Finding large quantities of high-quality data to train neural networks is one of the most challenging aspects for researchers since privacy restrictions and associated financial obligations make it difficult to gather the data. In recent years, Generative Adversarial Networks (GANs) have been extensively used in the generation of different types of datasets. Despite this, we cannot control the attributes that will be associated with a data sample generated by GANs. Combining GANs with Variational Autoencoders (VAEs) is an effective way of obtaining outputs which have the desired attributes. Our study examined the sampling capacity and quality of VAE and VAE-GAN for MNIST and Fashion MNIST datasets. Additionally, we introduce a new metric, Transformer Score (TS), to determine the quality of the generated data. It is based on a vision transformers network and demonstrates superiority over Inception Score.