class_docstr: Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).
  
  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 ([`~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.
      processor ([`~transformers.CLIPProcessor`]):
          A `CLIPProcessor` to procces reference image.
      image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
          Frozen image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      image_project ([`CLIPImageProjection`]):
          A `CLIPImageProjection` to project image embedding into phrases embedding space.
      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`.
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
  import warnings
  from typing import Any, Callable, Dict, List, Optional, Union
  import PIL.Image
  import torch
  from transformers import CLIPFeatureExtractor, CLIPProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...models.attention import GatedSelfAttentionDense
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from ..stable_diffusion import StableDiffusionPipelineOutput
  from ..stable_diffusion.clip_image_project_model import CLIPImageProjection
  from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 3 for calling the StableDiffusionGLIGENTextImagePipeline obj's underlying member method code with example usage: obj.disable_vae_slicing() without return types