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: Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
  compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
code: def enable_vae_slicing(self):
      r"""
      Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
      compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
      """
      self.vae.enable_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 2 for calling the StableDiffusionGLIGENTextImagePipeline obj's underlying member method code with example usage: obj.enable_vae_slicing() without return types