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fixed client bug; converted kling, luma nodes

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bigcat88 2025-08-01 16:31:35 +03:00
parent 1e00ee1073
commit ed354f08fd
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GPG Key ID: 1F0BF0EC3CF22721
3 changed files with 104 additions and 99 deletions
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3
comfy_api_nodes/apis/client.py
View File

@ -859,6 +859,7 @@ class PollingOperation(Generic[T, R]):
if self.progress_extractor:
progress = utils.ProgressBar(PROGRESS_BAR_MAX)
status = TaskStatus.PENDING
for poll_count in range(1, self.max_poll_attempts + 1):
try:
logging.debug(f"[DEBUG] Polling attempt #{poll_count}")
@ -928,7 +929,7 @@ class PollingOperation(Generic[T, R]):
except Exception as e:
# For other errors, increment count and potentially abort
consecutive_errors += 1
if consecutive_errors >= max_consecutive_errors:
if consecutive_errors >= max_consecutive_errors or status == TaskStatus.FAILED:
raise Exception(
f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}"
) from e

130
comfy_api_nodes/nodes_kling.py
View File

@ -109,7 +109,7 @@ class KlingApiError(Exception):
pass
def poll_until_finished(
async def poll_until_finished(
auth_kwargs: dict[str, str],
api_endpoint: ApiEndpoint[Any, R],
result_url_extractor: Optional[Callable[[R], str]] = None,
@ -117,7 +117,7 @@ def poll_until_finished(
node_id: Optional[str] = None,
) -> R:
"""Polls the Kling API endpoint until the task reaches a terminal state, then returns the response."""
return PollingOperation(
return await PollingOperation(
poll_endpoint=api_endpoint,
completed_statuses=[
KlingTaskStatus.succeed.value,
@ -278,18 +278,18 @@ def get_images_urls_from_response(response) -> Optional[str]:
return None
def video_result_to_node_output(
async def video_result_to_node_output(
video: KlingVideoResult,
) -> tuple[VideoFromFile, str, str]:
"""Converts a KlingVideoResult to a tuple of (VideoFromFile, str, str) to be used as a ComfyUI node output."""
return (
download_url_to_video_output(video.url),
await download_url_to_video_output(str(video.url)),
str(video.id),
str(video.duration),
)
def image_result_to_node_output(
async def image_result_to_node_output(
images: list[KlingImageResult],
) -> torch.Tensor:
"""
@ -297,9 +297,9 @@ def image_result_to_node_output(
If multiple images are returned, they will be stacked along the batch dimension.
"""
if len(images) == 1:
return download_url_to_image_tensor(images[0].url)
return await download_url_to_image_tensor(str(images[0].url))
else:
return torch.cat([download_url_to_image_tensor(image.url) for image in images])
return torch.cat([await download_url_to_image_tensor(str(image.url)) for image in images])
class KlingNodeBase(ComfyNodeABC):
@ -467,10 +467,10 @@ class KlingTextToVideoNode(KlingNodeBase):
RETURN_NAMES = ("VIDEO", "video_id", "duration")
DESCRIPTION = "Kling Text to Video Node"
def get_response(
async def get_response(
self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
) -> KlingText2VideoResponse:
return poll_until_finished(
return await poll_until_finished(
auth_kwargs,
ApiEndpoint(
path=f"{PATH_TEXT_TO_VIDEO}/{task_id}",
@ -483,7 +483,7 @@ class KlingTextToVideoNode(KlingNodeBase):
node_id=node_id,
)
def api_call(
async def api_call(
self,
prompt: str,
negative_prompt: str,
@ -519,17 +519,17 @@ class KlingTextToVideoNode(KlingNodeBase):
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_video_result_response(final_response)
video = get_video_from_response(final_response)
return video_result_to_node_output(video)
return await video_result_to_node_output(video)
class KlingCameraControlT2VNode(KlingTextToVideoNode):
@ -581,7 +581,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode):
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."
def api_call(
async def api_call(
self,
prompt: str,
negative_prompt: str,
@ -591,7 +591,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode):
unique_id: Optional[str] = None,
**kwargs,
):
return super().api_call(
return await super().api_call(
model_name=KlingVideoGenModelName.kling_v1,
cfg_scale=cfg_scale,
mode=KlingVideoGenMode.std,
@ -670,10 +670,10 @@ class KlingImage2VideoNode(KlingNodeBase):
RETURN_NAMES = ("VIDEO", "video_id", "duration")
DESCRIPTION = "Kling Image to Video Node"
def get_response(
async def get_response(
self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
) -> KlingImage2VideoResponse:
return poll_until_finished(
return await poll_until_finished(
auth_kwargs,
ApiEndpoint(
path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}",
@ -686,7 +686,7 @@ class KlingImage2VideoNode(KlingNodeBase):
node_id=node_id,
)
def api_call(
async def api_call(
self,
start_frame: torch.Tensor,
prompt: str,
@ -733,17 +733,17 @@ class KlingImage2VideoNode(KlingNodeBase):
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_video_result_response(final_response)
video = get_video_from_response(final_response)
return video_result_to_node_output(video)
return await video_result_to_node_output(video)
class KlingCameraControlI2VNode(KlingImage2VideoNode):
@ -798,7 +798,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
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."
def api_call(
async def api_call(
self,
start_frame: torch.Tensor,
prompt: str,
@ -809,7 +809,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
unique_id: Optional[str] = None,
**kwargs,
):
return super().api_call(
return await super().api_call(
model_name=KlingVideoGenModelName.kling_v1_5,
start_frame=start_frame,
cfg_scale=cfg_scale,
@ -897,7 +897,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
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."
def api_call(
async def api_call(
self,
start_frame: torch.Tensor,
end_frame: torch.Tensor,
@ -912,7 +912,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[
mode
]
return super().api_call(
return await super().api_call(
prompt=prompt,
negative_prompt=negative_prompt,
model_name=model_name,
@ -964,10 +964,10 @@ class KlingVideoExtendNode(KlingNodeBase):
RETURN_NAMES = ("VIDEO", "video_id", "duration")
DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes."
def get_response(
async def get_response(
self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
) -> KlingVideoExtendResponse:
return poll_until_finished(
return await poll_until_finished(
auth_kwargs,
ApiEndpoint(
path=f"{PATH_VIDEO_EXTEND}/{task_id}",
@ -980,7 +980,7 @@ class KlingVideoExtendNode(KlingNodeBase):
node_id=node_id,
)
def api_call(
async def api_call(
self,
prompt: str,
negative_prompt: str,
@ -1006,17 +1006,17 @@ class KlingVideoExtendNode(KlingNodeBase):
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_video_result_response(final_response)
video = get_video_from_response(final_response)
return video_result_to_node_output(video)
return await video_result_to_node_output(video)
class KlingVideoEffectsBase(KlingNodeBase):
@ -1025,10 +1025,10 @@ class KlingVideoEffectsBase(KlingNodeBase):
RETURN_TYPES = ("VIDEO", "STRING", "STRING")
RETURN_NAMES = ("VIDEO", "video_id", "duration")
def get_response(
async def get_response(
self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
) -> KlingVideoEffectsResponse:
return poll_until_finished(
return await poll_until_finished(
auth_kwargs,
ApiEndpoint(
path=f"{PATH_VIDEO_EFFECTS}/{task_id}",
@ -1041,7 +1041,7 @@ class KlingVideoEffectsBase(KlingNodeBase):
node_id=node_id,
)
def api_call(
async def api_call(
self,
dual_character: bool,
effect_scene: KlingDualCharacterEffectsScene | KlingSingleImageEffectsScene,
@ -1084,17 +1084,17 @@ class KlingVideoEffectsBase(KlingNodeBase):
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_video_result_response(final_response)
video = get_video_from_response(final_response)
return video_result_to_node_output(video)
return await video_result_to_node_output(video)
class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
@ -1142,7 +1142,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
RETURN_TYPES = ("VIDEO", "STRING")
RETURN_NAMES = ("VIDEO", "duration")
def api_call(
async def api_call(
self,
image_left: torch.Tensor,
image_right: torch.Tensor,
@ -1153,7 +1153,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
unique_id: Optional[str] = None,
**kwargs,
):
video, _, duration = super().api_call(
video, _, duration = await super().api_call(
dual_character=True,
effect_scene=effect_scene,
model_name=model_name,
@ -1208,7 +1208,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene."
def api_call(
async def api_call(
self,
image: torch.Tensor,
effect_scene: KlingSingleImageEffectsScene,
@ -1217,7 +1217,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
unique_id: Optional[str] = None,
**kwargs,
):
return super().api_call(
return await super().api_call(
dual_character=False,
effect_scene=effect_scene,
model_name=model_name,
@ -1253,11 +1253,11 @@ class KlingLipSyncBase(KlingNodeBase):
f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters."
)
def get_response(
async def get_response(
self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
) -> KlingLipSyncResponse:
"""Polls the Kling API endpoint until the task reaches a terminal state."""
return poll_until_finished(
return await poll_until_finished(
auth_kwargs,
ApiEndpoint(
path=f"{PATH_LIP_SYNC}/{task_id}",
@ -1270,7 +1270,7 @@ class KlingLipSyncBase(KlingNodeBase):
node_id=node_id,
)
def api_call(
async def api_call(
self,
video: VideoInput,
audio: Optional[AudioInput] = None,
@ -1287,12 +1287,12 @@ class KlingLipSyncBase(KlingNodeBase):
self.validate_lip_sync_video(video)
# Upload video to Comfy API and get download URL
video_url = upload_video_to_comfyapi(video, auth_kwargs=kwargs)
video_url = await upload_video_to_comfyapi(video, auth_kwargs=kwargs)
logging.info("Uploaded video to Comfy API. URL: %s", video_url)
# Upload the audio file to Comfy API and get download URL
if audio:
audio_url = upload_audio_to_comfyapi(audio, auth_kwargs=kwargs)
audio_url = await upload_audio_to_comfyapi(audio, auth_kwargs=kwargs)
logging.info("Uploaded audio to Comfy API. URL: %s", audio_url)
else:
audio_url = None
@ -1319,17 +1319,17 @@ class KlingLipSyncBase(KlingNodeBase):
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_video_result_response(final_response)
video = get_video_from_response(final_response)
return video_result_to_node_output(video)
return await video_result_to_node_output(video)
class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
@ -1357,7 +1357,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
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."
def api_call(
async def api_call(
self,
video: VideoInput,
audio: AudioInput,
@ -1365,7 +1365,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
unique_id: Optional[str] = None,
**kwargs,
):
return super().api_call(
return await super().api_call(
video=video,
audio=audio,
voice_language=voice_language,
@ -1469,7 +1469,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
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):
**kwargs,
):
voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice]
return super().api_call(
return await super().api_call(
video=video,
text=text,
voice_language=voice_language,
@ -1533,10 +1533,10 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
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 = {

70
comfy_api_nodes/nodes_luma.py
View File

@ -38,7 +38,7 @@ from comfy_api_nodes.apinode_utils import (
)
from server import PromptServer
import requests
import aiohttp
import torch
from io import BytesIO
@ -217,7 +217,7 @@ class LumaImageGenerationNode(ComfyNodeABC):
},
}
def api_call(
async def api_call(
self,
prompt: str,
model: str,
@ -234,19 +234,19 @@ class LumaImageGenerationNode(ComfyNodeABC):
# handle image_luma_ref
api_image_ref = None
if image_luma_ref is not None:
api_image_ref = self._convert_luma_refs(
api_image_ref = await self._convert_luma_refs(
image_luma_ref, max_refs=4, auth_kwargs=kwargs,
)
# handle style_luma_ref
api_style_ref = None
if style_image is not None:
api_style_ref = self._convert_style_image(
api_style_ref = await self._convert_style_image(
style_image, weight=style_image_weight, auth_kwargs=kwargs,
)
# handle character_ref images
character_ref = None
if character_image is not None:
download_urls = upload_images_to_comfyapi(
download_urls = await upload_images_to_comfyapi(
character_image, max_images=4, auth_kwargs=kwargs,
)
character_ref = LumaCharacterRef(
@ -270,7 +270,7 @@ class LumaImageGenerationNode(ComfyNodeABC):
),
auth_kwargs=kwargs,
)
response_api: LumaGeneration = operation.execute()
response_api: LumaGeneration = await operation.execute()
operation = PollingOperation(
poll_endpoint=ApiEndpoint(
@ -286,19 +286,20 @@ class LumaImageGenerationNode(ComfyNodeABC):
node_id=unique_id,
auth_kwargs=kwargs,
)
response_poll = operation.execute()
response_poll = await operation.execute()
img_response = requests.get(response_poll.assets.image)
img = process_image_response(img_response)
async with aiohttp.ClientSession() as session:
async with session.get(response_poll.assets.image) as img_response:
img = process_image_response(await img_response.content.read())
return (img,)
def _convert_luma_refs(
async def _convert_luma_refs(
self, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None
):
luma_urls = []
ref_count = 0
for ref in luma_ref.refs:
download_urls = upload_images_to_comfyapi(
download_urls = await upload_images_to_comfyapi(
ref.image, max_images=1, auth_kwargs=auth_kwargs
)
luma_urls.append(download_urls[0])
@ -307,13 +308,13 @@ class LumaImageGenerationNode(ComfyNodeABC):
break
return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs)
def _convert_style_image(
async def _convert_style_image(
self, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None
):
chain = LumaReferenceChain(
first_ref=LumaReference(image=style_image, weight=weight)
)
return self._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs)
return await self._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs)
class LumaImageModifyNode(ComfyNodeABC):
@ -370,7 +371,7 @@ class LumaImageModifyNode(ComfyNodeABC):
},
}
def api_call(
async def api_call(
self,
prompt: str,
model: str,
@ -381,7 +382,7 @@ class LumaImageModifyNode(ComfyNodeABC):
**kwargs,
):
# first, upload image
download_urls = upload_images_to_comfyapi(
download_urls = await upload_images_to_comfyapi(
image, max_images=1, auth_kwargs=kwargs,
)
image_url = download_urls[0]
@ -402,7 +403,7 @@ class LumaImageModifyNode(ComfyNodeABC):
),
auth_kwargs=kwargs,
)
response_api: LumaGeneration = operation.execute()
response_api: LumaGeneration = await operation.execute()
operation = PollingOperation(
poll_endpoint=ApiEndpoint(
@ -418,10 +419,11 @@ class LumaImageModifyNode(ComfyNodeABC):
node_id=unique_id,
auth_kwargs=kwargs,
)
response_poll = operation.execute()
response_poll = await operation.execute()
img_response = requests.get(response_poll.assets.image)
img = process_image_response(img_response)
async with aiohttp.ClientSession() as session:
async with session.get(response_poll.assets.image) as img_response:
img = process_image_response(await img_response.content.read())
return (img,)
@ -494,7 +496,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
},
}
def api_call(
async def api_call(
self,
prompt: str,
model: str,
@ -529,7 +531,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
),
auth_kwargs=kwargs,
)
response_api: LumaGeneration = operation.execute()
response_api: LumaGeneration = await operation.execute()
if unique_id:
PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
@ -549,10 +551,11 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
estimated_duration=LUMA_T2V_AVERAGE_DURATION,
auth_kwargs=kwargs,
)
response_poll = operation.execute()
response_poll = await operation.execute()
vid_response = requests.get(response_poll.assets.video)
return (VideoFromFile(BytesIO(vid_response.content)),)
async with aiohttp.ClientSession() as session:
async with session.get(response_poll.assets.video) as vid_response:
return (VideoFromFile(BytesIO(await vid_response.content.read())),)
class LumaImageToVideoGenerationNode(ComfyNodeABC):
@ -626,7 +629,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
},
}
def api_call(
async def api_call(
self,
prompt: str,
model: str,
@ -644,7 +647,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
raise Exception(
"At least one of first_image and last_image requires an input."
)
keyframes = self._convert_to_keyframes(first_image, last_image, auth_kwargs=kwargs)
keyframes = await self._convert_to_keyframes(first_image, last_image, auth_kwargs=kwargs)
duration = duration if model != LumaVideoModel.ray_1_6 else None
resolution = resolution if model != LumaVideoModel.ray_1_6 else None
@ -667,7 +670,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
),
auth_kwargs=kwargs,
)
response_api: LumaGeneration = operation.execute()
response_api: LumaGeneration = await operation.execute()
if unique_id:
PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
@ -687,12 +690,13 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
estimated_duration=LUMA_I2V_AVERAGE_DURATION,
auth_kwargs=kwargs,
)
response_poll = operation.execute()
response_poll = await operation.execute()
vid_response = requests.get(response_poll.assets.video)
return (VideoFromFile(BytesIO(vid_response.content)),)
async with aiohttp.ClientSession() as session:
async with session.get(response_poll.assets.video) as vid_response:
return (VideoFromFile(BytesIO(await vid_response.content.read())),)
def _convert_to_keyframes(
async def _convert_to_keyframes(
self,
first_image: torch.Tensor = None,
last_image: torch.Tensor = None,
@ -703,12 +707,12 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
frame0 = None
frame1 = None
if first_image is not None:
download_urls = upload_images_to_comfyapi(
download_urls = await upload_images_to_comfyapi(
first_image, max_images=1, auth_kwargs=auth_kwargs,
)
frame0 = LumaImageReference(type="image", url=download_urls[0])
if last_image is not None:
download_urls = upload_images_to_comfyapi(
download_urls = await upload_images_to_comfyapi(
last_image, max_images=1, auth_kwargs=auth_kwargs,
)
frame1 = LumaImageReference(type="image", url=download_urls[0])