gensbi.diffusion.path.edm_path

EDM probability path implementation.

This module implements the probability path for EDM-based diffusion models, supporting various noise schedules (EDM, EDM-VP, EDM-VE).

Based on the paper “Elucidating the Design Space of Diffusion-Based Generative Models” by Karras et al., 2022. https://arxiv.org/abs/2206.00364

Classes

EDMPath

EDM probability path.

Module Contents

class gensbi.diffusion.path.edm_path.EDMPath(scheduler)

Bases: gensbi.diffusion.path.path.ProbPath

EDM probability path.

This class implements the probability path for EDM-based diffusion models, supporting different noise schedules (EDM, EDM-VP, EDM-VE).

Parameters:

scheduler (The scheduler object for noise generation, must be one of 'EDM', 'EDM-VP', or 'EDM-VE'.)

Example

from gensbi.diffusion.path import EDMPath
from gensbi.diffusion.path.scheduler import EDMScheduler
import jax, jax.numpy as jnp
scheduler = EDMScheduler()
path = EDMPath(scheduler)
key = jax.random.PRNGKey(0)
x_1 = jax.random.normal(key, (32, 2))
x_0 = jax.random.normal(jax.random.PRNGKey(1), (32, 2))
sigma = jnp.ones((32, 1))
sample = path.sample(x_0, x_1, sigma)
print(sample.x_t.shape)
# (32, 2)

get_loss_fn()

Returns the loss function for the EDM path.

Returns:

The loss function as provided by the scheduler.

Return type:

Callable

sample(x_0, x_1, sigma)

Sample from the EDM probability path.

Constructs x_t = x_1 + sigma * x_0 where x_0 is standard normal noise.

Parameters:

• x_0 (Array) – Source noise sample from N(0, 1), shape (batch_size, …).

• x_1 (Array) – Target data point, shape (batch_size, …).

• sigma (Array) – Noise scale, shape (batch_size, …).

Returns:

A sample from the EDM path.

Return type:

EDMPathSample

sample_sigma(key, batch_size)

Sample the noise scale sigma from the scheduler.

Parameters:

• key (Array) – JAX random key.

• batch_size (int) – Number of samples to generate.

Returns:

Samples of sigma, shape (batch_size, …).

Return type:

Array

scheduler