  Generative Adversarial Networks (GANs) [Goodfellow et al. 2014] convergence
in a high-resolution setting with a computational constrain of GPU memory
capacity has been beset with difficulty due to the known lack of convergence
rate stability. In order to boost network convergence of DCGAN (Deep
Convolutional Generative Adversarial Networks) [Radford et al. 2016] and
achieve good-looking high-resolution results we propose a new layered network,
HDCGAN, that incorporates current state-of-the-art techniques for this effect.
Glasses, a mechanism to arbitrarily improve the final GAN generated results by
enlarging the input size by a telescope {\zeta} is also presented. A novel
bias-free dataset, Curt\'o & Zarza, containing human faces from different
ethnical groups in a wide variety of illumination conditions and image
resolutions is introduced. Curt\'o is enhanced with HDCGAN synthetic images,
thus being the first GAN augmented dataset of faces. We conduct extensive
experiments on CelebA [Liu et al. 2015], CelebA-hq [Karras et al. 2018] and
Curt\'o. HDCGAN is the current state-of-the-art in synthetic image generation
on CelebA achieving a MS-SSIM of 0.1978 and a FR\'ECHET Inception Distance of
8.44.
