Abstract: Aiming at the problem of scarcity of solar cell defect images, a dual-dimensional attention integrated adversarial network for defect images generation is proposed for solar cell defect detection model training. Firstly, an integrated adversarial network model with dual generators and dual discriminators is constructed; secondly, channel attention and improved spatial attention are combined into dual-dimensional attention which is incorporated into generators and discriminators; finally, for solving unstable problems when training model, a dual-generator time-sharing training approach is designed. Compared with the existing optimal generation methods on the solar cell electroluminescence (EL) defect dataset, the Fréchet inception distance (FID) and structural similarity index measure (SSIM) of the five kinds generated defect images are increased by 53.87 and 0.46. In addition, the mean average precision (MAP) of the five kinds defect images reaches 96.56% by using the generated defect images to train the yolov5 detection model.