title: ViPer: Visual Personalization of Generative Models via Individual
    Preference Learning
summary: Different users find different images generated for the same prompt
  desirable. This gives rise to personalized image generation which involves
  creating images aligned with an individual's visual preference. Current
  generative models are, however, unpersonalized, as they are tuned to produce
  outputs that appeal to a broad audience. Using them to generate images aligned
  with individual users relies on iterative manual prompt engineering by the user
  which is inefficient and undesirable. We propose to personalize the image
  generation process by first capturing the generic preferences of the user in a
  one-time process by inviting them to comment on a small selection of images,
  explaining why they like or dislike each. Based on these comments, we infer a
  user's structured liked and disliked visual attributes, i.e., their visual
  preference, using a large language model. These attributes are used to guide a
  text-to-image model toward producing images that are tuned towards the
  individual user's visual preference. Through a series of user studies and large
  language model guided evaluations, we demonstrate that the proposed method
  results in generations that are well aligned with individual users' visual
  preferences.
paper_authors._id: 66a1b4598f0d2327e0a2ce03
paper_authors.name: Sogand Salehi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-25T11:07:38.414Z
paper_authors._id: 63179ba4212fce5a3cd66a67
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63179ba4212fce5a3cd66a67/qvXI90u5gWi7MP5coxkc2.png
paper_authors.fullname: Sogand Salehi
paper_authors.type: user
paper_authors.user: sogandstormesalehi
paper_authors._id: 66a1b4598f0d2327e0a2ce04
paper_authors.name: Mahdi Shafiei
paper_authors._id: 66a1b4598f0d2327e0a2ce05
paper_authors.name: Teresa Yeo
paper_authors._id: 66a1b4598f0d2327e0a2ce06
paper_authors.name: Roman Bachmann
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-25T12:23:23.301Z
paper_authors._id: 624dc3c5953e603497e3a28c
paper_authors.avatarUrl: /avatars/955e0269f7e5fe3dbefeafa803a06d76.svg
paper_authors.fullname: Roman Bachmann
paper_authors.type: user
paper_authors.user: roman-bachmann
paper_authors._id: 66a1b4598f0d2327e0a2ce07
paper_authors.name: Amir Zamir
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-25T12:23:28.403Z
paper_authors._id: 666c7e0d29b9f769d6b2ee68
paper_authors.avatarUrl: /avatars/8261e3a8e6fdd2012a2e3f91279df110.svg
paper_authors.fullname: Amir Zamir
paper_authors.type: user
paper_authors.user: amir32002
paper_title: ViPer: Visual Personalization of Generative Models via Individual
    Preference Learning
paper_summary: Different users find different images generated for the same prompt
  desirable. This gives rise to personalized image generation which involves
  creating images aligned with an individual's visual preference. Current
  generative models are, however, unpersonalized, as they are tuned to produce
  outputs that appeal to a broad audience. Using them to generate images aligned
  with individual users relies on iterative manual prompt engineering by the user
  which is inefficient and undesirable. We propose to personalize the image
  generation process by first capturing the generic preferences of the user in a
  one-time process by inviting them to comment on a small selection of images,
  explaining why they like or dislike each. Based on these comments, we infer a
  user's structured liked and disliked visual attributes, i.e., their visual
  preference, using a large language model. These attributes are used to guide a
  text-to-image model toward producing images that are tuned towards the
  individual user's visual preference. Through a series of user studies and large
  language model guided evaluations, we demonstrate that the proposed method
  results in generations that are well aligned with individual users' visual
  preferences.
paper_ai_summary: A method to personalize image generation using user feedback and a large language model to guide a text-to-image model.
paper_ai_keywords: large language model text-to-image model user feedback structured visual attributes visual preference