The CS-Flow method based on normalizing flows has achieved good results in the field of defect detection， but its way of repeatedly stacking single coupling blocks increases the complexity of the network. Therefore， we propose a network ValidFlow composed of two coupling blocks stacking： Feature advection flow （FA flow） and feature blending flow （FB flow）. In the subnetwork of FA flow， the short-cut branch of up and down sampling is removed and depth-separable convolution is introduced. The subnetworks within FB flow are fused across scales at three scales. This allows ValidFlow to reduce the number of parameters while keeping the information well mixed. Compared with the existing methods on MVTec AD，MTD and DAGM datasets， it can be seen that on MVTec AD datasets， the average AUROC of ValidFlow in 15 categories is 99.2%， and the AUROC of ValidFlow in four categories is 100%. On the MTD dataset， AUROC achieves 99.6%. At the same time， compared with CS-Flow， ValidFlow has 207.61M fewer parameters and 22 higher reasoning speed FPS. On the DAGM dataset， the average AUROC of the 10 categories is 99.0%， which is very close to the monitored method in terms of performance.