title: Art-Free Generative Models: Art Creation Without Graphic Art Knowledge
summary: We explore the question: "How much prior art knowledge is needed to create
  art?" To investigate this, we propose a text-to-image generation model trained
  without access to art-related content. We then introduce a simple yet effective
  method to learn an art adapter using only a few examples of selected artistic
  styles. Our experiments show that art generated using our method is perceived
  by users as comparable to art produced by models trained on large, art-rich
  datasets. Finally, through data attribution techniques, we illustrate how
  examples from both artistic and non-artistic datasets contributed to the
  creation of new artistic styles.
paper_authors._id: 674ea7dcb311aeec3f329ae5
paper_authors.name: Hui Ren
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-03T09:56:40.473Z
paper_authors._id: 65c30a5ffc592f2e565ea69f
paper_authors.avatarUrl: /avatars/5b7b42fb96cc88dbeca530fedf3c2571.svg
paper_authors.fullname: Hui Ren
paper_authors.type: user
paper_authors.user: rhfeiyang
paper_authors._id: 674ea7dcb311aeec3f329ae6
paper_authors.name: Joanna Materzynska
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-03T15:25:28.758Z
paper_authors._id: 60ef074b89938e400b470da8
paper_authors.avatarUrl: /avatars/c914070df46fb2dce9067d0eafc88238.svg
paper_authors.fullname: Joanna Materzynska
paper_authors.type: user
paper_authors.user: jomat
paper_authors._id: 674ea7dcb311aeec3f329ae7
paper_authors.name: Rohit Gandikota
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-03T16:04:26.266Z
paper_authors._id: 636daf1b56c0762cfda074b5
paper_authors.avatarUrl: /avatars/f44be5eb110acfa2efbd09de6b416239.svg
paper_authors.fullname: Rohit Gandikota
paper_authors.type: user
paper_authors.user: RohitGandikota
paper_authors._id: 674ea7dcb311aeec3f329ae8
paper_authors.name: David Bau
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-03T16:04:19.664Z
paper_authors._id: 6214d6c01e35c843d42d1f77
paper_authors.avatarUrl: /avatars/ac208cd180b4f3ed1ec367e581facfcf.svg
paper_authors.fullname: David Bau
paper_authors.type: user
paper_authors.user: davidbau
paper_authors._id: 674ea7dcb311aeec3f329ae9
paper_authors.name: Antonio Torralba
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-03T16:04:13.407Z
paper_authors._id: 6744c6ec6ec99a37d4ba9235
paper_authors.avatarUrl: /avatars/a5384b63bb615192f6fa157c6ea89e92.svg
paper_authors.fullname: Antonio
paper_authors.type: user
paper_authors.user: Antoniotorralbaborruel
paper_title: Art-Free Generative Models: Art Creation Without Graphic Art Knowledge
paper_summary: We explore the question: "How much prior art knowledge is needed to create
  art?" To investigate this, we propose a text-to-image generation model trained
  without access to art-related content. We then introduce a simple yet effective
  method to learn an art adapter using only a few examples of selected artistic
  styles. Our experiments show that art generated using our method is perceived
  by users as comparable to art produced by models trained on large, art-rich
  datasets. Finally, through data attribution techniques, we illustrate how
  examples from both artistic and non-artistic datasets contributed to the
  creation of new artistic styles.
paper_ai_keywords: text-to-image generation model art adapter artistic styles data attribution techniques artistic datasets non-artistic datasets