title: Measuring Style Similarity in Diffusion Models
summary: Generative models are now widely used by graphic designers and artists. Prior
  works have shown that these models remember and often replicate content from
  their training data during generation. Hence as their proliferation increases,
  it has become important to perform a database search to determine whether the
  properties of the image are attributable to specific training data, every time
  before a generated image is used for professional purposes. Existing tools for
  this purpose focus on retrieving images of similar semantic content. Meanwhile,
  many artists are concerned with style replication in text-to-image models. We
  present a framework for understanding and extracting style descriptors from
  images. Our framework comprises a new dataset curated using the insight that
  style is a subjective property of an image that captures complex yet meaningful
  interactions of factors including but not limited to colors, textures, shapes,
  etc. We also propose a method to extract style descriptors that can be used to
  attribute style of a generated image to the images used in the training dataset
  of a text-to-image model. We showcase promising results in various style
  retrieval tasks. We also quantitatively and qualitatively analyze style
  attribution and matching in the Stable Diffusion model. Code and artifacts are
  available at https://github.com/learn2phoenix/CSD.
paper_authors._id: 660c212e5c4bb2bcc485e41a
paper_authors.name: Gowthami Somepalli
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-03T09:27:52.938Z
paper_authors._id: 62ae853a4b5d6165083a12f8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62ae853a4b5d6165083a12f8/3_Q1yB3Qfpd9PngzzBlgZ.jpeg
paper_authors.fullname: Gowthami Somepalli
paper_authors.type: user
paper_authors.user: somepago
paper_authors._id: 660c212e5c4bb2bcc485e41b
paper_authors.name: Anubhav Gupta
paper_authors._id: 660c212e5c4bb2bcc485e41c
paper_authors.name: Kamal Gupta
paper_authors._id: 660c212e5c4bb2bcc485e41d
paper_authors.name: Shramay Palta
paper_authors._id: 660c212e5c4bb2bcc485e41e
paper_authors.name: Micah Goldblum
paper_authors._id: 660c212e5c4bb2bcc485e41f
paper_authors.name: Jonas Geiping
paper_authors._id: 660c212e5c4bb2bcc485e420
paper_authors.name: Abhinav Shrivastava
paper_authors._id: 660c212e5c4bb2bcc485e421
paper_authors.name: Tom Goldstein
paper_title: Measuring Style Similarity in Diffusion Models
paper_summary: Generative models are now widely used by graphic designers and artists. Prior
  works have shown that these models remember and often replicate content from
  their training data during generation. Hence as their proliferation increases,
  it has become important to perform a database search to determine whether the
  properties of the image are attributable to specific training data, every time
  before a generated image is used for professional purposes. Existing tools for
  this purpose focus on retrieving images of similar semantic content. Meanwhile,
  many artists are concerned with style replication in text-to-image models. We
  present a framework for understanding and extracting style descriptors from
  images. Our framework comprises a new dataset curated using the insight that
  style is a subjective property of an image that captures complex yet meaningful
  interactions of factors including but not limited to colors, textures, shapes,
  etc. We also propose a method to extract style descriptors that can be used to
  attribute style of a generated image to the images used in the training dataset
  of a text-to-image model. We showcase promising results in various style
  retrieval tasks. We also quantitatively and qualitatively analyze style
  attribution and matching in the Stable Diffusion model. Code and artifacts are
  available at https://github.com/learn2phoenix/CSD.
paper_ai_keywords: generative models database search semantic content style replication style descriptors text-to-image models Stable Diffusion model style attribution style matching