Diffusers documentation

improved pseudo numerical methods for diffusion models (iPNDM)

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improved pseudo numerical methods for diffusion models (iPNDM)


Original implementation can be found here.


class diffusers.IPNDMScheduler

< >

( num_train_timesteps: int = 1000 trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None )


  • num_train_timesteps (int) — number of diffusion steps used to train the model.

Improved Pseudo numerical methods for diffusion models (iPNDM) ported from @crowsonkb’s amazing k-diffusion library

~ConfigMixin takes care of storing all config attributes that are passed in the scheduler’s __init__ function, such as num_train_timesteps. They can be accessed via scheduler.config.num_train_timesteps. SchedulerMixin provides general loading and saving functionality via the SchedulerMixin.save_pretrained() and from_pretrained() functions.

For more details, see the original paper: https://arxiv.org/abs/2202.09778


< >

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


  • sample (torch.FloatTensor) — input sample



scaled input sample

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


< >

( num_inference_steps: int device: typing.Union[str, torch.device] = None )


  • num_inference_steps (int) — the number of diffusion steps used when generating samples with a pre-trained model.

Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.


< >

( model_output: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) ~scheduling_utils.SchedulerOutput or tuple


  • model_output (torch.FloatTensor) — direct output from learned diffusion model.
  • timestep (int) — current discrete timestep in the diffusion chain.
  • sample (torch.FloatTensor) — current instance of sample being created by diffusion process.
  • return_dict (bool) — option for returning tuple rather than SchedulerOutput class


~scheduling_utils.SchedulerOutput or tuple

~scheduling_utils.SchedulerOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is the sample tensor.

Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple times to approximate the solution.