ViPer: Visual Personalization of Generative Models via Individual Preference Learning

Sogand Salehi, Mahdi Shafiei, Teresa Yeo, Roman Bachmann, Amir Zamir

Abstract: 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, tuned to produce outputs that appeal to a broad audience are unpersonalized. Using them to generate images aligned with individual users relies on iterative manual prompt engineering by the user which is inefficient and undesirable.