  Current state-of-the-art photorealistic generators are computationally
expensive, involve unstable training processes, and have real and synthetic
distributions that are dissimilar in higher-dimensional spaces. To solve these
issues, we propose a variant of the StackGAN architecture. The new architecture
incorporates conditional generators to construct an image in many stages. In
our model, we generate grayscale facial images in two different stages: noise
to edges (stage one) and edges to grayscale (stage two). Our model is trained
with the CelebA facial image dataset and achieved a Fr\'echet Inception
Distance (FID) score of 73 for edge images and a score of 59 for grayscale
images generated using the synthetic edge images. Although our model achieved
subpar results in relation to state-of-the-art models, dropout layers could
reduce the overfitting in our conditional mapping. Additionally, since most
images can be broken down into important features, improvements to our model
can generalize to other datasets. Therefore, our model can potentially serve as
a superior alternative to traditional means of generating photorealistic
images.
