This paper investigates how self-supervised spectral representation learning can improve diffusion generative models by aligning hidden state representations. The authors propose a spectral regularization method that leverages shared perspectives on perturbation kernels, demonstrating consistent improvements in generation quality across images and 3D point clouds.