基于扩散模型的文本图像生成对比研究综述

Comparative Review of Text-to-Image Generation Techniques Based on Diffusion Models

随着深度学习的不断发展,人工智能生成内容成为了一个热门话题,特别是扩散模型作为一种新兴的生成模型,在文本图像生成领域取得了显著进展。全面描述了扩散模型在文本图像生成任务中的应用,并与生成对抗网络和自回归模型的对比分析,揭示了扩散模型的优势和局限性。同时深入探讨了扩散模型在提升图像质量、优化模型效率以及多语言文本图像生成方面的具体方法,通过在CUB、COCO和T2I-CompBench数据集上进行了实验分析,不仅验证了扩散模型零样本生成的能力,还凸显了其根据复杂文本提示生成高质量图像的能力。介绍了扩散模型在文本图像编辑、3D生成、视频及医学图像生成等领域的应用前景。总结了扩散模型在文本图像生成任务上面临的挑战以及未来的发展趋势,有助于研究者更深入地推进这一领域的研究。

With the continuous development of deep learning,artificial intelligence generated content has become a hot topic,especially diffusion models,as an emerging generation model,have made significant progress in the field of text-to-image generation.This article comprehensively describes the application of diffusion models in text and image generation tasks,and compares them with generative adversarial networks and autoregressive models,revealing the advantages and limitations of diffusion models.Meanwhile,it delves into the specific methods of diffusion models in improving image quality,optimizing model efficiency and generating images from multilingual text prompts.Experimental analyses on CUB,COCO and T2I-CompBench datasets not only validates the zero-shot generation capability of diffusion models but also highlights their ability to generate high-quality images based on complex text prompts.The paper introduces the promising applications of diffusion models in fields such as text-guided image editing,3D generation,video generation,and medical image generation.It summarizes the challenges faced by diffusion models in text-to-image generation tasks and their future development trends,aiming to facilitate further research in this domain.