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Google Veo2 Video - ComfyUI Native Node Documentation
Google Veo2 Video - ComfyUI Native Node Documentation
A node that generates videos from text descriptions using Google’s Veo2 technology
The Google Veo2 Video node generates high-quality videos from text descriptions using Google’s Veo2 API technology, converting text prompts into dynamic video content.
Parameters
Basic Parameters
Parameter | Type | Default | Description |
---|---|---|---|
prompt | string | "" | Text description of the video content to generate |
aspect_ratio | select | ”16:9” | Output video aspect ratio, “16:9” or “9:16” |
negative_prompt | string | "" | Text describing what to avoid in the video |
duration_seconds | integer | 5 | Video duration, 5-8 seconds |
enhance_prompt | boolean | True | Whether to use AI to enhance the prompt |
person_generation | select | ”ALLOW” | Allow or block person generation, “ALLOW” or “BLOCK” |
seed | integer | 0 | Random seed, 0 means randomly generated |
Optional Parameters
Parameter | Type | Default | Description |
---|---|---|---|
image | image | None | Optional reference image to guide video creation |
Output
Output | Type | Description |
---|---|---|
VIDEO | video | Generated video |
Source Code
[Node Source Code (Updated 2025-05-03)]
class VeoVideoGenerationNode(ComfyNodeABC):
"""
Generates videos from text prompts using Google's Veo API.
This node can create videos from text descriptions and optional image inputs,
with control over parameters like aspect ratio, duration, and more.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "Text description of the video",
},
),
"aspect_ratio": (
IO.COMBO,
{
"options": ["16:9", "9:16"],
"default": "16:9",
"tooltip": "Aspect ratio of the output video",
},
),
},
"optional": {
"negative_prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "Negative text prompt to guide what to avoid in the video",
},
),
"duration_seconds": (
IO.INT,
{
"default": 5,
"min": 5,
"max": 8,
"step": 1,
"display": "number",
"tooltip": "Duration of the output video in seconds",
},
),
"enhance_prompt": (
IO.BOOLEAN,
{
"default": True,
"tooltip": "Whether to enhance the prompt with AI assistance",
}
),
"person_generation": (
IO.COMBO,
{
"options": ["ALLOW", "BLOCK"],
"default": "ALLOW",
"tooltip": "Whether to allow generating people in the video",
},
),
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 0xFFFFFFFF,
"step": 1,
"display": "number",
"control_after_generate": True,
"tooltip": "Seed for video generation (0 for random)",
},
),
"image": (IO.IMAGE, {
"default": None,
"tooltip": "Optional reference image to guide video generation",
}),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
},
}
RETURN_TYPES = (IO.VIDEO,)
FUNCTION = "generate_video"
CATEGORY = "api node/video/Veo"
DESCRIPTION = "Generates videos from text prompts using Google's Veo API"
API_NODE = True
def generate_video(
self,
prompt,
aspect_ratio="16:9",
negative_prompt="",
duration_seconds=5,
enhance_prompt=True,
person_generation="ALLOW",
seed=0,
image=None,
auth_token=None,
):
# Prepare the instances for the request
instances = []
instance = {
"prompt": prompt
}
# Add image if provided
if image is not None:
image_base64 = convert_image_to_base64(image)
if image_base64:
instance["image"] = {
"bytesBase64Encoded": image_base64,
"mimeType": "image/png"
}
instances.append(instance)
# Create parameters dictionary
parameters = {
"aspectRatio": aspect_ratio,
"personGeneration": person_generation,
"durationSeconds": duration_seconds,
"enhancePrompt": enhance_prompt,
}
# Add optional parameters if provided
if negative_prompt:
parameters["negativePrompt"] = negative_prompt
if seed > 0:
parameters["seed"] = seed
# Initial request to start video generation
initial_operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/veo/generate",
method=HttpMethod.POST,
request_model=Veo2GenVidRequest,
response_model=Veo2GenVidResponse
),
request=Veo2GenVidRequest(
instances=instances,
parameters=parameters
),
auth_token=auth_token
)
initial_response = initial_operation.execute()
operation_name = initial_response.name
logging.info(f"Veo generation started with operation name: {operation_name}")
# Define status extractor function
def status_extractor(response):
# Only return "completed" if the operation is done, regardless of success or failure
# We'll check for errors after polling completes
return "completed" if response.done else "pending"
# Define progress extractor function
def progress_extractor(response):
# Could be enhanced if the API provides progress information
return None
# Define the polling operation
poll_operation = PollingOperation(
poll_endpoint=ApiEndpoint(
path="/proxy/veo/poll",
method=HttpMethod.POST,
request_model=Veo2GenVidPollRequest,
response_model=Veo2GenVidPollResponse
),
completed_statuses=["completed"],
failed_statuses=[], # No failed statuses, we'll handle errors after polling
status_extractor=status_extractor,
progress_extractor=progress_extractor,
request=Veo2GenVidPollRequest(
operationName=operation_name
),
auth_token=auth_token,
poll_interval=5.0
)
# Execute the polling operation
poll_response = poll_operation.execute()
# Now check for errors in the final response
# Check for error in poll response
if hasattr(poll_response, 'error') and poll_response.error:
error_message = f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})"
logging.error(error_message)
raise Exception(error_message)
# Check for RAI filtered content
if (hasattr(poll_response.response, 'raiMediaFilteredCount') and
poll_response.response.raiMediaFilteredCount > 0):
# Extract reason message if available
if (hasattr(poll_response.response, 'raiMediaFilteredReasons') and
poll_response.response.raiMediaFilteredReasons):
reason = poll_response.response.raiMediaFilteredReasons[0]
error_message = f"Content filtered by Google's Responsible AI practices: {reason} ({poll_response.response.raiMediaFilteredCount} videos filtered.)"
else:
error_message = f"Content filtered by Google's Responsible AI practices ({poll_response.response.raiMediaFilteredCount} videos filtered.)"
logging.error(error_message)
raise Exception(error_message)
# Extract video data
video_data = None
if poll_response.response and hasattr(poll_response.response, 'videos') and poll_response.response.videos and len(poll_response.response.videos) > 0:
video = poll_response.response.videos[0]
# Check if video is provided as base64 or URL
if hasattr(video, 'bytesBase64Encoded') and video.bytesBase64Encoded:
# Decode base64 string to bytes
video_data = base64.b64decode(video.bytesBase64Encoded)
elif hasattr(video, 'gcsUri') and video.gcsUri:
# Download from URL
video_url = video.gcsUri
video_response = requests.get(video_url)
video_data = video_response.content
else:
raise Exception("Video returned but no data or URL was provided")
else:
raise Exception("Video generation completed but no video was returned")
if not video_data:
raise Exception("No video data was returned")
logging.info("Video generation completed successfully")
# Convert video data to BytesIO object
video_io = io.BytesIO(video_data)
# Return VideoFromFile object
return (VideoFromFile(video_io),)