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1495 lines (1414 loc) · 57.1 KB
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import torch
from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.apis.luma import (
LUMA_KEYFRAME_MODE_FRACTION,
LUMA_KEYFRAME_MODE_SECONDS,
Luma2Generation,
Luma2GenerationRequest,
Luma2ImageRef,
Luma2VideoEdit,
Luma2VideoOptions,
LumaAspectRatio,
LumaCharacterRef,
LumaConceptChain,
LumaGeneration,
LumaGenerationRequest,
LumaImageGenerationRequest,
LumaImageIdentity,
LumaImageModel,
LumaImageReference,
LumaIO,
LumaKeyframes,
LumaModifyImageRef,
LumaRay32KeyframeChain,
LumaRay32KeyframeItem,
LumaReference,
LumaReferenceChain,
LumaVideoModel,
LumaVideoModelOutputDuration,
LumaVideoOutputResolution,
get_luma_concepts,
)
from comfy_api_nodes.util import (
ApiEndpoint,
download_url_to_image_tensor,
download_url_to_video_output,
poll_op,
sync_op,
upload_image_to_comfyapi,
upload_images_to_comfyapi,
upload_video_to_comfyapi,
validate_string,
)
LUMA_T2V_AVERAGE_DURATION = 105
LUMA_I2V_AVERAGE_DURATION = 100
class LumaReferenceNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaReferenceNode",
display_name="Luma Reference",
category="partner/image/Luma",
description="Holds an image and weight for use with Luma Generate Image node.",
inputs=[
IO.Image.Input(
"image",
tooltip="Image to use as reference.",
),
IO.Float.Input(
"weight",
default=1.0,
min=0.0,
max=1.0,
step=0.01,
tooltip="Weight of image reference.",
),
IO.Custom(LumaIO.LUMA_REF).Input(
"luma_ref",
optional=True,
),
],
outputs=[IO.Custom(LumaIO.LUMA_REF).Output(display_name="luma_ref")],
)
@classmethod
def execute(cls, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None) -> IO.NodeOutput:
if luma_ref is not None:
luma_ref = luma_ref.clone()
else:
luma_ref = LumaReferenceChain()
luma_ref.add(LumaReference(image=image, weight=round(weight, 2)))
return IO.NodeOutput(luma_ref)
class LumaConceptsNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaConceptsNode",
display_name="Luma Concepts",
category="partner/video/Luma",
description="Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.",
inputs=[
IO.Combo.Input(
"concept1",
options=get_luma_concepts(include_none=True),
),
IO.Combo.Input(
"concept2",
options=get_luma_concepts(include_none=True),
),
IO.Combo.Input(
"concept3",
options=get_luma_concepts(include_none=True),
),
IO.Combo.Input(
"concept4",
options=get_luma_concepts(include_none=True),
),
IO.Custom(LumaIO.LUMA_CONCEPTS).Input(
"luma_concepts",
tooltip="Optional Camera Concepts to add to the ones chosen here.",
optional=True,
),
],
outputs=[IO.Custom(LumaIO.LUMA_CONCEPTS).Output(display_name="luma_concepts")],
)
@classmethod
def execute(
cls,
concept1: str,
concept2: str,
concept3: str,
concept4: str,
luma_concepts: LumaConceptChain = None,
) -> IO.NodeOutput:
chain = LumaConceptChain(str_list=[concept1, concept2, concept3, concept4])
if luma_concepts is not None:
chain = luma_concepts.clone_and_merge(chain)
return IO.NodeOutput(chain)
class LumaImageGenerationNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaImageNode",
display_name="Luma Text to Image",
category="partner/image/Luma",
description="Generates images synchronously based on prompt and aspect ratio.",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the image generation",
),
IO.Combo.Input(
"model",
options=LumaImageModel,
),
IO.Combo.Input(
"aspect_ratio",
options=LumaAspectRatio,
default=LumaAspectRatio.ratio_16_9,
),
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.Float.Input(
"style_image_weight",
default=1.0,
min=0.0,
max=1.0,
step=0.01,
tooltip="Weight of style image. Ignored if no style_image provided.",
),
IO.Custom(LumaIO.LUMA_REF).Input(
"image_luma_ref",
tooltip="Luma Reference node connection to influence generation with input images; up to 4 images can be considered.",
optional=True,
),
IO.Image.Input(
"style_image",
tooltip="Style reference image; only 1 image will be used.",
optional=True,
),
IO.Image.Input(
"character_image",
tooltip="Character reference images; can be a batch of multiple, up to 4 images can be considered.",
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"]),
expr="""
(
$m := widgets.model;
$contains($m,"photon-flash-1")
? {"type":"usd","usd":0.0027}
: $contains($m,"photon-1")
? {"type":"usd","usd":0.0104}
: {"type":"usd","usd":0.0246}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
model: str,
aspect_ratio: str,
seed,
style_image_weight: float,
image_luma_ref: LumaReferenceChain | None = None,
style_image: torch.Tensor | None = None,
character_image: torch.Tensor | None = None,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=3)
# handle image_luma_ref
api_image_ref = None
if image_luma_ref is not None:
api_image_ref = await cls._convert_luma_refs(image_luma_ref, max_refs=4)
# handle style_luma_ref
api_style_ref = None
if style_image is not None:
api_style_ref = await cls._convert_style_image(style_image, weight=style_image_weight)
# handle character_ref images
character_ref = None
if character_image is not None:
download_urls = await upload_images_to_comfyapi(cls, character_image, max_images=4)
character_ref = LumaCharacterRef(identity0=LumaImageIdentity(images=download_urls))
response_api = await sync_op(
cls,
ApiEndpoint(path="/proxy/luma/generations/image", method="POST"),
response_model=LumaGeneration,
data=LumaImageGenerationRequest(
prompt=prompt,
model=model,
aspect_ratio=aspect_ratio,
image_ref=api_image_ref,
style_ref=api_style_ref,
character_ref=character_ref,
),
)
response_poll = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
response_model=LumaGeneration,
status_extractor=lambda x: x.state,
)
return IO.NodeOutput(await download_url_to_image_tensor(response_poll.assets.image))
@classmethod
async def _convert_luma_refs(cls, luma_ref: LumaReferenceChain, max_refs: int):
luma_urls = []
ref_count = 0
for ref in luma_ref.refs:
download_urls = await upload_images_to_comfyapi(cls, ref.image, max_images=1)
luma_urls.append(download_urls[0])
ref_count += 1
if ref_count >= max_refs:
break
return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs)
@classmethod
async def _convert_style_image(cls, style_image: torch.Tensor, weight: float):
chain = LumaReferenceChain(first_ref=LumaReference(image=style_image, weight=weight))
return await cls._convert_luma_refs(chain, max_refs=1)
class LumaImageModifyNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaImageModifyNode",
display_name="Luma Image to Image",
category="partner/image/Luma",
description="Modifies images synchronously based on prompt and aspect ratio.",
inputs=[
IO.Image.Input(
"image",
),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the image generation",
),
IO.Float.Input(
"image_weight",
default=0.1,
min=0.0,
max=0.98,
step=0.01,
tooltip="Weight of the image; the closer to 1.0, the less the image will be modified.",
),
IO.Combo.Input(
"model",
options=LumaImageModel,
),
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.",
),
],
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"]),
expr="""
(
$m := widgets.model;
$contains($m,"photon-flash-1")
? {"type":"usd","usd":0.0027}
: $contains($m,"photon-1")
? {"type":"usd","usd":0.0104}
: {"type":"usd","usd":0.0246}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
model: str,
image: torch.Tensor,
image_weight: float,
seed,
) -> IO.NodeOutput:
download_urls = await upload_images_to_comfyapi(cls, image, max_images=1)
image_url = download_urls[0]
response_api = await sync_op(
cls,
ApiEndpoint(path="/proxy/luma/generations/image", method="POST"),
response_model=LumaGeneration,
data=LumaImageGenerationRequest(
prompt=prompt,
model=model,
modify_image_ref=LumaModifyImageRef(
url=image_url, weight=round(max(min(1.0 - image_weight, 0.98), 0.0), 2)
),
),
)
response_poll = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
response_model=LumaGeneration,
status_extractor=lambda x: x.state,
)
return IO.NodeOutput(await download_url_to_image_tensor(response_poll.assets.image))
class LumaTextToVideoGenerationNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaVideoNode",
display_name="Luma Text to Video",
category="partner/video/Luma",
description="Generates videos synchronously based on prompt and output_size.",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the video generation",
),
IO.Combo.Input(
"model",
options=LumaVideoModel,
),
IO.Combo.Input(
"aspect_ratio",
options=LumaAspectRatio,
default=LumaAspectRatio.ratio_16_9,
),
IO.Combo.Input(
"resolution",
options=LumaVideoOutputResolution,
default=LumaVideoOutputResolution.res_540p,
),
IO.Combo.Input(
"duration",
options=LumaVideoModelOutputDuration,
),
IO.Boolean.Input(
"loop",
default=False,
),
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(LumaIO.LUMA_CONCEPTS).Input(
"luma_concepts",
tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
optional=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=PRICE_BADGE_VIDEO,
)
@classmethod
async def execute(
cls,
prompt: str,
model: str,
aspect_ratio: str,
resolution: str,
duration: str,
loop: bool,
seed,
luma_concepts: LumaConceptChain | None = None,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False, min_length=3)
duration = duration if model != LumaVideoModel.ray_1_6 else None
resolution = resolution if model != LumaVideoModel.ray_1_6 else None
response_api = await sync_op(
cls,
ApiEndpoint(path="/proxy/luma/generations", method="POST"),
response_model=LumaGeneration,
data=LumaGenerationRequest(
prompt=prompt,
model=model,
resolution=resolution,
aspect_ratio=aspect_ratio,
duration=duration,
loop=loop,
concepts=luma_concepts.create_api_model() if luma_concepts else None,
),
)
response_poll = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
response_model=LumaGeneration,
status_extractor=lambda x: x.state,
estimated_duration=LUMA_T2V_AVERAGE_DURATION,
)
return IO.NodeOutput(await download_url_to_video_output(response_poll.assets.video))
class LumaImageToVideoGenerationNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaImageToVideoNode",
display_name="Luma Image to Video",
category="partner/video/Luma",
description="Generates videos synchronously based on prompt, input images, and output_size.",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the video generation",
),
IO.Combo.Input(
"model",
options=LumaVideoModel,
),
# IO.Combo.Input(
# "aspect_ratio",
# options=[ratio.value for ratio in LumaAspectRatio],
# default=LumaAspectRatio.ratio_16_9,
# ),
IO.Combo.Input(
"resolution",
options=LumaVideoOutputResolution,
default=LumaVideoOutputResolution.res_540p,
),
IO.Combo.Input(
"duration",
options=[dur.value for dur in LumaVideoModelOutputDuration],
),
IO.Boolean.Input(
"loop",
default=False,
),
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.Image.Input(
"first_image",
tooltip="First frame of generated video.",
optional=True,
),
IO.Image.Input(
"last_image",
tooltip="Last frame of generated video.",
optional=True,
),
IO.Custom(LumaIO.LUMA_CONCEPTS).Input(
"luma_concepts",
tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
optional=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=PRICE_BADGE_VIDEO,
)
@classmethod
async def execute(
cls,
prompt: str,
model: str,
resolution: str,
duration: str,
loop: bool,
seed,
first_image: torch.Tensor = None,
last_image: torch.Tensor = None,
luma_concepts: LumaConceptChain = None,
) -> IO.NodeOutput:
if first_image is None and last_image is None:
raise Exception("At least one of first_image and last_image requires an input.")
keyframes = await cls._convert_to_keyframes(first_image, last_image)
duration = duration if model != LumaVideoModel.ray_1_6 else None
resolution = resolution if model != LumaVideoModel.ray_1_6 else None
response_api = await sync_op(
cls,
ApiEndpoint(path="/proxy/luma/generations", method="POST"),
response_model=LumaGeneration,
data=LumaGenerationRequest(
prompt=prompt,
model=model,
aspect_ratio=LumaAspectRatio.ratio_16_9, # ignored, but still needed by the API for some reason
resolution=resolution,
duration=duration,
loop=loop,
keyframes=keyframes,
concepts=luma_concepts.create_api_model() if luma_concepts else None,
),
)
response_poll = await poll_op(
cls,
poll_endpoint=ApiEndpoint(path=f"/proxy/luma/generations/{response_api.id}"),
response_model=LumaGeneration,
status_extractor=lambda x: x.state,
estimated_duration=LUMA_I2V_AVERAGE_DURATION,
)
return IO.NodeOutput(await download_url_to_video_output(response_poll.assets.video))
@classmethod
async def _convert_to_keyframes(
cls,
first_image: torch.Tensor = None,
last_image: torch.Tensor = None,
):
if first_image is None and last_image is None:
return None
frame0 = None
frame1 = None
if first_image is not None:
download_urls = await upload_images_to_comfyapi(cls, first_image, max_images=1)
frame0 = LumaImageReference(type="image", url=download_urls[0])
if last_image is not None:
download_urls = await upload_images_to_comfyapi(cls, last_image, max_images=1)
frame1 = LumaImageReference(type="image", url=download_urls[0])
return LumaKeyframes(frame0=frame0, frame1=frame1)
PRICE_BADGE_VIDEO = IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["model", "resolution", "duration"]),
expr="""
(
$p := {
"ray-flash-2": {
"5s": {"4k":3.13,"1080p":0.79,"720p":0.34,"540p":0.2},
"9s": {"4k":5.65,"1080p":1.42,"720p":0.61,"540p":0.36}
},
"ray-2": {
"5s": {"4k":9.11,"1080p":2.27,"720p":1.02,"540p":0.57},
"9s": {"4k":16.4,"1080p":4.1,"720p":1.83,"540p":1.03}
}
};
$m := widgets.model;
$d := widgets.duration;
$r := widgets.resolution;
$modelKey :=
$contains($m,"ray-flash-2") ? "ray-flash-2" :
$contains($m,"ray-2") ? "ray-2" :
$contains($m,"ray-1-6") ? "ray-1-6" :
"other";
$durKey := $contains($d,"5s") ? "5s" : $contains($d,"9s") ? "9s" : "";
$resKey :=
$contains($r,"4k") ? "4k" :
$contains($r,"1080p") ? "1080p" :
$contains($r,"720p") ? "720p" :
$contains($r,"540p") ? "540p" : "";
$modelPrices := $lookup($p, $modelKey);
$durPrices := $lookup($modelPrices, $durKey);
$v := $lookup($durPrices, $resKey);
$price :=
($modelKey = "ray-1-6") ? 0.5 :
($modelKey = "other") ? 0.79 :
($exists($v) ? $v : 0.79);
{"type":"usd","usd": $price}
)
""",
)
def _luma2_uni1_common_inputs(max_image_refs: int) -> list:
return [
IO.Combo.Input(
"style",
options=["auto", "manga"],
default="auto",
tooltip="Style preset. 'auto' picks based on the prompt; "
"'manga' applies a manga/anime aesthetic and requires a portrait "
"aspect ratio (2:3, 9:16, 1:2, 1:3).",
),
IO.Boolean.Input(
"web_search",
default=False,
tooltip="Search the web for visual references before generating.",
),
IO.Autogrow.Input(
"image_ref",
template=IO.Autogrow.TemplateNames(
IO.Image.Input("image"),
names=[f"image_{i}" for i in range(1, max_image_refs + 1)],
min=0,
),
optional=True,
tooltip=f"Up to {max_image_refs} reference images for style/content guidance.",
),
]
async def _luma2_upload_image_refs(
cls: type[IO.ComfyNode],
refs: dict | None,
max_count: int,
) -> list[Luma2ImageRef] | None:
if not refs:
return None
out: list[Luma2ImageRef] = []
for key in refs:
url = await upload_image_to_comfyapi(cls, refs[key])
out.append(Luma2ImageRef(url=url))
if len(out) > max_count:
raise ValueError(f"Maximum {max_count} reference images are allowed.")
return out or None
async def _luma2_submit_and_poll(
cls: type[IO.ComfyNode],
request: Luma2GenerationRequest,
*,
estimated_duration: int | None = None,
) -> Luma2Generation:
"""Submit a Luma Agents generation and poll until done; returns the completed generation."""
initial = await sync_op(
cls,
ApiEndpoint(path="/proxy/luma_2/generations", method="POST"),
response_model=Luma2Generation,
data=request,
)
if not initial.id:
raise RuntimeError("Luma API did not return a generation id.")
final = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/luma_2/generations/{initial.id}", method="GET"),
response_model=Luma2Generation,
status_extractor=lambda r: r.state,
progress_extractor=lambda r: None,
estimated_duration=estimated_duration,
)
if not final.output or not final.output[0].url:
msg = final.failure_reason or "no output returned"
if final.failure_code:
msg = f"{msg} [{final.failure_code}]"
raise RuntimeError(f"Luma generation failed: {msg}")
return final
class LumaImageNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaImageNode2",
display_name="Luma UNI-1 Image",
category="partner/image/Luma",
description="Generate images from text using the Luma UNI-1 model.",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Text description of the desired image. 1–6000 characters.",
),
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option(
"uni-1",
[
IO.Combo.Input(
"aspect_ratio",
options=[
"auto",
"3:1",
"2:1",
"16:9",
"3:2",
"1:1",
"2:3",
"9:16",
"1:2",
"1:3",
],
default="auto",
tooltip="Output image aspect ratio. 'auto' lets "
"the model pick based on the prompt.",
),
*_luma2_uni1_common_inputs(max_image_refs=9),
],
),
IO.DynamicCombo.Option(
"uni-1-max",
[
IO.Combo.Input(
"aspect_ratio",
options=[
"auto",
"3:1",
"2:1",
"16:9",
"3:2",
"1:1",
"2:3",
"9:16",
"1:2",
"1:3",
],
default="auto",
tooltip="Output image aspect ratio. 'auto' lets "
"the model pick based on the prompt.",
),
*_luma2_uni1_common_inputs(max_image_refs=9),
],
),
],
tooltip="Model to use for generation.",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
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(
depends_on=IO.PriceBadgeDepends(widgets=["model"], input_groups=["model.image_ref"]),
expr="""
(
$m := widgets.model;
$refs := $lookup(inputGroups, "model.image_ref");
$base := $m = "uni-1-max" ? 0.1 : 0.0404;
{"type":"usd","usd": $round($base + 0.003 * $refs, 4)}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
model: dict,
seed: int,
) -> IO.NodeOutput:
validate_string(prompt, min_length=1, max_length=6000)
aspect_ratio = model["aspect_ratio"]
style = model["style"]
allowed_manga_ratios = {"2:3", "9:16", "1:2", "1:3"}
if style == "manga" and aspect_ratio != "auto" and aspect_ratio not in allowed_manga_ratios:
raise ValueError(
f"'manga' style requires a portrait aspect ratio "
f"({', '.join(sorted(allowed_manga_ratios))}) or 'auto'; got '{aspect_ratio}'."
)
request = Luma2GenerationRequest(
prompt=prompt,
model=model["model"],
type="image",
aspect_ratio=aspect_ratio if aspect_ratio != "auto" else None,
style=style if style != "auto" else None,
output_format="png",
web_search=model["web_search"],
image_ref=await _luma2_upload_image_refs(cls, model.get("image_ref"), max_count=9),
)
final = await _luma2_submit_and_poll(cls, request)
return IO.NodeOutput(await download_url_to_image_tensor(final.output[0].url))
class LumaImageEditNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaImageEditNode2",
display_name="Luma UNI-1 Image Edit",
category="partner/image/Luma",
description="Edit an existing image with a text prompt using the Luma UNI-1 model.",
inputs=[
IO.Image.Input(
"source",
tooltip="Source image to edit.",
),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Description of the desired edit. 1–6000 characters.",
),
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option(
"uni-1",
_luma2_uni1_common_inputs(max_image_refs=8),
),
IO.DynamicCombo.Option(
"uni-1-max",
_luma2_uni1_common_inputs(max_image_refs=8),
),
],
tooltip="Model to use for editing.",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
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(
depends_on=IO.PriceBadgeDepends(widgets=["model"], input_groups=["model.image_ref"]),
expr="""
(
$m := widgets.model;
$refs := $lookup(inputGroups, "model.image_ref");
$base := $m = "uni-1-max" ? 0.103 : 0.0434;
{"type":"usd","usd": $round($base + 0.003 * $refs, 4)}
)
""",
),
)
@classmethod
async def execute(
cls,
source: Input.Image,
prompt: str,
model: dict,
seed: int,
) -> IO.NodeOutput:
validate_string(prompt, min_length=1, max_length=6000)
request = Luma2GenerationRequest(
prompt=prompt,
model=model["model"],
type="image_edit",
source=Luma2ImageRef(url=await upload_image_to_comfyapi(cls, source)),
style=model["style"] if model["style"] != "auto" else None,
output_format="png",
web_search=model["web_search"],
image_ref=await _luma2_upload_image_refs(cls, model.get("image_ref"), max_count=8),
)
final = await _luma2_submit_and_poll(cls, request)
return IO.NodeOutput(await download_url_to_image_tensor(final.output[0].url))
_BADGE_RAY32_VIDEO = IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["resolution", "duration"]),
expr="""
(
$p := {
"360p": {"5s": 0.06, "10s": 0.18},
"540p": {"5s": 0.15, "10s": 0.45},
"720p": {"5s": 0.3, "10s": 0.9},
"1080p": {"5s": 1.2, "10s": 3.6}
};
{"type": "usd", "usd": $lookup($lookup($p, widgets.resolution), widgets.duration)}
)
""",
)
_BADGE_RAY32_VIDEO_5S = IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
expr="""
(
$p := {"360p": 0.06, "540p": 0.15, "720p": 0.3, "1080p": 1.2};
{"type": "usd", "usd": $lookup($p, widgets.resolution)}
)
""",
)
_BADGE_RAY32_EDIT = IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
expr="""
(
$p := {
"360p": {"min": 0.54, "max": 1.08},
"540p": {"min": 0.72, "max": 1.44},
"720p": {"min": 1.08, "max": 2.16},
"1080p": {"min": 2.16, "max": 4.32}
};
$r := $lookup($p, widgets.resolution);
{"type": "range_usd", "min_usd": $r.min, "max_usd": $r.max, "format": {"note": "(by source length)"}}
)
""",
)
_BADGE_RAY32_REFRAME = IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["resolution"]),
expr="""
(
$p := {"360p": 0.03, "540p": 0.06, "720p": 0.12, "1080p": 0.36};
{"type": "usd", "usd": $lookup($p, widgets.resolution), "format": {"suffix": "/second"}}
)
""",
)
def _ray32_seed_input() -> IO.Input:
return IO.Int.Input(
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