Stylecodes: Encoding Stylistic Information For Image Generation
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.
673f1d0e68595672b8cefd0d
Ciara Rowles
extracted_confirmed
2024-11-21T11:46:26.588Z
63357214eb6132ca653020e7
https://cdn-avatars.huggingface.co/v1/production/uploads/63357214eb6132ca653020e7/A_imoyFDx30wgi0guG_s8.png
Ciara
user
CiaraRowles
Stylecodes: Encoding Stylistic Information For Image Generation
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.
StyleCodes is an open-source style encoder enabling users to generate style-reference codes for image generation with minimal quality loss.
diffusion models style-conditioned image generation style-reference codes MidJourney StyleCodes base64 code image-to-style techniques