generax 0.1.2

Generative Models using Jax

generax

generax provides implementations of flow based generative models. The library is built on top of Equinox which removes the need to worry about keeping track of model parameters.

key = random.PRNGKey(0) # JAX random key
x = ... # some data

# Create a flow model
model = NeuralSpline(input_shape=x.shape[1:],
                     n_flow_layers=3,
                     n_blocks=4,
                     hidden_size=32,
                     working_size=16,
                     n_spline_knots=8,
                     key=key)

# Data dependent initialization
model = model.data_dependent_init(x, key=key)

# Take multiple samples using vmap
keys = random.split(key, 1000)
samples = eqx.filter_vmap(model.sample)(keys)

# Compute the log probability of data
log_prob = eqx.filter_vmap(model.log_prob)(x)

There is also support for probability paths (time-dependent probability distributions) which can be used to train continuous normalizing flows with flow matching. See the examples on flow matching and multi-sample flow matching for more details.

Installation

generax is available on pip:

pip install generax

Training

Generax provides an easy interface to train these models:

trainer = Trainer(checkpoint_path='tmp/model_path')

model = trainer.train(model=model,              # Generax model
                      objective=my_objective,   # Objective function
                      evaluate_model=tester,    # Testing function
                      optimizer=optimizer,      # Optax optimizer
                      num_steps=10000,          # Number of training steps
                      data_iterator=train_ds,   # Training data iterator
                      double_batch=1000,        # Train these many batches in a scan loop
                      checkpoint_every=1000,    # Checkpoint interval
                      test_every=1000,          # Test interval
                      retrain=True)             # Retrain from checkpoint

See the examples folder for more details.