Euler scheduler
Overview
Euler scheduler (Algorithm 2) from the paper Elucidating the Design Space of Diffusion-Based Generative Models by Karras et al. (2022). Based on the original k-diffusion implementation by Katherine Crowson. Fast scheduler which often times generates good outputs with 20-30 steps.
EulerDiscreteScheduler
class diffusers.EulerDiscreteScheduler
< 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' )
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)
Euler scheduler (Algorithm 2) from Karras et al. (2022) https://arxiv.org/abs/2206.00364. . Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51
~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
s_churn: float = 0.0
s_tmin: float = 0.0
s_tmax: float = inf
s_noise: float = 1.0
generator: typing.Optional[torch._C.Generator] = None
return_dict: bool = True
)
→
~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput 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. -
s_churn (
float) — -
s_tmin (
float) — -
s_tmax (
float) — -
s_noise (
float) — -
generator (
torch.Generator, optional) — Random number generator. -
return_dict (
bool) — option for returning tuple rather than EulerDiscreteSchedulerOutput class
Returns
~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput or tuple
~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput 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).