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.
      timesteps (`List[int]`, *optional*):
          Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
          in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
          passed will be used. Must be in descending order.
      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`.
      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.
      ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
      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.
      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).
      guidance_rescale (`float`, *optional*, defaults to 0.0):
          Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
          Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
          using zero terminal SNR.
      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,
      timesteps: List[int] = None,
      guidance_scale: float = 7.5,
      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,
      cross_attention_kwargs: Optional[Dict[str, Any]] = None,
      guidance_rescale: float = 0.0,
      clip_skip: Optional[int] = None,
      **kwargs,
  ):
      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.
          timesteps (`List[int]`, *optional*):
              Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
              in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
              passed will be used. Must be in descending order.
          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`.
          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.
          ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
          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.
          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).
          guidance_rescale (`float`, *optional*, defaults to 0.0):
              Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
              Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
              using zero terminal SNR.
          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
      )
      # to deal with lora scaling and other possible forward hooks
  
      # 1. Check inputs. Raise error if not correct
      self.check_inputs(
          prompt,
          height,
          width,
          negative_prompt,
          prompt_embeds,
          negative_prompt_embeds,
      )
  
      self._guidance_scale = guidance_scale
      self._guidance_rescale = guidance_rescale
      self._clip_skip = clip_skip
      self._cross_attention_kwargs = cross_attention_kwargs
      self._interrupt = False
  
      # 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
  
      # 3. Encode input prompt
      lora_scale = (
          self.cross_attention_kwargs.get("scale", None)
          if self.cross_attention_kwargs is not None
          else None
      )
  
      prompt_embeds, negative_prompt_embeds = self.encode_prompt(
          prompt,
          device,
          num_images_per_prompt,
          self.do_classifier_free_guidance,
          negative_prompt,
          prompt_embeds=prompt_embeds,
          negative_prompt_embeds=negative_prompt_embeds,
          lora_scale=lora_scale,
          clip_skip=self.clip_skip,
      )
  
      # For classifier free guidance, we need to do two forward passes.
      # Here we concatenate the unconditional and text embeddings into a single batch
      # to avoid doing two forward passes
      if self.do_classifier_free_guidance:
          prompt_embeds = torch.cat(
              [negative_prompt_embeds, prompt_embeds]
          )
  
      # 4. Prepare timesteps
      timesteps, num_inference_steps = retrieve_timesteps(
          self.scheduler, num_inference_steps, device, timesteps
      )
  
      # 5. Prepare latent variables
      num_channels_latents = self.unet.config.in_channels
      latents = self.prepare_latents(
          batch_size * num_images_per_prompt,
          num_channels_latents,
          height,
          width,
          prompt_embeds.dtype,
          device,
          generator,
          latents,
      )
  
      # 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)
  
      # 6.2 Optionally get Guidance Scale Embedding
      timestep_cond = None
      if self.unet.config.time_cond_proj_dim is not None:
          guidance_scale_tensor = torch.tensor(
              self.guidance_scale - 1
          ).repeat(batch_size * num_images_per_prompt)
          timestep_cond = self.get_guidance_scale_embedding(
              guidance_scale_tensor,
              embedding_dim=self.unet.config.time_cond_proj_dim,
          ).to(device=device, dtype=latents.dtype)
  
      # 7. Denoising loop
      num_warmup_steps = (
          len(timesteps) - num_inference_steps * self.scheduler.order
      )
      self._num_timesteps = len(timesteps)
      with self.progress_bar(total=num_inference_steps) as progress_bar:
          for i, t in enumerate(timesteps):
              if self.interrupt:
                  continue
  
              # expand the latents if we are doing classifier free guidance
              latent_model_input = (
                  torch.cat([latents] * 2)
                  if self.do_classifier_free_guidance
                  else latents
              )
              latent_model_input = self.scheduler.scale_model_input(
                  latent_model_input, t
              )
  
              # predict the noise residual
              noise_pred = self.unet(
                  latent_model_input,
                  t,
                  encoder_hidden_states=prompt_embeds,
                  timestep_cond=timestep_cond,
                  cross_attention_kwargs=self.cross_attention_kwargs,
                  return_dict=False,
              )[0]
  
              # perform guidance
              if self.do_classifier_free_guidance:
                  noise_pred_uncond, noise_pred_text = noise_pred.chunk(
                      2
                  )
                  noise_pred = (
                      noise_pred_uncond
                      + self.guidance_scale
                      * (noise_pred_text - noise_pred_uncond)
                  )
  
              if (
                  self.do_classifier_free_guidance
                  and self.guidance_rescale > 0.0
              ):
                  # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                  noise_pred = rescale_noise_cfg(
                      noise_pred,
                      noise_pred_text,
                      guidance_rescale=self.guidance_rescale,
                  )
  
              # compute the previous noisy sample x_t -> x_t-1
              latents = self.scheduler.step(
                  noise_pred,
                  t,
                  latents,
                  **extra_step_kwargs,
                  return_dict=False,
              )[0]
  
              # 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 not output_type == "latent":
          image = self.vae.decode(
              latents / self.vae.config.scaling_factor,
              return_dict=False,
              generator=generator,
          )[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 all models
      self.maybe_free_model_hooks()
  
      if not return_dict:
          return (image, has_nsfw_concept)
  
      return StableDiffusionPipelineOutput(
          images=image, nsfw_content_detected=has_nsfw_concept
      )
  
imports: import inspect
  from typing import Any, Dict, List, Optional, Union
  import torch
  import torch.nn
  from transformers import AutoModel, AutoTokenizer, CLIPImageProcessor
  from diffusers import DiffusionPipeline
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import LoraLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.lora import adjust_lora_scale_text_encoder
  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, logging, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 23 for calling the GlueGenStableDiffusionPipeline obj's underlying member method code with example usage: obj.__call__(prompt, height, width, num_inference_steps, timesteps, guidance_scale, negative_prompt, num_images_per_prompt, eta, generator, latents, prompt_embeds, negative_prompt_embeds, output_type, return_dict, cross_attention_kwargs, guidance_rescale, clip_skip) and returns: StableDiffusionPipelineOutput, image,  has_nsfw_concept