Prompt Expansion for Adaptive Text-to-Image Generation

• URL: http://arxiv.org/abs/2312.16720v1
• Date: Wed, 27 Dec 2023 21:12:21 GMT
• Title: Prompt Expansion for Adaptive Text-to-Image Generation
• Authors: Siddhartha Datta, Alexander Ku, Deepak Ramachandran, Peter Anderson
• Abstract summary: 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. 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.
• Score: 51.67811570987088
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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