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https://github.com/comfyanonymous/ComfyUI.git
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feat(api-nodes): add Vidu Video nodes (#9368)
This commit is contained in:
Alexander Piskun
GitHub
622
comfy_api_nodes/nodes_vidu.py
Normal file
622
comfy_api_nodes/nodes_vidu.py
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@@ -0,0 +1,622 @@
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import logging
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from enum import Enum
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from typing import Any, Callable, Optional, Literal, TypeVar
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from typing_extensions import override
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import torch
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from pydantic import BaseModel, Field
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from comfy_api.latest import ComfyExtension, io as comfy_io
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from comfy_api_nodes.util.validation_utils import (
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validate_aspect_ratio_closeness,
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validate_image_dimensions,
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validate_image_aspect_ratio_range,
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get_number_of_images,
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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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PollingOperation,
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EmptyRequest,
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)
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from comfy_api_nodes.apinode_utils import download_url_to_video_output, upload_images_to_comfyapi
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VIDU_TEXT_TO_VIDEO = "/proxy/vidu/text2video"
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VIDU_IMAGE_TO_VIDEO = "/proxy/vidu/img2video"
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VIDU_REFERENCE_VIDEO = "/proxy/vidu/reference2video"
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VIDU_START_END_VIDEO = "/proxy/vidu/start-end2video"
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VIDU_GET_GENERATION_STATUS = "/proxy/vidu/tasks/%s/creations"
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R = TypeVar("R")
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class VideoModelName(str, Enum):
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vidu_q1 = 'viduq1'
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class AspectRatio(str, Enum):
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r_16_9 = "16:9"
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r_9_16 = "9:16"
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r_1_1 = "1:1"
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class Resolution(str, Enum):
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r_1080p = "1080p"
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class MovementAmplitude(str, Enum):
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auto = "auto"
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small = "small"
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medium = "medium"
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large = "large"
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class TaskCreationRequest(BaseModel):
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model: VideoModelName = VideoModelName.vidu_q1
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prompt: Optional[str] = Field(None, max_length=1500)
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duration: Optional[Literal[5]] = 5
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seed: Optional[int] = Field(0, ge=0, le=2147483647)
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aspect_ratio: Optional[AspectRatio] = AspectRatio.r_16_9
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resolution: Optional[Resolution] = Resolution.r_1080p
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movement_amplitude: Optional[MovementAmplitude] = MovementAmplitude.auto
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images: Optional[list[str]] = Field(None, description="Base64 encoded string or image URL")
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class TaskStatus(str, Enum):
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created = "created"
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queueing = "queueing"
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processing = "processing"
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success = "success"
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failed = "failed"
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class TaskCreationResponse(BaseModel):
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task_id: str = Field(...)
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state: TaskStatus = Field(...)
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created_at: str = Field(...)
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code: Optional[int] = Field(None, description="Error code")
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class TaskResult(BaseModel):
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id: str = Field(..., description="Creation id")
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url: str = Field(..., description="The URL of the generated results, valid for one hour")
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cover_url: str = Field(..., description="The cover URL of the generated results, valid for one hour")
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class TaskStatusResponse(BaseModel):
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state: TaskStatus = Field(...)
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err_code: Optional[str] = Field(None)
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creations: list[TaskResult] = Field(..., description="Generated results")
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async def poll_until_finished(
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auth_kwargs: dict[str, str],
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api_endpoint: ApiEndpoint[Any, R],
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result_url_extractor: Optional[Callable[[R], str]] = None,
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estimated_duration: Optional[int] = None,
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node_id: Optional[str] = None,
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) -> R:
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return await PollingOperation(
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poll_endpoint=api_endpoint,
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completed_statuses=[TaskStatus.success.value],
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failed_statuses=[TaskStatus.failed.value],
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status_extractor=lambda response: response.state.value,
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auth_kwargs=auth_kwargs,
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result_url_extractor=result_url_extractor,
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estimated_duration=estimated_duration,
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node_id=node_id,
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poll_interval=16.0,
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max_poll_attempts=256,
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).execute()
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def get_video_url_from_response(response) -> Optional[str]:
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if response.creations:
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return response.creations[0].url
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return None
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def get_video_from_response(response) -> TaskResult:
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if not response.creations:
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error_msg = f"Vidu request does not contain results. State: {response.state}, Error Code: {response.err_code}"
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logging.info(error_msg)
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raise RuntimeError(error_msg)
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logging.info("Vidu task %s succeeded. Video URL: %s", response.creations[0].id, response.creations[0].url)
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return response.creations[0]
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async def execute_task(
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vidu_endpoint: str,
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auth_kwargs: Optional[dict[str, str]],
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payload: TaskCreationRequest,
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estimated_duration: int,
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node_id: str,
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) -> R:
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response = await SynchronousOperation(
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endpoint=ApiEndpoint(
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path=vidu_endpoint,
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method=HttpMethod.POST,
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request_model=TaskCreationRequest,
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response_model=TaskCreationResponse,
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),
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request=payload,
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auth_kwargs=auth_kwargs,
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).execute()
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if response.state == TaskStatus.failed:
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error_msg = f"Vidu request failed. Code: {response.code}"
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logging.error(error_msg)
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raise RuntimeError(error_msg)
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return await poll_until_finished(
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auth_kwargs,
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ApiEndpoint(
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path=VIDU_GET_GENERATION_STATUS % response.task_id,
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method=HttpMethod.GET,
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request_model=EmptyRequest,
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response_model=TaskStatusResponse,
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),
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result_url_extractor=get_video_url_from_response,
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estimated_duration=estimated_duration,
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node_id=node_id,
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)
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class ViduTextToVideoNode(comfy_io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return comfy_io.Schema(
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node_id="ViduTextToVideoNode",
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display_name="Vidu Text To Video Generation",
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category="api node/video/Vidu",
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description="Generate video from text prompt",
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inputs=[
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comfy_io.Combo.Input(
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"model",
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options=[model.value for model in VideoModelName],
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default=VideoModelName.vidu_q1.value,
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tooltip="Model name",
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),
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comfy_io.String.Input(
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"prompt",
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multiline=True,
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tooltip="A textual description for video generation",
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),
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comfy_io.Int.Input(
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"duration",
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default=5,
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min=5,
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max=5,
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step=1,
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display_mode=comfy_io.NumberDisplay.number,
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tooltip="Duration of the output video in seconds",
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optional=True,
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),
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comfy_io.Int.Input(
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"seed",
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default=0,
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min=0,
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max=2147483647,
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step=1,
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display_mode=comfy_io.NumberDisplay.number,
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control_after_generate=True,
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tooltip="Seed for video generation (0 for random)",
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optional=True,
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),
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comfy_io.Combo.Input(
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"aspect_ratio",
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options=[model.value for model in AspectRatio],
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default=AspectRatio.r_16_9.value,
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tooltip="The aspect ratio of the output video",
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optional=True,
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),
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comfy_io.Combo.Input(
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"resolution",
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options=[model.value for model in Resolution],
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default=Resolution.r_1080p.value,
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tooltip="Supported values may vary by model & duration",
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optional=True,
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),
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comfy_io.Combo.Input(
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"movement_amplitude",
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options=[model.value for model in MovementAmplitude],
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default=MovementAmplitude.auto.value,
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tooltip="The movement amplitude of objects in the frame",
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optional=True,
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),
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],
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outputs=[
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comfy_io.Video.Output(),
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],
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hidden=[
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comfy_io.Hidden.auth_token_comfy_org,
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comfy_io.Hidden.api_key_comfy_org,
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comfy_io.Hidden.unique_id,
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],
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is_api_node=True,
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)
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@classmethod
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async def execute(
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cls,
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model: str,
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prompt: str,
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duration: int,
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seed: int,
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aspect_ratio: str,
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resolution: str,
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movement_amplitude: str,
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) -> comfy_io.NodeOutput:
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if not prompt:
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raise ValueError("The prompt field is required and cannot be empty.")
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payload = TaskCreationRequest(
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model_name=model,
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prompt=prompt,
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duration=duration,
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seed=seed,
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aspect_ratio=aspect_ratio,
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resolution=resolution,
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movement_amplitude=movement_amplitude,
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)
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auth = {
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"auth_token": cls.hidden.auth_token_comfy_org,
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"comfy_api_key": cls.hidden.api_key_comfy_org,
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}
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results = await execute_task(VIDU_TEXT_TO_VIDEO, auth, payload, 320, cls.hidden.unique_id)
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return comfy_io.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url))
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class ViduImageToVideoNode(comfy_io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return comfy_io.Schema(
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node_id="ViduImageToVideoNode",
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display_name="Vidu Image To Video Generation",
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category="api node/video/Vidu",
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description="Generate video from image and optional prompt",
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inputs=[
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comfy_io.Combo.Input(
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"model",
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options=[model.value for model in VideoModelName],
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default=VideoModelName.vidu_q1.value,
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tooltip="Model name",
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),
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comfy_io.Image.Input(
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"image",
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tooltip="An image to be used as the start frame of the generated video",
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),
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comfy_io.String.Input(
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"prompt",
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multiline=True,
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default="",
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tooltip="A textual description for video generation",
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optional=True,
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),
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comfy_io.Int.Input(
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"duration",
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default=5,
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min=5,
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max=5,
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step=1,
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display_mode=comfy_io.NumberDisplay.number,
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tooltip="Duration of the output video in seconds",
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optional=True,
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),
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comfy_io.Int.Input(
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"seed",
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default=0,
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min=0,
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max=2147483647,
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step=1,
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display_mode=comfy_io.NumberDisplay.number,
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control_after_generate=True,
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tooltip="Seed for video generation (0 for random)",
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optional=True,
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),
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comfy_io.Combo.Input(
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"resolution",
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options=[model.value for model in Resolution],
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default=Resolution.r_1080p.value,
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tooltip="Supported values may vary by model & duration",
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optional=True,
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),
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comfy_io.Combo.Input(
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"movement_amplitude",
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options=[model.value for model in MovementAmplitude],
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default=MovementAmplitude.auto.value,
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tooltip="The movement amplitude of objects in the frame",
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optional=True,
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),
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],
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outputs=[
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comfy_io.Video.Output(),
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],
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hidden=[
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comfy_io.Hidden.auth_token_comfy_org,
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comfy_io.Hidden.api_key_comfy_org,
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comfy_io.Hidden.unique_id,
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],
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is_api_node=True,
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)
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@classmethod
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async def execute(
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cls,
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model: str,
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image: torch.Tensor,
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prompt: str,
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duration: int,
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seed: int,
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resolution: str,
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movement_amplitude: str,
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) -> comfy_io.NodeOutput:
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if get_number_of_images(image) > 1:
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raise ValueError("Only one input image is allowed.")
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validate_image_aspect_ratio_range(image, (1, 4), (4, 1))
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payload = TaskCreationRequest(
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model_name=model,
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prompt=prompt,
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duration=duration,
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seed=seed,
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resolution=resolution,
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movement_amplitude=movement_amplitude,
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)
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auth = {
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"auth_token": cls.hidden.auth_token_comfy_org,
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"comfy_api_key": cls.hidden.api_key_comfy_org,
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}
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payload.images = await upload_images_to_comfyapi(
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image,
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max_images=1,
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mime_type="image/png",
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auth_kwargs=auth,
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)
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results = await execute_task(VIDU_IMAGE_TO_VIDEO, auth, payload, 120, cls.hidden.unique_id)
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return comfy_io.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url))
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class ViduReferenceVideoNode(comfy_io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return comfy_io.Schema(
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node_id="ViduReferenceVideoNode",
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display_name="Vidu Reference To Video Generation",
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category="api node/video/Vidu",
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description="Generate video from multiple images and prompt",
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inputs=[
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comfy_io.Combo.Input(
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"model",
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options=[model.value for model in VideoModelName],
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default=VideoModelName.vidu_q1.value,
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tooltip="Model name",
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),
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comfy_io.Image.Input(
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"images",
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tooltip="Images to use as references to generate a video with consistent subjects (max 7 images).",
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),
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comfy_io.String.Input(
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"prompt",
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multiline=True,
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tooltip="A textual description for video generation",
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),
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comfy_io.Int.Input(
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"duration",
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default=5,
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min=5,
|
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max=5,
|
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step=1,
|
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display_mode=comfy_io.NumberDisplay.number,
|
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tooltip="Duration of the output video in seconds",
|
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optional=True,
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),
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comfy_io.Int.Input(
|
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"seed",
|
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default=0,
|
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min=0,
|
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max=2147483647,
|
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step=1,
|
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display_mode=comfy_io.NumberDisplay.number,
|
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control_after_generate=True,
|
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tooltip="Seed for video generation (0 for random)",
|
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optional=True,
|
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),
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comfy_io.Combo.Input(
|
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"aspect_ratio",
|
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options=[model.value for model in AspectRatio],
|
||||
default=AspectRatio.r_16_9.value,
|
||||
tooltip="The aspect ratio of the output video",
|
||||
optional=True,
|
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),
|
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comfy_io.Combo.Input(
|
||||
"resolution",
|
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options=[model.value for model in Resolution],
|
||||
default=Resolution.r_1080p.value,
|
||||
tooltip="Supported values may vary by model & duration",
|
||||
optional=True,
|
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),
|
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comfy_io.Combo.Input(
|
||||
"movement_amplitude",
|
||||
options=[model.value for model in MovementAmplitude],
|
||||
default=MovementAmplitude.auto.value,
|
||||
tooltip="The movement amplitude of objects in the frame",
|
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optional=True,
|
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),
|
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],
|
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outputs=[
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comfy_io.Video.Output(),
|
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],
|
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hidden=[
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comfy_io.Hidden.auth_token_comfy_org,
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comfy_io.Hidden.api_key_comfy_org,
|
||||
comfy_io.Hidden.unique_id,
|
||||
],
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||||
is_api_node=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
model: str,
|
||||
images: torch.Tensor,
|
||||
prompt: str,
|
||||
duration: int,
|
||||
seed: int,
|
||||
aspect_ratio: str,
|
||||
resolution: str,
|
||||
movement_amplitude: str,
|
||||
) -> comfy_io.NodeOutput:
|
||||
if not prompt:
|
||||
raise ValueError("The prompt field is required and cannot be empty.")
|
||||
a = get_number_of_images(images)
|
||||
if a > 7:
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raise ValueError("Too many images, maximum allowed is 7.")
|
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for image in images:
|
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validate_image_aspect_ratio_range(image, (1, 4), (4, 1))
|
||||
validate_image_dimensions(image, min_width=128, min_height=128)
|
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payload = TaskCreationRequest(
|
||||
model_name=model,
|
||||
prompt=prompt,
|
||||
duration=duration,
|
||||
seed=seed,
|
||||
aspect_ratio=aspect_ratio,
|
||||
resolution=resolution,
|
||||
movement_amplitude=movement_amplitude,
|
||||
)
|
||||
auth = {
|
||||
"auth_token": cls.hidden.auth_token_comfy_org,
|
||||
"comfy_api_key": cls.hidden.api_key_comfy_org,
|
||||
}
|
||||
payload.images = await upload_images_to_comfyapi(
|
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images,
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max_images=7,
|
||||
mime_type="image/png",
|
||||
auth_kwargs=auth,
|
||||
)
|
||||
results = await execute_task(VIDU_REFERENCE_VIDEO, auth, payload, 120, cls.hidden.unique_id)
|
||||
return comfy_io.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url))
|
||||
|
||||
|
||||
class ViduStartEndToVideoNode(comfy_io.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return comfy_io.Schema(
|
||||
node_id="ViduStartEndToVideoNode",
|
||||
display_name="Vidu Start End To Video Generation",
|
||||
category="api node/video/Vidu",
|
||||
description="Generate a video from start and end frames and a prompt",
|
||||
inputs=[
|
||||
comfy_io.Combo.Input(
|
||||
"model",
|
||||
options=[model.value for model in VideoModelName],
|
||||
default=VideoModelName.vidu_q1.value,
|
||||
tooltip="Model name",
|
||||
),
|
||||
comfy_io.Image.Input(
|
||||
"first_frame",
|
||||
tooltip="Start frame",
|
||||
),
|
||||
comfy_io.Image.Input(
|
||||
"end_frame",
|
||||
tooltip="End frame",
|
||||
),
|
||||
comfy_io.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
tooltip="A textual description for video generation",
|
||||
optional=True,
|
||||
),
|
||||
comfy_io.Int.Input(
|
||||
"duration",
|
||||
default=5,
|
||||
min=5,
|
||||
max=5,
|
||||
step=1,
|
||||
display_mode=comfy_io.NumberDisplay.number,
|
||||
tooltip="Duration of the output video in seconds",
|
||||
optional=True,
|
||||
),
|
||||
comfy_io.Int.Input(
|
||||
"seed",
|
||||
default=0,
|
||||
min=0,
|
||||
max=2147483647,
|
||||
step=1,
|
||||
display_mode=comfy_io.NumberDisplay.number,
|
||||
control_after_generate=True,
|
||||
tooltip="Seed for video generation (0 for random)",
|
||||
optional=True,
|
||||
),
|
||||
comfy_io.Combo.Input(
|
||||
"resolution",
|
||||
options=[model.value for model in Resolution],
|
||||
default=Resolution.r_1080p.value,
|
||||
tooltip="Supported values may vary by model & duration",
|
||||
optional=True,
|
||||
),
|
||||
comfy_io.Combo.Input(
|
||||
"movement_amplitude",
|
||||
options=[model.value for model in MovementAmplitude],
|
||||
default=MovementAmplitude.auto.value,
|
||||
tooltip="The movement amplitude of objects in the frame",
|
||||
optional=True,
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
comfy_io.Video.Output(),
|
||||
],
|
||||
hidden=[
|
||||
comfy_io.Hidden.auth_token_comfy_org,
|
||||
comfy_io.Hidden.api_key_comfy_org,
|
||||
comfy_io.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
model: str,
|
||||
first_frame: torch.Tensor,
|
||||
end_frame: torch.Tensor,
|
||||
prompt: str,
|
||||
duration: int,
|
||||
seed: int,
|
||||
resolution: str,
|
||||
movement_amplitude: str,
|
||||
) -> comfy_io.NodeOutput:
|
||||
validate_aspect_ratio_closeness(first_frame, end_frame, min_rel=0.8, max_rel=1.25, strict=False)
|
||||
payload = TaskCreationRequest(
|
||||
model_name=model,
|
||||
prompt=prompt,
|
||||
duration=duration,
|
||||
seed=seed,
|
||||
resolution=resolution,
|
||||
movement_amplitude=movement_amplitude,
|
||||
)
|
||||
auth = {
|
||||
"auth_token": cls.hidden.auth_token_comfy_org,
|
||||
"comfy_api_key": cls.hidden.api_key_comfy_org,
|
||||
}
|
||||
payload.images = [
|
||||
(await upload_images_to_comfyapi(frame, max_images=1, mime_type="image/png", auth_kwargs=auth))[0]
|
||||
for frame in (first_frame, end_frame)
|
||||
]
|
||||
results = await execute_task(VIDU_START_END_VIDEO, auth, payload, 96, cls.hidden.unique_id)
|
||||
return comfy_io.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url))
|
||||
|
||||
|
||||
class ViduExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
|
||||
return [
|
||||
ViduTextToVideoNode,
|
||||
ViduImageToVideoNode,
|
||||
ViduReferenceVideoNode,
|
||||
ViduStartEndToVideoNode,
|
||||
]
|
||||
|
||||
async def comfy_entrypoint() -> ViduExtension:
|
||||
return ViduExtension()
|
||||
@@ -53,6 +53,53 @@ def validate_image_aspect_ratio(
|
||||
)
|
||||
|
||||
|
||||
def validate_image_aspect_ratio_range(
|
||||
image: torch.Tensor,
|
||||
min_ratio: tuple[float, float], # e.g. (1, 4)
|
||||
max_ratio: tuple[float, float], # e.g. (4, 1)
|
||||
*,
|
||||
strict: bool = True, # True -> (min, max); False -> [min, max]
|
||||
) -> float:
|
||||
a1, b1 = min_ratio
|
||||
a2, b2 = max_ratio
|
||||
if a1 <= 0 or b1 <= 0 or a2 <= 0 or b2 <= 0:
|
||||
raise ValueError("Ratios must be positive, like (1, 4) or (4, 1).")
|
||||
lo, hi = (a1 / b1), (a2 / b2)
|
||||
if lo > hi:
|
||||
lo, hi = hi, lo
|
||||
a1, b1, a2, b2 = a2, b2, a1, b1 # swap only for error text
|
||||
w, h = get_image_dimensions(image)
|
||||
if w <= 0 or h <= 0:
|
||||
raise ValueError(f"Invalid image dimensions: {w}x{h}")
|
||||
ar = w / h
|
||||
ok = (lo < ar < hi) if strict else (lo <= ar <= hi)
|
||||
if not ok:
|
||||
op = "<" if strict else "≤"
|
||||
raise ValueError(f"Image aspect ratio {ar:.6g} is outside allowed range: {a1}:{b1} {op} ratio {op} {a2}:{b2}")
|
||||
return ar
|
||||
|
||||
|
||||
def validate_aspect_ratio_closeness(
|
||||
start_img,
|
||||
end_img,
|
||||
min_rel: float,
|
||||
max_rel: float,
|
||||
*,
|
||||
strict: bool = False, # True => exclusive, False => inclusive
|
||||
) -> None:
|
||||
w1, h1 = get_image_dimensions(start_img)
|
||||
w2, h2 = get_image_dimensions(end_img)
|
||||
if min(w1, h1, w2, h2) <= 0:
|
||||
raise ValueError("Invalid image dimensions")
|
||||
ar1 = w1 / h1
|
||||
ar2 = w2 / h2
|
||||
# Normalize so it is symmetric (no need to check both ar1/ar2 and ar2/ar1)
|
||||
closeness = max(ar1, ar2) / min(ar1, ar2)
|
||||
limit = max(max_rel, 1.0 / min_rel) # for 0.8..1.25 this is 1.25
|
||||
if (closeness >= limit) if strict else (closeness > limit):
|
||||
raise ValueError(f"Aspect ratios must be close: start/end={ar1/ar2:.4f}, allowed range {min_rel}–{max_rel}.")
|
||||
|
||||
|
||||
def validate_video_dimensions(
|
||||
video: VideoInput,
|
||||
min_width: Optional[int] = None,
|
||||
@@ -98,3 +145,9 @@ def validate_video_duration(
|
||||
raise ValueError(
|
||||
f"Video duration must be at most {max_duration}s, got {duration}s"
|
||||
)
|
||||
|
||||
|
||||
def get_number_of_images(images):
|
||||
if isinstance(images, torch.Tensor):
|
||||
return images.shape[0] if images.ndim >= 4 else 1
|
||||
return len(images)
|
||||
|
||||
Reference in New Issue
Block a user