ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation

Paper | Project Page

Installation

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 on torch1.12.1+cu113 and torch2.0.0+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