title: Stylebreeder: Exploring and Democratizing Artistic Styles through
    Text-to-Image Models
summary: 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.
paper_authors._id: 6678f524fac70db9e8e5c173
paper_authors.name: Matthew Zheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-24T13:36:18.005Z
paper_authors._id: 64f8eec98a234f114e3b46f7
paper_authors.avatarUrl: /avatars/298224cf71dfac26c4142bf2f3f8869b.svg
paper_authors.fullname: Matthew Zheng
paper_authors.type: user
paper_authors.user: matthewz03
paper_authors._id: 6678f524fac70db9e8e5c174
paper_authors.name: Enis Simsar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-24T07:27:37.005Z
paper_authors._id: 63412f2add8853dc7e306a4f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/nqbteeQhS2dPA5LVy7dTD.png
paper_authors.fullname: Enis Simsar
paper_authors.type: user
paper_authors.user: enisimsar
paper_authors._id: 6678f524fac70db9e8e5c175
paper_authors.name: Hidir Yesiltepe
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-24T13:36:29.705Z
paper_authors._id: 63597e84d72fc0539e72b507
paper_authors.avatarUrl: /avatars/f568302b70064220e3c824577e5bece4.svg
paper_authors.fullname: Hidir Yesiltepe
paper_authors.type: user
paper_authors.user: Hidir
paper_authors._id: 6678f524fac70db9e8e5c176
paper_authors.name: Federico Tombari
paper_authors._id: 6678f524fac70db9e8e5c177
paper_authors.name: Joel Simon
paper_authors._id: 6678f524fac70db9e8e5c178
paper_authors.name: Pinar Yanardag
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:57:59.182Z
paper_authors._id: 683b9c7d4568ffd33e7334e1
paper_authors.avatarUrl: /avatars/046bd5a78a24f3bc4e1f6cb6076970e0.svg
paper_authors.fullname: Pinar Yanardag
paper_authors.type: user
paper_authors.user: Pinguar
paper_title: Stylebreeder: Exploring and Democratizing Artistic Styles through
    Text-to-Image Models
paper_summary: 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.
paper_ai_summary: A comprehensive dataset and series of tasks for text-to-image models explore diverse user-generated styles and enhance personalization methods, supporting unique artistic expressions.
paper_ai_keywords: text-to-image diffusion models style atlas LoRA format