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 a 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.
code: def upcast_vae(self):
      dtype = self.vae.dtype
      self.vae.to(dtype=torch.float32)
      use_torch_2_0_or_xformers = isinstance(
          self.vae.decoder.mid_block.attentions[0].processor,
          (
              AttnProcessor2_0,
              XFormersAttnProcessor,
              LoRAXFormersAttnProcessor,
              LoRAAttnProcessor2_0,
          ),
      )
      # if xformers or torch_2_0 is used attention block does not need
      # to be in float32 which can save lots of memory
      if use_torch_2_0_or_xformers:
          self.vae.post_quant_conv.to(dtype)
          self.vae.decoder.conv_in.to(dtype)
          self.vae.decoder.mid_block.to(dtype)
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  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, deprecate, 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 14 for calling the StableDiffusionXLInpaintPipeline obj's underlying member method code with example usage: obj.upcast_vae() without return types