In this study, we develop a super-resolution (SR) model for homogeneous isotropic turbulence (HIT) inspired by the recently proposed low-inference-cost ResShift diffusion model. The training data are obtained from direct numerical simulation of two three-dimensional HIT cases with varying grid resolutions and Reynolds numbers ( $ \textit{Re}_\lambda = 94$ and 173) to increase the model’s generalisability. The model is trained on two-dimensional snapshots rather than full three-dimensional fields, as training and inference on three-dimensional data would increase the computational cost significantly. Both the data from the whole domain and the data from a quarter of the domain are considered in the dataset to increase the diversity and quantity of training samples. This strategy also helps the model learn more localised flow structures and reduces dependence on global domain-specific patterns. The model is trained using single snapshots of velocity components for three upsampling factors of 4, 8 and 16. To assess the generalisability of the trained model, it is tested for flows under conditions different from those of the training data. Additionally, the high-resolution reconstruction of flow fields from low-resolution turbulent boundary layer data is performed to evaluate the model’s performance in anisotropic turbulence. The results show that the diffusion model presented in this study performs well in predicting the velocity field even for high upsampling factors, and unlike bicubic interpolation, convolutional neural network (CNN)- and U-Net-based models, it does not generate a visually blurry flow field when applied to high upsampling factors. It also outperforms bicubic interpolation, CNN- and U-Net-based models, as well as the traditional conditional denoising diffusion probabilistic model designed for SR, in predicting flow statistics. The model effectively extracts flow features, generates flow structures of varying sizes and shows strong performance in predicting vorticity. It also reproduces the energy spectrum at high wavenumbers with reasonable accuracy, indicating the recovery of small-scale structures often lost in coarse data. This capability is valuable for subgrid-scale stress estimation and helps improve the physical fidelity of large eddy simulation frameworks.