Euler Ancestral scheduler
Overview
Ancestral sampling with Euler method steps. Based on the original k-diffusion implementation by Katherine Crowson. Fast scheduler which often times generates good outputs with 20-30 steps.
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 )
Parameters
-
num_train_timesteps (
int) — number of diffusion steps used to train the model. -
beta_start (
float) — the startingbetavalue of inference. -
beta_end (
float) — the finalbetavalue. -
beta_schedule (
str) — 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) — option to pass an array of betas directly to the constructor to bypassbeta_start,beta_endetc. -
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) orv_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf) -
timestep_spacing (
str, default"linspace") — The way the timesteps should be scaled. Refer to Table 2. of Common Diffusion Noise Schedules and Sample Steps are Flawed for more information. -
steps_offset (
int, default0) — an offset added to the inference steps. You can use a combination ofoffset=1andset_alpha_to_one=False, to make the last step use step 0 for the previous alpha product, as done in stable diffusion.
Ancestral sampling with Euler method steps. Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72
~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
Scales the denoising model input by (sigma**2 + 1) ** 0.5 to match the Euler algorithm.
set_timesteps
< source >( num_inference_steps: int device: typing.Union[str, torch.device] = None )
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
step
< source >(
model_output: FloatTensor
timestep: typing.Union[float, torch.FloatTensor]
sample: FloatTensor
generator: typing.Optional[torch._C.Generator] = None
return_dict: bool = True
)
→
~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput or tuple
Parameters
-
model_output (
torch.FloatTensor) — direct output from learned diffusion model. -
timestep (
float) — current timestep in the diffusion chain. -
sample (
torch.FloatTensor) — current instance of sample being created by diffusion process. -
generator (
torch.Generator, optional) — Random number generator. -
return_dict (
bool) — option for returning tuple rather than EulerAncestralDiscreteSchedulerOutput class
Returns
~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput or tuple
~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput if return_dict is True, otherwise
a tuple. When returning a tuple, the first element is the sample tensor.
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).