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Merge branch 'master' into partition-advanced-widgets

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This commit is contained in:
Jedrzej Kosinski
2026-02-19 19:10:17 -08:00
committed by GitHub
parent f7817d0303 5632b2df9d
commit 0358446ef0
93 changed files with 4651 additions and 898 deletions
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8
comfy_api_nodes/apis/__init__.py generated
View File

@@ -1197,12 +1197,6 @@ class KlingImageGenImageReferenceType(str, Enum):
face = 'face'
class KlingImageGenModelName(str, Enum):
kling_v1 = 'kling-v1'
kling_v1_5 = 'kling-v1-5'
kling_v2 = 'kling-v2'
class KlingImageGenerationsRequest(BaseModel):
aspect_ratio: Optional[KlingImageGenAspectRatio] = '16:9'
callback_url: Optional[AnyUrl] = Field(
@@ -1218,7 +1212,7 @@ class KlingImageGenerationsRequest(BaseModel):
0.5, description='Reference intensity for user-uploaded images', ge=0.0, le=1.0
)
image_reference: Optional[KlingImageGenImageReferenceType] = None
model_name: Optional[KlingImageGenModelName] = 'kling-v1'
model_name: str = Field(...)
n: Optional[int] = Field(1, description='Number of generated images', ge=1, le=9)
negative_prompt: Optional[str] = Field(
None, description='Negative text prompt', max_length=200

44
comfy_api_nodes/apis/bria.py
View File

@@ -45,17 +45,55 @@ class BriaEditImageRequest(BaseModel):
)
class BriaRemoveBackgroundRequest(BaseModel):
image: str = Field(...)
sync: bool = Field(False)
visual_input_content_moderation: bool = Field(
False, description="If true, returns 422 on input image moderation failure."
)
visual_output_content_moderation: bool = Field(
False, description="If true, returns 422 on visual output moderation failure."
)
seed: int = Field(...)
class BriaStatusResponse(BaseModel):
request_id: str = Field(...)
status_url: str = Field(...)
warning: str | None = Field(None)
class BriaResult(BaseModel):
class BriaRemoveBackgroundResult(BaseModel):
image_url: str = Field(...)
class BriaRemoveBackgroundResponse(BaseModel):
status: str = Field(...)
result: BriaRemoveBackgroundResult | None = Field(None)
class BriaImageEditResult(BaseModel):
structured_prompt: str = Field(...)
image_url: str = Field(...)
class BriaResponse(BaseModel):
class BriaImageEditResponse(BaseModel):
status: str = Field(...)
result: BriaResult | None = Field(None)
result: BriaImageEditResult | None = Field(None)
class BriaRemoveVideoBackgroundRequest(BaseModel):
video: str = Field(...)
background_color: str = Field(default="transparent", description="Background color for the output video.")
output_container_and_codec: str = Field(...)
preserve_audio: bool = Field(True)
seed: int = Field(...)
class BriaRemoveVideoBackgroundResult(BaseModel):
video_url: str = Field(...)
class BriaRemoveVideoBackgroundResponse(BaseModel):
status: str = Field(...)
result: BriaRemoveVideoBackgroundResult | None = Field(None)

6
comfy_api_nodes/apis/gemini.py
View File

@@ -116,9 +116,15 @@ class GeminiGenerationConfig(BaseModel):
topP: float | None = Field(None, ge=0.0, le=1.0)
class GeminiImageOutputOptions(BaseModel):
mimeType: str = Field("image/png")
compressionQuality: int | None = Field(None)
class GeminiImageConfig(BaseModel):
aspectRatio: str | None = Field(None)
imageSize: str | None = Field(None)
imageOutputOptions: GeminiImageOutputOptions = Field(default_factory=GeminiImageOutputOptions)
class GeminiImageGenerationConfig(GeminiGenerationConfig):

20
comfy_api_nodes/apis/hunyuan3d.py
View File

@@ -64,3 +64,23 @@ class To3DProTaskResultResponse(BaseModel):
class To3DProTaskQueryRequest(BaseModel):
JobId: str = Field(...)
class To3DUVFileInput(BaseModel):
Type: str = Field(..., description="File type: GLB, OBJ, or FBX")
Url: str = Field(...)
class To3DUVTaskRequest(BaseModel):
File: To3DUVFileInput = Field(...)
class TextureEditImageInfo(BaseModel):
Url: str = Field(...)
class TextureEditTaskRequest(BaseModel):
File3D: To3DUVFileInput = Field(...)
Image: TextureEditImageInfo | None = Field(None)
Prompt: str | None = Field(None)
EnablePBR: bool | None = Field(None)

46
comfy_api_nodes/apis/kling.py
View File

@@ -1,12 +1,22 @@
from pydantic import BaseModel, Field
class MultiPromptEntry(BaseModel):
index: int = Field(...)
prompt: str = Field(...)
duration: str = Field(...)
class OmniProText2VideoRequest(BaseModel):
model_name: str = Field(..., description="kling-video-o1")
aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
duration: str = Field(..., description="'5' or '10'")
prompt: str = Field(...)
mode: str = Field("pro")
multi_shot: bool | None = Field(None)
multi_prompt: list[MultiPromptEntry] | None = Field(None)
shot_type: str | None = Field(None)
sound: str = Field(..., description="'on' or 'off'")
class OmniParamImage(BaseModel):
@@ -26,6 +36,10 @@ class OmniProFirstLastFrameRequest(BaseModel):
duration: str = Field(..., description="'5' or '10'")
prompt: str = Field(...)
mode: str = Field("pro")
sound: str | None = Field(None, description="'on' or 'off'")
multi_shot: bool | None = Field(None)
multi_prompt: list[MultiPromptEntry] | None = Field(None)
shot_type: str | None = Field(None)
class OmniProReferences2VideoRequest(BaseModel):
@@ -38,6 +52,10 @@ class OmniProReferences2VideoRequest(BaseModel):
duration: str | None = Field(..., description="From 3 to 10.")
prompt: str = Field(...)
mode: str = Field("pro")
sound: str | None = Field(None, description="'on' or 'off'")
multi_shot: bool | None = Field(None)
multi_prompt: list[MultiPromptEntry] | None = Field(None)
shot_type: str | None = Field(None)
class TaskStatusVideoResult(BaseModel):
@@ -54,6 +72,7 @@ class TaskStatusImageResult(BaseModel):
class TaskStatusResults(BaseModel):
videos: list[TaskStatusVideoResult] | None = Field(None)
images: list[TaskStatusImageResult] | None = Field(None)
series_images: list[TaskStatusImageResult] | None = Field(None)
class TaskStatusResponseData(BaseModel):
@@ -77,31 +96,42 @@ class OmniImageParamImage(BaseModel):
class OmniProImageRequest(BaseModel):
model_name: str = Field(..., description="kling-image-o1")
resolution: str = Field(..., description="'1k' or '2k'")
model_name: str = Field(...)
resolution: str = Field(...)
aspect_ratio: str | None = Field(...)
prompt: str = Field(...)
mode: str = Field("pro")
n: int | None = Field(1, le=9)
image_list: list[OmniImageParamImage] | None = Field(..., max_length=10)
result_type: str | None = Field(None, description="Set to 'series' for series generation")
series_amount: int | None = Field(None, ge=2, le=9, description="Number of images in a series")
class TextToVideoWithAudioRequest(BaseModel):
model_name: str = Field(..., description="kling-v2-6")
model_name: str = Field(...)
aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
duration: str = Field(..., description="'5' or '10'")
prompt: str = Field(...)
duration: str = Field(...)
prompt: str | None = Field(...)
negative_prompt: str | None = Field(None)
mode: str = Field("pro")
sound: str = Field(..., description="'on' or 'off'")
multi_shot: bool | None = Field(None)
multi_prompt: list[MultiPromptEntry] | None = Field(None)
shot_type: str | None = Field(None)
class ImageToVideoWithAudioRequest(BaseModel):
model_name: str = Field(..., description="kling-v2-6")
model_name: str = Field(...)
image: str = Field(...)
duration: str = Field(..., description="'5' or '10'")
prompt: str = Field(...)
image_tail: str | None = Field(None)
duration: str = Field(...)
prompt: str | None = Field(...)
negative_prompt: str | None = Field(None)
mode: str = Field("pro")
sound: str = Field(..., description="'on' or 'off'")
multi_shot: bool | None = Field(None)
multi_prompt: list[MultiPromptEntry] | None = Field(None)
shot_type: str | None = Field(None)
class MotionControlRequest(BaseModel):

45
comfy_api_nodes/apis/recraft.py
View File

@@ -198,11 +198,6 @@ dict_recraft_substyles_v3 = {
}
class RecraftModel(str, Enum):
recraftv3 = 'recraftv3'
recraftv2 = 'recraftv2'
class RecraftImageSize(str, Enum):
res_1024x1024 = '1024x1024'
res_1365x1024 = '1365x1024'
@@ -221,6 +216,41 @@ class RecraftImageSize(str, Enum):
res_1707x1024 = '1707x1024'
RECRAFT_V4_SIZES = [
"1024x1024",
"1536x768",
"768x1536",
"1280x832",
"832x1280",
"1216x896",
"896x1216",
"1152x896",
"896x1152",
"832x1344",
"1280x896",
"896x1280",
"1344x768",
"768x1344",
]
RECRAFT_V4_PRO_SIZES = [
"2048x2048",
"3072x1536",
"1536x3072",
"2560x1664",
"1664x2560",
"2432x1792",
"1792x2432",
"2304x1792",
"1792x2304",
"1664x2688",
"1434x1024",
"1024x1434",
"2560x1792",
"1792x2560",
]
class RecraftColorObject(BaseModel):
rgb: list[int] = Field(..., description='An array of 3 integer values in range of 0...255 defining RGB Color Model')
@@ -234,17 +264,16 @@ class RecraftControlsObject(BaseModel):
class RecraftImageGenerationRequest(BaseModel):
prompt: str = Field(..., description='The text prompt describing the image to generate')
size: RecraftImageSize | None = Field(None, description='The size of the generated image (e.g., "1024x1024")')
size: str | None = Field(None, description='The size of the generated image (e.g., "1024x1024")')
n: int = Field(..., description='The number of images to generate')
negative_prompt: str | None = Field(None, description='A text description of undesired elements on an image')
model: RecraftModel | None = Field(RecraftModel.recraftv3, description='The model to use for generation (e.g., "recraftv3")')
model: str = Field(...)
style: str | None = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")')
substyle: str | None = Field(None, description='The substyle to apply to the generated image, depending on the style input')
controls: RecraftControlsObject | None = Field(None, description='A set of custom parameters to tweak generation process')
style_id: str | None = Field(None, description='Use a previously uploaded style as a reference; UUID')
strength: float | None = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity')
random_seed: int | None = Field(None, description="Seed for video generation")
# text_layout
class RecraftReturnedObject(BaseModel):

220
comfy_api_nodes/nodes_bria.py
View File

@@ -3,7 +3,11 @@ from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.apis.bria import (
BriaEditImageRequest,
BriaResponse,
BriaRemoveBackgroundRequest,
BriaRemoveBackgroundResponse,
BriaRemoveVideoBackgroundRequest,
BriaRemoveVideoBackgroundResponse,
BriaImageEditResponse,
BriaStatusResponse,
InputModerationSettings,
)
@@ -11,10 +15,12 @@ from comfy_api_nodes.util import (
ApiEndpoint,
convert_mask_to_image,
download_url_to_image_tensor,
get_number_of_images,
download_url_to_video_output,
poll_op,
sync_op,
upload_images_to_comfyapi,
upload_image_to_comfyapi,
upload_video_to_comfyapi,
validate_video_duration,
)
@@ -73,21 +79,15 @@ class BriaImageEditNode(IO.ComfyNode):
IO.DynamicCombo.Input(
"moderation",
options=[
IO.DynamicCombo.Option("false", []),
IO.DynamicCombo.Option(
"true",
[
IO.Boolean.Input(
"prompt_content_moderation", default=False
),
IO.Boolean.Input(
"visual_input_moderation", default=False
),
IO.Boolean.Input(
"visual_output_moderation", default=True
),
IO.Boolean.Input("prompt_content_moderation", default=False),
IO.Boolean.Input("visual_input_moderation", default=False),
IO.Boolean.Input("visual_output_moderation", default=True),
],
),
IO.DynamicCombo.Option("false", []),
],
tooltip="Moderation settings",
),
@@ -127,50 +127,26 @@ class BriaImageEditNode(IO.ComfyNode):
mask: Input.Image | None = None,
) -> IO.NodeOutput:
if not prompt and not structured_prompt:
raise ValueError(
"One of prompt or structured_prompt is required to be non-empty."
)
if get_number_of_images(image) != 1:
raise ValueError("Exactly one input image is required.")
raise ValueError("One of prompt or structured_prompt is required to be non-empty.")
mask_url = None
if mask is not None:
mask_url = (
await upload_images_to_comfyapi(
cls,
convert_mask_to_image(mask),
max_images=1,
mime_type="image/png",
wait_label="Uploading mask",
)
)[0]
mask_url = await upload_image_to_comfyapi(cls, convert_mask_to_image(mask), wait_label="Uploading mask")
response = await sync_op(
cls,
ApiEndpoint(path="proxy/bria/v2/image/edit", method="POST"),
data=BriaEditImageRequest(
instruction=prompt if prompt else None,
structured_instruction=structured_prompt if structured_prompt else None,
images=await upload_images_to_comfyapi(
cls,
image,
max_images=1,
mime_type="image/png",
wait_label="Uploading image",
),
images=[await upload_image_to_comfyapi(cls, image, wait_label="Uploading image")],
mask=mask_url,
negative_prompt=negative_prompt if negative_prompt else None,
guidance_scale=guidance_scale,
seed=seed,
model_version=model,
steps_num=steps,
prompt_content_moderation=moderation.get(
"prompt_content_moderation", False
),
visual_input_content_moderation=moderation.get(
"visual_input_moderation", False
),
visual_output_content_moderation=moderation.get(
"visual_output_moderation", False
),
prompt_content_moderation=moderation.get("prompt_content_moderation", False),
visual_input_content_moderation=moderation.get("visual_input_moderation", False),
visual_output_content_moderation=moderation.get("visual_output_moderation", False),
),
response_model=BriaStatusResponse,
)
@@ -178,7 +154,7 @@ class BriaImageEditNode(IO.ComfyNode):
cls,
ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
status_extractor=lambda r: r.status,
response_model=BriaResponse,
response_model=BriaImageEditResponse,
)
return IO.NodeOutput(
await download_url_to_image_tensor(response.result.image_url),
@@ -186,11 +162,167 @@ class BriaImageEditNode(IO.ComfyNode):
)
class BriaRemoveImageBackground(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="BriaRemoveImageBackground",
display_name="Bria Remove Image Background",
category="api node/image/Bria",
description="Remove the background from an image using Bria RMBG 2.0.",
inputs=[
IO.Image.Input("image"),
IO.DynamicCombo.Input(
"moderation",
options=[
IO.DynamicCombo.Option("false", []),
IO.DynamicCombo.Option(
"true",
[
IO.Boolean.Input("visual_input_moderation", default=False),
IO.Boolean.Input("visual_output_moderation", default=True),
],
),
],
tooltip="Moderation settings",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed controls whether the node should re-run; "
"results are non-deterministic regardless of seed.",
),
],
outputs=[IO.Image.Output()],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
expr="""{"type":"usd","usd":0.018}""",
),
)
@classmethod
async def execute(
cls,
image: Input.Image,
moderation: dict,
seed: int,
) -> IO.NodeOutput:
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/bria/v2/image/edit/remove_background", method="POST"),
data=BriaRemoveBackgroundRequest(
image=await upload_image_to_comfyapi(cls, image, wait_label="Uploading image"),
sync=False,
visual_input_content_moderation=moderation.get("visual_input_moderation", False),
visual_output_content_moderation=moderation.get("visual_output_moderation", False),
seed=seed,
),
response_model=BriaStatusResponse,
)
response = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
status_extractor=lambda r: r.status,
response_model=BriaRemoveBackgroundResponse,
)
return IO.NodeOutput(await download_url_to_image_tensor(response.result.image_url))
class BriaRemoveVideoBackground(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="BriaRemoveVideoBackground",
display_name="Bria Remove Video Background",
category="api node/video/Bria",
description="Remove the background from a video using Bria. ",
inputs=[
IO.Video.Input("video"),
IO.Combo.Input(
"background_color",
options=[
"Black",
"White",
"Gray",
"Red",
"Green",
"Blue",
"Yellow",
"Cyan",
"Magenta",
"Orange",
],
tooltip="Background color for the output video.",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed controls whether the node should re-run; "
"results are non-deterministic regardless of seed.",
),
],
outputs=[IO.Video.Output()],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
),
)
@classmethod
async def execute(
cls,
video: Input.Video,
background_color: str,
seed: int,
) -> IO.NodeOutput:
validate_video_duration(video, max_duration=60.0)
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/bria/v2/video/edit/remove_background", method="POST"),
data=BriaRemoveVideoBackgroundRequest(
video=await upload_video_to_comfyapi(cls, video),
background_color=background_color,
output_container_and_codec="mp4_h264",
seed=seed,
),
response_model=BriaStatusResponse,
)
response = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
status_extractor=lambda r: r.status,
response_model=BriaRemoveVideoBackgroundResponse,
)
return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
class BriaExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
BriaImageEditNode,
BriaRemoveImageBackground,
BriaRemoveVideoBackground,
]

18
comfy_api_nodes/nodes_gemini.py
View File

@@ -6,6 +6,7 @@ See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/infer
import base64
import os
from enum import Enum
from fnmatch import fnmatch
from io import BytesIO
from typing import Literal
@@ -119,6 +120,13 @@ async def create_image_parts(
return image_parts
def _mime_matches(mime: GeminiMimeType | None, pattern: str) -> bool:
"""Check if a MIME type matches a pattern. Supports fnmatch globs (e.g. 'image/*')."""
if mime is None:
return False
return fnmatch(mime.value, pattern)
def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Literal["text"] | str) -> list[GeminiPart]:
"""
Filter response parts by their type.
@@ -151,9 +159,9 @@ def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Litera
for part in candidate.content.parts:
if part_type == "text" and part.text:
parts.append(part)
elif part.inlineData and part.inlineData.mimeType == part_type:
elif part.inlineData and _mime_matches(part.inlineData.mimeType, part_type):
parts.append(part)
elif part.fileData and part.fileData.mimeType == part_type:
elif part.fileData and _mime_matches(part.fileData.mimeType, part_type):
parts.append(part)
if not parts and blocked_reasons:
@@ -178,7 +186,7 @@ def get_text_from_response(response: GeminiGenerateContentResponse) -> str:
async def get_image_from_response(response: GeminiGenerateContentResponse) -> Input.Image:
image_tensors: list[Input.Image] = []
parts = get_parts_by_type(response, "image/png")
parts = get_parts_by_type(response, "image/*")
for part in parts:
if part.inlineData:
image_data = base64.b64decode(part.inlineData.data)
@@ -629,7 +637,7 @@ class GeminiImage(IO.ComfyNode):
if not aspect_ratio:
aspect_ratio = "auto" # for backward compatability with old workflows; to-do remove this in December
image_config = GeminiImageConfig(aspectRatio=aspect_ratio)
image_config = GeminiImageConfig() if aspect_ratio == "auto" else GeminiImageConfig(aspectRatio=aspect_ratio)
if images is not None:
parts.extend(await create_image_parts(cls, images))
@@ -649,7 +657,7 @@ class GeminiImage(IO.ComfyNode):
],
generationConfig=GeminiImageGenerationConfig(
responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
imageConfig=None if aspect_ratio == "auto" else image_config,
imageConfig=image_config,
),
systemInstruction=gemini_system_prompt,
),

297
comfy_api_nodes/nodes_hunyuan3d.py
View File

@@ -1,31 +1,48 @@
from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api.latest import IO, ComfyExtension, Input, Types
from comfy_api_nodes.apis.hunyuan3d import (
Hunyuan3DViewImage,
InputGenerateType,
ResultFile3D,
TextureEditTaskRequest,
To3DProTaskCreateResponse,
To3DProTaskQueryRequest,
To3DProTaskRequest,
To3DProTaskResultResponse,
To3DUVFileInput,
To3DUVTaskRequest,
)
from comfy_api_nodes.util import (
ApiEndpoint,
download_url_to_file_3d,
download_url_to_image_tensor,
downscale_image_tensor_by_max_side,
poll_op,
sync_op,
upload_3d_model_to_comfyapi,
upload_image_to_comfyapi,
validate_image_dimensions,
validate_string,
)
def get_file_from_response(response_objs: list[ResultFile3D], file_type: str) -> ResultFile3D | None:
def _is_tencent_rate_limited(status: int, body: object) -> bool:
return (
status == 400
and isinstance(body, dict)
and "RequestLimitExceeded" in str(body.get("Response", {}).get("Error", {}).get("Code", ""))
)
def get_file_from_response(
response_objs: list[ResultFile3D], file_type: str, raise_if_not_found: bool = True
) -> ResultFile3D | None:
for i in response_objs:
if i.Type.lower() == file_type.lower():
return i
if raise_if_not_found:
raise ValueError(f"'{file_type}' file type is not found in the response.")
return None
@@ -35,8 +52,9 @@ class TencentTextToModelNode(IO.ComfyNode):
def define_schema(cls):
return IO.Schema(
node_id="TencentTextToModelNode",
display_name="Hunyuan3D: Text to Model (Pro)",
display_name="Hunyuan3D: Text to Model",
category="api node/3d/Tencent",
essentials_category="3D",
inputs=[
IO.Combo.Input(
"model",
@@ -120,6 +138,7 @@ class TencentTextToModelNode(IO.ComfyNode):
EnablePBR=generate_type.get("pbr", None),
PolygonType=generate_type.get("polygon_type", None),
),
is_rate_limited=_is_tencent_rate_limited,
)
if response.Error:
raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
@@ -131,11 +150,14 @@ class TencentTextToModelNode(IO.ComfyNode):
response_model=To3DProTaskResultResponse,
status_extractor=lambda r: r.Status,
)
glb_result = get_file_from_response(result.ResultFile3Ds, "glb")
obj_result = get_file_from_response(result.ResultFile3Ds, "obj")
file_glb = await download_url_to_file_3d(glb_result.Url, "glb", task_id=task_id) if glb_result else None
return IO.NodeOutput(
file_glb, file_glb, await download_url_to_file_3d(obj_result.Url, "obj", task_id=task_id) if obj_result else None
f"{task_id}.glb",
await download_url_to_file_3d(
get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
),
await download_url_to_file_3d(
get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
),
)
@@ -145,8 +167,9 @@ class TencentImageToModelNode(IO.ComfyNode):
def define_schema(cls):
return IO.Schema(
node_id="TencentImageToModelNode",
display_name="Hunyuan3D: Image(s) to Model (Pro)",
display_name="Hunyuan3D: Image(s) to Model",
category="api node/3d/Tencent",
essentials_category="3D",
inputs=[
IO.Combo.Input(
"model",
@@ -268,6 +291,7 @@ class TencentImageToModelNode(IO.ComfyNode):
EnablePBR=generate_type.get("pbr", None),
PolygonType=generate_type.get("polygon_type", None),
),
is_rate_limited=_is_tencent_rate_limited,
)
if response.Error:
raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
@@ -279,11 +303,257 @@ class TencentImageToModelNode(IO.ComfyNode):
response_model=To3DProTaskResultResponse,
status_extractor=lambda r: r.Status,
)
glb_result = get_file_from_response(result.ResultFile3Ds, "glb")
obj_result = get_file_from_response(result.ResultFile3Ds, "obj")
file_glb = await download_url_to_file_3d(glb_result.Url, "glb", task_id=task_id) if glb_result else None
return IO.NodeOutput(
file_glb, file_glb, await download_url_to_file_3d(obj_result.Url, "obj", task_id=task_id) if obj_result else None
f"{task_id}.glb",
await download_url_to_file_3d(
get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
),
await download_url_to_file_3d(
get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
),
)
class TencentModelTo3DUVNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="TencentModelTo3DUVNode",
display_name="Hunyuan3D: Model to UV",
category="api node/3d/Tencent",
description="Perform UV unfolding on a 3D model to generate UV texture. "
"Input model must have less than 30000 faces.",
inputs=[
IO.MultiType.Input(
"model_3d",
types=[IO.File3DGLB, IO.File3DOBJ, IO.File3DFBX, IO.File3DAny],
tooltip="Input 3D model (GLB, OBJ, or FBX)",
),
IO.Int.Input(
"seed",
default=1,
min=0,
max=2147483647,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed controls whether the node should re-run; "
"results are non-deterministic regardless of seed.",
),
],
outputs=[
IO.File3DOBJ.Output(display_name="OBJ"),
IO.File3DFBX.Output(display_name="FBX"),
IO.Image.Output(),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(expr='{"type":"usd","usd":0.2}'),
)
SUPPORTED_FORMATS = {"glb", "obj", "fbx"}
@classmethod
async def execute(
cls,
model_3d: Types.File3D,
seed: int,
) -> IO.NodeOutput:
_ = seed
file_format = model_3d.format.lower()
if file_format not in cls.SUPPORTED_FORMATS:
raise ValueError(
f"Unsupported file format: '{file_format}'. "
f"Supported formats: {', '.join(sorted(cls.SUPPORTED_FORMATS))}."
)
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/tencent/hunyuan/3d-uv", method="POST"),
response_model=To3DProTaskCreateResponse,
data=To3DUVTaskRequest(
File=To3DUVFileInput(
Type=file_format.upper(),
Url=await upload_3d_model_to_comfyapi(cls, model_3d, file_format),
)
),
is_rate_limited=_is_tencent_rate_limited,
)
if response.Error:
raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
result = await poll_op(
cls,
ApiEndpoint(path="/proxy/tencent/hunyuan/3d-uv/query", method="POST"),
data=To3DProTaskQueryRequest(JobId=response.JobId),
response_model=To3DProTaskResultResponse,
status_extractor=lambda r: r.Status,
)
return IO.NodeOutput(
await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
await download_url_to_image_tensor(get_file_from_response(result.ResultFile3Ds, "image").Url),
)
class Tencent3DTextureEditNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="Tencent3DTextureEditNode",
display_name="Hunyuan3D: 3D Texture Edit",
category="api node/3d/Tencent",
description="After inputting the 3D model, perform 3D model texture redrawing.",
inputs=[
IO.MultiType.Input(
"model_3d",
types=[IO.File3DFBX, IO.File3DAny],
tooltip="3D model in FBX format. Model should have less than 100000 faces.",
),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Describes texture editing. Supports up to 1024 UTF-8 characters.",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed controls whether the node should re-run; "
"results are non-deterministic regardless of seed.",
),
],
outputs=[
IO.File3DGLB.Output(display_name="GLB"),
IO.File3DFBX.Output(display_name="FBX"),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
expr="""{"type":"usd","usd": 0.6}""",
),
)
@classmethod
async def execute(
cls,
model_3d: Types.File3D,
prompt: str,
seed: int,
) -> IO.NodeOutput:
_ = seed
file_format = model_3d.format.lower()
if file_format != "fbx":
raise ValueError(f"Unsupported file format: '{file_format}'. Only FBX format is supported.")
validate_string(prompt, field_name="prompt", min_length=1, max_length=1024)
model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/tencent/hunyuan/3d-texture-edit", method="POST"),
response_model=To3DProTaskCreateResponse,
data=TextureEditTaskRequest(
File3D=To3DUVFileInput(Type=file_format.upper(), Url=model_url),
Prompt=prompt,
EnablePBR=True,
),
is_rate_limited=_is_tencent_rate_limited,
)
if response.Error:
raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
result = await poll_op(
cls,
ApiEndpoint(path="/proxy/tencent/hunyuan/3d-texture-edit/query", method="POST"),
data=To3DProTaskQueryRequest(JobId=response.JobId),
response_model=To3DProTaskResultResponse,
status_extractor=lambda r: r.Status,
)
return IO.NodeOutput(
await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb"),
await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
)
class Tencent3DPartNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="Tencent3DPartNode",
display_name="Hunyuan3D: 3D Part",
category="api node/3d/Tencent",
description="Automatically perform component identification and generation based on the model structure.",
inputs=[
IO.MultiType.Input(
"model_3d",
types=[IO.File3DFBX, IO.File3DAny],
tooltip="3D model in FBX format. Model should have less than 30000 faces.",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed controls whether the node should re-run; "
"results are non-deterministic regardless of seed.",
),
],
outputs=[
IO.File3DFBX.Output(display_name="FBX"),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(expr='{"type":"usd","usd":0.6}'),
)
@classmethod
async def execute(
cls,
model_3d: Types.File3D,
seed: int,
) -> IO.NodeOutput:
_ = seed
file_format = model_3d.format.lower()
if file_format != "fbx":
raise ValueError(f"Unsupported file format: '{file_format}'. Only FBX format is supported.")
model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/tencent/hunyuan/3d-part", method="POST"),
response_model=To3DProTaskCreateResponse,
data=To3DUVTaskRequest(
File=To3DUVFileInput(Type=file_format.upper(), Url=model_url),
),
is_rate_limited=_is_tencent_rate_limited,
)
if response.Error:
raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
result = await poll_op(
cls,
ApiEndpoint(path="/proxy/tencent/hunyuan/3d-part/query", method="POST"),
data=To3DProTaskQueryRequest(JobId=response.JobId),
response_model=To3DProTaskResultResponse,
status_extractor=lambda r: r.Status,
)
return IO.NodeOutput(
await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
)
@@ -293,6 +563,9 @@ class TencentHunyuan3DExtension(ComfyExtension):
return [
TencentTextToModelNode,
TencentImageToModelNode,
# TencentModelTo3DUVNode,
# Tencent3DTextureEditNode,
Tencent3DPartNode,
]

765
comfy_api_nodes/nodes_kling.py
View File

File diff suppressed because it is too large Load Diff

62
comfy_api_nodes/nodes_magnific.py
View File

@@ -30,6 +30,30 @@ from comfy_api_nodes.util import (
validate_image_dimensions,
)
_EUR_TO_USD = 1.19
def _tier_price_eur(megapixels: float) -> float:
"""Price in EUR for a single Magnific upscaling step based on input megapixels."""
if megapixels <= 1.3:
return 0.143
if megapixels <= 3.0:
return 0.286
if megapixels <= 6.4:
return 0.429
return 1.716
def _calculate_magnific_upscale_price_usd(width: int, height: int, scale: int) -> float:
"""Calculate total Magnific upscale price in USD for given input dimensions and scale factor."""
num_steps = int(math.log2(scale))
total_eur = 0.0
pixels = width * height
for _ in range(num_steps):
total_eur += _tier_price_eur(pixels / 1_000_000)
pixels *= 4
return round(total_eur * _EUR_TO_USD, 2)
class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
@classmethod
@@ -105,11 +129,20 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
],
is_api_node=True,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["scale_factor"]),
depends_on=IO.PriceBadgeDepends(widgets=["scale_factor", "auto_downscale"]),
expr="""
(
$max := widgets.scale_factor = "2x" ? 1.326 : 1.657;
{"type": "range_usd", "min_usd": 0.11, "max_usd": $max}
$ad := widgets.auto_downscale;
$mins := $ad
? {"2x": 0.172, "4x": 0.343, "8x": 0.515, "16x": 0.515}
: {"2x": 0.172, "4x": 0.343, "8x": 0.515, "16x": 0.844};
$maxs := {"2x": 0.515, "4x": 0.844, "8x": 1.015, "16x": 1.187};
{
"type": "range_usd",
"min_usd": $lookup($mins, widgets.scale_factor),
"max_usd": $lookup($maxs, widgets.scale_factor),
"format": { "approximate": true }
}
)
""",
),
@@ -170,6 +203,10 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
f"Use a smaller input image or lower scale factor."
)
final_height, final_width = get_image_dimensions(image)
actual_scale = int(scale_factor.rstrip("x"))
price_usd = _calculate_magnific_upscale_price_usd(final_width, final_height, actual_scale)
initial_res = await sync_op(
cls,
ApiEndpoint(path="/proxy/freepik/v1/ai/image-upscaler", method="POST"),
@@ -191,6 +228,7 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-upscaler/{initial_res.task_id}"),
response_model=TaskResponse,
status_extractor=lambda x: x.status,
price_extractor=lambda _: price_usd,
poll_interval=10.0,
max_poll_attempts=480,
)
@@ -260,8 +298,14 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
depends_on=IO.PriceBadgeDepends(widgets=["scale_factor"]),
expr="""
(
$max := widgets.scale_factor = "2x" ? 1.326 : 1.657;
{"type": "range_usd", "min_usd": 0.11, "max_usd": $max}
$mins := {"2x": 0.172, "4x": 0.343, "8x": 0.515, "16x": 0.844};
$maxs := {"2x": 2.045, "4x": 2.545, "8x": 2.889, "16x": 3.06};
{
"type": "range_usd",
"min_usd": $lookup($mins, widgets.scale_factor),
"max_usd": $lookup($maxs, widgets.scale_factor),
"format": { "approximate": true }
}
)
""",
),
@@ -324,6 +368,9 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
f"Use a smaller input image or lower scale factor."
)
final_height, final_width = get_image_dimensions(image)
price_usd = _calculate_magnific_upscale_price_usd(final_width, final_height, requested_scale)
initial_res = await sync_op(
cls,
ApiEndpoint(path="/proxy/freepik/v1/ai/image-upscaler-precision-v2", method="POST"),
@@ -342,6 +389,7 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
ApiEndpoint(path=f"/proxy/freepik/v1/ai/image-upscaler-precision-v2/{initial_res.task_id}"),
response_model=TaskResponse,
status_extractor=lambda x: x.status,
price_extractor=lambda _: price_usd,
poll_interval=10.0,
max_poll_attempts=480,
)
@@ -885,8 +933,8 @@ class MagnificExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
# MagnificImageUpscalerCreativeNode,
# MagnificImageUpscalerPreciseV2Node,
MagnificImageUpscalerCreativeNode,
MagnificImageUpscalerPreciseV2Node,
MagnificImageStyleTransferNode,
MagnificImageRelightNode,
MagnificImageSkinEnhancerNode,

14
comfy_api_nodes/nodes_moonvalley.py
View File

@@ -219,8 +219,8 @@ class MoonvalleyImg2VideoNode(IO.ComfyNode):
),
IO.Int.Input(
"steps",
default=33,
min=1,
default=80,
min=75, # steps should be greater or equal to cooldown_steps(75) + warmup_steps(0)
max=100,
step=1,
tooltip="Number of denoising steps",
@@ -340,8 +340,8 @@ class MoonvalleyVideo2VideoNode(IO.ComfyNode):
),
IO.Int.Input(
"steps",
default=33,
min=1,
default=60,
min=60, # steps should be greater or equal to cooldown_steps(36) + warmup_steps(24)
max=100,
step=1,
display_mode=IO.NumberDisplay.number,
@@ -370,7 +370,7 @@ class MoonvalleyVideo2VideoNode(IO.ComfyNode):
video: Input.Video | None = None,
control_type: str = "Motion Transfer",
motion_intensity: int | None = 100,
steps=33,
steps=60,
prompt_adherence=4.5,
) -> IO.NodeOutput:
validated_video = validate_video_to_video_input(video)
@@ -465,8 +465,8 @@ class MoonvalleyTxt2VideoNode(IO.ComfyNode):
),
IO.Int.Input(
"steps",
default=33,
min=1,
default=80,
min=75, # steps should be greater or equal to cooldown_steps(75) + warmup_steps(0)
max=100,
step=1,
tooltip="Inference steps",

7
comfy_api_nodes/nodes_openai.py
View File

@@ -43,7 +43,6 @@ class SupportedOpenAIModel(str, Enum):
o1 = "o1"
o3 = "o3"
o1_pro = "o1-pro"
gpt_4o = "gpt-4o"
gpt_4_1 = "gpt-4.1"
gpt_4_1_mini = "gpt-4.1-mini"
gpt_4_1_nano = "gpt-4.1-nano"
@@ -576,6 +575,7 @@ class OpenAIChatNode(IO.ComfyNode):
node_id="OpenAIChatNode",
display_name="OpenAI ChatGPT",
category="api node/text/OpenAI",
essentials_category="Text Generation",
description="Generate text responses from an OpenAI model.",
inputs=[
IO.String.Input(
@@ -650,11 +650,6 @@ class OpenAIChatNode(IO.ComfyNode):
"usd": [0.01, 0.04],
"format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
}
: $contains($m, "gpt-4o") ? {
"type": "list_usd",
"usd": [0.0025, 0.01],
"format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
}
: $contains($m, "gpt-4.1-nano") ? {
"type": "list_usd",
"usd": [0.0001, 0.0004],

273
comfy_api_nodes/nodes_recraft.py
View File

@@ -1,5 +1,4 @@
from io import BytesIO
from typing import Optional, Union
import aiohttp
import torch
@@ -9,6 +8,8 @@ from typing_extensions import override
from comfy.utils import ProgressBar
from comfy_api.latest import IO, ComfyExtension
from comfy_api_nodes.apis.recraft import (
RECRAFT_V4_PRO_SIZES,
RECRAFT_V4_SIZES,
RecraftColor,
RecraftColorChain,
RecraftControls,
@@ -18,7 +19,6 @@ from comfy_api_nodes.apis.recraft import (
RecraftImageGenerationResponse,
RecraftImageSize,
RecraftIO,
RecraftModel,
RecraftStyle,
RecraftStyleV3,
get_v3_substyles,
@@ -39,7 +39,7 @@ async def handle_recraft_file_request(
cls: type[IO.ComfyNode],
image: torch.Tensor,
path: str,
mask: Optional[torch.Tensor] = None,
mask: torch.Tensor | None = None,
total_pixels: int = 4096 * 4096,
timeout: int = 1024,
request=None,
@@ -73,11 +73,11 @@ async def handle_recraft_file_request(
def recraft_multipart_parser(
data,
parent_key=None,
formatter: Optional[type[callable]] = None,
converted_to_check: Optional[list[list]] = None,
formatter: type[callable] | None = None,
converted_to_check: list[list] | None = None,
is_list: bool = False,
return_mode: str = "formdata", # "dict" | "formdata"
) -> Union[dict, aiohttp.FormData]:
) -> dict | aiohttp.FormData:
"""
Formats data such that multipart/form-data will work with aiohttp library when both files and data are present.
@@ -309,7 +309,7 @@ class RecraftStyleInfiniteStyleLibrary(IO.ComfyNode):
node_id="RecraftStyleV3InfiniteStyleLibrary",
display_name="Recraft Style - Infinite Style Library",
category="api node/image/Recraft",
description="Select style based on preexisting UUID from Recraft's Infinite Style Library.",
description="Choose style based on preexisting UUID from Recraft's Infinite Style Library.",
inputs=[
IO.String.Input("style_id", default="", tooltip="UUID of style from Infinite Style Library."),
],
@@ -485,7 +485,7 @@ class RecraftTextToImageNode(IO.ComfyNode):
data=RecraftImageGenerationRequest(
prompt=prompt,
negative_prompt=negative_prompt,
model=RecraftModel.recraftv3,
model="recraftv3",
size=size,
n=n,
style=recraft_style.style,
@@ -598,7 +598,7 @@ class RecraftImageToImageNode(IO.ComfyNode):
request = RecraftImageGenerationRequest(
prompt=prompt,
negative_prompt=negative_prompt,
model=RecraftModel.recraftv3,
model="recraftv3",
n=n,
strength=round(strength, 2),
style=recraft_style.style,
@@ -698,7 +698,7 @@ class RecraftImageInpaintingNode(IO.ComfyNode):
request = RecraftImageGenerationRequest(
prompt=prompt,
negative_prompt=negative_prompt,
model=RecraftModel.recraftv3,
model="recraftv3",
n=n,
style=recraft_style.style,
substyle=recraft_style.substyle,
@@ -810,7 +810,7 @@ class RecraftTextToVectorNode(IO.ComfyNode):
data=RecraftImageGenerationRequest(
prompt=prompt,
negative_prompt=negative_prompt,
model=RecraftModel.recraftv3,
model="recraftv3",
size=size,
n=n,
style=recraft_style.style,
@@ -933,7 +933,7 @@ class RecraftReplaceBackgroundNode(IO.ComfyNode):
request = RecraftImageGenerationRequest(
prompt=prompt,
negative_prompt=negative_prompt,
model=RecraftModel.recraftv3,
model="recraftv3",
n=n,
style=recraft_style.style,
substyle=recraft_style.substyle,
@@ -963,6 +963,7 @@ class RecraftRemoveBackgroundNode(IO.ComfyNode):
node_id="RecraftRemoveBackgroundNode",
display_name="Recraft Remove Background",
category="api node/image/Recraft",
essentials_category="Image Tools",
description="Remove background from image, and return processed image and mask.",
inputs=[
IO.Image.Input("image"),
@@ -1078,6 +1079,252 @@ class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode):
)
class RecraftV4TextToImageNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="RecraftV4TextToImageNode",
display_name="Recraft V4 Text to Image",
category="api node/image/Recraft",
description="Generates images using Recraft V4 or V4 Pro models.",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
tooltip="Prompt for the image generation. Maximum 10,000 characters.",
),
IO.String.Input(
"negative_prompt",
multiline=True,
tooltip="An optional text description of undesired elements on an image.",
),
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option(
"recraftv4",
[
IO.Combo.Input(
"size",
options=RECRAFT_V4_SIZES,
default="1024x1024",
tooltip="The size of the generated image.",
),
],
),
IO.DynamicCombo.Option(
"recraftv4_pro",
[
IO.Combo.Input(
"size",
options=RECRAFT_V4_PRO_SIZES,
default="2048x2048",
tooltip="The size of the generated image.",
),
],
),
],
tooltip="The model to use for generation.",
),
IO.Int.Input(
"n",
default=1,
min=1,
max=6,
tooltip="The number of images to generate.",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="Seed to determine if node should re-run; "
"actual results are nondeterministic regardless of seed.",
),
IO.Custom(RecraftIO.CONTROLS).Input(
"recraft_controls",
tooltip="Optional additional controls over the generation via the Recraft Controls node.",
optional=True,
),
],
outputs=[
IO.Image.Output(),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["model", "n"]),
expr="""
(
$prices := {"recraftv4": 0.04, "recraftv4_pro": 0.25};
{"type":"usd","usd": $lookup($prices, widgets.model) * widgets.n}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
negative_prompt: str,
model: dict,
n: int,
seed: int,
recraft_controls: RecraftControls | None = None,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False, min_length=1, max_length=10000)
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/recraft/image_generation", method="POST"),
response_model=RecraftImageGenerationResponse,
data=RecraftImageGenerationRequest(
prompt=prompt,
negative_prompt=negative_prompt if negative_prompt else None,
model=model["model"],
size=model["size"],
n=n,
controls=recraft_controls.create_api_model() if recraft_controls else None,
),
max_retries=1,
)
images = []
for data in response.data:
with handle_recraft_image_output():
image = bytesio_to_image_tensor(await download_url_as_bytesio(data.url, timeout=1024))
if len(image.shape) < 4:
image = image.unsqueeze(0)
images.append(image)
return IO.NodeOutput(torch.cat(images, dim=0))
class RecraftV4TextToVectorNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="RecraftV4TextToVectorNode",
display_name="Recraft V4 Text to Vector",
category="api node/image/Recraft",
description="Generates SVG using Recraft V4 or V4 Pro models.",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
tooltip="Prompt for the image generation. Maximum 10,000 characters.",
),
IO.String.Input(
"negative_prompt",
multiline=True,
tooltip="An optional text description of undesired elements on an image.",
),
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option(
"recraftv4",
[
IO.Combo.Input(
"size",
options=RECRAFT_V4_SIZES,
default="1024x1024",
tooltip="The size of the generated image.",
),
],
),
IO.DynamicCombo.Option(
"recraftv4_pro",
[
IO.Combo.Input(
"size",
options=RECRAFT_V4_PRO_SIZES,
default="2048x2048",
tooltip="The size of the generated image.",
),
],
),
],
tooltip="The model to use for generation.",
),
IO.Int.Input(
"n",
default=1,
min=1,
max=6,
tooltip="The number of images to generate.",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="Seed to determine if node should re-run; "
"actual results are nondeterministic regardless of seed.",
),
IO.Custom(RecraftIO.CONTROLS).Input(
"recraft_controls",
tooltip="Optional additional controls over the generation via the Recraft Controls node.",
optional=True,
),
],
outputs=[
IO.SVG.Output(),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["model", "n"]),
expr="""
(
$prices := {"recraftv4": 0.08, "recraftv4_pro": 0.30};
{"type":"usd","usd": $lookup($prices, widgets.model) * widgets.n}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
negative_prompt: str,
model: dict,
n: int,
seed: int,
recraft_controls: RecraftControls | None = None,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False, min_length=1, max_length=10000)
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/recraft/image_generation", method="POST"),
response_model=RecraftImageGenerationResponse,
data=RecraftImageGenerationRequest(
prompt=prompt,
negative_prompt=negative_prompt if negative_prompt else None,
model=model["model"],
size=model["size"],
n=n,
style="vector_illustration",
substyle=None,
controls=recraft_controls.create_api_model() if recraft_controls else None,
),
max_retries=1,
)
svg_data = []
for data in response.data:
svg_data.append(await download_url_as_bytesio(data.url, timeout=1024))
return IO.NodeOutput(SVG(svg_data))
class RecraftExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1098,6 +1345,8 @@ class RecraftExtension(ComfyExtension):
RecraftCreateStyleNode,
RecraftColorRGBNode,
RecraftControlsNode,
RecraftV4TextToImageNode,
RecraftV4TextToVectorNode,
]

3
comfy_api_nodes/nodes_rodin.py
View File

@@ -505,6 +505,9 @@ class Rodin3D_Gen2(IO.ComfyNode):
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
expr="""{"type":"usd","usd":0.4}""",
),
)
@classmethod

1
comfy_api_nodes/nodes_stability.py
View File

@@ -631,6 +631,7 @@ class StabilityTextToAudio(IO.ComfyNode):
node_id="StabilityTextToAudio",
display_name="Stability AI Text To Audio",
category="api node/audio/Stability AI",
essentials_category="Audio",
description=cleandoc(cls.__doc__ or ""),
inputs=[
IO.Combo.Input(

226
comfy_api_nodes/nodes_vidu.py
View File

@@ -54,6 +54,7 @@ async def execute_task(
response_model=TaskStatusResponse,
status_extractor=lambda r: r.state,
progress_extractor=lambda r: r.progress,
price_extractor=lambda r: r.credits * 0.005 if r.credits is not None else None,
max_poll_attempts=max_poll_attempts,
)
if not response.creations:
@@ -1320,6 +1321,36 @@ class Vidu3TextToVideoNode(IO.ComfyNode):
),
],
),
IO.DynamicCombo.Option(
"viduq3-turbo",
[
IO.Combo.Input(
"aspect_ratio",
options=["16:9", "9:16", "3:4", "4:3", "1:1"],
tooltip="The aspect ratio of the output video.",
),
IO.Combo.Input(
"resolution",
options=["720p", "1080p"],
tooltip="Resolution of the output video.",
),
IO.Int.Input(
"duration",
default=5,
min=1,
max=16,
step=1,
display_mode=IO.NumberDisplay.slider,
tooltip="Duration of the output video in seconds.",
),
IO.Boolean.Input(
"audio",
default=False,
tooltip="When enabled, outputs video with sound "
"(including dialogue and sound effects).",
),
],
),
],
tooltip="Model to use for video generation.",
),
@@ -1348,13 +1379,20 @@ class Vidu3TextToVideoNode(IO.ComfyNode):
],
is_api_node=True,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["model.duration", "model.resolution"]),
depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
expr="""
(
$res := $lookup(widgets, "model.resolution");
$base := $lookup({"720p": 0.075, "1080p": 0.1}, $res);
$perSec := $lookup({"720p": 0.025, "1080p": 0.05}, $res);
{"type":"usd","usd": $base + $perSec * ($lookup(widgets, "model.duration") - 1)}
$d := $lookup(widgets, "model.duration");
$contains(widgets.model, "turbo")
? (
$rate := $lookup({"720p": 0.06, "1080p": 0.08}, $res);
{"type":"usd","usd": $rate * $d}
)
: (
$rate := $lookup({"720p": 0.15, "1080p": 0.16}, $res);
{"type":"usd","usd": $rate * $d}
)
)
""",
),
@@ -1423,6 +1461,31 @@ class Vidu3ImageToVideoNode(IO.ComfyNode):
),
],
),
IO.DynamicCombo.Option(
"viduq3-turbo",
[
IO.Combo.Input(
"resolution",
options=["720p", "1080p"],
tooltip="Resolution of the output video.",
),
IO.Int.Input(
"duration",
default=5,
min=1,
max=16,
step=1,
display_mode=IO.NumberDisplay.slider,
tooltip="Duration of the output video in seconds.",
),
IO.Boolean.Input(
"audio",
default=False,
tooltip="When enabled, outputs video with sound "
"(including dialogue and sound effects).",
),
],
),
],
tooltip="Model to use for video generation.",
),
@@ -1456,13 +1519,20 @@ class Vidu3ImageToVideoNode(IO.ComfyNode):
],
is_api_node=True,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["model.duration", "model.resolution"]),
depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
expr="""
(
$res := $lookup(widgets, "model.resolution");
$base := $lookup({"720p": 0.075, "1080p": 0.275, "2k": 0.35}, $res);
$perSec := $lookup({"720p": 0.05, "1080p": 0.075, "2k": 0.075}, $res);
{"type":"usd","usd": $base + $perSec * ($lookup(widgets, "model.duration") - 1)}
$d := $lookup(widgets, "model.duration");
$contains(widgets.model, "turbo")
? (
$rate := $lookup({"720p": 0.06, "1080p": 0.08}, $res);
{"type":"usd","usd": $rate * $d}
)
: (
$rate := $lookup({"720p": 0.15, "1080p": 0.16, "2k": 0.2}, $res);
{"type":"usd","usd": $rate * $d}
)
)
""",
),
@@ -1495,6 +1565,145 @@ class Vidu3ImageToVideoNode(IO.ComfyNode):
return IO.NodeOutput(await download_url_to_video_output(results[0].url))
class Vidu3StartEndToVideoNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="Vidu3StartEndToVideoNode",
display_name="Vidu Q3 Start/End Frame-to-Video Generation",
category="api node/video/Vidu",
description="Generate a video from a start frame, an end frame, and a prompt.",
inputs=[
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option(
"viduq3-pro",
[
IO.Combo.Input(
"resolution",
options=["720p", "1080p"],
tooltip="Resolution of the output video.",
),
IO.Int.Input(
"duration",
default=5,
min=1,
max=16,
step=1,
display_mode=IO.NumberDisplay.slider,
tooltip="Duration of the output video in seconds.",
),
IO.Boolean.Input(
"audio",
default=False,
tooltip="When enabled, outputs video with sound "
"(including dialogue and sound effects).",
),
],
),
IO.DynamicCombo.Option(
"viduq3-turbo",
[
IO.Combo.Input(
"resolution",
options=["720p", "1080p"],
tooltip="Resolution of the output video.",
),
IO.Int.Input(
"duration",
default=5,
min=1,
max=16,
step=1,
display_mode=IO.NumberDisplay.slider,
tooltip="Duration of the output video in seconds.",
),
IO.Boolean.Input(
"audio",
default=False,
tooltip="When enabled, outputs video with sound "
"(including dialogue and sound effects).",
),
],
),
],
tooltip="Model to use for video generation.",
),
IO.Image.Input("first_frame"),
IO.Image.Input("end_frame"),
IO.String.Input(
"prompt",
multiline=True,
tooltip="Prompt description (max 2000 characters).",
),
IO.Int.Input(
"seed",
default=1,
min=0,
max=2147483647,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
),
],
outputs=[
IO.Video.Output(),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
expr="""
(
$res := $lookup(widgets, "model.resolution");
$d := $lookup(widgets, "model.duration");
$contains(widgets.model, "turbo")
? (
$rate := $lookup({"720p": 0.06, "1080p": 0.08}, $res);
{"type":"usd","usd": $rate * $d}
)
: (
$rate := $lookup({"720p": 0.15, "1080p": 0.16}, $res);
{"type":"usd","usd": $rate * $d}
)
)
""",
),
)
@classmethod
async def execute(
cls,
model: dict,
first_frame: Input.Image,
end_frame: Input.Image,
prompt: str,
seed: int,
) -> IO.NodeOutput:
validate_string(prompt, max_length=2000)
validate_images_aspect_ratio_closeness(first_frame, end_frame, min_rel=0.8, max_rel=1.25, strict=False)
payload = TaskCreationRequest(
model=model["model"],
prompt=prompt,
duration=model["duration"],
seed=seed,
resolution=model["resolution"],
audio=model["audio"],
images=[
(await upload_images_to_comfyapi(cls, frame, max_images=1, mime_type="image/png"))[0]
for frame in (first_frame, end_frame)
],
)
results = await execute_task(cls, VIDU_START_END_VIDEO, payload)
return IO.NodeOutput(await download_url_to_video_output(results[0].url))
class ViduExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1511,6 +1720,7 @@ class ViduExtension(ComfyExtension):
ViduMultiFrameVideoNode,
Vidu3TextToVideoNode,
Vidu3ImageToVideoNode,
Vidu3StartEndToVideoNode,
]

2
comfy_api_nodes/util/__init__.py
View File

@@ -33,6 +33,7 @@ from .download_helpers import (
download_url_to_video_output,
)
from .upload_helpers import (
upload_3d_model_to_comfyapi,
upload_audio_to_comfyapi,
upload_file_to_comfyapi,
upload_image_to_comfyapi,
@@ -62,6 +63,7 @@ __all__ = [
"sync_op",
"sync_op_raw",
# Upload helpers
"upload_3d_model_to_comfyapi",
"upload_audio_to_comfyapi",
"upload_file_to_comfyapi",
"upload_image_to_comfyapi",

215
comfy_api_nodes/util/client.py
View File

@@ -57,6 +57,7 @@ class _RequestConfig:
files: dict[str, Any] | list[tuple[str, Any]] | None
multipart_parser: Callable | None
max_retries: int
max_retries_on_rate_limit: int
retry_delay: float
retry_backoff: float
wait_label: str = "Waiting"
@@ -65,6 +66,7 @@ class _RequestConfig:
final_label_on_success: str | None = "Completed"
progress_origin_ts: float | None = None
price_extractor: Callable[[dict[str, Any]], float | None] | None = None
is_rate_limited: Callable[[int, Any], bool] | None = None
@dataclass
@@ -78,7 +80,7 @@ class _PollUIState:
active_since: float | None = None # start time of current active interval (None if queued)
_RETRY_STATUS = {408, 429, 500, 502, 503, 504}
_RETRY_STATUS = {408, 500, 502, 503, 504} # status 429 is handled separately
COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done", "complete"]
FAILED_STATUSES = ["cancelled", "canceled", "canceling", "fail", "failed", "error"]
QUEUED_STATUSES = ["created", "queued", "queueing", "submitted", "initializing"]
@@ -103,6 +105,8 @@ async def sync_op(
final_label_on_success: str | None = "Completed",
progress_origin_ts: float | None = None,
monitor_progress: bool = True,
max_retries_on_rate_limit: int = 16,
is_rate_limited: Callable[[int, Any], bool] | None = None,
) -> M:
raw = await sync_op_raw(
cls,
@@ -122,6 +126,8 @@ async def sync_op(
final_label_on_success=final_label_on_success,
progress_origin_ts=progress_origin_ts,
monitor_progress=monitor_progress,
max_retries_on_rate_limit=max_retries_on_rate_limit,
is_rate_limited=is_rate_limited,
)
if not isinstance(raw, dict):
raise Exception("Expected JSON response to validate into a Pydantic model, got non-JSON (binary or text).")
@@ -143,9 +149,9 @@ async def poll_op(
poll_interval: float = 5.0,
max_poll_attempts: int = 160,
timeout_per_poll: float = 120.0,
max_retries_per_poll: int = 3,
max_retries_per_poll: int = 10,
retry_delay_per_poll: float = 1.0,
retry_backoff_per_poll: float = 2.0,
retry_backoff_per_poll: float = 1.4,
estimated_duration: int | None = None,
cancel_endpoint: ApiEndpoint | None = None,
cancel_timeout: float = 10.0,
@@ -194,6 +200,8 @@ async def sync_op_raw(
final_label_on_success: str | None = "Completed",
progress_origin_ts: float | None = None,
monitor_progress: bool = True,
max_retries_on_rate_limit: int = 16,
is_rate_limited: Callable[[int, Any], bool] | None = None,
) -> dict[str, Any] | bytes:
"""
Make a single network request.
@@ -222,6 +230,8 @@ async def sync_op_raw(
final_label_on_success=final_label_on_success,
progress_origin_ts=progress_origin_ts,
price_extractor=price_extractor,
max_retries_on_rate_limit=max_retries_on_rate_limit,
is_rate_limited=is_rate_limited,
)
return await _request_base(cfg, expect_binary=as_binary)
@@ -240,9 +250,9 @@ async def poll_op_raw(
poll_interval: float = 5.0,
max_poll_attempts: int = 160,
timeout_per_poll: float = 120.0,
max_retries_per_poll: int = 3,
max_retries_per_poll: int = 10,
retry_delay_per_poll: float = 1.0,
retry_backoff_per_poll: float = 2.0,
retry_backoff_per_poll: float = 1.4,
estimated_duration: int | None = None,
cancel_endpoint: ApiEndpoint | None = None,
cancel_timeout: float = 10.0,
@@ -506,7 +516,7 @@ def _friendly_http_message(status: int, body: Any) -> str:
if status == 409:
return "There is a problem with your account. Please contact support@comfy.org."
if status == 429:
return "Rate Limit Exceeded: Please try again later."
return "Rate Limit Exceeded: The server returned 429 after all retry attempts. Please wait and try again."
try:
if isinstance(body, dict):
err = body.get("error")
@@ -586,6 +596,8 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
start_time = cfg.progress_origin_ts if cfg.progress_origin_ts is not None else time.monotonic()
attempt = 0
delay = cfg.retry_delay
rate_limit_attempts = 0
rate_limit_delay = cfg.retry_delay
operation_succeeded: bool = False
final_elapsed_seconds: int | None = None
extracted_price: float | None = None
@@ -653,17 +665,14 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
payload_headers["Content-Type"] = "application/json"
payload_kw["json"] = cfg.data or {}
try:
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
request_headers=dict(payload_headers) if payload_headers else None,
request_params=dict(params) if params else None,
request_data=request_body_log,
)
except Exception as _log_e:
logging.debug("[DEBUG] request logging failed: %s", _log_e)
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
request_headers=dict(payload_headers) if payload_headers else None,
request_params=dict(params) if params else None,
request_data=request_body_log,
)
req_coro = sess.request(method, url, params=params, **payload_kw)
req_task = asyncio.create_task(req_coro)
@@ -688,41 +697,33 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
body = await resp.json()
except (ContentTypeError, json.JSONDecodeError):
body = await resp.text()
if resp.status in _RETRY_STATUS and attempt <= cfg.max_retries:
should_retry = False
wait_time = 0.0
retry_label = ""
is_rl = resp.status == 429 or (
cfg.is_rate_limited is not None and cfg.is_rate_limited(resp.status, body)
)
if is_rl and rate_limit_attempts < cfg.max_retries_on_rate_limit:
rate_limit_attempts += 1
wait_time = min(rate_limit_delay, 30.0)
rate_limit_delay *= cfg.retry_backoff
retry_label = f"rate-limit retry {rate_limit_attempts} of {cfg.max_retries_on_rate_limit}"
should_retry = True
elif resp.status in _RETRY_STATUS and (attempt - rate_limit_attempts) <= cfg.max_retries:
wait_time = delay
delay *= cfg.retry_backoff
retry_label = f"retry {attempt - rate_limit_attempts} of {cfg.max_retries}"
should_retry = True
if should_retry:
logging.warning(
"HTTP %s %s -> %s. Retrying in %.2fs (retry %d of %d).",
"HTTP %s %s -> %s. Waiting %.2fs (%s).",
method,
url,
resp.status,
delay,
attempt,
cfg.max_retries,
wait_time,
retry_label,
)
try:
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content=body,
error_message=_friendly_http_message(resp.status, body),
)
except Exception as _log_e:
logging.debug("[DEBUG] response logging failed: %s", _log_e)
await sleep_with_interrupt(
delay,
cfg.node_cls,
cfg.wait_label if cfg.monitor_progress else None,
start_time if cfg.monitor_progress else None,
cfg.estimated_total,
display_callback=_display_time_progress if cfg.monitor_progress else None,
)
delay *= cfg.retry_backoff
continue
msg = _friendly_http_message(resp.status, body)
try:
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
@@ -730,10 +731,27 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content=body,
error_message=msg,
error_message=f"HTTP {resp.status} ({retry_label}, will retry in {wait_time:.1f}s)",
)
except Exception as _log_e:
logging.debug("[DEBUG] response logging failed: %s", _log_e)
await sleep_with_interrupt(
wait_time,
cfg.node_cls,
cfg.wait_label if cfg.monitor_progress else None,
start_time if cfg.monitor_progress else None,
cfg.estimated_total,
display_callback=_display_time_progress if cfg.monitor_progress else None,
)
continue
msg = _friendly_http_message(resp.status, body)
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content=body,
error_message=msg,
)
raise Exception(msg)
if expect_binary:
@@ -753,17 +771,14 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
bytes_payload = bytes(buff)
operation_succeeded = True
final_elapsed_seconds = int(time.monotonic() - start_time)
try:
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content=bytes_payload,
)
except Exception as _log_e:
logging.debug("[DEBUG] response logging failed: %s", _log_e)
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content=bytes_payload,
)
return bytes_payload
else:
try:
@@ -780,45 +795,39 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
extracted_price = cfg.price_extractor(payload) if cfg.price_extractor else None
operation_succeeded = True
final_elapsed_seconds = int(time.monotonic() - start_time)
try:
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content=response_content_to_log,
)
except Exception as _log_e:
logging.debug("[DEBUG] response logging failed: %s", _log_e)
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content=response_content_to_log,
)
return payload
except ProcessingInterrupted:
logging.debug("Polling was interrupted by user")
raise
except (ClientError, OSError) as e:
if attempt <= cfg.max_retries:
if (attempt - rate_limit_attempts) <= cfg.max_retries:
logging.warning(
"Connection error calling %s %s. Retrying in %.2fs (%d/%d): %s",
method,
url,
delay,
attempt,
attempt - rate_limit_attempts,
cfg.max_retries,
str(e),
)
try:
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
request_headers=dict(payload_headers) if payload_headers else None,
request_params=dict(params) if params else None,
request_data=request_body_log,
error_message=f"{type(e).__name__}: {str(e)} (will retry)",
)
except Exception as _log_e:
logging.debug("[DEBUG] request error logging failed: %s", _log_e)
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
request_headers=dict(payload_headers) if payload_headers else None,
request_params=dict(params) if params else None,
request_data=request_body_log,
error_message=f"{type(e).__name__}: {str(e)} (will retry)",
)
await sleep_with_interrupt(
delay,
cfg.node_cls,
@@ -831,23 +840,6 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
continue
diag = await _diagnose_connectivity()
if not diag["internet_accessible"]:
try:
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
request_headers=dict(payload_headers) if payload_headers else None,
request_params=dict(params) if params else None,
request_data=request_body_log,
error_message=f"LocalNetworkError: {str(e)}",
)
except Exception as _log_e:
logging.debug("[DEBUG] final error logging failed: %s", _log_e)
raise LocalNetworkError(
"Unable to connect to the API server due to local network issues. "
"Please check your internet connection and try again."
) from e
try:
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
@@ -855,10 +847,21 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
request_headers=dict(payload_headers) if payload_headers else None,
request_params=dict(params) if params else None,
request_data=request_body_log,
error_message=f"ApiServerError: {str(e)}",
error_message=f"LocalNetworkError: {str(e)}",
)
except Exception as _log_e:
logging.debug("[DEBUG] final error logging failed: %s", _log_e)
raise LocalNetworkError(
"Unable to connect to the API server due to local network issues. "
"Please check your internet connection and try again."
) from e
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
request_headers=dict(payload_headers) if payload_headers else None,
request_params=dict(params) if params else None,
request_data=request_body_log,
error_message=f"ApiServerError: {str(e)}",
)
raise ApiServerError(
f"The API server at {default_base_url()} is currently unreachable. "
f"The service may be experiencing issues."

2
comfy_api_nodes/util/conversions.py
View File

@@ -57,7 +57,7 @@ def tensor_to_bytesio(
image: torch.Tensor,
*,
total_pixels: int | None = 2048 * 2048,
mime_type: str = "image/png",
mime_type: str | None = "image/png",
) -> BytesIO:
"""Converts a torch.Tensor image to a named BytesIO object.

30
comfy_api_nodes/util/download_helpers.py
View File

@@ -167,27 +167,25 @@ async def download_url_to_bytesio(
with contextlib.suppress(Exception):
dest.seek(0)
with contextlib.suppress(Exception):
request_logger.log_request_response(
operation_id=op_id,
request_method="GET",
request_url=url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content=f"[streamed {written} bytes to dest]",
)
request_logger.log_request_response(
operation_id=op_id,
request_method="GET",
request_url=url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content=f"[streamed {written} bytes to dest]",
)
return
except asyncio.CancelledError:
raise ProcessingInterrupted("Task cancelled") from None
except (ClientError, OSError) as e:
if attempt <= max_retries:
with contextlib.suppress(Exception):
request_logger.log_request_response(
operation_id=op_id,
request_method="GET",
request_url=url,
error_message=f"{type(e).__name__}: {str(e)} (will retry)",
)
request_logger.log_request_response(
operation_id=op_id,
request_method="GET",
request_url=url,
error_message=f"{type(e).__name__}: {str(e)} (will retry)",
)
await sleep_with_interrupt(delay, cls, None, None, None)
delay *= retry_backoff
continue

66
comfy_api_nodes/util/request_logger.py
View File

@@ -8,7 +8,6 @@ from typing import Any
import folder_paths
# Get the logger instance
logger = logging.getLogger(__name__)
@@ -91,38 +90,41 @@ def log_request_response(
Filenames are sanitized and length-limited for cross-platform safety.
If we still fail to write, we fall back to appending into api.log.
"""
log_dir = get_log_directory()
filepath = _build_log_filepath(log_dir, operation_id, request_url)
log_content: list[str] = []
log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}")
log_content.append(f"Operation ID: {operation_id}")
log_content.append("-" * 30 + " REQUEST " + "-" * 30)
log_content.append(f"Method: {request_method}")
log_content.append(f"URL: {request_url}")
if request_headers:
log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
if request_params:
log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
if request_data is not None:
log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}")
log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30)
if response_status_code is not None:
log_content.append(f"Status Code: {response_status_code}")
if response_headers:
log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}")
if response_content is not None:
log_content.append(f"Content:\n{_format_data_for_logging(response_content)}")
if error_message:
log_content.append(f"Error:\n{error_message}")
try:
with open(filepath, "w", encoding="utf-8") as f:
f.write("\n".join(log_content))
logger.debug("API log saved to: %s", filepath)
except Exception as e:
logger.error("Error writing API log to %s: %s", filepath, str(e))
log_dir = get_log_directory()
filepath = _build_log_filepath(log_dir, operation_id, request_url)
log_content: list[str] = []
log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}")
log_content.append(f"Operation ID: {operation_id}")
log_content.append("-" * 30 + " REQUEST " + "-" * 30)
log_content.append(f"Method: {request_method}")
log_content.append(f"URL: {request_url}")
if request_headers:
log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
if request_params:
log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
if request_data is not None:
log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}")
log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30)
if response_status_code is not None:
log_content.append(f"Status Code: {response_status_code}")
if response_headers:
log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}")
if response_content is not None:
log_content.append(f"Content:\n{_format_data_for_logging(response_content)}")
if error_message:
log_content.append(f"Error:\n{error_message}")
try:
with open(filepath, "w", encoding="utf-8") as f:
f.write("\n".join(log_content))
logger.debug("API log saved to: %s", filepath)
except Exception as e:
logger.error("Error writing API log to %s: %s", filepath, str(e))
except Exception as _log_e:
logging.debug("[DEBUG] log_request_response failed: %s", _log_e)
if __name__ == '__main__':

76
comfy_api_nodes/util/upload_helpers.py
View File

@@ -164,6 +164,27 @@ async def upload_video_to_comfyapi(
return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type, wait_label)
_3D_MIME_TYPES = {
"glb": "model/gltf-binary",
"obj": "model/obj",
"fbx": "application/octet-stream",
}
async def upload_3d_model_to_comfyapi(
cls: type[IO.ComfyNode],
model_3d: Types.File3D,
file_format: str,
) -> str:
"""Uploads a 3D model file to ComfyUI API and returns its download URL."""
return await upload_file_to_comfyapi(
cls,
model_3d.get_data(),
f"{uuid.uuid4()}.{file_format}",
_3D_MIME_TYPES.get(file_format, "application/octet-stream"),
)
async def upload_file_to_comfyapi(
cls: type[IO.ComfyNode],
file_bytes_io: BytesIO,
@@ -255,17 +276,14 @@ async def upload_file(
monitor_task = asyncio.create_task(_monitor())
sess: aiohttp.ClientSession | None = None
try:
try:
request_logger.log_request_response(
operation_id=operation_id,
request_method="PUT",
request_url=upload_url,
request_headers=headers or None,
request_params=None,
request_data=f"[File data {len(data)} bytes]",
)
except Exception as e:
logging.debug("[DEBUG] upload request logging failed: %s", e)
request_logger.log_request_response(
operation_id=operation_id,
request_method="PUT",
request_url=upload_url,
request_headers=headers or None,
request_params=None,
request_data=f"[File data {len(data)} bytes]",
)
sess = aiohttp.ClientSession(timeout=timeout)
req = sess.put(upload_url, data=data, headers=headers, skip_auto_headers=skip_auto_headers)
@@ -311,31 +329,27 @@ async def upload_file(
delay *= retry_backoff
continue
raise Exception(f"Failed to upload (HTTP {resp.status}).")
try:
request_logger.log_request_response(
operation_id=operation_id,
request_method="PUT",
request_url=upload_url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content="File uploaded successfully.",
)
except Exception as e:
logging.debug("[DEBUG] upload response logging failed: %s", e)
request_logger.log_request_response(
operation_id=operation_id,
request_method="PUT",
request_url=upload_url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content="File uploaded successfully.",
)
return
except asyncio.CancelledError:
raise ProcessingInterrupted("Task cancelled") from None
except (aiohttp.ClientError, OSError) as e:
if attempt <= max_retries:
with contextlib.suppress(Exception):
request_logger.log_request_response(
operation_id=operation_id,
request_method="PUT",
request_url=upload_url,
request_headers=headers or None,
request_data=f"[File data {len(data)} bytes]",
error_message=f"{type(e).__name__}: {str(e)} (will retry)",
)
request_logger.log_request_response(
operation_id=operation_id,
request_method="PUT",
request_url=upload_url,
request_headers=headers or None,
request_data=f"[File data {len(data)} bytes]",
error_message=f"{type(e).__name__}: {str(e)} (will retry)",
)
await sleep_with_interrupt(
delay,
cls,