Installation

1. Create and activate conda environment

$ conda create -n cleandiffuser python==3.9
$ conda activate cleandiffuser

2. Install PyTorch

Install torch>1.0.0,<2.3.0 that is compatible with your CUDA version. For example, PyTorch 2.2.2 with CUDA 12.1:

$ conda install pytorch==2.2.2 torchvision==0.17.2 pytorch-cuda=12.1 -c pytorch -c nvidia

3. Install CleanDiffuser from source

$ git clone https://github.com/CleanDiffuserTeam/CleanDiffuser.git
$ cd CleanDiffuser
$ pip install -e .

4. Additional installations (Optional)

For users who need to run pipelines and reproduce the results of the paper, they will need to install RL simulators.

First, install the dependencies related to the mujoco-py environment. For more details, see https://github.com/openai/mujoco-py#install-mujoco

$ sudo apt-get install libosmesa6-dev libgl1-mesa-glx libglfw3 libglew-dev patchelf

# Install D4RL from source (recommended)
$ cd <PATH_TO_D4RL_INSTALL_DIR>
$ git clone https://github.com/Farama-Foundation/D4RL.git
$ cd D4RL
$ pip install -e .
# Install Robomimic from source (recommended)
$ cd <PATH_TO_ROBOMIMIC_INSTALL_DIR>
$ git clone https://github.com/ARISE-Initiative/robomimic.git
$ cd robomimic
$ pip install -e .
$ cd <PATH_TO_ROBOSUITE_INSTALL_DIR>
$ git clone https://github.com/ARISE-Initiative/robosuite.git
$ cd robosuite
$ pip install -e .

Note: The latest version of dependencies running the robomimic image still has compatibility issues, and we are actively working on a fix. The temporary solution is to downgrade the gym version to 0.21.0: pip install setuptools==65.5.0 pip==21, pip install gym==0.21.0

Try it now!

# Tutorial
$ python tutorials/1_a_minimal_DBC_implementation.py
# Reinforcement Learning
$ python pipelines/diffuser_d4rl_mujoco.py
# Imitation Learning (need to download the dataset, see below)
$ python pipelines/dp_pusht.py

If you need to reproduce Imitation Learning environments (pusht, kitchen, robomimic), you need to download the datasets additionally. We recommend downloading the corresponding compressed files from Datasets. We provide the default dataset path as dev/:

dev/
.
├── kitchen
├── pusht
├── robomimic