LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
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Cited by 67 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Attaining a high degree of user controllability in visual generation often requires intricate, fine-grained inputs like layouts. However, such inputs impose a substantial burden on users when compared to simple text inputs. To address the issue, we study how Large Language Models (LLMs) can serve as visual planners by generating layouts from text conditions, and thus collaborate with visual generative models. We propose LayoutGPT, a method to compose in-context visual demonstrations in style sheet language to enhance the visual planning skills of LLMs. LayoutGPT can generate plausible layouts in multiple domains, ranging from 2D images to 3D indoor scenes. LayoutGPT also shows superior performance in converting challenging language concepts like numerical and spatial relations to layout arrangements for faithful text-to-image generation. When combined with a downstream image generation model, LayoutGPT outperforms text-to-image models/systems by 20-40% and achieves comparable performance as human users in designing visual layouts for numerical and spatial correctness. Lastly, LayoutGPT achieves comparable performance to supervised methods in 3D indoor scene synthesis, demonstrating its effectiveness and potential in multiple visual domains.
Key facts. Cited by 347 papers in the map (data through 14 Oct 2026). Builds on 54 of its 65 references that are in the map. First posted on arXiv 24 May 2023.
Builds on
54 of 65 references.
References · 54
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- 2019LayoutGAN: Generating Graphic Layouts with Wireframe Discriminatorscited by 131
- 2020LayoutTransformer: Layout Generation and Completion with Self-attentioncited by 123
- 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
- 2018Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Modelscited by 106
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- 2021BLT: Bidirectional Layout Transformer for Controllable Layout Generationcited by 69
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- 2020End-to-End Optimization of Scene Layoutcited by 52
50 most cited of 54 All 54 (JSON)
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Citing papers per year: 2023: 31; 2024: 87; 2025: 119; 2026: 110.
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