#
KDPM2DiscreteScheduler

The `KDPM2DiscreteScheduler` is inspired by the Elucidating the Design Space of Diffusion-Based Generative Models paper, and the scheduler is ported from and created by Katherine Crowson.

The original codebase can be found at crowsonkb/k-diffusion.

##
KDPM2DiscreteScheduler

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class diffusers.KDPM2DiscreteScheduler

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( 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 use_karras_sigmas: typing.Optional[bool] = False use_exponential_sigmas: typing.Optional[bool] = False use_beta_sigmas: typing.Optional[bool] = False prediction_type: str = 'epsilon' timestep_spacing: str = 'linspace' 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 0.00085) — The starting `beta` value of inference.
- beta_end (`float`, defaults to 0.012) — 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` or `scaled_linear`.
- trained_betas (`np.ndarray`, optional) — Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
- use_karras_sigmas (`bool`, optional, defaults to `False`) — Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`, the sigmas are determined according to a sequence of noise levels {σi}.
- use_exponential_sigmas (`bool`, optional, defaults to `False`) — Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
- use_beta_sigmas (`bool`, optional, defaults to `False`) — Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to Beta Sampling is All You Need for more information.
- prediction_type (`str`, defaults to `epsilon`, optional) — Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), `sample` (directly predicts the noisy sample`) or` v_prediction` (see section 2.4 of Imagen Video paper).
- timestep_spacing (`str`, defaults to `"linspace"`) — 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, as required by some model families.

KDPM2DiscreteScheduler is inspired by the DPMSolver2 and Algorithm 2 from the Elucidating the Design Space of Diffusion-Based Generative Models paper.

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.

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scale_model_input

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

Parameters

- sample (`torch.Tensor`) — The input sample.
- timestep (`int`, optional) — The current timestep in the diffusion chain.

Returns

`torch.Tensor`

A scaled input sample.

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

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set_begin_index

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( begin_index: int = 0 )

Parameters

- begin_index (`int`) — The begin index for the scheduler.

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

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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 discrete timesteps used for the diffusion chain (to be run before inference).

####
step

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( model_output: typing.Union[torch.Tensor, numpy.ndarray] timestep: typing.Union[float, torch.Tensor] sample: typing.Union[torch.Tensor, numpy.ndarray] return_dict: bool = True ) →
`KDPM2DiscreteSchedulerOutput` or `tuple`

Parameters

- model_output (`torch.Tensor`) — The direct output from learned diffusion model.
- timestep (`float`) — 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 `KDPM2DiscreteSchedulerOutput` or tuple.

Returns

`KDPM2DiscreteSchedulerOutput` or `tuple`

If return_dict is `True`, `KDPM2DiscreteSchedulerOutput` 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).

##
SchedulerOutput

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class diffusers.schedulers.scheduling_utils.SchedulerOutput

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( 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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