Generative AI models have revolutionized various fields by enabling the creation of realistic and diverse data samples. Among these models, diffusion models have emerged as a powerful approach for generating high-quality images, text, and audio. This survey paper provides a comprehensive overview of generative AI diffusion and legacy models, focusing on their underlying techniques, applications across different domains, and their challenges. We delve into the theoretical foundations of diffusion models, including concepts such as denoising diffusion probabilistic models (DDPM) and score-based generative modeling. Furthermore, we explore the diverse applications of these models in text-to-image, image inpainting, and image super-resolution, along with others, showcasing their potential in creative tasks and data augmentation. By synthesizing existing research and highlighting critical advancements in this field, this survey aims to provide researchers and practitioners with a comprehensive understanding of generative AI diffusion and legacy models and inspire future innovations in this exciting area of artificial intelligence.

翻译：生成式人工智能模型通过生成逼真且多样化的数据样本，已彻底改变了多个领域。在这些模型中，扩散模型作为一种生成高质量图像、文本和音频的强大方法崭露头角。本综述论文全面概述了生成式人工智能的扩散模型及传统模型，重点关注其底层技术、跨不同领域的应用以及面临的挑战。我们深入探讨了扩散模型的理论基础，包括去噪扩散概率模型（DDPM）和基于分数的生成建模等概念。此外，我们探索了这些模型在文本到图像、图像修复、图像超分辨率及其他多种应用中的多样性，展示了它们在创意任务和数据增强方面的潜力。通过综合现有研究并突出该领域的关键进展，本综述旨在为研究人员和实践者提供对生成式人工智能扩散模型及传统模型的全面理解，并激发这一激动人心的人工智能领域的未来创新。