def __call__(
        self,
        image: PipelineImageInput,
        prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        height: int = 480,
        width: int = 720,
        num_frames: int = 49,
        num_inference_steps: int = 50,
        guidance_scale: float = 6.0,
        use_dynamic_cfg: bool = False,
        num_videos_per_prompt: int = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: str = "pil",
        return_dict: bool = True,
        attention_kwargs: Optional[Dict[str, Any]] = None,
        callback_on_step_end: Optional[
            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
        ] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        max_sequence_length: int = 226,
        id_vit_hidden: Optional[torch.Tensor] = None,
        id_cond: Optional[torch.Tensor] = None,
        kps_cond: Optional[torch.Tensor] = None,
    ) -> Union[ConsisIDPipelineOutput, Tuple]:
        """
        Function invoked when calling the pipeline for generation.

        Args:
            image (`PipelineImageInput`):
                The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            height (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
                The height in pixels of the generated image. This is set to 480 by default for the best results.
            width (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
                The width in pixels of the generated image. This is set to 720 by default for the best results.
            num_frames (`int`, defaults to `49`):
                Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
                contain 1 extra frame because ConsisID is conditioned with (num_seconds * fps + 1) frames where
                num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
                needs to be satisfied is that of divisibility mentioned above.
            num_inference_steps (`int`, *optional*, defaults to 50):
                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 6):
                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.
            use_dynamic_cfg (`bool`, *optional*, defaults to `False`):
                If True, dynamically adjusts the guidance scale during inference. This allows the model to use a
                progressive guidance scale, improving the balance between text-guided generation and image quality over
                the course of the inference steps. Typically, early inference steps use a higher guidance scale for
                more faithful image generation, while later steps reduce it for more diverse and natural results.
            num_videos_per_prompt (`int`, *optional*, defaults to 1):
                The number of videos 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.
            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`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
                of a plain tuple.
            attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.
            max_sequence_length (`int`, defaults to `226`):
                Maximum sequence length in encoded prompt. Must be consistent with
                `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
            id_vit_hidden (`Optional[torch.Tensor]`, *optional*):
                The tensor representing the hidden features extracted from the face model, which are used to condition
                the local facial extractor. This is crucial for the model to obtain high-frequency information of the
                face. If not provided, the local facial extractor will not run normally.
            id_cond (`Optional[torch.Tensor]`, *optional*):
                The tensor representing the hidden features extracted from the clip model, which are used to condition
                the local facial extractor. This is crucial for the model to edit facial features If not provided, the
                local facial extractor will not run normally.
            kps_cond (`Optional[torch.Tensor]`, *optional*):
                A tensor that determines whether the global facial extractor use keypoint information for conditioning.
                If provided, this tensor controls whether facial keypoints such as eyes, nose, and mouth landmarks are
                used during the generation process. This helps ensure the model retains more facial low-frequency
                information.

        Examples:

        Returns:
            [`~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput`] or `tuple`:
            [`~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput`] if `return_dict` is True, otherwise a
            `tuple`. When returning a tuple, the first element is a list with the generated images.
        """

        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs

        height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial
        width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial
        num_frames = num_frames or self.transformer.config.sample_frames

        num_videos_per_prompt = 1

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            image=image,
            prompt=prompt,
            height=height,
            width=width,
            negative_prompt=negative_prompt,
            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
            latents=latents,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )
        self._guidance_scale = guidance_scale
        self._attention_kwargs = attention_kwargs
        self._interrupt = False

        # 2. Default call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
            prompt=prompt,
            negative_prompt=negative_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            num_videos_per_prompt=num_videos_per_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            max_sequence_length=max_sequence_length,
            device=device,
        )
        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)

        # 4. Prepare timesteps
        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device)
        self._num_timesteps = len(timesteps)

        # 5. Prepare latents
        is_kps = getattr(self.transformer.config, "is_kps", False)
        kps_cond = kps_cond if is_kps else None
        if kps_cond is not None:
            kps_cond = draw_kps(image, kps_cond)
            kps_cond = self.video_processor.preprocess(kps_cond, height=height, width=width).to(
                device, dtype=prompt_embeds.dtype
            )

        image = self.video_processor.preprocess(image, height=height, width=width).to(
            device, dtype=prompt_embeds.dtype
        )

        latent_channels = self.transformer.config.in_channels // 2
        latents, image_latents = self.prepare_latents(
            image,
            batch_size * num_videos_per_prompt,
            latent_channels,
            num_frames,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
            kps_cond,
        )

        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Create rotary embeds if required
        image_rotary_emb = (
            self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
            if self.transformer.config.use_rotary_positional_embeddings
            else None
        )

        # 8. Denoising loop
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

        with self.progress_bar(total=num_inference_steps) as progress_bar:
            # for DPM-solver++
            old_pred_original_sample = None
            timesteps_cpu = timesteps.cpu()
            for i, t in enumerate(timesteps):
                if self.interrupt:
                    continue

                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                latent_image_input = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents
                latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=2)

                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                timestep = t.expand(latent_model_input.shape[0])

                # predict noise model_output
                noise_pred = self.transformer(
                    hidden_states=latent_model_input,
                    encoder_hidden_states=prompt_embeds,
                    timestep=timestep,
                    image_rotary_emb=image_rotary_emb,
                    attention_kwargs=attention_kwargs,
                    return_dict=False,
                    id_vit_hidden=id_vit_hidden,
                    id_cond=id_cond,
                )[0]
                noise_pred = noise_pred.float()

                # perform guidance
                if use_dynamic_cfg:
                    self._guidance_scale = 1 + guidance_scale * (
                        (
                            1
                            - math.cos(
                                math.pi
                                * ((num_inference_steps - timesteps_cpu[i].item()) / num_inference_steps) ** 5.0
                            )
                        )
                        / 2
                    )
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                if not isinstance(self.scheduler, CogVideoXDPMScheduler):
                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
                else:
                    latents, old_pred_original_sample = self.scheduler.step(
                        noise_pred,
                        old_pred_original_sample,
                        t,
                        timesteps[i - 1] if i > 0 else None,
                        latents,
                        **extra_step_kwargs,
                        return_dict=False,
                    )
                latents = latents.to(prompt_embeds.dtype)

                # call the callback, if provided
                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)
                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)

                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()

        if not output_type == "latent":
            video = self.decode_latents(latents)
            video = self.video_processor.postprocess_video(video=video, output_type=output_type)
        else:
            video = latents

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (video,)

        return ConsisIDPipelineOutput(frames=video)

        Function invoked when calling the pipeline for generation.

        Args:
            image (`PipelineImageInput`):
                The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            height (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
                The height in pixels of the generated image. This is set to 480 by default for the best results.
            width (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
                The width in pixels of the generated image. This is set to 720 by default for the best results.
            num_frames (`int`, defaults to `49`):
                Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
                contain 1 extra frame because ConsisID is conditioned with (num_seconds * fps + 1) frames where
                num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
                needs to be satisfied is that of divisibility mentioned above.
            num_inference_steps (`int`, *optional*, defaults to 50):
                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 6):
                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.
            use_dynamic_cfg (`bool`, *optional*, defaults to `False`):
                If True, dynamically adjusts the guidance scale during inference. This allows the model to use a
                progressive guidance scale, improving the balance between text-guided generation and image quality over
                the course of the inference steps. Typically, early inference steps use a higher guidance scale for
                more faithful image generation, while later steps reduce it for more diverse and natural results.
            num_videos_per_prompt (`int`, *optional*, defaults to 1):
                The number of videos 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.
            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`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
                of a plain tuple.
            attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.
            max_sequence_length (`int`, defaults to `226`):
                Maximum sequence length in encoded prompt. Must be consistent with
                `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
            id_vit_hidden (`Optional[torch.Tensor]`, *optional*):
                The tensor representing the hidden features extracted from the face model, which are used to condition
                the local facial extractor. This is crucial for the model to obtain high-frequency information of the
                face. If not provided, the local facial extractor will not run normally.
            id_cond (`Optional[torch.Tensor]`, *optional*):
                The tensor representing the hidden features extracted from the clip model, which are used to condition
                the local facial extractor. This is crucial for the model to edit facial features If not provided, the
                local facial extractor will not run normally.
            kps_cond (`Optional[torch.Tensor]`, *optional*):
                A tensor that determines whether the global facial extractor use keypoint information for conditioning.
                If provided, this tensor controls whether facial keypoints such as eyes, nose, and mouth landmarks are
                used during the generation process. This helps ensure the model retains more facial low-frequency
                information.

        Examples:

        Returns:
            [`~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput`] or `tuple`:
            [`~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput`] if `return_dict` is True, otherwise a
            `tuple`. When returning a tuple, the first element is a list with the generated images.
        
src/diffusers/pipelines/consisid/pipeline_consisid.py