Noam Aigerman, Thibault Groueix, SIGGRAPH 2024

[paper] [website]

Torch need to be > 2.0 for the sparse solver. We use CUDA118 and python3.8 but other might work.

git clone https://github.com/ThibaultGROUEIX/GenerativeEscherPatterns.git
cd GenerativeEscherPatterns

Run ./install.sh or follow these steps :

conda create -y -n  escher python=3.8
conda activate escher

then

conda install -y suitesparse
conda install -y -c conda-forge igl ffmpeg

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
pip install -e .

Install the CUDA TOOLKIT 11.8.

export CUDA_HOME=/usr/local/cuda
sudo chmod -R 777 /usr/local/cuda 
git clone https://github.com/NVlabs/nvdiffrast.git
cd nvdiffrast
sudo apt-get update && sudo apt-get install -y --no-install-recommends \
    pkg-config \
    libglvnd0 \
    libgl1 \
    libglx0 \
    libegl1 \
    libgles2 \
    libglvnd-dev \
    libgl1-mesa-dev \
    libegl1-mesa-dev \
    libgles2-mesa-dev \
    cmake \
    ninja-build \
    curl
export PYTHONDONTWRITEBYTECODE=1
export NVIDIA_VISIBLE_DEVICES=all
export NVIDIA_DRIVER_CAPABILITIES=compute,utility,graphics
export PYTHONUNBUFFERED=1
export LD_LIBRARY_PATH=/usr/lib64:$LD_LIBRARY_PATH
export PYOPENGL_PLATFORM=egl
pip install --upgrade pip
# python setup.py install : NotADirectoryError
pip install .

Follow these additional steps to install DeepFloyd

pip install bitsandbytes
pip install sentencepiece

Remember to always activate the conda environment : conda activate escher We use OmegaConf to load arguments. Base arguments are defined in configs/base.yaml. They can be overwritten by the command line :

python -m escher.main TILING_TYPE="OrbifoldIII" PROMPT="A beautiful illustration of a flower, a masterpiece" OUTPUT_DIR="./output"

• TILING_TYPE can be either of : Cylinder, KleinBottle, MobiusStrip, OrbifoldI, OrbifoldIHybrid, OrbifoldII,OrbifoldIIHybrid, OrbifoldIII, OrbifoldIV,OrbifoldIVHybrid, PinnedBoundary, ProjectivePlane, ReflectSquare, RightAngleHybrid, Torus

If you want to reuse a specific config file from a prior experiment: python -m escher.main CONF_FILE=/path/to/config/config.yaml

Check out all the arguments in configs/base.yaml.

By default, the code will generate tilings at different resolution, as well as a video of the camera moving over the liting. You can achieve the same result from a checkpoint (all logs from an experiment are stored in a .pkl )

python -m escher.rendering.render_tiling_from_pkl --path path/to/pkl --make_infinite_video --grid_sizes 10 --make_video --num_labels 1

This will produce a video of the camera moving over the tiling, as well as a video where tiles appear one by one, as well as static images of the tiling at different resolutions.

• Deepfloyd is twice faster than SD (6it/sec versus 3it/sec)
• SD with latent opt is even faster (10it / seconds)
• High guidance is critical
• Larger batch-sizes help

• Distorsion is probably bad for optimization. It would be great to continuously remesh and update the constraints accordingly
• Post-process the texture with generative fill to create texture variations with the same shape
• Autoconvert the output to Adobe Illustrator file format