Code associated with: Régaldo-Saint Blancard, B., Allys, E., Auclair, C., Boulanger, F., Eickenberg, M., Levrier, F., Vacher, L. & Zhang, S. (submitted). "Generative Models of Multi-channel Data Based on a Single Example - Application to Dust Emission". arXiv:2208.03538

The computation of the WPH statistics relies on the Python package PyWPH (v1.1), first released in arXiv:2102.03160.

We provide:

• The data used in the paper, that is the set of simulated multi-frequency $(I, E, B)$ maps of the thermal emission of interstellar dust on which the generative models are based on.
• The code allowing to build and sample these models.

To run the code, make sure that your Python environment includes PyTorch (torch>=1.9.0) as well as the other dependencies (run 'pip install -r requirements.txt'). To run it with a GPU (which is highly recommended for optimal performance), you will also need an installation of CUDA. See the PyTorch installation guide.