class_docstr: Combined Pipeline for image-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.
docstr: Function invoked when calling the pipeline for generation.
  
  Args:
      prompt (`str` or `List[str]`):
          The prompt or prompts to guide the image generation.
      image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
          `Image`, or tensor representing an image batch, that will be used as the starting point for the
          process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded
          again.
      negative_prompt (`str` or `List[str]`, *optional*):
          The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
          if `guidance_scale` is less than `1`).
      num_images_per_prompt (`int`, *optional*, defaults to 1):
          The number of images to generate per prompt.
      guidance_scale (`float`, *optional*, defaults to 4.0):
          Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
          `guidance_scale` is defined as `w` of equation 2. of [Imagen
          Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
          1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
          usually at the expense of lower image quality.
      strength (`float`, *optional*, defaults to 0.3):
          Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
          will be used as a starting point, adding more noise to it the larger the `strength`. The number of
          denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
          be maximum and the denoising process will run for the full number of iterations specified in
          `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
      num_inference_steps (`int`, *optional*, defaults to 100):
          The number of denoising steps. More denoising steps usually lead to a higher quality image at the
          expense of slower inference.
      height (`int`, *optional*, defaults to 512):
          The height in pixels of the generated image.
      width (`int`, *optional*, defaults to 512):
          The width in pixels of the generated image.
      prior_guidance_scale (`float`, *optional*, defaults to 4.0):
          Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
          `guidance_scale` is defined as `w` of equation 2. of [Imagen
          Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
          1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
          usually at the expense of lower image quality.
      prior_num_inference_steps (`int`, *optional*, defaults to 100):
          The number of denoising steps. More denoising steps usually lead to a higher quality image at the
          expense of slower inference.
      generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
          One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
          to make generation deterministic.
      latents (`torch.FloatTensor`, *optional*):
          Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
          generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
          tensor will ge generated by sampling using the supplied random `generator`.
      output_type (`str`, *optional*, defaults to `"pil"`):
          The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
          (`np.array`) or `"pt"` (`torch.Tensor`).
      callback (`Callable`, *optional*):
          A function that calls every `callback_steps` steps during inference. The function is called with the
          following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
      callback_steps (`int`, *optional*, defaults to 1):
          The frequency at which the `callback` function is called. If not specified, the callback is called at
          every step.
      return_dict (`bool`, *optional*, defaults to `True`):
          Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
  
  Examples:
  
  Returns:
      [`~pipelines.ImagePipelineOutput`] or `tuple`
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code: def __call__(
      self,
      prompt: Union[str, List[str]],
      image: Union[
          torch.FloatTensor,
          PIL.Image.Image,
          List[torch.FloatTensor],
          List[PIL.Image.Image],
      ],
      negative_prompt: Optional[Union[str, List[str]]] = None,
      num_inference_steps: int = 100,
      guidance_scale: float = 4.0,
      strength: float = 0.3,
      num_images_per_prompt: int = 1,
      height: int = 512,
      width: int = 512,
      prior_guidance_scale: float = 4.0,
      prior_num_inference_steps: int = 25,
      generator: Optional[
          Union[torch.Generator, List[torch.Generator]]
      ] = None,
      latents: Optional[torch.FloatTensor] = None,
      output_type: Optional[str] = "pil",
      callback: Optional[
          Callable[[int, int, torch.FloatTensor], None]
      ] = None,
      callback_steps: int = 1,
      return_dict: bool = True,
  ):
      """
      Function invoked when calling the pipeline for generation.
  
      Args:
          prompt (`str` or `List[str]`):
              The prompt or prompts to guide the image generation.
          image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
              `Image`, or tensor representing an image batch, that will be used as the starting point for the
              process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded
              again.
          negative_prompt (`str` or `List[str]`, *optional*):
              The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
              if `guidance_scale` is less than `1`).
          num_images_per_prompt (`int`, *optional*, defaults to 1):
              The number of images to generate per prompt.
          guidance_scale (`float`, *optional*, defaults to 4.0):
              Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
              `guidance_scale` is defined as `w` of equation 2. of [Imagen
              Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
              1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
              usually at the expense of lower image quality.
          strength (`float`, *optional*, defaults to 0.3):
              Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
              will be used as a starting point, adding more noise to it the larger the `strength`. The number of
              denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
              be maximum and the denoising process will run for the full number of iterations specified in
              `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
          num_inference_steps (`int`, *optional*, defaults to 100):
              The number of denoising steps. More denoising steps usually lead to a higher quality image at the
              expense of slower inference.
          height (`int`, *optional*, defaults to 512):
              The height in pixels of the generated image.
          width (`int`, *optional*, defaults to 512):
              The width in pixels of the generated image.
          prior_guidance_scale (`float`, *optional*, defaults to 4.0):
              Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
              `guidance_scale` is defined as `w` of equation 2. of [Imagen
              Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
              1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
              usually at the expense of lower image quality.
          prior_num_inference_steps (`int`, *optional*, defaults to 100):
              The number of denoising steps. More denoising steps usually lead to a higher quality image at the
              expense of slower inference.
          generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
              One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
              to make generation deterministic.
          latents (`torch.FloatTensor`, *optional*):
              Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
              generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
              tensor will ge generated by sampling using the supplied random `generator`.
          output_type (`str`, *optional*, defaults to `"pil"`):
              The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
              (`np.array`) or `"pt"` (`torch.Tensor`).
          callback (`Callable`, *optional*):
              A function that calls every `callback_steps` steps during inference. The function is called with the
              following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
          callback_steps (`int`, *optional*, defaults to 1):
              The frequency at which the `callback` function is called. If not specified, the callback is called at
              every step.
          return_dict (`bool`, *optional*, defaults to `True`):
              Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
  
      Examples:
  
      Returns:
          [`~pipelines.ImagePipelineOutput`] or `tuple`
      """
      prior_outputs = self.prior_pipe(
          prompt=prompt,
          negative_prompt=negative_prompt,
          num_images_per_prompt=num_images_per_prompt,
          num_inference_steps=prior_num_inference_steps,
          generator=generator,
          latents=latents,
          guidance_scale=prior_guidance_scale,
          output_type="pt",
          return_dict=False,
      )
      image_embeds = prior_outputs[0]
      negative_image_embeds = prior_outputs[1]
  
      prompt = (
          [prompt] if not isinstance(prompt, (list, tuple)) else prompt
      )
      image = [image] if isinstance(prompt, PIL.Image.Image) else image
  
      if (
          len(prompt) < image_embeds.shape[0]
          and image_embeds.shape[0] % len(prompt) == 0
      ):
          prompt = (image_embeds.shape[0] // len(prompt)) * prompt
  
      if (
          isinstance(image, (list, tuple))
          and len(image) < image_embeds.shape[0]
          and image_embeds.shape[0] % len(image) == 0
      ):
          image = (image_embeds.shape[0] // len(image)) * image
  
      outputs = self.decoder_pipe(
          image=image,
          image_embeds=image_embeds,
          negative_image_embeds=negative_image_embeds,
          width=width,
          height=height,
          strength=strength,
          num_inference_steps=num_inference_steps,
          generator=generator,
          guidance_scale=guidance_scale,
          output_type=output_type,
          callback=callback,
          callback_steps=callback_steps,
          return_dict=return_dict,
      )
      return outputs
  
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 7 for calling the KandinskyV22Img2ImgCombinedPipeline obj's underlying member method code with example usage: obj.__call__(prompt, image, negative_prompt, num_inference_steps, guidance_scale, strength, num_images_per_prompt, height, width, prior_guidance_scale, prior_num_inference_steps, generator, latents, output_type, callback, callback_steps, return_dict) and returns: outputs