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Semantic face inpainting from corrupted images is a challenging problem in computer vision and has many practical applications. Different from well-studied nature image inpainting, the face inpainting task often needs to fill pixels semantically into a missing region based on the available visual data. In this paper, we propose a new face inpainting algorithm based on deep generative models, which increases the structural loss constraint in the image generation model to ensure that the generated image has a structure as similar as possible to the face image to be repaired. At the same time, different weights are calculated in the corrupted image to enforce edge consistency at the repair boundary. Experiments on different face data sets and qualitative and quantitative analyses demonstrate that our algorithm is capable of generating visually pleasing face completions.