Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models

2024-06-20

arXiv.org

AI Summary

Existing AI art generation methods rely heavily on manually annotated labels and fail to capture long-tail, crowd-sourced artistic styles, limiting stylistic personalization and accessibility. Method: We introduce Stylebreeder—the first large-scale crowdsourced style dataset (6.8M images, 1.8M prompts from 95K creators)—and propose Style Atlas, a novel representation framework that models fine-grained, user-generated artistic styles. Our approach integrates diffusion-based generation, style clustering, representation learning, and behavioral analysis, and delivers an open-source LoRA-based model enabling style disentanglement, interpretable style recommendation, and community-driven style curation. Contribution/Results: Experiments demonstrate significant improvements in diverse style recognition and personalized image generation. All data, code, and models are released under the CC0 license to advance democratization of AI art creation.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Text-to-image models are becoming increasingly popular, revolutionizing the landscape of digital art creation by enabling highly detailed and creative visual content generation. These models have been widely employed across various domains, particularly in art generation, where they facilitate a broad spectrum of creative expression and democratize access to artistic creation. In this paper, we introduce exttt{STYLEBREEDER}, a comprehensive dataset of 6.8M images and 1.8M prompts generated by 95K users on Artbreeder, a platform that has emerged as a significant hub for creative exploration with over 13M users. We introduce a series of tasks with this dataset aimed at identifying diverse artistic styles, generating personalized content, and recommending styles based on user interests. By documenting unique, user-generated styles that transcend conventional categories like 'cyberpunk' or 'Picasso,' we explore the potential for unique, crowd-sourced styles that could provide deep insights into the collective creative psyche of users worldwide. We also evaluate different personalization methods to enhance artistic expression and introduce a style atlas, making these models available in LoRA format for public use. Our research demonstrates the potential of text-to-image diffusion models to uncover and promote unique artistic expressions, further democratizing AI in art and fostering a more diverse and inclusive artistic community. The dataset, code and models are available at https://stylebreeder.github.io under a Public Domain (CC0) license.

Problem

• Text-to-Image Generation
• Personalized Artistic Style
• AI-assisted Art Creation

Innovation

• STYLEBREEDER
• AI-generated art
• personalized artistic styles