DiffusionSDE#

class deepinv.sampling.DiffusionSDE(forward_drift, forward_diffusion, alpha=1.0, denoiser=None, solver=None, minus_one_one=True, dtype=torch.float64, device=torch.device('cpu'), *args, **kwargs)[source]#

Bases: BaseSDE

Define the Reverse-time Diffusion Stochastic Differential Equation.

Given a forward-time SDE of the form:

\[d x_t = f(x_t, t) dt + g(t)d w_t\]

This class define the following reverse-time SDE:

\[d x_{t} = \left( f(x_t, t) - \frac{1 + \alpha}{2} g(t)^2 \nabla \log p_t(x_t) \right) dt + g(t) \sqrt{\alpha} d w_{t}.\]

Parameters:

drift (Callable) – a time-dependent drift function \(f(x, t)\) of the forward-time SDE.
diffusion (Callable) – a time-dependent diffusion function \(g(t)\) of the forward-time SDE.
alpha (Callable) – a scalar weighting the diffusion term. \(\alpha = 0\) corresponds to the ODE sampling and \(\alpha > 0\) corresponds to the SDE sampling.
deepinv.models.Denoiser – a denoiser used to provide an approximation of the score at time \(t\) \(\nabla \log p_t\).
solver (deepinv.sampling.BaseSDESolver) – the solver for solving the SDE.
minus_one_one (bool) – If True, wrap the denoiser so that SDE states x in [-1, 1] are converted to [0, 1] before denoising and mapped back afterward. Set True for denoisers trained on [0, 1] (all denoisers in deepinv.models.Denoiser); set False only if the denoiser natively expects [-1, 1]. This affects only the denoiser interface and usually improves quality when matched to the denoiser’s training range. Default: True.
dtype (torch.dtype) – data type of the computation, except for the denoiser which will use torch.float32. We recommend using torch.float64 for better stability and less numerical error when solving the SDE in discrete time, since most computation cost is from evaluating the denoiser, which will be always computed in torch.float32.
device (torch.device) – device on which the computation is performed.
*args – additional arguments for the deepinv.sampling.BaseSDE.
**kwargs – additional keyword arguments for the deepinv.sampling.BaseSDE.

scale_t(t)[source]#

The scale \(s(t)\) of the condition distribution \(p(x_t \vert x_0) \sim \mathcal{N}(s(t)x_0, s(t)^2 \sigma_t^2 \mathrm{Id})\).

Parameters:: t (torch.Tensor, float) – current time step
Returns:: the mean of the condition distribution at time step t.
Return type:: torch.Tensor

score(x, t, *args, **kwargs)[source]#

Approximating the score function \(\nabla \log p_t\) by the denoiser.

Parameters:

x (torch.Tensor) – current state
t (torch.Tensor, float) – current time step
*args – additional arguments for the denoiser.
**kwargs – additional keyword arguments for the denoiser, e.g., class_labels for class-conditional models.

Returns: