Track bundled vendor runtime sources
This commit is contained in:
452
vendor/ComfyUI/comfy_api_nodes/nodes_minimax.py
vendored
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452
vendor/ComfyUI/comfy_api_nodes/nodes_minimax.py
vendored
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from typing import Optional
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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
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from comfy_api_nodes.apis.minimax import (
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MinimaxFileRetrieveResponse,
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MiniMaxModel,
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MinimaxTaskResultResponse,
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MinimaxVideoGenerationRequest,
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MinimaxVideoGenerationResponse,
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SubjectReferenceItem,
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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_video_output,
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poll_op,
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sync_op,
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upload_images_to_comfyapi,
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validate_string,
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)
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I2V_AVERAGE_DURATION = 114
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T2V_AVERAGE_DURATION = 234
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async def _generate_mm_video(
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cls: type[IO.ComfyNode],
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*,
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prompt_text: str,
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seed: int,
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model: str,
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image: Optional[torch.Tensor] = None, # used for ImageToVideo
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subject: Optional[torch.Tensor] = None, # used for SubjectToVideo
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average_duration: Optional[int] = None,
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) -> IO.NodeOutput:
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if image is None:
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validate_string(prompt_text, field_name="prompt_text")
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image_url = None
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if image is not None:
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image_url = (await upload_images_to_comfyapi(cls, image, max_images=1))[0]
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# TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model
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subject_reference = None
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if subject is not None:
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subject_url = (await upload_images_to_comfyapi(cls, subject, max_images=1))[0]
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subject_reference = [SubjectReferenceItem(image=subject_url)]
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response = await sync_op(
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cls,
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ApiEndpoint(path="/proxy/minimax/video_generation", method="POST"),
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response_model=MinimaxVideoGenerationResponse,
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data=MinimaxVideoGenerationRequest(
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model=MiniMaxModel(model),
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prompt=prompt_text,
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callback_url=None,
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first_frame_image=image_url,
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subject_reference=subject_reference,
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prompt_optimizer=None,
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),
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)
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task_id = response.task_id
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if not task_id:
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raise Exception(f"MiniMax generation failed: {response.base_resp}")
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task_result = await poll_op(
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cls,
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ApiEndpoint(path="/proxy/minimax/query/video_generation", query_params={"task_id": task_id}),
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response_model=MinimaxTaskResultResponse,
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status_extractor=lambda x: x.status.value,
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estimated_duration=average_duration,
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)
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file_id = task_result.file_id
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if file_id is None:
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raise Exception("Request was not successful. Missing file ID.")
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file_result = await sync_op(
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cls,
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ApiEndpoint(path="/proxy/minimax/files/retrieve", query_params={"file_id": int(file_id)}),
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response_model=MinimaxFileRetrieveResponse,
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)
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file_url = file_result.file.download_url
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if file_url is None:
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raise Exception(f"No video was found in the response. Full response: {file_result.model_dump()}")
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if file_result.file.backup_download_url:
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try:
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return IO.NodeOutput(await download_url_to_video_output(file_url, timeout=10, max_retries=2))
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except Exception: # if we have a second URL to retrieve the result, try again using that one
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return IO.NodeOutput(
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await download_url_to_video_output(file_result.file.backup_download_url, max_retries=3)
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)
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return IO.NodeOutput(await download_url_to_video_output(file_url))
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class MinimaxTextToVideoNode(IO.ComfyNode):
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@classmethod
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def define_schema(cls) -> IO.Schema:
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return IO.Schema(
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node_id="MinimaxTextToVideoNode",
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display_name="MiniMax Text to Video",
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category="partner/video/MiniMax",
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description="Generates videos synchronously based on a prompt, and optional parameters.",
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inputs=[
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IO.String.Input(
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"prompt_text",
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multiline=True,
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default="",
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tooltip="Text prompt to guide the video generation",
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),
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IO.Combo.Input(
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"model",
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options=["T2V-01", "T2V-01-Director"],
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default="T2V-01",
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tooltip="Model to use for video generation",
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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=0xFFFFFFFFFFFFFFFF,
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step=1,
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control_after_generate=True,
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tooltip="The random seed used for creating the noise.",
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optional=True,
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),
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],
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outputs=[IO.Video.Output()],
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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":0.43}""",
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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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prompt_text: str,
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model: str = "T2V-01",
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seed: int = 0,
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) -> IO.NodeOutput:
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return await _generate_mm_video(
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cls,
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prompt_text=prompt_text,
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seed=seed,
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model=model,
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image=None,
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subject=None,
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average_duration=T2V_AVERAGE_DURATION,
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)
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class MinimaxImageToVideoNode(IO.ComfyNode):
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@classmethod
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def define_schema(cls) -> IO.Schema:
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return IO.Schema(
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node_id="MinimaxImageToVideoNode",
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display_name="MiniMax Image to Video",
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category="partner/video/MiniMax",
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description="Generates videos synchronously based on an image and prompt, and optional parameters.",
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inputs=[
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IO.Image.Input(
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"image",
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tooltip="Image to use as first frame of video generation",
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),
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IO.String.Input(
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"prompt_text",
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multiline=True,
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default="",
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tooltip="Text prompt to guide the video generation",
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),
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IO.Combo.Input(
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"model",
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options=["I2V-01-Director", "I2V-01", "I2V-01-live"],
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default="I2V-01",
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tooltip="Model to use for video generation",
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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=0xFFFFFFFFFFFFFFFF,
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step=1,
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control_after_generate=True,
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tooltip="The random seed used for creating the noise.",
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optional=True,
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),
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],
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outputs=[IO.Video.Output()],
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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":0.43}""",
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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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image: torch.Tensor,
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prompt_text: str,
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model: str = "I2V-01",
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seed: int = 0,
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) -> IO.NodeOutput:
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return await _generate_mm_video(
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cls,
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prompt_text=prompt_text,
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seed=seed,
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model=model,
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image=image,
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subject=None,
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average_duration=I2V_AVERAGE_DURATION,
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)
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class MinimaxSubjectToVideoNode(IO.ComfyNode):
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@classmethod
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def define_schema(cls) -> IO.Schema:
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return IO.Schema(
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node_id="MinimaxSubjectToVideoNode",
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display_name="MiniMax Subject to Video",
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category="partner/video/MiniMax",
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description="Generates videos synchronously based on an image and prompt, and optional parameters.",
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inputs=[
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IO.Image.Input(
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"subject",
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tooltip="Image of subject to reference for video generation",
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),
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IO.String.Input(
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"prompt_text",
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multiline=True,
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default="",
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tooltip="Text prompt to guide the video generation",
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),
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IO.Combo.Input(
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"model",
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options=["S2V-01"],
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default="S2V-01",
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tooltip="Model to use for video generation",
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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=0xFFFFFFFFFFFFFFFF,
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step=1,
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control_after_generate=True,
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tooltip="The random seed used for creating the noise.",
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optional=True,
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),
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],
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outputs=[IO.Video.Output()],
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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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)
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@classmethod
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async def execute(
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cls,
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subject: torch.Tensor,
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prompt_text: str,
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model: str = "S2V-01",
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seed: int = 0,
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) -> IO.NodeOutput:
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return await _generate_mm_video(
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cls,
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prompt_text=prompt_text,
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seed=seed,
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model=model,
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image=None,
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subject=subject,
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average_duration=T2V_AVERAGE_DURATION,
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)
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class MinimaxHailuoVideoNode(IO.ComfyNode):
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@classmethod
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def define_schema(cls) -> IO.Schema:
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return IO.Schema(
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node_id="MinimaxHailuoVideoNode",
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display_name="MiniMax Hailuo Video",
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category="partner/video/MiniMax",
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description="Generates videos from prompt, with optional start frame using the new MiniMax Hailuo-02 model.",
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inputs=[
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IO.String.Input(
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"prompt_text",
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multiline=True,
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default="",
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tooltip="Text prompt to guide the video generation.",
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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=0xFFFFFFFFFFFFFFFF,
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step=1,
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control_after_generate=True,
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tooltip="The random seed used for creating the noise.",
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optional=True,
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),
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IO.Image.Input(
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"first_frame_image",
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tooltip="Optional image to use as the first frame to generate a video.",
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optional=True,
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),
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IO.Boolean.Input(
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"prompt_optimizer",
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default=True,
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tooltip="Optimize prompt to improve generation quality when needed.",
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optional=True,
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),
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IO.Combo.Input(
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"duration",
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options=[6, 10],
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default=6,
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tooltip="The length of the output video in seconds.",
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optional=True,
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),
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IO.Combo.Input(
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"resolution",
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options=["768P", "1080P"],
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default="768P",
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tooltip="The dimensions of the video display. 1080p is 1920x1080, 768p is 1366x768.",
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optional=True,
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),
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],
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outputs=[IO.Video.Output()],
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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=["resolution", "duration"]),
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expr="""
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(
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$prices := {
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"768p": {"6": 0.28, "10": 0.56},
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"1080p": {"6": 0.49}
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};
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$resPrices := $lookup($prices, $lowercase(widgets.resolution));
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$price := $lookup($resPrices, $string(widgets.duration));
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{"type":"usd","usd": $price ? $price : 0.43}
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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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prompt_text: str,
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seed: int = 0,
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first_frame_image: Optional[torch.Tensor] = None, # used for ImageToVideo
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prompt_optimizer: bool = True,
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duration: int = 6,
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resolution: str = "768P",
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model: str = "MiniMax-Hailuo-02",
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) -> IO.NodeOutput:
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if first_frame_image is None:
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validate_string(prompt_text, field_name="prompt_text")
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if model == "MiniMax-Hailuo-02" and resolution.upper() == "1080P" and duration != 6:
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raise Exception(
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"When model is MiniMax-Hailuo-02 and resolution is 1080P, duration is limited to 6 seconds."
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)
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# upload image, if passed in
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image_url = None
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if first_frame_image is not None:
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image_url = (await upload_images_to_comfyapi(cls, first_frame_image, max_images=1))[0]
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response = await sync_op(
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cls,
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ApiEndpoint(path="/proxy/minimax/video_generation", method="POST"),
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response_model=MinimaxVideoGenerationResponse,
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data=MinimaxVideoGenerationRequest(
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model=MiniMaxModel(model),
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prompt=prompt_text,
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callback_url=None,
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first_frame_image=image_url,
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prompt_optimizer=prompt_optimizer,
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duration=duration,
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resolution=resolution,
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),
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)
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task_id = response.task_id
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if not task_id:
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raise Exception(f"MiniMax generation failed: {response.base_resp}")
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average_duration = 120 if resolution == "768P" else 240
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task_result = await poll_op(
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cls,
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ApiEndpoint(path="/proxy/minimax/query/video_generation", query_params={"task_id": task_id}),
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response_model=MinimaxTaskResultResponse,
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status_extractor=lambda x: x.status.value,
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estimated_duration=average_duration,
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)
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file_id = task_result.file_id
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if file_id is None:
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raise Exception("Request was not successful. Missing file ID.")
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file_result = await sync_op(
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cls,
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ApiEndpoint(path="/proxy/minimax/files/retrieve", query_params={"file_id": int(file_id)}),
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response_model=MinimaxFileRetrieveResponse,
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)
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file_url = file_result.file.download_url
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if file_url is None:
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raise Exception(f"No video was found in the response. Full response: {file_result.model_dump()}")
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if file_result.file.backup_download_url:
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try:
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return IO.NodeOutput(await download_url_to_video_output(file_url, timeout=10, max_retries=2))
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except Exception: # if we have a second URL to retrieve the result, try again using that one
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return IO.NodeOutput(
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await download_url_to_video_output(file_result.file.backup_download_url, max_retries=3)
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)
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return IO.NodeOutput(await download_url_to_video_output(file_url))
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class MinimaxExtension(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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MinimaxTextToVideoNode,
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MinimaxImageToVideoNode,
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# MinimaxSubjectToVideoNode,
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MinimaxHailuoVideoNode,
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]
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async def comfy_entrypoint() -> MinimaxExtension:
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return MinimaxExtension()
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Reference in New Issue
Block a user