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[API Nodes] add Flux.2 Pro node (#10880)

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Alexander Piskun 2025-11-25 18:09:07 +02:00 committed by GitHub
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3 changed files with 143 additions and 125 deletions
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28
comfy_api_nodes/apis/bfl_api.py
View File

@ -70,6 +70,29 @@ class BFLFluxProGenerateRequest(BaseModel):
# )
class Flux2ProGenerateRequest(BaseModel):
prompt: str = Field(...)
width: int = Field(1024, description="Must be a multiple of 32.")
height: int = Field(768, description="Must be a multiple of 32.")
seed: int | None = Field(None)
prompt_upsampling: bool | None = Field(None)
input_image: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_2: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_3: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_4: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_5: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_6: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_7: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_8: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_9: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
safety_tolerance: int | None = Field(
5, description="Tolerance level for input and output moderation. Value 0 being most strict.", ge=0, le=5
)
output_format: str | None = Field(
"png", description="Output format for the generated image. Can be 'jpeg' or 'png'."
)
class BFLFluxKontextProGenerateRequest(BaseModel):
prompt: str = Field(..., description='The text prompt for what you wannt to edit.')
input_image: Optional[str] = Field(None, description='Image to edit in base64 format')
@ -109,8 +132,9 @@ class BFLFluxProUltraGenerateRequest(BaseModel):
class BFLFluxProGenerateResponse(BaseModel):
id: str = Field(..., description='The unique identifier for the generation task.')
polling_url: str = Field(..., description='URL to poll for the generation result.')
id: str = Field(..., description="The unique identifier for the generation task.")
polling_url: str = Field(..., description="URL to poll for the generation result.")
cost: float | None = Field(None, description="Price in cents")
class BFLStatus(str, Enum):

238
comfy_api_nodes/nodes_bfl.py
View File

@ -1,7 +1,7 @@
from inspect import cleandoc
from typing import Optional
import torch
from pydantic import BaseModel
from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension
@ -9,15 +9,16 @@ from comfy_api_nodes.apis.bfl_api import (
BFLFluxExpandImageRequest,
BFLFluxFillImageRequest,
BFLFluxKontextProGenerateRequest,
BFLFluxProGenerateRequest,
BFLFluxProGenerateResponse,
BFLFluxProUltraGenerateRequest,
BFLFluxStatusResponse,
BFLStatus,
Flux2ProGenerateRequest,
)
from comfy_api_nodes.util import (
ApiEndpoint,
download_url_to_image_tensor,
get_number_of_images,
poll_op,
resize_mask_to_image,
sync_op,
@ -116,7 +117,7 @@ class FluxProUltraImageNode(IO.ComfyNode):
prompt_upsampling: bool = False,
raw: bool = False,
seed: int = 0,
image_prompt: Optional[torch.Tensor] = None,
image_prompt: torch.Tensor | None = None,
image_prompt_strength: float = 0.1,
) -> IO.NodeOutput:
if image_prompt is None:
@ -230,7 +231,7 @@ class FluxKontextProImageNode(IO.ComfyNode):
aspect_ratio: str,
guidance: float,
steps: int,
input_image: Optional[torch.Tensor] = None,
input_image: torch.Tensor | None = None,
seed=0,
prompt_upsampling=False,
) -> IO.NodeOutput:
@ -280,124 +281,6 @@ class FluxKontextMaxImageNode(FluxKontextProImageNode):
DISPLAY_NAME = "Flux.1 Kontext [max] Image"
class FluxProImageNode(IO.ComfyNode):
"""
Generates images synchronously based on prompt and resolution.
"""
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="FluxProImageNode",
display_name="Flux 1.1 [pro] Image",
category="api node/image/BFL",
description=cleandoc(cls.__doc__ or ""),
inputs=[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the image generation",
),
IO.Boolean.Input(
"prompt_upsampling",
default=False,
tooltip="Whether to perform upsampling on the prompt. "
"If active, automatically modifies the prompt for more creative generation, "
"but results are nondeterministic (same seed will not produce exactly the same result).",
),
IO.Int.Input(
"width",
default=1024,
min=256,
max=1440,
step=32,
),
IO.Int.Input(
"height",
default=768,
min=256,
max=1440,
step=32,
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="The random seed used for creating the noise.",
),
IO.Image.Input(
"image_prompt",
optional=True,
),
# "image_prompt_strength": (
# IO.FLOAT,
# {
# "default": 0.1,
# "min": 0.0,
# "max": 1.0,
# "step": 0.01,
# "tooltip": "Blend between the prompt and the image prompt.",
# },
# ),
],
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,
)
@classmethod
async def execute(
cls,
prompt: str,
prompt_upsampling,
width: int,
height: int,
seed=0,
image_prompt=None,
# image_prompt_strength=0.1,
) -> IO.NodeOutput:
image_prompt = image_prompt if image_prompt is None else tensor_to_base64_string(image_prompt)
initial_response = await sync_op(
cls,
ApiEndpoint(
path="/proxy/bfl/flux-pro-1.1/generate",
method="POST",
),
response_model=BFLFluxProGenerateResponse,
data=BFLFluxProGenerateRequest(
prompt=prompt,
prompt_upsampling=prompt_upsampling,
width=width,
height=height,
seed=seed,
image_prompt=image_prompt,
),
)
response = await poll_op(
cls,
ApiEndpoint(initial_response.polling_url),
response_model=BFLFluxStatusResponse,
status_extractor=lambda r: r.status,
progress_extractor=lambda r: r.progress,
completed_statuses=[BFLStatus.ready],
failed_statuses=[
BFLStatus.request_moderated,
BFLStatus.content_moderated,
BFLStatus.error,
BFLStatus.task_not_found,
],
queued_statuses=[],
)
return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
class FluxProExpandNode(IO.ComfyNode):
"""
Outpaints image based on prompt.
@ -640,16 +523,125 @@ class FluxProFillNode(IO.ComfyNode):
return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
class Flux2ProImageNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="Flux2ProImageNode",
display_name="Flux.2 [pro] Image",
category="api node/image/BFL",
description="Generates images synchronously based on prompt and resolution.",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the image generation or edit",
),
IO.Int.Input(
"width",
default=1024,
min=256,
max=2048,
step=32,
),
IO.Int.Input(
"height",
default=768,
min=256,
max=2048,
step=32,
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="The random seed used for creating the noise.",
),
IO.Boolean.Input(
"prompt_upsampling",
default=False,
tooltip="Whether to perform upsampling on the prompt. "
"If active, automatically modifies the prompt for more creative generation, "
"but results are nondeterministic (same seed will not produce exactly the same result).",
),
IO.Image.Input("images", optional=True, tooltip="Up to 4 images to be used as references."),
],
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,
)
@classmethod
async def execute(
cls,
prompt: str,
width: int,
height: int,
seed: int,
prompt_upsampling: bool,
images: torch.Tensor | None = None,
) -> IO.NodeOutput:
reference_images = {}
if images is not None:
if get_number_of_images(images) > 9:
raise ValueError("The current maximum number of supported images is 9.")
for image_index in range(images.shape[0]):
key_name = f"input_image_{image_index + 1}" if image_index else "input_image"
reference_images[key_name] = tensor_to_base64_string(images[image_index], total_pixels=2048 * 2048)
initial_response = await sync_op(
cls,
ApiEndpoint(path="/proxy/bfl/flux-2-pro/generate", method="POST"),
response_model=BFLFluxProGenerateResponse,
data=Flux2ProGenerateRequest(
prompt=prompt,
width=width,
height=height,
seed=seed,
prompt_upsampling=prompt_upsampling,
**reference_images,
),
)
def price_extractor(_r: BaseModel) -> float | None:
return None if initial_response.cost is None else initial_response.cost / 100
response = await poll_op(
cls,
ApiEndpoint(initial_response.polling_url),
response_model=BFLFluxStatusResponse,
status_extractor=lambda r: r.status,
progress_extractor=lambda r: r.progress,
price_extractor=price_extractor,
completed_statuses=[BFLStatus.ready],
failed_statuses=[
BFLStatus.request_moderated,
BFLStatus.content_moderated,
BFLStatus.error,
BFLStatus.task_not_found,
],
queued_statuses=[],
)
return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
class BFLExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
FluxProUltraImageNode,
# FluxProImageNode,
FluxKontextProImageNode,
FluxKontextMaxImageNode,
FluxProExpandNode,
FluxProFillNode,
Flux2ProImageNode,
]

2
comfy_api_nodes/util/__init__.py
View File

@ -36,6 +36,7 @@ from .upload_helpers import (
upload_video_to_comfyapi,
)
from .validation_utils import (
get_image_dimensions,
get_number_of_images,
validate_aspect_ratio_string,
validate_audio_duration,
@ -82,6 +83,7 @@ __all__ = [
"trim_video",
"video_to_base64_string",
# Validation utilities
"get_image_dimensions",
"get_number_of_images",
"validate_aspect_ratio_string",
"validate_audio_duration",