KDPM2AncestralDiscreteScheduler
译者:片刻小哥哥
项目地址:https://huggingface.apachecn.org/docs/diffusers/api/schedulers/dpm_discrete_ancestral
原始地址:https://huggingface.co/docs/diffusers/api/schedulers/dpm_discrete_ancestral
The
KDPM2DiscreteScheduler
with ancestral sampling is inspired by the
Elucidating the Design Space of Diffusion-Based Generative Models
paper, and the scheduler is ported from and created by
Katherine Crowson
.
The original codebase can be found at crowsonkb/k-diffusion .
KDPM2AncestralDiscreteScheduler
class
diffusers.
KDPM2AncestralDiscreteScheduler
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py#L72)
(
num_train_timesteps
: int = 1000
beta_start
: float = 0.00085
beta_end
: float = 0.012
beta_schedule
: str = 'linear'
trained_betas
: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None
use_karras_sigmas
: typing.Optional[bool] = False
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 0.00085) — The startingbeta
value of inference. - beta_end
(
float
, defaults to 0.012) — 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
. - use_karras_sigmas
(
bool
, optional , defaults toFalse
) — Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. IfTrue
, the sigmas are determined according to a sequence of noise levels {σi}. - 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.
KDPM2DiscreteScheduler with ancestral sampling is inspired by the DPMSolver2 and Algorithm 2 from the Elucidating the Design Space of Diffusion-Based Generative Models paper.
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_k_dpm_2_ancestral_discrete.py#L180)
(
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.
set_timesteps
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py#L210)
(
num_inference_steps
: int
device
: typing.Union[str, torch.device] = None
num_train_timesteps
: typing.Optional[int] = 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_k_dpm_2_ancestral_discrete.py#L361)
(
model_output
: typing.Union[torch.FloatTensor, numpy.ndarray]
timestep
: typing.Union[float, torch.FloatTensor]
sample
: typing.Union[torch.FloatTensor, numpy.ndarray]
generator
: typing.Optional[torch._C.Generator] = None
return_dict
: bool = True
)
→
export const metadata = 'undefined';
SchedulerOutput
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 SchedulerOutput or tuple.
Returns
export const metadata = 'undefined';
SchedulerOutput
or
tuple
export const metadata = 'undefined';
If return_dict is
True
,
~schedulers.scheduling_ddim.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).
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.