Posterior Overlap Reduction for Diverse Text-to-Image Generation

Text-to-image generative models can produce high-quality images yet often yield visually repetitive groups for a single prompt. Existing group diversification methods commonly compare the model's clean predictions directly or through visual embeddings. However, at an intermediate timestep, the model's conditional clean prediction estimates only the mean of a posterior over possible clean images. We argue that different posterior means can still correspond to distributions that share substantial probability mass. We call this shared mass posterior overlap and introduce Posterior Overlap Reduction (POR), a training-free method that reduces pairwise posterior overlap within a fixed group of noisy states. Under a condition on posterior variance, we derive an upper bound on overlap that decreases with symmetrized KL divergence. We obtain a model-based estimate of this divergence without evaluating posterior densities. Using this estimate, POR jointly updates the group at a single timestep early in sampling, then resumes the original sampler. Across Stable Diffusion 3 and FLUX.1-dev, POR shifts the quality–diversity frontier outward on COCO and achieves the highest Vendi Score and lowest mean pairwise similarity among the compared methods on COCO and DPG-Bench, while retaining competitive image quality and prompt alignment.