Page 413 - 《软件学报》2026年第4期

软件学报 ISSN 1000-9825, CODEN RUXUEW E-mail: jos@iscas.ac.cn
2026,37(4):1854−1884 [doi: 10.13328/j.cnki.jos.007511] [CSTR: 32375.14.jos.007511] http://www.jos.org.cn
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基于扩散模型的个性化图像生成方法综述

何子健, 李冠彬

(中山大学 计算机学院, 广东 广州 510006)
通信作者: 李冠彬, E-mail: liguanbin@mail.sysu.edu.cn

摘 要: 随着深度学习技术和扩散模型的快速发展, 图像及视频生成模型展示了高质量、多样化的强大生成能力.
如何利用这些模型实现高效、精准的个性化生成成为当前研究的热点. 个性化图像生成方法能够通过结合文本描
述和用户提供的特定概念或主体, 实现定制化图像的生成, 满足用户对个性化视觉内容的多样化需求. 综述基于扩
散模型的个性化图像生成方法, 从生成目标的角度将现有方法分为单主体驱动生成和多概念组合生成两类, 前者
聚焦于根据单一主体生成定制化图像, 重点研究如何精确捕捉和重建主体的视觉特征, 后者则专注于将多个概念
或主体融合到同一图像中, 解决跨概念语义对齐和视觉一致性等问题. 结合具体任务和应用场景, 对个性化生成代
表性工作进行了详细分析. 此外, 比较和总结了常用的数据集、生成模型的评估方法和个性化生成方法间的性能
对比, 进一步探讨了个性化生成方法在实际应用中面临的挑战及未来发展方向, 对研究趋势进行了展望. 旨在为相
关领域的研究者提供全面的参考, 推动个性化生成方法的发展与创新.
关键词: 扩散模型; 个性化生成; 特征表达; 图像生成; 概念保持
中图法分类号: TP391

中文引用格式: 何子健, 李冠彬. 基于扩散模型的个性化图像生成方法综述. 软件学报, 2026, 37(4): 1854–1884. http://www.jos.org.
cn/1000-9825/7511.htm
英文引用格式: He ZJ, Li GB. Review of Personalized Image Generation Methods Based on Diffusion Models. Ruan Jian Xue
Bao/Journal of Software, 2026, 37(4): 1854–1884 (in Chinese). http://www.jos.org.cn/1000-9825/7511.htm

Review of Personalized Image Generation Methods Based on Diffusion Models
HE Zi-Jian, LI Guan-Bin
(School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China)
Abstract: With the rapid development of deep learning technologies and diffusion models, image and video generation models have
displayed powerful capabilities to produce high-quality and diverse results. How to leverage these models for efficient and precise
personalized generation has become a current research hotspot. Personalized image generation methods can combine text descriptions with
specific concepts or subjects provided by users to enable the creation of customized images and meet the diverse needs of users for
personalized visual content. This study reviews personalized image generation methods based on diffusion models, categorizing existing
methods from the perspective of the generation target into single-subject driven generation and multi-concept combination generation. The
former focuses on generating customized images according to individual subjects, emphasizing the accurate capture and reconstruction of
the subjects’ visual features. The latter focuses on merging multiple concepts or subjects into a single image, addressing challenges like
semantic alignment across concepts and visual consistency. This study provides a detailed analysis of representative work in personalized
generation by combining specific tasks and application scenarios. Additionally, this study compares and summarizes common datasets,
evaluation methods of generation models, and performance comparisons between different personalized generation methods. It further
discusses the challenges that personalized generation methods face in practical applications and the future development directions, and
offers a prospect for the research trends. This study aims to provide comprehensive references for researchers in relevant fields, fostering
the development and innovation of personalized generation methods.

* 基金项目: 国家重点研发计划 (2024YFB3908503); 国家自然科学基金 (62322608); CCF 快手科研合作基金 (CCFKuaiShou 2024007)
收稿时间: 2025-04-17; 修改时间: 2025-06-16, 2025-07-21; 采用时间: 2025-08-12; jos 在线出版时间: 2025-11-26
CNKI 网络首发时间: 2025-11-27