from collections.abc import Sequence, Mapping
from typing import Any, Literal, Callable
import keras
from keras import ops
from bayesflow.types import Tensor, Shape
from bayesflow.utils import (
expand_right_as,
find_network,
integrate,
integrate_stochastic,
jacobian_trace,
layer_kwargs,
logging,
maybe_mask_tensor,
random_mask,
randomly_mask_along_axis,
weighted_mean,
STOCHASTIC_METHODS,
DETERMINISTIC_METHODS,
)
from bayesflow.utils.serialization import serialize, serializable
from .schedules.noise_schedule import NoiseSchedule
from .dispatch import find_noise_schedule
from ...inference import InferenceNetwork
from ...defaults import TIME_MLP_DEFAULTS, DIFFUSION_INTEGRATE_DEFAULTS
[docs]
@serializable("bayesflow.networks")
class DiffusionModel(InferenceNetwork):
"""Score-based diffusion model for simulation-based inference (SBI).
Implements a score-based diffusion model with configurable subnet architecture,
noise schedule, and prediction/loss types for amortized SBI as described in [1].
Note that score-based diffusion is the most sluggish of all available samplers,
so expect slower inference times than flow matching and much slower than
normalizing flows.
Parameters
----------
subnet : str, type, or keras.Layer, optional
A neural network type for the diffusion model, will be instantiated using
*subnet_kwargs*. If a string is provided, it should be a registered name
(e.g., ``"time_mlp"``). If a type or ``keras.Layer`` is provided, it will
be directly instantiated with the given *subnet_kwargs*. Any subnet must
accept a tuple of tensors ``(target, time, conditions)``.
noise_schedule : {'edm', 'cosine'} or NoiseSchedule or type, optional
Noise schedule controlling the diffusion dynamics. Can be a string
identifier, a schedule class, or a pre-initialised schedule instance.
Default is ``"edm"``.
prediction_type : {'velocity', 'noise', 'F', 'x'}, optional
Output format of the model's prediction. Default is ``"F"``.
loss_type : {'velocity', 'noise', 'F'}, optional
Loss function used to train the model. Default is ``"noise"``.
subnet_kwargs : dict[str, Any], optional
Additional keyword arguments passed to the subnet constructor.
schedule_kwargs : dict[str, Any], optional
Additional keyword arguments passed to the noise schedule constructor.
integrate_kwargs : dict[str, Any], optional
Configuration dictionary for the ODE/SDE integrator used at inference time.
drop_cond_prob : float, optional
Probability of dropping conditions during training (i.e., classifier-free guidance).
Default is 0.0.
drop_target_prob : float, optional
Probability of dropping target values during training (i.e., learning arbitrary
distributions). Default is 0.0.
**kwargs
Additional keyword arguments passed to the base ``InferenceNetwork``.
References
----------
[1] Arruda, J., Bracher, N., Köthe, U., Hasenauer, J., & Radev, S. T. (2025).
Diffusion Models in Simulation-Based Inference: A Tutorial Review.
arXiv preprint arXiv:2512.20685.
"""
def __init__(
self,
*,
subnet: str | type | keras.Layer = "time_mlp",
noise_schedule: Literal["edm", "cosine"] | NoiseSchedule | type = "edm",
prediction_type: Literal["velocity", "noise", "F", "x"] = "F",
loss_type: Literal["velocity", "noise", "F"] = "noise",
subnet_kwargs: dict[str, Any] = None,
schedule_kwargs: dict[str, Any] = None,
integrate_kwargs: dict[str, Any] = None,
drop_cond_prob: float = 0.0,
drop_target_prob: float = 0.0,
**kwargs,
):
super().__init__(base_distribution="normal", **kwargs)
if prediction_type not in ["noise", "velocity", "F", "x"]:
raise ValueError(f"Unknown prediction type: {prediction_type}")
if loss_type not in ["noise", "velocity", "F"]:
raise ValueError(f"Unknown loss type: {loss_type}")
if loss_type != "noise":
logging.warning(
"The standard schedules have weighting functions defined for the noise prediction loss. "
"You might want to replace them if you are using a different loss function."
)
self._prediction_type = prediction_type
self._loss_type = loss_type
schedule_kwargs = schedule_kwargs or {}
self.noise_schedule = find_noise_schedule(noise_schedule, **schedule_kwargs)
self.noise_schedule.validate()
self.integrate_kwargs = DIFFUSION_INTEGRATE_DEFAULTS | (integrate_kwargs or {})
self.seed_generator = keras.random.SeedGenerator()
subnet_kwargs = subnet_kwargs or {}
if subnet == "time_mlp":
subnet_kwargs = TIME_MLP_DEFAULTS | subnet_kwargs
self.subnet = find_network(subnet, **subnet_kwargs)
self.output_projector = None
self.drop_cond_prob = drop_cond_prob
self.unconditional_mode = False
self.drop_target_prob = drop_target_prob
[docs]
def compute_metrics(
self,
x: Tensor | Sequence[Tensor],
conditions: Tensor = None,
sample_weight: Tensor = None,
stage: str = "training",
**kwargs,
) -> dict[str, Tensor]:
subnet_kwargs = self._collect_mask_kwargs(self._SUBNET_MASK_KEYS, kwargs)
training = stage == "training"
noise_schedule_training_stage = stage == "training" or stage == "validation"
if conditions is not None:
conditions = randomly_mask_along_axis(conditions, self.drop_cond_prob, seed_generator=self.seed_generator)
# Sample training diffusion time as a low discrepancy sequence to decrease variance
u0 = keras.random.uniform(shape=(1,), dtype=ops.dtype(x), seed=self.seed_generator)
i = ops.arange(0, ops.shape(x)[0], dtype=ops.dtype(x))
t = (u0 + i / ops.cast(ops.shape(x)[0], dtype=ops.dtype(x))) % 1
# Calculate the noise level
log_snr_t = self.noise_schedule.get_log_snr(t, training=noise_schedule_training_stage)
log_snr_t = expand_right_as(log_snr_t, x)
alpha_t, sigma_t = self.noise_schedule.get_alpha_sigma(log_snr_t=log_snr_t)
weights_for_snr = self.noise_schedule.get_weights_for_snr(log_snr_t=log_snr_t)
# Generate noise vector
eps_t = keras.random.normal(ops.shape(x), dtype=ops.dtype(x), seed=self.seed_generator)
# Diffuse x to get noisy input to the network
diffused_x = alpha_t * x + sigma_t * eps_t
# Generate optional target dropout mask
mask_x = random_mask(ops.shape(x), self.drop_target_prob, self.seed_generator)
diffused_x = maybe_mask_tensor(diffused_x, mask=mask_x, replacement=x)
# Obtain output of the network and transform to prediction of the clean signal x
norm_log_snr_t = self._transform_log_snr(log_snr_t)
subnet_out = self.subnet((diffused_x, norm_log_snr_t, conditions), training=training, **subnet_kwargs)
pred = self.output_projector(subnet_out)
x_pred = self.convert_prediction_to_x(
pred=pred, z=diffused_x, alpha_t=alpha_t, sigma_t=sigma_t, log_snr_t=log_snr_t
)
# Finally, compute the loss according to the configured loss type. Note that the standard weighting
# functions are defined for the noise prediction loss, so if you use a different loss type, you might want
# to adjust the weighting accordingly.
match self._loss_type:
case "noise":
noise_pred = (diffused_x - alpha_t * x_pred) / sigma_t
loss = weights_for_snr * ops.mean(mask_x * (noise_pred - eps_t) ** 2, axis=-1)
case "velocity":
velocity_pred = (alpha_t * diffused_x - x_pred) / sigma_t
v_t = alpha_t * eps_t - sigma_t * x
loss = weights_for_snr * ops.mean(mask_x * (velocity_pred - v_t) ** 2, axis=-1)
case "F":
sigma_data = self.noise_schedule.sigma_data if hasattr(self.noise_schedule, "sigma_data") else 1.0
x1 = ops.sqrt(ops.exp(-log_snr_t) + sigma_data**2) / (ops.exp(-log_snr_t / 2) * sigma_data)
x2 = (sigma_data * alpha_t) / (ops.exp(-log_snr_t / 2) * ops.sqrt(ops.exp(-log_snr_t) + sigma_data**2))
f_pred = x1 * x_pred - x2 * diffused_x
f_t = x1 * x - x2 * diffused_x
loss = weights_for_snr * ops.mean(mask_x * (f_pred - f_t) ** 2, axis=-1)
case _:
raise ValueError(f"Unknown loss type: {self._loss_type}")
loss = weighted_mean(loss, sample_weight)
return {"loss": loss}
[docs]
def build(self, xz_shape: Shape, conditions_shape: Shape = None):
if self.built:
return
self.base_distribution.build(xz_shape)
self.output_projector = keras.layers.Dense(units=xz_shape[-1], bias_initializer="zeros")
# construct input shape for subnet and subnet projector
time_shape = (xz_shape[0], 1) # same batch dims, 1 feature
self.subnet.build((xz_shape, time_shape, conditions_shape))
out_shape = self.subnet.compute_output_shape((xz_shape, time_shape, conditions_shape))
self.output_projector.build(out_shape)
[docs]
def get_config(self):
base_config = super().get_config()
base_config = layer_kwargs(base_config)
config = {
"subnet": self.subnet,
"noise_schedule": self.noise_schedule,
"prediction_type": self._prediction_type,
"loss_type": self._loss_type,
"integrate_kwargs": self.integrate_kwargs,
"drop_cond_prob": self.drop_cond_prob,
"drop_target_prob": self.drop_target_prob,
}
return base_config | serialize(config)
[docs]
def guidance_constraint_term(
self,
x: Tensor,
time: Tensor,
constraints: Callable | Sequence[Callable],
guidance_strength: float = 1.0,
scaling_function: Callable | None = None,
reduce: Literal["sum", "mean"] = "sum",
) -> Tensor:
"""
Backend-agnostic implementation of:
`∇_x Σ_k log sigmoid( -s(t) * c_k(x) )`
Parameters
----------
x : Tensor
The denoised target at time t.
time : Tensor
The time corresponding to x.
constraints : Callable or Sequence[Callable]
A single constraint function or a list/tuple of constraint functions.
Each function should take x as input and return a tensor of constraint values.
guidance_strength : float, optional
A positive scaling factor for the guidance term. Default is 1.0.
scaling_function : Callable, optional
A function that takes time t as input and returns a scaling factor s(t).
If None, a default scaling based on the noise schedule is used. Default is None.
reduce : {'sum', 'mean'}, optional
Method to reduce the log-probabilities from multiple constraints. Default is 'sum'.
Returns
-------
Tensor
The computed guidance term of the same shape as zt.
"""
if not isinstance(constraints, Sequence):
constraints = [constraints]
if scaling_function is None:
def scaling_function(t: Tensor):
log_snr = self.noise_schedule.get_log_snr(t, training=False)
alpha_t, sigma_t = self.noise_schedule.get_alpha_sigma(log_snr)
return ops.square(alpha_t) / ops.square(sigma_t)
def objective_fn(z):
st = scaling_function(time)
logp = keras.ops.zeros((), dtype=z.dtype)
for c in constraints:
ck = c(z)
logp = logp - keras.ops.softplus(st * ck)
return keras.ops.sum(logp) if reduce == "sum" else keras.ops.mean(logp)
backend = keras.backend.backend()
match backend:
case "jax":
import jax
grad = jax.grad(objective_fn)(x)
case "tensorflow":
import tensorflow as tf
with tf.GradientTape() as tape:
tape.watch(x)
objective = objective_fn(x)
grad = tape.gradient(objective, x)
case "torch":
import torch
with torch.enable_grad():
x_grad = x.clone().detach().requires_grad_(True)
objective = objective_fn(x_grad)
grad = torch.autograd.grad(
outputs=objective,
inputs=x_grad,
)[0]
case _:
raise NotImplementedError(f"Unsupported backend: {backend}")
return guidance_strength * grad
[docs]
def convert_prediction_to_x(
self, pred: Tensor, z: Tensor, alpha_t: Tensor, sigma_t: Tensor, log_snr_t: Tensor
) -> Tensor:
"""
Converts the neural network prediction into the denoised data `x`, depending on
the prediction type configured for the model.
Parameters
----------
pred : Tensor
The output prediction from the neural network, typically representing noise,
velocity, or a transformation of the clean signal.
z : Tensor
The noisy latent variable `z` to be denoised.
alpha_t : Tensor
The noise schedule's scaling factor for the clean signal at time `t`.
sigma_t : Tensor
The standard deviation of the noise at time `t`.
log_snr_t : Tensor
The log signal-to-noise ratio at time `t`.
Returns
-------
Tensor
The reconstructed clean signal `x` from the model prediction.
"""
match self._prediction_type:
case "velocity":
return alpha_t * z - sigma_t * pred
case "noise":
return (z - sigma_t * pred) / alpha_t
case "F":
sigma_data = getattr(self.noise_schedule, "sigma_data", 1.0)
x1 = (sigma_data**2 * alpha_t) / (ops.exp(-log_snr_t) + sigma_data**2)
x2 = ops.exp(-log_snr_t / 2) * sigma_data / ops.sqrt(ops.exp(-log_snr_t) + sigma_data**2)
return x1 * z + x2 * pred
case "x":
return pred
case "score":
return (z + sigma_t**2 * pred) / alpha_t
case _:
raise ValueError(f"Unknown prediction type {self._prediction_type}.")
[docs]
def score(
self,
xz: Tensor,
time: float | Tensor = None,
log_snr_t: Tensor = None,
conditions: Tensor = None,
training: bool = False,
guidance_constraints: Mapping[str, Any] = None,
guidance_function: Callable[[Tensor, Tensor], Tensor] = None,
**kwargs,
) -> Tensor:
"""
Computes the score of the target or latent variable `xz`.
Parameters
----------
xz : Tensor
The current state of the latent variable `z`, typically of shape (..., D),
where D is the dimensionality of the latent space.
time : float or Tensor
Scalar or tensor representing the time (or noise level) at which the velocity
should be computed. Will be broadcasted to xz. If None, log_snr_t must be provided.
log_snr_t : Tensor
The log signal-to-noise ratio at time `t`. If None, time must be provided.
conditions : Tensor, optional
Conditional inputs to the network, such as conditioning variables
or encoder outputs. Shape must be broadcastable with `xz`. Default is None.
training : bool, optional
Whether the model is in training mode. Affects behavior of dropout, batch norm,
or other stochastic layers. Default is False.
guidance_constraints : dict[str, Any], optional
A dictionary of parameters for computing a guidance constraint term, which is
added to the score for guided sampling. The specific keys and values depend on
the implementation of `guidance_constraint_term`.
guidance_function : Callable[[Tensor, Tensor], Tensor], optional
A custom function for computing a guidance term, which is added to the score
for guided sampling. The function should accept the predicted clean signal
`x_pred` and the current time `time` as inputs and return a tensor of the same
shape as `xz`.
**kwargs
Subnet kwargs (e.g., attention_mask, mask) for the subnet layer.
Also supports guidance_constraints and guidance_function for custom guidance.
Returns
-------
Tensor
The velocity tensor of the same shape as `xz`, representing the right-hand
side of the probability-flow SDE or ODE at the given `time`.
"""
subnet_kwargs = self._collect_mask_kwargs(self._SUBNET_MASK_KEYS, kwargs)
if log_snr_t is None:
log_snr_t = self.noise_schedule.get_log_snr(t=time, training=training)
log_snr_t = expand_right_as(log_snr_t, xz)
log_snr_t = ops.broadcast_to(log_snr_t, ops.shape(xz)[:-1] + (1,))
if time is None:
time = self.noise_schedule.get_t_from_log_snr(log_snr_t, training=training)
alpha_t, sigma_t = self.noise_schedule.get_alpha_sigma(log_snr_t=log_snr_t)
norm_log_snr = self._transform_log_snr(log_snr_t)
subnet_out = self.subnet((xz, norm_log_snr, conditions), training=training, **subnet_kwargs)
pred = self.output_projector(subnet_out)
x_pred = self.convert_prediction_to_x(pred=pred, z=xz, alpha_t=alpha_t, sigma_t=sigma_t, log_snr_t=log_snr_t)
score = (alpha_t * x_pred - xz) / ops.square(sigma_t)
if guidance_constraints is not None:
guidance = self.guidance_constraint_term(x=x_pred, time=time, **guidance_constraints)
score = score + guidance
if guidance_function is not None:
guidance = guidance_function(x=x_pred, time=time)
score = score + guidance
return score
[docs]
def velocity(
self,
xz: Tensor,
time: float | Tensor,
stochastic_solver: bool,
conditions: Tensor = None,
training: bool = False,
**kwargs,
) -> Tensor:
"""
Computes the velocity (i.e., time derivative) of the target or latent variable `xz` for either
a stochastic differential equation (SDE) or ordinary differential equation (ODE).
Parameters
----------
xz : Tensor
The current state of the latent variable `z`, typically of shape (..., D),
where D is the dimensionality of the latent space.
time : float or Tensor
Scalar or tensor representing the time (or noise level) at which the velocity
should be computed. Will be broadcasted to xz.
stochastic_solver : bool
If True, computes the velocity for the stochastic formulation (SDE).
If False, uses the deterministic formulation (ODE).
conditions : Tensor, optional
Conditional inputs to the network, such as conditioning variables
or encoder outputs. Shape must be broadcastable with `xz`. Default is None.
training : bool, optional
Whether the model is in training mode. Affects behavior of dropout, batch norm,
or other stochastic layers. Default is False.
Returns
-------
Tensor
The velocity tensor of the same shape as `xz`, representing the right-hand
side of the SDE or ODE at the given `time`.
"""
log_snr_t = expand_right_as(self.noise_schedule.get_log_snr(t=time, training=training), xz)
log_snr_t = ops.broadcast_to(log_snr_t, ops.shape(xz)[:-1] + (1,))
score = self.score(xz, log_snr_t=log_snr_t, conditions=conditions, training=training, **kwargs)
# compute velocity f, g of the SDE or ODE
f, g_squared = self.noise_schedule.get_drift(log_snr_t=log_snr_t, x=xz, training=training)
if stochastic_solver:
# for the SDE: d(z) = [f(z, t) - g(t) ^ 2 * score(z, lambda )] dt + g(t) dW
out = f - g_squared * score
else:
# for the ODE: d(z) = [f(z, t) - 0.5 * g(t) ^ 2 * score(z, lambda )] dt
out = f - 0.5 * g_squared * score
# Zero out velocity where target is fixed (during inference only)
if not training:
target_mask = kwargs.get("target_mask", None)
out = maybe_mask_tensor(out, mask=target_mask)
return out
[docs]
def diffusion_term(
self,
xz: Tensor,
time: float | Tensor,
training: bool = False,
**kwargs,
) -> Tensor:
"""
Compute the diffusion term (standard deviation of the noise) at a given time.
Parameters
----------
xz : Tensor
Input tensor of shape (..., D), typically representing the target or latent variables at given time.
time : float or Tensor
The diffusion time step(s). Can be a scalar or a tensor broadcastable to the shape of `xz`.
training : bool, optional
Whether to use the training noise schedule (default is False).
Returns
-------
Tensor
The diffusion term tensor with shape matching `xz` except for the last dimension, which is set to 1.
"""
log_snr_t = expand_right_as(self.noise_schedule.get_log_snr(t=time, training=training), xz)
log_snr_t = ops.broadcast_to(log_snr_t, ops.shape(xz)[:-1] + (1,))
g_squared = self.noise_schedule.get_drift(log_snr_t=log_snr_t)
g = ops.sqrt(g_squared)
# Zero out diffusion where target is fixed (during inference only)
if not training:
target_mask = kwargs.get("target_mask", None)
g = maybe_mask_tensor(g, mask=target_mask)
return g
def _velocity_trace(
self,
xz: Tensor,
time: Tensor,
conditions: Tensor = None,
max_steps: int = None,
training: bool = False,
**kwargs,
) -> tuple[Tensor, Tensor]:
def f(x):
return self.velocity(
x, time=time, stochastic_solver=False, conditions=conditions, training=training, **kwargs
)
v, trace = jacobian_trace(f, xz, max_steps=max_steps, seed=self.seed_generator, return_output=True)
return v, ops.expand_dims(trace, axis=-1)
def _transform_log_snr(self, log_snr: Tensor) -> Tensor:
"""Transform the log_snr to the range [-1, 1] for the diffusion process."""
log_snr_min = self.noise_schedule.log_snr_min
log_snr_max = self.noise_schedule.log_snr_max
normalized_snr = (log_snr - log_snr_min) / (log_snr_max - log_snr_min)
scaled_value = 2 * normalized_snr - 1
return scaled_value
def _forward(
self,
x: Tensor,
conditions: Tensor = None,
density: bool = False,
training: bool = False,
**kwargs,
) -> Tensor | tuple[Tensor, Tensor]:
# Note: integrators will cherry-pick necessary kwargs, so
# we can be general (i.e., sloppy) here
integrate_kwargs = {"start_time": 0.0, "stop_time": 1.0}
integrate_kwargs |= self.integrate_kwargs
integrate_kwargs |= kwargs
if integrate_kwargs["method"] in STOCHASTIC_METHODS:
logging.warning(
"Stochastic methods are not supported for density evaluation."
" Falling back to tsit5 ODE solver."
" To suppress this warning, explicitly pass a method from " + str(DETERMINISTIC_METHODS) + "."
)
integrate_kwargs["method"] = "tsit5"
# Apply user-provided target mask if available
target_mask = kwargs.get("target_mask", None)
targets_fixed = kwargs.get("targets_fixed", None)
if target_mask is not None:
target_mask = keras.ops.broadcast_to(target_mask, keras.ops.shape(x))
targets_fixed = keras.ops.broadcast_to(targets_fixed, keras.ops.shape(x))
x = maybe_mask_tensor(x, target_mask, replacement=targets_fixed)
if self.unconditional_mode and conditions is not None:
conditions = keras.ops.zeros_like(conditions)
logging.info("Condition masking is applied: conditions are set to zero.")
if density:
def deltas(time, xz):
v, trace = self._velocity_trace(xz, time=time, conditions=conditions, training=training, **kwargs)
return {"xz": v, "trace": trace}
state = {
"xz": x,
"trace": ops.zeros(ops.shape(x)[:-1] + (1,), dtype=ops.dtype(x)),
}
state = integrate(deltas, state, **integrate_kwargs)
z = state["xz"]
log_density = self.base_distribution.log_prob(z) + ops.squeeze(state["trace"], axis=-1)
return z, log_density
def deltas(time, xz):
return {
"xz": self.velocity(
xz, time=time, stochastic_solver=False, conditions=conditions, training=training, **kwargs
)
}
state = {"xz": x}
state = integrate(deltas, state, **integrate_kwargs)
z = state["xz"]
return z
def _inverse(
self, z: Tensor, conditions: Tensor = None, density: bool = False, training: bool = False, **kwargs
) -> Tensor | tuple[Tensor, Tensor]:
# Build integrate kwargs: hardcoded defaults → instance config → call-time overrides
integrate_kwargs = {"start_time": 1.0, "stop_time": 0.0}
integrate_kwargs |= self.integrate_kwargs
integrate_kwargs |= kwargs
# Apply user-provided target mask if available
target_mask = kwargs.get("target_mask", None)
targets_fixed = kwargs.get("targets_fixed", None)
if target_mask is not None:
target_mask = keras.ops.broadcast_to(target_mask, keras.ops.shape(z))
targets_fixed = keras.ops.broadcast_to(targets_fixed, keras.ops.shape(z))
z = maybe_mask_tensor(z, target_mask, replacement=targets_fixed)
if self.unconditional_mode and conditions is not None:
conditions = keras.ops.zeros_like(conditions)
logging.info("Condition masking is applied: conditions are set to zero.")
if density:
if integrate_kwargs["method"] in STOCHASTIC_METHODS:
logging.warning(
"Stochastic methods are not supported for density computation."
" Falling back to ODE solver."
" Use one of the deterministic methods: " + str(DETERMINISTIC_METHODS) + "."
)
integrate_kwargs["method"] = "tsit5"
def deltas(time, xz):
v, trace = self._velocity_trace(xz, time=time, conditions=conditions, training=training, **kwargs)
return {"xz": v, "trace": trace}
state = {
"xz": z,
"trace": ops.zeros(ops.shape(z)[:-1] + (1,), dtype=ops.dtype(z)),
}
state = integrate(deltas, state, **integrate_kwargs)
x = state["xz"]
log_density = self.base_distribution.log_prob(z) - ops.squeeze(state["trace"], axis=-1)
return x, log_density
state = {"xz": z}
if integrate_kwargs["method"] in STOCHASTIC_METHODS:
def deltas(time, xz):
return {
"xz": self.velocity(
xz, time=time, stochastic_solver=True, conditions=conditions, training=training, **kwargs
)
}
def diffusion(time, xz):
return {"xz": self.diffusion_term(xz, time=time, training=training, **kwargs)}
score_fn = None
if "corrector_steps" in integrate_kwargs or integrate_kwargs.get("method") == "langevin":
def score_fn(time, xz):
return {"xz": self.score(xz, time=time, conditions=conditions, training=training, **kwargs)}
state = integrate_stochastic(
drift_fn=deltas,
diffusion_fn=diffusion,
score_fn=score_fn,
noise_schedule=self.noise_schedule,
state=state,
seed=self.seed_generator,
**integrate_kwargs,
)
else:
def deltas(time, xz):
return {
"xz": self.velocity(
xz, time=time, stochastic_solver=False, conditions=conditions, training=training, **kwargs
)
}
state = integrate(deltas, state, **integrate_kwargs)
x = state["xz"]
return x
