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Generating images that fit a given text description using machine learninghas improved greatly with the release of technologies such as the CLIPimage-text encoder model; however, current methods lack artistic control of thestyle of image to be generated. We introduce StyleCLIPDraw which adds a styleloss to the CLIPDraw text-to-drawing synthesis model to allow artistic controlof the synthesized drawings in addition to control of the content via text.Whereas performing decoupled style transfer on a generated image only affectsthe texture, our proposed coupled approach is able to capture a style in bothtexture and shape, suggesting that the style of the drawing is coupled with thedrawing process itself. More results and our code are available athttps://github.com/pschaldenbrand/StyleCLIPDraw

StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Synthesis