code: def __init__(
      self,
      vae: AutoencoderKL,
      text_encoder: CLIPTextModel,
      tokenizer: CLIPTokenizer,
      unet: UNet2DConditionModel,
      scheduler: Union[DDIMScheduler, PNDMScheduler],
      safety_checker: StableDiffusionSafetyChecker,
      feature_extractor: CLIPFeatureExtractor,
  ):
      super().__init__()
      self.register_modules(
          vae=vae,
          text_encoder=text_encoder,
          tokenizer=tokenizer,
          unet=unet,
          scheduler=scheduler,
          safety_checker=safety_checker,
          feature_extractor=feature_extractor,
      )
  
imports: import inspect
  from copy import deepcopy
  from enum import Enum
  from typing import List, Optional, Tuple, Union
  import torch
  from tqdm.auto import tqdm
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
  from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils import logging
label_desc: Use image node_id 1 to create a new StableDiffusionTilingPipeline object from inherited base classes: DiffusionPipeline, StableDiffusionExtrasMixin with example: obj = StableDiffusionTilingPipeline(vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor)