Beyond Pixel Mimicry: Disentangled Self-Similarity Rewards for Diverse Subject-Driven Generation

Subject-driven image generation faces an “Identity-DiversityParadox”, where strong identity preservation often leads to rigid and low-diversity outputs. We propose a post-training framework called DivRLthat jointly optimizes identity consistency and structural diversity si-multaneously by leveraging disentangled visual features from a robustsimilarity model. Specifically, we introduce a Negative Self-SimilarityMeasure (nSSM) to quantify structural diversity, and Visual Seman-tic Matching (VSM) to evaluate identity consistency. We propose an“Explore-and-Suppress” strategy that treats VSM as a gated constraint:the model freely explores structurally diverse configurations, and onlysamples that violate the identity threshold are penalized via a quadratichinge loss. This converts identity preservation from a competing objectiveinto a feasibility constraint, allowing nSSM and VSM to improve jointly.Experiments demonstrate that our method effectively pushes the modelto generate both consistent and diverse images and improves structuraldiversity while maintaining comparable identity consistency through agated optimization formulation.