zea.models.diffusion¶
Diffusion models
Classes
|
Diffusion Posterior Sampling guidance. |
|
Base class for diffusion guidance methods. |
|
Implementation of a diffusion generative model. |
- class zea.models.diffusion.DPS(diffusion_model, operator, disable_jit=False)[source]¶
Bases:
DiffusionGuidance
Diffusion Posterior Sampling guidance.
- compute_error(noisy_images, measurements, noise_rates, signal_rates, omega, **kwargs)[source]¶
Compute measurement error for diffusion posterior sampling.
- Parameters:
noisy_images – Noisy images.
measurement – Target measurement.
operator – Forward operator.
noise_rates – Current noise rates.
signal_rates – Current signal rates.
omega – Weight for the measurement error.
**kwargs – Additional arguments for the operator.
- Returns:
Tuple of (measurement_error, (pred_noises, pred_images))
- class zea.models.diffusion.DiffusionGuidance(diffusion_model, operator, disable_jit=False)[source]¶
Bases:
ABC
,Object
Base class for diffusion guidance methods.
- class zea.models.diffusion.DiffusionModel(*args, **kwargs)[source]¶
Bases:
DeepGenerativeModel
Implementation of a diffusion generative model. Heavily inspired from https://keras.io/examples/generative/ddim/
- call(inputs, training=False, network=None, **kwargs)[source]¶
Calls the score network.
If network is not provided, will use the exponential moving average network if training is False, otherwise the regular network.
- denoise(noisy_images, noise_rates, signal_rates, training, network=None)[source]¶
Predict noise component and calculate the image component using it.
- diffusion_schedule(diffusion_times)[source]¶
Cosine diffusion schedule https://arxiv.org/abs/2102.09672
- Parameters:
diffusion_times – tensor with diffusion times in [0, 1]
- Returns:
tensor with noise rates signal_rates: tensor with signal rates
according to: - x_t = signal_rate * x_0 + noise_rate * noise - x_t = sqrt(alpha_t) * x_0 + sqrt(1 - alpha_t) * noise
or with stochastic sampling: - x_t = sqrt(alpha_t) * x_0 + sqrt(1 - alpha_t - sigma_t^2) * noise + sigma_t * epsilon
where: - sigma_t = sqrt((1 - alpha_t) / (1 - alpha_{t+1})) * sqrt(1 - alpha_{t+1} / alpha_t)
- Return type:
noise_rates
Note
t+1 = previous time step t = current time step
- get_config()[source]¶
Returns the config of the object.
An object config is a Python dictionary (serializable) containing the information needed to re-instantiate it.
- log_likelihood(data, **kwargs)[source]¶
Approximate log-likelihood of the data under the model.
- Parameters:
data – Data to compute log-likelihood for.
**kwargs – Additional arguments.
- Returns:
Approximate log-likelihood.
- property metrics¶
Metrics for training.
- posterior_sample(measurements, n_samples=1, n_steps=20, initial_step=0, initial_samples=None, seed=None, **kwargs)[source]¶
Sample from the posterior distribution given measurements.
- Parameters:
measurements – Input measurements. Typically of shape (batch_size, *input_shape).
n_samples – Number of posterior samples to generate. Will generate n_samples samples for each measurement in the measurements batch.
n_steps – Number of diffusion steps.
initial_step – Initial step to start from. Can warm start the diffusion process with a partially noised image, thereby skipping part of the diffusion process. Initial step closer to n_steps, will result in a shorter diffusion process (i.e. less noise added to the initial image). A value of 0 means that the diffusion process starts from pure noise.
initial_samples – Optional initial samples to start from. If provided, these samples will be used as the starting point for the diffusion process. Only used if initial_step is greater than 0. Must be of shape (batch_size, n_samples, *input_shape).
seed – Random seed generator.
**kwargs – Additional arguments.
- Returns:
(batch_size, n_samples, *input_shape).
- Return type:
Posterior samples p(x|y), of shape
- prepare_diffusion(diffusion_steps, initial_step, verbose, disable_jit=False)[source]¶
Prepare the diffusion process.
This method sets up the parameters for the diffusion process, including validation of the initial step and calculation of the step size.
- prepare_schedule(base_diffusion_times, initial_noise, initial_samples, initial_step, step_size)[source]¶
Prepare the diffusion schedule.
This method sets up the initial noisy images based on the provided initial noise and samples. It handles the case where the initial step is greater than 0, allowing for the use of partially noised images for initialization of the diffusion process.
- Parameters:
base_diffusion_times – Base diffusion times.
initial_noise – Initial noise tensor.
initial_samples – Optional initial samples to start from.
initial_step – Initial step to start from.
step_size – Step size for the diffusion process.
- Returns:
Noisy images after the initial step.
- Return type:
next_noisy_images
- reverse_conditional_diffusion(measurements, initial_noise, diffusion_steps, initial_samples=None, initial_step=0, stochastic_sampling=False, seed=None, verbose=False, track_progress_type='x_0', disable_jit=False, **kwargs)[source]¶
Reverse diffusion process conditioned on some measurement.
Effectively performs diffusion posterior sampling p(x_0 | y).
- Parameters:
measurements – Conditioning data.
initial_noise – Initial noise tensor.
diffusion_steps (
int
) – Number of diffusion steps.initial_samples – Optional initial samples to start from.
initial_step (
int
) – Initial step to start from.stochastic_sampling (
bool
) – Whether to use stochastic sampling (DDPM).seed – Random seed generator.
verbose (
bool
) – Whether to show a progress bar.track_progress_type (
Literal
[None
,'x_0'
,'x_t'
]) – Type of progress tracking (“x_0” or “x_t”).**kwargs – Additional arguments. These are passed to the guidance function and the operator. Examples are omega, mask, etc.
- Returns:
Generated images.
- reverse_diffusion(initial_noise, diffusion_steps, initial_samples=None, initial_step=0, stochastic_sampling=False, seed=None, verbose=True, track_progress_type='x_0', disable_jit=False, training=False, network_type=None)[source]¶
Reverse diffusion process to generate images from noise.
- Parameters:
initial_noise – Initial noise tensor.
diffusion_steps (
int
) – Number of diffusion steps.initial_samples – Optional initial samples to start from.
initial_step (
int
) – Initial step to start from.stochastic_sampling (
bool
) – Whether to use stochastic sampling (DDPM).seed (
SeedGenerator
|None
) – Random seed generator.verbose (
bool
) – Whether to show a progress bar.track_progress_type (
Literal
[None
,'x_0'
,'x_t'
]) – Type of progress tracking (“x_0” or “x_t”).disable_jit (
bool
) – Whether to disable JIT compilation.training (
bool
) – Whether to use the training mode of the network.network_type (
Literal
[None
,'main'
,'ema'
]) – Which network to use (“main” or “ema”). If None, uses the network based on the training argument.
- Returns:
Generated images.
- reverse_diffusion_step(shape, pred_images, pred_noises, signal_rates, next_signal_rates, next_noise_rates, seed=None, stochastic_sampling=False)[source]¶
A single reverse diffusion step.
- Parameters:
shape – Shape of the input tensor.
pred_images – Predicted images.
pred_noises – Predicted noises.
signal_rates – Current signal rates.
next_signal_rates – Next signal rates.
next_noise_rates – Next noise rates.
seed – Random seed generator.
stochastic_sampling – Whether to use stochastic sampling (DDPM).
- Returns:
Noisy images after the reverse diffusion step.
- Return type:
next_noisy_images
- sample(n_samples=1, n_steps=20, seed=None, **kwargs)[source]¶
Sample from the model.
- Parameters:
n_samples – Number of samples to generate.
n_steps – Number of diffusion steps.
seed – Random seed generator.
**kwargs – Additional arguments.
- Returns:
Generated samples.
- start_track_progress(diffusion_steps)[source]¶
Initialize the progress tracking for the diffusion process.
For diffusion animation we keep track of the diffusion progress. For large number of steps, we do not store all the images due to memory constraints.
- store_progress(step, track_progress_type, next_noisy_images, pred_images)[source]¶
Store the progress of the diffusion process.
- Parameters:
step – Current diffusion step.
track_progress_type – Type of progress tracking (“x_0” or “x_t”).
next_noisy_images – Noisy images after the current step.
pred_images – Predicted images.
Notes
x_0 is considered the predicted image (aka Tweedie estimate)
x_t is the noisy intermediate image