In digital art, artificial intelligence (AI) has found ubiquitous applications in (semi-)automatically producing captivating visuals with aesthetic appeal, pushing the boundaries of creativity and productivity. Pixel-based photo generation AI models and stroke-based neural painting methods have been successfully developed for creating photorealistic and artistic images. On the one hand, pixel-based models can directly predict the pixel values of raster images, allowing for detailed and realistic representations. On the other hand, stroke-based techniques provide a more aesthetic approach to art creation, mimicking the way humans draw and create paintings. By combining these two techniques, artists can achieve a harmonious blend of high fidelity and artistic interpretation, bringing exciting possibilities to the realm of digital art. In this work, we propose a pipeline for combining state-of-the-art AI methodologies in order to generate a collection of multiple procedural paintings via a single text prompt. Specifically, we employ an integration of Quality-Diversity (QD) optimization and Stable Diffusion (SD) to generate diverse high-quality images, which then become inputs for the Compositional Neural Painter (CNP) model to render sequences of painting strokes artistically drawing the images.