In recent years, obtaining multi-modality magnetic resonance (MR) images with automatic generation methods has been widely studied. However, it is still difficult to generate images of all the other modalities by one given modality. To solve this problem, this study proposes a dynamic generative adversarial network (DyGAN) model. By combining the generative adversarial network and dynamic convolution and introducing a task label, the new model can simultaneously generate other three MR modalities from one modality. In addition, a multi-scale discrimination strategy is further proposed to improve the quality of image generation by fusing multiple scales. Image generation is verified on the BRATS19 dataset. The experimental results show that the new method can not only simultaneously generate multi-modality images but also improve the quality of the generated images.