ViPer: Visual Personalization of Generative Models via Individual
  Preference Learning
Authors: name: Sogand Salehi, name: Mahdi Shafiei, name: Teresa Yeo, name: Roman Bachmann, name: Amir Zamir

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.