IPNDMScheduler
Last updated
Last updated
IPNDMScheduler is a fourth-order Improved Pseudo Linear Multistep scheduler. The original implementation can be found at .
( num_train_timesteps: int = 1000trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None )
Parameters
num_train_timesteps (int, defaults to 1000) — The number of diffusion steps to train the model.
trained_betas (np.ndarray, optional) — Pass an array of betas directly to the constructor to bypass beta_start and beta_end.
A fourth-order Improved Pseudo Linear Multistep scheduler.
This model inherits from and . Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.
scale_model_input
( sample: FloatTensor*args**kwargs ) → torch.FloatTensor
Parameters
sample (torch.FloatTensor) — The input sample.
Returns
torch.FloatTensor
A scaled input sample.
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
set_timesteps
( num_inference_steps: intdevice: typing.Union[str, torch.device] = None )
Parameters
num_inference_steps (int) — The number of diffusion steps used when generating samples with a pre-trained model.
device (str or torch.device, optional) — The device to which the timesteps should be moved to. If None, the timesteps are not moved.
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
Parameters
model_output (torch.FloatTensor) — The direct output from learned diffusion model.
timestep (int) — The current discrete timestep in the diffusion chain.
sample (torch.FloatTensor) — A current instance of a sample created by the diffusion process.
Returns
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the linear multistep method. It performs one forward pass multiple times to approximate the solution.
( prev_sample: FloatTensor )
Parameters
prev_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) — Computed sample (x_{t-1}) of previous timestep. prev_sample should be used as next model input in the denoising loop.
Base class for the output of a scheduler’s step function.
( model_output: FloatTensortimestep: intsample: FloatTensorreturn_dict: bool = True ) → or tuple
return_dict (bool) — Whether or not to return a or tuple.
or tuple
If return_dict is True, is returned, otherwise a tuple is returned where the first element is the sample tensor.