class_docstr: 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:
      text_encoder ([`MultilingualCLIP`]):
          Frozen text-encoder.
      tokenizer ([`XLMRobertaTokenizer`]):
          Tokenizer of class
      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.
code: def __init__(
      self,
      text_encoder: MultilingualCLIP,
      tokenizer: XLMRobertaTokenizer,
      unet: UNet2DConditionModel,
      scheduler: Union[DDIMScheduler, DDPMScheduler],
      movq: VQModel,
  ):
      super().__init__()
  
      self.register_modules(
          text_encoder=text_encoder,
          tokenizer=tokenizer,
          unet=unet,
          scheduler=scheduler,
          movq=movq,
      )
      self.movq_scale_factor = 2 ** (
          len(self.movq.config.block_out_channels) - 1
      )
  
file_path: diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py
imports: from typing import Callable, List, Optional, Union
  import torch
  from transformers import XLMRobertaTokenizer
  from ...models import UNet2DConditionModel, VQModel
  from ...schedulers import DDIMScheduler, DDPMScheduler
  from ...utils import logging, replace_example_docstring
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  from .text_encoder import MultilingualCLIP
label_desc: Use image node_id 1 to create a new KandinskyPipeline object from inherited base classes: DiffusionPipeline with example: obj = KandinskyPipeline(text_encoder, tokenizer, unet, scheduler, movq)