class_docstr: Pipeline for image-to-image generation 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
  
  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`.
docstr: Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
  computing decoding in one step.
docstr_tok: ["Disable","tiled","VAE","decoding",".","If","`","enable_vae_tiling","`","was","previously","enabled",",","this","method","will","go","back","to","computing","decoding","in","one","step","."]
code: def disable_vae_tiling(self):
      r"""
      Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
      computing decoding in one step.
      """
      self.vae.disable_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 5 for calling the StableDiffusionControlNetImg2ImgPipeline obj's underlying member method code with example usage: obj.disable_vae_tiling() without return types