  A word-as-image is a semantic typography technique where a word illustration
presents a visualization of the meaning of the word, while also preserving its
readability. We present a method to create word-as-image illustrations
automatically. This task is highly challenging as it requires semantic
understanding of the word and a creative idea of where and how to depict these
semantics in a visually pleasing and legible manner. We rely on the remarkable
ability of recent large pretrained language-vision models to distill textual
concepts visually. We target simple, concise, black-and-white designs that
convey the semantics clearly. We deliberately do not change the color or
texture of the letters and do not use embellishments. Our method optimizes the
outline of each letter to convey the desired concept, guided by a pretrained
Stable Diffusion model. We incorporate additional loss terms to ensure the
legibility of the text and the preservation of the style of the font. We show
high quality and engaging results on numerous examples and compare to
alternative techniques.
