Kling Image to Video 节点可以通过Kling的图像到视频API，将静态图像转换为动态视频内容。

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参数说明

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基本参数

以下所有参数均为必需参数：

参数类型默认值说明
start_frame图像-输入的源图像
prompt字符串""描述视频动作和内容的文本提示词
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-v1-5”使用的模型类型
aspect_ratio选择项”16:9”输出视频的宽高比
duration选择项”5s”生成视频的持续时间
mode选择项”pro”视频生成模式

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输出

输出类型说明
VIDEO视频生成的视频结果
video_id字符串视频的唯一标识符
duration字符串视频的实际持续时间

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源码参考

[节点源码 (更新于2025-05-03)]

class KlingImage2VideoNode(KlingNodeBase):
    """Kling Image to Video Node"""

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "start_frame": model_field_to_node_input(
                    IO.IMAGE, KlingImage2VideoRequest, "image"
                ),
                "prompt": model_field_to_node_input(
                    IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True
                ),
                "negative_prompt": model_field_to_node_input(
                    IO.STRING,
                    KlingImage2VideoRequest,
                    "negative_prompt",
                    multiline=True,
                ),
                "model_name": model_field_to_node_input(
                    IO.COMBO,
                    KlingImage2VideoRequest,
                    "model_name",
                    enum_type=KlingVideoGenModelName,
                ),
                "cfg_scale": model_field_to_node_input(
                    IO.FLOAT, KlingImage2VideoRequest, "cfg_scale"
                ),
                "mode": model_field_to_node_input(
                    IO.COMBO,
                    KlingImage2VideoRequest,
                    "mode",
                    enum_type=KlingVideoGenMode,
                ),
                "aspect_ratio": model_field_to_node_input(
                    IO.COMBO,
                    KlingImage2VideoRequest,
                    "aspect_ratio",
                    enum_type=KlingVideoGenAspectRatio,
                ),
                "duration": model_field_to_node_input(
                    IO.COMBO,
                    KlingImage2VideoRequest,
                    "duration",
                    enum_type=KlingVideoGenDuration,
                ),
            },
            "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"},
        }

    RETURN_TYPES = ("VIDEO", "STRING", "STRING")
    RETURN_NAMES = ("VIDEO", "video_id", "duration")
    DESCRIPTION = "Kling Image to Video Node"

    def get_response(self, task_id: str, auth_token: str) -> KlingImage2VideoResponse:
        return poll_until_finished(
            auth_token,
            ApiEndpoint(
                path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}",
                method=HttpMethod.GET,
                request_model=KlingImage2VideoRequest,
                response_model=KlingImage2VideoResponse,
            ),
        )

    def api_call(
        self,
        start_frame: torch.Tensor,
        prompt: str,
        negative_prompt: str,
        model_name: str,
        cfg_scale: float,
        mode: str,
        aspect_ratio: str,
        duration: str,
        camera_control: Optional[KlingCameraControl] = None,
        end_frame: Optional[torch.Tensor] = None,
        auth_token: Optional[str] = None,
    ) -> tuple[VideoFromFile]:
        validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V)
        initial_operation = SynchronousOperation(
            endpoint=ApiEndpoint(
                path=PATH_IMAGE_TO_VIDEO,
                method=HttpMethod.POST,
                request_model=KlingImage2VideoRequest,
                response_model=KlingImage2VideoResponse,
            ),
            request=KlingImage2VideoRequest(
                model_name=KlingVideoGenModelName(model_name),
                image=tensor_to_base64_string(start_frame),
                image_tail=(
                    tensor_to_base64_string(end_frame)
                    if end_frame is not None
                    else None
                ),
                prompt=prompt,
                negative_prompt=negative_prompt if negative_prompt else None,
                cfg_scale=cfg_scale,
                mode=KlingVideoGenMode(mode),
                aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
                duration=KlingVideoGenDuration(duration),
                camera_control=camera_control,
            ),
            auth_token=auth_token,
        )

        task_creation_response = initial_operation.execute()
        validate_task_creation_response(task_creation_response)
        task_id = task_creation_response.data.task_id

        final_response = self.get_response(task_id, auth_token)
        validate_video_result_response(final_response)

        video = get_video_from_response(final_response)
        return video_result_to_node_output(video)