def __call__(
        self,
        image_embeds: Union[torch.Tensor, List[torch.Tensor]],
        image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],
        negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],
        hint: torch.Tensor,
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 100,
        guidance_scale: float = 4.0,
        strength: float = 0.3,
        num_images_per_prompt: int = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        output_type: Optional[str] = "pil",
        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
        callback_steps: int = 1,
        return_dict: bool = True,
    ):
        """
        Function invoked when calling the pipeline for generation.

        Args:
            image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
                The clip image embeddings for text prompt, that will be used to condition the image generation.
            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `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.
            strength (`float`, *optional*, defaults to 0.8):
                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`.
            hint (`torch.Tensor`):
                The controlnet condition.
            negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
                The clip image embeddings for negative text prompt, will be used to condition the image generation.
            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.
            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.
            guidance_scale (`float`, *optional*, defaults to 4.0):
                Guidance scale as defined in [Classifier-Free Diffusion
                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
                of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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.
            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.Tensor)`.
            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`
        """
        device = self._execution_device

        do_classifier_free_guidance = guidance_scale > 1.0

        if isinstance(image_embeds, list):
            image_embeds = torch.cat(image_embeds, dim=0)
        if isinstance(negative_image_embeds, list):
            negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
        if isinstance(hint, list):
            hint = torch.cat(hint, dim=0)

        batch_size = image_embeds.shape[0]

        if do_classifier_free_guidance:
            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
            negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
            hint = hint.repeat_interleave(num_images_per_prompt, dim=0)

            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
                dtype=self.unet.dtype, device=device
            )
            hint = torch.cat([hint, hint], dim=0).to(dtype=self.unet.dtype, device=device)

        if not isinstance(image, list):
            image = [image]
        if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):
            raise ValueError(
                f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and pytorch tensor"
            )

        image = torch.cat([self.image_processor.preprocess(i, width, height) for i in image], dim=0)
        image = image.to(dtype=image_embeds.dtype, device=device)

        latents = self.movq.encode(image)["latents"]
        latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
        latents = self.prepare_latents(
            latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator
        )
        for i, t in enumerate(self.progress_bar(timesteps)):
            # expand the latents if we are doing classifier free guidance
            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents

            added_cond_kwargs = {"image_embeds": image_embeds, "hint": hint}
            noise_pred = self.unet(
                sample=latent_model_input,
                timestep=t,
                encoder_hidden_states=None,
                added_cond_kwargs=added_cond_kwargs,
                return_dict=False,
            )[0]

            if do_classifier_free_guidance:
                noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                _, variance_pred_text = variance_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)

            if not (
                hasattr(self.scheduler.config, "variance_type")
                and self.scheduler.config.variance_type in ["learned", "learned_range"]
            ):
                noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)

            # compute the previous noisy sample x_t -> x_t-1

            latents = self.scheduler.step(
                noise_pred,
                t,
                latents,
                generator=generator,
            )[0]

            if callback is not None and i % callback_steps == 0:
                step_idx = i // getattr(self.scheduler, "order", 1)
                callback(step_idx, t, latents)

            if XLA_AVAILABLE:
                xm.mark_step()

        # post-processing
        image = self.movq.decode(latents, force_not_quantize=True)["sample"]

        # Offload all models
        self.maybe_free_model_hooks()

        if output_type not in ["pt", "np", "pil"]:
            raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")

        image = self.image_processor.postprocess(image, output_type)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)

        Function invoked when calling the pipeline for generation.

        Args:
            image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
                The clip image embeddings for text prompt, that will be used to condition the image generation.
            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `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.
            strength (`float`, *optional*, defaults to 0.8):
                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`.
            hint (`torch.Tensor`):
                The controlnet condition.
            negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
                The clip image embeddings for negative text prompt, will be used to condition the image generation.
            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.
            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.
            guidance_scale (`float`, *optional*, defaults to 4.0):
                Guidance scale as defined in [Classifier-Free Diffusion
                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
                of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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.
            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.Tensor)`.
            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`
        
src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py