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.

这篇综述论文提供了关于生成式 AI 扩散和传统模型的全面概述，主要介绍了它们的基本技术、在不同领域的应用以及所面临的挑战，旨在为研究人员和从业者提供全面了解，并激发未来在这个令人兴奋的人工智能领域的创新。