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Visual metaphors are powerful rhetorical devices used to persuade orcommunicate creative ideas through images. Similar to linguistic metaphors,they convey meaning implicitly through symbolism and juxtaposition of thesymbols. We propose a new task of generating visual metaphors from linguisticmetaphors. This is a challenging task for diffusion-based text-to-image models,such as DALL$\cdot$E 2, since it requires the ability to model implicit meaningand compositionality. We propose to solve the task through the collaborationbetween Large Language Models (LLMs) and Diffusion Models: Instruct GPT-3(davinci-002) with Chain-of-Thought prompting generates text that represents avisual elaboration of the linguistic metaphor containing the implicit meaningand relevant objects, which is then used as input to the diffusion-basedtext-to-image models.Using a human-AI collaboration framework, where humansinteract both with the LLM and the top-performing diffusion model, we create ahigh-quality dataset containing 6,476 visual metaphors for 1,540 linguisticmetaphors and their associated visual elaborations. Evaluation by professionalillustrators shows the promise of LLM-Diffusion Model collaboration for thistask . To evaluate the utility of our Human-AI collaboration framework and thequality of our dataset, we perform both an intrinsic human-based evaluation andan extrinsic evaluation using visual entailment as a downstream task.

I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors