We introduce the new task of generating Illustrated Instructions, i.e.,
visual instructions customized to a user's needs. We identify desiderata unique
to this task, and formalize it through a suite of automatic and human
evaluation metrics, designed to measure the validity, consistency, and efficacy
of the generations. We combine the power of large language models (LLMs)
together with strong text-to-image generation diffusion models to propose a
simple approach called StackedDiffusion, which generates such illustrated
instructions given text as input. The resulting model strongly outperforms
baseline approaches and state-of-the-art multimodal LLMs; and in 30% of cases,
users even prefer it to human-generated articles. Most notably, it enables
various new and exciting applications far beyond what static articles on the
web can provide, such as personalized instructions complete with intermediate
steps and pictures in response to a user's individual situation.

生成用户定制的视觉说明书的新任务，通过结合大型语言模型和文本到图像生成扩散模型实现了一种简单方法（StackedDiffusion），在有效性、一致性和效果的度量上远远超过基线方法和最先进的多模态语言模型。