As a class of generative models, generative adversarial networks (GANs) are now capable of achieving excellent results, especially relating to facial images, which could reach 1024*1024 resolution in high-quality face synthesis. Recently, some studies have tried to map real face images into the latent space of GANs, for pre-trained GANs to represent real faces (E.g., optimizing style latent space on StyleGANs). However, the application of latent space to real face morphing is still in its infancy. Currently, training costs are high and/or require huge samples with labels. By adding regularization to the latent optimization, we propose a novel method to morph real faces based on StyleGAN. We labelled 12,000 face images with 32 attributes and utilise logistic-regression models to discover independent attribute vectors in latent space. Appropriate regularization helps us to regularize both latent vectors (a face with its attribute) so that they can fall into an ideal area. Moreover, we use those attribute vectors under different layer representations to handle real face morphing. Compared to the existing baseline, our method yields a larger output with higher quality.