code: def __init__(
      self,
      sigma_sampler_config: dict,
      loss_weighting_config: dict,
      loss_type: str = "l2",
      offset_noise_level: float = 0.0,
      batch2model_keys: Optional[Union[str, List[str]]] = None,
  ):
      super().__init__()
  
      assert loss_type in ["l2", "l1", "lpips"]
  
      self.sigma_sampler = instantiate_from_config(sigma_sampler_config)
      self.loss_weighting = instantiate_from_config(
          loss_weighting_config
      )
  
      self.loss_type = loss_type
      self.offset_noise_level = offset_noise_level
  
      if loss_type == "lpips":
          self.lpips = LPIPS().eval()
  
      if not batch2model_keys:
          batch2model_keys = []
  
      if isinstance(batch2model_keys, str):
          batch2model_keys = [batch2model_keys]
  
      self.batch2model_keys = set(batch2model_keys)
  
file_path: generative-models/sgm/modules/diffusionmodules/loss.py
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from ...modules.autoencoding.lpips.loss.lpips import LPIPS
  from ...modules.encoders.modules import GeneralConditioner
  from ...util import append_dims, instantiate_from_config
  from .denoiser import Denoiser
label_desc: Use image node_id 1 to create a new StandardDiffusionLoss object from inherited base classes: nn with example: obj = StandardDiffusionLoss(sigma_sampler_config, loss_weighting_config, loss_type, offset_noise_level, batch2model_keys)