class_docstr: Pipeline for a bimodal image-text model which supports unconditional text and image generation, text-conditioned
  image generation, image-conditioned text generation, and joint image-text generation.
  
  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.).
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. This
          is part of the UniDiffuser image representation along with the CLIP vision encoding.
      text_encoder ([`CLIPTextModel`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      image_encoder ([`CLIPVisionModel`]):
          A [`~transformers.CLIPVisionModel`] to encode images as part of its image representation along with the VAE
          latent representation.
      image_processor ([`CLIPImageProcessor`]):
          [`~transformers.CLIPImageProcessor`] to preprocess an image before CLIP encoding it with `image_encoder`.
      clip_tokenizer ([`CLIPTokenizer`]):
           A [`~transformers.CLIPTokenizer`] to tokenize the prompt before encoding it with `text_encoder`.
      text_decoder ([`UniDiffuserTextDecoder`]):
          Frozen text decoder. This is a GPT-style model which is used to generate text from the UniDiffuser
          embedding.
      text_tokenizer ([`GPT2Tokenizer`]):
          A [`~transformers.GPT2Tokenizer`] to decode text for text generation; used along with the `text_decoder`.
      unet ([`UniDiffuserModel`]):
          A [U-ViT](https://github.com/baofff/U-ViT) model with UNNet-style skip connections between transformer
          layers to denoise the encoded image latents.
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image and/or text latents. The
          original UniDiffuser paper uses the [`DPMSolverMultistepScheduler`] scheduler.
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docstr: Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
  compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
  processing larger images.
docstr_tok: ["Enable","tiled","VAE","decoding",".","When","this","option","is","enabled",",","the","VAE","will","split","the","input","tensor","into","tiles","to","compute","decoding","and","encoding","in","several","steps",".","This","is","useful","for","saving","a","large","amount","of","memory","and","to","allow","processing","larger","images","."]
code: def enable_vae_tiling(self):
      r"""
      Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
      compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
      processing larger images.
      """
      self.vae.enable_tiling()
  
imports: import inspect
  from dataclasses import dataclass
  from typing import Callable, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection, GPT2Tokenizer
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
  from ...utils.outputs import BaseOutput
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from .modeling_text_decoder import UniDiffuserTextDecoder
  from .modeling_uvit import UniDiffuserModel
label_desc: Use image node_id 4 for calling the UniDiffuserPipeline obj's underlying member method code with example usage: obj.enable_vae_tiling() without return types