code: def from_pretrained(cls, pretrained_model_path, **kwargs):
      pipe = cls()
      pipe.device = torch.device("cpu")
      pipe.vae = AutoencoderKL.from_pretrained(
          pretrained_model_path, subfolder="vae"
      )
      vae_decoder_path = pipe._get_cache_path(
          pretrained_model_path, vae=True, **kwargs
      )
      if vae_decoder_path:
          print(
              f"Loading existing optimized vae decoder from {vae_decoder_path}..."
          )
          decoder = InferenceOptimizer.load(
              vae_decoder_path, device=kwargs["device"]
          )
          setattr(pipe.vae, "decoder", decoder)
      cache_path = pipe._get_cache_path(
          pretrained_model_path, **kwargs
      )  # get model path by optimization pipeline(accelerator), precision
      if "int8" in cache_path:
          # load inc model needs original model
          unet = UNet2DConditionModel.from_pretrained(
              pretrained_model_path, subfolder="unet"
          )
          pipe.unet = InferenceOptimizer.load(cache_path, model=unet)
      else:
          pipe.unet = InferenceOptimizer.load(
              cache_path, device=kwargs["device"]
          )
      setattr(pipe.unet, "in_channels", 4)
      pipe.tokenizer = CLIPTokenizer.from_pretrained(
          pretrained_model_path + "/tokenizer"
      )
      pipe.text_encoder = CLIPTextModel.from_pretrained(
          pretrained_model_path + "/text_encoder"
      )
      pipe.safety_checker = None
      pipe.feature_extractor = None
      pipe.scheduler = None
      return pipe
  
imports: import os
  import torch
  import inspect
  import numpy
  from tqdm.auto import tqdm
  import multiprocessing
  from bigdl.nano.pytorch import InferenceOptimizer
  from typing import Callable, List, Optional, Union
  from diffusers import AutoencoderKL
  from transformers import CLIPTokenizer, CLIPTextModel
  from diffusers.schedulers import *
  from diffusers.utils import logging
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipeline_utils import DiffusionPipeline
  from diffusers.models import UNet2DConditionModel
label_desc: Use image node_id 2 for calling the NanoStableDiffusionPipeline obj's underlying member method code with example usage: obj.from_pretrained(cls, pretrained_model_path) and returns: pipe