KDPM2AncestralDiscreteScheduler
Last updated
Last updated
The KDPM2DiscreteScheduler with ancestral sampling is inspired by the paper, and the scheduler is ported from and created by .
The original codebase can be found at .
( num_train_timesteps: int = 1000beta_start: float = 0.00085beta_end: float = 0.012beta_schedule: str = 'linear'trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = Noneprediction_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.
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 paper).
timestep_spacing (str, defaults to "linspace") — The way the timesteps should be scaled. Refer to Table 2 of the 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.
scale_model_input
( sample: FloatTensortimestep: 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: intdevice: typing.Union[str, torch.device] = Nonenum_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
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.
Returns
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).
( 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.
KDPM2DiscreteScheduler with ancestral sampling is inspired by the DPMSolver2 and Algorithm 2 from the paper.
This model inherits from and . Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.
( 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] = Nonereturn_dict: bool = True ) → or tuple
return_dict (bool) — Whether or not to return a or tuple.
or tuple