Vidar: Embodied Video Diffusion Model for Generalist Bimanual Manipulation

📝Paper | 🌍Project Page | Pre-trained HunyuanVideo Checkpoint

Also refer to here for the latest version with the Wan 2.2 model.

Introduction

Here is the codebase for Vidar: Embodied Video Diffusion Model for Generalist Bimanual Manipulation.

Below you will find setup instructions and basic usage guidance for the code within the vidar folder.

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Environment Setup

Our code has been tested with CUDA 12.4.
If you encounter errors, please also refer to known issues in HunyuanVideo-I2V.

1. Create a Conda Environment

conda create -n vidar python==3.11.9

2. Activate the Environment

conda activate vidar

3. Install PyTorch and CUDA Dependencies

For CUDA 12.4:

conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia

(Optional) Install the full CUDA toolkit:

conda install -c nvidia cuda-toolkit=12.4

4. Install Python Requirements

python -m pip install -r requirements.txt

5. Install Flash Attention v2 for Acceleration

Requires CUDA 11.8 or newer:

python -m pip install ninja
python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3

6. Install xDiT for Parallel Inference

We recommend using PyTorch 2.4.0 and flash-attn 2.6.3:

python -m pip install xfuser==0.4.0

Troubleshooting: Floating Point Exceptions

If you encounter floating point exceptions (core dump) on certain GPUs, try:

pip install nvidia-cublas-cu12==12.4.5.8
export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/nvidia/cublas/lib/

Ensure you have CUDA 12.4, CUBLAS >= 12.4.5.8, and CUDNN >= 9.00 installed.

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Video Diffusion Model

Data Preparation

Prepare your metadata as follows:

{
    "video_path": "{VIDEO_PATH}",
    "raw_caption": {
        "long caption": "{PROMPT}"
    }
}

You also need to encode the videos in your dataset before training:

vm/hyvae_extract/start.sh

For more details, refer to Hunyuan VAE extract.

Training

Edit scripts/vm/train.sh to match your platform settings, then run:

scripts/vm/train.sh

Inference

To test your trained model:

scripts/vm/sample.sh

This generates a video based on the first frame and your instruction.

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Masked Inverse Dynamic Model

Data Preparation

• Training data (default folder): assets/train
• Testing data (default folder): assets/test
• Files are organized as task_name/episode_idx.mp4 (a multi-view video) and task_name/episode_idx_qpos.pt (a 2D tensor with corresponding actions).

Training

Edit scripts/idm/train.sh as needed, then run:

scripts/idm/train.sh

Inference

To evaluate your model:

scripts/idm/eval.sh