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KDPM2AncestralDiscreteScheduler

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KDPM2AncestralDiscreteScheduler

The KDPM2DiscreteScheduler with ancestral sampling is inspired by the Elucidating the Design Space of Diffusion-Based Generative Models paper, and the scheduler is ported from and created by Katherine Crowson.

The original codebase can be found at crowsonkb/k-diffusion.

KDPM2AncestralDiscreteScheduler

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 use_karras_sigmas: typing.Optional[bool] = False prediction_type: str = 'epsilon' timestep_spacing: str = 'linspace' steps_offset: int = 0 )

Parameters

  • num_train_timesteps (int, defaults to 1000) — The number of diffusion steps to train the model.
  • beta_start (float, defaults to 0.00085) — The starting beta value of inference.
  • beta_end (float, defaults to 0.012) — The final beta value.
  • beta_schedule (str, defaults to "linear") — 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) — Pass an array of betas directly to the constructor to bypass beta_start and beta_end.
  • use_karras_sigmas (bool, optional, defaults to False) — Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True, the sigmas are determined according to a sequence of noise levels {σi}.
  • prediction_type (str, defaults to epsilon, optional) — Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process), sample (directly predicts the noisy sample) or v_prediction` (see section 2.4 of Imagen Video paper).
  • timestep_spacing (str, defaults to "linspace") — The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and Sample Steps are Flawed for more information.
  • steps_offset (int, defaults to 0) — An offset added to the inference steps. You can use a combination of offset=1 and set_alpha_to_one=False to make the last step use step 0 for the previous alpha product like in Stable Diffusion.

KDPM2DiscreteScheduler with ancestral sampling is inspired by the DPMSolver2 and Algorithm 2 from the Elucidating the Design Space of Diffusion-Based Generative Models paper.

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

Parameters

  • sample (torch.FloatTensor) — The input sample.
  • timestep (int, optional) — The current timestep in the diffusion chain.

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_timesteps

< >

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

Parameters

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

Returns

SchedulerOutput or tuple

If return_dict is True, ~schedulers.scheduling_ddim.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 diffusion process from the learned model outputs (most often the predicted noise).

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.

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