Prompt Expansion for Adaptive Text-to-Image Generation

Siddhartha Datta, Alexander Ku, Deepak Ramachandran, Peter Anderson

Prompt Expansion for Adaptive Text-to-Image Generation: 5 upvotes on Hugging Face Daily Papers, #18 of 19 papers on 2023-12-29. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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