# Attribute-conditioned Layout GAN for Automatic Graphic Design

arXiv: https://arxiv.org/abs/2009.05284 · HTML: https://mlsanity.com/paper/2009.05284/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 30 of 68)

- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [CanvasVAE: Learning to Generate Vector Graphic Documents](https://mlsanity.com/paper/2108.01249/) (2021)
- [Constrained Graphic Layout Generation via Latent Optimization](https://mlsanity.com/paper/2108.00871/) (2021)
- [DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception](https://mlsanity.com/paper/2410.12628/) (2024)
- [BLT: Bidirectional Layout Transformer for Controllable Layout Generation](https://mlsanity.com/paper/2112.05112/) (2021)
- [PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout](https://mlsanity.com/paper/2303.15937/) (2023)
- [Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs](https://mlsanity.com/paper/2205.00303/) (2022)
- [LayoutDM: Transformer-based Diffusion Model for Layout Generation](https://mlsanity.com/paper/2305.02567/) (2023)
- [LayoutPrompter: Awaken the Design Ability of Large Language Models](https://mlsanity.com/paper/2311.06495/) (2023)
- [LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction](https://mlsanity.com/paper/2208.08037/) (2022)
- [Unifying Layout Generation with a Decoupled Diffusion Model](https://mlsanity.com/paper/2303.05049/) (2023)
- [LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models](https://mlsanity.com/paper/2303.11589/) (2023)
- [Graphic Design with Large Multimodal Model](https://mlsanity.com/paper/2404.14368/) (2024)
- [Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation](https://mlsanity.com/paper/2311.13602/) (2023)
- [PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generation](https://mlsanity.com/paper/2404.00995/) (2024)
- [Geometry Aligned Variational Transformer for Image-conditioned Layout Generation](https://mlsanity.com/paper/2209.00852/) (2022)
- [Towards Flexible Multi-modal Document Models](https://mlsanity.com/paper/2303.18248/) (2023)
- [UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback](https://mlsanity.com/paper/2406.07739/) (2024)
- [Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints](https://mlsanity.com/paper/2402.04754/) (2024)
- [PLay: Parametrically Conditioned Layout Generation using Latent Diffusion](https://mlsanity.com/paper/2301.11529/) (2023)
- [LayoutFlow: Flow Matching for Layout Generation](https://mlsanity.com/paper/2403.18187/) (2024)
- [Aesthetic Text Logo Synthesis via Content-aware Layout Inferring](https://mlsanity.com/paper/2204.02701/) (2022)
- [Desigen: A Pipeline for Controllable Design Template Generation](https://mlsanity.com/paper/2403.09093/) (2024)
- [GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networks](https://mlsanity.com/paper/2101.09978/) (2021)
- [PosterO: Structuring Layout Trees to Enable Language Models in Generalized Content-Aware Layout Generation](https://mlsanity.com/paper/2505.07843/) (2025)
- [LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer](https://mlsanity.com/paper/2212.09877/) (2022)
- [A Parse-Then-Place Approach for Generating Graphic Layouts from Textual Descriptions](https://mlsanity.com/paper/2308.12700/) (2023)
- [VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correction](https://mlsanity.com/paper/2412.04237/) (2024)
- [SciPostLayout: A Dataset for Layout Analysis and Layout Generation of Scientific Posters](https://mlsanity.com/paper/2407.19787/) (2024)
- [Can GPTs Evaluate Graphic Design Based on Design Principles?](https://mlsanity.com/paper/2410.08885/) (2024)

## Abstract (arXiv)

Modeling layout is an important first step for graphic design. Recently, methods for generating graphic layouts have progressed, particularly with Generative Adversarial Networks (GANs). However, the problem of specifying the locations and sizes of design elements usually involves constraints with respect to element attributes, such as area, aspect ratio and reading-order. Automating attribute conditional graphic layouts remains a complex and unsolved problem. In this paper, we introduce Attribute-conditioned Layout GAN to incorporate the attributes of design elements for graphic layout generation by forcing both the generator and the discriminator to meet attribute conditions. Due to the complexity of graphic designs, we further propose an element dropout method to make the discriminator look at partial lists of elements and learn their local patterns. In addition, we introduce various loss designs following different design principles for layout optimization. We demonstrate that the proposed method can synthesize graphic layouts conditioned on different element attributes. It can also adjust well-designed layouts to new sizes while retaining elements' original reading-orders. The effectiveness of our method is validated through a user study.