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@ -65,6 +65,12 @@ from comfy_api_nodes.apinode_utils import (
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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_nodes.util.validation_utils 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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)
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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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@ -80,18 +86,16 @@ 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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# TODO: adjust based on tests
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AVERAGE_DURATION_T2V = 319 # 319,
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AVERAGE_DURATION_I2V = 164 # 164,
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AVERAGE_DURATION_LIP_SYNC = 120
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AVERAGE_DURATION_VIRTUAL_TRY_ON = 19 # 19,
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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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@ -211,23 +215,8 @@ def validate_input_image(image: torch.Tensor) -> None:
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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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validate_image_dimensions(image, min_width=300, min_height=300)
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validate_image_aspect_ratio(image, min_aspect_ratio=1 / 2.5, max_aspect_ratio=2.5)
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def get_camera_control_input_config(
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@ -1243,6 +1232,17 @@ class KlingLipSyncBase(KlingNodeBase):
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RETURN_TYPES = ("VIDEO", "STRING", "STRING")
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RETURN_NAMES = ("VIDEO", "video_id", "duration")
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def validate_lip_sync_video(self, video: VideoInput):
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"""
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Validates the input video adheres to the expectations of the Kling Lip Sync API:
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- Video length does not exceed 10s and is not shorter than 2s
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- Length and width dimensions should both be between 720px and 1920px
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See: https://app.klingai.com/global/dev/document-api/apiReference/model/videoTolip
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"""
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validate_video_dimensions(video, 720, 1920)
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validate_video_duration(video, 2, 10)
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def validate_text(self, text: str):
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if not text:
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raise ValueError("Text is required")
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@ -1282,6 +1282,7 @@ class KlingLipSyncBase(KlingNodeBase):
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) -> tuple[VideoFromFile, str, str]:
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if text:
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self.validate_text(text)
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self.validate_lip_sync_video(video)
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# Upload video to Comfy API and get download URL
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video_url = upload_video_to_comfyapi(video, auth_kwargs=kwargs)
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@ -1352,7 +1353,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
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},
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}
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DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file."
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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."
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def api_call(
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self,
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@ -1464,7 +1465,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
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},
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}
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DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt."
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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."
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def api_call(
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self,
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0
comfy_api_nodes/util/__init__.py
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0
comfy_api_nodes/util/__init__.py
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100
comfy_api_nodes/util/validation_utils.py
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100
comfy_api_nodes/util/validation_utils.py
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@ -0,0 +1,100 @@
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import logging
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from typing import Optional
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import torch
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from comfy_api.input.video_types import VideoInput
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def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]:
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if len(image.shape) == 4:
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return image.shape[1], image.shape[2]
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elif len(image.shape) == 3:
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return 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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def validate_image_dimensions(
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image: torch.Tensor,
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min_width: Optional[int] = None,
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max_width: Optional[int] = None,
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min_height: Optional[int] = None,
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max_height: Optional[int] = None,
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):
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height, width = get_image_dimensions(image)
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if min_width is not None and width < min_width:
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raise ValueError(f"Image width must be at least {min_width}px, got {width}px")
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if max_width is not None and width > max_width:
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raise ValueError(f"Image width must be at most {max_width}px, got {width}px")
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if min_height is not None and height < min_height:
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raise ValueError(
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f"Image height must be at least {min_height}px, got {height}px"
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)
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if max_height is not None and height > max_height:
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raise ValueError(f"Image height must be at most {max_height}px, got {height}px")
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def validate_image_aspect_ratio(
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image: torch.Tensor,
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min_aspect_ratio: Optional[float] = None,
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max_aspect_ratio: Optional[float] = None,
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):
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width, height = get_image_dimensions(image)
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aspect_ratio = width / height
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if min_aspect_ratio is not None and aspect_ratio < min_aspect_ratio:
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raise ValueError(
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f"Image aspect ratio must be at least {min_aspect_ratio}, got {aspect_ratio}"
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)
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if max_aspect_ratio is not None and aspect_ratio > max_aspect_ratio:
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raise ValueError(
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f"Image aspect ratio must be at most {max_aspect_ratio}, got {aspect_ratio}"
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)
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def validate_video_dimensions(
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video: VideoInput,
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min_width: Optional[int] = None,
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max_width: Optional[int] = None,
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min_height: Optional[int] = None,
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max_height: Optional[int] = None,
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):
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try:
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width, height = video.get_dimensions()
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except Exception as e:
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logging.error("Error getting dimensions of video: %s", e)
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return
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if min_width is not None and width < min_width:
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raise ValueError(f"Video width must be at least {min_width}px, got {width}px")
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if max_width is not None and width > max_width:
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raise ValueError(f"Video width must be at most {max_width}px, got {width}px")
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if min_height is not None and height < min_height:
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raise ValueError(
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f"Video height must be at least {min_height}px, got {height}px"
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)
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if max_height is not None and height > max_height:
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raise ValueError(f"Video height must be at most {max_height}px, got {height}px")
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def validate_video_duration(
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video: VideoInput,
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min_duration: Optional[float] = None,
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max_duration: Optional[float] = None,
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):
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try:
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duration = video.get_duration()
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except Exception as e:
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logging.error("Error getting duration of video: %s", e)
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return
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epsilon = 0.0001
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if min_duration is not None and min_duration - epsilon > duration:
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raise ValueError(
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f"Video duration must be at least {min_duration}s, got {duration}s"
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)
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if max_duration is not None and duration > max_duration + epsilon:
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raise ValueError(
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f"Video duration must be at most {max_duration}s, got {duration}s"
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)
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@ -31,6 +31,7 @@ class T5TokenizerOptions:
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}
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}
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CATEGORY = "_for_testing/conditioning"
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RETURN_TYPES = ("CLIP",)
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FUNCTION = "set_options"
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@ -13,6 +13,7 @@ import os
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import re
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from io import BytesIO
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from inspect import cleandoc
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import torch
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from comfy.comfy_types import FileLocator
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@ -74,6 +75,24 @@ class ImageFromBatch:
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s = s_in[batch_index:batch_index + length].clone()
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return (s,)
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class ImageAddNoise:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "image": ("IMAGE",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
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"strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "repeat"
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CATEGORY = "image"
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def repeat(self, image, seed, strength):
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generator = torch.manual_seed(seed)
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s = torch.clip((image + strength * torch.randn(image.size(), generator=generator, device="cpu").to(image)), min=0.0, max=1.0)
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return (s,)
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class SaveAnimatedWEBP:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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@ -295,6 +314,7 @@ NODE_CLASS_MAPPINGS = {
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"ImageCrop": ImageCrop,
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"RepeatImageBatch": RepeatImageBatch,
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"ImageFromBatch": ImageFromBatch,
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"ImageAddNoise": ImageAddNoise,
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"SaveAnimatedWEBP": SaveAnimatedWEBP,
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"SaveAnimatedPNG": SaveAnimatedPNG,
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"SaveSVGNode": SaveSVGNode,
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@ -8,7 +8,8 @@ class StringConcatenate():
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return {
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"required": {
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"string_a": (IO.STRING, {"multiline": True}),
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"string_b": (IO.STRING, {"multiline": True})
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"string_b": (IO.STRING, {"multiline": True}),
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"delimiter": (IO.STRING, {"multiline": False, "default": ""})
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}
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}
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@ -16,8 +17,8 @@ class StringConcatenate():
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FUNCTION = "execute"
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CATEGORY = "utils/string"
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def execute(self, string_a, string_b, **kwargs):
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return string_a + string_b,
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def execute(self, string_a, string_b, delimiter, **kwargs):
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return delimiter.join((string_a, string_b)),
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class StringSubstring():
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@classmethod
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