mirror of
https://github.com/Comfy-Org/ComfyUI.git
synced 2026-08-05 18:05:08 +08:00
[Partner Nodes] new Flux2ImageNode and GrokImageEditNodeV2 nodes with DynamicCombo and Autogrow (#13814)
This commit is contained in:
Alexander Piskun
@@ -596,6 +596,7 @@ class Flux2ProImageNode(IO.ComfyNode):
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["width", "height"], inputs=["images"]),
|
||||
expr=cls.PRICE_BADGE_EXPR,
|
||||
),
|
||||
is_deprecated=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
@@ -674,6 +675,175 @@ class Flux2MaxImageNode(Flux2ProImageNode):
|
||||
"""
|
||||
|
||||
|
||||
_FLUX2_MODEL_ENDPOINTS = {
|
||||
"Flux.2 [pro]": "/proxy/bfl/flux-2-pro/generate",
|
||||
"Flux.2 [max]": "/proxy/bfl/flux-2-max/generate",
|
||||
}
|
||||
|
||||
|
||||
def _flux2_model_inputs():
|
||||
return [
|
||||
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.Autogrow.Input(
|
||||
"images",
|
||||
template=IO.Autogrow.TemplateNames(
|
||||
IO.Image.Input("image"),
|
||||
names=[f"image_{i}" for i in range(1, 9)],
|
||||
min=0,
|
||||
),
|
||||
tooltip="Optional reference image(s) for image-to-image generation. Up to 8 images.",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
class Flux2ImageNode(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> IO.Schema:
|
||||
return IO.Schema(
|
||||
node_id="Flux2ImageNode",
|
||||
display_name="Flux.2 Image",
|
||||
category="api node/image/BFL",
|
||||
description="Generate images via Flux.2 [pro] or Flux.2 [max] from a prompt and optional reference images.",
|
||||
inputs=[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Prompt for the image generation or edit",
|
||||
),
|
||||
IO.DynamicCombo.Input(
|
||||
"model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option("Flux.2 [pro]", _flux2_model_inputs()),
|
||||
IO.DynamicCombo.Option("Flux.2 [max]", _flux2_model_inputs()),
|
||||
],
|
||||
),
|
||||
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(
|
||||
depends_on=IO.PriceBadgeDepends(
|
||||
widgets=["model", "model.width", "model.height"],
|
||||
input_groups=["model.images"],
|
||||
),
|
||||
expr="""
|
||||
(
|
||||
$isMax := widgets.model = "flux.2 [max]";
|
||||
$MP := 1024 * 1024;
|
||||
$w := $lookup(widgets, "model.width");
|
||||
$h := $lookup(widgets, "model.height");
|
||||
$outMP := $max([1, $floor((($w * $h) + $MP - 1) / $MP)]);
|
||||
$outputCost := $isMax
|
||||
? (0.07 + 0.03 * ($outMP - 1))
|
||||
: (0.03 + 0.015 * ($outMP - 1));
|
||||
$refMin := $isMax ? 0.03 : 0.015;
|
||||
$refMax := $isMax ? 0.24 : 0.12;
|
||||
$hasRefs := $lookup(inputGroups, "model.images") > 0;
|
||||
$hasRefs
|
||||
? {
|
||||
"type": "range_usd",
|
||||
"min_usd": $outputCost + $refMin,
|
||||
"max_usd": $outputCost + $refMax,
|
||||
"format": { "approximate": true }
|
||||
}
|
||||
: {"type": "usd", "usd": $outputCost}
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
prompt: str,
|
||||
model: dict,
|
||||
seed: int,
|
||||
) -> IO.NodeOutput:
|
||||
model_choice = model["model"]
|
||||
endpoint = _FLUX2_MODEL_ENDPOINTS[model_choice]
|
||||
width = model["width"]
|
||||
height = model["height"]
|
||||
images_dict = model.get("images") or {}
|
||||
|
||||
image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
|
||||
n_images = sum(get_number_of_images(t) for t in image_tensors)
|
||||
if n_images > 8:
|
||||
raise ValueError("The current maximum number of supported images is 8.")
|
||||
|
||||
flat_tensors: list[torch.Tensor] = []
|
||||
for tensor in image_tensors:
|
||||
if len(tensor.shape) == 4:
|
||||
flat_tensors.extend(tensor[i] for i in range(tensor.shape[0]))
|
||||
else:
|
||||
flat_tensors.append(tensor)
|
||||
|
||||
reference_images: dict[str, str] = {}
|
||||
for idx, tensor in enumerate(flat_tensors):
|
||||
key_name = f"input_image_{idx + 1}" if idx else "input_image"
|
||||
reference_images[key_name] = tensor_to_base64_string(tensor, total_pixels=2048 * 2048)
|
||||
|
||||
initial_response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path=endpoint, method="POST"),
|
||||
response_model=BFLFluxProGenerateResponse,
|
||||
data=Flux2ProGenerateRequest(
|
||||
prompt=prompt,
|
||||
width=width,
|
||||
height=height,
|
||||
seed=seed,
|
||||
**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]]:
|
||||
@@ -685,6 +855,7 @@ class BFLExtension(ComfyExtension):
|
||||
FluxProFillNode,
|
||||
Flux2ProImageNode,
|
||||
Flux2MaxImageNode,
|
||||
Flux2ImageNode,
|
||||
]
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user