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*: [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`]
      - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
  
  as well as the following saving methods:
      - *LoRA*: [`loaders.StableDiffusionXLLoraLoaderMixin.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`].
      requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
          Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the
          config of `stabilityai/stable-diffusion-xl-refiner-1-0`.
      force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
          Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
          `stabilityai/stable-diffusion-xl-base-1-0`.
      add_watermarker (`bool`, *optional*):
          Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
          watermark output images. If not defined, it will default to True if the package is installed, otherwise no
          watermarker will be used.
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docstr: Function invoked when calling the pipeline for generation.
  
  Args:
      prompt (`str` or `List[str]`, *optional*):
          The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
          instead.
      prompt_2 (`str` or `List[str]`, *optional*):
          The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
          used in both text-encoders
      image (`torch.FloatTensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.FloatTensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`):
          The image(s) to modify with the pipeline.
      strength (`float`, *optional*, defaults to 0.3):
          Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
          will be used as a starting point, adding more noise to it the larger the `strength`. The number of
          denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
          be maximum and the denoising process will run for the full number of iterations specified in
          `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of
          `denoising_start` being declared as an integer, the value of `strength` will be ignored.
      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_start (`float`, *optional*):
          When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
          bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
          it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
          strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
          is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refine Image
          Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
      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 (ca. final 20% of timesteps still needed) and should be
          denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
          final 20% of 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 [**Refine Image
          Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
      guidance_scale (`float`, *optional*, defaults to 7.5):
          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` or `List[str]`, *optional*):
          The prompt or prompts 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` or `List[str]`, *optional*):
          The prompt or prompts 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.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).
      negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
          To negatively condition the generation process based on a specific image resolution. 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). For more
          information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
      negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
          To negatively condition the generation process based on a specific crop coordinates. 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). For more
          information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
      negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
          To negatively condition the generation process based on a target image resolution. It should be as same
          as the `target_size` for most cases. 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). For more
          information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
      aesthetic_score (`float`, *optional*, defaults to 6.0):
          Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
          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).
      negative_aesthetic_score (`float`, *optional*, defaults to 2.5):
          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). Can be used to
          simulate an aesthetic score of the generated image by influencing the negative text condition.
      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.StableDiffusionXLPipelineOutput`] or `tuple`:
      [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
      `tuple. When returning a tuple, the first element is a list with the generated images.
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code: def __call__(
      self,
      prompt: Union[str, List[str]] = None,
      prompt_2: Optional[Union[str, List[str]]] = None,
      image: PipelineImageInput = None,
      strength: float = 0.3,
      num_inference_steps: int = 50,
      denoising_start: Optional[float] = None,
      denoising_end: Optional[float] = None,
      guidance_scale: float = 5.0,
      negative_prompt: Optional[Union[str, List[str]]] = None,
      negative_prompt_2: 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,
      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: Tuple[int, int] = None,
      crops_coords_top_left: Tuple[int, int] = (0, 0),
      target_size: Tuple[int, int] = None,
      negative_original_size: Optional[Tuple[int, int]] = None,
      negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
      negative_target_size: Optional[Tuple[int, int]] = None,
      aesthetic_score: float = 6.0,
      negative_aesthetic_score: float = 2.5,
      clip_skip: Optional[int] = None,
  ):
      r"""
      Function invoked when calling the pipeline for generation.
  
      Args:
          prompt (`str` or `List[str]`, *optional*):
              The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
              instead.
          prompt_2 (`str` or `List[str]`, *optional*):
              The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
              used in both text-encoders
          image (`torch.FloatTensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.FloatTensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`):
              The image(s) to modify with the pipeline.
          strength (`float`, *optional*, defaults to 0.3):
              Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
              will be used as a starting point, adding more noise to it the larger the `strength`. The number of
              denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
              be maximum and the denoising process will run for the full number of iterations specified in
              `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of
              `denoising_start` being declared as an integer, the value of `strength` will be ignored.
          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_start (`float`, *optional*):
              When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
              bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
              it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
              strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
              is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refine Image
              Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
          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 (ca. final 20% of timesteps still needed) and should be
              denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
              final 20% of 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 [**Refine Image
              Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
          guidance_scale (`float`, *optional*, defaults to 7.5):
              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` or `List[str]`, *optional*):
              The prompt or prompts 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` or `List[str]`, *optional*):
              The prompt or prompts 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.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).
          negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
              To negatively condition the generation process based on a specific image resolution. 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). For more
              information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
          negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
              To negatively condition the generation process based on a specific crop coordinates. 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). For more
              information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
          negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
              To negatively condition the generation process based on a target image resolution. It should be as same
              as the `target_size` for most cases. 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). For more
              information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
          aesthetic_score (`float`, *optional*, defaults to 6.0):
              Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
              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).
          negative_aesthetic_score (`float`, *optional*, defaults to 2.5):
              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). Can be used to
              simulate an aesthetic score of the generated image by influencing the negative text condition.
          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.StableDiffusionXLPipelineOutput`] or `tuple`:
          [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
          `tuple. When returning a tuple, the first element is a list with the generated images.
      """
      # 1. Check inputs. Raise error if not correct
      self.check_inputs(
          prompt,
          prompt_2,
          strength,
          num_inference_steps,
          callback_steps,
          negative_prompt,
          negative_prompt_2,
          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
      text_encoder_lora_scale = (
          cross_attention_kwargs.get("scale", None)
          if cross_attention_kwargs is not None
          else None
      )
      (
          prompt_embeds,
          negative_prompt_embeds,
          pooled_prompt_embeds,
          negative_pooled_prompt_embeds,
      ) = self.encode_prompt(
          prompt=prompt,
          prompt_2=prompt_2,
          device=device,
          num_images_per_prompt=num_images_per_prompt,
          do_classifier_free_guidance=do_classifier_free_guidance,
          negative_prompt=negative_prompt,
          negative_prompt_2=negative_prompt_2,
          prompt_embeds=prompt_embeds,
          negative_prompt_embeds=negative_prompt_embeds,
          pooled_prompt_embeds=pooled_prompt_embeds,
          negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
          lora_scale=text_encoder_lora_scale,
          clip_skip=clip_skip,
      )
  
      # 4. Preprocess image
      image = self.image_processor.preprocess(image)
  
      # 5. Prepare timesteps
      def denoising_value_valid(dnv):
          return isinstance(denoising_end, float) and 0 < dnv < 1
  
      self.scheduler.set_timesteps(num_inference_steps, device=device)
      timesteps, num_inference_steps = self.get_timesteps(
          num_inference_steps,
          strength,
          device,
          denoising_start=denoising_start
          if denoising_value_valid
          else None,
      )
      latent_timestep = timesteps[:1].repeat(
          batch_size * num_images_per_prompt
      )
  
      add_noise = True if denoising_start is None else False
      # 6. Prepare latent variables
      latents = self.prepare_latents(
          image,
          latent_timestep,
          batch_size,
          num_images_per_prompt,
          prompt_embeds.dtype,
          device,
          generator,
          add_noise,
      )
      # 7. Prepare extra step kwargs.
      extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
  
      height, width = latents.shape[-2:]
      height = height * self.vae_scale_factor
      width = width * self.vae_scale_factor
  
      original_size = original_size or (height, width)
      target_size = target_size or (height, width)
  
      # 8. Prepare added time ids & embeddings
      if negative_original_size is None:
          negative_original_size = original_size
      if negative_target_size is None:
          negative_target_size = target_size
  
      add_text_embeds = pooled_prompt_embeds
      add_time_ids, add_neg_time_ids = self._get_add_time_ids(
          original_size,
          crops_coords_top_left,
          target_size,
          aesthetic_score,
          negative_aesthetic_score,
          negative_original_size,
          negative_crops_coords_top_left,
          negative_target_size,
          dtype=prompt_embeds.dtype,
      )
      add_time_ids = add_time_ids.repeat(
          batch_size * num_images_per_prompt, 1
      )
  
      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_neg_time_ids = add_neg_time_ids.repeat(
              batch_size * num_images_per_prompt, 1
          )
          add_time_ids = torch.cat(
              [add_neg_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)
  
      # 9. Denoising loop
      num_warmup_steps = max(
          len(timesteps) - num_inference_steps * self.scheduler.order, 0
      )
  
      # 9.1 Apply denoising_end
      if (
          denoising_end is not None
          and denoising_start is not None
          and denoising_value_valid(denoising_end)
          and denoising_value_valid(denoising_start)
          and denoising_start >= denoising_end
      ):
          raise ValueError(
              f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: "
              + f" {denoising_end} when using type float."
          )
      elif denoising_end is not None and denoising_value_valid(
          denoising_end
      ):
          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 XLA_AVAILABLE:
                  xm.mark_step()
  
      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 all models
      self.maybe_free_model_hooks()
  
      if not return_dict:
          return (image,)
  
      return StableDiffusionXLPipelineOutput(images=image)
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import PIL.Image
  import torch
  from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...models.attention_processor import AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, is_invisible_watermark_available, is_torch_xla_available, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from .pipeline_output import StableDiffusionXLPipelineOutput
label_desc: Use image node_id 15 for calling the StableDiffusionXLImg2ImgPipeline obj's underlying member method code with example usage: obj.__call__(prompt, prompt_2, image, strength, num_inference_steps, denoising_start, 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, negative_original_size, negative_crops_coords_top_left, negative_target_size, aesthetic_score, negative_aesthetic_score, clip_skip) and returns: StableDiffusionXLPipelineOutput, unknown, StableDiffusionXLPipelineOutput, image