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[Partner Nodes] feat: add Flux Virtual Try-On and Erase nodes (#14207)

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This commit is contained in:
Alexander Piskun
2026-06-01 17:12:12 +03:00
committed by GitHub
parent 462c27fdb2
commit af58c5e674
2 changed files with 243 additions and 107 deletions
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172
comfy_api_nodes/nodes_bfl.py
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@@ -4,17 +4,20 @@ from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.apis.bfl import (
BFLFluxEraseRequest,
BFLFluxExpandImageRequest,
BFLFluxFillImageRequest,
BFLFluxKontextProGenerateRequest,
BFLFluxProGenerateResponse,
BFLFluxProUltraGenerateRequest,
BFLFluxStatusResponse,
BFLFluxVTORequest,
BFLStatus,
Flux2ProGenerateRequest,
)
from comfy_api_nodes.util import (
ApiEndpoint,
convert_mask_to_image,
download_url_to_image_tensor,
get_number_of_images,
poll_op,
@@ -22,19 +25,11 @@ from comfy_api_nodes.util import (
sync_op,
tensor_to_base64_string,
validate_aspect_ratio_string,
validate_image_dimensions,
validate_string,
)
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.
"""
mask = mask.unsqueeze(-1)
mask = torch.cat([mask] * 3, dim=-1)
return mask
class FluxProUltraImageNode(IO.ComfyNode):
@classmethod
@@ -519,6 +514,163 @@ class FluxProFillNode(IO.ComfyNode):
return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
class FluxEraseNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="FluxEraseNode",
display_name="Flux Erase Image",
category="image/partner/BFL",
description="Removes the masked object from an image and reconstructs the background. "
"Paint the mask over what you want to erase.",
inputs=[
IO.Image.Input("image"),
IO.Mask.Input("mask", tooltip="White areas are removed; black areas are preserved."),
IO.Int.Input(
"dilate_pixels",
default=10,
min=0,
max=25,
tooltip="Expands the mask boundaries to ensure clean coverage of the object's edges.",
),
],
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":"range_usd","min_usd":0.03,"max_usd":0.06,"format":{"approximate":true}}""",
),
)
@classmethod
async def execute(
cls,
image: Input.Image,
mask: Input.Image,
dilate_pixels: int = 10,
) -> IO.NodeOutput:
validate_image_dimensions(image, min_width=256, min_height=256)
mask = resize_mask_to_image(mask, image)
mask = tensor_to_base64_string(convert_mask_to_image(mask))
initial_response = await sync_op(
cls,
ApiEndpoint(path="/proxy/bfl/v1/flux-tools/erase-v1", method="POST"),
response_model=BFLFluxProGenerateResponse,
data=BFLFluxEraseRequest(
image=tensor_to_base64_string(image[:, :, :, :3]), # make sure image will have alpha channel removed
mask=mask,
dilate_pixels=dilate_pixels,
),
)
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 FluxVTONode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="FluxVTONode",
display_name="Flux Virtual Try-On",
category="image/partner/BFL",
description="Virtual try-on: dresses the person in the provided garment.",
inputs=[
IO.Image.Input("person", tooltip="Image of the person to dress."),
IO.Image.Input("garment", tooltip="Image of the garment to apply."),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Optional natural-language styling instruction (e.g. how the garment should fit).",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="The random seed used for creating the noise.",
),
],
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":"range_usd","min_usd":0.0375,"max_usd":0.075,"format":{"approximate":true}}""",
),
)
@classmethod
async def execute(
cls,
person: Input.Image,
garment: Input.Image,
prompt: str = "",
seed: int = 0,
) -> IO.NodeOutput:
initial_response = await sync_op(
cls,
ApiEndpoint(path="/proxy/bfl/v1/flux-tools/vto-v1", method="POST"),
response_model=BFLFluxProGenerateResponse,
data=BFLFluxVTORequest(
prompt=prompt,
person=tensor_to_base64_string(person[:, :, :, :3]),
garment=tensor_to_base64_string(garment[:, :, :, :3]),
seed=seed,
),
)
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 Flux2ProImageNode(IO.ComfyNode):
NODE_ID = "Flux2ProImageNode"
@@ -853,6 +1005,8 @@ class BFLExtension(ComfyExtension):
FluxKontextMaxImageNode,
FluxProExpandNode,
FluxProFillNode,
FluxEraseNode,
FluxVTONode,
Flux2ProImageNode,
Flux2MaxImageNode,
Flux2ImageNode,