PNDMScheduler
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 >( 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 startingbetavalue of inference. - beta_end (
float, defaults to 0.02) — The finalbetavalue. - 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_startandbeta_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 isTruethe 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=1andset_alpha_to_one=Falseto 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 >( sample: FloatTensor *args **kwargs ) → torch.FloatTensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
set_timesteps
< source >( 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).
step
< source >( model_output: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) → 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
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.
step_plms
< source >( model_output: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) → 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
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.
step_prk
< source >( model_output: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) → 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
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.
SchedulerOutput
class diffusers.schedulers.scheduling_utils.SchedulerOutput
< source >( prev_sample: FloatTensor )
Base class for the output of a scheduler’s step function.