EulerAncestralDiscreteScheduler
译者:片刻小哥哥
项目地址:https://huggingface.apachecn.org/docs/diffusers/api/schedulers/euler_ancestral
原始地址:https://huggingface.co/docs/diffusers/api/schedulers/euler_ancestral
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
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py#L95)
(
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
, 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). - 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. You can use a combination ofoffset=1
andset_alpha_to_one=False
to make the last step use step 0 for the previous alpha product like in Stable Diffusion.
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
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py#L187)
(
sample
: FloatTensor
timestep
: typing.Union[float, torch.FloatTensor]
)
→
export const metadata = 'undefined';
torch.FloatTensor
Parameters
- sample
(
torch.FloatTensor
) — The input sample. - timestep
(
int
, optional ) — The current timestep in the diffusion chain.
Returns
export const metadata = 'undefined';
torch.FloatTensor
export const metadata = 'undefined';
A scaled input sample.
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_timesteps
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py#L213)
(
num_inference_steps
: int
device
: typing.Union[str, torch.device] = 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.
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py#L273)
(
model_output
: FloatTensor
timestep
: typing.Union[float, torch.FloatTensor]
sample
: FloatTensor
generator
: typing.Optional[torch._C.Generator] = None
return_dict
: bool = True
)
→
export const metadata = 'undefined';
EulerAncestralDiscreteSchedulerOutput
or
tuple
Parameters
- model_output
(
torch.FloatTensor
) — The direct output from learned diffusion model. - timestep
(
float
) — The current discrete timestep in the diffusion chain. - sample
(
torch.FloatTensor
) — 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
export const metadata = 'undefined';
EulerAncestralDiscreteSchedulerOutput
or
tuple
export const metadata = 'undefined';
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
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py#L33)
(
prev_sample
: FloatTensor
pred_original_sample
: typing.Optional[torch.FloatTensor] = None
)
Parameters
- prev_sample
(
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. - pred_original_sample
(
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.