Abstract: With the development of Generative Adversarial Networks (GAN), more and more researches have been conducted in the field of Chinese fonts transformation and researchers are able to generate high-quality images of Chinese characters. These font transformation models can transform a source font to a target font using GAN. However, current methods have limitations that 1) generated images are oftentimes blurry and 2) models can only learn and produce one target font at a time. To address these problems, we have developed a brand-new model to perform Chinese font transformation. First, font information is attached to images to tell the generator the fonts that we want to transform. Then, the generator extracts and learns feature mappings through convolutional networks and generates photo-realistic images using transposed convolutional networks. The ground truth images are then used as supervisory information to ensure that characters and fonts generated are consistent with themselves. This model only needs to be trained once, but it is able to transform one font to multiple fonts and produce new fonts. Extensive experiments on seven Chinese font datasets show the superiority of the proposed method over several other methods in Chinese font transformation.