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

LMSDiscreteScheduler

LMSDiscreteScheduler is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by Katherine Crowson, and the original implementation can be found at crowsonkb/k-diffusion.

LMSDiscreteScheduler[[diffusers.LMSDiscreteScheduler]]

diffusers.LMSDiscreteScheduler[[diffusers.LMSDiscreteScheduler]]

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A linear multistep scheduler for discrete beta schedules.

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.LMSDiscreteScheduler.add_noisehttps://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/schedulers/scheduling_lms_discrete.py#L623[{"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 ("linear", "scaled_linear", or "squaredcos_cap_v2", defaults to "linear") : The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model.

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

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.

prediction_type ("epsilon", "sample", or "v_prediction", defaults to "epsilon") : 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).

timestep_spacing ("linspace", "leading", or "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.

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

Returns:

torch.Tensor

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

get_lms_coefficient[[diffusers.LMSDiscreteScheduler.get_lms_coefficient]]

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Compute the linear multistep coefficient.

Parameters:

order (int) : The order of the linear multistep method.

t (int) : The current timestep index.

current_order (int) : The current order for which to compute the coefficient.

Returns:

float

The computed linear multistep coefficient.

index_for_timestep[[diffusers.LMSDiscreteScheduler.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.LMSDiscreteScheduler.scale_model_input]]

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

Parameters:

sample (torch.Tensor) : The input sample.

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

Returns:

torch.Tensor

A scaled input sample.

set_begin_index[[diffusers.LMSDiscreteScheduler.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.LMSDiscreteScheduler.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) : 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.

step[[diffusers.LMSDiscreteScheduler.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 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.

order (int, defaults to 4) : The order of the linear multistep method.

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

Returns:

[SchedulerOutput](/docs/diffusers/pr_11739/en/api/schedulers/overview#diffusers.schedulers.scheduling_utils.SchedulerOutput) or tuple``

If return_dict is True, SchedulerOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

LMSDiscreteSchedulerOutput[[diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput]]

diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput[[diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput]]

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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.