code: def unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]:
      r"""
      Returns:
          a state dict containing just the attention processor parameters.
      """
      attn_processors = unet.attn_processors
  
      attn_processors_state_dict = {}
  
      for attn_processor_key, attn_processor in attn_processors.items():
          for (
              parameter_key,
              parameter,
          ) in attn_processor.state_dict().items():
              attn_processors_state_dict[
                  f"{attn_processor_key}.{parameter_key}"
              ] = parameter
  
      return attn_processors_state_dict
  
imports: import hashlib
  import itertools
  import os
  from pathlib import Path
  from typing import Dict
  import torch
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionXLPipeline, UNet2DConditionModel
  from diffusers.utils.import_utils import is_xformers_available
  from packaging import version
  from tqdm import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  from autotrain import logger
  from autotrain.trainers.dreambooth.datasets import PromptDataset
label_desc: Use image node_id 6 for calling a global function with example usage: unet_attn_processors_state_dict(unet) and returns: attn_processors_state_dict