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See installation.md for additional information, including installation via Docker.
- You must have an NVIDIA graphics card with at least 6GB VRAM and have CUDA installed.
- Install
Python >= 3.8
. - (Optional, Recommended) Create a virtual environment:
python3 -m virtualenv venv
. venv/bin/activate
# Newer pip versions, e.g. pip-23.x, can be much faster than old versions, e.g. pip-20.x.
# For instance, it caches the wheels of git packages to avoid unnecessarily rebuilding them later.
python3 -m pip install --upgrade pip
- Install
PyTorch >= 1.12
. We have tested ontorch1.12.1+cu113
andtorch2.0.0+cu118
, but other versions should also work fine.
# mostly prefer torch 2.2
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
# torch1.12.1+cu113
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
# or torch2.0.0+cu118
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
- (Optional, Recommended) Install ninja to speed up the compilation of CUDA extensions:
pip install ninja
- Install major dependencies:
pip install -r requirements.txt
- Install xformer, assume the CUDA version is cu118.
pip3 install -U xformers --index-url https://download.pytorch.org/whl/cu118
- Install iNGP dependencies (according to your default CUDA):
export PATH="/usr/local/cuda/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch