Content-Edited Layout Generation for Graphic Design

Abstract

In this paper, we formulate a novel form of conditional layout generation for graphic design, termed content-edited layout generation(CELG). CELG extends conventional content-aware layout generation (CLG), which generates a foreground layout for a fixed background image. The key distinction is that CELG treats the input image as an editable visual asset and, conditioned on a textual design intention and a foreground requirement that may be empty, produces an adapted background together with its layout. To study this task, we introduce Search-Compare-Edit(SCE), a data construction framework that supplies the paired supervision missing from existing design datasets. Starting from professional design canvases and their layouts, SCE synthesizes plausible pre-edit source images and corresponding editing instructions, yielding InstructEdit, a dataset of aligned source images, target canvases, instructions, and layouts. Based on the generated data, we train a two-flow multimodal flow matching model that couples a pretrained image editor with a layout flow transformer, enabling background editing and layout generation simultaneously within a unified framework that more closely reflects real-world design workflows. Our experiments systematically demonstrate the value of CELG as a distinct task and the performance advantages of the proposed model.