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DPM Discrete Scheduler with ancestral sampling inspired by Karras et. al paper

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DPM Discrete Scheduler with ancestral sampling inspired by Karras et. al paper


Inspired by Karras et. al. Scheduler ported from @crowsonkb’s https://github.com/crowsonkb/k-diffusion library:

All credit for making this scheduler work goes to Katherine Crowson


class diffusers.KDPM2AncestralDiscreteScheduler

< >

( num_train_timesteps: int = 1000 beta_start: float = 0.00085 beta_end: float = 0.012 beta_schedule: str = 'linear' trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None prediction_type: str = 'epsilon' )


  • num_train_timesteps (int) — number of diffusion steps used to train the model. beta_start (float): the
  • starting beta value of inference. beta_end (float) — the final beta value. beta_schedule (str): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from linear or scaled_linear.
  • trained_betas (np.ndarray, optional) — option to pass an array of betas directly to the constructor to bypass beta_start, beta_end etc. options to clip the variance used when adding noise to the denoised sample. Choose from fixed_small, fixed_small_log, fixed_large, fixed_large_log, learned or learned_range.
  • prediction_type (str, default epsilon, optional) — prediction type of the scheduler function, one of epsilon (predicting the noise of the diffusion process), sample (directly predicting the noisy sample) or v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf)

Scheduler created by @crowsonkb in k_diffusion, see: https://github.com/crowsonkb/k-diffusion/blob/5b3af030dd83e0297272d861c19477735d0317ec/k_diffusion/sampling.py#L188

Scheduler inspired by DPM-Solver-2 and Algorthim 2 from Karras et al. (2022).

~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.


< >

( sample: FloatTensor timestep: typing.Union[float, torch.FloatTensor] ) torch.FloatTensor


  • Ensures interchangeability with schedulers that need to scale the denoising model input depending on the —
  • current timestep. — sample (torch.FloatTensor): input sample timestep (int, optional): current timestep



scaled input sample


< >

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


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


< >

( model_output: typing.Union[torch.FloatTensor, numpy.ndarray] timestep: typing.Union[float, torch.FloatTensor] sample: typing.Union[torch.FloatTensor, numpy.ndarray] generator: typing.Optional[torch._C.Generator] = None return_dict: bool = True ) SchedulerOutput or tuple


  • Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion —
  • process from the learned model outputs (most often the predicted noise). — model_output (torch.FloatTensor or np.ndarray): direct output from learned diffusion model. timestep (int): current discrete timestep in the diffusion chain. sample (torch.FloatTensor or np.ndarray): current instance of sample being created by diffusion process. return_dict (bool): option for returning tuple rather than SchedulerOutput class


SchedulerOutput or tuple

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