ConsisID

Identity-Preserving Text-to-Video Generation by Frequency Decomposition from Peking University & University of Rochester & etc, by Shenghai Yuan, Jinfa Huang, Xianyi He, Yunyang Ge, Yujun Shi, Liuhan Chen, Jiebo Luo, Li Yuan.

The abstract from the paper is:

Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity. It is an important task in video generation but remains an open problem for generative models. This paper pushes the technical frontier of IPT2V in two directions that have not been resolved in the literature: (1) A tuning-free pipeline without tedious case-by-case finetuning, and (2) A frequency-aware heuristic identity-preserving Diffusion Transformer (DiT)-based control scheme. To achieve these goals, we propose ConsisID, a tuning-free DiT-based controllable IPT2V model to keep human-id**entity **consis**tent in the generated video. Inspired by prior findings in frequency analysis of vision/diffusion transformers, it employs identity-control signals in the frequency domain, where facial features can be decomposed into low-frequency global features (e.g., profile, proportions) and high-frequency intrinsic features (e.g., identity markers that remain unaffected by pose changes). First, from a low-frequency perspective, we introduce a global facial extractor, which encodes the reference image and facial key points into a latent space, generating features enriched with low-frequency information. These features are then integrated into the shallow layers of the network to alleviate training challenges associated with DiT. Second, from a high-frequency perspective, we design a local facial extractor to capture high-frequency details and inject them into the transformer blocks, enhancing the model's ability to preserve fine-grained features. To leverage the frequency information for identity preservation, we propose a hierarchical training strategy, transforming a vanilla pre-trained video generation model into an IPT2V model. Extensive experiments demonstrate that our frequency-aware heuristic scheme provides an optimal control solution for DiT-based models. Thanks to this scheme, our **ConsisID achieves excellent results in generating high-quality, identity-preserving videos, making strides towards more effective IPT2V. The model weight of ConsID is publicly available at https://github.com/PKU-YuanGroup/ConsisID.

Tip

Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

This pipeline was contributed by SHYuanBest. The original codebase can be found here. The original weights can be found under hf.co/BestWishYsh.

There are two official ConsisID checkpoints for identity-preserving text-to-video.

checkpoints	recommended inference dtype
BestWishYsh/ConsisID-preview	mindspore.bfloat16
BestWishYsh/ConsisID-1.5	mindspore.bfloat16

Memory optimization

ConsisID requires about 44 GB of device memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H). The following memory optimizations could be used to reduce the memory footprint. For replication, you can refer to this script.

Feature (overlay the previous)	Max Memory Allocated	Max Memory Reserved
-	37 GB	44 GB
enable_model_cpu_offload	22 GB	25 GB
enable_sequential_cpu_offload	16 GB	22 GB
vae.enable_slicing	16 GB	22 GB
vae.enable_tiling	5 GB	7 GB

mindone.diffusers.pipelines.consisid.ConsisIDPipeline

Bases: DiffusionPipeline, CogVideoXLoraLoaderMixin

Pipeline for image-to-video generation using ConsisID.

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.)

PARAMETER	DESCRIPTION
vae	Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. TYPE: [`AutoencoderKL`]
text_encoder	Frozen text-encoder. ConsisID uses T5; specifically the t5-v1_1-xxl variant. TYPE: [`T5EncoderModel`]
tokenizer	Tokenizer of class T5Tokenizer. TYPE: `T5Tokenizer`
transformer	A text conditioned ConsisIDTransformer3DModel to denoise the encoded video latents. TYPE: [`ConsisIDTransformer3DModel`]
scheduler	A scheduler to be used in combination with transformer to denoise the encoded video latents. TYPE: [`SchedulerMixin`]

Source code in mindone/diffusers/pipelines/consisid/pipeline_consisid.py

class ConsisIDPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
    r"""
    Pipeline for image-to-video generation using ConsisID.

    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:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
        text_encoder ([`T5EncoderModel`]):
            Frozen text-encoder. ConsisID uses
            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
        tokenizer (`T5Tokenizer`):
            Tokenizer of class
            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
        transformer ([`ConsisIDTransformer3DModel`]):
            A text conditioned `ConsisIDTransformer3DModel` to denoise the encoded video latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
    """

    _optional_components = []
    model_cpu_offload_seq = "text_encoder->transformer->vae"

    _callback_tensor_inputs = [
        "latents",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]

    def __init__(
        self,
        tokenizer: T5Tokenizer,
        text_encoder: T5EncoderModel,
        vae: AutoencoderKLCogVideoX,
        transformer: ConsisIDTransformer3DModel,
        scheduler: CogVideoXDPMScheduler,
    ):
        super().__init__()

        self.register_modules(
            tokenizer=tokenizer,
            text_encoder=text_encoder,
            vae=vae,
            transformer=transformer,
            scheduler=scheduler,
        )
        self.vae_scale_factor_spatial = (
            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
        )
        self.vae_scale_factor_temporal = (
            self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
        )
        self.vae_scaling_factor_image = (
            self.vae.config.scaling_factor if hasattr(self, "vae") and self.vae is not None else 0.7
        )

        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)

    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds
    def _get_t5_prompt_embeds(
        self,
        prompt: Union[str, List[str]] = None,
        num_videos_per_prompt: int = 1,
        max_sequence_length: int = 226,
        dtype: Optional[ms.Type] = None,
    ):
        dtype = dtype or self.text_encoder.dtype

        prompt = [prompt] if isinstance(prompt, str) else prompt
        batch_size = len(prompt)

        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            add_special_tokens=True,
            return_tensors="np",
        )
        text_input_ids = text_inputs.input_ids
        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="np").input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not np.array_equal(
            text_input_ids, untruncated_ids
        ):
            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
            logger.warning(
                "The following part of your input was truncated because `max_sequence_length` is set to "
                f" {max_sequence_length} tokens: {removed_text}"
            )

        prompt_embeds = self.text_encoder(ms.Tensor.from_numpy(text_input_ids))[0]
        prompt_embeds = prompt_embeds.to(dtype=dtype)

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        _, seq_len, _ = prompt_embeds.shape
        prompt_embeds = prompt_embeds.tile((1, num_videos_per_prompt, 1))
        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)

        return prompt_embeds

    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt
    def encode_prompt(
        self,
        prompt: Union[str, List[str]],
        negative_prompt: Optional[Union[str, List[str]]] = None,
        do_classifier_free_guidance: bool = True,
        num_videos_per_prompt: int = 1,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = None,
        max_sequence_length: int = 226,
        dtype: Optional[ms.Type] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            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`).
            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
                Whether to use classifier free guidance or not.
            num_videos_per_prompt (`int`, *optional*, defaults to 1):
                Number of videos that should be generated per prompt.
            prompt_embeds (`ms.Tensor`, *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 (`ms.Tensor`, *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.
            dtype: (`ms.Type`, *optional*):
                torch dtype
        """
        prompt = [prompt] if isinstance(prompt, str) else prompt
        if prompt is not None:
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if prompt_embeds is None:
            prompt_embeds = self._get_t5_prompt_embeds(
                prompt=prompt,
                num_videos_per_prompt=num_videos_per_prompt,
                max_sequence_length=max_sequence_length,
                dtype=dtype,
            )

        if do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ""
            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt

            if prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )

            negative_prompt_embeds = self._get_t5_prompt_embeds(
                prompt=negative_prompt,
                num_videos_per_prompt=num_videos_per_prompt,
                max_sequence_length=max_sequence_length,
                dtype=dtype,
            )

        return prompt_embeds, negative_prompt_embeds

    def prepare_latents(
        self,
        image: ms.Tensor,
        batch_size: int = 1,
        num_channels_latents: int = 16,
        num_frames: int = 13,
        height: int = 60,
        width: int = 90,
        dtype: Optional[ms.Type] = None,
        generator: Optional[np.random.Generator] = None,
        latents: Optional[ms.Tensor] = None,
        kps_cond: Optional[ms.Tensor] = None,
    ):
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        num_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
        shape = (
            batch_size,
            num_frames,
            num_channels_latents,
            height // self.vae_scale_factor_spatial,
            width // self.vae_scale_factor_spatial,
        )

        image = image.unsqueeze(2)  # [B, C, F, H, W]

        if isinstance(generator, list):
            image_latents = [
                retrieve_latents(self.vae, self.vae.encode(image[i].unsqueeze(0))[0], generator[i])
                for i in range(batch_size)
            ]
            if kps_cond is not None:
                kps_cond = kps_cond.unsqueeze(2)
                kps_cond_latents = [
                    retrieve_latents(self.vae, self.vae.encode(kps_cond[i].unsqueeze(0))[0], generator[i])
                    for i in range(batch_size)
                ]
        else:
            image_latents = [
                retrieve_latents(self.vae, self.vae.encode(img.unsqueeze(0))[0], generator) for img in image
            ]
            if kps_cond is not None:
                kps_cond = kps_cond.unsqueeze(2)
                kps_cond_latents = [
                    retrieve_latents(self.vae, self.vae.encode(img.unsqueeze(0))[0], generator) for img in kps_cond
                ]

        image_latents = mint.cat(image_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4)  # [B, F, C, H, W]
        image_latents = self.vae_scaling_factor_image * image_latents

        if kps_cond is not None:
            kps_cond_latents = mint.cat(kps_cond_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4)  # [B, F, C, H, W]
            kps_cond_latents = self.vae_scaling_factor_image * kps_cond_latents

            padding_shape = (
                batch_size,
                num_frames - 2,
                num_channels_latents,
                height // self.vae_scale_factor_spatial,
                width // self.vae_scale_factor_spatial,
            )
        else:
            padding_shape = (
                batch_size,
                num_frames - 1,
                num_channels_latents,
                height // self.vae_scale_factor_spatial,
                width // self.vae_scale_factor_spatial,
            )

        latent_padding = mint.zeros(padding_shape, dtype=dtype)
        if kps_cond is not None:
            image_latents = mint.cat([image_latents, kps_cond_latents, latent_padding], dim=1)
        else:
            image_latents = mint.cat([image_latents, latent_padding], dim=1)

        if latents is None:
            latents = randn_tensor(shape, generator=generator, dtype=dtype)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents, image_latents

    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.decode_latents
    def decode_latents(self, latents: ms.Tensor) -> ms.Tensor:
        latents = latents.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
        latents = 1 / self.vae_scaling_factor_image * latents
        # vae decode only support pynative
        with pynative_context():
            frames = self.vae.decode(latents)[0]
        return frames

    # Copied from diffusers.pipelines.animatediff.pipeline_animatediff_video2video.AnimateDiffVideoToVideoPipeline.get_timesteps
    def get_timesteps(self, num_inference_steps, timesteps, strength):
        # get the original timestep using init_timestep
        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

        t_start = max(num_inference_steps - init_timestep, 0)
        timesteps = timesteps[t_start * self.scheduler.order :]

        return timesteps, num_inference_steps - t_start

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def check_inputs(
        self,
        image,
        prompt,
        height,
        width,
        negative_prompt,
        callback_on_step_end_tensor_inputs,
        latents=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
    ):
        if not isinstance(image, ms.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list):
            raise ValueError(
                "`image` has to be of type `ms.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
                f" {type(image)}"
            )

        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        if callback_on_step_end_tensor_inputs is not None and not all(
            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
        ):
            raise ValueError(
                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"  # noqa: E501
            )
        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

    def _prepare_rotary_positional_embeddings(
        self,
        height: int,
        width: int,
        num_frames: int,
    ) -> Tuple[ms.Tensor, ms.Tensor]:
        grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
        grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
        base_size_width = self.transformer.config.sample_width // self.transformer.config.patch_size
        base_size_height = self.transformer.config.sample_height // self.transformer.config.patch_size

        grid_crops_coords = get_resize_crop_region_for_grid(
            (grid_height, grid_width), base_size_width, base_size_height
        )
        freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
            embed_dim=self.transformer.config.attention_head_dim,
            crops_coords=grid_crops_coords,
            grid_size=(grid_height, grid_width),
            temporal_size=num_frames,
        )

        return freqs_cos, freqs_sin

    @property
    def guidance_scale(self):
        return self._guidance_scale

    @property
    def num_timesteps(self):
        return self._num_timesteps

    @property
    def attention_kwargs(self):
        return self._attention_kwargs

    @property
    def interrupt(self):
        return self._interrupt

    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[np.random.Generator, List[np.random.Generator]]] = None,
        latents: Optional[ms.Tensor] = None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = 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[ms.Tensor] = None,
        id_cond: Optional[ms.Tensor] = None,
        kps_cond: Optional[ms.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 `ms.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://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.
            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 (`ms.Tensor`, *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 (`ms.Tensor`, *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 (`ms.Tensor`, *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[ms.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[ms.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[ms.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]

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `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,
        )
        if do_classifier_free_guidance:
            prompt_embeds = mint.cat([negative_prompt_embeds, prompt_embeds], dim=0)

        # 4. Prepare timesteps
        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps)
        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(
                dtype=prompt_embeds.dtype
            )

        image = self.video_processor.preprocess(image, height=height, width=width).to(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,
            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.shape[1])
            if self.transformer.config.use_rotary_positional_embeddings
            else None
        )

        # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
        # to the transformer and will raise RuntimeError.
        lora_scale = self.attention_kwargs.pop("scale", None) if self.attention_kwargs is not None else None
        if lora_scale is not None:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self.transformer, lora_scale)

        # 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
            for i, t in enumerate(timesteps):
                if self.interrupt:
                    continue

                latent_model_input = mint.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 = mint.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents
                latent_model_input = mint.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.broadcast_to((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[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 lora_scale is not None:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self.transformer, lora_scale)

        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

        if not return_dict:
            return (video,)

        return ConsisIDPipelineOutput(frames=video)

mindone.diffusers.pipelines.consisid.ConsisIDPipeline.__call__(image, prompt=None, negative_prompt=None, height=480, width=720, num_frames=49, num_inference_steps=50, guidance_scale=6.0, use_dynamic_cfg=False, num_videos_per_prompt=1, eta=0.0, generator=None, latents=None, prompt_embeds=None, negative_prompt_embeds=None, output_type='pil', return_dict=True, attention_kwargs=None, callback_on_step_end=None, callback_on_step_end_tensor_inputs=['latents'], max_sequence_length=226, id_vit_hidden=None, id_cond=None, kps_cond=None)

Function invoked when calling the pipeline for generation.

PARAMETER	DESCRIPTION
image	The input image to condition the generation on. Must be an image, a list of images or a ms.Tensor. TYPE: `PipelineImageInput`
prompt	The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. instead. TYPE: `str` or `List[str]`, *optional* DEFAULT: None
negative_prompt	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). TYPE: `str` or `List[str]`, *optional* DEFAULT: None
height	The height in pixels of the generated image. This is set to 480 by default for the best results. TYPE: `int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial DEFAULT: 480
width	The width in pixels of the generated image. This is set to 720 by default for the best results. TYPE: `int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial DEFAULT: 720
num_frames	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. TYPE: `int`, defaults to `49` DEFAULT: 49
num_inference_steps	The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. TYPE: `int`, *optional*, defaults to 50 DEFAULT: 50
guidance_scale	Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. 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. TYPE: `float`, *optional*, defaults to 6 DEFAULT: 6.0
use_dynamic_cfg	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. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
num_videos_per_prompt	The number of videos to generate per prompt. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
generator	One or a list of torch generator(s) to make generation deterministic. TYPE: `torch.Generator` or `List[torch.Generator]`, *optional* DEFAULT: None
latents	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. TYPE: `ms.Tensor`, *optional* DEFAULT: None
prompt_embeds	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. TYPE: `ms.Tensor`, *optional* DEFAULT: None
negative_prompt_embeds	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. TYPE: `ms.Tensor`, *optional* DEFAULT: None
output_type	The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array. TYPE: `str`, *optional*, defaults to `"pil"` DEFAULT: 'pil'
return_dict	Whether or not to return a [~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True
attention_kwargs	A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor. TYPE: `dict`, *optional* DEFAULT: None
callback_on_step_end	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. TYPE: `Callable`, *optional* DEFAULT: None
callback_on_step_end_tensor_inputs	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. TYPE: `List`, *optional* DEFAULT: ['latents']
max_sequence_length	Maximum sequence length in encoded prompt. Must be consistent with self.transformer.config.max_text_seq_length otherwise may lead to poor results. TYPE: `int`, defaults to `226` DEFAULT: 226
id_vit_hidden	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. TYPE: `Optional[ms.Tensor]`, *optional* DEFAULT: None
id_cond	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. TYPE: `Optional[ms.Tensor]`, *optional* DEFAULT: None
kps_cond	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. TYPE: `Optional[ms.Tensor]`, *optional* DEFAULT: None

RETURNS	DESCRIPTION
Union[ConsisIDPipelineOutput, Tuple]	[~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput] or tuple:
Union[ConsisIDPipelineOutput, Tuple]	[~pipelines.consisid.pipeline_output.ConsisIDPipelineOutput] if return_dict is True, otherwise a
Union[ConsisIDPipelineOutput, Tuple]	tuple. When returning a tuple, the first element is a list with the generated images.

Source code in mindone/diffusers/pipelines/consisid/pipeline_consisid.py

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[np.random.Generator, List[np.random.Generator]]] = None,
    latents: Optional[ms.Tensor] = None,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = 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[ms.Tensor] = None,
    id_cond: Optional[ms.Tensor] = None,
    kps_cond: Optional[ms.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 `ms.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://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.
        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 (`ms.Tensor`, *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 (`ms.Tensor`, *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 (`ms.Tensor`, *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[ms.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[ms.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[ms.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]

    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `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,
    )
    if do_classifier_free_guidance:
        prompt_embeds = mint.cat([negative_prompt_embeds, prompt_embeds], dim=0)

    # 4. Prepare timesteps
    timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps)
    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(
            dtype=prompt_embeds.dtype
        )

    image = self.video_processor.preprocess(image, height=height, width=width).to(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,
        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.shape[1])
        if self.transformer.config.use_rotary_positional_embeddings
        else None
    )

    # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
    # to the transformer and will raise RuntimeError.
    lora_scale = self.attention_kwargs.pop("scale", None) if self.attention_kwargs is not None else None
    if lora_scale is not None:
        # weight the lora layers by setting `lora_scale` for each PEFT layer
        scale_lora_layers(self.transformer, lora_scale)

    # 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
        for i, t in enumerate(timesteps):
            if self.interrupt:
                continue

            latent_model_input = mint.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 = mint.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents
            latent_model_input = mint.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.broadcast_to((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[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 lora_scale is not None:
        # remove `lora_scale` from each PEFT layer
        unscale_lora_layers(self.transformer, lora_scale)

    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

    if not return_dict:
        return (video,)

    return ConsisIDPipelineOutput(frames=video)

mindone.diffusers.pipelines.consisid.ConsisIDPipeline.encode_prompt(prompt, negative_prompt=None, do_classifier_free_guidance=True, num_videos_per_prompt=1, prompt_embeds=None, negative_prompt_embeds=None, max_sequence_length=226, dtype=None)

Encodes the prompt into text encoder hidden states.

PARAMETER	DESCRIPTION
prompt	prompt to be encoded TYPE: `str` or `List[str]`, *optional*
negative_prompt	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). TYPE: `str` or `List[str]`, *optional* DEFAULT: None
do_classifier_free_guidance	Whether to use classifier free guidance or not. TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True
num_videos_per_prompt	Number of videos that should be generated per prompt. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
prompt_embeds	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. TYPE: `ms.Tensor`, *optional* DEFAULT: None
negative_prompt_embeds	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. TYPE: `ms.Tensor`, *optional* DEFAULT: None
dtype	(ms.Type, optional): torch dtype TYPE: Optional[Type] DEFAULT: None

Source code in mindone/diffusers/pipelines/consisid/pipeline_consisid.py

def encode_prompt(
    self,
    prompt: Union[str, List[str]],
    negative_prompt: Optional[Union[str, List[str]]] = None,
    do_classifier_free_guidance: bool = True,
    num_videos_per_prompt: int = 1,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = None,
    max_sequence_length: int = 226,
    dtype: Optional[ms.Type] = None,
):
    r"""
    Encodes the prompt into text encoder hidden states.

    Args:
        prompt (`str` or `List[str]`, *optional*):
            prompt to be encoded
        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`).
        do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
            Whether to use classifier free guidance or not.
        num_videos_per_prompt (`int`, *optional*, defaults to 1):
            Number of videos that should be generated per prompt.
        prompt_embeds (`ms.Tensor`, *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 (`ms.Tensor`, *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.
        dtype: (`ms.Type`, *optional*):
            torch dtype
    """
    prompt = [prompt] if isinstance(prompt, str) else prompt
    if prompt is not None:
        batch_size = len(prompt)
    else:
        batch_size = prompt_embeds.shape[0]

    if prompt_embeds is None:
        prompt_embeds = self._get_t5_prompt_embeds(
            prompt=prompt,
            num_videos_per_prompt=num_videos_per_prompt,
            max_sequence_length=max_sequence_length,
            dtype=dtype,
        )

    if do_classifier_free_guidance and negative_prompt_embeds is None:
        negative_prompt = negative_prompt or ""
        negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt

        if prompt is not None and type(prompt) is not type(negative_prompt):
            raise TypeError(
                f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                f" {type(prompt)}."
            )
        elif batch_size != len(negative_prompt):
            raise ValueError(
                f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                " the batch size of `prompt`."
            )

        negative_prompt_embeds = self._get_t5_prompt_embeds(
            prompt=negative_prompt,
            num_videos_per_prompt=num_videos_per_prompt,
            max_sequence_length=max_sequence_length,
            dtype=dtype,
        )

    return prompt_embeds, negative_prompt_embeds

mindone.diffusers.pipelines.consisid.pipeline_output.ConsisIDPipelineOutput dataclass

Bases: BaseOutput

Output class for ConsisID pipelines.

PARAMETER	DESCRIPTION
frames	List of video outputs - It can be a nested list of length batch_size, with each sub-list containing denoised PIL image sequences of length num_frames. It can also be a NumPy array or Torch tensor of shape (batch_size, num_frames, channels, height, width). TYPE: `torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]

Source code in mindone/diffusers/pipelines/consisid/pipeline_output.py

@dataclass
class ConsisIDPipelineOutput(BaseOutput):
    r"""
    Output class for ConsisID pipelines.

    Args:
        frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
            `(batch_size, num_frames, channels, height, width)`.
    """

    frames: ms.Tensor