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

By Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, In-So Kweon. arXiv.org, 2023

439 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/35ccd924de9e8483bdcf144cbf2edf09be157b7e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Generative artificial intelligence (2023, neutral): “Relying on GANs or diffusion models as DGMs, synthetic images are created that find use cases in marketing, design and fashion, or creative fields in the form of new visual art (Haase et al., 2023; Mayahi & Vidrih, 2022; Zhang et al., 2023a).”
Learning Interactive Real-World Simulators (2023, background): “The history conditioned base model operates at temporal and spatial resolution [16 , 24 , 40] , and the two spatial super-resolution models operate at spatial resolution [24 , 40] → [48 , 80] and [48 , 80] → [192 , 320] , respectively.”
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI (2023, neutral): “, ChatGPT [138]), text-to-image synthesis [139] and text-to-speech synthesis [31, 140].”