Installation With Docker#

Note: the first time building and running with Docker can take several minutes, please be patient.

Clone Modified Viser Library#

The interactive demo relies on a fork of Viser that implements a timeline interface and more. Clone it within the kimodo directory before building with Docker using:

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

Quick Install#

Before running Docker, make sure your Hugging Face token is available at ~/.cache/huggingface/token on the host, for example by running hf auth login once outside the container (see the Installation instructions).

The easiest way to build and immediately run the interactive demo webapp (with the text-encoder service) in one command is:

docker compose up -d --build

Step-by-Step Installation#

Alternatively, you can first build with:

docker compose build

This builds text-encoder and demo containers corresponding to the text encoding service and the interactive motion authoring webapp, respectively. Please see the quick start guide for more information on these.

Advanced Configuration of Dependencies

This repo uses:

  • docker_requirements.in: human-maintained, top-level dependencies

  • docker_requirements.txt: pinned lockfile (automatically generated)

Notes:

  • We keep a lockfile for reproducible Docker builds (so a rebuild next week pulls the same deps).

  • The lockfile intentionally omits torch/CUDA wheels because the Docker base image (nvcr.io/nvidia/pytorch) already provides a tested PyTorch build (avoids slow installs and CUDA mismatches).


After building, you will need to manually start the text-encoder service before doing any motion generation:

docker compose up text-encoder

Note, the first time running this command will take a long time as the Llama-based text encoder is downloaded.

Finally, to start the interactive demo:

docker compose up demo

For more information on using the Docker setup, see the Quick Start guide next.