Method save_pretrained

diffusers/src/diffusers/training_utils.py:375–388

(self, path)

        return ema_model

    def save_pretrained(self, path):
        if self.model_cls is None:
            raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__.")

        if self.model_config is None:
            raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__.")

        model = self.model_cls.from_config(self.model_config)
        state_dict = self.state_dict()
        state_dict.pop("shadow_params", None)

        model.register_to_config(**state_dict)
        self.copy_to(model.parameters())
        model.save_pretrained(path)

    def get_decay(self, optimization_step: int) -> float:
        """