PNDMScheduler
Last updated
Last updated
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 .
( num_train_timesteps: int = 1000beta_start: float = 0.0001beta_end: float = 0.02beta_schedule: str = 'linear'trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = Noneskip_prk_steps: bool = Falseset_alpha_to_one: bool = Falseprediction_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 starting beta value of inference.
beta_end (float, defaults to 0.02) — The final beta 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 from linear, scaled_linear, or squaredcos_cap_v2.
trained_betas (np.ndarray, optional) — Pass an array of betas directly to the constructor to bypass beta_start and beta_end.
skip_prk_steps (bool, defaults to False) — 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 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.
prediction_type (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 paper).
timestep_spacing (str, defaults to "leading") — The way the timesteps should be scaled. Refer to Table 2 of the for more information.
steps_offset (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.
scale_model_input
( sample: FloatTensor*args**kwargs ) → torch.FloatTensor
Parameters
sample (torch.FloatTensor) — The input sample.
Returns
torch.FloatTensor
A scaled input sample.
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
set_timesteps
( num_inference_steps: intdevice: 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 or torch.device, optional) — The device to which the timesteps should be moved to. If None, the timesteps are not moved.
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
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.
Returns
step_plms
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.
Returns
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
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.
Returns
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 )
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.
This model inherits from and . Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.
( model_output: FloatTensortimestep: intsample: FloatTensorreturn_dict: bool = True ) → or tuple
return_dict (bool) — Whether or not to return a or tuple.
or tuple
If return_dict is True, 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 or depending on the internal variable counter.
( model_output: FloatTensortimestep: intsample: FloatTensorreturn_dict: bool = True ) → or tuple
return_dict (bool) — Whether or not to return a or tuple.
or tuple
If return_dict is True, is returned, otherwise a tuple is returned where the first element is the sample tensor.
( model_output: FloatTensortimestep: intsample: FloatTensorreturn_dict: bool = True ) → or tuple
return_dict (bool) — Whether or not to return a or tuple.
or tuple
If return_dict is True, is returned, otherwise a tuple is returned where the first element is the sample tensor.