Dynamic latent scale GAN is a method to train an encoder that inverts the generator of GAN with maximum likelihood estimation. In this paper, we propose a method to improve the performance of dynamic latent scale GAN by integrating perceptual VAE loss into dynamic latent scale GAN efficiently. When training dynamic latent scale GAN with normal i.i.d. latent random variable, and latent encoder is integrated into discriminator, a sum of a predicted latent random variable of real data and a scaled normal noise follows normal i.i.d. random variable. This random variable can be used for both VAE and GAN training. Considering the intermediate layer output of the discriminator as a feature encoder output, the generator can be trained to minimize perceptual VAE loss. Also, inference & backpropagation for perceptual VAE loss can be integrated into those for GAN training. Therefore, perceptual VAE training does not require additional computation. Also, the proposed method does not require prior loss or variance estimation like VAE.