class RecraftTextToImageNode:
    """
    Generates images synchronously based on prompt and resolution.
    """
    RETURN_TYPES = (IO.IMAGE,)
    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
    FUNCTION = "api_call"
    API_NODE = True
    CATEGORY = "api node/image/Recraft"
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "prompt": (
                    IO.STRING,
                    {
                        "multiline": True,
                        "default": "",
                        "tooltip": "Prompt for the image generation.",
                    },
                ),
                "size": (
                    [res.value for res in RecraftImageSize],
                    {
                        "default": RecraftImageSize.res_1024x1024,
                        "tooltip": "The size of the generated image.",
                    },
                ),
                "n": (
                    IO.INT,
                    {
                        "default": 1,
                        "min": 1,
                        "max": 6,
                        "tooltip": "The number of images to generate.",
                    },
                ),
                "seed": (
                    IO.INT,
                    {
                        "default": 0,
                        "min": 0,
                        "max": 0xFFFFFFFFFFFFFFFF,
                        "control_after_generate": True,
                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
                    },
                ),
            },
            "optional": {
                "recraft_style": (RecraftIO.STYLEV3,),
                "negative_prompt": (
                    IO.STRING,
                    {
                        "default": "",
                        "forceInput": True,
                        "tooltip": "An optional text description of undesired elements on an image.",
                    },
                ),
                "recraft_controls": (
                    RecraftIO.CONTROLS,
                    {
                        "tooltip": "Optional additional controls over the generation via the Recraft Controls node."
                    },
                ),
            },
            "hidden": {
                "auth_token": "AUTH_TOKEN_COMFY_ORG",
            },
        }
    def api_call(
        self,
        prompt: str,
        size: str,
        n: int,
        seed,
        recraft_style: RecraftStyle = None,
        negative_prompt: str = None,
        recraft_controls: RecraftControls = None,
        auth_token=None,
        **kwargs,
    ):
        default_style = RecraftStyle(RecraftStyleV3.realistic_image)
        if recraft_style is None:
            recraft_style = default_style
        controls_api = None
        if recraft_controls:
            controls_api = recraft_controls.create_api_model()
        if not negative_prompt:
            negative_prompt = None
        operation = SynchronousOperation(
            endpoint=ApiEndpoint(
                path="/proxy/recraft/image_generation",
                method=HttpMethod.POST,
                request_model=RecraftImageGenerationRequest,
                response_model=RecraftImageGenerationResponse,
            ),
            request=RecraftImageGenerationRequest(
                prompt=prompt,
                negative_prompt=negative_prompt,
                model=RecraftModel.recraftv3,
                size=size,
                n=n,
                style=recraft_style.style,
                substyle=recraft_style.substyle,
                style_id=recraft_style.style_id,
                controls=controls_api,
            ),
            auth_token=auth_token,
        )
        response: RecraftImageGenerationResponse = operation.execute()
        images = []
        for data in response.data:
            image = bytesio_to_image_tensor(
                download_url_to_bytesio(data.url, timeout=1024)
            )
            if len(image.shape) < 4:
                image = image.unsqueeze(0)
            images.append(image)
        output_image = torch.cat(images, dim=0)
        return (output_image,)