#
DPM Discrete Scheduler inspired by Karras et. al paper

##
Overview

Inspired by Karras et. al. Scheduler ported from @crowsonkb’s https://github.com/crowsonkb/k-diffusion library:

All credit for making this scheduler work goes to Katherine Crowson

##
KDPM2DiscreteScheduler

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class diffusers.KDPM2DiscreteScheduler

< source >

( num_train_timesteps: int = 1000 beta_start: float = 0.00085 beta_end: float = 0.012 beta_schedule: str = 'linear' trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None prediction_type: str = 'epsilon' )

Parameters

- num_train_timesteps (`int`) — number of diffusion steps used to train the model. beta_start (`float`): the
- starting `beta` value of inference. beta_end (`float`) — the final `beta` value. beta_schedule (`str`): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from `linear` or `scaled_linear`.
- trained_betas (`np.ndarray`, optional) — option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`, `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
- prediction_type (`str`, default `epsilon`, optional) — prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process), `sample` (directly predicting the noisy sample`) or` v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf)

Scheduler created by @crowsonkb in k_diffusion, see: https://github.com/crowsonkb/k-diffusion/blob/5b3af030dd83e0297272d861c19477735d0317ec/k_diffusion/sampling.py#L188

Scheduler inspired by DPM-Solver-2 and Algorthim 2 from Karras et al. (2022).

~ConfigMixin takes care of storing all config attributes that are passed in the scheduler’s `__init__` function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. SchedulerMixin provides general loading and saving functionality via the SchedulerMixin.save_pretrained() and from_pretrained() functions.

####
scale_model_input

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( sample: FloatTensor timestep: typing.Union[float, torch.FloatTensor] ) → `torch.FloatTensor`

Parameters

- Ensures interchangeability with schedulers that need to scale the denoising model input depending on the —
- current timestep. — sample (`torch.FloatTensor`): input sample timestep (`int`, optional): current timestep

Returns

`torch.FloatTensor`

scaled input sample

####
set_timesteps

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( num_inference_steps: int device: typing.Union[str, torch.device] = None num_train_timesteps: typing.Optional[int] = 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 timesteps used for the diffusion chain. Supporting function to be run before inference.

####
step

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( model_output: typing.Union[torch.FloatTensor, numpy.ndarray] timestep: typing.Union[float, torch.FloatTensor] sample: typing.Union[torch.FloatTensor, numpy.ndarray] return_dict: bool = True ) → SchedulerOutput or `tuple`

Parameters

- Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion —
- process from the learned model outputs (most often the predicted noise). — model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model. timestep (`int`): current discrete timestep in the diffusion chain. sample (`torch.FloatTensor` or `np.ndarray`): current instance of sample being created by diffusion process. return_dict (`bool`): option for returning tuple rather than SchedulerOutput class

Returns

SchedulerOutput or `tuple`

SchedulerOutput if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.