class_docstr: Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet-XS 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
      - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
      - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
  
  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)).
      text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):
          Second frozen text-encoder
          ([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).
      tokenizer ([`~transformers.CLIPTokenizer`]):
          A `CLIPTokenizer` to tokenize text.
      tokenizer_2 ([`~transformers.CLIPTokenizer`]):
          A `CLIPTokenizer` to tokenize text.
      unet ([`UNet2DConditionModel`]):
          A `UNet2DConditionModel` to denoise the encoded image latents.
      controlnet ([`ControlNetXSModel`]:
          Provides additional conditioning to the `unet` during the denoising process.
      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`].
      force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
          Whether the negative prompt embeddings should 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](https://github.com/ShieldMnt/invisible-watermark/) library to
          watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no
          watermarker is used.
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 CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
  from diffusers.loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, ControlNetXSModel, UNet2DConditionModel
  from diffusers.models.attention_processor import AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
  from diffusers.models.lora import adjust_lora_scale_text_encoder
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.import_utils import is_invisible_watermark_available
  from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
label_desc: Use image node_id 4 for calling the StableDiffusionXLControlNetXSPipeline obj's underlying member method code with example usage: obj.enable_vae_tiling() without return types