DPM Discrete Scheduler with ancestral sampling inspired by Karras et. al paper
Overview
Inspired by Karras et. al. Scheduler ported from @crowsonkb’s https://github.com/crowsonkb/kdiffusion library:
All credit for making this scheduler work goes to Katherine Crowson
KDPM2AncestralDiscreteScheduler
class diffusers.KDPM2AncestralDiscreteScheduler
< source >( 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' )
Parameters

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 finalbeta
value. beta_schedule (str
): the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose fromlinear
orscaled_linear
. 
trained_betas (
np.ndarray
, optional) — option to pass an array of betas directly to the constructor to bypassbeta_start
,beta_end
etc. options to clip the variance used when adding noise to the denoised sample. Choose fromfixed_small
,fixed_small_log
,fixed_large
,fixed_large_log
,learned
orlearned_range
. 
prediction_type (
str
, defaultepsilon
, optional) — prediction type of the scheduler function, one ofepsilon
(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/kdiffusion/blob/5b3af030dd83e0297272d861c19477735d0317ec/k_diffusion/sampling.py#L188
Scheduler inspired by DPMSolver2 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.
scale_model_input
< source >(
sample: FloatTensor
timestep: typing.Union[float, torch.FloatTensor]
)
→
torch.FloatTensor
set_timesteps
< source >( num_inference_steps: int device: typing.Union[str, torch.device] = None num_train_timesteps: typing.Optional[int] = None )
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
step
< source >(
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
 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
ornp.ndarray
): direct output from learned diffusion model. timestep (int
): current discrete timestep in the diffusion chain. sample (torch.FloatTensor
ornp.ndarray
): current instance of sample being created by diffusion process. return_dict (bool
): option for returning tuple rather than SchedulerOutput class
Returns
SchedulerOutput or tuple
SchedulerOutput if return_dict
is True, otherwise a tuple
. When
returning a tuple, the first element is the sample tensor.