Function load_or_create_model_card

src/diffusers/utils/hub_utils.py:98–185

Loads or creates a model card. Args: repo_id_or_path (`str`): The repo id (e.g., "stable-diffusion-v1-5/stable-diffusion-v1-5") or local path where to look for the model card. token (`str`, *optional*): Authentication token. Will default

(
    repo_id_or_path: str = None,
    token: str | None = None,
    is_pipeline: bool = False,
    from_training: bool = False,
    model_description: str | None = None,
    base_model: str = None,
    prompt: str | None = None,
    license: str | None = None,
    widget: list[dict] | None = None,
    inference: bool | None = None,
    is_modular: bool = False,
    update_model_card: bool = False,
)

def load_or_create_model_card(
    repo_id_or_path: str = None,
    token: str | None = None,
    is_pipeline: bool = False,
    from_training: bool = False,
    model_description: str | None = None,
    base_model: str = None,
    prompt: str | None = None,
    license: str | None = None,
    widget: list[dict] | None = None,
    inference: bool | None = None,
    is_modular: bool = False,
    update_model_card: bool = False,
) -> ModelCard:
    """
    Loads or creates a model card.

    Args:
        repo_id_or_path (`str`):
            The repo id (e.g., "stable-diffusion-v1-5/stable-diffusion-v1-5") or local path where to look for the model
            card.
        token (`str`, *optional*):
            Authentication token. Will default to the stored token. See https://huggingface.co/settings/token for more
            details.
        is_pipeline (`bool`):
            Boolean to indicate if we're adding tag to a [`DiffusionPipeline`].
        from_training: (`bool`): Boolean flag to denote if the model card is being created from a training script.
        model_description (`str`, *optional*): Model description to add to the model card. Helpful when using
            `load_or_create_model_card` from a training script.
        base_model (`str`): Base model identifier (e.g., "stabilityai/stable-diffusion-xl-base-1.0"). Useful
            for DreamBooth-like training.
        prompt (`str`, *optional*): Prompt used for training. Useful for DreamBooth-like training.
        license: (`str`, *optional*): License of the output artifact. Helpful when using
            `load_or_create_model_card` from a training script.
        widget (`list[dict]`, *optional*): Widget to accompany a gallery template.
        inference: (`bool`, optional): Whether to turn on inference widget. Helpful when using
            `load_or_create_model_card` from a training script.
        is_modular: (`bool`, optional): Boolean flag to denote if the model card is for a modular pipeline.
            When True, uses model_description as-is without additional template formatting.
        update_model_card: (`bool`, optional): When True, regenerates the model card content even if one
            already exists on the remote repo. Existing card metadata (tags, license, etc.) is preserved. Only
            supported for modular pipelines (i.e., `is_modular=True`).
    """
    if not is_jinja_available():
        raise ValueError(
            "Modelcard rendering is based on Jinja templates."
            " Please make sure to have `jinja` installed before using `load_or_create_model_card`."
            " To install it, please run `pip install Jinja2`."
        )

    if update_model_card and not is_modular:
        raise ValueError("`update_model_card=True` is only supported for modular pipelines (`is_modular=True`).")

    try:
        # Check if the model card is present on the remote repo
        model_card = ModelCard.load(repo_id_or_path, token=token)
        # For modular pipelines, regenerate card content when requested (preserve existing metadata)
        if update_model_card and is_modular and model_description is not None: