DeepVecFont v2

This is the official pytorch implementation of:

DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality. CVPR 2023.

Paper: Arxiv Video: Youtube

Installation

conda create -n dvf_v2 python=3.9
conda activate dvf_v2
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
pip install tensorboardX einops timm scikit-image cairosvg

Dataset

The dataset used can be found in Onedrive or Baiduyun (Password: pmr2). Put the data directory in the root path. This directory contains:

(1) char_set: the character set used for Chinese and English. (2) font_ttfs: the TTF/OTF files of fonts. (3) font_sfds: the sfd files extracted by FontForge. (4) vecfont_dataset: the processed files ready for training/testing.

Note: The train/test split in this released dataset is slightly different to what was used in our paper. The original train/test split in our paper can be found in v1_train_font_ids.txt and v1_test_font_ids.txt. These ids are corresponding to the ttf/off files in data/font_ttfs.

Trained Checkpoints

Our trained checkpoints (English and Chinese) can be found in Onedrive or Baiduyun (Password: pih5). We provided 3 checkpoints on epochs 500, 550, 600. If you use our trained checkpoints, you can directly go to the Testing section.

Train

English Dataset:

CUDA_VISIBLE_DEVICES=0 python train.py --mode train --name_exp dvf_base_exp_eng --model_name main_model --batch_size 32 --max_seq_len 51 --lang eng --ref_nshot 4

Chinese Dataset:

CUDA_VISIBLE_DEVICES=0 python train.py --mode train --name_exp dvf_base_exp_chn --model_name main_model --batch_size 32 --max_seq_len 71 --lang chn --ref_nshot 8

Testing (Few-shot Generation)

English:

CUDA_VISIBLE_DEVICES=0 python test_few_shot.py --mode test --name_exp dvf_base_exp_eng --model_name main_model --batch_size 1 --n_samples 20 --name_ckpt {name_ckpt}

Chinese:

CUDA_VISIBLE_DEVICES=0 python test_few_shot.py --mode test --name_exp dvf_base_exp_chn --language chn --max_seq_len 71 --model_name main_model --batch_size 1 --n_samples 50 --model_name main_model --batch_size 1 --n_samples 50 --ref_nshot 8 --ref_char_ids 0,1,2,3,26,27,28,29 --name_ckpt {name_ckpt}

Note that you can modify ref_char_ids to define which characters are used as references. The synthesized candidates are in ./experiments/{exp_name}/results/{font_id}/svgs_single, and the selected results (by IOU) is in ./experiments/{exp_name}/results/{font_id}/svgs_merge.

In the testing phase, we run the model for n_samples times to generate multiple candidates, and in each time a random noise is injected (see code). Currently we use IOU as the metric to pick the candidate, which sometimes cannot find the best result. You can manually check all the candidates.

Testing (Font interpolation and Random Generation)

Will be updated soon ...

Customize Dataset

Taking --language 'eng' (English) as an example (it also could be 'chn' (Chinese)):

Install Fontforge in non-Conda env:

conda deactivate
apt install python3-fontforge

Step1: Convert TTF to Sdfs

cd data_utils
python3 convert_ttf_to_sfd.py --split train --language eng
python3 convert_ttf_to_sfd.py --split test --language eng

By now you can re-enter the conda env:

conda activate dvf_v2

Step2: Render glyph images:

python write_glyph_imgs.py --split train --language eng
python write_glyph_imgs.py --split test --language eng

Step3: Filter and Package Them into Directories:

Modify MAX_SEQ_LEN (the maximum sequence length) in svg_utils.py. We set MAX_SEQ_LEN to 50 for English and 70 for Chinese. You can also change the number according to your need.

python write_data_to_dirs.py --split train --language eng
python write_data_to_dirs.py --split test --language eng

Step3.1: Data Augmentation (ONLY for Chinese when training)

python augment.py --split train --language chn --max_len 71
python augment.py --split test --language chn --max_len 71

Step4: Relaxation Processing and Calculating Auxiliary Bezier Points:

python relax_rep.py --split train --language eng --max_len 51
python relax_rep.py --split test --language eng --max_len 51

when language is chn, set max_len to 71.

Font Copyrights

Please note that all the Chinese fonts are collected from Founder, and the fonts CANNOT be used for any commercial uses without permission from Founder.

Acknowledgment

• SVG-VAE
• DeepVecFont

Citation

If you use this code or find our work is helpful, please consider citing our work:

@inproceedings{wang2023deepvecfont,
  title={DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality},
  author={Wang, Yuqing and Wang, Yizhi and Yu, Longhui and Zhu, Yuesheng and Lian, Zhouhui},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={18320--18328},
  year={2023}
}