We propose a normalizing flow based on the wavelet framework for super-resolution (SR) called WDFSR. It learns the conditional distribution mapping between low-resolution images in the RGB domain and high-resolution images in the wavelet domain to simultaneously generate high-resolution images of different styles. To address the issue of some flow-based models being sensitive to datasets, which results in training fluctuations that reduce the mapping ability of the model and weaken generalization, we designed a method that combines a T-distribution and QR decomposition layer. Our method alleviates this problem while maintaining the ability of the model to map different distributions and produce higher-quality images. Good contextual conditional features can promote model training and enhance the distribution mapping capabilities for conditional distribution mapping. Therefore, we propose a Refinement layer combined with an attention mechanism to refine and fuse the extracted condition features to improve image quality. Extensive experiments on several SR datasets demonstrate that WDFSR outperforms most general CNN- and flow-based models in terms of PSNR value and perception quality. We also demonstrated that our framework works well for other low-level vision tasks, such as low-light enhancement. The pretrained models and source code with guidance for reference are available at https://github.com/Lisbegin/WDFSR.