Stylecodes: Encoding Stylistic Information For Image Generation
Ciara Rowles
2024-11-21
Summary
This paper introduces StyleCodes, a new method for encoding the style of images into short numeric codes, making it easier to generate images with specific styles without needing to share the original images.
What's the problem?
While diffusion models can create high-quality images, controlling their style is challenging. Current methods often require sharing example images, which can be cumbersome and impractical. Additionally, users cannot generate style codes from their own images, limiting their ability to customize styles easily.
What's the solution?
StyleCodes solves this problem by providing an open-source architecture that allows users to encode image styles into a compact 20-symbol code. This method enables users to express the style of any image in a simple format that can be easily shared and applied to new images. The paper demonstrates that using StyleCodes results in minimal loss of image quality compared to traditional methods of transferring styles.
Why it matters?
This research is important because it simplifies the process of style transfer in image generation, empowering artists and creators to easily apply specific styles to their work. By making style encoding more accessible, StyleCodes can enhance creativity and streamline workflows in digital art and design.
Abstract
Diffusion models excel in image generation, but controlling them remains a challenge. We focus on the problem of style-conditioned image generation. Although example images work, they are cumbersome: srefs (style-reference codes) from MidJourney solve this issue by expressing a specific image style in a short numeric code. These have seen widespread adoption throughout social media due to both their ease of sharing and the fact they allow using an image for style control, without having to post the source images themselves. However, users are not able to generate srefs from their own images, nor is the underlying training procedure public. We propose StyleCodes: an open-source and open-research style encoder architecture and training procedure to express image style as a 20-symbol base64 code. Our experiments show that our encoding results in minimal loss in quality compared to traditional image-to-style techniques.