Resolving the Identity Crisis in Text-to-Image Generation

Abstract

State-of-the-art text-to-image models demonstrate impressive realism but suffer from a persistent identity crisis when generating scenes with multiple humans: producing duplicate faces, merging identities, and miscounting individuals. We present DisCo (Reinforcement with DiverSity Constraints), a novel reinforcement learning framework that directly optimizes identity diversity both within images and across groups of generated samples. DisCo fine-tunes flow-matching models using Group-Relative Policy Optimization (GRPO), guided by a compositional reward that: (i) penalizes facial similarity within images, (ii) discourages identity repetition across samples, (iii) enforces accurate person counts, and (iv) preserves visual fidelity via human preference scores. A single-stage curriculum stabilizes training as prompt complexity increases, requiring no additional annotations. On the DiverseHumans Testset, DisCo achieves 98.6% Unique Face Accuracy and near-perfect Global Identity Spread, outperforming both open-source and proprietary models (e.g., Gemini, GPT-Image) while maintaining competitive perceptual quality. Our results establish cross-sample diversity as a critical axis for resolving identity collapse in generative models, and position DisCo as a scalable, annotation-free solution for multi-human image synthesis.