mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2025-12-09 05:54:24 +08:00
1541 lines
58 KiB
Python
1541 lines
58 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
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import math
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import logging
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from typing_extensions import override
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import torch
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from comfy_api_nodes.apis import (
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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.util import (
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validate_image_dimensions,
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validate_image_aspect_ratio,
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validate_video_dimensions,
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validate_video_duration,
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tensor_to_base64_string,
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validate_string,
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upload_audio_to_comfyapi,
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download_url_to_image_tensor,
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upload_video_to_comfyapi,
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download_url_to_video_output,
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sync_op,
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ApiEndpoint,
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poll_op,
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)
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from comfy_api.input_impl import VideoFromFile
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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.latest import ComfyExtension, IO
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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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AVERAGE_DURATION_T2V = 319
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AVERAGE_DURATION_I2V = 164
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AVERAGE_DURATION_LIP_SYNC = 455
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AVERAGE_DURATION_VIRTUAL_TRY_ON = 19
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AVERAGE_DURATION_IMAGE_GEN = 32
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AVERAGE_DURATION_VIDEO_EFFECTS = 320
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AVERAGE_DURATION_VIDEO_EXTEND = 320
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R = TypeVar("R")
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MODE_TEXT2VIDEO = {
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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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"pro mode / 5s duration / kling-v2-1-master": ("pro", "5", "kling-v2-1-master"),
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"pro mode / 10s duration / kling-v2-1-master": ("pro", "10", "kling-v2-1-master"),
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"pro mode / 5s duration / kling-v2-5-turbo": ("pro", "5", "kling-v2-5-turbo"),
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"pro mode / 10s duration / kling-v2-5-turbo": ("pro", "10", "kling-v2-5-turbo"),
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}
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"""
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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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MODE_START_END_FRAME = {
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"standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"),
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"pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"),
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"pro mode / 5s duration / kling-v1-5": ("pro", "5", "kling-v1-5"),
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"pro mode / 10s duration / kling-v1-5": ("pro", "10", "kling-v1-5"),
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"pro mode / 5s duration / kling-v1-6": ("pro", "5", "kling-v1-6"),
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"pro mode / 10s duration / kling-v1-6": ("pro", "10", "kling-v1-6"),
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"pro mode / 5s duration / kling-v2-1": ("pro", "5", "kling-v2-1"),
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"pro mode / 10s duration / kling-v2-1": ("pro", "10", "kling-v2-1"),
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}
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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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VOICES_CONFIG = {
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# English voices
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"Melody": ("girlfriend_4_speech02", "en"),
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"Sunny": ("genshin_vindi2", "en"),
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"Sage": ("zhinen_xuesheng", "en"),
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"Ace": ("AOT", "en"),
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"Blossom": ("ai_shatang", "en"),
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"Peppy": ("genshin_klee2", "en"),
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"Dove": ("genshin_kirara", "en"),
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"Shine": ("ai_kaiya", "en"),
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"Anchor": ("oversea_male1", "en"),
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"Lyric": ("ai_chenjiahao_712", "en"),
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"Tender": ("chat1_female_new-3", "en"),
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"Siren": ("chat_0407_5-1", "en"),
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"Zippy": ("cartoon-boy-07", "en"),
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"Bud": ("uk_boy1", "en"),
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"Sprite": ("cartoon-girl-01", "en"),
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"Candy": ("PeppaPig_platform", "en"),
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"Beacon": ("ai_huangzhong_712", "en"),
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"Rock": ("ai_huangyaoshi_712", "en"),
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"Titan": ("ai_laoguowang_712", "en"),
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"Grace": ("chengshu_jiejie", "en"),
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"Helen": ("you_pingjing", "en"),
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"Lore": ("calm_story1", "en"),
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"Crag": ("uk_man2", "en"),
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"Prattle": ("laopopo_speech02", "en"),
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"Hearth": ("heainainai_speech02", "en"),
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"The Reader": ("reader_en_m-v1", "en"),
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"Commercial Lady": ("commercial_lady_en_f-v1", "en"),
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# Chinese voices
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"阳光少年": ("genshin_vindi2", "zh"),
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"懂事小弟": ("zhinen_xuesheng", "zh"),
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"运动少年": ("tiyuxi_xuedi", "zh"),
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"青春少女": ("ai_shatang", "zh"),
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"温柔小妹": ("genshin_klee2", "zh"),
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"元气少女": ("genshin_kirara", "zh"),
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"阳光男生": ("ai_kaiya", "zh"),
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"幽默小哥": ("tiexin_nanyou", "zh"),
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"文艺小哥": ("ai_chenjiahao_712", "zh"),
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"甜美邻家": ("girlfriend_1_speech02", "zh"),
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"温柔姐姐": ("chat1_female_new-3", "zh"),
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"职场女青": ("girlfriend_2_speech02", "zh"),
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"活泼男童": ("cartoon-boy-07", "zh"),
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"俏皮女童": ("cartoon-girl-01", "zh"),
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"稳重老爸": ("ai_huangyaoshi_712", "zh"),
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"温柔妈妈": ("you_pingjing", "zh"),
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"严肃上司": ("ai_laoguowang_712", "zh"),
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"优雅贵妇": ("chengshu_jiejie", "zh"),
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"慈祥爷爷": ("zhuxi_speech02", "zh"),
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"唠叨爷爷": ("uk_oldman3", "zh"),
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"唠叨奶奶": ("laopopo_speech02", "zh"),
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"和蔼奶奶": ("heainainai_speech02", "zh"),
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"东北老铁": ("dongbeilaotie_speech02", "zh"),
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"重庆小伙": ("chongqingxiaohuo_speech02", "zh"),
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"四川妹子": ("chuanmeizi_speech02", "zh"),
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"潮汕大叔": ("chaoshandashu_speech02", "zh"),
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"台湾男生": ("ai_taiwan_man2_speech02", "zh"),
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"西安掌柜": ("xianzhanggui_speech02", "zh"),
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"天津姐姐": ("tianjinjiejie_speech02", "zh"),
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"新闻播报男": ("diyinnansang_DB_CN_M_04-v2", "zh"),
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"译制片男": ("yizhipiannan-v1", "zh"),
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"撒娇女友": ("tianmeixuemei-v1", "zh"),
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"刀片烟嗓": ("daopianyansang-v1", "zh"),
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"乖巧正太": ("mengwa-v1", "zh"),
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}
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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_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 Exception(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("Error: %s.\nResponse: %s", error_msg, response)
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raise Exception(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("Error: %s.\nResponse: %s", error_msg, response)
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raise Exception(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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validate_image_dimensions(image, min_width=300, min_height=300)
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validate_image_aspect_ratio(image, (1, 2.5), (2.5, 1))
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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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Will raise an error if the response is not valid.
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"""
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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_video_url_from_response(response) -> Optional[str]:
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"""Returns the first video url from the Kling video generation task result.
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Will not raise an error if the response is not valid.
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"""
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if response and is_valid_video_response(response):
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return str(get_video_from_response(response).url)
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else:
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return None
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def get_images_from_response(response) -> list[KlingImageResult]:
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"""Returns the list of image objects from the Kling image generation task result.
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Will raise an error if the response is not valid.
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"""
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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 get_images_urls_from_response(response) -> Optional[str]:
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"""Returns the list of image urls from the Kling image generation task result.
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Will not raise an error if the response is not valid. If there is only one image, returns the url as a string. If there are multiple images, returns a list of urls.
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"""
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if response and is_valid_image_response(response):
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images = get_images_from_response(response)
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image_urls = [str(image.url) for image in images]
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return "\n".join(image_urls)
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else:
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return None
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async 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 await download_url_to_image_tensor(str(images[0].url))
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else:
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return torch.cat([await download_url_to_image_tensor(str(image.url)) for image in images])
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async def execute_text2video(
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cls: type[IO.ComfyNode],
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prompt: str,
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negative_prompt: str,
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cfg_scale: float,
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model_name: str,
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model_mode: str,
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duration: str,
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aspect_ratio: str,
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camera_control: Optional[KlingCameraControl] = None,
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) -> IO.NodeOutput:
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validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
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task_creation_response = await sync_op(
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cls,
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ApiEndpoint(path=PATH_TEXT_TO_VIDEO, method="POST"),
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response_model=KlingText2VideoResponse,
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data=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(model_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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)
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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 = await poll_op(
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cls,
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ApiEndpoint(path=f"{PATH_TEXT_TO_VIDEO}/{task_id}"),
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response_model=KlingText2VideoResponse,
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estimated_duration=AVERAGE_DURATION_T2V,
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status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
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)
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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 IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration))
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async def execute_image2video(
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cls: type[IO.ComfyNode],
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start_frame: torch.Tensor,
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prompt: str,
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negative_prompt: str,
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model_name: str,
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cfg_scale: float,
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model_mode: str,
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aspect_ratio: str,
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duration: str,
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camera_control: Optional[KlingCameraControl] = None,
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end_frame: Optional[torch.Tensor] = None,
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) -> IO.NodeOutput:
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validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V)
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validate_input_image(start_frame)
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if camera_control is not None:
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# Camera control type for image 2 video is always `simple`
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camera_control.type = KlingCameraControlType.simple
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if model_mode == "std" and model_name == KlingVideoGenModelName.kling_v2_5_turbo.value:
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model_mode = "pro" # October 5: currently "std" mode is not supported for this model
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task_creation_response = await sync_op(
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cls,
|
|
ApiEndpoint(path=PATH_IMAGE_TO_VIDEO, method="POST"),
|
|
response_model=KlingImage2VideoResponse,
|
|
data=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(model_mode),
|
|
duration=KlingVideoGenDuration(duration),
|
|
camera_control=camera_control,
|
|
),
|
|
)
|
|
|
|
validate_task_creation_response(task_creation_response)
|
|
task_id = task_creation_response.data.task_id
|
|
|
|
final_response = await poll_op(
|
|
cls,
|
|
ApiEndpoint(path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}"),
|
|
response_model=KlingImage2VideoResponse,
|
|
estimated_duration=AVERAGE_DURATION_I2V,
|
|
status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
|
|
)
|
|
validate_video_result_response(final_response)
|
|
|
|
video = get_video_from_response(final_response)
|
|
return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration))
|
|
|
|
|
|
async def execute_video_effect(
|
|
cls: type[IO.ComfyNode],
|
|
dual_character: bool,
|
|
effect_scene: KlingDualCharacterEffectsScene | KlingSingleImageEffectsScene,
|
|
model_name: str,
|
|
duration: KlingVideoGenDuration,
|
|
image_1: torch.Tensor,
|
|
image_2: Optional[torch.Tensor] = None,
|
|
model_mode: Optional[KlingVideoGenMode] = None,
|
|
) -> tuple[VideoFromFile, str, str]:
|
|
if dual_character:
|
|
request_input_field = KlingDualCharacterEffectInput(
|
|
model_name=model_name,
|
|
mode=model_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,
|
|
)
|
|
|
|
task_creation_response = await sync_op(
|
|
cls,
|
|
endpoint=ApiEndpoint(path=PATH_VIDEO_EFFECTS, method="POST"),
|
|
response_model=KlingVideoEffectsResponse,
|
|
data=KlingVideoEffectsRequest(
|
|
effect_scene=effect_scene,
|
|
input=request_input_field,
|
|
),
|
|
)
|
|
|
|
validate_task_creation_response(task_creation_response)
|
|
task_id = task_creation_response.data.task_id
|
|
|
|
final_response = await poll_op(
|
|
cls,
|
|
ApiEndpoint(path=f"{PATH_VIDEO_EFFECTS}/{task_id}"),
|
|
response_model=KlingVideoEffectsResponse,
|
|
estimated_duration=AVERAGE_DURATION_VIDEO_EFFECTS,
|
|
status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
|
|
)
|
|
validate_video_result_response(final_response)
|
|
|
|
video = get_video_from_response(final_response)
|
|
return await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration)
|
|
|
|
|
|
async def execute_lipsync(
|
|
cls: type[IO.ComfyNode],
|
|
video: VideoInput,
|
|
audio: Optional[AudioInput] = None,
|
|
voice_language: Optional[str] = None,
|
|
model_mode: Optional[str] = None,
|
|
text: Optional[str] = None,
|
|
voice_speed: Optional[float] = None,
|
|
voice_id: Optional[str] = None,
|
|
) -> IO.NodeOutput:
|
|
if text:
|
|
validate_string(text, field_name="Text", max_length=MAX_PROMPT_LENGTH_LIP_SYNC)
|
|
validate_video_dimensions(video, 720, 1920)
|
|
validate_video_duration(video, 2, 10)
|
|
|
|
# Upload video to Comfy API and get download URL
|
|
video_url = await upload_video_to_comfyapi(cls, video)
|
|
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 = await upload_audio_to_comfyapi(
|
|
cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg", filename="output.mp3"
|
|
)
|
|
logging.info("Uploaded audio to Comfy API. URL: %s", audio_url)
|
|
else:
|
|
audio_url = None
|
|
|
|
task_creation_response = await sync_op(
|
|
cls,
|
|
ApiEndpoint(PATH_LIP_SYNC, "POST"),
|
|
response_model=KlingLipSyncResponse,
|
|
data=KlingLipSyncRequest(
|
|
input=KlingLipSyncInputObject(
|
|
video_url=video_url,
|
|
mode=model_mode,
|
|
text=text,
|
|
voice_language=voice_language,
|
|
voice_speed=voice_speed,
|
|
audio_type="url",
|
|
audio_url=audio_url,
|
|
voice_id=voice_id,
|
|
),
|
|
),
|
|
)
|
|
|
|
validate_task_creation_response(task_creation_response)
|
|
task_id = task_creation_response.data.task_id
|
|
|
|
final_response = await poll_op(
|
|
cls,
|
|
ApiEndpoint(path=f"{PATH_LIP_SYNC}/{task_id}"),
|
|
response_model=KlingLipSyncResponse,
|
|
estimated_duration=AVERAGE_DURATION_LIP_SYNC,
|
|
status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
|
|
)
|
|
validate_video_result_response(final_response)
|
|
|
|
video = get_video_from_response(final_response)
|
|
return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration))
|
|
|
|
|
|
class KlingCameraControls(IO.ComfyNode):
|
|
"""Kling Camera Controls Node"""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingCameraControls",
|
|
display_name="Kling Camera Controls",
|
|
category="api node/video/Kling",
|
|
description="Allows specifying configuration options for Kling Camera Controls and motion control effects.",
|
|
inputs=[
|
|
IO.Combo.Input("camera_control_type", options=KlingCameraControlType),
|
|
IO.Float.Input(
|
|
"horizontal_movement",
|
|
default=0.0,
|
|
min=-10.0,
|
|
max=10.0,
|
|
step=0.25,
|
|
display_mode=IO.NumberDisplay.slider,
|
|
tooltip="Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right",
|
|
),
|
|
IO.Float.Input(
|
|
"vertical_movement",
|
|
default=0.0,
|
|
min=-10.0,
|
|
max=10.0,
|
|
step=0.25,
|
|
display_mode=IO.NumberDisplay.slider,
|
|
tooltip="Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward.",
|
|
),
|
|
IO.Float.Input(
|
|
"pan",
|
|
default=0.5,
|
|
min=-10.0,
|
|
max=10.0,
|
|
step=0.25,
|
|
display_mode=IO.NumberDisplay.slider,
|
|
tooltip="Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.",
|
|
),
|
|
IO.Float.Input(
|
|
"tilt",
|
|
default=0.0,
|
|
min=-10.0,
|
|
max=10.0,
|
|
step=0.25,
|
|
display_mode=IO.NumberDisplay.slider,
|
|
tooltip="Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.",
|
|
),
|
|
IO.Float.Input(
|
|
"roll",
|
|
default=0.0,
|
|
min=-10.0,
|
|
max=10.0,
|
|
step=0.25,
|
|
display_mode=IO.NumberDisplay.slider,
|
|
tooltip="Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.",
|
|
),
|
|
IO.Float.Input(
|
|
"zoom",
|
|
default=0.0,
|
|
min=-10.0,
|
|
max=10.0,
|
|
step=0.25,
|
|
display_mode=IO.NumberDisplay.slider,
|
|
tooltip="Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.",
|
|
),
|
|
],
|
|
outputs=[IO.Custom("CAMERA_CONTROL").Output(display_name="camera_control")],
|
|
)
|
|
|
|
@classmethod
|
|
def validate_inputs(
|
|
cls,
|
|
horizontal_movement: float,
|
|
vertical_movement: float,
|
|
pan: float,
|
|
tilt: float,
|
|
roll: float,
|
|
zoom: float,
|
|
) -> bool | str:
|
|
if not is_valid_camera_control_configs(
|
|
[
|
|
horizontal_movement,
|
|
vertical_movement,
|
|
pan,
|
|
tilt,
|
|
roll,
|
|
zoom,
|
|
]
|
|
):
|
|
return "Invalid camera control configs: at least one of the values must be non-zero"
|
|
return True
|
|
|
|
@classmethod
|
|
def execute(
|
|
cls,
|
|
camera_control_type: str,
|
|
horizontal_movement: float,
|
|
vertical_movement: float,
|
|
pan: float,
|
|
tilt: float,
|
|
roll: float,
|
|
zoom: float,
|
|
) -> IO.NodeOutput:
|
|
return IO.NodeOutput(
|
|
KlingCameraControl(
|
|
type=KlingCameraControlType(camera_control_type),
|
|
config=KlingCameraConfig(
|
|
horizontal=horizontal_movement,
|
|
vertical=vertical_movement,
|
|
pan=pan,
|
|
roll=roll,
|
|
tilt=tilt,
|
|
zoom=zoom,
|
|
),
|
|
)
|
|
)
|
|
|
|
|
|
class KlingTextToVideoNode(IO.ComfyNode):
|
|
"""Kling Text to Video Node"""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
modes = list(MODE_TEXT2VIDEO.keys())
|
|
return IO.Schema(
|
|
node_id="KlingTextToVideoNode",
|
|
display_name="Kling Text to Video",
|
|
category="api node/video/Kling",
|
|
description="Kling Text to Video Node",
|
|
inputs=[
|
|
IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
|
|
IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
|
|
IO.Float.Input("cfg_scale", default=1.0, min=0.0, max=1.0),
|
|
IO.Combo.Input(
|
|
"aspect_ratio",
|
|
options=KlingVideoGenAspectRatio,
|
|
default="16:9",
|
|
),
|
|
IO.Combo.Input(
|
|
"mode",
|
|
options=modes,
|
|
default=modes[4],
|
|
tooltip="The configuration to use for the video generation following the format: mode / duration / model_name.",
|
|
),
|
|
],
|
|
outputs=[
|
|
IO.Video.Output(),
|
|
IO.String.Output(display_name="video_id"),
|
|
IO.String.Output(display_name="duration"),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
prompt: str,
|
|
negative_prompt: str,
|
|
cfg_scale: float,
|
|
mode: str,
|
|
aspect_ratio: str,
|
|
) -> IO.NodeOutput:
|
|
model_mode, duration, model_name = MODE_TEXT2VIDEO[mode]
|
|
return await execute_text2video(
|
|
cls,
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
cfg_scale=cfg_scale,
|
|
model_mode=model_mode,
|
|
aspect_ratio=aspect_ratio,
|
|
model_name=model_name,
|
|
duration=duration,
|
|
)
|
|
|
|
|
|
class KlingCameraControlT2VNode(IO.ComfyNode):
|
|
"""
|
|
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 define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingCameraControlT2VNode",
|
|
display_name="Kling Text to Video (Camera Control)",
|
|
category="api node/video/Kling",
|
|
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.",
|
|
inputs=[
|
|
IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
|
|
IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
|
|
IO.Float.Input("cfg_scale", default=0.75, min=0.0, max=1.0),
|
|
IO.Combo.Input(
|
|
"aspect_ratio",
|
|
options=KlingVideoGenAspectRatio,
|
|
default="16:9",
|
|
),
|
|
IO.Custom("CAMERA_CONTROL").Input(
|
|
"camera_control",
|
|
tooltip="Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.",
|
|
),
|
|
],
|
|
outputs=[
|
|
IO.Video.Output(),
|
|
IO.String.Output(display_name="video_id"),
|
|
IO.String.Output(display_name="duration"),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
prompt: str,
|
|
negative_prompt: str,
|
|
cfg_scale: float,
|
|
aspect_ratio: str,
|
|
camera_control: Optional[KlingCameraControl] = None,
|
|
) -> IO.NodeOutput:
|
|
return await execute_text2video(
|
|
cls,
|
|
model_name=KlingVideoGenModelName.kling_v1,
|
|
cfg_scale=cfg_scale,
|
|
model_mode=KlingVideoGenMode.std,
|
|
aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
|
|
duration=KlingVideoGenDuration.field_5,
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
camera_control=camera_control,
|
|
)
|
|
|
|
|
|
class KlingImage2VideoNode(IO.ComfyNode):
|
|
"""Kling Image to Video Node"""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingImage2VideoNode",
|
|
display_name="Kling Image to Video",
|
|
category="api node/video/Kling",
|
|
description="Kling Image to Video Node",
|
|
inputs=[
|
|
IO.Image.Input("start_frame", tooltip="The reference image used to generate the video."),
|
|
IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
|
|
IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
|
|
IO.Combo.Input(
|
|
"model_name",
|
|
options=KlingVideoGenModelName,
|
|
default="kling-v2-master",
|
|
),
|
|
IO.Float.Input("cfg_scale", default=0.8, min=0.0, max=1.0),
|
|
IO.Combo.Input("mode", options=KlingVideoGenMode, default=KlingVideoGenMode.std),
|
|
IO.Combo.Input(
|
|
"aspect_ratio",
|
|
options=KlingVideoGenAspectRatio,
|
|
default=KlingVideoGenAspectRatio.field_16_9,
|
|
),
|
|
IO.Combo.Input("duration", options=KlingVideoGenDuration, default=KlingVideoGenDuration.field_5),
|
|
],
|
|
outputs=[
|
|
IO.Video.Output(),
|
|
IO.String.Output(display_name="video_id"),
|
|
IO.String.Output(display_name="duration"),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
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,
|
|
) -> IO.NodeOutput:
|
|
return await execute_image2video(
|
|
cls,
|
|
start_frame=start_frame,
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
cfg_scale=cfg_scale,
|
|
model_name=model_name,
|
|
aspect_ratio=aspect_ratio,
|
|
model_mode=mode,
|
|
duration=duration,
|
|
camera_control=camera_control,
|
|
end_frame=end_frame,
|
|
)
|
|
|
|
|
|
class KlingCameraControlI2VNode(IO.ComfyNode):
|
|
"""
|
|
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 define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingCameraControlI2VNode",
|
|
display_name="Kling Image to Video (Camera Control)",
|
|
category="api node/video/Kling",
|
|
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.",
|
|
inputs=[
|
|
IO.Image.Input(
|
|
"start_frame",
|
|
tooltip="Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.",
|
|
),
|
|
IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
|
|
IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
|
|
IO.Float.Input("cfg_scale", default=0.75, min=0.0, max=1.0),
|
|
IO.Combo.Input(
|
|
"aspect_ratio",
|
|
options=KlingVideoGenAspectRatio,
|
|
default=KlingVideoGenAspectRatio.field_16_9,
|
|
),
|
|
IO.Custom("CAMERA_CONTROL").Input(
|
|
"camera_control",
|
|
tooltip="Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.",
|
|
),
|
|
],
|
|
outputs=[
|
|
IO.Video.Output(),
|
|
IO.String.Output(display_name="video_id"),
|
|
IO.String.Output(display_name="duration"),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
start_frame: torch.Tensor,
|
|
prompt: str,
|
|
negative_prompt: str,
|
|
cfg_scale: float,
|
|
aspect_ratio: str,
|
|
camera_control: KlingCameraControl,
|
|
) -> IO.NodeOutput:
|
|
return await execute_image2video(
|
|
cls,
|
|
model_name=KlingVideoGenModelName.kling_v1_5,
|
|
start_frame=start_frame,
|
|
cfg_scale=cfg_scale,
|
|
model_mode=KlingVideoGenMode.pro,
|
|
aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
|
|
duration=KlingVideoGenDuration.field_5,
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
camera_control=camera_control,
|
|
)
|
|
|
|
|
|
class KlingStartEndFrameNode(IO.ComfyNode):
|
|
"""
|
|
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.
|
|
"""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
modes = list(MODE_START_END_FRAME.keys())
|
|
return IO.Schema(
|
|
node_id="KlingStartEndFrameNode",
|
|
display_name="Kling Start-End Frame to Video",
|
|
category="api node/video/Kling",
|
|
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.",
|
|
inputs=[
|
|
IO.Image.Input(
|
|
"start_frame",
|
|
tooltip="Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.",
|
|
),
|
|
IO.Image.Input(
|
|
"end_frame",
|
|
tooltip="Reference Image - End frame control. URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px. Base64 should not include data:image prefix.",
|
|
),
|
|
IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
|
|
IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
|
|
IO.Float.Input("cfg_scale", default=0.5, min=0.0, max=1.0),
|
|
IO.Combo.Input(
|
|
"aspect_ratio",
|
|
options=[i.value for i in KlingVideoGenAspectRatio],
|
|
default="16:9",
|
|
),
|
|
IO.Combo.Input(
|
|
"mode",
|
|
options=modes,
|
|
default=modes[2],
|
|
tooltip="The configuration to use for the video generation following the format: mode / duration / model_name.",
|
|
),
|
|
],
|
|
outputs=[
|
|
IO.Video.Output(),
|
|
IO.String.Output(display_name="video_id"),
|
|
IO.String.Output(display_name="duration"),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
start_frame: torch.Tensor,
|
|
end_frame: torch.Tensor,
|
|
prompt: str,
|
|
negative_prompt: str,
|
|
cfg_scale: float,
|
|
aspect_ratio: str,
|
|
mode: str,
|
|
) -> IO.NodeOutput:
|
|
mode, duration, model_name = MODE_START_END_FRAME[mode]
|
|
return await execute_image2video(
|
|
cls,
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
model_name=model_name,
|
|
start_frame=start_frame,
|
|
cfg_scale=cfg_scale,
|
|
model_mode=mode,
|
|
aspect_ratio=aspect_ratio,
|
|
duration=duration,
|
|
end_frame=end_frame,
|
|
)
|
|
|
|
|
|
class KlingVideoExtendNode(IO.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingVideoExtendNode",
|
|
display_name="Kling Video Extend",
|
|
category="api node/video/Kling",
|
|
description="Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes.",
|
|
inputs=[
|
|
IO.String.Input(
|
|
"prompt",
|
|
multiline=True,
|
|
tooltip="Positive text prompt for guiding the video extension",
|
|
),
|
|
IO.String.Input(
|
|
"negative_prompt",
|
|
multiline=True,
|
|
tooltip="Negative text prompt for elements to avoid in the extended video",
|
|
),
|
|
IO.Float.Input("cfg_scale", default=0.5, min=0.0, max=1.0),
|
|
IO.String.Input(
|
|
"video_id",
|
|
force_input=True,
|
|
tooltip="The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.",
|
|
),
|
|
],
|
|
outputs=[
|
|
IO.Video.Output(),
|
|
IO.String.Output(display_name="video_id"),
|
|
IO.String.Output(display_name="duration"),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
prompt: str,
|
|
negative_prompt: str,
|
|
cfg_scale: float,
|
|
video_id: str,
|
|
) -> IO.NodeOutput:
|
|
validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
|
|
task_creation_response = await sync_op(
|
|
cls,
|
|
ApiEndpoint(path=PATH_VIDEO_EXTEND, method="POST"),
|
|
response_model=KlingVideoExtendResponse,
|
|
data=KlingVideoExtendRequest(
|
|
prompt=prompt if prompt else None,
|
|
negative_prompt=negative_prompt if negative_prompt else None,
|
|
cfg_scale=cfg_scale,
|
|
video_id=video_id,
|
|
),
|
|
)
|
|
|
|
validate_task_creation_response(task_creation_response)
|
|
task_id = task_creation_response.data.task_id
|
|
|
|
final_response = await poll_op(
|
|
cls,
|
|
ApiEndpoint(path=f"{PATH_VIDEO_EXTEND}/{task_id}"),
|
|
response_model=KlingVideoExtendResponse,
|
|
estimated_duration=AVERAGE_DURATION_VIDEO_EXTEND,
|
|
status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
|
|
)
|
|
validate_video_result_response(final_response)
|
|
|
|
video = get_video_from_response(final_response)
|
|
return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration))
|
|
|
|
|
|
class KlingDualCharacterVideoEffectNode(IO.ComfyNode):
|
|
"""Kling Dual Character Video Effect Node"""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingDualCharacterVideoEffectNode",
|
|
display_name="Kling Dual Character Video Effects",
|
|
category="api node/video/Kling",
|
|
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.",
|
|
inputs=[
|
|
IO.Image.Input("image_left", tooltip="Left side image"),
|
|
IO.Image.Input("image_right", tooltip="Right side image"),
|
|
IO.Combo.Input(
|
|
"effect_scene",
|
|
options=[i.value for i in KlingDualCharacterEffectsScene],
|
|
),
|
|
IO.Combo.Input(
|
|
"model_name",
|
|
options=[i.value for i in KlingCharacterEffectModelName],
|
|
default="kling-v1",
|
|
),
|
|
IO.Combo.Input(
|
|
"mode",
|
|
options=[i.value for i in KlingVideoGenMode],
|
|
default="std",
|
|
),
|
|
IO.Combo.Input(
|
|
"duration",
|
|
options=[i.value for i in KlingVideoGenDuration],
|
|
),
|
|
],
|
|
outputs=[
|
|
IO.Video.Output(),
|
|
IO.String.Output(display_name="duration"),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
image_left: torch.Tensor,
|
|
image_right: torch.Tensor,
|
|
effect_scene: KlingDualCharacterEffectsScene,
|
|
model_name: KlingCharacterEffectModelName,
|
|
mode: KlingVideoGenMode,
|
|
duration: KlingVideoGenDuration,
|
|
) -> IO.NodeOutput:
|
|
video, _, duration = await execute_video_effect(
|
|
cls,
|
|
dual_character=True,
|
|
effect_scene=effect_scene,
|
|
model_name=model_name,
|
|
model_mode=mode,
|
|
duration=duration,
|
|
image_1=image_left,
|
|
image_2=image_right,
|
|
)
|
|
return IO.NodeOutput(video, duration)
|
|
|
|
|
|
class KlingSingleImageVideoEffectNode(IO.ComfyNode):
|
|
"""Kling Single Image Video Effect Node"""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingSingleImageVideoEffectNode",
|
|
display_name="Kling Video Effects",
|
|
category="api node/video/Kling",
|
|
description="Achieve different special effects when generating a video based on the effect_scene.",
|
|
inputs=[
|
|
IO.Image.Input("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"),
|
|
IO.Combo.Input(
|
|
"effect_scene",
|
|
options=[i.value for i in KlingSingleImageEffectsScene],
|
|
),
|
|
IO.Combo.Input(
|
|
"model_name",
|
|
options=[i.value for i in KlingSingleImageEffectModelName],
|
|
),
|
|
IO.Combo.Input(
|
|
"duration",
|
|
options=[i.value for i in KlingVideoGenDuration],
|
|
),
|
|
],
|
|
outputs=[
|
|
IO.Video.Output(),
|
|
IO.String.Output(display_name="video_id"),
|
|
IO.String.Output(display_name="duration"),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
image: torch.Tensor,
|
|
effect_scene: KlingSingleImageEffectsScene,
|
|
model_name: KlingSingleImageEffectModelName,
|
|
duration: KlingVideoGenDuration,
|
|
) -> IO.NodeOutput:
|
|
return IO.NodeOutput(
|
|
*(
|
|
await execute_video_effect(
|
|
cls,
|
|
dual_character=False,
|
|
effect_scene=effect_scene,
|
|
model_name=model_name,
|
|
duration=duration,
|
|
image_1=image,
|
|
)
|
|
)
|
|
)
|
|
|
|
|
|
class KlingLipSyncAudioToVideoNode(IO.ComfyNode):
|
|
"""Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file."""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingLipSyncAudioToVideoNode",
|
|
display_name="Kling Lip Sync Video with Audio",
|
|
category="api node/video/Kling",
|
|
description="Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.",
|
|
inputs=[
|
|
IO.Video.Input("video"),
|
|
IO.Audio.Input("audio"),
|
|
IO.Combo.Input(
|
|
"voice_language",
|
|
options=[i.value for i in KlingLipSyncVoiceLanguage],
|
|
default="en",
|
|
),
|
|
],
|
|
outputs=[
|
|
IO.Video.Output(),
|
|
IO.String.Output(display_name="video_id"),
|
|
IO.String.Output(display_name="duration"),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
video: VideoInput,
|
|
audio: AudioInput,
|
|
voice_language: str,
|
|
) -> IO.NodeOutput:
|
|
return await execute_lipsync(
|
|
cls,
|
|
video=video,
|
|
audio=audio,
|
|
voice_language=voice_language,
|
|
model_mode="audio2video",
|
|
)
|
|
|
|
|
|
class KlingLipSyncTextToVideoNode(IO.ComfyNode):
|
|
"""Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt."""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingLipSyncTextToVideoNode",
|
|
display_name="Kling Lip Sync Video with Text",
|
|
category="api node/video/Kling",
|
|
description="Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.",
|
|
inputs=[
|
|
IO.Video.Input("video"),
|
|
IO.String.Input(
|
|
"text",
|
|
multiline=True,
|
|
tooltip="Text Content for Lip-Sync Video Generation. Required when mode is text2video. Maximum length is 120 characters.",
|
|
),
|
|
IO.Combo.Input(
|
|
"voice",
|
|
options=list(VOICES_CONFIG.keys()),
|
|
default="Melody",
|
|
),
|
|
IO.Float.Input(
|
|
"voice_speed",
|
|
default=1,
|
|
min=0.8,
|
|
max=2.0,
|
|
display_mode=IO.NumberDisplay.slider,
|
|
tooltip="Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.",
|
|
),
|
|
],
|
|
outputs=[
|
|
IO.Video.Output(),
|
|
IO.String.Output(display_name="video_id"),
|
|
IO.String.Output(display_name="duration"),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
video: VideoInput,
|
|
text: str,
|
|
voice: str,
|
|
voice_speed: float,
|
|
) -> IO.NodeOutput:
|
|
voice_id, voice_language = VOICES_CONFIG[voice]
|
|
return await execute_lipsync(
|
|
cls,
|
|
video=video,
|
|
text=text,
|
|
voice_language=voice_language,
|
|
voice_id=voice_id,
|
|
voice_speed=voice_speed,
|
|
model_mode="text2video",
|
|
)
|
|
|
|
|
|
class KlingVirtualTryOnNode(IO.ComfyNode):
|
|
"""Kling Virtual Try On Node."""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingVirtualTryOnNode",
|
|
display_name="Kling Virtual Try On",
|
|
category="api node/image/Kling",
|
|
description="Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background.",
|
|
inputs=[
|
|
IO.Image.Input("human_image"),
|
|
IO.Image.Input("cloth_image"),
|
|
IO.Combo.Input(
|
|
"model_name",
|
|
options=[i.value for i in KlingVirtualTryOnModelName],
|
|
default="kolors-virtual-try-on-v1",
|
|
),
|
|
],
|
|
outputs=[
|
|
IO.Image.Output(),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
human_image: torch.Tensor,
|
|
cloth_image: torch.Tensor,
|
|
model_name: KlingVirtualTryOnModelName,
|
|
) -> IO.NodeOutput:
|
|
task_creation_response = await sync_op(
|
|
cls,
|
|
ApiEndpoint(path=PATH_VIRTUAL_TRY_ON, method="POST"),
|
|
response_model=KlingVirtualTryOnResponse,
|
|
data=KlingVirtualTryOnRequest(
|
|
human_image=tensor_to_base64_string(human_image),
|
|
cloth_image=tensor_to_base64_string(cloth_image),
|
|
model_name=model_name,
|
|
),
|
|
)
|
|
|
|
validate_task_creation_response(task_creation_response)
|
|
task_id = task_creation_response.data.task_id
|
|
|
|
final_response = await poll_op(
|
|
cls,
|
|
ApiEndpoint(path=f"{PATH_VIRTUAL_TRY_ON}/{task_id}"),
|
|
response_model=KlingVirtualTryOnResponse,
|
|
estimated_duration=AVERAGE_DURATION_VIRTUAL_TRY_ON,
|
|
status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
|
|
)
|
|
validate_image_result_response(final_response)
|
|
|
|
images = get_images_from_response(final_response)
|
|
return IO.NodeOutput(await image_result_to_node_output(images))
|
|
|
|
|
|
class KlingImageGenerationNode(IO.ComfyNode):
|
|
"""Kling Image Generation Node. Generate an image from a text prompt with an optional reference image."""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> IO.Schema:
|
|
return IO.Schema(
|
|
node_id="KlingImageGenerationNode",
|
|
display_name="Kling Image Generation",
|
|
category="api node/image/Kling",
|
|
description="Kling Image Generation Node. Generate an image from a text prompt with an optional reference image.",
|
|
inputs=[
|
|
IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
|
|
IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"),
|
|
IO.Combo.Input(
|
|
"image_type",
|
|
options=[i.value for i in KlingImageGenImageReferenceType],
|
|
),
|
|
IO.Float.Input(
|
|
"image_fidelity",
|
|
default=0.5,
|
|
min=0.0,
|
|
max=1.0,
|
|
step=0.01,
|
|
display_mode=IO.NumberDisplay.slider,
|
|
tooltip="Reference intensity for user-uploaded images",
|
|
),
|
|
IO.Float.Input(
|
|
"human_fidelity",
|
|
default=0.45,
|
|
min=0.0,
|
|
max=1.0,
|
|
step=0.01,
|
|
display_mode=IO.NumberDisplay.slider,
|
|
tooltip="Subject reference similarity",
|
|
),
|
|
IO.Combo.Input(
|
|
"model_name",
|
|
options=[i.value for i in KlingImageGenModelName],
|
|
default="kling-v1",
|
|
),
|
|
IO.Combo.Input(
|
|
"aspect_ratio",
|
|
options=[i.value for i in KlingImageGenAspectRatio],
|
|
default="16:9",
|
|
),
|
|
IO.Int.Input(
|
|
"n",
|
|
default=1,
|
|
min=1,
|
|
max=9,
|
|
tooltip="Number of generated images",
|
|
),
|
|
IO.Image.Input("image", optional=True),
|
|
],
|
|
outputs=[
|
|
IO.Image.Output(),
|
|
],
|
|
hidden=[
|
|
IO.Hidden.auth_token_comfy_org,
|
|
IO.Hidden.api_key_comfy_org,
|
|
IO.Hidden.unique_id,
|
|
],
|
|
is_api_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
async def execute(
|
|
cls,
|
|
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,
|
|
) -> IO.NodeOutput:
|
|
validate_string(prompt, field_name="prompt", min_length=1, max_length=MAX_PROMPT_LENGTH_IMAGE_GEN)
|
|
validate_string(negative_prompt, field_name="negative_prompt", max_length=MAX_PROMPT_LENGTH_IMAGE_GEN)
|
|
|
|
if image is None:
|
|
image_type = None
|
|
elif model_name == KlingImageGenModelName.kling_v1:
|
|
raise ValueError(f"The model {KlingImageGenModelName.kling_v1.value} does not support reference images.")
|
|
else:
|
|
image = tensor_to_base64_string(image)
|
|
|
|
task_creation_response = await sync_op(
|
|
cls,
|
|
ApiEndpoint(path=PATH_IMAGE_GENERATIONS, method="POST"),
|
|
response_model=KlingImageGenerationsResponse,
|
|
data=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,
|
|
),
|
|
)
|
|
|
|
validate_task_creation_response(task_creation_response)
|
|
task_id = task_creation_response.data.task_id
|
|
|
|
final_response = await poll_op(
|
|
cls,
|
|
ApiEndpoint(path=f"{PATH_IMAGE_GENERATIONS}/{task_id}"),
|
|
response_model=KlingImageGenerationsResponse,
|
|
estimated_duration=AVERAGE_DURATION_IMAGE_GEN,
|
|
status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
|
|
)
|
|
validate_image_result_response(final_response)
|
|
|
|
images = get_images_from_response(final_response)
|
|
return IO.NodeOutput(await image_result_to_node_output(images))
|
|
|
|
|
|
class KlingExtension(ComfyExtension):
|
|
@override
|
|
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
|
return [
|
|
KlingCameraControls,
|
|
KlingTextToVideoNode,
|
|
KlingImage2VideoNode,
|
|
KlingCameraControlI2VNode,
|
|
KlingCameraControlT2VNode,
|
|
KlingStartEndFrameNode,
|
|
KlingVideoExtendNode,
|
|
KlingLipSyncAudioToVideoNode,
|
|
KlingLipSyncTextToVideoNode,
|
|
KlingVirtualTryOnNode,
|
|
KlingImageGenerationNode,
|
|
KlingSingleImageVideoEffectNode,
|
|
KlingDualCharacterVideoEffectNode,
|
|
]
|
|
|
|
|
|
async def comfy_entrypoint() -> KlingExtension:
|
|
return KlingExtension()
|