Use score-based generative models to generate new images.
Project description
Diffusion SDE - A score-based generative modelling with SDEs package
Synthesize new images using the score-based generative models.
Installation
Currently, diffusion_sde
supports release of Python 3.7 onwards.
To install the current release:
$ pip install -U diffusion_sde
Getting Started
Start by instantiating a dataset class with a path where the custom dataset is located
from diffusion_sde import datasets
# Specify the path of the custom dataset in the dataset class
ds = datasets(path_to_dataset)
Then, instantiate the diffSDE
class to train the model and generate samples and pass the dataset using .set_loaders()
method
from diffusion_sde import diffSDE
# Instantiate the diffSDE class
cls_diff = diffSDE()
# Set the dataloaders by passing the dataset instantiation as above
cls_diff.set_loaders(dataset=ds)
Begin the model training using the .train()
method and select the desired number of epochs for training.
# Train the model
cls_diff.train(n_iters)
Generate the samples from the trained model with the .generate_samples()
method and specify the desired number of steps for the sampler. We suggest setting the value of n_steps
in the range of $\sim1500$-$2000$ steps to produce high-quality samples
# Generate samples from the trained model
cls_diff.generate_samples(n_steps)
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