Abstract: There is a strong correlation between semantic attributes in face editing models; editing one attribute may result in changes to other semantic attributes and non-edited areas. To enhance user editing experience and achieve more precise editing of facial details, this paper proposes a face editing model based on semantic separation and feature fusion in the image domain, termed Independent Semantic and Fused Latent (ISFL). Firstly, facial image semantics are separated using image masks and organized into a hierarchical tree structure. Then, ISFL is used to achieve local separation and global fusion of image semantics. Users can independently edit the structure and appearance of different semantics in the image using masks. Finally, two methods, encoder-based and optimization-based, are employed to optimize the details of the generated image. Experimental results demonstrate that the proposed image editing method can produce more realistic and detail-rich images.