diffusers/pr_14421/en/api/schedulers/helios_dmd.md

HeliosDMDScheduler

HeliosDMDScheduler is based on the pyramidal flow-matching sampling introduced in Helios.

HeliosDMDScheduler[[diffusers.HeliosDMDScheduler]]

diffusers.HeliosDMDScheduler[[diffusers.HeliosDMDScheduler]]

diffusers.HeliosDMDScheduler(num_train_timesteps: int = 1000, shift: float = 1.0, stages: int = 3, stage_range: list = [0, 0.3333333333333333, 0.6666666666666666, 1], gamma: float = 0.3333333333333333, prediction_type: str = 'flow_prediction', use_flow_sigmas: bool = True, use_dynamic_shifting: bool = False, time_shift_type: typing.Literal['exponential', 'linear'] = 'linear')

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init_sigmas[[diffusers.HeliosDMDScheduler.init_sigmas]]

init_sigmas()

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initialize the global timesteps and sigmas

init_sigmas_for_each_stage[[diffusers.HeliosDMDScheduler.init_sigmas_for_each_stage]]

init_sigmas_for_each_stage()

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Init the timesteps for each stage

set_begin_index[[diffusers.HeliosDMDScheduler.set_begin_index]]

set_begin_index(begin_index: int = 0)

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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.

set_timesteps[[diffusers.HeliosDMDScheduler.set_timesteps]]

set_timesteps(num_inference_steps: int, stage_index: int | None = None, device: typing.Union[str, torch.device] = None, sigmas: bool | None = None, mu: bool | None = None, is_amplify_first_chunk: bool = False)

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Setting the timesteps and sigmas for each stage

time_shift[[diffusers.HeliosDMDScheduler.time_shift]]

time_shift(mu: float, sigma: float, t: Tensor)

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Parameters:

mu (float) : The mu parameter for the time shift.

sigma (float) : The sigma parameter for the time shift.

t (torch.Tensor) : The input timesteps.

Returns: torch.Tensor

The time-shifted timesteps.

Apply time shifting to the sigmas.

scheduling_helios_dmd