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Prompt Expansion for Adaptive Text-to-Image Generation (2312.16720v1)
Published 27 Dec 2023 in cs.CV
Abstract: 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.
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- Siddhartha Datta (19 papers)
- Alexander Ku (15 papers)
- Deepak Ramachandran (28 papers)
- Peter Anderson (30 papers)