Installation#

Note: This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

Note: This repo was developed and primarily tested on Linux

There are two ways to install Kimodo: (1) as a package, or (2) download the source code and install. Both require setting up a Hugging Face token to use the text encoder at generation time.

Set Up Hugging Face Token#

The Kimodo text encoder relies on the gated meta-llama/Meta-Llama-3-8B-Instruct model, which requires:

  • Your HF account has been granted access to the model page.

  • You provide a HF token for runtime

After receiving access to the Llama repo, please create an access token here. Then use it to log in on your command line:

hf auth login

or alternatively, paste the token in this file ~/.cache/huggingface/token. If you don’t have hf installed, you will first need to run pip install --upgrade huggingface_hub.

Kimodo Install Option 1: Package Install#

The easiest way to get started is simply installing Kimodo as a package without needing to clone the codebase. This will allow you to generate motions and run the demo as a black box.

We suggest creating a new Python environment for the install, for example with venv or conda:

conda create -n kimodo python=3.10
conda activate kimodo

To ensure you have a version of PyTorch that is compatible with your system and CUDA version, it is recommended to manually install the best version of PyTorch for you before installing Kimodo. Anything over PyTorch 2.0 is sufficient. We strongly suggest using a GPU-capable version of PyTorch to generate motions in a reasonable amount of time.

Installing the base Kimodo package will allow you generate motions with the command line:

pip install git+https://github.com/nv-tlabs/kimodo.git

If you want to be able to run the interactive demo as well, use this command which installs additional dependencies:

pip install "kimodo[all] @ git+https://github.com/nv-tlabs/kimodo.git"

Now should be ready to use Kimodo. Check out the quick start guide to see how to generate motions.

If you experience issues with package or system compatibility using the above install strategy, we recommend downloading the codebase and using the Docker install detailed below.

Kimodo Install Option 2: Source Code Install#

If you plan to build on Kimodo or dig into the codebase, you’ll want to clone and install the repo.

Clone Kimodo Repository#

git clone https://github.com/nv-tlabs/kimodo.git
cd kimodo

Choose Your Installation Route#

Kimodo can be installed by building and running through a virtual environment (e.g., conda) or within a Docker container.