Abstract: To solve the difficulty in obtaining annotated pedestrian images in the field of pedestrian re-identification research, a novel data augmentation method guided by multi-factor is proposed in this paper. Firstly, a local multi-scale guidance mechanism is designed in the generator network. It can suppress the local artifacts in generated images through feature fusion. Secondly, a long-distance correlation guidance mechanism is proposed to improve the overall visual quality of the generated pedestrian image by guiding the long-distance dependence of the generated image with external attention. Lastly, an adversarial discrimination network is designed and embed into original generative adversarial networks. The three network stability architecture model increases the stability of generative adversarial network training. The experiment are validated on the VIPeR, Market-1501 and DukeMTMC-reID benchmark datasets. The results demonstrate our method outperforms the state-of-the-art with the mAP and rank-1 scores, especially in small-scale datasets.