IPNDMScheduler
译者：片刻小哥哥
项目地址：https://huggingface.apachecn.org/docs/diffusers/api/schedulers/ipndm
原始地址：https://huggingface.co/docs/diffusers/api/schedulers/ipndm
IPNDMScheduler
is a fourth-order Improved Pseudo Linear Multistep scheduler. The original implementation can be found at
crowsonkb/v-diffusion-pytorch
.
IPNDMScheduler
class
diffusers.
IPNDMScheduler
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_ipndm.py#L25)
(
num_train_timesteps
: int = 1000
trained_betas
: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None
)
Parameters
- num_train_timesteps
(
int, defaults to 1000) — The number of diffusion steps to train the model. - trained_betas
(
np.ndarray, optional ) — Pass an array of betas directly to the constructor to bypassbeta_startandbeta_end.
A fourth-order Improved Pseudo Linear Multistep 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.
scale_model_input
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_ipndm.py#L170)
(
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_ipndm.py#L67)
(
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
(
strortorch.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_ipndm.py#L112)
(
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.
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.FloatTensorof shape(batch_size, num_channels, height, width)for images) — Computed sample(x_{t-1})of previous timestep.prev_sampleshould be used as next model input in the denoising loop.
Base class for the output of a scheduler’s
step
function.
