class_docstr: Pipeline for generating images from the Wuerstchen model.
  
  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:
      tokenizer (`CLIPTokenizer`):
          The CLIP tokenizer.
      text_encoder (`CLIPTextModel`):
          The CLIP text encoder.
      decoder ([`WuerstchenDiffNeXt`]):
          The WuerstchenDiffNeXt unet decoder.
      vqgan ([`PaellaVQModel`]):
          The VQGAN model.
      scheduler ([`DDPMWuerstchenScheduler`]):
          A scheduler to be used in combination with `prior` to generate image embedding.
      latent_dim_scale (float, `optional`, defaults to 10.67):
          Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are
          height=24 and width=24, the VQ latent shape needs to be height=int(24*10.67)=256 and
          width=int(24*10.67)=256 in order to match the training conditions.
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code: def __init__(
      self,
      tokenizer: CLIPTokenizer,
      text_encoder: CLIPTextModel,
      decoder: WuerstchenDiffNeXt,
      scheduler: DDPMWuerstchenScheduler,
      vqgan: PaellaVQModel,
      latent_dim_scale: float = 10.67,
  ) -> None:
      super().__init__()
      self.register_modules(
          tokenizer=tokenizer,
          text_encoder=text_encoder,
          decoder=decoder,
          scheduler=scheduler,
          vqgan=vqgan,
      )
      self.register_to_config(latent_dim_scale=latent_dim_scale)
  
file_path: diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py
imports: from typing import Callable, Dict, List, Optional, Union
  import numpy
  import torch
  from transformers import CLIPTextModel, CLIPTokenizer
  from ...schedulers import DDPMWuerstchenScheduler
  from ...utils import deprecate, logging, replace_example_docstring
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  from .modeling_paella_vq_model import PaellaVQModel
  from .modeling_wuerstchen_diffnext import WuerstchenDiffNeXt
label_desc: Use image node_id 1 to create a new WuerstchenDecoderPipeline object from inherited base classes: DiffusionPipeline with example: obj = WuerstchenDecoderPipeline(tokenizer, text_encoder, decoder, scheduler, vqgan, latent_dim_scale)