  Generating images that fit a given text description using machine learning
has improved greatly with the release of technologies such as the CLIP
image-text encoder model; however, current methods lack artistic control of the
style of image to be generated. We present an approach for generating styled
drawings for a given text description where a user can specify a desired
drawing style using a sample image. Inspired by a theory in art that style and
content are generally inseparable during the creative process, we propose a
coupled approach, known here as StyleCLIPDraw, whereby the drawing is generated
by optimizing for style and content simultaneously throughout the process as
opposed to applying style transfer after creating content in a sequence. Based
on human evaluation, the styles of images generated by StyleCLIPDraw are
strongly preferred to those by the sequential approach. Although the quality of
content generation degrades for certain styles, overall considering both
content \textit{and} style, StyleCLIPDraw is found far more preferred,
indicating the importance of style, look, and feel of machine generated images
to people as well as indicating that style is coupled in the drawing process
itself. Our code (https://github.com/pschaldenbrand/StyleCLIPDraw), a
demonstration (https://replicate.com/pschaldenbrand/style-clip-draw), and style
evaluation data
(https://www.kaggle.com/pittsburghskeet/drawings-with-style-evaluation-styleclipdraw)
are publicly available.
