mirror of
https://github.com/comfyanonymous/ComfyUI.git
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feat(api-nodes): add Grok Imagine nodes (#12136)
This commit is contained in:
Alexander Piskun
GitHub
parent
4f5bd39b1c
commit
6c5f906bf2
67
comfy_api_nodes/apis/grok.py
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67
comfy_api_nodes/apis/grok.py
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from pydantic import BaseModel, Field
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class ImageGenerationRequest(BaseModel):
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model: str = Field(...)
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prompt: str = Field(...)
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aspect_ratio: str = Field(...)
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n: int = Field(...)
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seed: int = Field(...)
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response_for: str = Field("url")
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class InputUrlObject(BaseModel):
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url: str = Field(...)
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class ImageEditRequest(BaseModel):
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model: str = Field(...)
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image: InputUrlObject = Field(...)
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prompt: str = Field(...)
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resolution: str = Field(...)
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n: int = Field(...)
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seed: int = Field(...)
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response_for: str = Field("url")
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class VideoGenerationRequest(BaseModel):
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model: str = Field(...)
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prompt: str = Field(...)
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image: InputUrlObject | None = Field(...)
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duration: int = Field(...)
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aspect_ratio: str | None = Field(...)
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resolution: str = Field(...)
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seed: int = Field(...)
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class VideoEditRequest(BaseModel):
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model: str = Field(...)
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prompt: str = Field(...)
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video: InputUrlObject = Field(...)
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seed: int = Field(...)
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class ImageResponseObject(BaseModel):
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url: str | None = Field(None)
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b64_json: str | None = Field(None)
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revised_prompt: str | None = Field(None)
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class ImageGenerationResponse(BaseModel):
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data: list[ImageResponseObject] = Field(...)
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class VideoGenerationResponse(BaseModel):
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request_id: str = Field(...)
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class VideoResponseObject(BaseModel):
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url: str = Field(...)
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upsampled_prompt: str | None = Field(None)
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duration: int = Field(...)
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class VideoStatusResponse(BaseModel):
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status: str | None = Field(None)
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video: VideoResponseObject | None = Field(None)
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model: str | None = Field(None)
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417
comfy_api_nodes/nodes_grok.py
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417
comfy_api_nodes/nodes_grok.py
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import torch
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from typing_extensions import override
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from comfy_api.latest import IO, ComfyExtension, Input
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from comfy_api_nodes.apis.grok import (
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ImageEditRequest,
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ImageGenerationRequest,
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ImageGenerationResponse,
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InputUrlObject,
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VideoEditRequest,
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VideoGenerationRequest,
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VideoGenerationResponse,
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VideoStatusResponse,
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)
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from comfy_api_nodes.util import (
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ApiEndpoint,
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download_url_to_image_tensor,
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download_url_to_video_output,
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get_fs_object_size,
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get_number_of_images,
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poll_op,
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sync_op,
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tensor_to_base64_string,
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upload_video_to_comfyapi,
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validate_string,
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validate_video_duration,
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)
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class GrokImageNode(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="GrokImageNode",
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display_name="Grok Image",
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category="api node/image/Grok",
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description="Generate images using Grok based on a text prompt",
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inputs=[
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IO.Combo.Input("model", options=["grok-imagine-image-beta"]),
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IO.String.Input(
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"prompt",
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multiline=True,
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tooltip="The text prompt used to generate the image",
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),
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IO.Combo.Input(
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"aspect_ratio",
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options=[
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"1:1",
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"2:3",
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"3:2",
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"3:4",
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"4:3",
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"9:16",
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"16:9",
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"9:19.5",
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"19.5:9",
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"9:20",
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"20:9",
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"1:2",
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"2:1",
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],
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),
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IO.Int.Input(
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"number_of_images",
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default=1,
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min=1,
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max=10,
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step=1,
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tooltip="Number of images to generate",
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display_mode=IO.NumberDisplay.number,
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),
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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=IO.NumberDisplay.number,
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control_after_generate=True,
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tooltip="Seed to determine if node should re-run; "
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"actual results are nondeterministic regardless of seed.",
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),
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],
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outputs=[
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IO.Image.Output(),
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],
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hidden=[
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IO.Hidden.auth_token_comfy_org,
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IO.Hidden.api_key_comfy_org,
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IO.Hidden.unique_id,
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],
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is_api_node=True,
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price_badge=IO.PriceBadge(
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depends_on=IO.PriceBadgeDepends(widgets=["number_of_images"]),
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expr="""{"type":"usd","usd":0.033 * widgets.number_of_images}""",
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),
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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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aspect_ratio: str,
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number_of_images: int,
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seed: int,
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) -> IO.NodeOutput:
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validate_string(prompt, strip_whitespace=True, min_length=1)
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response = await sync_op(
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cls,
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ApiEndpoint(path="/proxy/xai/v1/images/generations", method="POST"),
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data=ImageGenerationRequest(
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model=model,
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prompt=prompt,
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aspect_ratio=aspect_ratio,
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n=number_of_images,
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seed=seed,
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),
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response_model=ImageGenerationResponse,
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)
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if len(response.data) == 1:
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return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
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return IO.NodeOutput(
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torch.cat(
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[await download_url_to_image_tensor(i) for i in [str(d.url) for d in response.data if d.url]],
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)
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)
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class GrokImageEditNode(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="GrokImageEditNode",
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display_name="Grok Image Edit",
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category="api node/image/Grok",
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description="Modify an existing image based on a text prompt",
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inputs=[
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IO.Combo.Input("model", options=["grok-imagine-image-beta"]),
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IO.Image.Input("image"),
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IO.String.Input(
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"prompt",
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multiline=True,
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tooltip="The text prompt used to generate the image",
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),
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IO.Combo.Input("resolution", options=["1K"]),
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IO.Int.Input(
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"number_of_images",
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default=1,
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min=1,
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max=10,
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step=1,
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tooltip="Number of edited images to generate",
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display_mode=IO.NumberDisplay.number,
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),
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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=IO.NumberDisplay.number,
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control_after_generate=True,
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tooltip="Seed to determine if node should re-run; "
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"actual results are nondeterministic regardless of seed.",
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),
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],
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outputs=[
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IO.Image.Output(),
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],
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hidden=[
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IO.Hidden.auth_token_comfy_org,
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IO.Hidden.api_key_comfy_org,
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IO.Hidden.unique_id,
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],
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is_api_node=True,
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price_badge=IO.PriceBadge(
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depends_on=IO.PriceBadgeDepends(widgets=["number_of_images"]),
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expr="""{"type":"usd","usd":0.002 + 0.033 * widgets.number_of_images}""",
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),
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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: Input.Image,
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prompt: str,
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resolution: str,
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number_of_images: int,
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seed: int,
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) -> IO.NodeOutput:
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validate_string(prompt, strip_whitespace=True, min_length=1)
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if get_number_of_images(image) != 1:
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raise ValueError("Only one input image is supported.")
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response = await sync_op(
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cls,
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ApiEndpoint(path="/proxy/xai/v1/images/edits", method="POST"),
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data=ImageEditRequest(
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model=model,
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image=InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(image)}"),
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prompt=prompt,
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resolution=resolution.lower(),
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n=number_of_images,
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seed=seed,
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),
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response_model=ImageGenerationResponse,
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)
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if len(response.data) == 1:
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return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
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return IO.NodeOutput(
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torch.cat(
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[await download_url_to_image_tensor(i) for i in [str(d.url) for d in response.data if d.url]],
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)
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)
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class GrokVideoNode(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="GrokVideoNode",
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display_name="Grok Video",
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category="api node/video/Grok",
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description="Generate video from a prompt or an image",
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inputs=[
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IO.Combo.Input("model", options=["grok-imagine-video-beta"]),
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IO.String.Input(
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"prompt",
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multiline=True,
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tooltip="Text description of the desired video.",
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),
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IO.Combo.Input(
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"resolution",
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options=["480p", "720p"],
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tooltip="The resolution of the output video.",
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),
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IO.Combo.Input(
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"aspect_ratio",
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options=["auto", "16:9", "4:3", "3:2", "1:1", "2:3", "3:4", "9:16"],
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tooltip="The aspect ratio of the output video.",
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),
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IO.Int.Input(
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"duration",
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default=6,
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min=1,
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max=15,
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step=1,
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tooltip="The duration of the output video in seconds.",
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display_mode=IO.NumberDisplay.slider,
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),
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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=IO.NumberDisplay.number,
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control_after_generate=True,
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tooltip="Seed to determine if node should re-run; "
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"actual results are nondeterministic regardless of seed.",
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),
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IO.Image.Input("image", optional=True),
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],
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outputs=[
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IO.Video.Output(),
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],
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hidden=[
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IO.Hidden.auth_token_comfy_org,
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IO.Hidden.api_key_comfy_org,
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IO.Hidden.unique_id,
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],
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is_api_node=True,
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price_badge=IO.PriceBadge(
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depends_on=IO.PriceBadgeDepends(widgets=["duration"], inputs=["image"]),
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expr="""
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(
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$base := 0.181 * widgets.duration;
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{"type":"usd","usd": inputs.image.connected ? $base + 0.002 : $base}
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)
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""",
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),
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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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resolution: str,
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aspect_ratio: str,
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duration: int,
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seed: int,
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image: Input.Image | None = None,
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) -> IO.NodeOutput:
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image_url = None
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if image is not None:
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if get_number_of_images(image) != 1:
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raise ValueError("Only one input image is supported.")
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image_url = InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(image)}")
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validate_string(prompt, strip_whitespace=True, min_length=1)
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initial_response = await sync_op(
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cls,
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ApiEndpoint(path="/proxy/xai/v1/videos/generations", method="POST"),
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data=VideoGenerationRequest(
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model=model,
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image=image_url,
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prompt=prompt,
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resolution=resolution,
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duration=duration,
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aspect_ratio=None if aspect_ratio == "auto" else aspect_ratio,
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seed=seed,
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),
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response_model=VideoGenerationResponse,
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)
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response = await poll_op(
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cls,
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ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
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status_extractor=lambda r: r.status if r.status is not None else "complete",
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response_model=VideoStatusResponse,
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)
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return IO.NodeOutput(await download_url_to_video_output(response.video.url))
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class GrokVideoEditNode(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="GrokVideoEditNode",
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display_name="Grok Video Edit",
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category="api node/video/Grok",
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description="Edit an existing video based on a text prompt.",
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inputs=[
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IO.Combo.Input("model", options=["grok-imagine-video-beta"]),
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IO.String.Input(
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"prompt",
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multiline=True,
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tooltip="Text description of the desired video.",
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),
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IO.Video.Input("video", tooltip="Maximum supported duration is 8.7 seconds and 50MB file size."),
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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=IO.NumberDisplay.number,
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control_after_generate=True,
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tooltip="Seed to determine if node should re-run; "
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"actual results are nondeterministic regardless of seed.",
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),
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],
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outputs=[
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IO.Video.Output(),
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],
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hidden=[
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IO.Hidden.auth_token_comfy_org,
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IO.Hidden.api_key_comfy_org,
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IO.Hidden.unique_id,
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],
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is_api_node=True,
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price_badge=IO.PriceBadge(
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expr="""{"type":"usd","usd": 0.191, "format": {"suffix": "/sec", "approximate": true}}""",
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),
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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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video: Input.Video,
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seed: int,
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) -> IO.NodeOutput:
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validate_string(prompt, strip_whitespace=True, min_length=1)
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validate_video_duration(video, min_duration=1, max_duration=8.7)
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video_stream = video.get_stream_source()
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video_size = get_fs_object_size(video_stream)
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if video_size > 50 * 1024 * 1024:
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raise ValueError(f"Video size ({video_size / 1024 / 1024:.1f}MB) exceeds 50MB limit.")
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initial_response = await sync_op(
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cls,
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ApiEndpoint(path="/proxy/xai/v1/videos/edits", method="POST"),
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data=VideoEditRequest(
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model=model,
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video=InputUrlObject(url=await upload_video_to_comfyapi(cls, video)),
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prompt=prompt,
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seed=seed,
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),
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response_model=VideoGenerationResponse,
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)
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response = await poll_op(
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cls,
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ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
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status_extractor=lambda r: r.status if r.status is not None else "complete",
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response_model=VideoStatusResponse,
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)
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return IO.NodeOutput(await download_url_to_video_output(response.video.url))
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class GrokExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[IO.ComfyNode]]:
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return [
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GrokImageNode,
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GrokImageEditNode,
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GrokVideoNode,
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GrokVideoEditNode,
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]
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async def comfy_entrypoint() -> GrokExtension:
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return GrokExtension()
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