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HeliosScheduler

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HeliosScheduler

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

HeliosScheduler

class 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' )

convert_model_output

< >

( model_output: Tensor *args sample: Tensor = None sigma: Tensor = None **kwargs ) → torch.Tensor

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.

Returns

torch.Tensor

The converted model output.

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

init_sigmas

< >

( )

initialize the global timesteps and sigmas

init_sigmas_for_each_stage

< >

( )

Init the timesteps for each stage

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 ) → torch.Tensor

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.

Returns

torch.Tensor

The corrected sample tensor at the current timestep.

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

multistep_uni_p_bh_update

< >

( model_output: Tensor *args sample: Tensor = None order: int = None sigma: Tensor = None sigma_next: Tensor = None **kwargs ) → torch.Tensor

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).

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

< >

( begin_index: int = 0 )

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

< >

( num_inference_steps: int stage_index: int | None = None device: str | torch.device = None sigmas: bool | None = None mu: bool | None = None is_amplify_first_chunk: bool = False )

Setting the timesteps and sigmas for each stage

time_shift

< >

( mu: float sigma: float t: Tensor ) → torch.Tensor

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

Update on GitHub

←HeliosDMDScheduler HeunDiscreteScheduler→