layout-prompter 0.1.0

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LayoutPrompter: Awaken the Design Ability of Large Language Models (NeurIPS2023)

LayoutPrompter is a versatile method for graphic layout generation, capable of solving various conditional layout generation tasks (as illustrated on the left side) across a range of layout domains (as illustrated on the right side) without any model training or fine-tuning.

Installation

pip install git+https://github.com/creative-graphic-design/layout-prompter

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Results

We conduct experiments on three groups of layout generation tasks, including

• constraint-explicit layout generation
• content-aware layout generation
• text-to-layout

Below are the qualitative results.

Constraint-Explicit Layout Generation

Content-Aware Layout Generation

Text-to-Layout

Installation

1. Clone this repository

git clone https://github.com/microsoft/LayoutGeneration.git
cd LayoutGeneration/LayoutPrompter

2. Create a conda environment

conda create -n layoutprompter python=3.8
conda activate layoutprompter

3. Install PyTorch and other dependencies

conda install pytorch=1.13.1 torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt
pip install -e src/

Datasets

We use 4 datasets in this work, including RICO, PubLayNet, PosterLayout and WebUI. They can be downloaded from HuggingFace using the following commands:

git lfs install
git clone https://huggingface.co/datasets/KyleLin/LayoutPrompter

Move the contents to the dataset directory as follows:

dataset/
├── posterlayout
├── publaynet
├── rico
├── webui

Notebooks

We include three jupyter notebooks here, each corresponding to a type of layout generation task. They all consist of the following components:

• Configuration
• Process raw data
• Dynamic exemplar selection
• Input-output serialization
• Call GPT
• Parsing
• Layout ranking
• Visualization

Try it!

Citation

If you find this code useful for your research, please cite our paper:

@inproceedings{lin2023layoutprompter,
  title={LayoutPrompter: Awaken the Design Ability of Large Language Models},
  author={Lin, Jiawei and Guo, Jiaqi and Sun, Shizhao and Yang, Zijiang James and Lou, Jian-Guang and Zhang, Dongmei},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}