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
  implemented for all pipelines (downloading, saving, running on a particular device, etc.).
  
  The pipeline also inherits the following loading methods:
      - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
      - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
      - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
      - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
  
  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`].
      feature_extractor ([`~transformers.CLIPImageProcessor`]):
          A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
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code: def save_lora_weights(
      self,
      save_directory: Union[str, os.PathLike],
      unet_lora_layers: Dict[
          str, Union[torch.nn.Module, torch.Tensor]
      ] = None,
      text_encoder_lora_layers: Dict[
          str, Union[torch.nn.Module, torch.Tensor]
      ] = None,
      text_encoder_2_lora_layers: Dict[
          str, Union[torch.nn.Module, torch.Tensor]
      ] = None,
      is_main_process: bool = True,
      weight_name: str = None,
      save_function: Callable = None,
      safe_serialization: bool = False,
  ):
      state_dict = {}
  
      def pack_weights(layers, prefix):
          layers_weights = (
              layers.state_dict()
              if isinstance(layers, torch.nn.Module)
              else layers
          )
          layers_state_dict = {
              f"{prefix}.{module_name}": param
              for module_name, param in layers_weights.items()
          }
          return layers_state_dict
  
      state_dict.update(pack_weights(unet_lora_layers, "unet"))
  
      if text_encoder_lora_layers and text_encoder_2_lora_layers:
          state_dict.update(
              pack_weights(text_encoder_lora_layers, "text_encoder")
          )
          state_dict.update(
              pack_weights(text_encoder_2_lora_layers, "text_encoder_2")
          )
  
      self.write_lora_layers(
          state_dict=state_dict,
          save_directory=save_directory,
          is_main_process=is_main_process,
          weight_name=weight_name,
          save_function=save_function,
          safe_serialization=safe_serialization,
      )
  
imports: import inspect
  import os
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
  from diffusers import DiffusionPipeline, StableDiffusionXLPipeline
  from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
  from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
  from diffusers.models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
  from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import deprecate, 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 35 for calling the SDXLLongPromptWeightingPipeline obj's underlying member method code with example usage: obj.save_lora_weights(save_directory, unet_lora_layers, text_encoder_lora_layers, text_encoder_2_lora_layers, is_main_process, weight_name, save_function, safe_serialization) and returns: layers_state_dict