class_docstr: Pipeline for image-to-image generation using Stable Diffusion XL with ControlNet guidance.
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
  library implements for all the pipelines (such as downloading or 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
      - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
      - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
      text_encoder ([`CLIPTextModel`]):
          Frozen text-encoder. Stable Diffusion uses the text portion of
          [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
          the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
      text_encoder_2 ([` CLIPTextModelWithProjection`]):
          Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
          [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
          specifically the
          [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
          variant.
      tokenizer (`CLIPTokenizer`):
          Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      tokenizer_2 (`CLIPTokenizer`):
          Second Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      unet ([`UNet2DConditionModel`]): Conditional U-Net architecture 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`].
      requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
          Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the
          config of `stabilityai/stable-diffusion-xl-refiner-1-0`.
      force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
          Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
          `stabilityai/stable-diffusion-xl-base-1-0`.
      add_watermarker (`bool`, *optional*):
          Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
          watermark output images. If not defined, it will default to True if the package is installed, otherwise no
          watermarker will be used.
      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.
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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, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
  from diffusers.utils.import_utils import is_invisible_watermark_available
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...loaders import IPAdapterMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel
  from ...models.attention_processor import AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
  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_xl.pipeline_output import StableDiffusionXLPipelineOutput
  from .multicontrolnet import MultiControlNetModel
label_desc: Use image node_id 4 for calling the StableDiffusionXLControlNetImg2ImgPipeline obj's underlying member method code with example usage: obj.enable_vae_tiling() without return types