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

TL;DR

This study investigates how user input influences the diversity of AI-generated images, highlighting that user behavior significantly impacts content originality and diversity in Text-to-Image models.

Contribution

It introduces three originality metrics for prompts and analyzes how user tendencies affect visual diversity and content homogenization in AI-generated images.

Findings

User originality correlates with increased visual diversity.

Topic choice and language originality influence image popularity.

User behavior plays a critical role in shaping AI-generated content diversity.

Abstract

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.