class_docstr: Combined Pipeline for text-to-image generation using Kandinsky
  
  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:
      scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
          A scheduler to be used in combination with `unet` to generate image latents.
      unet ([`UNet2DConditionModel`]):
          Conditional U-Net architecture to denoise the image embedding.
      movq ([`VQModel`]):
          MoVQ Decoder to generate the image from the latents.
      prior_prior ([`PriorTransformer`]):
          The canonincal unCLIP prior to approximate the image embedding from the text embedding.
      prior_image_encoder ([`CLIPVisionModelWithProjection`]):
          Frozen image-encoder.
      prior_text_encoder ([`CLIPTextModelWithProjection`]):
          Frozen text-encoder.
      prior_tokenizer (`CLIPTokenizer`):
          Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      prior_scheduler ([`UnCLIPScheduler`]):
          A scheduler to be used in combination with `prior` to generate image embedding.
      prior_image_processor ([`CLIPImageProcessor`]):
          A image_processor to be used to preprocess image from clip.
code: def set_progress_bar_config(self, **kwargs):
      self.prior_pipe.set_progress_bar_config(**kwargs)
      self.decoder_pipe.set_progress_bar_config(**kwargs)
  
imports: from typing import Callable, List, Optional, Union
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
  from ...models import PriorTransformer, UNet2DConditionModel, VQModel
  from ...schedulers import DDPMScheduler, UnCLIPScheduler
  from ...utils import logging, replace_example_docstring
  from ..pipeline_utils import DiffusionPipeline
  from .pipeline_kandinsky2_2 import KandinskyV22Pipeline
  from .pipeline_kandinsky2_2_img2img import KandinskyV22Img2ImgPipeline
  from .pipeline_kandinsky2_2_inpainting import KandinskyV22InpaintPipeline
  from .pipeline_kandinsky2_2_prior import KandinskyV22PriorPipeline
label_desc: Use image node_id 5 for calling the KandinskyV22CombinedPipeline obj's underlying member method code with example usage: obj.set_progress_bar_config() without return types