  We ask the question: to what extent can recent large-scale language and image
generation models blend visual concepts? Given an arbitrary object, we identify
a relevant object and generate a single-sentence description of the blend of
the two using a language model. We then generate a visual depiction of the
blend using a text-based image generation model. Quantitative and qualitative
evaluations demonstrate the superiority of language models over classical
methods for conceptual blending, and of recent large-scale image generation
models over prior models for the visual depiction.
