Albeit recent Generative Models have achieved notable progress in synthesizing realistic facial aging images, many of them, e.g., GAN-based methods, cannot accurately capture the continuous progression of age-related shape-to-texture changes over time. In this paper, we propose an innovative facial age transformation framework that enables the generation of continuous shape-to-texture aging facial images. Firstly, the Prior Latent Age Modulation (PLAM) is designed to leverage the advantages of continuous sampling in high-dimensional space by normalizing flows to achieve precise and reversible mapping between the age attribute variable distributions and the prior latent space, ensuring smooth transitions along with facial aging. Secondly, we introduce the Attentional Feature Fusion (AFF), which dynamically allocates weights to effectively fuse the age attribute features by the latent space manipulation with the content features in StyleGAN, thereby generating facial images that accurately depict facial characteristics from shape to texture corresponding to specific ages. Finally, through quantitative and qualitative analysis of existing datasets, we validate the effectiveness and superiority of our proposed method in facial aging tasks.