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.
( 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
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
, scaled_linear
, or squaredcos_cap_v2
. np.ndarray
, optional) —
Pass an array of betas directly to the constructor to bypass beta_start
and beta_end
. bool
, defaults to False
) —
Allows the scheduler to skip the Runge-Kutta steps defined in the original paper as being required before
PLMS steps. bool
, defaults to False
) —
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 is True
the previous alpha product is fixed to 1
,
otherwise it uses the alpha value at step 0. str
, defaults to epsilon
, optional) —
Prediction type of the scheduler function; can be epsilon
(predicts the noise of the diffusion process)
or v_prediction
(see section 2.4 of Imagen Video
paper). 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. 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. 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.
( sample: FloatTensor *args **kwargs ) → torch.FloatTensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
( 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: int sample: FloatTensor return_dict: bool = True ) → SchedulerOutput or tuple
Parameters
torch.FloatTensor
) —
The direct output from learned diffusion model. int
) —
The current discrete timestep in the diffusion chain. torch.FloatTensor
) —
A current instance of a sample created by the diffusion process. bool
) —
Whether or not to return a SchedulerOutput or tuple
. Returns
SchedulerOutput or tuple
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
.
( model_output: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) → SchedulerOutput or tuple
Parameters
torch.FloatTensor
) —
The direct output from learned diffusion model. int
) —
The current discrete timestep in the diffusion chain. torch.FloatTensor
) —
A current instance of a sample created by the diffusion process. bool
) —
Whether or not to return a SchedulerOutput or tuple. Returns
SchedulerOutput or tuple
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.
( model_output: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) → SchedulerOutput or tuple
Parameters
torch.FloatTensor
) —
The direct output from learned diffusion model. int
) —
The current discrete timestep in the diffusion chain. torch.FloatTensor
) —
A current instance of a sample created by the diffusion process. bool
) —
Whether or not to return a SchedulerOutput or tuple. Returns
SchedulerOutput or tuple
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.
( prev_sample: FloatTensor )
Base class for the output of a scheduler’s step
function.