PNDMScheduler
译者:片刻小哥哥
项目地址:https://huggingface.apachecn.org/docs/diffusers/api/schedulers/pndm
原始地址:https://huggingface.co/docs/diffusers/api/schedulers/pndm
PNDMScheduler
, or pseudo numerical methods for diffusion models, uses more advanced ODE integration techniques like the Runge-Kutta and linear multi-step method. The original implementation can be found at
crowsonkb/k-diffusion
.
PNDMScheduler
class
diffusers.
PNDMScheduler
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_pndm.py#L72)
(
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
skip_prk_steps
: bool = False
set_alpha_to_one
: bool = False
prediction_type
: str = 'epsilon'
timestep_spacing
: str = 'leading'
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
,scaled_linear
, orsquaredcos_cap_v2
. - trained_betas
(
np.ndarray
, optional ) — Pass an array of betas directly to the constructor to bypassbeta_start
andbeta_end
. - skip_prk_steps
(
bool
, defaults toFalse
) — Allows the scheduler to skip the Runge-Kutta steps defined in the original paper as being required before PLMS steps. - set_alpha_to_one
(
bool
, defaults toFalse
) — Each diffusion step uses the alphas product value at that step and at the previous one. For the final step there is no previous alpha. When this option isTrue
the previous alpha product is fixed to1
, otherwise it uses the alpha value at step 0. - prediction_type
(
str
, defaults toepsilon
, optional ) — Prediction type of the scheduler function; can beepsilon
(predicts the noise of the diffusion process) orv_prediction
(see section 2.4 of Imagen Video paper). - timestep_spacing
(
str
, defaults to"leading"
) — 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.
PNDMScheduler
uses pseudo numerical methods for diffusion models such as the Runge-Kutta and linear multi-step
method.
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_pndm.py#L394)
(
sample
: FloatTensor
*args
**kwargs
)
→
export const metadata = 'undefined';
torch.FloatTensor
Parameters
- sample
(
torch.FloatTensor
) — The input sample.
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.
set_timesteps
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_pndm.py#L170)
(
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_pndm.py#L230)
(
model_output
: FloatTensor
timestep
: int
sample
: FloatTensor
return_dict
: bool = True
)
→
export const metadata = 'undefined';
SchedulerOutput
or
tuple
Parameters
- model_output
(
torch.FloatTensor
) — The direct output from learned diffusion model. - timestep
(
int
) — The current discrete timestep in the diffusion chain. - sample
(
torch.FloatTensor
) — A current instance of a sample created by the diffusion process. - return_dict
(
bool
) — Whether or not to return a SchedulerOutput ortuple
.
Returns
export const metadata = 'undefined';
SchedulerOutput
or
tuple
export const metadata = 'undefined';
If return_dict is
True
,
SchedulerOutput
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), and calls
step_prk()
or
step_plms()
depending on the internal variable
counter
.
step_plms
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_pndm.py#L323)
(
model_output
: FloatTensor
timestep
: int
sample
: FloatTensor
return_dict
: bool = True
)
→
export const metadata = 'undefined';
SchedulerOutput
or
tuple
Parameters
- model_output
(
torch.FloatTensor
) — The direct output from learned diffusion model. - timestep
(
int
) — The current discrete timestep in the diffusion chain. - sample
(
torch.FloatTensor
) — A current instance of a sample created by the diffusion process. - return_dict
(
bool
) — Whether or not to return a SchedulerOutput or tuple.
Returns
export const metadata = 'undefined';
SchedulerOutput
or
tuple
export const metadata = 'undefined';
If return_dict is
True
,
SchedulerOutput
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 sample with the linear multistep method. It performs one forward pass multiple times to approximate the solution.
step_prk
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_pndm.py#L263)
(
model_output
: FloatTensor
timestep
: int
sample
: FloatTensor
return_dict
: bool = True
)
→
export const metadata = 'undefined';
SchedulerOutput
or
tuple
Parameters
- model_output
(
torch.FloatTensor
) — The direct output from learned diffusion model. - timestep
(
int
) — The current discrete timestep in the diffusion chain. - sample
(
torch.FloatTensor
) — A current instance of a sample created by the diffusion process. - return_dict
(
bool
) — Whether or not to return a SchedulerOutput or tuple.
Returns
export const metadata = 'undefined';
SchedulerOutput
or
tuple
export const metadata = 'undefined';
If return_dict is
True
,
SchedulerOutput
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 sample with the Runge-Kutta method. It performs four forward passes to approximate the solution to the differential equation.
SchedulerOutput
class
diffusers.schedulers.scheduling_utils.
SchedulerOutput
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_utils.py#L50)
(
prev_sample
: FloatTensor
)
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.
Base class for the output of a scheduler’s
step
function.