EulerAncestralDiscreteScheduler
A scheduler that uses ancestral sampling with Euler method steps. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original k-diffusion implementation by Katherine Crowson.
EulerAncestralDiscreteScheduler
class diffusers.EulerAncestralDiscreteScheduler
< source >( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None prediction_type: str = 'epsilon' timestep_spacing: str = 'linspace' steps_offset: int = 0 rescale_betas_zero_snr: bool = False )
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 startingbetavalue of inference. - beta_end (
float, defaults to 0.02) — The finalbetavalue. - 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 fromlinearorscaled_linear. - trained_betas (
np.ndarray, optional) — Pass an array of betas directly to the constructor to bypassbeta_startandbeta_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) orv_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, as required by some model families. - rescale_betas_zero_snr (
bool, defaults toFalse) — Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and dark samples instead of limiting it to samples with medium brightness. Loosely related to--offset_noise.
Ancestral sampling with Euler method steps.
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: typing.Union[float, torch.Tensor] ) → torch.Tensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep. Scales the denoising model input by (sigma**2 + 1) ** 0.5 to match the Euler algorithm.
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: int device: typing.Union[str, torch.device] = None )
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
< source >( model_output: Tensor timestep: typing.Union[float, torch.Tensor] sample: Tensor generator: typing.Optional[torch._C.Generator] = None return_dict: bool = True ) → EulerAncestralDiscreteSchedulerOutput 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. - generator (
torch.Generator, optional) — A random number generator. - return_dict (
bool) — Whether or not to return a EulerAncestralDiscreteSchedulerOutput or tuple.
Returns
If return_dict is True,
EulerAncestralDiscreteSchedulerOutput 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).
EulerAncestralDiscreteSchedulerOutput
class diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput
< source >( prev_sample: Tensor pred_original_sample: typing.Optional[torch.Tensor] = None )
Parameters
- prev_sample (
torch.Tensorof shape(batch_size, num_channels, height, width)for images) — Computed sample(x_{t-1})of previous timestep.prev_sampleshould be used as next model input in the denoising loop. - pred_original_sample (
torch.Tensorof shape(batch_size, num_channels, height, width)for images) — The predicted denoised sample(x_{0})based on the model output from the current timestep.pred_original_samplecan be used to preview progress or for guidance.
Output class for the scheduler’s step function output.