class_docstr: Pipeline for upscaling Stable Diffusion output image resolution by a factor of 2.
  
  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.
      text_encoder ([`~transformers.CLIPTextModel`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      tokenizer ([`~transformers.CLIPTokenizer`]):
          A `CLIPTokenizer` to tokenize text.
      unet ([`UNet2DConditionModel`]):
          A `UNet2DConditionModel` to denoise the encoded image latents.
      scheduler ([`SchedulerMixin`]):
          A [`EulerDiscreteScheduler`] to be used in combination with `unet` to denoise the encoded image latents.
class_docstr_tok: ["Pipeline","for","upscaling","Stable","Diffusion","output","image","resolution","by","a","factor","of","2",".","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",".","text_encoder","(","[","`","~transformers.CLIPTextModel","`","]",")",":","Frozen","text-encoder","(","[","clip-vit-large-patch14","]","(","https",":","\/\/huggingface.co\/openai\/clip-vit-large-patch14",")",")",".","tokenizer","(","[","`","~transformers.CLIPTokenizer","`","]",")",":","A","`","CLIPTokenizer","`","to","tokenize","text",".","unet","(","[","`","UNet2DConditionModel","`","]",")",":","A","`","UNet2DConditionModel","`","to","denoise","the","encoded","image","latents",".","scheduler","(","[","`","SchedulerMixin","`","]",")",":","A","[","`","EulerDiscreteScheduler","`","]","to","be","used","in","combination","with","`","unet","`","to","denoise","the","encoded","image","latents","."]
code: def __init__(
      self,
      vae: AutoencoderKL,
      text_encoder: CLIPTextModel,
      tokenizer: CLIPTokenizer,
      unet: UNet2DConditionModel,
      scheduler: EulerDiscreteScheduler,
  ):
      super().__init__()
  
      self.register_modules(
          vae=vae,
          text_encoder=text_encoder,
          tokenizer=tokenizer,
          unet=unet,
          scheduler=scheduler,
      )
      self.vae_scale_factor = 2 ** (
          len(self.vae.config.block_out_channels) - 1
      )
      self.image_processor = VaeImageProcessor(
          vae_scale_factor=self.vae_scale_factor, resample="bicubic"
      )
  
imports: import warnings
  from typing import Callable, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from transformers import CLIPTextModel, CLIPTokenizer
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...loaders import FromSingleFileMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...schedulers import EulerDiscreteScheduler
  from ...utils import deprecate, logging
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 1 to create a new StableDiffusionLatentUpscalePipeline object from inherited base classes: DiffusionPipeline, FromSingleFileMixin with example: obj = StableDiffusionLatentUpscalePipeline(vae, text_encoder, tokenizer, unet, scheduler)