Awesome

diffvg

Differentiable Rasterizer for Vector Graphics https://people.csail.mit.edu/tzumao/diffvg

diffvg is a differentiable rasterizer for 2D vector graphics. See the webpage for more info.

Install

git submodule update --init --recursive
conda install -y pytorch torchvision -c pytorch
conda install -y numpy
conda install -y scikit-image
conda install -y -c anaconda cmake
conda install -y -c conda-forge ffmpeg
pip install svgwrite
pip install svgpathtools
pip install cssutils
pip install numba
pip install torch-tools
pip install visdom
python setup.py install

Install using poetry

prerequisite

install python 3.7, poetry and ffmpeg

# install poetry (mac, linux)
curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -

# install ffmpeg

(macos)
brew install ffmpeg

(linux)
sudo apt install ffmpeg

or use conda
conda install -y -c conda-forge ffmpeg

Install python packages

# install all python dependencies
poetry install

# install pydiffvg
poetry run python setup.py install

Now to run the apps, just add poetry run before each of the commands below, e.g.

poetry run python single_circle.py

Building in debug mode

python setup.py build --debug install

Run

cd apps

Optimizing a single circle to a target.

python single_circle.py

Finite difference comparison.

finite_difference_comp.py [-h] [--size_scale SIZE_SCALE]
                               [--clamping_factor CLAMPING_FACTOR]
                               [--use_prefiltering USE_PREFILTERING]
                               svg_file

e.g.,

python finite_difference_comp.py imgs/tiger.svg

Interactive editor

python svg_brush.py

Painterly rendering

painterly_rendering.py [-h] [--num_paths NUM_PATHS]
                       [--max_width MAX_WIDTH] [--use_lpips_loss]
                       [--num_iter NUM_ITER] [--use_blob]
                       target

e.g.,

python painterly_rendering.py imgs/fallingwater.jpg --num_paths 2048 --max_width 4.0 --use_lpips_loss

Image vectorization

python refine_svg.py [-h] [--use_lpips_loss] [--num_iter NUM_ITER] svg target

e.g.,

python refine_svg.py imgs/flower.svg imgs/flower.jpg

Seam carving

python seam_carving.py [-h] [--svg SVG] [--optim_steps OPTIM_STEPS]

e.g.,

python seam_carving.py imgs/hokusai.svg

Vector variational autoencoder & vector GAN:

For the GAN models, see apps/generative_models/train_gan.py. Generate samples from a pretrained using apps/generative_models/eval_gan.py.

For the VAE models, see apps/generative_models/mnist_vae.py.

If you use diffvg in your academic work, please cite

@article{Li:2020:DVG,
    title = {Differentiable Vector Graphics Rasterization for Editing and Learning},
    author = {Li, Tzu-Mao and Luk\'{a}\v{c}, Michal and Gharbi Micha\"{e}l and Jonathan Ragan-Kelley},
    journal = {ACM Trans. Graph. (Proc. SIGGRAPH Asia)},
    volume = {39},
    number = {6},
    pages = {193:1--193:15},
    year = {2020}
}