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

HeliosScheduler

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

HeliosScheduler[[diffusers.HeliosScheduler]]

diffusers.HeliosScheduler[[diffusers.HeliosScheduler]]

diffusers.HeliosScheduler(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, thresholding: bool = False, prediction_type: str = 'flow_prediction', solver_order: int = 2, predict_x0: bool = True, solver_type: str = 'bh2', lower_order_final: bool = True, disable_corrector: list = [], solver_p: SchedulerMixin = None, use_flow_sigmas: bool = True, scheduler_type: str = 'unipc', use_dynamic_shifting: bool = False, time_shift_type: typing.Literal['exponential', 'linear'] = 'exponential')

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convert_model_output[[diffusers.HeliosScheduler.convert_model_output]]

convert_model_output(model_output: Tensor, *args, sample: Tensor = None, sigma: Tensor = None, **kwargs)

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Parameters:

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

timestep (int) : The current discrete timestep in the diffusion chain.

sample (torch.Tensor) : A current instance of a sample created by the diffusion process.

sigma (torch.Tensor, optional) : The sigma of the current step in the noise schedule.

Returns: torch.Tensor

The converted model output.

Convert the model output to the corresponding type the UniPC algorithm needs.

init_sigmas[[diffusers.HeliosScheduler.init_sigmas]]

init_sigmas()

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initialize the global timesteps and sigmas

init_sigmas_for_each_stage[[diffusers.HeliosScheduler.init_sigmas_for_each_stage]]

init_sigmas_for_each_stage()

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Init the timesteps for each stage

multistep_uni_c_bh_update[[diffusers.HeliosScheduler.multistep_uni_c_bh_update]]

multistep_uni_c_bh_update(this_model_output: Tensor, *args, last_sample: Tensor = None, this_sample: Tensor = None, order: int = None, sigma_before: Tensor = None, sigma: Tensor = None, **kwargs)

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Parameters:

this_model_output (torch.Tensor) : The model outputs at x_t.

this_timestep (int) : The current timestep t.

last_sample (torch.Tensor) : The generated sample before the last predictor x_{t-1}.

this_sample (torch.Tensor) : The generated sample after the last predictor x_{t}.

order (int) : The p of UniC-p at this step. The effective order of accuracy should be order + 1.

sigma_before (torch.Tensor, optional) : The sigma of the previous step in the noise schedule.

sigma (torch.Tensor, optional) : The sigma of the current step in the noise schedule.

Returns: torch.Tensor

The corrected sample tensor at the current timestep.

One step for the UniC (B(h) version).

multistep_uni_p_bh_update[[diffusers.HeliosScheduler.multistep_uni_p_bh_update]]

multistep_uni_p_bh_update(model_output: Tensor, *args, sample: Tensor = None, order: int = None, sigma: Tensor = None, sigma_next: Tensor = None, **kwargs)

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Parameters:

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

prev_timestep (int) : The previous discrete timestep in the diffusion chain.

sample (torch.Tensor) : A current instance of a sample created by the diffusion process.

order (int) : The order of UniP at this timestep (corresponds to the p in UniPC-p).

sigma (torch.Tensor, optional) : The sigma of the current step in the noise schedule.

sigma_next (torch.Tensor, optional) : The sigma of the next step in the noise schedule.

Returns: torch.Tensor

The sample tensor at the previous timestep.

One step for the UniP (B(h) version). Alternatively, self.solver_p is used if is specified.

set_begin_index[[diffusers.HeliosScheduler.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.HeliosScheduler.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.HeliosScheduler.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