Pre-trained models are available at https://www.dropbox.com/scl/fo/zmwsav82kgqtc0tzgpj3l/h?dl=0&rlkey=k6d2bp73k4ifavcg9ldjhgu0s
To generate CIFAR10 samples, open the notebooks/image.ipynb notebook.
Change the paths at the top of the config/eval/cifar10.py config file to point to a folder where CIFAR10 can be downloaded and the paths to the model and config downloaded from the dropbox link.
To generate piano samples, open the notebooks/piano.ipynb notebook.
Change the paths at the top of the config/eval/piano.py config file to point to the dataset downloaded from the dropbox link as well as the model weights and config file.
The sampling settings can be set in the config files, switching between standard tau-leaping and with predictor-corrector steps.
The CIFAR10 model can be trained using
python train.py cifar10
Paths to store the output and to download the CIFAR10 dataset should be set in the training config, config/train/cifar10.py.
To train the model over multiple GPUs, use
python dist_train.py cifar10
with settings found in the config/train/cifar10_distributed.py config file.
The piano model can be trained using
python train.py piano
Paths to store the output and to the dataset downloaded from the dropbox link should be set in config/train/piano.py.
pytorch
ml_collections
tensorboard
pyyaml
tqdm
scipy
torchtyping
matplotlib
These are 4 pairs of audio samples. The first is a music sequence generated by the model conditioned on the first 2 bars (~2.5 secs) of the piece. The second is the ground truth song from the test dataset.