  Converting text descriptions into images using Generative Adversarial
Networks has become a popular research area. Visually appealing images have
been generated successfully in recent years. Inspired by these studies, we
investigated the generation of artistic images on a large variance dataset.
This dataset includes images with variations, for example, in shape, color, and
content. These variations in images provide originality which is an important
factor for artistic essence. One major characteristic of our work is that we
used keywords as image descriptions, instead of sentences. As the network
architecture, we proposed a sequential Generative Adversarial Network model.
The first stage of this sequential model processes the word vectors and creates
a base image whereas the next stages focus on creating high-resolution
artistic-style images without working on word vectors. To deal with the
unstable nature of GANs, we proposed a mixture of techniques like Wasserstein
loss, spectral normalization, and minibatch discrimination. Ultimately, we were
able to generate painting images, which have a variety of styles. We evaluated
our results by using the Fr\'echet Inception Distance score and conducted a
user study with 186 participants.
