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Equidiff
Lillianwei/Equidiff
Equidiff is a machine learning model from Lillianwei. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
- foldername: the name of the folder - filename: the name of your file
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Updated Mar 9, 2025
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At a glance
- Access
- Public
- Created
- Mar 9, 2025
- Updated
- Mar 9, 2025
- SHA
- 1501ed77
Model card
From the Hugging Face model README
Equidiff
- folder_name: the name of the folder
- file_name: the name of your file
Prepare data
Use mimicgen to generate data.
Use EmbodiedBM/equidiff/combinehdf5.py to combine data from multiple .hdf5 files if needed.
Put hdf5 data at EmbodiedBM/equidiff/data/robomimic/datasets with the format [folder_name]/[file_name].hdf5
Convert data
python equi_diffpo/scripts/robomimic_dataset_conversion.py -i data/robomimic/datasets/square_d2_test/demo.hdf5 -o data/robomimic/datasets/square_d2_test/demo_abs.hdf5 -n 12
Train
Use another CUDA device if 7 is currently in use.
CUDA_VISIBLE_DEVICES=5 MUJOCO_GL=osmesa PYOPENGL_PLATFORM=osmesa HYDRA_FULL_ERROR=1 python train.py --config-name=train_sq2_5000 folder_name=square_d2_5000 file_name=demo n_demo=5000
If you use another task than square_d2, you should change the task_name config by adding task_name=[task_name]
Test
Change the ckpt_path to the trained policy's weight's path in EmbodiedBM/equidiff/equi_diffpo/config/test_sq2.yaml
If you use another task than square_d2, you should change the dataset config in test_sq2.yaml and download the corresponding dataset from Huggingface.
python test.py
- Created
- Mar 9, 2025
- Updated
- Mar 9, 2025