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