Text-to-image Diffusion Models in Generative AI: A Survey

• URL: http://arxiv.org/abs/2303.07909v3
• Date: Fri, 08 Nov 2024 06:19:33 GMT
• Title: Text-to-image Diffusion Models in Generative AI: A Survey
• Authors: Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, In So Kweon, Junmo Kim,
• Abstract summary: This survey reviews the progress of diffusion models in generating images from text. We discuss applications beyond image generation, such as text-guided generation for various modalities like videos, and text-guided image editing.
• Score: 86.11421833017693
• License:
• Abstract: 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.

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