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feat(api-nodes): network client v2: async ops, cancellation, downloads, refactor (#10390)

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* feat(api-nodes): implement new API client for V3 nodes

* feat(api-nodes): implement new API client for V3 nodes

* feat(api-nodes): implement new API client for V3 nodes

* converted WAN nodes to use new client; polishing

* fix(auth): do not leak authentification for the absolute urls

* convert BFL API nodes to use new API client; remove deprecated BFL nodes

* converted Google Veo nodes

* fix(Veo3.1 model): take into account "generate_audio" parameter
This commit is contained in:
Alexander Piskun
2025-10-24 08:37:16 +03:00
committed by GitHub
parent 24188b3141
commit 388b306a2b
29 changed files with 2935 additions and 2298 deletions
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279
comfy_api_nodes/nodes_bytedance.py
View File

@@ -1,35 +1,27 @@
import logging
import math
from enum import Enum
from typing import Literal, Optional, Type, Union
from typing_extensions import override
from typing import Literal, Optional, Union
import torch
from pydantic import BaseModel, Field
from typing_extensions import override
from comfy_api.latest import ComfyExtension, IO
from comfy_api_nodes.util.validation_utils import (
validate_image_aspect_ratio_range,
get_number_of_images,
validate_image_dimensions,
)
from comfy_api_nodes.apis.client import (
from comfy_api.latest import IO, ComfyExtension
from comfy_api_nodes.util import (
ApiEndpoint,
EmptyRequest,
HttpMethod,
SynchronousOperation,
PollingOperation,
T,
)
from comfy_api_nodes.apinode_utils import (
download_url_to_image_tensor,
download_url_to_video_output,
upload_images_to_comfyapi,
validate_string,
get_number_of_images,
image_tensor_pair_to_batch,
poll_op,
sync_op,
upload_images_to_comfyapi,
validate_image_aspect_ratio_range,
validate_image_dimensions,
validate_string,
)
BYTEPLUS_IMAGE_ENDPOINT = "/proxy/byteplus/api/v3/images/generations"
# Long-running tasks endpoints(e.g., video)
@@ -46,13 +38,14 @@ class Image2ImageModelName(str, Enum):
class Text2VideoModelName(str, Enum):
seedance_1_pro = "seedance-1-0-pro-250528"
seedance_1_pro = "seedance-1-0-pro-250528"
seedance_1_lite = "seedance-1-0-lite-t2v-250428"
class Image2VideoModelName(str, Enum):
"""note(August 31): Pro model only supports FirstFrame: https://docs.byteplus.com/en/docs/ModelArk/1520757"""
seedance_1_pro = "seedance-1-0-pro-250528"
seedance_1_pro = "seedance-1-0-pro-250528"
seedance_1_lite = "seedance-1-0-lite-i2v-250428"
@@ -208,35 +201,6 @@ def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, N
return None
async def poll_until_finished(
auth_kwargs: dict[str, str],
task_id: str,
estimated_duration: Optional[int] = None,
node_id: Optional[str] = None,
) -> TaskStatusResponse:
"""Polls the ByteDance API endpoint until the task reaches a terminal state, then returns the response."""
return await PollingOperation(
poll_endpoint=ApiEndpoint(
path=f"{BYTEPLUS_TASK_STATUS_ENDPOINT}/{task_id}",
method=HttpMethod.GET,
request_model=EmptyRequest,
response_model=TaskStatusResponse,
),
completed_statuses=[
"succeeded",
],
failed_statuses=[
"cancelled",
"failed",
],
status_extractor=lambda response: response.status,
auth_kwargs=auth_kwargs,
result_url_extractor=get_video_url_from_task_status,
estimated_duration=estimated_duration,
node_id=node_id,
).execute()
class ByteDanceImageNode(IO.ComfyNode):
@classmethod
@@ -303,7 +267,7 @@ class ByteDanceImageNode(IO.ComfyNode):
IO.Boolean.Input(
"watermark",
default=True,
tooltip="Whether to add an \"AI generated\" watermark to the image",
tooltip='Whether to add an "AI generated" watermark to the image',
optional=True,
),
],
@@ -341,8 +305,7 @@ class ByteDanceImageNode(IO.ComfyNode):
w, h = width, height
if not (512 <= w <= 2048) or not (512 <= h <= 2048):
raise ValueError(
f"Custom size out of range: {w}x{h}. "
"Both width and height must be between 512 and 2048 pixels."
f"Custom size out of range: {w}x{h}. " "Both width and height must be between 512 and 2048 pixels."
)
payload = Text2ImageTaskCreationRequest(
@@ -353,20 +316,12 @@ class ByteDanceImageNode(IO.ComfyNode):
guidance_scale=guidance_scale,
watermark=watermark,
)
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
response = await SynchronousOperation(
endpoint=ApiEndpoint(
path=BYTEPLUS_IMAGE_ENDPOINT,
method=HttpMethod.POST,
request_model=Text2ImageTaskCreationRequest,
response_model=ImageTaskCreationResponse,
),
request=payload,
auth_kwargs=auth_kwargs,
).execute()
response = await sync_op(
cls,
ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
data=payload,
response_model=ImageTaskCreationResponse,
)
return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
@@ -420,7 +375,7 @@ class ByteDanceImageEditNode(IO.ComfyNode):
IO.Boolean.Input(
"watermark",
default=True,
tooltip="Whether to add an \"AI generated\" watermark to the image",
tooltip='Whether to add an "AI generated" watermark to the image',
optional=True,
),
],
@@ -449,16 +404,7 @@ class ByteDanceImageEditNode(IO.ComfyNode):
if get_number_of_images(image) != 1:
raise ValueError("Exactly one input image is required.")
validate_image_aspect_ratio_range(image, (1, 3), (3, 1))
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
source_url = (await upload_images_to_comfyapi(
image,
max_images=1,
mime_type="image/png",
auth_kwargs=auth_kwargs,
))[0]
source_url = (await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png"))[0]
payload = Image2ImageTaskCreationRequest(
model=model,
prompt=prompt,
@@ -467,16 +413,12 @@ class ByteDanceImageEditNode(IO.ComfyNode):
guidance_scale=guidance_scale,
watermark=watermark,
)
response = await SynchronousOperation(
endpoint=ApiEndpoint(
path=BYTEPLUS_IMAGE_ENDPOINT,
method=HttpMethod.POST,
request_model=Image2ImageTaskCreationRequest,
response_model=ImageTaskCreationResponse,
),
request=payload,
auth_kwargs=auth_kwargs,
).execute()
response = await sync_op(
cls,
ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
data=payload,
response_model=ImageTaskCreationResponse,
)
return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
@@ -504,7 +446,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
IO.Image.Input(
"image",
tooltip="Input image(s) for image-to-image generation. "
"List of 1-10 images for single or multi-reference generation.",
"List of 1-10 images for single or multi-reference generation.",
optional=True,
),
IO.Combo.Input(
@@ -534,9 +476,9 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
"sequential_image_generation",
options=["disabled", "auto"],
tooltip="Group image generation mode. "
"'disabled' generates a single image. "
"'auto' lets the model decide whether to generate multiple related images "
"(e.g., story scenes, character variations).",
"'disabled' generates a single image. "
"'auto' lets the model decide whether to generate multiple related images "
"(e.g., story scenes, character variations).",
optional=True,
),
IO.Int.Input(
@@ -547,7 +489,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
step=1,
display_mode=IO.NumberDisplay.number,
tooltip="Maximum number of images to generate when sequential_image_generation='auto'. "
"Total images (input + generated) cannot exceed 15.",
"Total images (input + generated) cannot exceed 15.",
optional=True,
),
IO.Int.Input(
@@ -564,7 +506,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
IO.Boolean.Input(
"watermark",
default=True,
tooltip="Whether to add an \"AI generated\" watermark to the image.",
tooltip='Whether to add an "AI generated" watermark to the image.',
optional=True,
),
IO.Boolean.Input(
@@ -611,8 +553,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
w, h = width, height
if not (1024 <= w <= 4096) or not (1024 <= h <= 4096):
raise ValueError(
f"Custom size out of range: {w}x{h}. "
"Both width and height must be between 1024 and 4096 pixels."
f"Custom size out of range: {w}x{h}. " "Both width and height must be between 1024 and 4096 pixels."
)
n_input_images = get_number_of_images(image) if image is not None else 0
if n_input_images > 10:
@@ -621,41 +562,31 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
raise ValueError(
"The maximum number of generated images plus the number of reference images cannot exceed 15."
)
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
reference_images_urls = []
if n_input_images:
for i in image:
validate_image_aspect_ratio_range(i, (1, 3), (3, 1))
reference_images_urls = (await upload_images_to_comfyapi(
reference_images_urls = await upload_images_to_comfyapi(
cls,
image,
max_images=n_input_images,
mime_type="image/png",
auth_kwargs=auth_kwargs,
))
payload = Seedream4TaskCreationRequest(
model=model,
prompt=prompt,
image=reference_images_urls,
size=f"{w}x{h}",
seed=seed,
sequential_image_generation=sequential_image_generation,
sequential_image_generation_options=Seedream4Options(max_images=max_images),
watermark=watermark,
)
response = await SynchronousOperation(
endpoint=ApiEndpoint(
path=BYTEPLUS_IMAGE_ENDPOINT,
method=HttpMethod.POST,
request_model=Seedream4TaskCreationRequest,
response_model=ImageTaskCreationResponse,
)
response = await sync_op(
cls,
ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
response_model=ImageTaskCreationResponse,
data=Seedream4TaskCreationRequest(
model=model,
prompt=prompt,
image=reference_images_urls,
size=f"{w}x{h}",
seed=seed,
sequential_image_generation=sequential_image_generation,
sequential_image_generation_options=Seedream4Options(max_images=max_images),
watermark=watermark,
),
request=payload,
auth_kwargs=auth_kwargs,
).execute()
)
if len(response.data) == 1:
return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
urls = [str(d["url"]) for d in response.data if isinstance(d, dict) and "url" in d]
@@ -719,13 +650,13 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
"camera_fixed",
default=False,
tooltip="Specifies whether to fix the camera. The platform appends an instruction "
"to fix the camera to your prompt, but does not guarantee the actual effect.",
"to fix the camera to your prompt, but does not guarantee the actual effect.",
optional=True,
),
IO.Boolean.Input(
"watermark",
default=True,
tooltip="Whether to add an \"AI generated\" watermark to the video.",
tooltip='Whether to add an "AI generated" watermark to the video.',
optional=True,
),
],
@@ -764,19 +695,9 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
f"--camerafixed {str(camera_fixed).lower()} "
f"--watermark {str(watermark).lower()}"
)
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
return await process_video_task(
request_model=Text2VideoTaskCreationRequest,
payload=Text2VideoTaskCreationRequest(
model=model,
content=[TaskTextContent(text=prompt)],
),
auth_kwargs=auth_kwargs,
node_id=cls.hidden.unique_id,
cls,
payload=Text2VideoTaskCreationRequest(model=model, content=[TaskTextContent(text=prompt)]),
estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
)
@@ -840,13 +761,13 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
"camera_fixed",
default=False,
tooltip="Specifies whether to fix the camera. The platform appends an instruction "
"to fix the camera to your prompt, but does not guarantee the actual effect.",
"to fix the camera to your prompt, but does not guarantee the actual effect.",
optional=True,
),
IO.Boolean.Input(
"watermark",
default=True,
tooltip="Whether to add an \"AI generated\" watermark to the video.",
tooltip='Whether to add an "AI generated" watermark to the video.',
optional=True,
),
],
@@ -879,13 +800,7 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000)
validate_image_aspect_ratio_range(image, (2, 5), (5, 2), strict=False) # 0.4 to 2.5
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
image_url = (await upload_images_to_comfyapi(image, max_images=1, auth_kwargs=auth_kwargs))[0]
image_url = (await upload_images_to_comfyapi(cls, image, max_images=1))[0]
prompt = (
f"{prompt} "
f"--resolution {resolution} "
@@ -897,13 +812,11 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
)
return await process_video_task(
request_model=Image2VideoTaskCreationRequest,
cls,
payload=Image2VideoTaskCreationRequest(
model=model,
content=[TaskTextContent(text=prompt), TaskImageContent(image_url=TaskImageContentUrl(url=image_url))],
),
auth_kwargs=auth_kwargs,
node_id=cls.hidden.unique_id,
estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
)
@@ -971,13 +884,13 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
"camera_fixed",
default=False,
tooltip="Specifies whether to fix the camera. The platform appends an instruction "
"to fix the camera to your prompt, but does not guarantee the actual effect.",
"to fix the camera to your prompt, but does not guarantee the actual effect.",
optional=True,
),
IO.Boolean.Input(
"watermark",
default=True,
tooltip="Whether to add an \"AI generated\" watermark to the video.",
tooltip='Whether to add an "AI generated" watermark to the video.',
optional=True,
),
],
@@ -1012,16 +925,11 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
validate_image_dimensions(i, min_width=300, min_height=300, max_width=6000, max_height=6000)
validate_image_aspect_ratio_range(i, (2, 5), (5, 2), strict=False) # 0.4 to 2.5
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
download_urls = await upload_images_to_comfyapi(
cls,
image_tensor_pair_to_batch(first_frame, last_frame),
max_images=2,
mime_type="image/png",
auth_kwargs=auth_kwargs,
)
prompt = (
@@ -1035,7 +943,7 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
)
return await process_video_task(
request_model=Image2VideoTaskCreationRequest,
cls,
payload=Image2VideoTaskCreationRequest(
model=model,
content=[
@@ -1044,8 +952,6 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
TaskImageContent(image_url=TaskImageContentUrl(url=str(download_urls[1])), role="last_frame"),
],
),
auth_kwargs=auth_kwargs,
node_id=cls.hidden.unique_id,
estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
)
@@ -1108,7 +1014,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
IO.Boolean.Input(
"watermark",
default=True,
tooltip="Whether to add an \"AI generated\" watermark to the video.",
tooltip='Whether to add an "AI generated" watermark to the video.',
optional=True,
),
],
@@ -1141,15 +1047,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000)
validate_image_aspect_ratio_range(image, (2, 5), (5, 2), strict=False) # 0.4 to 2.5
auth_kwargs = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
image_urls = await upload_images_to_comfyapi(
images, max_images=4, mime_type="image/png", auth_kwargs=auth_kwargs
)
image_urls = await upload_images_to_comfyapi(cls, images, max_images=4, mime_type="image/png")
prompt = (
f"{prompt} "
f"--resolution {resolution} "
@@ -1160,42 +1058,32 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
)
x = [
TaskTextContent(text=prompt),
*[TaskImageContent(image_url=TaskImageContentUrl(url=str(i)), role="reference_image") for i in image_urls]
*[TaskImageContent(image_url=TaskImageContentUrl(url=str(i)), role="reference_image") for i in image_urls],
]
return await process_video_task(
request_model=Image2VideoTaskCreationRequest,
payload=Image2VideoTaskCreationRequest(
model=model,
content=x,
),
auth_kwargs=auth_kwargs,
node_id=cls.hidden.unique_id,
cls,
payload=Image2VideoTaskCreationRequest(model=model, content=x),
estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
)
async def process_video_task(
request_model: Type[T],
cls: type[IO.ComfyNode],
payload: Union[Text2VideoTaskCreationRequest, Image2VideoTaskCreationRequest],
auth_kwargs: dict,
node_id: str,
estimated_duration: Optional[int],
) -> IO.NodeOutput:
initial_response = await SynchronousOperation(
endpoint=ApiEndpoint(
path=BYTEPLUS_TASK_ENDPOINT,
method=HttpMethod.POST,
request_model=request_model,
response_model=TaskCreationResponse,
),
request=payload,
auth_kwargs=auth_kwargs,
).execute()
response = await poll_until_finished(
auth_kwargs,
initial_response.id,
initial_response = await sync_op(
cls,
ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"),
data=payload,
response_model=TaskCreationResponse,
)
response = await poll_op(
cls,
ApiEndpoint(path=f"{BYTEPLUS_TASK_STATUS_ENDPOINT}/{initial_response.id}"),
status_extractor=lambda r: r.status,
estimated_duration=estimated_duration,
node_id=node_id,
response_model=TaskStatusResponse,
)
return IO.NodeOutput(await download_url_to_video_output(get_video_url_from_task_status(response)))
@@ -1221,5 +1109,6 @@ class ByteDanceExtension(ComfyExtension):
ByteDanceImageReferenceNode,
]
async def comfy_entrypoint() -> ByteDanceExtension:
return ByteDanceExtension()