[NeurIPS'20] Code for the Paper Compositional Visual Generation and Inference with Energy Based Models
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Code for Compositional Visual Generation with Energy Based Models A pytorch codebase for compositionality can be found here.

Install Prerequisites

Please install the required python packages by running the command below:

pip install -r requirements.txt

Download Datasets

We run experiments on Mujoco Scenes and CelebA dataset. To generate data used in the Mujoco Scenes dataset, look in the image_comb directory (you will need to appropriately modify the path) and run the corresponding files inside. For example to generate the continual learning dataset, you can use the command:

python image_comb/cube_continual.py

Feel free to reach out to us for pre-generated Mujoco Scenes Datasets

You can download the CelebA dataset here


Models are trained using the following command:

python train.py --dataset=<dataset> --exp=<exp_name> --cclass --step_lr=100.0 --swish_act --num_steps=60 --num_gpus=<gpu_num> 


The files ebm_sandbox.py and celeba_combine.py contains evaluation functions used to reproduce results in the paper. Different models can be set in the celeba_combine.py file, and different tasks evaluated using the --task flag in ebm_sandbox.py. You can use the command below to generate compositions of young, female, smiling and wavy hair faces:

python celeba_combine.py

High Resolution CelebA Generation

High resolution images in CelebA are composed using the training method here. Code for composing and training models can be found here as well as pretrained models.

Cubes Dataset

The dataset used for 3D cube experiments can be found at:


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