#
EulerDiscreteScheduler

The Euler scheduler (Algorithm 2) is 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.

##
EulerDiscreteScheduler

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class diffusers.EulerDiscreteScheduler

< 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 prediction_type: str = 'epsilon' interpolation_type: str = 'linear' use_karras_sigmas: typing.Optional[bool] = False 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 0.0001) — 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` or `scaled_linear`.
- trained_betas (`np.ndarray`, optional) — Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
- 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).
- interpolation_type(`str`, defaults to `"linear"`, optional) — The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be on of `"linear"` or `"log_linear"`.
- 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}.
- 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. 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.

Euler scheduler.

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

< source >

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

Parameters

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

Returns

`torch.FloatTensor`

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_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

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

Parameters

- model_output (`torch.FloatTensor`) — The direct output from learned diffusion model.
- timestep (`float`) — The current discrete timestep in the diffusion chain.
- sample (`torch.FloatTensor`) — 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 EulerDiscreteSchedulerOutput or tuple.

Returns

EulerDiscreteSchedulerOutput or `tuple`

If return_dict is `True`, EulerDiscreteSchedulerOutput 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).

##
EulerDiscreteSchedulerOutput

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class diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput

< source >

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

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.
- pred_original_sample (`torch.FloatTensor` 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.