class_docstr: Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter
  https://arxiv.org/abs/2302.08453
  
  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.)
  
  Args:
      adapter ([`T2IAdapter`] or [`MultiAdapter`] or `List[T2IAdapter]`):
          Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a
          list, the outputs from each Adapter are added together to create one combined additional conditioning.
      adapter_weights (`List[float]`, *optional*, defaults to None):
          List of floats representing the weight which will be multiply to each adapter's output before adding them
          together.
      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 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.
      tokenizer (`CLIPTokenizer`):
          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`].
      safety_checker ([`StableDiffusionSafetyChecker`]):
          Classification module that estimates whether generated images could be considered offensive or harmful.
          Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
      feature_extractor ([`CLIPFeatureExtractor`]):
          Model that extracts features from generated images to be used as inputs for the `safety_checker`.
code: def __init__(
      self,
      vae: AutoencoderKL,
      text_encoder: CLIPTextModel,
      text_encoder_2: CLIPTextModelWithProjection,
      tokenizer: CLIPTokenizer,
      tokenizer_2: CLIPTokenizer,
      unet: UNet2DConditionModel,
      adapter: Union[T2IAdapter, MultiAdapter, List[T2IAdapter]],
      scheduler: KarrasDiffusionSchedulers,
      force_zeros_for_empty_prompt: bool = True,
  ):
      super().__init__()
  
      self.register_modules(
          vae=vae,
          text_encoder=text_encoder,
          text_encoder_2=text_encoder_2,
          tokenizer=tokenizer,
          tokenizer_2=tokenizer_2,
          unet=unet,
          adapter=adapter,
          scheduler=scheduler,
      )
      self.register_to_config(
          force_zeros_for_empty_prompt=force_zeros_for_empty_prompt
      )
      self.vae_scale_factor = 2 ** (
          len(self.vae.config.block_out_channels) - 1
      )
      self.image_processor = VaeImageProcessor(
          vae_scale_factor=self.vae_scale_factor
      )
      self.default_sample_size = self.unet.config.sample_size
  
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 VaeImageProcessor
  from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, MultiAdapter, T2IAdapter, 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 PIL_INTERPOLATION, USE_PEFT_BACKEND, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
label_desc: Use image node_id 1 to create a new StableDiffusionXLAdapterPipeline object from inherited base classes: DiffusionPipeline, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin with example: obj = StableDiffusionXLAdapterPipeline(vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, unet, adapter, scheduler, force_zeros_for_empty_prompt)