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: Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
  computing decoding in one step.
code: def disable_vae_slicing(self):
      r"""
      Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
      computing decoding in one step.
      """
      self.vae.disable_slicing()
  
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 3 for calling the StableDiffusionXLControlNetXSPipeline obj's underlying member method code with example usage: obj.disable_vae_slicing() without return types