Denoising Diffusion Gamma Models

Running the Experiments

The code has been tested on PyTorch 1.7.

Train a model

python main.py --config {DATASET_NOISE}.yml --exp {PROJECT_PATH} --doc {MODEL_NAME} --ni

We provide 6 config files for Celeba and Church to train with gaussian noise and gamma noise.

Sampling from the model

Sampling from the generalized model for FID evaluation

python main.py --config {DATASET_NOISE}.yml --exp {PROJECT_PATH} --doc {MODEL_NAME} --sample --fid --timesteps {STEPS} --eta {ETA} --ni

where

• ETA controls the scale of the variance (0 is DDIM, and 1 is one type of DDPM).
• STEPS controls how many timesteps used in the process.
• MODEL_NAME finds the pre-trained checkpoint according to its inferred path.

Sampling from the model for image inpainting

Use --interpolation option instead of --fid.

Sampling from the sequence of images that lead to the sample

Use --sequence option instead.

The above two cases contain some hard-coded lines specific to producing the image, so modify them according to your needs.

References and Acknowledgements

This implementation is based on the code at https://github.com/ermongroup/ddim