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add Flux2MaxImage API Node (#11420)

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Alexander Piskun 2025-12-19 20:02:49 +02:00 committed by GitHub
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commit 5b4d0664c8
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1 changed files with 31 additions and 37 deletions
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68
comfy_api_nodes/nodes_bfl.py
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@ -1,10 +1,8 @@
from inspect import cleandoc
import torch
from pydantic import BaseModel
from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.apis.bfl_api import (
BFLFluxExpandImageRequest,
BFLFluxFillImageRequest,
@ -28,7 +26,7 @@ from comfy_api_nodes.util import (
)
def convert_mask_to_image(mask: torch.Tensor):
def convert_mask_to_image(mask: Input.Image):
"""
Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image.
"""
@ -38,9 +36,6 @@ def convert_mask_to_image(mask: torch.Tensor):
class FluxProUltraImageNode(IO.ComfyNode):
"""
Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution.
"""
@classmethod
def define_schema(cls) -> IO.Schema:
@ -48,7 +43,7 @@ class FluxProUltraImageNode(IO.ComfyNode):
node_id="FluxProUltraImageNode",
display_name="Flux 1.1 [pro] Ultra Image",
category="api node/image/BFL",
description=cleandoc(cls.__doc__ or ""),
description="Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution.",
inputs=[
IO.String.Input(
"prompt",
@ -117,7 +112,7 @@ class FluxProUltraImageNode(IO.ComfyNode):
prompt_upsampling: bool = False,
raw: bool = False,
seed: int = 0,
image_prompt: torch.Tensor | None = None,
image_prompt: Input.Image | None = None,
image_prompt_strength: float = 0.1,
) -> IO.NodeOutput:
if image_prompt is None:
@ -155,9 +150,6 @@ class FluxProUltraImageNode(IO.ComfyNode):
class FluxKontextProImageNode(IO.ComfyNode):
"""
Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.
"""
@classmethod
def define_schema(cls) -> IO.Schema:
@ -165,7 +157,7 @@ class FluxKontextProImageNode(IO.ComfyNode):
node_id=cls.NODE_ID,
display_name=cls.DISPLAY_NAME,
category="api node/image/BFL",
description=cleandoc(cls.__doc__ or ""),
description="Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.",
inputs=[
IO.String.Input(
"prompt",
@ -231,7 +223,7 @@ class FluxKontextProImageNode(IO.ComfyNode):
aspect_ratio: str,
guidance: float,
steps: int,
input_image: torch.Tensor | None = None,
input_image: Input.Image | None = None,
seed=0,
prompt_upsampling=False,
) -> IO.NodeOutput:
@ -271,20 +263,14 @@ class FluxKontextProImageNode(IO.ComfyNode):
class FluxKontextMaxImageNode(FluxKontextProImageNode):
"""
Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio.
"""
DESCRIPTION = cleandoc(__doc__ or "")
DESCRIPTION = "Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio."
BFL_PATH = "/proxy/bfl/flux-kontext-max/generate"
NODE_ID = "FluxKontextMaxImageNode"
DISPLAY_NAME = "Flux.1 Kontext [max] Image"
class FluxProExpandNode(IO.ComfyNode):
"""
Outpaints image based on prompt.
"""
@classmethod
def define_schema(cls) -> IO.Schema:
@ -292,7 +278,7 @@ class FluxProExpandNode(IO.ComfyNode):
node_id="FluxProExpandNode",
display_name="Flux.1 Expand Image",
category="api node/image/BFL",
description=cleandoc(cls.__doc__ or ""),
description="Outpaints image based on prompt.",
inputs=[
IO.Image.Input("image"),
IO.String.Input(
@ -371,7 +357,7 @@ class FluxProExpandNode(IO.ComfyNode):
@classmethod
async def execute(
cls,
image: torch.Tensor,
image: Input.Image,
prompt: str,
prompt_upsampling: bool,
top: int,
@ -418,9 +404,6 @@ class FluxProExpandNode(IO.ComfyNode):
class FluxProFillNode(IO.ComfyNode):
"""
Inpaints image based on mask and prompt.
"""
@classmethod
def define_schema(cls) -> IO.Schema:
@ -428,7 +411,7 @@ class FluxProFillNode(IO.ComfyNode):
node_id="FluxProFillNode",
display_name="Flux.1 Fill Image",
category="api node/image/BFL",
description=cleandoc(cls.__doc__ or ""),
description="Inpaints image based on mask and prompt.",
inputs=[
IO.Image.Input("image"),
IO.Mask.Input("mask"),
@ -480,8 +463,8 @@ class FluxProFillNode(IO.ComfyNode):
@classmethod
async def execute(
cls,
image: torch.Tensor,
mask: torch.Tensor,
image: Input.Image,
mask: Input.Image,
prompt: str,
prompt_upsampling: bool,
steps: int,
@ -525,11 +508,15 @@ class FluxProFillNode(IO.ComfyNode):
class Flux2ProImageNode(IO.ComfyNode):
NODE_ID = "Flux2ProImageNode"
DISPLAY_NAME = "Flux.2 [pro] Image"
API_ENDPOINT = "/proxy/bfl/flux-2-pro/generate"
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="Flux2ProImageNode",
display_name="Flux.2 [pro] Image",
node_id=cls.NODE_ID,
display_name=cls.DISPLAY_NAME,
category="api node/image/BFL",
description="Generates images synchronously based on prompt and resolution.",
inputs=[
@ -563,12 +550,11 @@ class Flux2ProImageNode(IO.ComfyNode):
),
IO.Boolean.Input(
"prompt_upsampling",
default=False,
default=True,
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).",
"If active, automatically modifies the prompt for more creative generation.",
),
IO.Image.Input("images", optional=True, tooltip="Up to 4 images to be used as references."),
IO.Image.Input("images", optional=True, tooltip="Up to 9 images to be used as references."),
],
outputs=[IO.Image.Output()],
hidden=[
@ -587,7 +573,7 @@ class Flux2ProImageNode(IO.ComfyNode):
height: int,
seed: int,
prompt_upsampling: bool,
images: torch.Tensor | None = None,
images: Input.Image | None = None,
) -> IO.NodeOutput:
reference_images = {}
if images is not None:
@ -598,7 +584,7 @@ class Flux2ProImageNode(IO.ComfyNode):
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"),
ApiEndpoint(path=cls.API_ENDPOINT, method="POST"),
response_model=BFLFluxProGenerateResponse,
data=Flux2ProGenerateRequest(
prompt=prompt,
@ -632,6 +618,13 @@ class Flux2ProImageNode(IO.ComfyNode):
return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
class Flux2MaxImageNode(Flux2ProImageNode):
NODE_ID = "Flux2MaxImageNode"
DISPLAY_NAME = "Flux.2 [max] Image"
API_ENDPOINT = "/proxy/bfl/flux-2-max/generate"
class BFLExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@ -642,6 +635,7 @@ class BFLExtension(ComfyExtension):
FluxProExpandNode,
FluxProFillNode,
Flux2ProImageNode,
Flux2MaxImageNode,
]