Realistic image super-resolution (RISR) has been a challenging research topic in image restoration, aiming to address complex degradation. However, existing methods often struggle to handle various unknown degradation factors presented in low-quality images, limiting their effectiveness to simplify degradation models. This gap between current RISR methods and real-world scenarios hinders their ability to generate realistic details. In this paper, we propose a novel realistic image super-resolution method based on stable diffusion to enhance degradation perception and detail generation. The framework comprises a Degradation-aware Module (DAM), a Detail-enhanced Module (DEM), and a General Restoration Module (GRM). DAM adjusts the weights of deep features in different channels using the Residual Composite Attention Network (RCAN) to remove perceived degradation, producing a smooth image containing only essential information. DEM employs a feature enhancement structure from coarse to fine to transform low-dimensional feature data obtained by downsampling the image data into corresponding high-resolution image data. GRM utilizes a pre-trained stable diffusion model based on the inverse diffusion process, and we add Advanced Nets in the diffusion stage to provide additional semantic information for noise restoration. Experimental results demonstrate the superiority of our method over existing state-of-the-art methods on both synthetic and real-world datasets.