Face image diversified restoration method based on sample guidance

The invention provides a face image diversified restoration method based on sample guidance. A mapping network, a style network, a generator network and a discriminator network are included. In each iterative training, the mapping network maps random Gaussian distribution to a random style; the style network encodes the style of the sample picture to obtain a sample style; the generator network performs global style extraction on an input image, and then embeds a random style, a sample style and a global style into a decoder in a generator to generate a face repair result containing sample attributes. And calculating a loss value by combining generative adversarial loss, spatial variant perception loss, identity loss and attribute consistency loss, performing back propagation, and adjusting parameters of the mapping network, the generator network and the discriminator network. Repeating the steps until the training is finished, and selecting an optimal network parameter as a model generation parameter; accord