class_docstr: Pipeline for text-to-image generation using Stable Diffusion.
  
  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.)
  
  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.
      tokenizer (`CLIPTokenizer`):
          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.
      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 details.
      feature_extractor ([`CLIPFeatureExtractor`]):
          Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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docstr: Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
  computing decoding in one step.
docstr_tok: ["Disable","sliced","VAE","decoding",".","If","`","enable_vae_slicing","`","was","previously","invoked",",","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 invoked, 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, Union
  import torch
  from packaging import version
  from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
  from diffusers.configuration_utils import FrozenDict
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import deprecate, is_accelerate_available, is_accelerate_version, logging, replace_example_docstring
  from diffusers.utils.torch_utils import randn_tensor
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 3 for calling the StableDiffusionPipeline obj's underlying member method code with example usage: obj.disable_vae_slicing() without return types