class_docstr: Pipeline for text-to-image generation using Stable Diffusion.
  
  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.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
      - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
      - [`~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 ([`~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.
      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`.
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docstr: Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
  computing decoding in one step.
docstr_tok: ["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, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from packaging import version
  from safetensors import safe_open
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
  from diffusers.configuration_utils import FrozenDict
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.attention_processor import FusedAttnProcessor2_0
  from diffusers.models.lora import LoRALinearLayer, adjust_lora_scale_text_encoder
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import USE_PEFT_BACKEND, _get_model_file, deprecate, logging, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 7 for calling the IPAdapterFaceIDStableDiffusionPipeline obj's underlying member method code with example usage: obj.disable_vae_slicing() without return types