  Creating an image reflecting the content of a long text is a complex process
that requires a sense of creativity. For example, creating a book cover or a
movie poster based on their summary or a food image based on its recipe. In
this paper we present the new task of generating images from long text that
does not describe the visual content of the image directly. For this, we build
a system for generating high-resolution 256 $\times$ 256 images of food
conditioned on their recipes. The relation between the recipe text (without its
title) to the visual content of the image is vague, and the textual structure
of recipes is complex, consisting of two sections (ingredients and
instructions) both containing multiple sentences.
  We used the recipe1M dataset to train and evaluate our model that is based on
a the StackGAN-v2 architecture.
