Patterns of Creativity: How User Input Shapes AI-Generated Visual Diversity

Recent critiques of Artificial-intelligence (AI)-generated visual content highlight concerns about the erosion of artistic originality, as these systems often replicate patterns from their training datasets, leading to significant uniformity and reduced diversity. Our research adopts a novel approac...

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Bibliographic Details
Published inarXiv.org
Main Authors Maria-Teresa De Rosa Palmini, Cetinic, Eva
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 09.10.2024
Subjects
Artificial intelligence
Creativity
Datasets
Regression models
User behavior
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Summary:Recent critiques of Artificial-intelligence (AI)-generated visual content highlight concerns about the erosion of artistic originality, as these systems often replicate patterns from their training datasets, leading to significant uniformity and reduced diversity. Our research adopts a novel approach by focusing on user behavior during interactions with Text-to-Image models. Instead of solely analyzing training data patterns, we examine how users' tendencies to create original prompts or rely on common templates influence content homogenization. We developed three originality metrics -- lexical, thematic, and word-sequence originality -- and applied them to user-generated prompts from two datasets, DiffusionDB and Civiverse. Additionally, we explored how characteristics such as topic choice, language originality, and the presence of NSFW content affect image popularity, using a linear regression model to predict user engagement. Our research enhances the discourse on AI's impact on creativity by emphasizing the critical role of user behavior in shaping the diversity of AI-generated visual content.
ISSN:2331-8422