diffusers/pr_12652/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]]

Source

convert_model_outputdiffusers.HeliosScheduler.convert_model_outputhttps://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/schedulers/scheduling_helios.py#L371[{"name": "model_output", "val": ": Tensor"}, {"name": "*args", "val": ""}, {"name": "sample", "val": ": Tensor = None"}, {"name": "sigma", "val": ": Tensor = None"}, {"name": "**kwargs", "val": ""}]- 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.0torch.TensorThe converted model output.

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

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.

init_sigmas[[diffusers.HeliosScheduler.init_sigmas]]

Source

initialize the global timesteps and sigmas

init_sigmas_for_each_stage[[diffusers.HeliosScheduler.init_sigmas_for_each_stage]]

Source

Init the timesteps for each stage

multistep_uni_c_bh_update[[diffusers.HeliosScheduler.multistep_uni_c_bh_update]]

Source

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

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.

multistep_uni_p_bh_update[[diffusers.HeliosScheduler.multistep_uni_p_bh_update]]

Source

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

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.

set_begin_index[[diffusers.HeliosScheduler.set_begin_index]]

Source

Sets the begin index for the scheduler. This function should be run from pipeline before the inference.

Parameters:

begin_index (int) : The begin index for the scheduler.

set_timesteps[[diffusers.HeliosScheduler.set_timesteps]]

Source

Setting the timesteps and sigmas for each stage

time_shift[[diffusers.HeliosScheduler.time_shift]]

Source

Apply time shifting to the sigmas.

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.

scheduling_helios