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: The call function to the pipeline for generation.
  
  Args:
      prompt (`str` or `List[str]`, *optional*):
          The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
      height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
          The height in pixels of the generated image.
      width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
          The width in pixels of the generated image.
      num_inference_steps (`int`, *optional*, defaults to 50):
          The number of denoising steps. More denoising steps usually lead to a higher quality image at the
          expense of slower inference.
      guidance_scale (`float`, *optional*, defaults to 7.5):
          A higher guidance scale value encourages the model to generate images closely linked to the text
          `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
      gligen_phrases (`List[str]`):
          The phrases to guide what to include in each of the regions defined by the corresponding
          `gligen_boxes`. There should only be one phrase per bounding box.
      gligen_images (`List[PIL.Image.Image]`):
          The images to guide what to include in each of the regions defined by the corresponding `gligen_boxes`.
          There should only be one image per bounding box
      input_phrases_mask (`int` or `List[int]`):
          pre phrases mask input defined by the correspongding `input_phrases_mask`
      input_images_mask (`int` or `List[int]`):
          pre images mask input defined by the correspongding `input_images_mask`
      gligen_boxes (`List[List[float]]`):
          The bounding boxes that identify rectangular regions of the image that are going to be filled with the
          content described by the corresponding `gligen_phrases`. Each rectangular box is defined as a
          `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].
      gligen_inpaint_image (`PIL.Image.Image`, *optional*):
          The input image, if provided, is inpainted with objects described by the `gligen_boxes` and
          `gligen_phrases`. Otherwise, it is treated as a generation task on a blank input image.
      gligen_scheduled_sampling_beta (`float`, defaults to 0.3):
          Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image
          Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for
          scheduled sampling during inference for improved quality and controllability.
      negative_prompt (`str` or `List[str]`, *optional*):
          The prompt or prompts to guide what to not include in image generation. If not defined, you need to
          pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
      num_images_per_prompt (`int`, *optional*, defaults to 1):
          The number of images to generate per prompt.
      eta (`float`, *optional*, defaults to 0.0):
          Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
          to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
      generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
          A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
          generation deterministic.
      latents (`torch.FloatTensor`, *optional*):
          Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
          generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
          tensor is generated by sampling using the supplied random `generator`.
      prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
          provided, text embeddings are generated from the `prompt` input argument.
      negative_prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
          not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
      output_type (`str`, *optional*, defaults to `"pil"`):
          The output format of the generated image. Choose between `PIL.Image` or `np.array`.
      return_dict (`bool`, *optional*, defaults to `True`):
          Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
          plain tuple.
      callback (`Callable`, *optional*):
          A function that calls every `callback_steps` steps during inference. The function is called with the
          following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
      callback_steps (`int`, *optional*, defaults to 1):
          The frequency at which the `callback` function is called. If not specified, the callback is called at
          every step.
      cross_attention_kwargs (`dict`, *optional*):
          A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
          [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
      gligen_normalize_constant (`float`, *optional*, defaults to 28.7):
          The normalize value of the image embedding.
      clip_skip (`int`, *optional*):
          Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
          the output of the pre-final layer will be used for computing the prompt embeddings.
  
  Examples:
  
  Returns:
      [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
          If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
          otherwise a `tuple` is returned where the first element is a list with the generated images and the
          second element is a list of `bool`s indicating whether the corresponding generated image contains
          "not-safe-for-work" (nsfw) content.
code: def __call__(
      self,
      prompt: Union[str, List[str]] = None,
      height: Optional[int] = None,
      width: Optional[int] = None,
      num_inference_steps: int = 50,
      guidance_scale: float = 7.5,
      gligen_scheduled_sampling_beta: float = 0.3,
      gligen_phrases: List[str] = None,
      gligen_images: List[PIL.Image.Image] = None,
      input_phrases_mask: Union[int, List[int]] = None,
      input_images_mask: Union[int, List[int]] = None,
      gligen_boxes: List[List[float]] = None,
      gligen_inpaint_image: Optional[PIL.Image.Image] = None,
      negative_prompt: Optional[Union[str, List[str]]] = None,
      num_images_per_prompt: Optional[int] = 1,
      eta: float = 0.0,
      generator: Optional[
          Union[torch.Generator, List[torch.Generator]]
      ] = None,
      latents: Optional[torch.FloatTensor] = None,
      prompt_embeds: Optional[torch.FloatTensor] = None,
      negative_prompt_embeds: Optional[torch.FloatTensor] = None,
      output_type: Optional[str] = "pil",
      return_dict: bool = True,
      callback: Optional[
          Callable[[int, int, torch.FloatTensor], None]
      ] = None,
      callback_steps: int = 1,
      cross_attention_kwargs: Optional[Dict[str, Any]] = None,
      gligen_normalize_constant: float = 28.7,
      clip_skip: int = None,
  ):
      r"""
      The call function to the pipeline for generation.
  
      Args:
          prompt (`str` or `List[str]`, *optional*):
              The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
          height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
              The height in pixels of the generated image.
          width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
              The width in pixels of the generated image.
          num_inference_steps (`int`, *optional*, defaults to 50):
              The number of denoising steps. More denoising steps usually lead to a higher quality image at the
              expense of slower inference.
          guidance_scale (`float`, *optional*, defaults to 7.5):
              A higher guidance scale value encourages the model to generate images closely linked to the text
              `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
          gligen_phrases (`List[str]`):
              The phrases to guide what to include in each of the regions defined by the corresponding
              `gligen_boxes`. There should only be one phrase per bounding box.
          gligen_images (`List[PIL.Image.Image]`):
              The images to guide what to include in each of the regions defined by the corresponding `gligen_boxes`.
              There should only be one image per bounding box
          input_phrases_mask (`int` or `List[int]`):
              pre phrases mask input defined by the correspongding `input_phrases_mask`
          input_images_mask (`int` or `List[int]`):
              pre images mask input defined by the correspongding `input_images_mask`
          gligen_boxes (`List[List[float]]`):
              The bounding boxes that identify rectangular regions of the image that are going to be filled with the
              content described by the corresponding `gligen_phrases`. Each rectangular box is defined as a
              `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].
          gligen_inpaint_image (`PIL.Image.Image`, *optional*):
              The input image, if provided, is inpainted with objects described by the `gligen_boxes` and
              `gligen_phrases`. Otherwise, it is treated as a generation task on a blank input image.
          gligen_scheduled_sampling_beta (`float`, defaults to 0.3):
              Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image
              Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for
              scheduled sampling during inference for improved quality and controllability.
          negative_prompt (`str` or `List[str]`, *optional*):
              The prompt or prompts to guide what to not include in image generation. If not defined, you need to
              pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
          num_images_per_prompt (`int`, *optional*, defaults to 1):
              The number of images to generate per prompt.
          eta (`float`, *optional*, defaults to 0.0):
              Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
              to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
          generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
              A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
              generation deterministic.
          latents (`torch.FloatTensor`, *optional*):
              Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
              generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
              tensor is generated by sampling using the supplied random `generator`.
          prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
              provided, text embeddings are generated from the `prompt` input argument.
          negative_prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
              not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
          output_type (`str`, *optional*, defaults to `"pil"`):
              The output format of the generated image. Choose between `PIL.Image` or `np.array`.
          return_dict (`bool`, *optional*, defaults to `True`):
              Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
              plain tuple.
          callback (`Callable`, *optional*):
              A function that calls every `callback_steps` steps during inference. The function is called with the
              following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
          callback_steps (`int`, *optional*, defaults to 1):
              The frequency at which the `callback` function is called. If not specified, the callback is called at
              every step.
          cross_attention_kwargs (`dict`, *optional*):
              A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
              [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
          gligen_normalize_constant (`float`, *optional*, defaults to 28.7):
              The normalize value of the image embedding.
          clip_skip (`int`, *optional*):
              Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
              the output of the pre-final layer will be used for computing the prompt embeddings.
  
      Examples:
  
      Returns:
          [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
              If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
              otherwise a `tuple` is returned where the first element is a list with the generated images and the
              second element is a list of `bool`s indicating whether the corresponding generated image contains
              "not-safe-for-work" (nsfw) content.
      """
      # 0. Default height and width to unet
      height = (
          height or self.unet.config.sample_size * self.vae_scale_factor
      )
      width = (
          width or self.unet.config.sample_size * self.vae_scale_factor
      )
  
      # 1. Check inputs. Raise error if not correct
      self.check_inputs(
          prompt,
          height,
          width,
          callback_steps,
          negative_prompt,
          prompt_embeds,
          negative_prompt_embeds,
      )
  
      # 2. Define call parameters
      if prompt is not None and isinstance(prompt, str):
          batch_size = 1
      elif prompt is not None and isinstance(prompt, list):
          batch_size = len(prompt)
      else:
          batch_size = prompt_embeds.shape[0]
  
      device = self._execution_device
      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      do_classifier_free_guidance = guidance_scale > 1.0
  
      # 3. Encode input prompt
      prompt_embeds, negative_prompt_embeds = self.encode_prompt(
          prompt,
          device,
          num_images_per_prompt,
          do_classifier_free_guidance,
          negative_prompt,
          prompt_embeds=prompt_embeds,
          negative_prompt_embeds=negative_prompt_embeds,
          clip_skip=clip_skip,
      )
  
      if do_classifier_free_guidance:
          prompt_embeds = torch.cat(
              [negative_prompt_embeds, prompt_embeds]
          )
  
      # 4. Prepare timesteps
      self.scheduler.set_timesteps(num_inference_steps, device=device)
      timesteps = self.scheduler.timesteps
  
      # 5. Prepare latent variables
      num_channels_latents = self.unet.in_channels
      latents = self.prepare_latents(
          batch_size * num_images_per_prompt,
          num_channels_latents,
          height,
          width,
          prompt_embeds.dtype,
          device,
          generator,
          latents,
      )
  
      # 5.1 Prepare GLIGEN variables
      max_objs = 30
      if len(gligen_boxes) > max_objs:
          warnings.warn(
              f"More that {max_objs} objects found. Only first {max_objs} objects will be processed.",
              FutureWarning,
          )
          gligen_phrases = gligen_phrases[:max_objs]
          gligen_boxes = gligen_boxes[:max_objs]
          gligen_images = gligen_images[:max_objs]
  
      repeat_batch = batch_size * num_images_per_prompt
  
      if do_classifier_free_guidance:
          repeat_batch = repeat_batch * 2
  
      if cross_attention_kwargs is None:
          cross_attention_kwargs = {}
  
      hidden_size = prompt_embeds.shape[2]
  
      cross_attention_kwargs[
          "gligen"
      ] = self.get_cross_attention_kwargs_with_grounded(
          hidden_size=hidden_size,
          gligen_phrases=gligen_phrases,
          gligen_images=gligen_images,
          gligen_boxes=gligen_boxes,
          input_phrases_mask=input_phrases_mask,
          input_images_mask=input_images_mask,
          repeat_batch=repeat_batch,
          normalize_constant=gligen_normalize_constant,
          max_objs=max_objs,
          device=device,
      )
  
      cross_attention_kwargs_without_grounded = {}
      cross_attention_kwargs_without_grounded[
          "gligen"
      ] = self.get_cross_attention_kwargs_without_grounded(
          hidden_size=hidden_size,
          repeat_batch=repeat_batch,
          max_objs=max_objs,
          device=device,
      )
  
      # Prepare latent variables for GLIGEN inpainting
      if gligen_inpaint_image is not None:
          # if the given input image is not of the same size as expected by VAE
          # center crop and resize the input image to expected shape
          if gligen_inpaint_image.size != (
              self.vae.sample_size,
              self.vae.sample_size,
          ):
              gligen_inpaint_image = self.target_size_center_crop(
                  gligen_inpaint_image, self.vae.sample_size
              )
          # Convert a single image into a batch of images with a batch size of 1
          # The resulting shape becomes (1, C, H, W), where C is the number of channels,
          # and H and W are the height and width of the image.
          # scales the pixel values to a range [-1, 1]
          gligen_inpaint_image = self.image_processor.preprocess(
              gligen_inpaint_image
          )
          gligen_inpaint_image = gligen_inpaint_image.to(
              dtype=self.vae.dtype, device=self.vae.device
          )
          # Run AutoEncoder to get corresponding latents
          gligen_inpaint_latent = self.vae.encode(
              gligen_inpaint_image
          ).latent_dist.sample()
          gligen_inpaint_latent = (
              self.vae.config.scaling_factor * gligen_inpaint_latent
          )
          # Generate an inpainting mask
          # pixel value = 0, where the object is present (defined by bounding boxes above)
          #               1, everywhere else
          gligen_inpaint_mask = self.draw_inpaint_mask_from_boxes(
              gligen_boxes, gligen_inpaint_latent.shape[2:]
          )
          gligen_inpaint_mask = gligen_inpaint_mask.to(
              dtype=gligen_inpaint_latent.dtype,
              device=gligen_inpaint_latent.device,
          )
          gligen_inpaint_mask = gligen_inpaint_mask[None, None]
          gligen_inpaint_mask_addition = torch.cat(
              (
                  gligen_inpaint_latent * gligen_inpaint_mask,
                  gligen_inpaint_mask,
              ),
              dim=1,
          )
          # Convert a single mask into a batch of masks with a batch size of 1
          gligen_inpaint_mask_addition = (
              gligen_inpaint_mask_addition.expand(
                  repeat_batch, -1, -1, -1
              ).clone()
          )
  
      int(gligen_scheduled_sampling_beta * len(timesteps))
      self.enable_fuser(True)
  
      # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
      extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
  
      # 7. Denoising loop
      num_warmup_steps = (
          len(timesteps) - num_inference_steps * self.scheduler.order
      )
      with self.progress_bar(total=num_inference_steps) as progress_bar:
          for i, t in enumerate(timesteps):
              if latents.shape[1] != 4:
                  latents = torch.randn_like(latents[:, :4])
  
              if gligen_inpaint_image is not None:
                  gligen_inpaint_latent_with_noise = (
                      self.scheduler.add_noise(
                          gligen_inpaint_latent,
                          torch.randn_like(gligen_inpaint_latent),
                          torch.tensor([t]),
                      )
                      .expand(latents.shape[0], -1, -1, -1)
                      .clone()
                  )
                  latents = (
                      gligen_inpaint_latent_with_noise
                      * gligen_inpaint_mask
                      + latents * (1 - gligen_inpaint_mask)
                  )
  
              # expand the latents if we are doing classifier free guidance
              latent_model_input = (
                  torch.cat([latents] * 2)
                  if do_classifier_free_guidance
                  else latents
              )
              latent_model_input = self.scheduler.scale_model_input(
                  latent_model_input, t
              )
  
              if gligen_inpaint_image is not None:
                  latent_model_input = torch.cat(
                      (
                          latent_model_input,
                          gligen_inpaint_mask_addition,
                      ),
                      dim=1,
                  )
  
              # predict the noise residual with grounded information
              noise_pred_with_grounding = self.unet(
                  latent_model_input,
                  t,
                  encoder_hidden_states=prompt_embeds,
                  cross_attention_kwargs=cross_attention_kwargs,
              ).sample
  
              # predict the noise residual without grounded information
              noise_pred_without_grounding = self.unet(
                  latent_model_input,
                  t,
                  encoder_hidden_states=prompt_embeds,
                  cross_attention_kwargs=cross_attention_kwargs_without_grounded,
              ).sample
  
              # perform guidance
              if do_classifier_free_guidance:
                  # Using noise_pred_text from noise residual with grounded information and noise_pred_uncond from noise residual without grounded information
                  _, noise_pred_text = noise_pred_with_grounding.chunk(
                      2
                  )
                  (
                      noise_pred_uncond,
                      _,
                  ) = noise_pred_without_grounding.chunk(2)
                  noise_pred = noise_pred_uncond + guidance_scale * (
                      noise_pred_text - noise_pred_uncond
                  )
              else:
                  noise_pred = noise_pred_with_grounding
  
              # compute the previous noisy sample x_t -> x_t-1
              latents = self.scheduler.step(
                  noise_pred, t, latents, **extra_step_kwargs
              ).prev_sample
  
              # call the callback, if provided
              if i == len(timesteps) - 1 or (
                  (i + 1) > num_warmup_steps
                  and (i + 1) % self.scheduler.order == 0
              ):
                  progress_bar.update()
                  if callback is not None and i % callback_steps == 0:
                      step_idx = i // getattr(
                          self.scheduler, "order", 1
                      )
                      callback(step_idx, t, latents)
  
      if not output_type == "latent":
          image = self.vae.decode(
              latents / self.vae.config.scaling_factor,
              return_dict=False,
          )[0]
          image, has_nsfw_concept = self.run_safety_checker(
              image, device, prompt_embeds.dtype
          )
      else:
          image = latents
          has_nsfw_concept = None
  
      if has_nsfw_concept is None:
          do_denormalize = [True] * image.shape[0]
      else:
          do_denormalize = [
              not has_nsfw for has_nsfw in has_nsfw_concept
          ]
  
      image = self.image_processor.postprocess(
          image, output_type=output_type, do_denormalize=do_denormalize
      )
  
      # Offload last model to CPU
      if (
          hasattr(self, "final_offload_hook")
          and self.final_offload_hook is not None
      ):
          self.final_offload_hook.offload()
  
      if not return_dict:
          return (image, has_nsfw_concept)
  
      return StableDiffusionPipelineOutput(
          images=image, nsfw_content_detected=has_nsfw_concept
      )
  
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 .None import StableDiffusionPipelineOutput
  from .clip_image_project_model import CLIPImageProjection
  from .safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 19 for calling the StableDiffusionGLIGENTextImagePipeline obj's underlying member method code with example usage: obj.__call__(prompt, height, width, num_inference_steps, guidance_scale, gligen_scheduled_sampling_beta, gligen_phrases, gligen_images, input_phrases_mask, input_images_mask, gligen_boxes, gligen_inpaint_image, negative_prompt, num_images_per_prompt, eta, generator, latents, prompt_embeds, negative_prompt_embeds, output_type, return_dict, callback, callback_steps, cross_attention_kwargs, gligen_normalize_constant, clip_skip) and returns: StableDiffusionPipelineOutput, image,  has_nsfw_concept