class_docstr: Pipeline for text-to-image generation using Stable Diffusion XL.
  
  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.)
  
  In addition the pipeline inherits the following loading methods:
      - *LoRA*: [`StableDiffusionXLPipeline.load_lora_weights`]
      - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
  
  as well as the following saving methods:
      - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`]
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
      text_encoder ([`CLIPTextModel`]):
          Frozen text-encoder. Stable Diffusion XL uses the text portion of
          [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
          the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
      text_encoder_2 ([` CLIPTextModelWithProjection`]):
          Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
          [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
          specifically the
          [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
          variant.
      tokenizer (`CLIPTokenizer`):
          Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      tokenizer_2 (`CLIPTokenizer`):
          Second Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      unet ([`UNet2DConditionModel`]): Conditional U-Net architecture 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`].
docstr: Function invoked when calling the pipeline for generation.
  
  Args:
      prompt (`str`):
          The prompt  to guide the image generation. If not defined, one has to pass `prompt_embeds`.
          instead.
      prompt_2 (`str`):
          The prompt to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
          used in both text-encoders
      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.
      denoising_end (`float`, *optional*):
          When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
          completed before it is intentionally prematurely terminated. As a result, the returned sample will
          still retain a substantial amount of noise as determined by the discrete timesteps selected by the
          scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
          "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
          Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
      guidance_scale (`float`, *optional*, defaults to 5.0):
          Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
          `guidance_scale` is defined as `w` of equation 2. of [Imagen
          Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
          1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
          usually at the expense of lower image quality.
      negative_prompt (`str`):
          The prompt not to guide the image generation. If not defined, one has to pass
          `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
          less than `1`).
      negative_prompt_2 (`str`):
          The prompt not to guide the image generation to be sent to `tokenizer_2` and
          `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
      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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
          [`schedulers.DDIMScheduler`], will be ignored for others.
      generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
          One or a list of [torch generator(s)](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 will ge 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, *e.g.* prompt weighting. If not
          provided, text embeddings will be generated from `prompt` input argument.
      negative_prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
          weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
          argument.
      pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
          If not provided, pooled text embeddings will be generated from `prompt` input argument.
      negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
          weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
          input argument.
      output_type (`str`, *optional*, defaults to `"pil"`):
          The output format of the generate image. Choose between
          [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
      return_dict (`bool`, *optional*, defaults to `True`):
          Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
          of a plain tuple.
      callback (`Callable`, *optional*):
          A function that will be called every `callback_steps` steps during inference. The function will be
          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 will be called. If not specified, the callback will be
          called at every step.
      cross_attention_kwargs (`dict`, *optional*):
          A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
          `self.processor` in
          [diffusers.models.attention_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 proposed by [Common Diffusion Noise Schedules and Sample Steps are
          Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
          [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.
      original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
          If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
          `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
          explained in section 2.2 of
          [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
      crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
          `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
          `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
          `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
          [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
      target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
          For most cases, `target_size` should be set to the desired height and width of the generated image. If
          not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
          section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
  
  Examples:
  
  Returns:
      [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
      [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
      `tuple`. When returning a tuple, the first element is a list with the generated images.
code: def __call__(
      self,
      prompt: str = None,
      prompt_2: Optional[str] = None,
      height: Optional[int] = None,
      width: Optional[int] = None,
      num_inference_steps: int = 50,
      denoising_end: Optional[float] = None,
      guidance_scale: float = 5.0,
      negative_prompt: Optional[str] = None,
      negative_prompt_2: Optional[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,
      pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
      negative_pooled_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,
      guidance_rescale: float = 0.0,
      original_size: Optional[Tuple[int, int]] = None,
      crops_coords_top_left: Tuple[int, int] = (0, 0),
      target_size: Optional[Tuple[int, int]] = None,
  ):
      r"""
      Function invoked when calling the pipeline for generation.
  
      Args:
          prompt (`str`):
              The prompt  to guide the image generation. If not defined, one has to pass `prompt_embeds`.
              instead.
          prompt_2 (`str`):
              The prompt to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
              used in both text-encoders
          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.
          denoising_end (`float`, *optional*):
              When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
              completed before it is intentionally prematurely terminated. As a result, the returned sample will
              still retain a substantial amount of noise as determined by the discrete timesteps selected by the
              scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
              "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
              Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
          guidance_scale (`float`, *optional*, defaults to 5.0):
              Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
              `guidance_scale` is defined as `w` of equation 2. of [Imagen
              Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
              1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
              usually at the expense of lower image quality.
          negative_prompt (`str`):
              The prompt not to guide the image generation. If not defined, one has to pass
              `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
              less than `1`).
          negative_prompt_2 (`str`):
              The prompt not to guide the image generation to be sent to `tokenizer_2` and
              `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
          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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
              [`schedulers.DDIMScheduler`], will be ignored for others.
          generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
              One or a list of [torch generator(s)](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 will ge 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, *e.g.* prompt weighting. If not
              provided, text embeddings will be generated from `prompt` input argument.
          negative_prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
              weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
              argument.
          pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
              If not provided, pooled text embeddings will be generated from `prompt` input argument.
          negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
              weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
              input argument.
          output_type (`str`, *optional*, defaults to `"pil"`):
              The output format of the generate image. Choose between
              [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
          return_dict (`bool`, *optional*, defaults to `True`):
              Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
              of a plain tuple.
          callback (`Callable`, *optional*):
              A function that will be called every `callback_steps` steps during inference. The function will be
              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 will be called. If not specified, the callback will be
              called at every step.
          cross_attention_kwargs (`dict`, *optional*):
              A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
              `self.processor` in
              [diffusers.models.attention_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 proposed by [Common Diffusion Noise Schedules and Sample Steps are
              Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
              [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.
          original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
              If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
              `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
              explained in section 2.2 of
              [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
          crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
              `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
              `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
              `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
              [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
          target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
              For most cases, `target_size` should be set to the desired height and width of the generated image. If
              not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
              section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
  
      Examples:
  
      Returns:
          [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
          [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
          `tuple`. When returning a tuple, the first element is a list with the generated images.
      """
      # 0. Default height and width to unet
      height = (
          height or self.default_sample_size * self.vae_scale_factor
      )
      width = width or self.default_sample_size * self.vae_scale_factor
  
      original_size = original_size or (height, width)
      target_size = target_size or (height, width)
  
      # 1. Check inputs. Raise error if not correct
      self.check_inputs(
          prompt,
          prompt_2,
          height,
          width,
          callback_steps,
          negative_prompt,
          negative_prompt_2,
          prompt_embeds,
          negative_prompt_embeds,
          pooled_prompt_embeds,
          negative_pooled_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
      (
          cross_attention_kwargs.get("scale", None)
          if cross_attention_kwargs is not None
          else None
      )
  
      negative_prompt = (
          negative_prompt if negative_prompt is not None else ""
      )
  
      (
          prompt_embeds,
          negative_prompt_embeds,
          pooled_prompt_embeds,
          negative_pooled_prompt_embeds,
      ) = get_weighted_text_embeddings_sdxl(
          pipe=self, prompt=prompt, neg_prompt=negative_prompt
      )
  
      # 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.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)
  
      # 7. Prepare added time ids & embeddings
      add_text_embeds = pooled_prompt_embeds
      add_time_ids = self._get_add_time_ids(
          original_size,
          crops_coords_top_left,
          target_size,
          dtype=prompt_embeds.dtype,
      )
  
      if do_classifier_free_guidance:
          prompt_embeds = torch.cat(
              [negative_prompt_embeds, prompt_embeds], dim=0
          )
          add_text_embeds = torch.cat(
              [negative_pooled_prompt_embeds, add_text_embeds], dim=0
          )
          add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
  
      prompt_embeds = prompt_embeds.to(device)
      add_text_embeds = add_text_embeds.to(device)
      add_time_ids = add_time_ids.to(device).repeat(
          batch_size * num_images_per_prompt, 1
      )
  
      # 8. Denoising loop
      num_warmup_steps = max(
          len(timesteps) - num_inference_steps * self.scheduler.order, 0
      )
  
      # 7.1 Apply denoising_end
      if (
          denoising_end is not None
          and isinstance(denoising_end, float)
          and denoising_end > 0
          and denoising_end < 1
      ):
          discrete_timestep_cutoff = int(
              round(
                  self.scheduler.config.num_train_timesteps
                  - (
                      denoising_end
                      * self.scheduler.config.num_train_timesteps
                  )
              )
          )
          num_inference_steps = len(
              list(
                  filter(
                      lambda ts: ts >= discrete_timestep_cutoff,
                      timesteps,
                  )
              )
          )
          timesteps = timesteps[:num_inference_steps]
  
      with self.progress_bar(total=num_inference_steps) as progress_bar:
          for i, t in enumerate(timesteps):
              # 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
              )
  
              # predict the noise residual
              added_cond_kwargs = {
                  "text_embeds": add_text_embeds,
                  "time_ids": add_time_ids,
              }
              noise_pred = self.unet(
                  latent_model_input,
                  t,
                  encoder_hidden_states=prompt_embeds,
                  cross_attention_kwargs=cross_attention_kwargs,
                  added_cond_kwargs=added_cond_kwargs,
                  return_dict=False,
              )[0]
  
              # perform guidance
              if do_classifier_free_guidance:
                  noise_pred_uncond, noise_pred_text = noise_pred.chunk(
                      2
                  )
                  noise_pred = noise_pred_uncond + guidance_scale * (
                      noise_pred_text - noise_pred_uncond
                  )
  
              if do_classifier_free_guidance and 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=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 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":
          # make sure the VAE is in float32 mode, as it overflows in float16
          needs_upcasting = (
              self.vae.dtype == torch.float16
              and self.vae.config.force_upcast
          )
  
          if needs_upcasting:
              self.upcast_vae()
              latents = latents.to(
                  next(
                      iter(self.vae.post_quant_conv.parameters())
                  ).dtype
              )
  
          image = self.vae.decode(
              latents / self.vae.config.scaling_factor,
              return_dict=False,
          )[0]
  
          # cast back to fp16 if needed
          if needs_upcasting:
              self.vae.to(dtype=torch.float16)
      else:
          image = latents
          return StableDiffusionXLPipelineOutput(images=image)
  
      # apply watermark if available
      if self.watermark is not None:
          image = self.watermark.apply_watermark(image)
  
      image = self.image_processor.postprocess(
          image, output_type=output_type
      )
  
      # 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,)
  
      return StableDiffusionXLPipelineOutput(images=image)
  
imports: import inspect
  import os
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import DiffusionPipeline, StableDiffusionXLPipeline
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.attention_processor import AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
  from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import is_accelerate_available, is_accelerate_version, is_invisible_watermark_available, logging, replace_example_docstring
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 13 for calling the SDXLLongPromptWeightingPipeline obj's underlying member method code with example usage: obj.__call__(prompt, prompt_2, height, width, num_inference_steps, denoising_end, guidance_scale, negative_prompt, negative_prompt_2, num_images_per_prompt, eta, generator, latents, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, output_type, return_dict, callback, callback_steps, cross_attention_kwargs, guidance_rescale, original_size, crops_coords_top_left, target_size) and returns: StableDiffusionXLPipelineOutput, StableDiffusionXLPipelineOutput, image