def enable_gradient_checkpointing(self, gradient_checkpointing_func: Optional[Callable] = None) -> None:
        """
        Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or
        *checkpoint activations* in other frameworks).

        Args:
            gradient_checkpointing_func (`Callable`, *optional*):
                The function to use for gradient checkpointing. If `None`, the default PyTorch checkpointing function
                is used (`torch.utils.checkpoint.checkpoint`).
        """
        if not self._supports_gradient_checkpointing:
            raise ValueError(
                f"{self.__class__.__name__} does not support gradient checkpointing. Please make sure to set the boolean attribute "
                f"`_supports_gradient_checkpointing` to `True` in the class definition."
            )

        if gradient_checkpointing_func is None:

            def _gradient_checkpointing_func(module, *args):
                ckpt_kwargs = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                return torch.utils.checkpoint.checkpoint(
                    module.__call__,
                    *args,
                    **ckpt_kwargs,
                )

            gradient_checkpointing_func = _gradient_checkpointing_func

        self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)

        Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or
        *checkpoint activations* in other frameworks).

        Args:
            gradient_checkpointing_func (`Callable`, *optional*):
                The function to use for gradient checkpointing. If `None`, the default PyTorch checkpointing function
                is used (`torch.utils.checkpoint.checkpoint`).
        
src/diffusers/models/modeling_utils.py