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Add video background removal and video upscaler nodes

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- Add Runware Video Background Removal node with Bria support
- Add Runware Video Upscaler node with Bria support
- Add Runware Vectorize node for image vectorization
- Add Lightricks Provider Settings node
- Add Video Inference Inputs node for structured inputs
- Add Input Audios and Reference Videos nodes
- Update video inference with speech support and reference videos
- Add new video models: OmniHuman 1/1.5, PixVerse LipSync, LTX
- Update Bytedance provider settings with boolean controls
- Add corresponding workflow examples
This commit is contained in:
Sirshendu Ganguly
2025-10-23 22:36:24 -04:00
parent 1c7da18f11
commit 8d75a6933f
28 changed files with 757 additions and 34 deletions
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modules/videoInference.py
+70 -18
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@@ -84,16 +84,6 @@ class txt2vid:
"Model": ("RUNWAREVIDEOMODEL", {
"tooltip": "Connect a Runware Video Model From Runware Video Model Search Node.",
}),
"positivePrompt": ("STRING", {
"multiline": True,
"placeholder": "Positive Prompt: a text instruction to guide the model on generating the video. It is usually a sentence or a paragraph that provides positive guidance for the task. This parameter is essential to shape the desired results.\n\nYou Can Press (Ctrl + Alt + E) To Enhance The Prompt!",
"tooltip": "Positive Prompt: a text instruction to guide the model on generating the video. You Can Also Press (Ctrl + Alt + E) To Enhance The Prompt!"
}),
"negativePrompt": ("STRING", {
"multiline": True,
"placeholder": "Negative Prompt: a text instruction to guide the model on generating the video. It is usually a sentence or a paragraph that provides negative guidance for the task. This parameter helps to avoid certain undesired results.",
"tooltip": "Negative Prompt: a text instruction to guide the model on generating the video."
}),
"Multi Inference Mode": ("BOOLEAN", {
"tooltip": "If Enabled the node will skip the video generation process and will only return the Runware Task Object to be used in the Multi Inference Node.",
"default": False,
@@ -162,6 +152,16 @@ class txt2vid:
}),
},
"optional": {
"positivePrompt": ("STRING", {
"multiline": True,
"placeholder": "Positive Prompt: a text instruction to guide the model on generating the video. It is usually a sentence or a paragraph that provides positive guidance for the task. This parameter is essential to shape the desired results.\n\nYou Can Press (Ctrl + Alt + E) To Enhance The Prompt!",
"tooltip": "Positive Prompt: a text instruction to guide the model on generating the video. You Can Also Press (Ctrl + Alt + E) To Enhance The Prompt!"
}),
"negativePrompt": ("STRING", {
"multiline": True,
"placeholder": "Negative Prompt: a text instruction to guide the model on generating the video. It is usually a sentence or a paragraph that provides negative guidance for the task. This parameter helps to avoid certain undesired results.",
"tooltip": "Negative Prompt: a text instruction to guide the model on generating the video."
}),
"frameImages": ("RUNWAREFRAMEIMAGES", {
"tooltip": "Frame images configuration from Runware Frame Images node. Allows precise control over frame positioning.",
}),
@@ -174,17 +174,31 @@ class txt2vid:
"inputAudios": ("RUNWAREINPUTAUDIOS", {
"tooltip": "Connect input audio files for video generation with audio synchronization.",
}),
"referenceVideos": ("RUNWAREREFERENCEVIDEOS", {
"tooltip": "Connect reference video files for video generation.",
}),
"speechVoice": ("STRING", {
"tooltip": "Voice for speech synthesis. Specify the voice to use for text-to-speech generation.",
}),
"speechText": ("STRING", {
"multiline": True,
"tooltip": "Text for speech synthesis. The text that will be converted to speech using the specified voice.",
}),
"inputs": ("RUNWAREVIDEOINFERENCEINPUTS", {
"tooltip": "Connect a Runware Video Inference Inputs node to provide custom inputs like image, audio, mask, and parameters for OmniHuman 1.5 and other video models.",
}),
}
}
@classmethod
def VALIDATE_INPUTS(cls, positivePrompt, negativePrompt):
if (positivePrompt is not None and (positivePrompt == "" or len(positivePrompt) < 3 or len(positivePrompt) > 2000)):
raise Exception(
"Positive Prompt Must Be Between 3 And 2000 characters!")
if (negativePrompt is not None and negativePrompt != "" and (len(negativePrompt) < 3 or len(negativePrompt) > 2000)):
raise Exception(
"Negative Prompt Must Be Between 3 And 2000 characters!")
def VALIDATE_INPUTS(cls, positivePrompt=None, negativePrompt=None):
# Only validate if prompts are provided and not empty
if positivePrompt is not None and positivePrompt.strip() != "":
if len(positivePrompt) < 3 or len(positivePrompt) > 2000:
raise Exception("Positive Prompt Must Be Between 3 And 2000 characters!")
if negativePrompt is not None and negativePrompt.strip() != "":
if len(negativePrompt) < 3 or len(negativePrompt) > 2000:
raise Exception("Negative Prompt Must Be Between 3 And 2000 characters!")
return True
DESCRIPTION = "Generates Videos Lightning Fast With Runware Video Inference Engine."
@@ -203,6 +217,10 @@ class txt2vid:
frameImages = kwargs.get("frameImages", None)
referenceImages = kwargs.get("referenceImages", None)
inputAudios = kwargs.get("inputAudios", None)
referenceVideos = kwargs.get("referenceVideos", None)
speechVoice = kwargs.get("speechVoice", None)
speechText = kwargs.get("speechText", None)
inputs = kwargs.get("inputs", None)
useCustomDimensions = kwargs.get("useCustomDimensions", False)
customWidth = kwargs.get("width", 864)
customHeight = kwargs.get("height", 480)
@@ -235,7 +253,6 @@ class txt2vid:
{
"taskType": "videoInference",
"taskUUID": rwUtils.genRandUUID(),
"positivePrompt": positivePrompt,
"height": height,
"width": width,
"model": model,
@@ -245,6 +262,10 @@ class txt2vid:
}
]
# Only add positivePrompt if it's not empty
if positivePrompt and positivePrompt.strip() != "":
genConfig[0]["positivePrompt"] = positivePrompt
# Add fps parameter only if enabled
if useFps:
genConfig[0]["fps"] = fps
@@ -276,6 +297,37 @@ class txt2vid:
if inputAudios is not None and len(inputAudios) > 0:
genConfig[0]["inputAudios"] = inputAudios
print(f"[Debugging] Input audios array: {inputAudios}")
# Add referenceVideos if provided and not empty
if referenceVideos is not None:
# Handle both single mediaUUID (string) and multiple mediaUUIDs (list)
if isinstance(referenceVideos, str) and referenceVideos.strip() != "":
genConfig[0]["referenceVideos"] = [referenceVideos.strip()]
print(f"[Debugging] Reference videos: {genConfig[0].get('referenceVideos', [])}")
elif isinstance(referenceVideos, list) and len(referenceVideos) > 0:
genConfig[0]["referenceVideos"] = referenceVideos
print(f"[Debugging] Reference videos: {genConfig[0].get('referenceVideos', [])}")
# Add speech parameters if both voice and text are provided
if speechVoice and speechVoice.strip() != "" and speechText and speechText.strip() != "":
genConfig[0]["speech"] = {
"voice": speechVoice.strip(),
"text": speechText.strip()
}
print(f"[Debugging] Speech parameters: voice='{speechVoice.strip()}', text='{speechText.strip()[:50]}...'")
# Handle inputs - merge custom inputs from Video Inference Inputs node
if inputs is not None:
# Merge inputs from video inference inputs node
if "inputs" not in genConfig[0]:
genConfig[0]["inputs"] = {}
# Merge each input from inputs (only actual input data, not provider settings)
for key, value in inputs.items():
genConfig[0]["inputs"][key] = value
print(f"[Debugging] Video inference inputs merged: {inputs}")
# Handle providerSettings - extract provider name from model and merge with custom settings
if providerSettings is not None:
# Extract provider name from model (e.g., "pixverse:1@1" -> "pixverse")