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

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[[diffusers.EDMEulerScheduler]]

diffusers.EDMEulerScheduler[[diffusers.EDMEulerScheduler]]

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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.

add_noisediffusers.EDMEulerScheduler.add_noisehttps://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/schedulers/scheduling_edm_euler.py#L536[{"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:

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 (Literal["karras", "exponential"], optional, defaults to "karras") : Sigma schedule to compute the sigmas. By default, we use the schedule introduced in the EDM paper (https://huggingface.co/papers/2206.00364). The "exponential" schedule was incorporated in this model: https://huggingface.co/stabilityai/cosxl.

num_train_timesteps (int, optional, defaults to 1000) : The number of diffusion steps to train the model.

prediction_type (Literal["epsilon", "v_prediction"], optional, defaults to "epsilon") : Prediction type of the scheduler function. "epsilon" predicts the noise of the diffusion process, and "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 (Literal["zero", "sigma_min"], optional, 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.

index_for_timestep[[diffusers.EDMEulerScheduler.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).

precondition_inputs[[diffusers.EDMEulerScheduler.precondition_inputs]]

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Precondition the input sample by scaling it according to the EDM formulation.

Parameters:

sample (torch.Tensor) : The input sample tensor to precondition.

sigma (float or torch.Tensor) : The current sigma (noise level) value.

Returns:

torch.Tensor

The scaled input sample.

precondition_noise[[diffusers.EDMEulerScheduler.precondition_noise]]

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Precondition the noise level by applying a logarithmic transformation.

Parameters:

sigma (float or torch.Tensor) : The sigma (noise level) value to precondition.

Returns:

torch.Tensor

The preconditioned noise value computed as 0.25 * log(sigma).

precondition_outputs[[diffusers.EDMEulerScheduler.precondition_outputs]]

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Precondition the model outputs according to the EDM formulation.

Parameters:

sample (torch.Tensor) : The input sample tensor.

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

sigma (float or torch.Tensor) : The current sigma (noise level) value.

Returns:

torch.Tensor

The denoised sample computed by combining the skip connection and output scaling.

scale_model_input[[diffusers.EDMEulerScheduler.scale_model_input]]

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

Parameters:

sample (torch.Tensor) : The input sample tensor.

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

Returns:

torch.Tensor

A scaled input sample.

set_begin_index[[diffusers.EDMEulerScheduler.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.EDMEulerScheduler.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.

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

sigmas (torch.Tensor or 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.

step[[diffusers.EDMEulerScheduler.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) : The amount of stochasticity to add at each step. Higher values add more noise.

s_tmin (float, optional, defaults to 0.0) : The minimum sigma threshold below which no noise is added.

s_tmax (float, optional, defaults to float("inf")) : The maximum sigma threshold above which no noise is 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 reproducibility.

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

pred_original_sample (torch.Tensor, optional) : The predicted denoised sample from a previous step. If provided, skips recomputation.

Returns:

[EDMEulerSchedulerOutput](/docs/diffusers/pr_11739/en/api/schedulers/edm_euler#diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput) or tuple``

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

EDMEulerSchedulerOutput[[diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput]]

diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput[[diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput]]

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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.