2023

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

Zhang, Chenshuang, Zhang, Chaoning, Zhang, Mengchun et al.

Understand

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.

Built on

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C. Zhang, C. Zhang, C. Li, S. Zheng, Y. Qiao, S. K. Dam, M. Zhang, J. U. Kim, S. T. Kim, G.-M. Park, J. Choi, S.-H. Bae, L.-H. Lee, P. Hui, I. S. Kweon, and C. S. Hong, “One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era,” researchgate DOI:10.13140/RG.2.2.24789.70883 , 2023
2023
Closest in time.

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