mirror of
https://github.com/comfyanonymous/ComfyUI.git
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1630 lines
60 KiB
Python
1630 lines
60 KiB
Python
"""Kling API Nodes
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For source of truth on the allowed permutations of request fields, please reference:
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- [Compatibility Table](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
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"""
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from __future__ import annotations
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from typing import Optional, TypeVar, Any
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import math
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import logging
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import torch
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from comfy_api_nodes.apis import (
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KlingTaskStatus,
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KlingCameraControl,
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KlingCameraConfig,
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KlingCameraControlType,
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KlingVideoGenDuration,
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KlingVideoGenMode,
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KlingVideoGenAspectRatio,
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KlingVideoGenModelName,
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KlingText2VideoRequest,
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KlingText2VideoResponse,
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KlingImage2VideoRequest,
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KlingImage2VideoResponse,
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KlingVideoExtendRequest,
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KlingVideoExtendResponse,
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KlingLipSyncVoiceLanguage,
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KlingLipSyncInputObject,
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KlingLipSyncRequest,
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KlingLipSyncResponse,
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KlingVirtualTryOnModelName,
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KlingVirtualTryOnRequest,
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KlingVirtualTryOnResponse,
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KlingVideoResult,
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KlingImageResult,
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KlingImageGenerationsRequest,
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KlingImageGenerationsResponse,
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KlingImageGenImageReferenceType,
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KlingImageGenModelName,
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KlingImageGenAspectRatio,
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KlingVideoEffectsRequest,
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KlingVideoEffectsResponse,
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KlingDualCharacterEffectsScene,
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KlingSingleImageEffectsScene,
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KlingDualCharacterEffectInput,
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KlingSingleImageEffectInput,
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KlingCharacterEffectModelName,
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KlingSingleImageEffectModelName,
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)
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from comfy_api_nodes.apis.client import (
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ApiEndpoint,
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HttpMethod,
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SynchronousOperation,
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PollingOperation,
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EmptyRequest,
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)
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from comfy_api_nodes.apinode_utils import (
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tensor_to_base64_string,
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download_url_to_video_output,
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upload_video_to_comfyapi,
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upload_audio_to_comfyapi,
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download_url_to_image_tensor,
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)
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from comfy_api_nodes.mapper_utils import model_field_to_node_input
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from comfy_api.input.basic_types import AudioInput
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from comfy_api.input.video_types import VideoInput
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from comfy_api.input_impl import VideoFromFile
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from comfy.comfy_types.node_typing import IO, InputTypeOptions, ComfyNodeABC
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KLING_API_VERSION = "v1"
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PATH_TEXT_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/text2video"
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PATH_IMAGE_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/image2video"
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PATH_VIDEO_EXTEND = f"/proxy/kling/{KLING_API_VERSION}/videos/video-extend"
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PATH_LIP_SYNC = f"/proxy/kling/{KLING_API_VERSION}/videos/lip-sync"
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PATH_VIDEO_EFFECTS = f"/proxy/kling/{KLING_API_VERSION}/videos/effects"
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PATH_CHARACTER_IMAGE = f"/proxy/kling/{KLING_API_VERSION}/images/generations"
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PATH_VIRTUAL_TRY_ON = f"/proxy/kling/{KLING_API_VERSION}/images/kolors-virtual-try-on"
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PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations"
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MAX_PROMPT_LENGTH_T2V = 2500
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MAX_PROMPT_LENGTH_I2V = 500
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MAX_PROMPT_LENGTH_IMAGE_GEN = 500
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MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200
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MAX_PROMPT_LENGTH_LIP_SYNC = 120
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R = TypeVar("R")
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class KlingApiError(Exception):
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"""Base exception for Kling API errors."""
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pass
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def poll_until_finished(auth_kwargs: dict[str,str], api_endpoint: ApiEndpoint[Any, R]) -> R:
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"""Polls the Kling API endpoint until the task reaches a terminal state, then returns the response."""
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return PollingOperation(
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poll_endpoint=api_endpoint,
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completed_statuses=[
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KlingTaskStatus.succeed.value,
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],
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failed_statuses=[KlingTaskStatus.failed.value],
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status_extractor=lambda response: (
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response.data.task_status.value
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if response.data and response.data.task_status
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else None
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),
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auth_kwargs=auth_kwargs,
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).execute()
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def is_valid_camera_control_configs(configs: list[float]) -> bool:
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"""Verifies that at least one camera control configuration is non-zero."""
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return any(not math.isclose(value, 0.0) for value in configs)
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def is_valid_prompt(prompt: str) -> bool:
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"""Verifies that the prompt is not empty."""
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return bool(prompt)
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def is_valid_task_creation_response(response: KlingText2VideoResponse) -> bool:
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"""Verifies that the initial response contains a task ID."""
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return bool(response.data.task_id)
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def is_valid_video_response(response: KlingText2VideoResponse) -> bool:
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"""Verifies that the response contains a task result with at least one video."""
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return (
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response.data is not None
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and response.data.task_result is not None
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and response.data.task_result.videos is not None
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and len(response.data.task_result.videos) > 0
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)
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def is_valid_image_response(response: KlingVirtualTryOnResponse) -> bool:
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"""Verifies that the response contains a task result with at least one image."""
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return (
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response.data is not None
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and response.data.task_result is not None
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and response.data.task_result.images is not None
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and len(response.data.task_result.images) > 0
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)
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def validate_prompts(prompt: str, negative_prompt: str, max_length: int) -> bool:
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"""Verifies that the positive prompt is not empty and that neither promt is too long."""
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if not prompt:
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raise ValueError("Positive prompt is empty")
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if len(prompt) > max_length:
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raise ValueError(f"Positive prompt is too long: {len(prompt)} characters")
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if negative_prompt and len(negative_prompt) > max_length:
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raise ValueError(
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f"Negative prompt is too long: {len(negative_prompt)} characters"
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)
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return True
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def validate_task_creation_response(response) -> None:
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"""Validates that the Kling task creation request was successful."""
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if not is_valid_task_creation_response(response):
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error_msg = f"Kling initial request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
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logging.error(error_msg)
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raise KlingApiError(error_msg)
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def validate_video_result_response(response) -> None:
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"""Validates that the Kling task result contains a video."""
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if not is_valid_video_response(response):
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error_msg = f"Kling task {response.data.task_id} succeeded but no video data found in response."
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logging.error(f"Error: {error_msg}.\nResponse: {response}")
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raise KlingApiError(error_msg)
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def validate_image_result_response(response) -> None:
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"""Validates that the Kling task result contains an image."""
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if not is_valid_image_response(response):
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error_msg = f"Kling task {response.data.task_id} succeeded but no image data found in response."
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logging.error(f"Error: {error_msg}.\nResponse: {response}")
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raise KlingApiError(error_msg)
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def validate_input_image(image: torch.Tensor) -> None:
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"""
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Validates the input image adheres to the expectations of the Kling API:
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- The image resolution should not be less than 300*300px
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- The aspect ratio of the image should be between 1:2.5 ~ 2.5:1
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See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo
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"""
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if len(image.shape) == 4:
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height, width = image.shape[1], image.shape[2]
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elif len(image.shape) == 3:
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height, width = image.shape[0], image.shape[1]
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else:
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raise ValueError("Invalid image tensor shape.")
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# Ensure minimum resolution is met
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if height < 300:
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raise ValueError("Image height must be at least 300px")
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if width < 300:
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raise ValueError("Image width must be at least 300px")
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# Ensure aspect ratio is within acceptable range
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aspect_ratio = width / height
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if aspect_ratio < 1 / 2.5 or aspect_ratio > 2.5:
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raise ValueError("Image aspect ratio must be between 1:2.5 and 2.5:1")
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def get_camera_control_input_config(
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tooltip: str, default: float = 0.0
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) -> tuple[IO, InputTypeOptions]:
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"""Returns common InputTypeOptions for Kling camera control configurations."""
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input_config = {
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"default": default,
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"min": -10.0,
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"max": 10.0,
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"step": 0.25,
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"display": "slider",
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"tooltip": tooltip,
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}
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return IO.FLOAT, input_config
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def get_video_from_response(response) -> KlingVideoResult:
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"""Returns the first video object from the Kling video generation task result."""
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video = response.data.task_result.videos[0]
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logging.info(
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"Kling task %s succeeded. Video URL: %s", response.data.task_id, video.url
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)
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return video
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def get_images_from_response(response) -> list[KlingImageResult]:
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images = response.data.task_result.images
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logging.info("Kling task %s succeeded. Images: %s", response.data.task_id, images)
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return images
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def video_result_to_node_output(
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video: KlingVideoResult,
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) -> tuple[VideoFromFile, str, str]:
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"""Converts a KlingVideoResult to a tuple of (VideoFromFile, str, str) to be used as a ComfyUI node output."""
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return (
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download_url_to_video_output(video.url),
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str(video.id),
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str(video.duration),
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)
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def image_result_to_node_output(
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images: list[KlingImageResult],
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) -> torch.Tensor:
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"""
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Converts a KlingImageResult to a tuple containing a [B, H, W, C] tensor.
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If multiple images are returned, they will be stacked along the batch dimension.
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"""
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if len(images) == 1:
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return download_url_to_image_tensor(images[0].url)
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else:
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return torch.cat([download_url_to_image_tensor(image.url) for image in images])
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class KlingNodeBase(ComfyNodeABC):
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"""Base class for Kling nodes."""
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FUNCTION = "api_call"
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CATEGORY = "api node/video/Kling"
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API_NODE = True
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class KlingCameraControls(KlingNodeBase):
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"""Kling Camera Controls Node"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"camera_control_type": model_field_to_node_input(
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IO.COMBO,
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KlingCameraControl,
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"type",
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enum_type=KlingCameraControlType,
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),
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"horizontal_movement": get_camera_control_input_config(
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"Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right"
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),
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"vertical_movement": get_camera_control_input_config(
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"Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward."
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),
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"pan": get_camera_control_input_config(
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"Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.",
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default=0.5,
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),
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"tilt": get_camera_control_input_config(
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"Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.",
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),
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"roll": get_camera_control_input_config(
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"Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.",
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),
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"zoom": get_camera_control_input_config(
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"Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.",
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),
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}
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}
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DESCRIPTION = "Allows specifying configuration options for Kling Camera Controls and motion control effects."
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RETURN_TYPES = ("CAMERA_CONTROL",)
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RETURN_NAMES = ("camera_control",)
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FUNCTION = "main"
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@classmethod
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def VALIDATE_INPUTS(
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cls,
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horizontal_movement: float,
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vertical_movement: float,
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pan: float,
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tilt: float,
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roll: float,
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zoom: float,
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) -> bool | str:
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if not is_valid_camera_control_configs(
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[
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horizontal_movement,
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vertical_movement,
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pan,
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tilt,
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roll,
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zoom,
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]
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):
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return "Invalid camera control configs: at least one of the values must be non-zero"
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return True
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def main(
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self,
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camera_control_type: str,
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horizontal_movement: float,
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vertical_movement: float,
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pan: float,
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tilt: float,
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roll: float,
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zoom: float,
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) -> tuple[KlingCameraControl]:
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return (
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KlingCameraControl(
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type=KlingCameraControlType(camera_control_type),
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config=KlingCameraConfig(
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horizontal=horizontal_movement,
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vertical=vertical_movement,
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pan=pan,
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roll=roll,
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tilt=tilt,
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zoom=zoom,
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),
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),
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)
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class KlingTextToVideoNode(KlingNodeBase):
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"""Kling Text to Video Node"""
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@staticmethod
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def get_mode_string_mapping() -> dict[str, tuple[str, str, str]]:
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"""
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Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples.
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Only includes config combos that support the `image_tail` request field.
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See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
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"""
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return {
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"standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"),
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"standard mode / 10s duration / kling-v1": ("std", "10", "kling-v1"),
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"pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"),
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"pro mode / 10s duration / kling-v1": ("pro", "10", "kling-v1"),
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"standard mode / 5s duration / kling-v1-6": ("std", "5", "kling-v1-6"),
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"standard mode / 10s duration / kling-v1-6": ("std", "10", "kling-v1-6"),
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"pro mode / 5s duration / kling-v2-master": ("pro", "5", "kling-v2-master"),
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"pro mode / 10s duration / kling-v2-master": ("pro", "10", "kling-v2-master"),
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"standard mode / 5s duration / kling-v2-master": ("std", "5", "kling-v2-master"),
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"standard mode / 10s duration / kling-v2-master": ("std", "10", "kling-v2-master"),
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}
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@classmethod
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def INPUT_TYPES(s):
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modes = list(KlingTextToVideoNode.get_mode_string_mapping().keys())
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return {
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"required": {
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"prompt": model_field_to_node_input(
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IO.STRING, KlingText2VideoRequest, "prompt", multiline=True
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),
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"negative_prompt": model_field_to_node_input(
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IO.STRING, KlingText2VideoRequest, "negative_prompt", multiline=True
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),
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"cfg_scale": model_field_to_node_input(
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IO.FLOAT,
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KlingText2VideoRequest,
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"cfg_scale",
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default=1.0,
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min=0.0,
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max=1.0,
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),
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"aspect_ratio": model_field_to_node_input(
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IO.COMBO,
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KlingText2VideoRequest,
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"aspect_ratio",
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enum_type=KlingVideoGenAspectRatio,
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),
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"mode": (
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modes,
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{
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"default": modes[4],
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"tooltip": "The configuration to use for the video generation following the format: mode / duration / model_name.",
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},
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),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
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"comfy_api_key": "API_KEY_COMFY_ORG",
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},
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}
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RETURN_TYPES = ("VIDEO", "STRING", "STRING")
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RETURN_NAMES = ("VIDEO", "video_id", "duration")
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DESCRIPTION = "Kling Text to Video Node"
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def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingText2VideoResponse:
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return poll_until_finished(
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auth_kwargs,
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ApiEndpoint(
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path=f"{PATH_TEXT_TO_VIDEO}/{task_id}",
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method=HttpMethod.GET,
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request_model=EmptyRequest,
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response_model=KlingText2VideoResponse,
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),
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)
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def api_call(
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self,
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prompt: str,
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negative_prompt: str,
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cfg_scale: float,
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mode: str,
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aspect_ratio: str,
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camera_control: Optional[KlingCameraControl] = None,
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model_name: Optional[str] = None,
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duration: Optional[str] = None,
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**kwargs,
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) -> tuple[VideoFromFile, str, str]:
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validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
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if model_name is None:
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mode, duration, model_name = self.get_mode_string_mapping()[mode]
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initial_operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path=PATH_TEXT_TO_VIDEO,
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method=HttpMethod.POST,
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request_model=KlingText2VideoRequest,
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response_model=KlingText2VideoResponse,
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),
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request=KlingText2VideoRequest(
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prompt=prompt if prompt else None,
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negative_prompt=negative_prompt if negative_prompt else None,
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duration=KlingVideoGenDuration(duration),
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mode=KlingVideoGenMode(mode),
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model_name=KlingVideoGenModelName(model_name),
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cfg_scale=cfg_scale,
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aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
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camera_control=camera_control,
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),
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auth_kwargs=kwargs,
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)
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task_creation_response = initial_operation.execute()
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validate_task_creation_response(task_creation_response)
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task_id = task_creation_response.data.task_id
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final_response = self.get_response(task_id, auth_kwargs=kwargs)
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validate_video_result_response(final_response)
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video = get_video_from_response(final_response)
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return video_result_to_node_output(video)
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|
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class KlingCameraControlT2VNode(KlingTextToVideoNode):
|
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"""
|
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Kling Text to Video Camera Control Node. This node is a text to video node, but it supports controlling the camera.
|
|
Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02.
|
|
"""
|
|
|
|
@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,
|
|
),
|
|
"cfg_scale": model_field_to_node_input(
|
|
IO.FLOAT,
|
|
KlingText2VideoRequest,
|
|
"cfg_scale",
|
|
default=0.75,
|
|
min=0.0,
|
|
max=1.0,
|
|
),
|
|
"aspect_ratio": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingText2VideoRequest,
|
|
"aspect_ratio",
|
|
enum_type=KlingVideoGenAspectRatio,
|
|
),
|
|
"camera_control": (
|
|
"CAMERA_CONTROL",
|
|
{
|
|
"tooltip": "Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.",
|
|
},
|
|
),
|
|
},
|
|
"hidden": {
|
|
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
|
"comfy_api_key": "API_KEY_COMFY_ORG",
|
|
},
|
|
}
|
|
|
|
DESCRIPTION = "Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text."
|
|
|
|
def api_call(
|
|
self,
|
|
prompt: str,
|
|
negative_prompt: str,
|
|
cfg_scale: float,
|
|
aspect_ratio: str,
|
|
camera_control: Optional[KlingCameraControl] = None,
|
|
**kwargs,
|
|
):
|
|
return super().api_call(
|
|
model_name=KlingVideoGenModelName.kling_v1,
|
|
cfg_scale=cfg_scale,
|
|
mode=KlingVideoGenMode.std,
|
|
aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
|
|
duration=KlingVideoGenDuration.field_5,
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
camera_control=camera_control,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
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",
|
|
tooltip="The reference image used to generate the video.",
|
|
),
|
|
"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",
|
|
default=0.8,
|
|
min=0.0,
|
|
max=1.0,
|
|
),
|
|
"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",
|
|
"comfy_api_key": "API_KEY_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_kwargs: dict[str,str]) -> KlingImage2VideoResponse:
|
|
return poll_until_finished(
|
|
auth_kwargs,
|
|
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,
|
|
**kwargs,
|
|
) -> tuple[VideoFromFile]:
|
|
validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V)
|
|
validate_input_image(start_frame)
|
|
|
|
if camera_control is not None:
|
|
# Camera control type for image 2 video is always `simple`
|
|
camera_control.type = KlingCameraControlType.simple
|
|
|
|
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),
|
|
duration=KlingVideoGenDuration(duration),
|
|
camera_control=camera_control,
|
|
),
|
|
auth_kwargs=kwargs,
|
|
)
|
|
|
|
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_kwargs=kwargs)
|
|
validate_video_result_response(final_response)
|
|
|
|
video = get_video_from_response(final_response)
|
|
return video_result_to_node_output(video)
|
|
|
|
|
|
class KlingCameraControlI2VNode(KlingImage2VideoNode):
|
|
"""
|
|
Kling Image to Video Camera Control Node. This node is a image to video node, but it supports controlling the camera.
|
|
Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02.
|
|
"""
|
|
|
|
@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,
|
|
),
|
|
"cfg_scale": model_field_to_node_input(
|
|
IO.FLOAT,
|
|
KlingImage2VideoRequest,
|
|
"cfg_scale",
|
|
default=0.75,
|
|
min=0.0,
|
|
max=1.0,
|
|
),
|
|
"aspect_ratio": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingImage2VideoRequest,
|
|
"aspect_ratio",
|
|
enum_type=KlingVideoGenAspectRatio,
|
|
),
|
|
"camera_control": (
|
|
"CAMERA_CONTROL",
|
|
{
|
|
"tooltip": "Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.",
|
|
},
|
|
),
|
|
},
|
|
"hidden": {
|
|
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
|
"comfy_api_key": "API_KEY_COMFY_ORG",
|
|
},
|
|
}
|
|
|
|
DESCRIPTION = "Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image."
|
|
|
|
def api_call(
|
|
self,
|
|
start_frame: torch.Tensor,
|
|
prompt: str,
|
|
negative_prompt: str,
|
|
cfg_scale: float,
|
|
aspect_ratio: str,
|
|
camera_control: KlingCameraControl,
|
|
**kwargs,
|
|
):
|
|
return super().api_call(
|
|
model_name=KlingVideoGenModelName.kling_v1_5,
|
|
start_frame=start_frame,
|
|
cfg_scale=cfg_scale,
|
|
mode=KlingVideoGenMode.pro,
|
|
aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
|
|
duration=KlingVideoGenDuration.field_5,
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
camera_control=camera_control,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
class KlingStartEndFrameNode(KlingImage2VideoNode):
|
|
"""
|
|
Kling First Last Frame Node. This node allows creation of a video from a first and last frame. It calls the normal image to video endpoint, but only allows the subset of input options that support the `image_tail` request field.
|
|
"""
|
|
|
|
@staticmethod
|
|
def get_mode_string_mapping() -> dict[str, tuple[str, str, str]]:
|
|
"""
|
|
Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples.
|
|
Only includes config combos that support the `image_tail` request field.
|
|
|
|
See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
|
|
"""
|
|
return {
|
|
"standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"),
|
|
"pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"),
|
|
"pro mode / 5s duration / kling-v1-5": ("pro", "5", "kling-v1-5"),
|
|
"pro mode / 10s duration / kling-v1-5": ("pro", "10", "kling-v1-5"),
|
|
"pro mode / 5s duration / kling-v1-6": ("pro", "5", "kling-v1-6"),
|
|
"pro mode / 10s duration / kling-v1-6": ("pro", "10", "kling-v1-6"),
|
|
}
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
modes = list(KlingStartEndFrameNode.get_mode_string_mapping().keys())
|
|
return {
|
|
"required": {
|
|
"start_frame": model_field_to_node_input(
|
|
IO.IMAGE, KlingImage2VideoRequest, "image"
|
|
),
|
|
"end_frame": model_field_to_node_input(
|
|
IO.IMAGE, KlingImage2VideoRequest, "image_tail"
|
|
),
|
|
"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,
|
|
),
|
|
"cfg_scale": model_field_to_node_input(
|
|
IO.FLOAT,
|
|
KlingImage2VideoRequest,
|
|
"cfg_scale",
|
|
default=0.5,
|
|
min=0.0,
|
|
max=1.0,
|
|
),
|
|
"aspect_ratio": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingImage2VideoRequest,
|
|
"aspect_ratio",
|
|
enum_type=KlingVideoGenAspectRatio,
|
|
),
|
|
"mode": (
|
|
modes,
|
|
{
|
|
"default": modes[2],
|
|
"tooltip": "The configuration to use for the video generation following the format: mode / duration / model_name.",
|
|
},
|
|
),
|
|
},
|
|
"hidden": {
|
|
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
|
"comfy_api_key": "API_KEY_COMFY_ORG",
|
|
},
|
|
}
|
|
|
|
DESCRIPTION = "Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last."
|
|
|
|
def api_call(
|
|
self,
|
|
start_frame: torch.Tensor,
|
|
end_frame: torch.Tensor,
|
|
prompt: str,
|
|
negative_prompt: str,
|
|
cfg_scale: float,
|
|
aspect_ratio: str,
|
|
mode: str,
|
|
**kwargs,
|
|
):
|
|
mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[
|
|
mode
|
|
]
|
|
return super().api_call(
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
model_name=model_name,
|
|
start_frame=start_frame,
|
|
cfg_scale=cfg_scale,
|
|
mode=mode,
|
|
aspect_ratio=aspect_ratio,
|
|
duration=duration,
|
|
end_frame=end_frame,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
class KlingVideoExtendNode(KlingNodeBase):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"prompt": model_field_to_node_input(
|
|
IO.STRING, KlingVideoExtendRequest, "prompt", multiline=True
|
|
),
|
|
"negative_prompt": model_field_to_node_input(
|
|
IO.STRING,
|
|
KlingVideoExtendRequest,
|
|
"negative_prompt",
|
|
multiline=True,
|
|
),
|
|
"cfg_scale": model_field_to_node_input(
|
|
IO.FLOAT,
|
|
KlingVideoExtendRequest,
|
|
"cfg_scale",
|
|
default=0.5,
|
|
min=0.0,
|
|
max=1.0,
|
|
),
|
|
"video_id": model_field_to_node_input(
|
|
IO.STRING, KlingVideoExtendRequest, "video_id", forceInput=True
|
|
),
|
|
},
|
|
"hidden": {
|
|
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
|
"comfy_api_key": "API_KEY_COMFY_ORG",
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("VIDEO", "STRING", "STRING")
|
|
RETURN_NAMES = ("VIDEO", "video_id", "duration")
|
|
DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes."
|
|
|
|
def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingVideoExtendResponse:
|
|
return poll_until_finished(
|
|
auth_kwargs,
|
|
ApiEndpoint(
|
|
path=f"{PATH_VIDEO_EXTEND}/{task_id}",
|
|
method=HttpMethod.GET,
|
|
request_model=EmptyRequest,
|
|
response_model=KlingVideoExtendResponse,
|
|
),
|
|
)
|
|
|
|
def api_call(
|
|
self,
|
|
prompt: str,
|
|
negative_prompt: str,
|
|
cfg_scale: float,
|
|
video_id: str,
|
|
**kwargs,
|
|
) -> tuple[VideoFromFile, str, str]:
|
|
validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
|
|
initial_operation = SynchronousOperation(
|
|
endpoint=ApiEndpoint(
|
|
path=PATH_VIDEO_EXTEND,
|
|
method=HttpMethod.POST,
|
|
request_model=KlingVideoExtendRequest,
|
|
response_model=KlingVideoExtendResponse,
|
|
),
|
|
request=KlingVideoExtendRequest(
|
|
prompt=prompt if prompt else None,
|
|
negative_prompt=negative_prompt if negative_prompt else None,
|
|
cfg_scale=cfg_scale,
|
|
video_id=video_id,
|
|
),
|
|
auth_kwargs=kwargs,
|
|
)
|
|
|
|
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_kwargs=kwargs)
|
|
validate_video_result_response(final_response)
|
|
|
|
video = get_video_from_response(final_response)
|
|
return video_result_to_node_output(video)
|
|
|
|
|
|
class KlingVideoEffectsBase(KlingNodeBase):
|
|
"""Kling Video Effects Base"""
|
|
|
|
RETURN_TYPES = ("VIDEO", "STRING", "STRING")
|
|
RETURN_NAMES = ("VIDEO", "video_id", "duration")
|
|
|
|
def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingVideoEffectsResponse:
|
|
return poll_until_finished(
|
|
auth_kwargs,
|
|
ApiEndpoint(
|
|
path=f"{PATH_VIDEO_EFFECTS}/{task_id}",
|
|
method=HttpMethod.GET,
|
|
request_model=EmptyRequest,
|
|
response_model=KlingVideoEffectsResponse,
|
|
),
|
|
)
|
|
|
|
def api_call(
|
|
self,
|
|
dual_character: bool,
|
|
effect_scene: KlingDualCharacterEffectsScene | KlingSingleImageEffectsScene,
|
|
model_name: str,
|
|
duration: KlingVideoGenDuration,
|
|
image_1: torch.Tensor,
|
|
image_2: Optional[torch.Tensor] = None,
|
|
mode: Optional[KlingVideoGenMode] = None,
|
|
**kwargs,
|
|
):
|
|
if dual_character:
|
|
request_input_field = KlingDualCharacterEffectInput(
|
|
model_name=model_name,
|
|
mode=mode,
|
|
images=[
|
|
tensor_to_base64_string(image_1),
|
|
tensor_to_base64_string(image_2),
|
|
],
|
|
duration=duration,
|
|
)
|
|
else:
|
|
request_input_field = KlingSingleImageEffectInput(
|
|
model_name=model_name,
|
|
image=tensor_to_base64_string(image_1),
|
|
duration=duration,
|
|
)
|
|
|
|
initial_operation = SynchronousOperation(
|
|
endpoint=ApiEndpoint(
|
|
path=PATH_VIDEO_EFFECTS,
|
|
method=HttpMethod.POST,
|
|
request_model=KlingVideoEffectsRequest,
|
|
response_model=KlingVideoEffectsResponse,
|
|
),
|
|
request=KlingVideoEffectsRequest(
|
|
effect_scene=effect_scene,
|
|
input=request_input_field,
|
|
),
|
|
auth_kwargs=kwargs,
|
|
)
|
|
|
|
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_kwargs=kwargs)
|
|
validate_video_result_response(final_response)
|
|
|
|
video = get_video_from_response(final_response)
|
|
return video_result_to_node_output(video)
|
|
|
|
|
|
class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
|
|
"""Kling Dual Character Video Effect Node"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image_left": (IO.IMAGE, {"tooltip": "Left side image"}),
|
|
"image_right": (IO.IMAGE, {"tooltip": "Right side image"}),
|
|
"effect_scene": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingVideoEffectsRequest,
|
|
"effect_scene",
|
|
enum_type=KlingDualCharacterEffectsScene,
|
|
),
|
|
"model_name": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingDualCharacterEffectInput,
|
|
"model_name",
|
|
enum_type=KlingCharacterEffectModelName,
|
|
),
|
|
"mode": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingDualCharacterEffectInput,
|
|
"mode",
|
|
enum_type=KlingVideoGenMode,
|
|
),
|
|
"duration": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingDualCharacterEffectInput,
|
|
"duration",
|
|
enum_type=KlingVideoGenDuration,
|
|
),
|
|
},
|
|
"hidden": {
|
|
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
|
"comfy_api_key": "API_KEY_COMFY_ORG",
|
|
},
|
|
}
|
|
|
|
DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene. First image will be positioned on left side, second on right side of the composite."
|
|
RETURN_TYPES = ("VIDEO", "STRING")
|
|
RETURN_NAMES = ("VIDEO", "duration")
|
|
|
|
def api_call(
|
|
self,
|
|
image_left: torch.Tensor,
|
|
image_right: torch.Tensor,
|
|
effect_scene: KlingDualCharacterEffectsScene,
|
|
model_name: KlingCharacterEffectModelName,
|
|
mode: KlingVideoGenMode,
|
|
duration: KlingVideoGenDuration,
|
|
**kwargs,
|
|
):
|
|
video, _, duration = super().api_call(
|
|
dual_character=True,
|
|
effect_scene=effect_scene,
|
|
model_name=model_name,
|
|
mode=mode,
|
|
duration=duration,
|
|
image_1=image_left,
|
|
image_2=image_right,
|
|
**kwargs,
|
|
)
|
|
return video, duration
|
|
|
|
class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
|
|
"""Kling Single Image Video Effect Node"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": (
|
|
IO.IMAGE,
|
|
{
|
|
"tooltip": " Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1"
|
|
},
|
|
),
|
|
"effect_scene": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingVideoEffectsRequest,
|
|
"effect_scene",
|
|
enum_type=KlingSingleImageEffectsScene,
|
|
),
|
|
"model_name": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingSingleImageEffectInput,
|
|
"model_name",
|
|
enum_type=KlingSingleImageEffectModelName,
|
|
),
|
|
"duration": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingSingleImageEffectInput,
|
|
"duration",
|
|
enum_type=KlingVideoGenDuration,
|
|
),
|
|
},
|
|
"hidden": {
|
|
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
|
"comfy_api_key": "API_KEY_COMFY_ORG",
|
|
},
|
|
}
|
|
|
|
DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene."
|
|
|
|
def api_call(
|
|
self,
|
|
image: torch.Tensor,
|
|
effect_scene: KlingSingleImageEffectsScene,
|
|
model_name: KlingSingleImageEffectModelName,
|
|
duration: KlingVideoGenDuration,
|
|
**kwargs,
|
|
):
|
|
return super().api_call(
|
|
dual_character=False,
|
|
effect_scene=effect_scene,
|
|
model_name=model_name,
|
|
duration=duration,
|
|
image_1=image,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
class KlingLipSyncBase(KlingNodeBase):
|
|
"""Kling Lip Sync Base"""
|
|
|
|
RETURN_TYPES = ("VIDEO", "STRING", "STRING")
|
|
RETURN_NAMES = ("VIDEO", "video_id", "duration")
|
|
|
|
def validate_text(self, text: str):
|
|
if not text:
|
|
raise ValueError("Text is required")
|
|
if len(text) > MAX_PROMPT_LENGTH_LIP_SYNC:
|
|
raise ValueError(
|
|
f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters."
|
|
)
|
|
|
|
def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingLipSyncResponse:
|
|
"""Polls the Kling API endpoint until the task reaches a terminal state."""
|
|
return poll_until_finished(
|
|
auth_kwargs,
|
|
ApiEndpoint(
|
|
path=f"{PATH_LIP_SYNC}/{task_id}",
|
|
method=HttpMethod.GET,
|
|
request_model=EmptyRequest,
|
|
response_model=KlingLipSyncResponse,
|
|
),
|
|
)
|
|
|
|
def api_call(
|
|
self,
|
|
video: VideoInput,
|
|
audio: Optional[AudioInput] = None,
|
|
voice_language: Optional[str] = None,
|
|
mode: Optional[str] = None,
|
|
text: Optional[str] = None,
|
|
voice_speed: Optional[float] = None,
|
|
voice_id: Optional[str] = None,
|
|
**kwargs
|
|
) -> tuple[VideoFromFile, str, str]:
|
|
if text:
|
|
self.validate_text(text)
|
|
|
|
# Upload video to Comfy API and get download URL
|
|
video_url = upload_video_to_comfyapi(video, auth_kwargs=kwargs)
|
|
logging.info("Uploaded video to Comfy API. URL: %s", video_url)
|
|
|
|
# Upload the audio file to Comfy API and get download URL
|
|
if audio:
|
|
audio_url = upload_audio_to_comfyapi(audio, auth_kwargs=kwargs)
|
|
logging.info("Uploaded audio to Comfy API. URL: %s", audio_url)
|
|
else:
|
|
audio_url = None
|
|
|
|
initial_operation = SynchronousOperation(
|
|
endpoint=ApiEndpoint(
|
|
path=PATH_LIP_SYNC,
|
|
method=HttpMethod.POST,
|
|
request_model=KlingLipSyncRequest,
|
|
response_model=KlingLipSyncResponse,
|
|
),
|
|
request=KlingLipSyncRequest(
|
|
input=KlingLipSyncInputObject(
|
|
video_url=video_url,
|
|
mode=mode,
|
|
text=text,
|
|
voice_language=voice_language,
|
|
voice_speed=voice_speed,
|
|
audio_type="url",
|
|
audio_url=audio_url,
|
|
voice_id=voice_id,
|
|
),
|
|
),
|
|
auth_kwargs=kwargs,
|
|
)
|
|
|
|
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_kwargs=kwargs)
|
|
validate_video_result_response(final_response)
|
|
|
|
video = get_video_from_response(final_response)
|
|
return video_result_to_node_output(video)
|
|
|
|
|
|
class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
|
|
"""Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file."""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"video": (IO.VIDEO, {}),
|
|
"audio": (IO.AUDIO, {}),
|
|
"voice_language": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingLipSyncInputObject,
|
|
"voice_language",
|
|
enum_type=KlingLipSyncVoiceLanguage,
|
|
),
|
|
},
|
|
"hidden": {
|
|
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
|
"comfy_api_key": "API_KEY_COMFY_ORG",
|
|
},
|
|
}
|
|
|
|
DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file."
|
|
|
|
def api_call(
|
|
self,
|
|
video: VideoInput,
|
|
audio: AudioInput,
|
|
voice_language: str,
|
|
**kwargs,
|
|
):
|
|
return super().api_call(
|
|
video=video,
|
|
audio=audio,
|
|
voice_language=voice_language,
|
|
mode="audio2video",
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
|
|
"""Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt."""
|
|
|
|
@staticmethod
|
|
def get_voice_config() -> dict[str, tuple[str, str]]:
|
|
return {
|
|
# English voices
|
|
"Melody": ("girlfriend_4_speech02", "en"),
|
|
"Sunny": ("genshin_vindi2", "en"),
|
|
"Sage": ("zhinen_xuesheng", "en"),
|
|
"Ace": ("AOT", "en"),
|
|
"Blossom": ("ai_shatang", "en"),
|
|
"Peppy": ("genshin_klee2", "en"),
|
|
"Dove": ("genshin_kirara", "en"),
|
|
"Shine": ("ai_kaiya", "en"),
|
|
"Anchor": ("oversea_male1", "en"),
|
|
"Lyric": ("ai_chenjiahao_712", "en"),
|
|
"Tender": ("chat1_female_new-3", "en"),
|
|
"Siren": ("chat_0407_5-1", "en"),
|
|
"Zippy": ("cartoon-boy-07", "en"),
|
|
"Bud": ("uk_boy1", "en"),
|
|
"Sprite": ("cartoon-girl-01", "en"),
|
|
"Candy": ("PeppaPig_platform", "en"),
|
|
"Beacon": ("ai_huangzhong_712", "en"),
|
|
"Rock": ("ai_huangyaoshi_712", "en"),
|
|
"Titan": ("ai_laoguowang_712", "en"),
|
|
"Grace": ("chengshu_jiejie", "en"),
|
|
"Helen": ("you_pingjing", "en"),
|
|
"Lore": ("calm_story1", "en"),
|
|
"Crag": ("uk_man2", "en"),
|
|
"Prattle": ("laopopo_speech02", "en"),
|
|
"Hearth": ("heainainai_speech02", "en"),
|
|
"The Reader": ("reader_en_m-v1", "en"),
|
|
"Commercial Lady": ("commercial_lady_en_f-v1", "en"),
|
|
# Chinese voices
|
|
"阳光少年": ("genshin_vindi2", "zh"),
|
|
"懂事小弟": ("zhinen_xuesheng", "zh"),
|
|
"运动少年": ("tiyuxi_xuedi", "zh"),
|
|
"青春少女": ("ai_shatang", "zh"),
|
|
"温柔小妹": ("genshin_klee2", "zh"),
|
|
"元气少女": ("genshin_kirara", "zh"),
|
|
"阳光男生": ("ai_kaiya", "zh"),
|
|
"幽默小哥": ("tiexin_nanyou", "zh"),
|
|
"文艺小哥": ("ai_chenjiahao_712", "zh"),
|
|
"甜美邻家": ("girlfriend_1_speech02", "zh"),
|
|
"温柔姐姐": ("chat1_female_new-3", "zh"),
|
|
"职场女青": ("girlfriend_2_speech02", "zh"),
|
|
"活泼男童": ("cartoon-boy-07", "zh"),
|
|
"俏皮女童": ("cartoon-girl-01", "zh"),
|
|
"稳重老爸": ("ai_huangyaoshi_712", "zh"),
|
|
"温柔妈妈": ("you_pingjing", "zh"),
|
|
"严肃上司": ("ai_laoguowang_712", "zh"),
|
|
"优雅贵妇": ("chengshu_jiejie", "zh"),
|
|
"慈祥爷爷": ("zhuxi_speech02", "zh"),
|
|
"唠叨爷爷": ("uk_oldman3", "zh"),
|
|
"唠叨奶奶": ("laopopo_speech02", "zh"),
|
|
"和蔼奶奶": ("heainainai_speech02", "zh"),
|
|
"东北老铁": ("dongbeilaotie_speech02", "zh"),
|
|
"重庆小伙": ("chongqingxiaohuo_speech02", "zh"),
|
|
"四川妹子": ("chuanmeizi_speech02", "zh"),
|
|
"潮汕大叔": ("chaoshandashu_speech02", "zh"),
|
|
"台湾男生": ("ai_taiwan_man2_speech02", "zh"),
|
|
"西安掌柜": ("xianzhanggui_speech02", "zh"),
|
|
"天津姐姐": ("tianjinjiejie_speech02", "zh"),
|
|
"新闻播报男": ("diyinnansang_DB_CN_M_04-v2", "zh"),
|
|
"译制片男": ("yizhipiannan-v1", "zh"),
|
|
"撒娇女友": ("tianmeixuemei-v1", "zh"),
|
|
"刀片烟嗓": ("daopianyansang-v1", "zh"),
|
|
"乖巧正太": ("mengwa-v1", "zh"),
|
|
}
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
voice_options = list(s.get_voice_config().keys())
|
|
return {
|
|
"required": {
|
|
"video": (IO.VIDEO, {}),
|
|
"text": model_field_to_node_input(
|
|
IO.STRING, KlingLipSyncInputObject, "text", multiline=True
|
|
),
|
|
"voice": (voice_options, {"default": voice_options[0]}),
|
|
"voice_speed": model_field_to_node_input(
|
|
IO.FLOAT, KlingLipSyncInputObject, "voice_speed", slider=True
|
|
),
|
|
},
|
|
"hidden": {
|
|
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
|
"comfy_api_key": "API_KEY_COMFY_ORG",
|
|
},
|
|
}
|
|
|
|
DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt."
|
|
|
|
def api_call(
|
|
self,
|
|
video: VideoInput,
|
|
text: str,
|
|
voice: str,
|
|
voice_speed: float,
|
|
**kwargs,
|
|
):
|
|
voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice]
|
|
return super().api_call(
|
|
video=video,
|
|
text=text,
|
|
voice_language=voice_language,
|
|
voice_id=voice_id,
|
|
voice_speed=voice_speed,
|
|
mode="text2video",
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
class KlingImageGenerationBase(KlingNodeBase):
|
|
"""Kling Image Generation Base Node."""
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "api node/image/Kling"
|
|
|
|
def validate_prompt(self, prompt: str, negative_prompt: Optional[str] = None):
|
|
if not prompt or len(prompt) > MAX_PROMPT_LENGTH_IMAGE_GEN:
|
|
raise ValueError(
|
|
f"Prompt must be less than {MAX_PROMPT_LENGTH_IMAGE_GEN} characters"
|
|
)
|
|
if negative_prompt and len(negative_prompt) > MAX_PROMPT_LENGTH_IMAGE_GEN:
|
|
raise ValueError(
|
|
f"Negative prompt must be less than {MAX_PROMPT_LENGTH_IMAGE_GEN} characters"
|
|
)
|
|
|
|
|
|
class KlingVirtualTryOnNode(KlingImageGenerationBase):
|
|
"""Kling Virtual Try On Node."""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"human_image": (IO.IMAGE, {}),
|
|
"cloth_image": (IO.IMAGE, {}),
|
|
"model_name": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingVirtualTryOnRequest,
|
|
"model_name",
|
|
enum_type=KlingVirtualTryOnModelName,
|
|
),
|
|
},
|
|
"hidden": {
|
|
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
|
"comfy_api_key": "API_KEY_COMFY_ORG",
|
|
},
|
|
}
|
|
|
|
DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human."
|
|
|
|
def get_response(
|
|
self, task_id: str, auth_kwargs: dict[str,str] = None
|
|
) -> KlingVirtualTryOnResponse:
|
|
return poll_until_finished(
|
|
auth_kwargs,
|
|
ApiEndpoint(
|
|
path=f"{PATH_VIRTUAL_TRY_ON}/{task_id}",
|
|
method=HttpMethod.GET,
|
|
request_model=EmptyRequest,
|
|
response_model=KlingVirtualTryOnResponse,
|
|
),
|
|
)
|
|
|
|
def api_call(
|
|
self,
|
|
human_image: torch.Tensor,
|
|
cloth_image: torch.Tensor,
|
|
model_name: KlingVirtualTryOnModelName,
|
|
**kwargs,
|
|
):
|
|
initial_operation = SynchronousOperation(
|
|
endpoint=ApiEndpoint(
|
|
path=PATH_VIRTUAL_TRY_ON,
|
|
method=HttpMethod.POST,
|
|
request_model=KlingVirtualTryOnRequest,
|
|
response_model=KlingVirtualTryOnResponse,
|
|
),
|
|
request=KlingVirtualTryOnRequest(
|
|
human_image=tensor_to_base64_string(human_image),
|
|
cloth_image=tensor_to_base64_string(cloth_image),
|
|
model_name=model_name,
|
|
),
|
|
auth_kwargs=kwargs,
|
|
)
|
|
|
|
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_kwargs=kwargs)
|
|
validate_image_result_response(final_response)
|
|
|
|
images = get_images_from_response(final_response)
|
|
return (image_result_to_node_output(images),)
|
|
|
|
|
|
class KlingImageGenerationNode(KlingImageGenerationBase):
|
|
"""Kling Image Generation Node. Generate an image from a text prompt with an optional reference image."""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"prompt": model_field_to_node_input(
|
|
IO.STRING,
|
|
KlingImageGenerationsRequest,
|
|
"prompt",
|
|
multiline=True,
|
|
max_length=MAX_PROMPT_LENGTH_IMAGE_GEN,
|
|
),
|
|
"negative_prompt": model_field_to_node_input(
|
|
IO.STRING,
|
|
KlingImageGenerationsRequest,
|
|
"negative_prompt",
|
|
multiline=True,
|
|
),
|
|
"image_type": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingImageGenerationsRequest,
|
|
"image_reference",
|
|
enum_type=KlingImageGenImageReferenceType,
|
|
),
|
|
"image_fidelity": model_field_to_node_input(
|
|
IO.FLOAT,
|
|
KlingImageGenerationsRequest,
|
|
"image_fidelity",
|
|
slider=True,
|
|
step=0.01,
|
|
),
|
|
"human_fidelity": model_field_to_node_input(
|
|
IO.FLOAT,
|
|
KlingImageGenerationsRequest,
|
|
"human_fidelity",
|
|
slider=True,
|
|
step=0.01,
|
|
),
|
|
"model_name": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingImageGenerationsRequest,
|
|
"model_name",
|
|
enum_type=KlingImageGenModelName,
|
|
),
|
|
"aspect_ratio": model_field_to_node_input(
|
|
IO.COMBO,
|
|
KlingImageGenerationsRequest,
|
|
"aspect_ratio",
|
|
enum_type=KlingImageGenAspectRatio,
|
|
),
|
|
"n": model_field_to_node_input(
|
|
IO.INT,
|
|
KlingImageGenerationsRequest,
|
|
"n",
|
|
),
|
|
},
|
|
"optional": {
|
|
"image": (IO.IMAGE, {}),
|
|
},
|
|
"hidden": {
|
|
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
|
"comfy_api_key": "API_KEY_COMFY_ORG",
|
|
},
|
|
}
|
|
|
|
DESCRIPTION = "Kling Image Generation Node. Generate an image from a text prompt with an optional reference image."
|
|
|
|
def get_response(
|
|
self, task_id: str, auth_kwargs: Optional[dict[str,str]] = None
|
|
) -> KlingImageGenerationsResponse:
|
|
return poll_until_finished(
|
|
auth_kwargs,
|
|
ApiEndpoint(
|
|
path=f"{PATH_IMAGE_GENERATIONS}/{task_id}",
|
|
method=HttpMethod.GET,
|
|
request_model=EmptyRequest,
|
|
response_model=KlingImageGenerationsResponse,
|
|
),
|
|
)
|
|
|
|
def api_call(
|
|
self,
|
|
model_name: KlingImageGenModelName,
|
|
prompt: str,
|
|
negative_prompt: str,
|
|
image_type: KlingImageGenImageReferenceType,
|
|
image_fidelity: float,
|
|
human_fidelity: float,
|
|
n: int,
|
|
aspect_ratio: KlingImageGenAspectRatio,
|
|
image: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
):
|
|
self.validate_prompt(prompt, negative_prompt)
|
|
|
|
if image is not None:
|
|
image = tensor_to_base64_string(image)
|
|
|
|
initial_operation = SynchronousOperation(
|
|
endpoint=ApiEndpoint(
|
|
path=PATH_IMAGE_GENERATIONS,
|
|
method=HttpMethod.POST,
|
|
request_model=KlingImageGenerationsRequest,
|
|
response_model=KlingImageGenerationsResponse,
|
|
),
|
|
request=KlingImageGenerationsRequest(
|
|
model_name=model_name,
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
image=image,
|
|
image_reference=image_type,
|
|
image_fidelity=image_fidelity,
|
|
human_fidelity=human_fidelity,
|
|
n=n,
|
|
aspect_ratio=aspect_ratio,
|
|
),
|
|
auth_kwargs=kwargs,
|
|
)
|
|
|
|
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_kwargs=kwargs)
|
|
validate_image_result_response(final_response)
|
|
|
|
images = get_images_from_response(final_response)
|
|
return (image_result_to_node_output(images),)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"KlingCameraControls": KlingCameraControls,
|
|
"KlingTextToVideoNode": KlingTextToVideoNode,
|
|
"KlingImage2VideoNode": KlingImage2VideoNode,
|
|
"KlingCameraControlI2VNode": KlingCameraControlI2VNode,
|
|
"KlingCameraControlT2VNode": KlingCameraControlT2VNode,
|
|
"KlingStartEndFrameNode": KlingStartEndFrameNode,
|
|
"KlingVideoExtendNode": KlingVideoExtendNode,
|
|
"KlingLipSyncAudioToVideoNode": KlingLipSyncAudioToVideoNode,
|
|
"KlingLipSyncTextToVideoNode": KlingLipSyncTextToVideoNode,
|
|
"KlingVirtualTryOnNode": KlingVirtualTryOnNode,
|
|
"KlingImageGenerationNode": KlingImageGenerationNode,
|
|
"KlingSingleImageVideoEffectNode": KlingSingleImageVideoEffectNode,
|
|
"KlingDualCharacterVideoEffectNode": KlingDualCharacterVideoEffectNode,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"KlingCameraControls": "Kling Camera Controls",
|
|
"KlingTextToVideoNode": "Kling Text to Video",
|
|
"KlingImage2VideoNode": "Kling Image to Video",
|
|
"KlingCameraControlI2VNode": "Kling Image to Video (Camera Control)",
|
|
"KlingCameraControlT2VNode": "Kling Text to Video (Camera Control)",
|
|
"KlingStartEndFrameNode": "Kling Start-End Frame to Video",
|
|
"KlingVideoExtendNode": "Kling Video Extend",
|
|
"KlingLipSyncAudioToVideoNode": "Kling Lip Sync Video with Audio",
|
|
"KlingLipSyncTextToVideoNode": "Kling Lip Sync Video with Text",
|
|
"KlingVirtualTryOnNode": "Kling Virtual Try On",
|
|
"KlingImageGenerationNode": "Kling Image Generation",
|
|
"KlingSingleImageVideoEffectNode": "Kling Video Effects",
|
|
"KlingDualCharacterVideoEffectNode": "Kling Dual Character Video Effects",
|
|
}
|