The Euler scheduler (Algorithm 2) is from the Elucidating the Design Space of Diffusion-Based Generative Models paper by Karras et al. 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.
( 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' interpolation_type: str = 'linear' use_karras_sigmas: typing.Optional[bool] = False timestep_spacing: str = 'linspace' steps_offset: int = 0 )
Parameters
int
, defaults to 1000) —
The number of diffusion steps to train the model. float
, defaults to 0.0001) —
The starting beta
value of inference. float
, defaults to 0.02) —
The final beta
value. str
, defaults to "linear"
) —
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
linear
or scaled_linear
. np.ndarray
, optional) —
Pass an array of betas directly to the constructor to bypass beta_start
and beta_end
. str
, defaults to epsilon
, optional) —
Prediction type of the scheduler function; can be epsilon
(predicts the noise of the diffusion process),
sample
(directly predicts the noisy sample) or
v_prediction` (see section 2.4 of Imagen
Video paper). str
, defaults to "linear"
, optional) —
The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be on of
"linear"
or "log_linear"
. bool
, optional, defaults to False
) —
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True
,
the sigmas are determined according to a sequence of noise levels {σi}. 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. int
, defaults to 0) —
An offset added to the inference steps. You can use a combination of offset=1
and
set_alpha_to_one=False
to make the last step use step 0 for the previous alpha product like in Stable
Diffusion. Euler scheduler.
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.
( sample: FloatTensor timestep: typing.Union[float, torch.FloatTensor] ) → torch.FloatTensor
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.
( 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).
( 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 ) → EulerDiscreteSchedulerOutput or tuple
Parameters
torch.FloatTensor
) —
The direct output from learned diffusion model. float
) —
The current discrete timestep in the diffusion chain. torch.FloatTensor
) —
A current instance of a sample created by the diffusion process. float
) — float
) — float
) — float
, defaults to 1.0) —
Scaling factor for noise added to the sample. torch.Generator
, optional) —
A random number generator. bool
) —
Whether or not to return a EulerDiscreteSchedulerOutput or
tuple. Returns
EulerDiscreteSchedulerOutput or tuple
If return_dict is True
, EulerDiscreteSchedulerOutput 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).
( prev_sample: FloatTensor pred_original_sample: typing.Optional[torch.FloatTensor] = None )
Parameters
torch.FloatTensor
of shape (batch_size, num_channels, height, width)
for images) —
Computed sample (x_{t-1})
of previous timestep. prev_sample
should be used as next model input in the
denoising loop. torch.FloatTensor
of 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_sample
can be used to preview progress or for guidance. Output class for the scheduler’s step
function output.