diffusers/pr_11739/en/api/schedulers/euler.md

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[[diffusers.EulerDiscreteScheduler]]

diffusers.EulerDiscreteScheduler[[diffusers.EulerDiscreteScheduler]]

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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.

add_noisediffusers.EulerDiscreteScheduler.add_noisehttps://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/schedulers/scheduling_euler_discrete.py#L802[{"name": "original_samples", "val": ": Tensor"}, {"name": "noise", "val": ": Tensor"}, {"name": "timesteps", "val": ": Tensor"}]- original_samples (torch.Tensor) -- The original samples to which noise will be added.

• noise (torch.Tensor) -- The noise tensor to add to the original samples.
• timesteps (torch.Tensor) -- The timesteps at which to add noise, determining the noise level from the schedule.0torch.TensorThe noisy samples with added noise scaled according to the timestep schedule.

Add noise to the original samples according to the noise schedule at the specified timesteps.

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 (Literal["linear", "scaled_linear", "squaredcos_cap_v2"], defaults to "linear") : The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from "linear", "scaled_linear", or "squaredcos_cap_v2".

trained_betas (np.ndarray, optional) : Pass an array of betas directly to the constructor to bypass beta_start and beta_end.

prediction_type (Literal["epsilon", "sample", "v_prediction"], 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 (Literal["linear", "log_linear"], defaults to "linear", optional) : The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be one 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}.

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.

sigma_min (float, optional) : The minimum sigma value for the noise schedule. If not provided, defaults to the last sigma in the schedule.

sigma_max (float, optional) : The maximum sigma value for the noise schedule. If not provided, defaults to the first sigma in the schedule.

timestep_spacing (Literal["linspace", "leading", "trailing"], 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.

timestep_type (Literal["discrete", "continuous"], defaults to "discrete") : The type of timesteps to use. Can be "discrete" or "continuous".

steps_offset (int, defaults to 0) : An offset added to the inference steps, as required by some model families.

rescale_betas_zero_snr (bool, defaults to False) : Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and dark samples instead of limiting it to samples with medium brightness. Loosely related to --offset_noise.

final_sigmas_type (Literal["zero", "sigma_min"], 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.

Returns:

torch.Tensor

The noisy samples with added noise scaled according to the timestep schedule.

get_velocity[[diffusers.EulerDiscreteScheduler.get_velocity]]

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Compute the velocity prediction for the given sample and noise at the specified timesteps.

This method implements the velocity prediction used in v-prediction models, which predicts a linear combination of the sample and noise.

Parameters:

sample (torch.Tensor) : The input sample for which to compute the velocity.

noise (torch.Tensor) : The noise tensor corresponding to the sample.

timesteps (torch.Tensor) : The timesteps at which to compute the velocity.

Returns:

torch.Tensor

The velocity prediction computed as sqrt(alpha_prod) * noise - sqrt(1 - alpha_prod) * sample.

index_for_timestep[[diffusers.EulerDiscreteScheduler.index_for_timestep]]

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Find the index of a given timestep in the timestep schedule.

Parameters:

timestep (float or torch.Tensor) : The timestep value to find in the schedule.

schedule_timesteps (torch.Tensor, optional) : The timestep schedule to search in. If None, uses self.timesteps.

Returns:

int

The index of the timestep in the schedule. For the very first step, returns the second index if multiple matches exist to avoid skipping a sigma when starting mid-schedule (e.g., for image-to-image).

scale_model_input[[diffusers.EulerDiscreteScheduler.scale_model_input]]

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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.

Parameters:

sample (torch.Tensor) : The input sample to be scaled.

timestep (float or torch.Tensor) : The current timestep in the diffusion chain.

Returns:

torch.Tensor

A scaled input sample, divided by (sigma**2 + 1) ** 0.5.

set_begin_index[[diffusers.EulerDiscreteScheduler.set_begin_index]]

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

Parameters:

begin_index (int, defaults to 0) : The begin index for the scheduler.

set_timesteps[[diffusers.EulerDiscreteScheduler.set_timesteps]]

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

Parameters:

num_inference_steps (int, optional) : The number of diffusion steps used when generating samples with a pre-trained model. If None, timesteps or sigmas must be provided.

device (str or torch.device, optional) : The device to which the timesteps should be moved to. If None, the timesteps are not moved.

timesteps (List[int], optional) : Custom timesteps used to support arbitrary timesteps schedule. If None, timesteps will be generated based on the timestep_spacing attribute. If timesteps is passed, num_inference_steps and sigmas must be None, and timestep_spacing attribute will be ignored.

sigmas (List[float], optional) : Custom sigmas used to support arbitrary timesteps schedule. If None, timesteps and sigmas will be generated based on the relevant scheduler attributes. If sigmas is passed, num_inference_steps and timesteps must be None, and the timesteps will be generated based on the custom sigmas schedule.

step[[diffusers.EulerDiscreteScheduler.step]]

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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).

Parameters:

model_output (torch.Tensor) : The direct output from the learned diffusion model.

timestep (float or torch.Tensor) : 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, optional, defaults to 0.0) : Stochasticity parameter that controls the amount of noise added during sampling. Higher values increase randomness.

s_tmin (float, optional, defaults to 0.0) : Minimum timestep threshold for applying stochasticity. Only timesteps above this value will have noise added.

s_tmax (float, optional, defaults to inf) : Maximum timestep threshold for applying stochasticity. Only timesteps below this value will have noise added.

s_noise (float, optional, defaults to 1.0) : Scaling factor for noise added to the sample.

generator (torch.Generator, optional) : A random number generator for reproducible sampling.

return_dict (bool, optional, defaults to True) : Whether or not to return a EulerDiscreteSchedulerOutput or tuple.

Returns:

[EulerDiscreteSchedulerOutput](/docs/diffusers/pr_11739/en/api/schedulers/euler#diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput) or tuple``

If return_dict is True, EulerDiscreteSchedulerOutput is returned, otherwise a tuple is returned where the first element is the sample tensor and the second element is the predicted original sample.

EulerDiscreteSchedulerOutput[[diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput]]

diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput[[diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput]]

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Output class for the scheduler's step function output.

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.