ample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
This function 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

)
→
~scheduling_utils.SchedulerOutput or tuple

Parameters 

model_output (torch