  Current text-to-image generation models often struggle to follow textual
instructions, especially the ones requiring spatial reasoning. On the other
hand, Large Language Models (LLMs), such as GPT-4, have shown remarkable
precision in generating code snippets for sketching out text inputs
graphically, e.g., via TikZ. In this work, we introduce Control-GPT to guide
the diffusion-based text-to-image pipelines with programmatic sketches
generated by GPT-4, enhancing their abilities for instruction following.
Control-GPT works by querying GPT-4 to write TikZ code, and the generated
sketches are used as references alongside the text instructions for diffusion
models (e.g., ControlNet) to generate photo-realistic images. One major
challenge to training our pipeline is the lack of a dataset containing aligned
text, images, and sketches. We address the issue by converting instance masks
in existing datasets into polygons to mimic the sketches used at test time. As
a result, Control-GPT greatly boosts the controllability of image generation.
It establishes a new state-of-art on the spatial arrangement and object
positioning generation and enhances users' control of object positions, sizes,
etc., nearly doubling the accuracy of prior models. Our work, as a first
attempt, shows the potential for employing LLMs to enhance the performance in
computer vision tasks.

Computer Vision and Pattern Recognition;Artificial Intelligence