Kling Text to Video 节点通过连接Kling的API服务，实现文本到视频的生成功能。用户只需提供描述性文本，即可创建对应的视频内容。

​
参数说明

​
必需参数

参数类型默认值说明
prompt字符串""描述要生成视频内容的文本提示词
negative_prompt字符串""指定不希望在视频中出现的元素
cfg_scale浮点数7.0配置缩放值，控制对提示词的遵循程度
model_name选择项”kling-v2-master”使用的视频生成模型
aspect_ratio选择项AspectRatio枚举值输出视频的宽高比
duration选择项Duration枚举值生成视频的持续时间
mode选择项Mode枚举值视频生成模式

​
输出

输出类型说明
VIDEO视频生成的视频结果
Kling ID字符串任务识别ID
Duration (sec)字符串视频时长（秒）

​
工作原理

节点将文本提示词发送到Kling的API服务器，系统处理后返回生成的视频结果。处理过程包括初始请求和任务状态轮询，当任务完成后，节点会下载视频并输出结果。

用户可以通过调整各种参数来控制生成效果，包括负面提示词、配置缩放值以及视频属性等。系统会验证提示词长度，确保请求符合API要求。

​
源码参考

[节点源码 (更新于2025-05-03)]

class KlingTextToVideoNode(KlingNodeBase):
    """Kling Text to Video Node"""

    @staticmethod
    def poll_for_task_status(task_id: str, auth_token: str) -> KlingText2VideoResponse:
        """Polls the Kling API endpoint until the task reaches a terminal state."""
        polling_operation = PollingOperation(
            poll_endpoint=ApiEndpoint(
                path=f"{PATH_TEXT_TO_VIDEO}/{task_id}",
                method=HttpMethod.GET,
                request_model=EmptyRequest,
                response_model=KlingText2VideoResponse,
            ),
            completed_statuses=[
                TaskStatus.succeed.value,
            ],
            failed_statuses=[TaskStatus.failed.value],
            status_extractor=lambda response: (
                response.data.task_status.value
                if response.data and response.data.task_status
                else None
            ),
            auth_token=auth_token,
        )
        return polling_operation.execute()

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "prompt": model_field_to_node_input(
                    IO.STRING, KlingText2VideoRequest, "prompt", multiline=True
                ),
                "negative_prompt": model_field_to_node_input(
                    IO.STRING, KlingText2VideoRequest, "negative_prompt", multiline=True
                ),
                "model_name": model_field_to_node_input(
                    IO.COMBO,
                    KlingText2VideoRequest,
                    "model_name",
                    enum_type=ModelName,
                    default="kling-v2-master",
                ),
                "cfg_scale": model_field_to_node_input(
                    IO.FLOAT, KlingText2VideoRequest, "cfg_scale"
                ),
                "mode": model_field_to_node_input(
                    IO.COMBO, KlingText2VideoRequest, "mode", enum_type=Mode
                ),
                "duration": model_field_to_node_input(
                    IO.COMBO, KlingText2VideoRequest, "duration", enum_type=Duration
                ),
                "aspect_ratio": model_field_to_node_input(
                    IO.COMBO,
                    KlingText2VideoRequest,
                    "aspect_ratio",
                    enum_type=AspectRatio,
                ),
            },
            "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"},
        }

    RETURN_TYPES = ("VIDEO", "STRING", "STRING")
    RETURN_NAMES = ("VIDEO", "Kling ID", "Duration (sec)")
    DESCRIPTION = "Kling Text to Video Node"

    def api_call(
        self,
        prompt: str,
        negative_prompt: str,
        model_name: str,
        cfg_scale: float,
        mode: str,
        duration: int,
        aspect_ratio: str,
        camera_control: Optional[CameraControl] = None,
        auth_token: Optional[str] = None,
    ) -> tuple[VideoFromFile, str, str]:
        validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
        initial_operation = SynchronousOperation(
            endpoint=ApiEndpoint(
                path=PATH_TEXT_TO_VIDEO,
                method=HttpMethod.POST,
                request_model=KlingText2VideoRequest,
                response_model=KlingText2VideoResponse,
            ),
            request=KlingText2VideoRequest(
                prompt=prompt if prompt else None,
                negative_prompt=negative_prompt if negative_prompt else None,
                duration=Duration(duration),
                mode=Mode(mode),
                model_name=ModelName(model_name),
                cfg_scale=cfg_scale,
                aspect_ratio=AspectRatio(aspect_ratio),
                camera_control=camera_control,
            ),
            auth_token=auth_token,
        )

        initial_response = initial_operation.execute()
        if not is_valid_initial_response(initial_response):
            error_msg = f"Kling initial request failed. Code: {initial_response.code}, Message: {initial_response.message}, Data: {initial_response.data}"
            logging.error(error_msg)
            raise KlingApiError(error_msg)

        task_id = initial_response.data.task_id
        final_response = self.poll_for_task_status(task_id, auth_token)
        if not is_valid_video_response(final_response):
            error_msg = (
                f"Kling task {task_id} succeeded but no video data found in response."
            )
            logging.error(error_msg)
            raise KlingApiError(error_msg)

        video = final_response.data.task_result.videos[0]
        logging.debug("Kling task %s succeeded. Video URL: %s", task_id, video.url)
        return (
            download_url_to_video_output(video.url),
            str(video.id),
            str(video.duration),
        )