Input Image blue, dislikes pink rainbows, dislikes grey brown, dislikes black gold, dislikes black futuristic, dislikes pink

Text-to-image (T2I) diffusion models have made remarkable strides in generating and editing high-fidelity images from text. Yet, these models remain fundamentally generic, failing to adapt to the nuanced aesthetic preferences of individual users. In this models, work, introducing we present the Collaborati first frame ve w Di ork rect for Preference personalized Optimization image editing (C-DPO), in diffusion a novel method that aligns image edits with user-specific preferences while leveraging collaborati as a node in ve a signals dynamic from preference like-minded graph indi and viduals.