  The task of text-to-image generation has achieved remarkable progress due to
the advances in the conditional generative adversarial networks (GANs).
However, existing conditional text-to-image GANs approaches mostly concentrate
on improving both image quality and semantic relevance but ignore the
explainability of the model which plays a vital role in real-world
applications. In this paper, we present a variety of techniques to take a deep
look into the latent space and semantic space of the conditional text-to-image
GANs model. We introduce pairwise linear interpolation of latent codes and
`linguistic' linear interpolation to study what the model has learned within
the latent space and `linguistic' embeddings. Subsequently, we extend linear
interpolation to triangular interpolation conditioned on three corners to
further analyze the model. After that, we build a Good/Bad data set containing
unsuccessfully and successfully synthetic samples and corresponding latent
codes for the image-quality research. Based on this data set, we propose a
framework for finding good latent codes by utilizing a linear SVM. Experimental
results on the recent DiverGAN generator trained on two benchmark data sets
qualitatively prove the effectiveness of our presented techniques, with a
better than 94\% accuracy in predicting ${Good}$/${Bad}$ classes for latent
vectors. The Good/Bad data set is publicly available at
https://zenodo.org/record/5850224#.YeGMwP7MKUk.
