#
KarrasVeScheduler

`KarrasVeScheduler` is a stochastic sampler tailored to variance-expanding (VE) models. It is based on the Elucidating the Design Space of Diffusion-Based Generative Models and Score-based generative modeling through stochastic differential equations papers.

##
KarrasVeScheduler

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class diffusers.KarrasVeScheduler

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( sigma_min: float = 0.02 sigma_max: float = 100 s_noise: float = 1.007 s_churn: float = 80 s_min: float = 0.05 s_max: float = 50 )

Parameters

- sigma_min (`float`, defaults to 0.02) — The minimum noise magnitude.
- sigma_max (`float`, defaults to 100) — The maximum noise magnitude.
- s_noise (`float`, defaults to 1.007) — The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, 1.011].
- s_churn (`float`, defaults to 80) — The parameter controlling the overall amount of stochasticity. A reasonable range is [0, 100].
- s_min (`float`, defaults to 0.05) — The start value of the sigma range to add noise (enable stochasticity). A reasonable range is [0, 10].
- s_max (`float`, defaults to 50) — The end value of the sigma range to add noise. A reasonable range is [0.2, 80].

A stochastic scheduler tailored to variance-expanding models.

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.

For more details on the parameters, see Appendix E. The grid search values used to find the optimal `{s_noise, s_churn, s_min, s_max}` for a specific model are described in Table 5 of the paper.

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add_noise_to_input

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( sample: Tensor sigma: float generator: typing.Optional[torch._C.Generator] = None )

Parameters

- sample (`torch.Tensor`) — The input sample.
- sigma (`float`) —
- generator (`torch.Generator`, optional) — A random number generator.

Explicit Langevin-like “churn” step of adding noise to the sample according to a `gamma_i ≥ 0` to reach a higher noise level `sigma_hat = sigma_i + gamma_i*sigma_i`.

####
scale_model_input

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( sample: Tensor timestep: typing.Optional[int] = None ) →
`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_timesteps

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( 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

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( model_output: Tensor sigma_hat: float sigma_prev: float sample_hat: Tensor return_dict: bool = True ) →
`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput` or `tuple`

Parameters

- model_output (`torch.Tensor`) — The direct output from learned diffusion model.
- sigma_hat (`float`) —
- sigma_prev (`float`) —
- sample_hat (`torch.Tensor`) —
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput` or `tuple`.

Returns

`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput` or `tuple`

If return_dict is `True`, `~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput` 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).

####
step_correct

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( model_output: Tensor sigma_hat: float sigma_prev: float sample_hat: Tensor sample_prev: Tensor derivative: Tensor return_dict: bool = True ) →
prev_sample (TODO)

Parameters

- model_output (`torch.Tensor`) — The direct output from learned diffusion model.
- sigma_hat (`float`) — TODO
- sigma_prev (`float`) — TODO
- sample_hat (`torch.Tensor`) — TODO
- sample_prev (`torch.Tensor`) — TODO
- derivative (`torch.Tensor`) — TODO
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a DDPMSchedulerOutput or `tuple`.

Returns

prev_sample (TODO)

updated sample in the diffusion chain. derivative (TODO): TODO

Corrects the predicted sample based on the `model_output` of the network.

##
KarrasVeOutput

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class diffusers.schedulers.deprecated.scheduling_karras_ve.KarrasVeOutput

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( prev_sample: Tensor derivative: Tensor pred_original_sample: typing.Optional[torch.Tensor] = None )

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.
- derivative (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images) — Derivative of predicted original image sample (x_0).
- pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images) — The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance.

Output class for the scheduler’s step function output.

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