  Two distinct tasks - generating photorealistic pictures from given text
prompts and transferring the style of a painting to a real image to make it
appear as though it were done by an artist, have been addressed many times, and
several approaches have been proposed to accomplish them. However, the
intersection of these two, i.e., generating paintings from a given caption, is
a relatively unexplored area with little data available. In this paper, we have
explored two distinct strategies and have integrated them together. First
strategy is to generate photorealistic images and then apply style transfer and
the second strategy is to train an image generation model on real images with
captions and then fine-tune it on captioned paintings later. These two models
are evaluated using different metrics as well as a user study is conducted to
get human feedback on the produced results.
