code: def save_blip_diffusion_model(model, args):
      qformer = get_qformer(model)
      qformer.eval()
  
      text_encoder = ContextCLIPTextModel.from_pretrained(
          "runwayml/stable-diffusion-v1-5", subfolder="text_encoder"
      )
      vae = AutoencoderKL.from_pretrained(
          "runwayml/stable-diffusion-v1-5", subfolder="vae"
      )
  
      unet = UNet2DConditionModel.from_pretrained(
          "runwayml/stable-diffusion-v1-5", subfolder="unet"
      )
      vae.eval()
      text_encoder.eval()
      scheduler = PNDMScheduler(
          beta_start=0.00085,
          beta_end=0.012,
          beta_schedule="scaled_linear",
          set_alpha_to_one=False,
          skip_prk_steps=True,
      )
      tokenizer = CLIPTokenizer.from_pretrained(
          "runwayml/stable-diffusion-v1-5", subfolder="tokenizer"
      )
      image_processor = BlipImageProcessor()
      blip_diffusion = BlipDiffusionPipeline(
          tokenizer=tokenizer,
          text_encoder=text_encoder,
          vae=vae,
          unet=unet,
          scheduler=scheduler,
          qformer=qformer,
          image_processor=image_processor,
      )
      blip_diffusion.save_pretrained(args.checkpoint_path)
  
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 12 for calling a global function with example usage: save_blip_diffusion_model(model, args) without return types