Creativity Behind the Prompts: Automated Creativity Assessment in Prompting for Text-to-Image Models

Abstract

This thesis delves into the intersection of artificial intelligence and creativity, specifically focusing on the application of text-to-image synthesis models. These models, gaining significant attention in recent years, hold the potential to redefine the boundaries of human imagination and challenge conventional notions of creativity. However, they also raise pertinent questions about originality, copyright, and the role of human input in the creative process.

The study investigates the use of prompt engineering to augment the creativity of the generated artworks. Various prompt modifiers, including artist names and aesthetic quality descriptors, are employed to guide the synthesis process. The results indicate that the strategic use of these modifiers significantly enhances the creativity of the generated images, providing a concrete strategy for both novice and experienced users of these models.

The research also explores the use of topic modeling methods, such as Gibbs Sampling Dirichlet Mixture Model (GSDMM) and BERTopic. However, several challenges, including computational constraints and limitations in the clustering methods used, are identified. Despite these challenges, the research offers valuable insights into the potential of text-to-image synthesis models and the role of prompt engineering in enhancing creativity. Future work aims to address these challenges and further explore the potential of these models in various creative domains.