mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-06 20:37:07 +08:00
295 lines
10 KiB
Python
295 lines
10 KiB
Python
import io
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import logging
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import base64
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import requests
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import math
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import torch
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import numpy as np
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from PIL import Image
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from comfy.comfy_types.node_typing import IO, ComfyNodeABC
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from comfy.utils import common_upscale
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from comfy_api.input_impl.video_types import VideoFromFile
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from comfy_api_nodes.apis import (
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Veo2GenVidRequest,
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Veo2GenVidResponse,
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Veo2GenVidPollRequest,
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Veo2GenVidPollResponse
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)
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from comfy_api_nodes.apis.client import (
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ApiEndpoint,
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HttpMethod,
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SynchronousOperation,
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)
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def downscale_input(image, total_pixels=1536*1024):
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samples = image.movedim(-1,1)
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# Downscaling input images to roughly the same size as the outputs
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total = int(total_pixels)
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scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
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if scale_by >= 1:
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return image
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width = round(samples.shape[3] * scale_by)
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height = round(samples.shape[2] * scale_by)
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s = common_upscale(samples, width, height, "lanczos", "disabled")
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s = s.movedim(1,-1)
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return s
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class VeoVideoGenerationNode(ComfyNodeABC):
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"""
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Generates videos from text prompts using Google's Veo API.
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This node can create videos from text descriptions and optional image inputs,
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with control over parameters like aspect ratio, duration, and more.
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"""
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"prompt": (
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IO.STRING,
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{
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"multiline": True,
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"default": "",
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"tooltip": "Text description of the video",
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},
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),
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"aspect_ratio": (
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IO.COMBO,
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{
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"options": ["16:9", "9:16"],
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"default": "16:9",
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"tooltip": "Aspect ratio of the output video",
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},
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),
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},
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"optional": {
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"negative_prompt": (
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IO.STRING,
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{
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"multiline": True,
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"default": "",
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"tooltip": "Negative text prompt to guide what to avoid in the video",
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},
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),
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"duration_seconds": (
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IO.INT,
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{
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"default": 5,
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"min": 5,
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"max": 8,
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"step": 1,
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"display": "number",
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"tooltip": "Duration of the output video in seconds",
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},
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),
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"enhance_prompt": (
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IO.BOOLEAN,
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{
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"default": True,
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"tooltip": "Whether to enhance the prompt with AI assistance",
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}
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),
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"person_generation": (
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IO.COMBO,
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{
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"options": ["ALLOW", "BLOCK"],
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"default": "ALLOW",
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"tooltip": "Whether to allow generating people in the video",
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},
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),
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"seed": (
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IO.INT,
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{
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"default": 0,
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"min": 0,
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"max": 0xFFFFFFFF,
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"step": 1,
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"display": "number",
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"tooltip": "Seed for video generation (0 for random)",
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},
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),
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"image": (IO.IMAGE, {
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"default": None,
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"tooltip": "Optional reference image to guide video generation",
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}),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
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},
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}
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RETURN_TYPES = (IO.VIDEO,)
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FUNCTION = "generate_video"
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CATEGORY = "api node/video/Veo"
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DESCRIPTION = "Generates videos from text prompts using Google's Veo API"
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API_NODE = True
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def _convert_image_to_base64(self, image: torch.Tensor):
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if image is None:
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return None
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scaled_image = downscale_input(image, total_pixels=2048*2048)
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# Remove batch dimension if present
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if len(scaled_image.shape) > 3:
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scaled_image = scaled_image[0]
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# Convert to numpy array and then to PIL Image
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image_np = (scaled_image.numpy() * 255).astype(np.uint8)
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img = Image.fromarray(image_np)
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# Convert to base64
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buffer = io.BytesIO()
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img.save(buffer, format="PNG")
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return base64.b64encode(buffer.getvalue()).decode('utf-8')
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def generate_video(
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self,
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prompt,
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aspect_ratio="16:9",
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negative_prompt="",
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duration_seconds=5,
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enhance_prompt=True,
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person_generation="ALLOW",
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seed=0,
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image=None,
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auth_token=None,
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):
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# Prepare the instances for the request
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instances = []
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instance = {
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"prompt": prompt
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}
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# Add image if provided
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if image is not None:
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image_base64 = self._convert_image_to_base64(image)
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if image_base64:
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instance["image"] = {
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"bytesBase64Encoded": image_base64,
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"mimeType": "image/png"
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}
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instances.append(instance)
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# Create parameters dictionary
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parameters = {
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"aspectRatio": aspect_ratio,
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"personGeneration": person_generation,
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"durationSeconds": duration_seconds,
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"enhancePrompt": enhance_prompt,
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}
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# Add optional parameters if provided
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if negative_prompt:
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parameters["negativePrompt"] = negative_prompt
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if seed > 0:
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parameters["seed"] = seed
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# Initial request to start video generation
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initial_operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/proxy/veo/generate",
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method=HttpMethod.POST,
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request_model=Veo2GenVidRequest,
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response_model=Veo2GenVidResponse
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),
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request=Veo2GenVidRequest(
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instances=instances,
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parameters=parameters
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),
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auth_token=auth_token
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)
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initial_response = initial_operation.execute()
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operation_name = initial_response.name
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logging.info(f"Veo generation started with operation name: {operation_name}")
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# Poll until operation is complete
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video_data = None
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while True:
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poll_operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/proxy/veo/poll",
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method=HttpMethod.POST,
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request_model=Veo2GenVidPollRequest,
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response_model=Veo2GenVidPollResponse
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),
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request=Veo2GenVidPollRequest(
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operationName=operation_name
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),
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auth_token=auth_token
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)
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poll_response = poll_operation.execute()
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# Check for error in poll response
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if hasattr(poll_response, 'error') and poll_response.error:
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error_message = f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})"
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logging.error(error_message)
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raise Exception(error_message)
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if poll_response.done:
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# Check for RAI filtered content
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if (hasattr(poll_response.response, 'raiMediaFilteredCount') and
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poll_response.response.raiMediaFilteredCount > 0):
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# Extract reason message if available
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if (hasattr(poll_response.response, 'raiMediaFilteredReasons') and
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poll_response.response.raiMediaFilteredReasons):
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reason = poll_response.response.raiMediaFilteredReasons[0]
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error_message = f"Content filtered by Google's Responsible AI practices: {reason} ({poll_response.response.raiMediaFilteredCount} videos filtered.)"
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logging.error(error_message)
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raise Exception(error_message)
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# Process successful response
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if poll_response.response and hasattr(poll_response.response, 'videos') and poll_response.response.videos and len(poll_response.response.videos) > 0:
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video = poll_response.response.videos[0]
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# Check if video is provided as base64 or URL
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if hasattr(video, 'bytesBase64Encoded') and video.bytesBase64Encoded:
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# Decode base64 string to bytes
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video_data = base64.b64decode(video.bytesBase64Encoded)
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break
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elif hasattr(video, 'gcsUri') and video.gcsUri:
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# Download from URL
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video_url = video.gcsUri
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video_response = requests.get(video_url)
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video_data = video_response.content
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break
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else:
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raise Exception("Video returned but no data or URL was provided")
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else:
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raise Exception("Video generation completed but no video was returned")
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# Wait before polling again
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import time
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time.sleep(5)
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if not video_data:
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raise Exception("No video data was returned")
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logging.info("Video generation completed successfully")
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# Convert video data to BytesIO object
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video_io = io.BytesIO(video_data)
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# Return VideoFromFile object
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return (VideoFromFile(video_io),)
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# Register the node
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NODE_CLASS_MAPPINGS = {
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"VeoVideoGenerationNode": VeoVideoGenerationNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"VeoVideoGenerationNode": "Google Veo2 Video Generation",
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}
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