class_docstr: The variance exploding stochastic differential equation (SDE) scheduler.
  
  :param snr: coefficient weighting the step from the model_output sample (from the network) to the random noise.
  :param sigma_min: initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the
          distribution of the data.
  :param sigma_max: :param sampling_eps: the end value of sampling, where timesteps decrease progessively from 1 to
  epsilon. :param correct_steps: number of correction steps performed on a produced sample. :param tensor_format:
  "np" or "pd" for the expected format of samples passed to the Scheduler.
class_docstr_tok: ["The","variance","exploding","stochastic","differential","equation","(","SDE",")","scheduler",".",":","param","snr",":","coefficient","weighting","the","step","from","the","model_output","sample","(","from","the","network",")","to","the","random","noise",".",":","param","sigma_min",":","initial","noise","scale","for","sigma","sequence","in","sampling","procedure",".","The","minimum","sigma","should","mirror","the","distribution","of","the","data",".",":","param","sigma_max",":",":","param","sampling_eps",":","the","end","value","of","sampling",",","where","timesteps","decrease","progessively","from","1","to","epsilon",".",":","param","correct_steps",":","number","of","correction","steps","performed","on","a","produced","sample",".",":","param","tensor_format",":","''","np","''","or","``","pd","''","for","the","expected","format","of","samples","passed","to","the","Scheduler","."]
code: def __init__(
      self,
      num_train_timesteps=2000,
      snr=0.15,
      sigma_min=0.01,
      sigma_max=1348,
      sampling_eps=1e-5,
      correct_steps=1,
      tensor_format="pd",
  ):
      # self.sigmas = None
      # self.discrete_sigmas = None
      #
      # # setable values
      # self.num_inference_steps = None
      self.timesteps = None
  
      self.set_sigmas(
          num_train_timesteps, sigma_min, sigma_max, sampling_eps
      )
  
      self.tensor_format = tensor_format
      self.set_format(tensor_format=tensor_format)
  
file_path: PaddleHub/modules/image/text_to_image/stable_diffusion_inpainting/diffusers/schedulers/scheduling_sde_ve.py
imports: from typing import Union
  import numpy
  import paddle
  from ..configuration_utils import ConfigMixin
  from ..configuration_utils import register_to_config
  from .scheduling_utils import SchedulerMixin
label_desc: Use image node_id 1 to create a new ScoreSdeVeScheduler object from inherited base classes: SchedulerMixin, ConfigMixin with example: obj = ScoreSdeVeScheduler(num_train_timesteps, snr, sigma_min, sigma_max, sampling_eps, correct_steps, tensor_format)