Stylebreeder：通过文本到图像模型探索和民主化艺术风格

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

June 20, 2024

作者: Matthew Zheng, Enis Simsar, Hidir Yesiltepe, Federico Tombari, Joel Simon, Pinar Yanardag

cs.AI

摘要

文本到图像模型正变得越来越受欢迎，通过实现高度详细和创意的视觉内容生成，彻底改变了数字艺术创作的格局。这些模型已被广泛应用于各个领域，特别是在艺术生成领域，它们促进了广泛的创意表达，并使艺术创作变得更加民主化。在本文中，我们介绍了STYLEBREEDER，这是一个包含680万图像和180万提示的全面数据集，由Artbreeder上的95,000名用户生成，Artbreeder是一个拥有超过1300万用户的重要创意探索平台。我们利用这个数据集提出了一系列任务，旨在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“赛博朋克”或“毕加索”的独特用户生成风格，我们探讨了独特的、众包风格的潜力，这些风格可以深入洞察全球用户的集体创意心理。我们还评估了不同的个性化方法以增强艺术表达，并引入了一个风格图谱，将这些模型以LoRA格式提供给公众使用。我们的研究展示了文本到图像扩散模型揭示和推广独特艺术表达的潜力，进一步使AI在艺术中民主化，并促进更多元化和包容性的艺术社区。该数据集、代码和模型可在https://stylebreeder.github.io 下载，采用公共领域（CC0）许可。

English

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.