Spectral super-resolution aims to reconstruct high- dimensional hyperspectral images from low-dimensional multispectral or RGB inputs, enabling rich spectral information recovery for downstream vision tasks. In this paper, we propose EDSSR, a diffusion-based framework that reconstructs high-quality hyperspectral images by modeling complex spectral variations through an end-to-end trained sampling process. EDSSR incorporates a pretrained diffusion model as a prior to guide the reconstruction and improve spectral consistency. Additionally, a Physics-Guided Module is introduced to inject physical constraints into the U-Net backbone, enhancing high-frequency detail recovery in the reconstructed spectrum. Extensive experiments on two benchmark datasets demonstrate the effectiveness of our method in enhancing reconstruction accuracy and spectral fidelity.