def process_face_embeddings_infer(
    face_helper_1,
    clip_vision_model,
    face_helper_2,
    eva_transform_mean,
    eva_transform_std,
    app,
    device,
    weight_dtype,
    img_file_path,
    is_align_face=True,
):
    """
    Process face embeddings from an input image for inference, including alignment, feature extraction, and embedding
    concatenation.

    Args:
        face_helper_1: Face helper object (first helper) for alignment and landmark detection.
        clip_vision_model: Pre-trained CLIP vision model used for feature extraction.
        face_helper_2: Face helper object (second helper) for embedding extraction.
        eva_transform_mean: Mean values for image normalization before passing to EVA model.
        eva_transform_std: Standard deviation values for image normalization before passing to EVA model.
        app: Application instance used for face detection.
        device: Device (CPU or GPU) where the computations will be performed.
        weight_dtype: Data type of the weights for precision (e.g., `torch.float32`).
        img_file_path: Path to the input image file (string) or a numpy array representing an image.
        is_align_face: Boolean flag indicating whether face alignment should be performed (default: True).

    Returns:
        Tuple:
            - id_cond: Concatenated tensor of Ante face embedding and CLIP vision embedding.
            - id_vit_hidden: Hidden state of the CLIP vision model, a list of tensors.
            - image: Processed face image after feature extraction and alignment.
            - face_kps: Keypoints of the face detected in the image.
    """

    # Load and preprocess the input image
    if isinstance(img_file_path, str):
        image = np.array(load_image(image=img_file_path).convert("RGB"))
    else:
        image = np.array(ImageOps.exif_transpose(Image.fromarray(img_file_path)).convert("RGB"))

    # Resize image to ensure the longer side is 1024 pixels
    image = resize_numpy_image_long(image, 1024)
    original_id_image = image

    # Process the image to extract face embeddings and related features
    id_cond, id_vit_hidden, align_crop_face_image, face_kps = process_face_embeddings(
        face_helper_1,
        clip_vision_model,
        face_helper_2,
        eva_transform_mean,
        eva_transform_std,
        app,
        device,
        weight_dtype,
        image,
        original_id_image,
        is_align_face,
    )

    # Convert the aligned cropped face image (torch tensor) to a numpy array
    tensor = align_crop_face_image.cpu().detach()
    tensor = tensor.squeeze()
    tensor = tensor.permute(1, 2, 0)
    tensor = tensor.numpy() * 255
    tensor = tensor.astype(np.uint8)
    image = ImageOps.exif_transpose(Image.fromarray(tensor))

    return id_cond, id_vit_hidden, image, face_kps

    Process face embeddings from an input image for inference, including alignment, feature extraction, and embedding
    concatenation.

    Args:
        face_helper_1: Face helper object (first helper) for alignment and landmark detection.
        clip_vision_model: Pre-trained CLIP vision model used for feature extraction.
        face_helper_2: Face helper object (second helper) for embedding extraction.
        eva_transform_mean: Mean values for image normalization before passing to EVA model.
        eva_transform_std: Standard deviation values for image normalization before passing to EVA model.
        app: Application instance used for face detection.
        device: Device (CPU or GPU) where the computations will be performed.
        weight_dtype: Data type of the weights for precision (e.g., `torch.float32`).
        img_file_path: Path to the input image file (string) or a numpy array representing an image.
        is_align_face: Boolean flag indicating whether face alignment should be performed (default: True).

    Returns:
        Tuple:
            - id_cond: Concatenated tensor of Ante face embedding and CLIP vision embedding.
            - id_vit_hidden: Hidden state of the CLIP vision model, a list of tensors.
            - image: Processed face image after feature extraction and alignment.
            - face_kps: Keypoints of the face detected in the image.
    
src/diffusers/pipelines/consisid/consisid_utils.py