#
PNDMScheduler

`PNDMScheduler`, or pseudo numerical methods for diffusion models, uses more advanced ODE integration techniques like the Runge-Kutta and linear multi-step method. The original implementation can be found at crowsonkb/k-diffusion.

##
PNDMScheduler

###
class diffusers.PNDMScheduler

< source >

( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None skip_prk_steps: bool = False set_alpha_to_one: bool = False prediction_type: str = 'epsilon' timestep_spacing: str = 'leading' steps_offset: int = 0 )

Parameters

- num_train_timesteps (`int`, defaults to 1000) — The number of diffusion steps to train the model.
- beta_start (`float`, defaults to 0.0001) — The starting `beta` value of inference.
- beta_end (`float`, defaults to 0.02) — The final `beta` value.
- beta_schedule (`str`, defaults to `"linear"`) — The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
- trained_betas (`np.ndarray`, optional) — Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
- skip_prk_steps (`bool`, defaults to `False`) — Allows the scheduler to skip the Runge-Kutta steps defined in the original paper as being required before PLMS steps.
- set_alpha_to_one (`bool`, defaults to `False`) — Each diffusion step uses the alphas product value at that step and at the previous one. For the final step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, otherwise it uses the alpha value at step 0.
- prediction_type (`str`, defaults to `epsilon`, optional) — Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process) or `v_prediction` (see section 2.4 of Imagen Video paper).
- timestep_spacing (`str`, defaults to `"leading"`) — The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and Sample Steps are Flawed for more information.
- steps_offset (`int`, defaults to 0) — An offset added to the inference steps. You can use a combination of `offset=1` and `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable Diffusion.

`PNDMScheduler` uses pseudo numerical methods for diffusion models such as the Runge-Kutta and linear multi-step method.

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.

####
scale_model_input

< source >

( 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

< 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: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) →
SchedulerOutput or `tuple`

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.
- 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 diffusion process from the learned model outputs (most often the predicted noise), and calls step_prk() or step_plms() depending on the internal variable `counter`.

####
step_plms

< source >

( model_output: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) →
SchedulerOutput or `tuple`

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.
- 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.

####
step_prk

< source >

( model_output: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) →
SchedulerOutput or `tuple`

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.
- 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 Runge-Kutta method. It performs four forward passes to approximate the solution to the differential equation.

##
SchedulerOutput

###
class diffusers.schedulers.scheduling_utils.SchedulerOutput

< source >

( 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.