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Converting text descriptions into images using Generative AdversarialNetworks has become a popular research area. Visually appealing images havebeen generated successfully in recent years. Inspired by these studies, weinvestigated the generation of artistic images on a large variance dataset.This dataset includes images with variations, for example, in shape, color, andcontent. These variations in images provide originality which is an importantfactor for artistic essence. One major characteristic of our work is that weused keywords as image descriptions, instead of sentences. As the networkarchitecture, we proposed a sequential Generative Adversarial Network model.The first stage of this sequential model processes the word vectors and createsa base image whereas the next stages focus on creating high-resolutionartistic-style images without working on word vectors. To deal with theunstable nature of GANs, we proposed a mixture of techniques like Wassersteinloss, spectral normalization, and minibatch discrimination. Ultimately, we wereable to generate painting images, which have a variety of styles. We evaluatedour results by using the Fr\'echet Inception Distance score and conducted auser study with 186 participants.

Words as Art Materials: Generating Paintings with Sequential GANs