  Graphic layout generation, a growing research field, plays a significant role
in user engagement and information perception. Existing methods primarily treat
layout generation as a numerical optimization task, focusing on quantitative
aspects while overlooking the semantic information of layout, such as the
relationship between each layout element. In this paper, we propose LayoutNUWA,
the first model that treats layout generation as a code generation task to
enhance semantic information and harness the hidden layout expertise of large
language models~(LLMs). More concretely, we develop a Code Instruct Tuning
(CIT) approach comprising three interconnected modules: 1) the Code
Initialization (CI) module quantifies the numerical conditions and initializes
them as HTML code with strategically placed masks; 2) the Code Completion (CC)
module employs the formatting knowledge of LLMs to fill in the masked portions
within the HTML code; 3) the Code Rendering (CR) module transforms the
completed code into the final layout output, ensuring a highly interpretable
and transparent layout generation procedure that directly maps code to a
visualized layout. We attain significant state-of-the-art performance (even
over 50\% improvements) on multiple datasets, showcasing the strong
capabilities of LayoutNUWA. Our code is available at
https://github.com/ProjectNUWA/LayoutNUWA.
