Multi-contrast images acquired with magnetic resonance imaging (MRI) provide abundant diagnostic information. However, the applicability of multi-contrast MRI is often limited by slow acquisition speed and high scanning cost. To overcome this issue, we propose a contrast generation method for arbitrary missing contrast images. First, StyleGAN2-based multi-contrast generator is trained to generate paired multi-contrast images. Second, pSp-based encoder network is used to predict style vectors from input images. Consequently, the imputation for arbitrary missing contrast is achieved by the process of (1) embedding one or more kinds of contrast images and (2) forward-propagating the style vector to the multi-contrast generator.