Traditional diffusion models typically employ a U-Net architecture. Previous studies have unveiled the roles of attention blocks in the U-Net. However, they overlook the dynamic evolution of their importance during the inference process, which hinders their further exploitation to improve image applications. In this study, we first theoretically proved that, re-weighting the outputs of the Transformer blocks within the U-Net is a "free lunch" for improving the signal-to-noise ratio during the sampling process. Next, we proposed Importance Probe to uncover and quantify the dynamic shifts in importance of the Transformer blocks throughout the denoising process. Finally, we design an adaptive importance-based re-weighting schedule tailored to specific image generation and editing tasks. Experimental results demonstrate that, our approach significantly improves the efficiency of the inference process, and enhances the aesthetic quality of the samples with identity consistency. Our method can be seamlessly integrated into any U-Net-based architecture. Code: https://github.com/Hytidel/UNetReweighting

Unsupervised Re-ID methods aim at learning robust and discriminative features from unlabeled data. However, existing methods often ignore the noise from distribution discrepancy during network training, which may lead to feature misalignment and hinder the model performance. To address this problem, we propose a Dynamic Clustering and Cluster Contrastive Learning (DCCC) method. Specifically, we first design a Dynamic Clustering Parameters Scheduler (DCPS) which adjust the clustering algorithm to fit the variation of feature distances to alleviate the distribution noise caused by unreasonable hyper-parameter settings in a global aspect. Then, a Dynamic Cluster Contrastive Learning (DyCL) method is proposed to tackle the distribution discrepancy in batch training with re-weighting allocation in a local aspect. We also introduce a Label Smoothing Soft Contrastive Loss (Lss) to combine the DyCL loss and self-supervised loss with low consumption and high efficiency on computing. Experiments on several public datasets validate the effectiveness of our proposed DCCC which outperforms previous state-of-the-art methods by achieving the best performance. Code is available at https://github.com/theziqi/DCCC.