Prompt Expansion for Adaptive Text-to-Image Generation
Siddhartha Datta Alexander Ku Deepak Ramachandran Peter Anderson
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.