Personalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preference Optimization
Connor Dunlop, Matthew Zheng, Kavana Venkatesh, Pinar Yanardag Delul
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 work, we present the first framework for personalized image editing in diffusion models, introducing Collaborative Direct Preference Optimization (C-DPO) - a novel  method that aligns image edits with user-specific preferences while leveraging collaborative signals from like-minded individuals. Our approach encodes each user as a node in a dynamic preference graph and learns  embeddings via a lightweight graph neural network, enabling information sharing across users with overlapping visual tastes. We enhance a diffusion model's editing capabilities by integrating these personalized embeddings into a novel DPO objective, which jointly optimizes for individual alignment and neighborhood coherence. To support this setting, we curate the first personalized image editing benchmark, a large-scale synthetic dataset comprising 3,000 richly structured user profiles with diverse editing histories and aesthetic inclinations. Comprehensive experiments, including user studies and quantitative benchmarks, demonstrate that our method consistently outperforms baselines in generating edits that are not only visually appealing and semantically precise, but also aligned with user identity.