Research Area:  Machine Learning

Abstract:

This survey reviews text-to-image diffusion models in the context that diffusion models have emerged to be popular for a wide range of generative tasks. As a self-contained work, this survey starts with a brief introduction of how a basic diffusion model works for image synthesis, followed by how condition or guidance improves learning. Based on that, we present a review of state-of-the-art methods on text-conditioned image synthesis, i.e., text-to-image. We further summarize applications beyond text-to-image generation: text-guided creative generation and text-guided image editing. Beyond the progress made so far, we discuss existing challenges and promising future directions.

Keywords:  
text-to-image
diffusion models
image synthesis
creative generation

Author(s) Name:  Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, In So Kweon

Publisher name:  arXiv

DOI:  10.48550/arXiv.2303.07909

Volume Information:  Volume 2

Paper Link:   https://arxiv.org/abs/2303.07909