code: def superres_create_unet_diffusers_config(original_unet_config):
      attention_resolutions = parse_list(
          original_unet_config["attention_resolutions"]
      )
      attention_resolutions = [
          original_unet_config["image_size"] // int(res)
          for res in attention_resolutions
      ]
  
      channel_mult = parse_list(original_unet_config["channel_mult"])
      block_out_channels = [
          original_unet_config["model_channels"] * mult
          for mult in channel_mult
      ]
  
      down_block_types = []
      resolution = 1
  
      for i in range(len(block_out_channels)):
          if resolution in attention_resolutions:
              block_type = "SimpleCrossAttnDownBlock2D"
          elif original_unet_config["resblock_updown"]:
              block_type = "ResnetDownsampleBlock2D"
          else:
              block_type = "DownBlock2D"
  
          down_block_types.append(block_type)
  
          if i != len(block_out_channels) - 1:
              resolution *= 2
  
      up_block_types = []
      for i in range(len(block_out_channels)):
          if resolution in attention_resolutions:
              block_type = "SimpleCrossAttnUpBlock2D"
          elif original_unet_config["resblock_updown"]:
              block_type = "ResnetUpsampleBlock2D"
          else:
              block_type = "UpBlock2D"
          up_block_types.append(block_type)
          resolution //= 2
  
      head_dim = original_unet_config["num_head_channels"]
      use_linear_projection = (
          original_unet_config["use_linear_in_transformer"]
          if "use_linear_in_transformer" in original_unet_config
          else False
      )
      if use_linear_projection:
          # stable diffusion 2-base-512 and 2-768
          if head_dim is None:
              head_dim = [5, 10, 20, 20]
  
      class_embed_type = None
      projection_class_embeddings_input_dim = None
  
      if "num_classes" in original_unet_config:
          if original_unet_config["num_classes"] == "sequential":
              class_embed_type = "projection"
              assert "adm_in_channels" in original_unet_config
              projection_class_embeddings_input_dim = (
                  original_unet_config["adm_in_channels"]
              )
          else:
              raise NotImplementedError(
                  f"Unknown conditional unet num_classes config: {original_unet_config['num_classes']}"
              )
  
      config = {
          "in_channels": original_unet_config["in_channels"],
          "down_block_types": tuple(down_block_types),
          "block_out_channels": tuple(block_out_channels),
          "layers_per_block": tuple(
              original_unet_config["num_res_blocks"]
          ),
          "cross_attention_dim": original_unet_config[
              "encoder_channels"
          ],
          "attention_head_dim": head_dim,
          "use_linear_projection": use_linear_projection,
          "class_embed_type": class_embed_type,
          "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
          "out_channels": original_unet_config["out_channels"],
          "up_block_types": tuple(up_block_types),
          "upcast_attention": False,  # TODO: guessing
          "cross_attention_norm": "group_norm",
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "act_fn": "gelu",
      }
  
      if original_unet_config["use_scale_shift_norm"]:
          config["resnet_time_scale_shift"] = "scale_shift"
  
      return config
  
imports: import argparse
  import inspect
  import os
  import numpy
  import torch
  import yaml
  from torch.nn import functional
  from transformers import CLIPConfig, CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, T5Tokenizer
  from diffusers import DDPMScheduler, IFPipeline, IFSuperResolutionPipeline, UNet2DConditionModel
  from diffusers.pipelines.deepfloyd_if.safety_checker import IFSafetyChecker
label_desc: Use image node_id 17 for calling a global function with example usage: superres_create_unet_diffusers_config(original_unet_config) and returns: config