code: def attention_from_original_checkpoint(
      model, diffuser_attention_prefix, original_attention_prefix
  ):
      attention = {}
      attention.update(
          {
              f"{diffuser_attention_prefix}.attention.query.weight": model[
                  f"{original_attention_prefix}.self.query.weight"
              ]
          }
      )
      attention.update(
          {
              f"{diffuser_attention_prefix}.attention.query.bias": model[
                  f"{original_attention_prefix}.self.query.bias"
              ]
          }
      )
      attention.update(
          {
              f"{diffuser_attention_prefix}.attention.key.weight": model[
                  f"{original_attention_prefix}.self.key.weight"
              ]
          }
      )
      attention.update(
          {
              f"{diffuser_attention_prefix}.attention.key.bias": model[
                  f"{original_attention_prefix}.self.key.bias"
              ]
          }
      )
      attention.update(
          {
              f"{diffuser_attention_prefix}.attention.value.weight": model[
                  f"{original_attention_prefix}.self.value.weight"
              ]
          }
      )
      attention.update(
          {
              f"{diffuser_attention_prefix}.attention.value.bias": model[
                  f"{original_attention_prefix}.self.value.bias"
              ]
          }
      )
      attention.update(
          {
              f"{diffuser_attention_prefix}.output.dense.weight": model[
                  f"{original_attention_prefix}.output.dense.weight"
              ]
          }
      )
      attention.update(
          {
              f"{diffuser_attention_prefix}.output.dense.bias": model[
                  f"{original_attention_prefix}.output.dense.bias"
              ]
          }
      )
      attention.update(
          {
              f"{diffuser_attention_prefix}.output.LayerNorm.weight": model[
                  f"{original_attention_prefix}.output.LayerNorm.weight"
              ]
          }
      )
      attention.update(
          {
              f"{diffuser_attention_prefix}.output.LayerNorm.bias": model[
                  f"{original_attention_prefix}.output.LayerNorm.bias"
              ]
          }
      )
      return attention
  
imports: import argparse
  import os
  import tempfile
  import torch
  from lavis.models import load_model_and_preprocess
  from transformers import CLIPTokenizer
  from transformers.models.blip_2.configuration_blip_2 import Blip2Config
  from diffusers import AutoencoderKL, PNDMScheduler, UNet2DConditionModel
  from diffusers.pipelines import BlipDiffusionPipeline
  from diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor
  from diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel
  from diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel
label_desc: Use image node_id 4 for calling a global function with example usage: attention_from_original_checkpoint(model, diffuser_attention_prefix, original_attention_prefix) and returns: attention