Stylecodes: Encoding Stylistic Information For Image Generation
This addresses the need for users to generate and share style codes from their own images in image generation, though it is incremental as it builds on existing style-reference concepts.
The paper tackles the problem of generating style-conditioned images by proposing StyleCodes, an open-source encoder that converts image style into a 20-symbol base64 code, with experiments showing minimal quality loss compared to traditional methods.
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.