基于扩散模型的条件引导图像生成综述

A review of conditional image generation based on diffusion models

基于人工智能技术的生成内容(artificial intelligence generated content,AIGC)已成为当下的热门话题。在众多生成模型中,扩散模型因其高度可解释的数学特性及高质量和多样性的结果引起广泛关注,在条件引导的图像生成领域已取得显著成果,被广泛应用于电影、游戏、绘画和虚拟现实等领域,在文本引导的图像生成任务中,扩散模型不仅能生成高分辨率的图像,而且能保证生成图像的质量。首先介绍了扩散模型的定义和相关背景,然后重点介绍了扩散模型在条件引导的图像生成领域的发展历程和最新进展,最后探讨了扩散模型面临的挑战和潜在的发展方向,旨在为广大研究人员提供相关领域的研究概况和前沿动态。

Artificial intelligence generated content(AIGC)has received significant attention at present.As the numerous generative models proposed,the emerging diffusion model has attracted extensive attention due to its highly interpretable mathematical properties and the ability to generate high-quality and diverse results.Nowadays,diffusion models have achieved remarkable results in the field of condition-guided image generation.This achievement promotes the development of diffusion models in other conditional tasks and has various applications in areas such as movies,games,paintings,and virtual reality.For instance,the diffusion model can generate high-resolution images in textguided image generation tasks while ensuring the quality of the generated images.In this paper,we first introduce the definition and background of diffusion models.Then,we present a review of the development history and latest progress of conditional image generation based on diffusion models.Finally,we conclude this survey with discussions on challenges and future research directions of diffusion models.