Method execute

conditioning_loader.py:34–64

(cls, file_name: str, device: str)

    @classmethod
    def execute(cls, file_name: str, device: str) -> io.NodeOutput:
        file_path = folder_paths.get_full_path("embeddings", file_name)
        if not Path(file_path).exists():
            raise FileNotFoundError(f"Conditioning file not found: {file_path}")

        target_device = "cpu"
        if device == "gpu":
            target_device = "cuda" if torch.cuda.is_available() else "cpu"

        conditioning: list[list[Any]] = []

        with safetensors.safe_open(
            file_path, framework="pt", device=target_device
        ) as f:
            tensor_keys = [k for k in f.keys() if k.startswith("conditioning_data_")]

            for tensor_key in sorted(tensor_keys):
                idx = tensor_key.replace("conditioning_data_", "")
                tensor = f.get_tensor(tensor_key)

                options: dict[str, Any] = {}
                mask_key = f"attention_mask_{idx}"
                if mask_key in f.keys():
                    options["attention_mask"] = f.get_tensor(mask_key)

                conditioning.append([tensor, options])

        if not conditioning:
            raise ValueError(f"No conditioning data found in file: {file_name}")

        return io.NodeOutput(conditioning)

    @classmethod
    def fingerprint_inputs(cls, file_name: str, device: str) -> str: