Paper: Towards Diverse and Consistent Typography Generation

Wataru Shimoda¹, Daichi Haraguchi², Seiichi Uchida², Kota Yamaguchi^1
1CyberAgent.Inc, ² Kyushu University
Accepted to WACV2024. [Publication] [Arxiv] [Project-page]

Introduction

This repository contains the codes for "Towards Diverse and Consistent Typography Generation".

Requirements

We check the reproducibility under the environment.

• Ubuntu (>=20.04)
• Python3 (>=3.8, <3.11)

Install

Clone this repository and navigate to the folder

git clone https://github.com/CyberAgentAILab/tdc-typography-generation.git
cd tdc-typography-generation

We manage the dependencies of python libraries by pyproject.toml.
Please install the dependencies via pip or poetry using pyproject.toml.

If the version of setuptools is setuptools >=61.0.0, the following command installs the dependencies via pip:

pip install .

or
We recommend installing the dependencies using Poetry (see official docs):

poetry install

Note that we omit the head of commands poetry run in the after guidance for simplification.

Dataset

Our model is trained and tested on Crello dataset, and this dataset is open on Hugging Face.
We can download this dataset through the Hugging Face Dataset API, but this dataset does not contain high-resolution background images.

We provide background images via Google Drive (link, 3.6GB).
Please download the background images and locate them to data/generate_bg_png/.

tar zxvf generate_bg_png.tar.gz 
mv generate_bg_png data/
rm generate_bg_png.tar.gz

We also provide font files for rendering designs and computing appropriate text sizes via Google Drive (link, 43MB).
Please download the font files and locate them to data/font/.

tar zxvf font.tar.gz 
mv font data/
rm font.tar.gz

Usage

We prepare scripts for experiments as the following.

Preprocessing

We recommend adding features to the dataset in advance, it makes training and testing faster.
We provide a script for preprocessing:

python -m typography_generation map_features --datadir data

This script extends the dataset via map function.
The extended dataset is saved in data/map_featreus, and --use_extended_dataset option manages the use of the extended dataset.

Training

The following command trains a model, it takes a half day with the preprocessed dataset and a NVIDIA T4 machine.
We handle the detail of training via configuration files in data/config/*.yaml.
The basic configurations are in src/typography_generation/config/*.py.

python -m typography_generation train_eval \
          --configname bart \
          --jobdir ${OUTPUT_DIR} \
          --datadir data \
          --use_extended_dataset \
          --gpu \

The outputs are in ${OUTPUT_DIR}.

Sampling

The following command samples typographic attributes.
This command requires --weight option, which is a path for loading weights of a trained model.
A weight file obtained by the avobe training command is in ${OUTPUT_DIR}/weight.pth.
Please assign a path of a weight file to ${WEIGHT_FILE}.

python -m typography_generation structure_preserved_sample \
          --configname bart \
          --jobdir ${OUTPUT_DIR} \
          --datadir data \
          --weight=${WEIGHT_FILE} \
          --use_extended_dataset \
          --gpu \

Visualization

We provides notebooks for showing results.

• notebooks/score.ipnyb shows scores of the saved results in ${OUTPUT_DIR}.
• notebooks/vis.ipnyb shows generated graphic designs in ${OUTPUT_DIR}.

Reference

@misc{shimoda_2024_tdctg,
    author    = {Shimoda, Wataru and Haraguchi, Daichi and Uchida, Seiichi and Yamaguchi, Kota},
    title     = {Towards Diverse and Consistent Typography Generation},
    publisher = {Winter Conference on Applications of Computer Vision (WACV)},
    year      = {2024},
}

Contact

This repository is maintained by Wataru shimoda(wataru_shimoda[at]cyberagent.co.jp).