This survey reviews the progress of diffusion models in generating images from text, ~\textit{i.e.} text-to-image diffusion models. As a self-contained work, this survey starts with a brief introduction of how diffusion models work for image synthesis, followed by the background for text-conditioned image synthesis. Based on that, we present an organized review of pioneering methods and their improvements on text-to-image generation. We further summarize applications beyond image generation, such as text-guided generation for various modalities like videos, and text-guided image editing. Beyond the progress made so far, we discuss existing challenges and promising future directions.
@article{arxiv.2303.07909,
title = {Text-to-image Diffusion Models in Generative AI: A Survey},
author = {Chenshuang Zhang and Chaoning Zhang and Mengchun Zhang and In So Kweon and Junmo Kim},
journal= {arXiv preprint arXiv:2303.07909},
year = {2024}
}
Comments
First survey on the recent progress of text-to-image generation based on the diffusion model