Conditional Variational Diffusion Models

This code implements the Conditional Variational Diffusion Models as described in the paper.

Where to get the data?

The datasets that we are using are available online:

• BioSR, the data that we are using has been transformed to .npy files
• ImageNet from ILSVRC2012
• HCOCO - only used in model evaluation

It is assumed that for:

• BioSR super-resolution task, data can be found in the directory specified as dataset_path in configs/biosr.yaml, in two files, x.npy (input) and y.npy (ground truth)
• BioSR phase task, data can be found in the directory specified as dataset_path in configs/biosr_phase.yaml, in one file, y.npy (ground truth). Input to the model will be generated based on the ground truth.
• ImageNet super-resolution task, data can be found in the directory specified as dataset_path in configs/imagenet_sr.yaml as a collection of JPEG files. Input to the model will be generated based on the ground truth.
• ImageNet phase task, data can be found in the directory specified as dataset_path in configs/imagenet_phase.yaml as a collection of JPEG files. Input to the model will be generated based on the ground truth.
• HCOCO phase evaluation task, data can be found in the directory specified as dataset_path in configs/hcoco_phase_eval.yaml as a collection of JPEG files. Input to the model will be generated based on the ground truth.

How to prepare environment?

Run the following code:

conda create -n myenv python=3.10
conda activate myenv
pip install -r requirements.txt
pip install -e .

How to run the training code?

1. Download the data.
2. Modify the config in configs/ directory with the path to the data you want to use and the directory for outputs.
3. Run the code from the root directory: python scripts/train.py --config-path $PATH_TO_CONFIG --neptune-token $NEPTUNE_TOKEN.

--neptune-token argument is optional.

How to run the training code?

1. Download the data.
2. Modify the config in configs/ directory with the path to the data you want to use and the directory for outputs.
3. Run the code from the root directory: python scripts/eval.py --config-path $PATH_TO_CONFIG --neptune-token $NEPTUNE_TOKEN.

--neptune-token argument is optional.

License

This repository is released under the MIT License (refer to the LICENSE file for details).