Hugging Face's logo

Diffusers documentation

IPNDMScheduler

You are viewing main version, which requires installation from source. If you'd like regular pip install, checkout the latest stable version (v0.27.2).
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

IPNDMScheduler

IPNDMScheduler is a fourth-order Improved Pseudo Linear Multistep scheduler. The original implementation can be found at crowsonkb/v-diffusion-pytorch.

IPNDMScheduler

class diffusers.IPNDMScheduler

< >

( num_train_timesteps: int = 1000 trained_betas: Union = None )

Parameters

  • num_train_timesteps (int, defaults to 1000) — The number of diffusion steps to train the model.
  • trained_betas (np.ndarray, optional) — Pass an array of betas directly to the constructor to bypass beta_start and beta_end.

A fourth-order Improved Pseudo Linear Multistep scheduler.

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.

scale_model_input

< >

( sample: FloatTensor *args **kwargs ) → torch.FloatTensor

Parameters

  • sample (torch.FloatTensor) — The input sample.

Returns

torch.FloatTensor

A scaled input sample.

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

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 device: Union = None )

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.

Sets the discrete timesteps used for the diffusion chain (to be run before inference).

step

< >

( model_output: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) → SchedulerOutput or tuple

Parameters

  • model_output (torch.FloatTensor) — The direct output from learned diffusion model.
  • timestep (int) — The current discrete timestep in the diffusion chain.
  • sample (torch.FloatTensor) — A current instance of a sample created by the diffusion process.
  • return_dict (bool) — Whether or not to return a SchedulerOutput or tuple.

Returns

SchedulerOutput or tuple

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

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the linear multistep method. It performs one forward pass multiple times to approximate the solution.

SchedulerOutput

class diffusers.schedulers.scheduling_utils.SchedulerOutput

< >

( prev_sample: FloatTensor )

Parameters

  • prev_sample (torch.FloatTensor 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.

Base class for the output of a scheduler’s step function.

< > Update on GitHub

←HeunDiscreteScheduler KarrasVeScheduler→