-Image-Editing-Methods)
[](https://arxiv.org/abs/2402.17525)
[](https://visitor-badge.laobi.icu/badge?page_id=SiatMMLab.Awesome-Diffusion-Model-Based-Image-Editing-Methods)
The repository is based on our survey [Diffusion Model-Based Image Editing: A Survey](https://arxiv.org/pdf/2402.17525.pdf) (TPAMI 2025).
Yi Huang*, Jiancheng Huang*, Yifan Liu*, Mingfu Yan*, Jiaxi Lv*, Jianzhuang Liu*, Wei Xiong, He Zhang, Liangliang Cao, Shifeng Chen
Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Adobe Inc, Apple Inc, Southern University of Science and Technology (SUSTech)
## Abstract
Denoising diffusion models have emerged as a powerful tool for various image generation and editing tasks, facilitating the synthesis of visual content in an unconditional or input-conditional manner. The core idea behind them is learning to reverse the process of gradually adding noise to images, allowing them to generate high-quality samples from a complex distribution. In this survey, we provide an exhaustive overview of existing methods using diffusion models for image editing, covering both theoretical and practical aspects in the field.
We delve into a thorough analysis and categorization of these works from multiple perspectives, including learning strategies, user-input conditions, and the array of specific editing tasks that can be accomplished.
In addition, we pay special attention to image inpainting and outpainting, and explore both earlier traditional context-driven and current multimodal conditional methods, offering a comprehensive analysis of their methodologies.
To further evaluate the performance of text-guided image editing algorithms, we propose a systematic benchmark, EditEval, featuring an innovative metric, LMM Score.
Finally, we address current limitations and envision some potential directions for future research.
## 🔖 News!!!
📌 We are actively tracking the latest research and welcome contributions to our repository and survey paper. If your studies are relevant, please feel free to contact us.
📰 2025-02-11: 🥳 Congrats, our paper is accepted by TPAMI 2025!!
📰 2024-10-25: Our benchmark [EditEval_v2](#benchmark-editeval_v2) is now released.
📰 2024-03-22: The [template](EditEval_v1/Metric/LMM_Score_GPT4V_Prompt_Template.md) of computing LMM Score using GPT-4V, along with a corresponding [leaderboard](#leaderboard) comparing several leading methods, is released.
📰 2024-03-14: Our benchmark [EditEval_v1](#benchmark-editeval_v1) is now released.
📰 2024-03-06: We establish a template for paper submissions. This template is accessible by navigating to the `New Issue` button within `Issues` or by clicking [here](https://github.com/SiatMMLab/Awesome-Diffusion-Model-Based-Image-Editing-Methods/issues/new/choose). Once there, please select the `Paper Submission Form` and complete it following the guidelines provided.
📰 2024-02-28: Our comprehensive survey paper, summarizing related methods published before February 1, 2024, is now available.
## 🔍 BibTeX
If you find this work helpful in your research, welcome to cite the paper and give a ⭐.
```
@article{huang2025diffusion,
title={Diffusion Model-Based Image Editing: A Survey},
author={Huang, Yi and Huang, Jiancheng and Liu, Yifan and Yan, Mingfu and Lv, Jiaxi and Liu, Jianzhuang and Xiong, Wei and Zhang, He and Cao, Liangliang and Chen, ShiExcerpt of 32,020 characters
Read on GitHub
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USTC-liuchang · University of Science and Technology of China · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cd145749b9819914, name:diffusion model, desc:diffusion model, name:image editing