RSTFA: Efficient Training-Free Human-Preference Alignment via Rejection Sampling for Text-to-Image Diffusion Models

Given a text-to-image diffusion model pretrained on large-scale text-image pairs, can we align the model with human pReferences without further fine-tuning? In this paper, we analyze the effect of alignment tuning in diffusion models by comparing the diffusion denoising trajectory between base and aligned models. Our findings reveal that alignment tuning primarily affects superficial stylistic aspects during denoising, rather than fundamental content, suggesting superficial alignment behaviors. Based on this discovery, we introduce a novel, training-free alignment approach (RSTFA) that leverages rejection sampling at specific stylistic timesteps, ensuring human preference alignment without fine-tuning or heavy inference overhead. We provide a theoretical analysis and derive a bias bound for our rejection-sampling alignment scheme. Empirically, we show that RSTFA better preserves sample diversity than reinforcement-learning-based tuning methods. Extensive experiments on Pick-a-Pic, COCO, HPD V2, and PartiPrompts show that our method not only achieves superior alignment with human preferences compared to state-of-the-art methods, but also reduces computational demands, establishing efficient, human-centered diffusion model alignment.