mirror of
https://github.com/comfyanonymous/ComfyUI.git
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98ff01e148
* [Luma] Print download URL of successful task result directly on nodes (#177) [Veo] Print download URL of successful task result directly on nodes (#184) [Recraft] Print download URL of successful task result directly on nodes (#183) [Pixverse] Print download URL of successful task result directly on nodes (#182) [Kling] Print download URL of successful task result directly on nodes (#181) [MiniMax] Print progress text and download URL of successful task result directly on nodes (#179) [Docs] Link to docs in `API_NODE` class property type annotation comment (#178) [Ideogram] Print download URL of successful task result directly on nodes (#176) [Kling] Print download URL of successful task result directly on nodes (#181) [Veo] Print download URL of successful task result directly on nodes (#184) [Recraft] Print download URL of successful task result directly on nodes (#183) [Pixverse] Print download URL of successful task result directly on nodes (#182) [MiniMax] Print progress text and download URL of successful task result directly on nodes (#179) [Docs] Link to docs in `API_NODE` class property type annotation comment (#178) [Luma] Print download URL of successful task result directly on nodes (#177) [Ideogram] Print download URL of successful task result directly on nodes (#176) Show output URL and progress text on Pika nodes (#168) [BFL] Print download URL of successful task result directly on nodes (#175) [OpenAI ] Print download URL of successful task result directly on nodes (#174) * fix ruff errors * fix 3.10 syntax error
780 lines
26 KiB
Python
780 lines
26 KiB
Python
"""
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Pika x ComfyUI API Nodes
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Pika API docs: https://pika-827374fb.mintlify.app/api-reference
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"""
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from __future__ import annotations
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import io
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from typing import Optional, TypeVar
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import logging
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import torch
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import numpy as np
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from comfy_api_nodes.apis import (
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PikaBodyGenerate22T2vGenerate22T2vPost,
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PikaGenerateResponse,
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PikaBodyGenerate22I2vGenerate22I2vPost,
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PikaVideoResponse,
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PikaBodyGenerate22C2vGenerate22PikascenesPost,
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IngredientsMode,
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PikaDurationEnum,
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PikaResolutionEnum,
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PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
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PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
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PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
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PikaBodyGenerate22KeyframeGenerate22PikaframesPost,
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Pikaffect,
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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 (
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tensor_to_bytesio,
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download_url_to_video_output,
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)
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from comfy_api_nodes.mapper_utils import model_field_to_node_input
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from comfy_api.input_impl.video_types import VideoInput, VideoContainer, VideoCodec
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from comfy_api.input_impl import VideoFromFile
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from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions
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R = TypeVar("R")
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PATH_PIKADDITIONS = "/proxy/pika/generate/pikadditions"
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PATH_PIKASWAPS = "/proxy/pika/generate/pikaswaps"
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PATH_PIKAFFECTS = "/proxy/pika/generate/pikaffects"
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PIKA_API_VERSION = "2.2"
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PATH_TEXT_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/t2v"
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PATH_IMAGE_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/i2v"
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PATH_PIKAFRAMES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikaframes"
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PATH_PIKASCENES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikascenes"
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PATH_VIDEO_GET = "/proxy/pika/videos"
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class PikaApiError(Exception):
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"""Exception for Pika API errors."""
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pass
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def is_valid_video_response(response: PikaVideoResponse) -> bool:
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"""Check if the video response is valid."""
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return hasattr(response, "url") and response.url is not None
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def is_valid_initial_response(response: PikaGenerateResponse) -> bool:
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"""Check if the initial response is valid."""
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return hasattr(response, "video_id") and response.video_id is not None
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class PikaNodeBase(ComfyNodeABC):
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"""Base class for Pika nodes."""
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@classmethod
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def get_base_inputs_types(
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cls, request_model
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) -> dict[str, tuple[IO, InputTypeOptions]]:
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"""Get the base required inputs types common to all Pika nodes."""
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return {
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"prompt_text": model_field_to_node_input(
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IO.STRING,
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request_model,
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"promptText",
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multiline=True,
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),
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"negative_prompt": model_field_to_node_input(
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IO.STRING,
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request_model,
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"negativePrompt",
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multiline=True,
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),
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"seed": model_field_to_node_input(
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IO.INT,
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request_model,
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"seed",
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min=0,
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max=0xFFFFFFFF,
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control_after_generate=True,
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),
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"resolution": model_field_to_node_input(
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IO.COMBO,
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request_model,
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"resolution",
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enum_type=PikaResolutionEnum,
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),
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"duration": model_field_to_node_input(
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IO.COMBO,
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request_model,
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"duration",
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enum_type=PikaDurationEnum,
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),
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}
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CATEGORY = "api node/video/Pika"
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API_NODE = True
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FUNCTION = "api_call"
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RETURN_TYPES = ("VIDEO",)
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def poll_for_task_status(
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self,
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task_id: str,
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auth_kwargs: Optional[dict[str, str]] = None,
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node_id: Optional[str] = None,
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) -> PikaGenerateResponse:
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polling_operation = PollingOperation(
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poll_endpoint=ApiEndpoint(
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path=f"{PATH_VIDEO_GET}/{task_id}",
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method=HttpMethod.GET,
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request_model=EmptyRequest,
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response_model=PikaVideoResponse,
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),
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completed_statuses=[
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"finished",
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],
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failed_statuses=["failed", "cancelled"],
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status_extractor=lambda response: (
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response.status.value if response.status else None
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),
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progress_extractor=lambda response: (
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response.progress if hasattr(response, "progress") else None
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),
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auth_kwargs=auth_kwargs,
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result_url_extractor=lambda response: (
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response.url if hasattr(response, "url") else None
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),
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node_id=node_id,
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estimated_duration=60
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)
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return polling_operation.execute()
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def execute_task(
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self,
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initial_operation: SynchronousOperation[R, PikaGenerateResponse],
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auth_kwargs: Optional[dict[str, str]] = None,
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node_id: Optional[str] = None,
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) -> tuple[VideoFromFile]:
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"""Executes the initial operation then polls for the task status until it is completed.
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Args:
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initial_operation: The initial operation to execute.
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auth_kwargs: The authentication token(s) to use for the API call.
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Returns:
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A tuple containing the video file as a VIDEO output.
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"""
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initial_response = initial_operation.execute()
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if not is_valid_initial_response(initial_response):
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error_msg = f"Pika initial request failed. Code: {initial_response.code}, Message: {initial_response.message}, Data: {initial_response.data}"
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logging.error(error_msg)
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raise PikaApiError(error_msg)
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task_id = initial_response.video_id
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final_response = self.poll_for_task_status(task_id, auth_kwargs)
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if not is_valid_video_response(final_response):
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error_msg = (
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f"Pika task {task_id} succeeded but no video data found in response."
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)
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logging.error(error_msg)
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raise PikaApiError(error_msg)
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video_url = str(final_response.url)
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logging.info("Pika task %s succeeded. Video URL: %s", task_id, video_url)
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return (download_url_to_video_output(video_url),)
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class PikaImageToVideoV2_2(PikaNodeBase):
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"""Pika 2.2 Image to Video Node."""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": (
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IO.IMAGE,
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{"tooltip": "The image to convert to video"},
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),
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**cls.get_base_inputs_types(PikaBodyGenerate22I2vGenerate22I2vPost),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
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"comfy_api_key": "API_KEY_COMFY_ORG",
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},
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}
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DESCRIPTION = "Sends an image and prompt to the Pika API v2.2 to generate a video."
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def api_call(
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self,
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image: torch.Tensor,
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prompt_text: str,
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negative_prompt: str,
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seed: int,
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resolution: str,
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duration: int,
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unique_id: str,
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**kwargs,
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) -> tuple[VideoFromFile]:
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# Convert image to BytesIO
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image_bytes_io = tensor_to_bytesio(image)
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image_bytes_io.seek(0)
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pika_files = {"image": ("image.png", image_bytes_io, "image/png")}
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# Prepare non-file data
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pika_request_data = PikaBodyGenerate22I2vGenerate22I2vPost(
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promptText=prompt_text,
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negativePrompt=negative_prompt,
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seed=seed,
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resolution=resolution,
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duration=duration,
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)
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initial_operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path=PATH_IMAGE_TO_VIDEO,
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method=HttpMethod.POST,
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request_model=PikaBodyGenerate22I2vGenerate22I2vPost,
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response_model=PikaGenerateResponse,
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),
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request=pika_request_data,
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files=pika_files,
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content_type="multipart/form-data",
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auth_kwargs=kwargs,
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)
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return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
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class PikaTextToVideoNodeV2_2(PikaNodeBase):
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"""Pika Text2Video v2.2 Node."""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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**cls.get_base_inputs_types(PikaBodyGenerate22T2vGenerate22T2vPost),
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"aspect_ratio": model_field_to_node_input(
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IO.FLOAT,
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PikaBodyGenerate22T2vGenerate22T2vPost,
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"aspectRatio",
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step=0.001,
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min=0.4,
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max=2.5,
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default=1.7777777777777777,
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),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
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"comfy_api_key": "API_KEY_COMFY_ORG",
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"unique_id": "UNIQUE_ID",
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},
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}
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DESCRIPTION = "Sends a text prompt to the Pika API v2.2 to generate a video."
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def api_call(
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self,
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prompt_text: str,
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negative_prompt: str,
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seed: int,
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resolution: str,
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duration: int,
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aspect_ratio: float,
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unique_id: str,
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**kwargs,
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) -> tuple[VideoFromFile]:
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initial_operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path=PATH_TEXT_TO_VIDEO,
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method=HttpMethod.POST,
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request_model=PikaBodyGenerate22T2vGenerate22T2vPost,
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response_model=PikaGenerateResponse,
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),
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request=PikaBodyGenerate22T2vGenerate22T2vPost(
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promptText=prompt_text,
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negativePrompt=negative_prompt,
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seed=seed,
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resolution=resolution,
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duration=duration,
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aspectRatio=aspect_ratio,
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),
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auth_kwargs=kwargs,
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content_type="application/x-www-form-urlencoded",
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)
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return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
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class PikaScenesV2_2(PikaNodeBase):
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"""PikaScenes v2.2 Node."""
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@classmethod
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def INPUT_TYPES(cls):
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image_ingredient_input = (
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IO.IMAGE,
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{"tooltip": "Image that will be used as ingredient to create a video."},
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)
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return {
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"required": {
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**cls.get_base_inputs_types(
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PikaBodyGenerate22C2vGenerate22PikascenesPost,
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),
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"ingredients_mode": model_field_to_node_input(
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IO.COMBO,
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PikaBodyGenerate22C2vGenerate22PikascenesPost,
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"ingredientsMode",
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enum_type=IngredientsMode,
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default="creative",
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),
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"aspect_ratio": model_field_to_node_input(
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IO.FLOAT,
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PikaBodyGenerate22C2vGenerate22PikascenesPost,
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"aspectRatio",
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step=0.001,
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min=0.4,
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max=2.5,
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default=1.7777777777777777,
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),
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},
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"optional": {
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"image_ingredient_1": image_ingredient_input,
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"image_ingredient_2": image_ingredient_input,
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"image_ingredient_3": image_ingredient_input,
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"image_ingredient_4": image_ingredient_input,
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"image_ingredient_5": image_ingredient_input,
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
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"comfy_api_key": "API_KEY_COMFY_ORG",
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"unique_id": "UNIQUE_ID",
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},
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}
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DESCRIPTION = "Combine your images to create a video with the objects in them. Upload multiple images as ingredients and generate a high-quality video that incorporates all of them."
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def api_call(
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self,
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prompt_text: str,
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negative_prompt: str,
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seed: int,
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resolution: str,
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duration: int,
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ingredients_mode: str,
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aspect_ratio: float,
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unique_id: str,
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image_ingredient_1: Optional[torch.Tensor] = None,
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image_ingredient_2: Optional[torch.Tensor] = None,
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image_ingredient_3: Optional[torch.Tensor] = None,
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image_ingredient_4: Optional[torch.Tensor] = None,
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image_ingredient_5: Optional[torch.Tensor] = None,
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**kwargs,
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) -> tuple[VideoFromFile]:
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# Convert all passed images to BytesIO
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all_image_bytes_io = []
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for image in [
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image_ingredient_1,
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image_ingredient_2,
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image_ingredient_3,
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image_ingredient_4,
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image_ingredient_5,
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]:
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if image is not None:
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image_bytes_io = tensor_to_bytesio(image)
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image_bytes_io.seek(0)
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all_image_bytes_io.append(image_bytes_io)
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pika_files = [
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("images", (f"image_{i}.png", image_bytes_io, "image/png"))
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for i, image_bytes_io in enumerate(all_image_bytes_io)
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]
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pika_request_data = PikaBodyGenerate22C2vGenerate22PikascenesPost(
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ingredientsMode=ingredients_mode,
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promptText=prompt_text,
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negativePrompt=negative_prompt,
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seed=seed,
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resolution=resolution,
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duration=duration,
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aspectRatio=aspect_ratio,
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)
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initial_operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path=PATH_PIKASCENES,
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method=HttpMethod.POST,
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request_model=PikaBodyGenerate22C2vGenerate22PikascenesPost,
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response_model=PikaGenerateResponse,
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),
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request=pika_request_data,
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files=pika_files,
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content_type="multipart/form-data",
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auth_kwargs=kwargs,
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)
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return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
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class PikAdditionsNode(PikaNodeBase):
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"""Pika Pikadditions Node. Add an image into a video."""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"video": (IO.VIDEO, {"tooltip": "The video to add an image to."}),
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"image": (IO.IMAGE, {"tooltip": "The image to add to the video."}),
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"prompt_text": model_field_to_node_input(
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IO.STRING,
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PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
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"promptText",
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multiline=True,
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),
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"negative_prompt": model_field_to_node_input(
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IO.STRING,
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PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
|
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"negativePrompt",
|
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multiline=True,
|
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),
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"seed": model_field_to_node_input(
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IO.INT,
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PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
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"seed",
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min=0,
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max=0xFFFFFFFF,
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control_after_generate=True,
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),
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},
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"hidden": {
|
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
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"comfy_api_key": "API_KEY_COMFY_ORG",
|
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"unique_id": "UNIQUE_ID",
|
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},
|
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}
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|
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DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you’d like to add to create a seamlessly integrated result."
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|
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def api_call(
|
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self,
|
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video: VideoInput,
|
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image: torch.Tensor,
|
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prompt_text: str,
|
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negative_prompt: str,
|
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seed: int,
|
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unique_id: str,
|
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**kwargs,
|
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) -> tuple[VideoFromFile]:
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# Convert video to BytesIO
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video_bytes_io = io.BytesIO()
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video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264)
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video_bytes_io.seek(0)
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# Convert image to BytesIO
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image_bytes_io = tensor_to_bytesio(image)
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image_bytes_io.seek(0)
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pika_files = [
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("video", ("video.mp4", video_bytes_io, "video/mp4")),
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("image", ("image.png", image_bytes_io, "image/png")),
|
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]
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# Prepare non-file data
|
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pika_request_data = PikaBodyGeneratePikadditionsGeneratePikadditionsPost(
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promptText=prompt_text,
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negativePrompt=negative_prompt,
|
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seed=seed,
|
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)
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initial_operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path=PATH_PIKADDITIONS,
|
||
method=HttpMethod.POST,
|
||
request_model=PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
|
||
response_model=PikaGenerateResponse,
|
||
),
|
||
request=pika_request_data,
|
||
files=pika_files,
|
||
content_type="multipart/form-data",
|
||
auth_kwargs=kwargs,
|
||
)
|
||
|
||
return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
|
||
|
||
|
||
class PikaSwapsNode(PikaNodeBase):
|
||
"""Pika Pikaswaps Node."""
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"video": (IO.VIDEO, {"tooltip": "The video to swap an object in."}),
|
||
"image": (
|
||
IO.IMAGE,
|
||
{
|
||
"tooltip": "The image used to replace the masked object in the video."
|
||
},
|
||
),
|
||
"mask": (
|
||
IO.MASK,
|
||
{"tooltip": "Use the mask to define areas in the video to replace"},
|
||
),
|
||
"prompt_text": model_field_to_node_input(
|
||
IO.STRING,
|
||
PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
|
||
"promptText",
|
||
multiline=True,
|
||
),
|
||
"negative_prompt": model_field_to_node_input(
|
||
IO.STRING,
|
||
PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
|
||
"negativePrompt",
|
||
multiline=True,
|
||
),
|
||
"seed": model_field_to_node_input(
|
||
IO.INT,
|
||
PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
|
||
"seed",
|
||
min=0,
|
||
max=0xFFFFFFFF,
|
||
control_after_generate=True,
|
||
),
|
||
},
|
||
"hidden": {
|
||
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
||
"comfy_api_key": "API_KEY_COMFY_ORG",
|
||
"unique_id": "UNIQUE_ID",
|
||
},
|
||
}
|
||
|
||
DESCRIPTION = "Swap out any object or region of your video with a new image or object. Define areas to replace either with a mask or coordinates."
|
||
RETURN_TYPES = ("VIDEO",)
|
||
|
||
def api_call(
|
||
self,
|
||
video: VideoInput,
|
||
image: torch.Tensor,
|
||
mask: torch.Tensor,
|
||
prompt_text: str,
|
||
negative_prompt: str,
|
||
seed: int,
|
||
unique_id: str,
|
||
**kwargs,
|
||
) -> tuple[VideoFromFile]:
|
||
# Convert video to BytesIO
|
||
video_bytes_io = io.BytesIO()
|
||
video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264)
|
||
video_bytes_io.seek(0)
|
||
|
||
# Convert mask to binary mask with three channels
|
||
mask = torch.round(mask)
|
||
mask = mask.repeat(1, 3, 1, 1)
|
||
|
||
# Convert 3-channel binary mask to BytesIO
|
||
mask_bytes_io = io.BytesIO()
|
||
mask_bytes_io.write(mask.numpy().astype(np.uint8))
|
||
mask_bytes_io.seek(0)
|
||
|
||
# Convert image to BytesIO
|
||
image_bytes_io = tensor_to_bytesio(image)
|
||
image_bytes_io.seek(0)
|
||
|
||
pika_files = [
|
||
("video", ("video.mp4", video_bytes_io, "video/mp4")),
|
||
("image", ("image.png", image_bytes_io, "image/png")),
|
||
("modifyRegionMask", ("mask.png", mask_bytes_io, "image/png")),
|
||
]
|
||
|
||
# Prepare non-file data
|
||
pika_request_data = PikaBodyGeneratePikaswapsGeneratePikaswapsPost(
|
||
promptText=prompt_text,
|
||
negativePrompt=negative_prompt,
|
||
seed=seed,
|
||
)
|
||
|
||
initial_operation = SynchronousOperation(
|
||
endpoint=ApiEndpoint(
|
||
path=PATH_PIKADDITIONS,
|
||
method=HttpMethod.POST,
|
||
request_model=PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
|
||
response_model=PikaGenerateResponse,
|
||
),
|
||
request=pika_request_data,
|
||
files=pika_files,
|
||
content_type="multipart/form-data",
|
||
auth_kwargs=kwargs,
|
||
)
|
||
|
||
return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
|
||
|
||
|
||
class PikaffectsNode(PikaNodeBase):
|
||
"""Pika Pikaffects Node."""
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"image": (
|
||
IO.IMAGE,
|
||
{"tooltip": "The reference image to apply the Pikaffect to."},
|
||
),
|
||
"pikaffect": model_field_to_node_input(
|
||
IO.COMBO,
|
||
PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
|
||
"pikaffect",
|
||
enum_type=Pikaffect,
|
||
default="Cake-ify",
|
||
),
|
||
"prompt_text": model_field_to_node_input(
|
||
IO.STRING,
|
||
PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
|
||
"promptText",
|
||
multiline=True,
|
||
),
|
||
"negative_prompt": model_field_to_node_input(
|
||
IO.STRING,
|
||
PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
|
||
"negativePrompt",
|
||
multiline=True,
|
||
),
|
||
"seed": model_field_to_node_input(
|
||
IO.INT,
|
||
PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
|
||
"seed",
|
||
min=0,
|
||
max=0xFFFFFFFF,
|
||
control_after_generate=True,
|
||
),
|
||
},
|
||
"hidden": {
|
||
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
||
"comfy_api_key": "API_KEY_COMFY_ORG",
|
||
"unique_id": "UNIQUE_ID",
|
||
},
|
||
}
|
||
|
||
DESCRIPTION = "Generate a video with a specific Pikaffect. Supported Pikaffects: Cake-ify, Crumble, Crush, Decapitate, Deflate, Dissolve, Explode, Eye-pop, Inflate, Levitate, Melt, Peel, Poke, Squish, Ta-da, Tear"
|
||
|
||
def api_call(
|
||
self,
|
||
image: torch.Tensor,
|
||
pikaffect: str,
|
||
prompt_text: str,
|
||
negative_prompt: str,
|
||
seed: int,
|
||
unique_id: str,
|
||
**kwargs,
|
||
) -> tuple[VideoFromFile]:
|
||
|
||
initial_operation = SynchronousOperation(
|
||
endpoint=ApiEndpoint(
|
||
path=PATH_PIKAFFECTS,
|
||
method=HttpMethod.POST,
|
||
request_model=PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
|
||
response_model=PikaGenerateResponse,
|
||
),
|
||
request=PikaBodyGeneratePikaffectsGeneratePikaffectsPost(
|
||
pikaffect=pikaffect,
|
||
promptText=prompt_text,
|
||
negativePrompt=negative_prompt,
|
||
seed=seed,
|
||
),
|
||
files={"image": ("image.png", tensor_to_bytesio(image), "image/png")},
|
||
content_type="multipart/form-data",
|
||
auth_kwargs=kwargs,
|
||
)
|
||
|
||
return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
|
||
|
||
|
||
class PikaStartEndFrameNode2_2(PikaNodeBase):
|
||
"""PikaFrames v2.2 Node."""
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"image_start": (IO.IMAGE, {"tooltip": "The first image to combine."}),
|
||
"image_end": (IO.IMAGE, {"tooltip": "The last image to combine."}),
|
||
**cls.get_base_inputs_types(
|
||
PikaBodyGenerate22KeyframeGenerate22PikaframesPost
|
||
),
|
||
},
|
||
"hidden": {
|
||
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
||
"comfy_api_key": "API_KEY_COMFY_ORG",
|
||
"unique_id": "UNIQUE_ID",
|
||
},
|
||
}
|
||
|
||
DESCRIPTION = "Generate a video by combining your first and last frame. Upload two images to define the start and end points, and let the AI create a smooth transition between them."
|
||
|
||
def api_call(
|
||
self,
|
||
image_start: torch.Tensor,
|
||
image_end: torch.Tensor,
|
||
prompt_text: str,
|
||
negative_prompt: str,
|
||
seed: int,
|
||
resolution: str,
|
||
duration: int,
|
||
unique_id: str,
|
||
**kwargs,
|
||
) -> tuple[VideoFromFile]:
|
||
|
||
pika_files = [
|
||
(
|
||
"keyFrames",
|
||
("image_start.png", tensor_to_bytesio(image_start), "image/png"),
|
||
),
|
||
("keyFrames", ("image_end.png", tensor_to_bytesio(image_end), "image/png")),
|
||
]
|
||
|
||
initial_operation = SynchronousOperation(
|
||
endpoint=ApiEndpoint(
|
||
path=PATH_PIKAFRAMES,
|
||
method=HttpMethod.POST,
|
||
request_model=PikaBodyGenerate22KeyframeGenerate22PikaframesPost,
|
||
response_model=PikaGenerateResponse,
|
||
),
|
||
request=PikaBodyGenerate22KeyframeGenerate22PikaframesPost(
|
||
promptText=prompt_text,
|
||
negativePrompt=negative_prompt,
|
||
seed=seed,
|
||
resolution=resolution,
|
||
duration=duration,
|
||
),
|
||
files=pika_files,
|
||
content_type="multipart/form-data",
|
||
auth_kwargs=kwargs,
|
||
)
|
||
|
||
return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
|
||
|
||
|
||
NODE_CLASS_MAPPINGS = {
|
||
"PikaImageToVideoNode2_2": PikaImageToVideoV2_2,
|
||
"PikaTextToVideoNode2_2": PikaTextToVideoNodeV2_2,
|
||
"PikaScenesV2_2": PikaScenesV2_2,
|
||
"Pikadditions": PikAdditionsNode,
|
||
"Pikaswaps": PikaSwapsNode,
|
||
"Pikaffects": PikaffectsNode,
|
||
"PikaStartEndFrameNode2_2": PikaStartEndFrameNode2_2,
|
||
}
|
||
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"PikaImageToVideoNode2_2": "Pika Image to Video",
|
||
"PikaTextToVideoNode2_2": "Pika Text to Video",
|
||
"PikaScenesV2_2": "Pika Scenes (Video Image Composition)",
|
||
"Pikadditions": "Pikadditions (Video Object Insertion)",
|
||
"Pikaswaps": "Pika Swaps (Video Object Replacement)",
|
||
"Pikaffects": "Pikaffects (Video Effects)",
|
||
"PikaStartEndFrameNode2_2": "Pika Start and End Frame to Video",
|
||
}
|