code: def __init__(self):
      self.vae = AutoencoderKL(
          in_channels=3,
          out_channels=3,
          down_block_types=(
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
          ),
          up_block_types=(
              "UpDecoderBlock2D",
              "UpDecoderBlock2D",
              "UpDecoderBlock2D",
              "UpDecoderBlock2D",
          ),
          block_out_channels=(128, 256, 512, 512),
          layers_per_block=2,
          act_fn="silu",
          latent_channels=4,
          sample_size=512,
      )
  
      self.unet = UNet2DConditionModel(
          sample_size=64,
          in_channels=4,
          out_channels=4,
          center_input_sample=False,
          flip_sin_to_cos=True,
          freq_shift=0,
          down_block_types=(
              "CrossAttnDownBlock2D",
              "CrossAttnDownBlock2D",
              "CrossAttnDownBlock2D",
              "DownBlock2D",
          ),
          up_block_types=(
              "UpBlock2D",
              "CrossAttnUpBlock2D",
              "CrossAttnUpBlock2D",
              "CrossAttnUpBlock2D",
          ),
          block_out_channels=(320, 640, 1280, 1280),
          layers_per_block=2,
          downsample_padding=1,
          mid_block_scale_factor=1,
          act_fn="silu",
          norm_num_groups=32,
          norm_eps=1e-5,
          cross_attention_dim=768,
          attention_head_dim=8,
      )
  
      vae_path = os.path.join(
          self.directory, "pre_trained", "waifu-vae.pdparams"
      )
      unet_path = os.path.join(
          self.directory, "pre_trained", "waifu-unet.pdparams"
      )
      self.unet.set_dict(paddle.load(unet_path))
      self.vae.set_dict(paddle.load(vae_path))
      for parameter in self.unet.parameters():
          parameter.stop_gradient = True
      self.vae.eval()
      for parameter in self.vae.parameters():
          parameter.stop_gradient = True
      self.unet.eval()
  
      self.text_encoder = build_model()
      for parameter in self.text_encoder.parameters():
          parameter.stop_gradient = True
      self.scheduler = PNDMScheduler(
          beta_start=0.00085,
          beta_end=0.012,
          beta_schedule="scaled_linear",
          num_train_timesteps=1000,
          skip_prk_steps=True,
      )
  
imports: import argparse
  import ast
  import os
  import random
  import sys
  from functools import partial
  from typing import List
  from typing import Optional
  import numpy
  import paddle
  from docarray import Document
  from docarray import DocumentArray
  from IPython import display
  from PIL import Image
  from stable_diffusion.clip.clip.utils import build_model
  from stable_diffusion.clip.clip.utils import tokenize
  from stable_diffusion.diffusers import AutoencoderKL
  from stable_diffusion.diffusers import DDIMScheduler
  from stable_diffusion.diffusers import LMSDiscreteScheduler
  from stable_diffusion.diffusers import PNDMScheduler
  from stable_diffusion.diffusers import UNet2DConditionModel
  from tqdm.auto import tqdm
  import paddlehub
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import runnable
  from paddlehub.module.module import serving
label_desc: Use image node_id 1 to create a new StableDiffusion object  with example: obj = StableDiffusion()