In the industrial production process, unsupervised visual inspection methods have obvious advantages over supervised visual inspection methods due to the scarcity of defect samples, annotation costs and the uncertainty of defect generation. Currently, unsupervised defect detection and localization methods have demonstrated significant improvements in detection accuracy to find numerous applications in industrial inspection. Nonetheless, the complexity of these methods limits their practical application. In this paper, we integrate the FastFlow model plugin as a probability distribution by introducing a simpler and lightweight CNN pre-trained backbone. Concurrently, various training strategies are employed to optimize the 2D Flow module within the Lightweight unsupervised flow model (LightFlow). Notably, the number of model parameters in the LightFlow model is only 1/4 of the original model size of the typical Vision Transformer (ViT) model CaiT. Thereby, this offers heightened training efficiency and speed. Therefore, extensive experimental results on three challenging anomaly detection datasets (MVTec AD, VisA, and BTAD) using various CNN backbones and multiple current state-of-the-art vision algorithms demonstrate the effectiveness of our approach. Specifically, the existing method can achieve 99.1% and 95.2% image-level AUROC (area under the receiver operating characteristic) in MVTec AD and VisA, respectively. IEEE