HeunDiscreteScheduler
The Heun scheduler (Algorithm 1) is from the Elucidating the Design Space of Diffusion-Based Generative Models paper by Karras et al. The scheduler is ported from the k-diffusion library and created by Katherine Crowson.
HeunDiscreteScheduler
class diffusers.HeunDiscreteScheduler
< source >( num_train_timesteps: int = 1000 beta_start: float = 0.00085 beta_end: float = 0.012 beta_schedule: str = 'linear' trained_betas: Union = None prediction_type: str = 'epsilon' use_karras_sigmas: Optional = False clip_sample: Optional = False clip_sample_range: float = 1.0 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 0.0001) — The startingbeta
value of inference. - beta_end (
float
, defaults to 0.02) — The finalbeta
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 fromlinear
orscaled_linear
. - trained_betas (
np.ndarray
, optional) — Pass an array of betas directly to the constructor to bypassbeta_start
andbeta_end
. - prediction_type (
str
, defaults toepsilon
, optional) — Prediction type of the scheduler function; can beepsilon
(predicts the noise of the diffusion process),sample
(directly predicts the noisy sample) or
v_prediction` (see section 2.4 of Imagen Video paper). - clip_sample (
bool
, defaults toTrue
) — Clip the predicted sample for numerical stability. - clip_sample_range (
float
, defaults to 1.0) — The maximum magnitude for sample clipping. Valid only whenclip_sample=True
. - use_karras_sigmas (
bool
, optional, defaults toFalse
) — Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. IfTrue
, the sigmas are determined according to a sequence of noise levels {σi}. - 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, as required by some model families.
Scheduler with Heun steps for discrete beta schedules.
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
< source >( sample: Tensor timestep: Union ) → torch.Tensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
set_begin_index
< source >( begin_index: int = 0 )
Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
set_timesteps
< source >( num_inference_steps: Optional = None device: Union = None num_train_timesteps: Optional = None timesteps: Optional = None )
Parameters
- num_inference_steps (
int
) — The number of diffusion steps used when generating samples with a pre-trained model. - device (
str
ortorch.device
, optional) — The device to which the timesteps should be moved to. IfNone
, the timesteps are not moved. - num_train_timesteps (
int
, optional) — The number of diffusion steps used when training the model. IfNone
, the defaultnum_train_timesteps
attribute is used. - timesteps (
List[int]
, optional) — Custom timesteps used to support arbitrary spacing between timesteps. IfNone
, timesteps will be generated based on thetimestep_spacing
attribute. Iftimesteps
is passed,num_inference_steps
must beNone
, andtimestep_spacing
attribute will be ignored.
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
< source >( model_output: Union timestep: Union sample: Union return_dict: bool = True ) → SchedulerOutput or tuple
Parameters
- model_output (
torch.Tensor
) — The direct output from learned diffusion model. - timestep (
float
) — The current discrete timestep in the diffusion chain. - sample (
torch.Tensor
) — A current instance of a sample created by the diffusion process. - return_dict (
bool
) — Whether or not to return a SchedulerOutput or tuple.
Returns
SchedulerOutput or tuple
If return_dict is True
, 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
< source >( prev_sample: Tensor )
Base class for the output of a scheduler’s step
function.