Improving Tail-Class Diversity in Diffusion Models via Training-Free SNR-Adaptive Block Routing

Although diffusion models have achieved remarkable performance in image generation, their performance degrades substantially on class-imbalanced datasets that commonly arise in real-world applications, particularly for tail classes, where both generation quality and intra-class diversity deteriorate and the generated distribution tends to collapse toward a limited set of dominant modes. In this work, we study how to improve the coverage of real intra-class modes for tail classes in a class-conditional U-Net diffusion model that has already been trained on long-tailed data, while keeping all model parameters frozen. Through empirical evaluation and internal mechanism analysis, we find that lower training frequency is associated with stronger local class-conditional pressure and narrower representational coverage. Local interventions further show that the effects of class conditioning on local denoising and final generation coverage are not fully aligned, and that appropriately modulating conditioning in the low-resolution encoding region can improve tail-class coverage. Building on this observation, we propose SNR-Adaptive Block Routing (SABR), a training-free sampling strategy that adaptively modulates the strength of local class-conditioning injection according to the model’s internal conditional response and the signal-to-noise ratio (SNR), without requiring any additional training or fine-tuning. Extensive experiments across different long-tailed data distributions and generative baselines demonstrate that SABR generalizes well across settings, improves generation quality in most cases, and effectively mitigates mode concentration for tail classes while increasing coverage of real intra-class modes.