%0 Journal Article
%T 基于布局控制的文本到图像扩散模型研究进展
Research Progress on Text-to-Image Diffusion Models Based on Layout Control
%A 齐时达
%J Computer Science and Application
%P 443-452
%@ 2161-881X
%D 2025
%I Hans Publishing
%R 10.12677/csa.2025.154116
%X 随着计算机视觉和生成模型的迅猛发展，布局到图像生成(Layout-to-Image Generation)已成为一个重要的研究方向。该任务通过提供物体的空间布局信息，如边界框位置和类别标签，生成符合该布局要求的真实图像。近年来，扩散模型作为一种新兴的生成技术，凭借其在图像生成中的独特优势，逐渐成为布局到图像生成的主流方法之一。与生成对抗网络(GAN)相比，扩散模型在图像质量、稳定性和多样性方面表现出更好的性能。本文综述了近年来扩散模型在布局到图像生成中的研究进展，详细介绍了扩散模型的基本原理，并将现有的研究成果归纳为三类：1) 专用扩散模型方法；2) 基于预训练扩散模型的适配方法；3) 推理阶段的组合控制方法。本文还分析了不同布局生成方法的优缺点，并对未来可能的研究方向进行了展望。
With the rapid development of computer vision and generative models, Layout-to-Image Generation has become an important research direction. This task involves generating realistic images that conform to the given spatial layout of objects, such as bounding box positions and class labels. In recent years, diffusion models, as an emerging generative technique, have gradually become one of the main methods for Layout-to-Image Generation due to their unique advantages in image generation. Compared to Generative Adversarial Networks (GANs), diffusion models perform better in terms of image quality, stability, and diversity. This paper reviews the recent advancements of diffusion models in Layout-to-Image Generation, provides a detailed introduction to the fundamental principles of diffusion models, and categorizes the existing research into three types: 1) Dedicated diffusion model methods; 2) Adaptation methods based on pre-trained diffusion models; 3) Combination control methods during the inference stage. The paper also analyzes the advantages and disadvantages of different layout generation methods and discusses potential future research directions.
%K 扩散模型，
%K 布局控制，
%K 生成对抗网络，
%K 图像生成，
%K 预训练
Diffusion Models
%K Layout Control
%K Generative Adversarial Networks
%K Image Generation
%K Pre-Training
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=113447