Stylebreeder: Exploring and Democratizing Artistic Styles through
  Text-to-Image Models
Authors: name: Matthew Zheng, name: Enis Simsar, name: Hidir Yesiltepe, name: Federico Tombari, name: Joel Simon, name: Pinar Yanardag

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 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.