  This paper creates a novel method of deep neural style transfer by generating
style images from freeform user text input. The language model and style
transfer model form a seamless pipeline that can create output images with
similar losses and improved quality when compared to baseline style transfer
methods. The language model returns a closely matching image given a style text
and description input, which is then passed to the style transfer model with an
input content image to create a final output. A proof-of-concept tool is also
developed to integrate the models and demonstrate the effectiveness of deep
image style transfer from freeform text.
