This repo contains some naive implementations of various score-based generative models.
To install this library, simply run the following command after cloning the repo :
pip install -e .
The supported methods are:
- Noise Conditional Score Networks (NCSN) (Song and Ermon, 2019)
- Denoising Diffusion Probabilistic Models (DDPM) (Ho et al., 2020)
- Conditional DDPM - Cross-Attention (CDDPM) (Rombach et al., 2021)
-
2D gaussian example:
notebooks/ncsn_2d_example.ipynb
-
CIFAR-10:
cd scripts
python ncsn_train_cifar10.py --dataset cifar10
You can visualise the CIFAR-10 samples in notebooks/ncsn_view.ipynb
.
-
2D gaussian example:
notebooks/ddpm_2d_example.ipynb
-
CIFAR-10, Butterfiles:
cd scripts
python ddpm_train.py --dataset [dataset-name]
where [datase-name]
can be cifar10 | butterflies
.
You can visualise the CIFAR-10 and Butterflies samples in notebooks/ddpp_view.ipynb
.
cd scripts
python ddpm_cond_train.py --dataset m1guelpf/nouns
You can visualise the Nouns samples in notebooks/ddpp_cond_view.ipynb
.
- 2D Gaussian mixture
- CIFAR-10 (32 x 32)
- CIFAR-10 (32 x 32)
- Butterflies (128 x 128)
- Nouns (32 x 32)