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 \texttt{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 this https URL under a Public
Domain (CC0) license.

通过介绍 Artbreeder 上由 95K 用户生成的 680 万图片和 180 万提示的全面数据集，我们引入了一系列任务来识别多样的艺术风格、生成个性化内容并根据用户兴趣推荐风格。通过记录超越传统类别如 'cyberpunk' 或 'Picasso' 的独特用户生成风格，我们探索了用户全球集体创造心理的潜能。我们还评估了不同的个性化方法以增强艺术表达，并引入了一个风格地图集，以公共用户的方式呈现这些模型。我们的研究展示了文本到图像扩散模型发现和推广独特艺术表达的潜能，进一步使 AI 在艺术中具有民主化，促进更加多样和包容的艺术社区。数据集、代码和模型可以在此 https URL 获取，采用公共领域（CC0）许可证。