Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes a Prompt Expansion framework that helps users generate high-quality, diverse images with less effort. The Prompt Expansion model takes a text query as input and outputs a set of expanded text prompts that are optimized such that when passed to a text-to-image model, generates a wider variety of appealing images. We conduct a human evaluation study that shows that images generated through Prompt Expansion are more aesthetically pleasing and diverse than those generated by baseline methods. Overall, this paper presents a novel and effective approach to improving the text-to-image generation experience.
@article{arxiv.2312.16720,
title = {Prompt Expansion for Adaptive Text-to-Image Generation},
author = {Siddhartha Datta and Alexander Ku and Deepak Ramachandran and Peter Anderson},
journal= {arXiv preprint arXiv:2312.16720},
year = {2023}
}