Stylebreeder clusters AI art 🎨🤖
So essentially,
Stylebreeder clusters diverse art styles from 6.8M images on Artbreeder, enhancing personalized AI-driven artistic expression.
Paper: Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models (21 Pages)
Researchers from Virginia Tech, ETH Zürich, TUM, Google and Artbreeder want to make better democratized AI art styles. The existing datasets of artwork, including AI-generated ones, often lack the scope and depth to analyze emerging styles on Gen AI platforms.
Hmm..What’s the background?
Traditional datasets have limitations in style diversity, while AI-generated datasets might be limited by user base size, lack of original text prompts, or focus on specific themes. This underscores the need for a comprehensive dataset like STYLEBREEDER that captures the breadth of user-generated styles and provides insights into their characteristics.
Ok, So what is proposed in the research paper?
The STYLEBREEDER dataset was created by scraping 6.8 million images and 1.8 million associated text prompts from Artbreeder, all under a CC0 license.
The dataset encompasses a variety of diffusion models used for generation, including Stable Diffusion and SD-XL, and includes metadata such as positive and negative prompts, user IDs, timestamps, image sizes, and model hyperparameters.
Images were converted into style embeddings using the CSD feature extractor, which excels at capturing stylistic features. These embeddings were then grouped into 1000 clusters using the K-Means++ algorithm based on cosine similarity, revealing distinct stylistic groups.
What’s next?
The STYLEBREEDER dataset allows for a variety of future explorations in understanding and utilizing user-generated artistic styles including analyzing how text prompts can be refined for more effective style guidance, investigating the evolution of artistic trends over time, and creating a recommendation system that suggests styles based on both visual and textual elements. Further research could also focus on developing search functionalities for navigating the generated images and exploring the concept of "explainable creativity" within this context.
So essentially,
Stylebreeder clusters diverse art styles from 6.8M images on Artbreeder, enhancing personalized AI-driven artistic expression.