#
IPNDMScheduler

`IPNDMScheduler` is a fourth-order Improved Pseudo Linear Multistep scheduler. The original implementation can be found at crowsonkb/v-diffusion-pytorch.

##
IPNDMScheduler

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class diffusers.IPNDMScheduler

< source >

( num_train_timesteps: int = 1000 trained_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 SchedulerMixin and ConfigMixin. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.

####
index_for_timestep

< source >

( timestep: typing.Union[float, torch.Tensor] schedule_timesteps: typing.Optional[torch.Tensor] = None ) →
`int`

Parameters

- timestep (`float` or `torch.Tensor`) — The timestep value to find in the schedule.
- schedule_timesteps (`torch.Tensor`, optional) — The timestep schedule to search in. If `None`, uses `self.timesteps`.

Returns

`int`

The index of the timestep in the schedule. For the very first step, returns the second index if multiple matches exist to avoid skipping a sigma when starting mid-schedule (e.g., for image-to-image).

Find the index of a given timestep in the timestep schedule.

####
scale_model_input

< source >

( sample: Tensor *args **kwargs ) →
`torch.Tensor`

Parameters

- sample (`torch.Tensor`) — The input sample.

Returns

`torch.Tensor`

A scaled input sample.

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.

####
set_begin_index

< source >

( begin_index: int = 0 )

Parameters

- begin_index (`int`, defaults to `0`) — The begin index for the scheduler.

Sets the begin index for the scheduler. This function should be run from pipeline before the inference.

####
set_timesteps

< source >

( num_inference_steps: int device: 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

< source >

( model_output: Tensor timestep: typing.Union[int, torch.Tensor] sample: Tensor return_dict: bool = True ) →
SchedulerOutput or `tuple`

Parameters

- model_output (`torch.Tensor`) — The direct output from learned diffusion model.
- timestep (`int`) — The current discrete timestep in the diffusion chain.
- sample (`torch.Tensor`) — A current instance of a sample created by the diffusion process.
- return_dict (`bool`) — Whether or not to return a SchedulerOutput or tuple.

Returns

SchedulerOutput or `tuple`

If return_dict is `True`, SchedulerOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

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.

##
SchedulerOutput

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class diffusers.schedulers.scheduling_utils.SchedulerOutput

< source >

( prev_sample: Tensor )

Parameters

- prev_sample (`torch.Tensor` 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.

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