#
EDMEulerScheduler

The Karras formulation of the Euler scheduler (Algorithm 2) from the Elucidating the Design Space of Diffusion-Based Generative Models paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original k-diffusion implementation by Katherine Crowson.

##
EDMEulerScheduler

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class diffusers.EDMEulerScheduler

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( sigma_min: float = 0.002 sigma_max: float = 80.0 sigma_data: float = 0.5 sigma_schedule: str = 'karras' num_train_timesteps: int = 1000 prediction_type: str = 'epsilon' rho: float = 7.0 final_sigmas_type: str = 'zero' )

Parameters

- sigma_min (`float`, optional, defaults to 0.002) — Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable range is [0, 10].
- sigma_max (`float`, optional, defaults to 80.0) — Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable range is [0.2, 80.0].
- sigma_data (`float`, optional, defaults to 0.5) — The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1].
- sigma_schedule (`str`, optional, defaults to `karras`) — Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper (https://huggingface.co/papers/2206.00364). Other acceptable value is “exponential”. The exponential schedule was incorporated in this model: https://huggingface.co/stabilityai/cosxl.
- num_train_timesteps (`int`, defaults to 1000) — The number of diffusion steps to train the model.
- 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).
- rho (`float`, optional, defaults to 7.0) — The rho parameter used for calculating the Karras sigma schedule, which is set to 7.0 in the EDM paper [1].
- final_sigmas_type (`str`, defaults to `"zero"`) — The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.

Implements the Euler scheduler in EDM formulation as presented in Karras et al. 2022 [1].

[1] Karras, Tero, et al. “Elucidating the Design Space of Diffusion-Based Generative Models.” https://huggingface.co/papers/2206.00364

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. Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.

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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 = None device: typing.Union[str, torch.device] = None sigmas: typing.Union[torch.Tensor, typing.List[float], NoneType] = 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.
- sigmas (`Union[torch.Tensor, List[float]]`, optional) — Custom sigmas to use for the denoising process. If not defined, the default behavior when `num_inference_steps` is passed will be used.

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

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step

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( model_output: Tensor timestep: typing.Union[float, torch.Tensor] sample: Tensor s_churn: float = 0.0 s_tmin: float = 0.0 s_tmax: float = inf s_noise: float = 1.0 generator: typing.Optional[torch._C.Generator] = None return_dict: bool = True pred_original_sample: typing.Optional[torch.Tensor] = None ) →
`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput` 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.
- s_churn (`float`) —
- s_tmin (`float`) —
- s_tmax (`float`) —
- s_noise (`float`, defaults to 1.0) — Scaling factor for noise added to the sample.
- generator (`torch.Generator`, optional) — A random number generator.
- return_dict (`bool`) — Whether or not to return a `~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput` or tuple.

Returns

`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput` or `tuple`

If return_dict is `True`, `~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput` 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).

##
EDMEulerSchedulerOutput

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class diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput

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( prev_sample: 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.
- 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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