class_docstr: Pipeline for image inpainting using Stable Diffusion with ControlNet guidance.
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
  implemented for all pipelines (downloading, saving, running on a particular device, etc.).
  
  The pipeline also inherits the following loading methods:
      - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
  
  <Tip>
  
  This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting
  ([runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)) as well as
  default text-to-image Stable Diffusion checkpoints
  ([runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)). Default text-to-image
  Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned on those, such as
  [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).
  
  </Tip>
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      text_encoder ([`~transformers.CLIPTextModel`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      tokenizer ([`~transformers.CLIPTokenizer`]):
          A `CLIPTokenizer` to tokenize text.
      unet ([`UNet2DConditionModel`]):
          A `UNet2DConditionModel` to denoise the encoded image latents.
      controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
          Provides additional conditioning to the `unet` during the denoising process. If you set multiple
          ControlNets as a list, the outputs from each ControlNet are added together to create one combined
          additional conditioning.
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
          [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
      safety_checker ([`StableDiffusionSafetyChecker`]):
          Classification module that estimates whether generated images could be considered offensive or harmful.
          Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
          about a model's potential harms.
      feature_extractor ([`~transformers.CLIPImageProcessor`]):
          A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
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docstr: Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
  compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
  processing larger images.
code: def enable_vae_tiling(self):
      r"""
      Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
      compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
      processing larger images.
      """
      self.vae.enable_tiling()
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import is_compiled_module, randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from ..stable_diffusion import StableDiffusionPipelineOutput
  from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from .multicontrolnet import MultiControlNetModel
label_desc: Use image node_id 4 for calling the StableDiffusionControlNetInpaintPipeline obj's underlying member method code with example usage: obj.enable_vae_tiling() without return types