The traditional generation model causes image blurring and lack of details. Therefore, in this paper, we propose a conditional generation adversarial network combining with the powerful feature extraction capability of the variational autoencoder to realize high-quality photo generation. In training process of sketch-photo generation, sketches with the same style are used, leading to the monotonous input image. The hand-drawn sketches of various artists have different styles. Therefore, using sketches in multiple styles to extend the training dataset, the universality of the model is improved. The experimental results demonstrate that the similarity of the generated photos using the proposed method improves by 0.09 (to 0.77) based on CUHK student data set. In addition, the compared with the unexpanding training set, the similarity of the generating image using our training set also improves by 0.233 (to 0.603).