Measuring Style Similarity in Diffusion Models
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.
660c212e5c4bb2bcc485e41a
Gowthami Somepalli
claimed_verified
2024-04-03T09:27:52.938Z
62ae853a4b5d6165083a12f8
https://cdn-avatars.huggingface.co/v1/production/uploads/62ae853a4b5d6165083a12f8/3_Q1yB3Qfpd9PngzzBlgZ.jpeg
Gowthami Somepalli
user
somepago
660c212e5c4bb2bcc485e41b
Anubhav Gupta
660c212e5c4bb2bcc485e41c
Kamal Gupta
660c212e5c4bb2bcc485e41d
Shramay Palta
660c212e5c4bb2bcc485e41e
Micah Goldblum
660c212e5c4bb2bcc485e41f
Jonas Geiping
claimed_verified
2025-06-27T08:59:08.095Z
63d86dbf3130cadcaf8bdd11
/avatars/29d79a0c6dcec01111ef192fecd0fa7a.svg
Jonas Geiping
user
JonasGeiping
660c212e5c4bb2bcc485e420
Abhinav Shrivastava
660c212e5c4bb2bcc485e421
Tom Goldstein
Measuring Style Similarity in Diffusion Models
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.
A new framework extracts style descriptors from images to attribute style in generated images to training data, with applications demonstrated in style retrieval and analysis for the Stable Diffusion model.
generative models text-to-image models style descriptors style attribution style retrieval Stable Diffusion model