mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-09-12 12:37:01 +00:00
async API nodes (#9129)
* converted API nodes to async * converted BFL API nodes to async * fixed client bug; converted gemini, ideogram, minimax * fixed client bug; converted openai nodes * fixed client bug; converted moonvalley, pika nodes * fixed client bug; converted kling, luma nodes * converted pixverse, rodin nodes * converted tripo, veo2 * converted recraft nodes * add lost log_request_response call
This commit is contained in:
Alexander Piskun
GitHub
@@ -109,7 +109,7 @@ class KlingApiError(Exception):
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pass
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def poll_until_finished(
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async def poll_until_finished(
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auth_kwargs: dict[str, str],
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api_endpoint: ApiEndpoint[Any, R],
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result_url_extractor: Optional[Callable[[R], str]] = None,
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@@ -117,7 +117,7 @@ def poll_until_finished(
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node_id: Optional[str] = None,
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) -> R:
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"""Polls the Kling API endpoint until the task reaches a terminal state, then returns the response."""
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return PollingOperation(
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return await PollingOperation(
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poll_endpoint=api_endpoint,
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completed_statuses=[
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KlingTaskStatus.succeed.value,
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@@ -278,18 +278,18 @@ def get_images_urls_from_response(response) -> Optional[str]:
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return None
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def video_result_to_node_output(
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async def video_result_to_node_output(
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video: KlingVideoResult,
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) -> tuple[VideoFromFile, str, str]:
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"""Converts a KlingVideoResult to a tuple of (VideoFromFile, str, str) to be used as a ComfyUI node output."""
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return (
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download_url_to_video_output(video.url),
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await download_url_to_video_output(str(video.url)),
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str(video.id),
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str(video.duration),
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)
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def image_result_to_node_output(
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async def image_result_to_node_output(
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images: list[KlingImageResult],
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) -> torch.Tensor:
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"""
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@@ -297,9 +297,9 @@ def image_result_to_node_output(
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If multiple images are returned, they will be stacked along the batch dimension.
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"""
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if len(images) == 1:
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return download_url_to_image_tensor(images[0].url)
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return await download_url_to_image_tensor(str(images[0].url))
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else:
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return torch.cat([download_url_to_image_tensor(image.url) for image in images])
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return torch.cat([await download_url_to_image_tensor(str(image.url)) for image in images])
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class KlingNodeBase(ComfyNodeABC):
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@@ -467,10 +467,10 @@ class KlingTextToVideoNode(KlingNodeBase):
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RETURN_NAMES = ("VIDEO", "video_id", "duration")
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DESCRIPTION = "Kling Text to Video Node"
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def get_response(
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async def get_response(
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self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
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) -> KlingText2VideoResponse:
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return poll_until_finished(
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return await poll_until_finished(
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auth_kwargs,
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ApiEndpoint(
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path=f"{PATH_TEXT_TO_VIDEO}/{task_id}",
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@@ -483,7 +483,7 @@ class KlingTextToVideoNode(KlingNodeBase):
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node_id=node_id,
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)
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def api_call(
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async def api_call(
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self,
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prompt: str,
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negative_prompt: str,
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@@ -519,17 +519,17 @@ class KlingTextToVideoNode(KlingNodeBase):
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auth_kwargs=kwargs,
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)
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task_creation_response = initial_operation.execute()
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task_creation_response = await initial_operation.execute()
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validate_task_creation_response(task_creation_response)
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task_id = task_creation_response.data.task_id
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final_response = self.get_response(
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final_response = await self.get_response(
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task_id, auth_kwargs=kwargs, node_id=unique_id
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)
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validate_video_result_response(final_response)
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video = get_video_from_response(final_response)
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return video_result_to_node_output(video)
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return await video_result_to_node_output(video)
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class KlingCameraControlT2VNode(KlingTextToVideoNode):
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@@ -581,7 +581,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode):
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DESCRIPTION = "Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text."
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def api_call(
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async def api_call(
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self,
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prompt: str,
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negative_prompt: str,
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@@ -591,7 +591,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode):
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unique_id: Optional[str] = None,
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**kwargs,
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):
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return super().api_call(
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return await super().api_call(
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model_name=KlingVideoGenModelName.kling_v1,
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cfg_scale=cfg_scale,
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mode=KlingVideoGenMode.std,
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@@ -670,10 +670,10 @@ class KlingImage2VideoNode(KlingNodeBase):
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RETURN_NAMES = ("VIDEO", "video_id", "duration")
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DESCRIPTION = "Kling Image to Video Node"
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def get_response(
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async def get_response(
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self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
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) -> KlingImage2VideoResponse:
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return poll_until_finished(
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return await poll_until_finished(
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auth_kwargs,
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ApiEndpoint(
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path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}",
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@@ -686,7 +686,7 @@ class KlingImage2VideoNode(KlingNodeBase):
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node_id=node_id,
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)
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def api_call(
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async def api_call(
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self,
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start_frame: torch.Tensor,
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prompt: str,
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@@ -733,17 +733,17 @@ class KlingImage2VideoNode(KlingNodeBase):
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auth_kwargs=kwargs,
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)
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task_creation_response = initial_operation.execute()
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task_creation_response = await initial_operation.execute()
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validate_task_creation_response(task_creation_response)
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task_id = task_creation_response.data.task_id
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final_response = self.get_response(
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final_response = await self.get_response(
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task_id, auth_kwargs=kwargs, node_id=unique_id
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)
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validate_video_result_response(final_response)
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video = get_video_from_response(final_response)
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return video_result_to_node_output(video)
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return await video_result_to_node_output(video)
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class KlingCameraControlI2VNode(KlingImage2VideoNode):
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@@ -798,7 +798,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
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DESCRIPTION = "Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image."
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def api_call(
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async def api_call(
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self,
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start_frame: torch.Tensor,
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prompt: str,
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@@ -809,7 +809,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
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unique_id: Optional[str] = None,
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**kwargs,
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):
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return super().api_call(
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return await super().api_call(
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model_name=KlingVideoGenModelName.kling_v1_5,
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start_frame=start_frame,
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cfg_scale=cfg_scale,
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@@ -897,7 +897,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
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DESCRIPTION = "Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last."
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def api_call(
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async def api_call(
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self,
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start_frame: torch.Tensor,
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end_frame: torch.Tensor,
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@@ -912,7 +912,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
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mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[
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mode
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]
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return super().api_call(
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return await super().api_call(
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prompt=prompt,
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negative_prompt=negative_prompt,
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model_name=model_name,
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@@ -964,10 +964,10 @@ class KlingVideoExtendNode(KlingNodeBase):
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RETURN_NAMES = ("VIDEO", "video_id", "duration")
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DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes."
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def get_response(
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async def get_response(
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self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
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) -> KlingVideoExtendResponse:
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return poll_until_finished(
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return await poll_until_finished(
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auth_kwargs,
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ApiEndpoint(
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path=f"{PATH_VIDEO_EXTEND}/{task_id}",
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@@ -980,7 +980,7 @@ class KlingVideoExtendNode(KlingNodeBase):
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node_id=node_id,
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)
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def api_call(
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async def api_call(
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self,
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prompt: str,
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negative_prompt: str,
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@@ -1006,17 +1006,17 @@ class KlingVideoExtendNode(KlingNodeBase):
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auth_kwargs=kwargs,
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)
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task_creation_response = initial_operation.execute()
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task_creation_response = await initial_operation.execute()
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validate_task_creation_response(task_creation_response)
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task_id = task_creation_response.data.task_id
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final_response = self.get_response(
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final_response = await self.get_response(
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task_id, auth_kwargs=kwargs, node_id=unique_id
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)
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validate_video_result_response(final_response)
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video = get_video_from_response(final_response)
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return video_result_to_node_output(video)
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return await video_result_to_node_output(video)
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class KlingVideoEffectsBase(KlingNodeBase):
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@@ -1025,10 +1025,10 @@ class KlingVideoEffectsBase(KlingNodeBase):
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RETURN_TYPES = ("VIDEO", "STRING", "STRING")
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RETURN_NAMES = ("VIDEO", "video_id", "duration")
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def get_response(
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async def get_response(
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self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
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) -> KlingVideoEffectsResponse:
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return poll_until_finished(
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return await poll_until_finished(
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auth_kwargs,
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ApiEndpoint(
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path=f"{PATH_VIDEO_EFFECTS}/{task_id}",
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@@ -1041,7 +1041,7 @@ class KlingVideoEffectsBase(KlingNodeBase):
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node_id=node_id,
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)
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def api_call(
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async def api_call(
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self,
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dual_character: bool,
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effect_scene: KlingDualCharacterEffectsScene | KlingSingleImageEffectsScene,
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@@ -1084,17 +1084,17 @@ class KlingVideoEffectsBase(KlingNodeBase):
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auth_kwargs=kwargs,
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)
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task_creation_response = initial_operation.execute()
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task_creation_response = await initial_operation.execute()
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validate_task_creation_response(task_creation_response)
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task_id = task_creation_response.data.task_id
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final_response = self.get_response(
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final_response = await self.get_response(
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task_id, auth_kwargs=kwargs, node_id=unique_id
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)
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validate_video_result_response(final_response)
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video = get_video_from_response(final_response)
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return video_result_to_node_output(video)
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return await video_result_to_node_output(video)
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class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
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@@ -1142,7 +1142,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
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RETURN_TYPES = ("VIDEO", "STRING")
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RETURN_NAMES = ("VIDEO", "duration")
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def api_call(
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async def api_call(
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self,
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image_left: torch.Tensor,
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image_right: torch.Tensor,
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@@ -1153,7 +1153,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
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unique_id: Optional[str] = None,
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**kwargs,
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):
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video, _, duration = super().api_call(
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video, _, duration = await super().api_call(
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dual_character=True,
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effect_scene=effect_scene,
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model_name=model_name,
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@@ -1208,7 +1208,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
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DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene."
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def api_call(
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async def api_call(
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self,
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image: torch.Tensor,
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effect_scene: KlingSingleImageEffectsScene,
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@@ -1217,7 +1217,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
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unique_id: Optional[str] = None,
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**kwargs,
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):
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return super().api_call(
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return await super().api_call(
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dual_character=False,
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effect_scene=effect_scene,
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model_name=model_name,
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@@ -1253,11 +1253,11 @@ class KlingLipSyncBase(KlingNodeBase):
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f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters."
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)
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def get_response(
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async def get_response(
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self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
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) -> KlingLipSyncResponse:
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"""Polls the Kling API endpoint until the task reaches a terminal state."""
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return poll_until_finished(
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return await poll_until_finished(
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auth_kwargs,
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ApiEndpoint(
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path=f"{PATH_LIP_SYNC}/{task_id}",
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@@ -1270,7 +1270,7 @@ class KlingLipSyncBase(KlingNodeBase):
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node_id=node_id,
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)
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def api_call(
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async def api_call(
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self,
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video: VideoInput,
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audio: Optional[AudioInput] = None,
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@@ -1287,12 +1287,12 @@ class KlingLipSyncBase(KlingNodeBase):
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self.validate_lip_sync_video(video)
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# Upload video to Comfy API and get download URL
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video_url = upload_video_to_comfyapi(video, auth_kwargs=kwargs)
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video_url = await upload_video_to_comfyapi(video, auth_kwargs=kwargs)
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logging.info("Uploaded video to Comfy API. URL: %s", video_url)
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# Upload the audio file to Comfy API and get download URL
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if audio:
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audio_url = upload_audio_to_comfyapi(audio, auth_kwargs=kwargs)
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audio_url = await upload_audio_to_comfyapi(audio, auth_kwargs=kwargs)
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logging.info("Uploaded audio to Comfy API. URL: %s", audio_url)
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else:
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audio_url = None
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@@ -1319,17 +1319,17 @@ class KlingLipSyncBase(KlingNodeBase):
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auth_kwargs=kwargs,
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)
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task_creation_response = initial_operation.execute()
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task_creation_response = await initial_operation.execute()
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validate_task_creation_response(task_creation_response)
|
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task_id = task_creation_response.data.task_id
|
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|
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final_response = self.get_response(
|
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final_response = await self.get_response(
|
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task_id, auth_kwargs=kwargs, node_id=unique_id
|
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)
|
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validate_video_result_response(final_response)
|
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|
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video = get_video_from_response(final_response)
|
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return video_result_to_node_output(video)
|
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return await video_result_to_node_output(video)
|
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|
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|
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class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
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@@ -1357,7 +1357,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
|
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|
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DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length."
|
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|
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def api_call(
|
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async def api_call(
|
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self,
|
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video: VideoInput,
|
||||
audio: AudioInput,
|
||||
@@ -1365,7 +1365,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
|
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unique_id: Optional[str] = None,
|
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**kwargs,
|
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):
|
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return super().api_call(
|
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return await super().api_call(
|
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video=video,
|
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audio=audio,
|
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voice_language=voice_language,
|
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@@ -1469,7 +1469,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
|
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|
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DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length."
|
||||
|
||||
def api_call(
|
||||
async def api_call(
|
||||
self,
|
||||
video: VideoInput,
|
||||
text: str,
|
||||
@@ -1479,7 +1479,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
|
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**kwargs,
|
||||
):
|
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voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice]
|
||||
return super().api_call(
|
||||
return await super().api_call(
|
||||
video=video,
|
||||
text=text,
|
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voice_language=voice_language,
|
||||
@@ -1533,10 +1533,10 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
|
||||
|
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DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background."
|
||||
|
||||
def get_response(
|
||||
async def get_response(
|
||||
self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
|
||||
) -> KlingVirtualTryOnResponse:
|
||||
return poll_until_finished(
|
||||
return await poll_until_finished(
|
||||
auth_kwargs,
|
||||
ApiEndpoint(
|
||||
path=f"{PATH_VIRTUAL_TRY_ON}/{task_id}",
|
||||
@@ -1549,7 +1549,7 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
|
||||
node_id=node_id,
|
||||
)
|
||||
|
||||
def api_call(
|
||||
async def api_call(
|
||||
self,
|
||||
human_image: torch.Tensor,
|
||||
cloth_image: torch.Tensor,
|
||||
@@ -1572,17 +1572,17 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
|
||||
auth_kwargs=kwargs,
|
||||
)
|
||||
|
||||
task_creation_response = initial_operation.execute()
|
||||
task_creation_response = await initial_operation.execute()
|
||||
validate_task_creation_response(task_creation_response)
|
||||
task_id = task_creation_response.data.task_id
|
||||
|
||||
final_response = self.get_response(
|
||||
final_response = await self.get_response(
|
||||
task_id, auth_kwargs=kwargs, node_id=unique_id
|
||||
)
|
||||
validate_image_result_response(final_response)
|
||||
|
||||
images = get_images_from_response(final_response)
|
||||
return (image_result_to_node_output(images),)
|
||||
return (await image_result_to_node_output(images),)
|
||||
|
||||
|
||||
class KlingImageGenerationNode(KlingImageGenerationBase):
|
||||
@@ -1655,13 +1655,13 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
|
||||
|
||||
DESCRIPTION = "Kling Image Generation Node. Generate an image from a text prompt with an optional reference image."
|
||||
|
||||
def get_response(
|
||||
async def get_response(
|
||||
self,
|
||||
task_id: str,
|
||||
auth_kwargs: Optional[dict[str, str]],
|
||||
node_id: Optional[str] = None,
|
||||
) -> KlingImageGenerationsResponse:
|
||||
return poll_until_finished(
|
||||
return await poll_until_finished(
|
||||
auth_kwargs,
|
||||
ApiEndpoint(
|
||||
path=f"{PATH_IMAGE_GENERATIONS}/{task_id}",
|
||||
@@ -1674,7 +1674,7 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
|
||||
node_id=node_id,
|
||||
)
|
||||
|
||||
def api_call(
|
||||
async def api_call(
|
||||
self,
|
||||
model_name: KlingImageGenModelName,
|
||||
prompt: str,
|
||||
@@ -1714,17 +1714,17 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
|
||||
auth_kwargs=kwargs,
|
||||
)
|
||||
|
||||
task_creation_response = initial_operation.execute()
|
||||
task_creation_response = await initial_operation.execute()
|
||||
validate_task_creation_response(task_creation_response)
|
||||
task_id = task_creation_response.data.task_id
|
||||
|
||||
final_response = self.get_response(
|
||||
final_response = await self.get_response(
|
||||
task_id, auth_kwargs=kwargs, node_id=unique_id
|
||||
)
|
||||
validate_image_result_response(final_response)
|
||||
|
||||
images = get_images_from_response(final_response)
|
||||
return (image_result_to_node_output(images),)
|
||||
return (await image_result_to_node_output(images),)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
|
||||
Reference in New Issue
Block a user