Nondestructive testing of welding images is still a significant challenge due to the imaging characteristics of radiographic images and the extremely random distribution of welding defect types. The practical application of supervised methods for welding nondestructive testing encounters significant challenges due to the limited availability of densely annotated samples and the absence of prior information regarding unknown defects. Hence, this article first proposes a rapid screening method using only defect-free image training to screen suspected defect images and normal images of welds in real time. Specifically, we first combine the correspondence mechanism and the representation mechanism, aiming to: 1) alleviate the smoothing reconstruction behavior caused by small defects and weak texture defects in weld; and 2) mitigate the offset learning behavior resulting from the differences between natural images and industrial weld images in the normalizing flow method. We propose a memory-aware transformer-based encoder, thus improving representations of complex defect-free images. Moreover, a dual-decoder strategy is introduced, which remaps the latent dependencies generated by the encoder through a semantic correspondence mechanism and reconstruction-guided normalizing flow, enabling effective learning of knowledge from weld images. We apply this framework to an industrial case of weld images, the experimental results demonstrate that our method outperforms other existing approaches.