Appearance quality is one of the national mandatory verification for domestic gas meters (DGM). In view of the lack of defect samples in the appearance quality verification of DGM, which makes the detection method based on supervised learning difficult to generalize to the actual application scenario. This paper studies the unsupervised detection method of DGM appearance defects. EfficientFormerV2-l, the improved Vision Transformer (ViT), is introduced to extract normal sample features, fuse the bottom and highlevel feature maps, and map the normal features to the standard Gaussian distribution using two-dimensional normalizing flow called FastFlow. The appearance defects are scattered outside the distribution so that the abnormal score is higher than the normal sample. By setting an adaptive threshold, the DGM appearance defects are identified and located. The experiment collects DGM normal samples, real defect samples, synthetic defect samples as data sets and optimizes the detection model parameters. The optimized detection model achieves 99. 77% AUROC at image level indicators, 96. 3% AUPRO at pixel level indicators, and can detect more than 4 DGM images per second, indicating that the method in this paper can accurately and efficiently identify and locate DGM appearance defects.