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2 changed files with 54 additions and 652 deletions

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@ -10,7 +10,6 @@ from pydantic import BaseModel, Field, confloat
class LumaIO:
LUMA_REF = "LUMA_REF"
LUMA_CONCEPTS = "LUMA_CONCEPTS"
LUMA_RAY32_KEYFRAME = "LUMA_RAY32_KEYFRAME"
class LumaReference:
@ -21,14 +20,13 @@ class LumaReference:
def create_api_model(self, download_url: str):
return LumaImageRef(url=download_url, weight=self.weight)
class LumaReferenceChain:
def __init__(self, first_ref: LumaReference = None):
def __init__(self, first_ref: LumaReference=None):
self.refs: list[LumaReference] = []
if first_ref:
self.refs.append(first_ref)
def add(self, luma_ref: LumaReference = None):
def add(self, luma_ref: LumaReference=None):
self.refs.append(luma_ref)
def create_api_model(self, download_urls: list[str], max_refs=4):
@ -126,7 +124,7 @@ def get_luma_concepts(include_none=False):
"pull_out",
"aerial",
"crane_up",
"eye_level",
"eye_level"
]
@ -164,8 +162,8 @@ class LumaVideoModelOutputDuration(str, Enum):
class LumaGenerationType(str, Enum):
video = "video"
image = "image"
video = 'video'
image = 'image'
class LumaState(str, Enum):
@ -176,109 +174,86 @@ class LumaState(str, Enum):
class LumaAssets(BaseModel):
video: Optional[str] = Field(None, description="The URL of the video")
image: Optional[str] = Field(None, description="The URL of the image")
progress_video: Optional[str] = Field(None, description="The URL of the progress video")
video: Optional[str] = Field(None, description='The URL of the video')
image: Optional[str] = Field(None, description='The URL of the image')
progress_video: Optional[str] = Field(None, description='The URL of the progress video')
class LumaImageRef(BaseModel):
"""Used for image gen"""
url: str = Field(..., description="The URL of the image reference")
weight: confloat(ge=0.0, le=1.0) = Field(..., description="The weight of the image reference")
url: str = Field(..., description='The URL of the image reference')
weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference')
class LumaImageReference(BaseModel):
"""Used for video gen"""
type: Optional[str] = Field("image", description="Input type, defaults to image")
url: str = Field(..., description="The URL of the image")
type: Optional[str] = Field('image', description='Input type, defaults to image')
url: str = Field(..., description='The URL of the image')
class LumaModifyImageRef(BaseModel):
url: str = Field(..., description="The URL of the image reference")
weight: confloat(ge=0.0, le=1.0) = Field(..., description="The weight of the image reference")
url: str = Field(..., description='The URL of the image reference')
weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference')
class LumaCharacterRef(BaseModel):
identity0: LumaImageIdentity = Field(..., description="The image identity object")
identity0: LumaImageIdentity = Field(..., description='The image identity object')
class LumaImageIdentity(BaseModel):
images: list[str] = Field(..., description="The URLs of the image identity")
images: list[str] = Field(..., description='The URLs of the image identity')
class LumaGenerationReference(BaseModel):
type: str = Field("generation", description="Input type, defaults to generation")
id: str = Field(..., description="The ID of the generation")
type: str = Field('generation', description='Input type, defaults to generation')
id: str = Field(..., description='The ID of the generation')
class LumaKeyframes(BaseModel):
frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description="")
frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description="")
frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='')
frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='')
class LumaConceptObject(BaseModel):
key: str = Field(..., description="Camera Concept name")
key: str = Field(..., description='Camera Concept name')
class LumaImageGenerationRequest(BaseModel):
prompt: str = Field(..., description="The prompt of the generation")
model: LumaImageModel = Field(LumaImageModel.photon_1, description="The image model used for the generation")
aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9)
image_ref: Optional[list[LumaImageRef]] = Field(None, description="List of image reference objects")
style_ref: Optional[list[LumaImageRef]] = Field(None, description="List of style reference objects")
character_ref: Optional[LumaCharacterRef] = Field(None, description="The image identity object")
modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description="The modify image reference object")
prompt: str = Field(..., description='The prompt of the generation')
model: LumaImageModel = Field(LumaImageModel.photon_1, description='The image model used for the generation')
aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9, description='The aspect ratio of the generation')
image_ref: Optional[list[LumaImageRef]] = Field(None, description='List of image reference objects')
style_ref: Optional[list[LumaImageRef]] = Field(None, description='List of style reference objects')
character_ref: Optional[LumaCharacterRef] = Field(None, description='The image identity object')
modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description='The modify image reference object')
class LumaGenerationRequest(BaseModel):
prompt: str = Field(..., description="The prompt of the generation")
model: LumaVideoModel = Field(LumaVideoModel.ray_2, description="The video model used for the generation")
duration: Optional[LumaVideoModelOutputDuration] = Field(None, description="The duration of the generation")
aspect_ratio: Optional[LumaAspectRatio] = Field(None, description="The aspect ratio of the generation")
resolution: Optional[LumaVideoOutputResolution] = Field(None, description="The resolution of the generation")
loop: Optional[bool] = Field(None, description="Whether to loop the video")
keyframes: Optional[LumaKeyframes] = Field(None, description="The keyframes of the generation")
concepts: Optional[list[LumaConceptObject]] = Field(None, description="Camera Concepts to apply to generation")
prompt: str = Field(..., description='The prompt of the generation')
model: LumaVideoModel = Field(LumaVideoModel.ray_2, description='The video model used for the generation')
duration: Optional[LumaVideoModelOutputDuration] = Field(None, description='The duration of the generation')
aspect_ratio: Optional[LumaAspectRatio] = Field(None, description='The aspect ratio of the generation')
resolution: Optional[LumaVideoOutputResolution] = Field(None, description='The resolution of the generation')
loop: Optional[bool] = Field(None, description='Whether to loop the video')
keyframes: Optional[LumaKeyframes] = Field(None, description='The keyframes of the generation')
concepts: Optional[list[LumaConceptObject]] = Field(None, description='Camera Concepts to apply to generation')
class LumaGeneration(BaseModel):
id: str = Field(..., description="The ID of the generation")
generation_type: LumaGenerationType = Field(..., description="Generation type, image or video")
state: LumaState = Field(..., description="The state of the generation")
failure_reason: Optional[str] = Field(None, description="The reason for the state of the generation")
created_at: str = Field(..., description="The date and time when the generation was created")
assets: Optional[LumaAssets] = Field(None, description="The assets of the generation")
model: str = Field(..., description="The model used for the generation")
request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(...)
id: str = Field(..., description='The ID of the generation')
generation_type: LumaGenerationType = Field(..., description='Generation type, image or video')
state: LumaState = Field(..., description='The state of the generation')
failure_reason: Optional[str] = Field(None, description='The reason for the state of the generation')
created_at: str = Field(..., description='The date and time when the generation was created')
assets: Optional[LumaAssets] = Field(None, description='The assets of the generation')
model: str = Field(..., description='The model used for the generation')
request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(..., description="The request used for the generation")
class Luma2ImageRef(BaseModel):
url: str | None = None
data: str | None = None
media_type: str | None = None
generation_id: str | None = Field(None, description="reference a prior generation (extend / source reuse)")
class Luma2VideoEdit(BaseModel):
"""Edit controls for Ray 3.2 ``video_edit`` generations."""
auto_controls: bool | None = Field(None, description="derive a conditioning schedule from the source (recommended)")
strength: str | None = Field(None, description="'adhere_1' .. 'reimagine_3'; constrained by IO.Combo")
class Luma2VideoOptions(BaseModel):
"""Ray 3.2 ``video`` output settings (text / image / keyframe / edit / extend)."""
resolution: str | None = Field(None, description="360p | 540p | 720p | 1080p")
duration: str | None = Field(None, description="5s | 10s")
loop: bool | None = Field(None)
start_frame: Luma2ImageRef | None = Field(None)
end_frame: Luma2ImageRef | None = Field(None)
keyframes: list[Luma2ImageRef] | None = Field(None)
keyframe_indexes: list[int] | None = Field(None)
edit: Luma2VideoEdit | None = Field(None)
class Luma2GenerationRequest(BaseModel):
@ -291,7 +266,6 @@ class Luma2GenerationRequest(BaseModel):
web_search: bool | None = None
image_ref: list[Luma2ImageRef] | None = None
source: Luma2ImageRef | None = None
video: Luma2VideoOptions | None = Field(None)
class Luma2Generation(BaseModel):
@ -303,31 +277,3 @@ class Luma2Generation(BaseModel):
output: list[LumaImageReference] | None = None
failure_reason: str | None = None
failure_code: str | None = None
# --- Ray 3.2 multi-keyframe chain ---
LUMA_KEYFRAME_MODE_FRACTION = "fraction" # value in [0.0, 1.0] of the output video duration
LUMA_KEYFRAME_MODE_SECONDS = "seconds" # absolute time, in seconds, from the start of the output
class LumaRay32KeyframeItem:
"""One guide image anchored at a position on the Ray 3.2 output timeline."""
def __init__(self, image: torch.Tensor, mode: str, value: float):
self.image = image
self.mode = mode # LUMA_KEYFRAME_MODE_FRACTION | LUMA_KEYFRAME_MODE_SECONDS
self.value = value
class LumaRay32KeyframeChain:
def __init__(self):
self.items: list[LumaRay32KeyframeItem] = []
def add(self, item: LumaRay32KeyframeItem) -> None:
self.items.append(item)
def clone(self) -> "LumaRay32KeyframeChain":
c = LumaRay32KeyframeChain()
c.items = list(self.items)
return c

View File

@ -3,13 +3,9 @@ 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,
@ -22,8 +18,6 @@ from comfy_api_nodes.apis.luma import (
LumaIO,
LumaKeyframes,
LumaModifyImageRef,
LumaRay32KeyframeChain,
LumaRay32KeyframeItem,
LumaReference,
LumaReferenceChain,
LumaVideoModel,
@ -39,7 +33,6 @@ from comfy_api_nodes.util import (
sync_op,
upload_image_to_comfyapi,
upload_images_to_comfyapi,
upload_video_to_comfyapi,
validate_string,
)
@ -699,10 +692,7 @@ async def _luma2_upload_image_refs(
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."""
) -> Input.Image:
initial = await sync_op(
cls,
ApiEndpoint(path="/proxy/luma_2/generations", method="POST"),
@ -710,21 +700,21 @@ async def _luma2_submit_and_poll(
data=request,
)
if not initial.id:
raise RuntimeError("Luma API did not return a generation id.")
raise RuntimeError("Luma 2 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:
if not final.output:
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
raise RuntimeError(f"Luma 2 generation failed: {msg}")
url = final.output[0].url
if not url:
raise RuntimeError("Luma 2 generation completed without an output URL.")
return await download_url_to_image_tensor(url)
class LumaImageNode(IO.ComfyNode):
@ -853,8 +843,7 @@ class LumaImageNode(IO.ComfyNode):
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))
return IO.NodeOutput(await _luma2_submit_and_poll(cls, request))
class LumaImageEditNode(IO.ComfyNode):
@ -940,533 +929,7 @@ class LumaImageEditNode(IO.ComfyNode):
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(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="Seed to determine if node should re-run; results are nondeterministic regardless of seed.",
)
async def _ray32_generate(cls: type[IO.ComfyNode], request: Luma2GenerationRequest) -> IO.NodeOutput:
"""Run a ray-3.2 generation and return (video, generation_id)."""
final = await _luma2_submit_and_poll(cls, request, estimated_duration=120)
video = await download_url_to_video_output(final.output[0].url)
return IO.NodeOutput(video, final.id or "")
class LumaRay32TextToVideoNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaRay32TextToVideoNode",
display_name="Luma Ray 3.2 Text to Video",
category="partner/video/Luma",
description="Generate a video from a text prompt using Luma's Ray 3.2 model.",
inputs=[
IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1", "4:3", "3:4", "21:9"]),
IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
IO.Combo.Input("duration", options=["5s", "10s"]),
IO.Boolean.Input(
"loop",
default=False,
tooltip="Make the video loop seamlessly. Only available with 5s duration.",
),
_ray32_seed_input(),
],
outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=_BADGE_RAY32_VIDEO,
)
@classmethod
async def execute(
cls, prompt: str, aspect_ratio: str, resolution: str, duration: str, loop: bool, seed: int
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
if loop and duration == "10s":
raise ValueError("Looping is only available with 5s duration on Ray 3.2.")
request = Luma2GenerationRequest(
prompt=prompt,
model="ray-3.2",
type="video",
aspect_ratio=aspect_ratio,
video=Luma2VideoOptions(resolution=resolution, duration=duration, loop=loop or None),
)
return await _ray32_generate(cls, request)
class LumaRay32ImageToVideoNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaRay32ImageToVideoNode",
display_name="Luma Ray 3.2 Image to Video",
category="partner/video/Luma",
description="Generate a video from a start and/or end frame using Luma's Ray 3.2 model. "
"Image-anchored generations are always 5 seconds.",
inputs=[
IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
IO.Boolean.Input(
"loop",
default=False,
tooltip="Make the video loop seamlessly. Not available when an end_frame is set.",
),
_ray32_seed_input(),
IO.Image.Input("start_frame", optional=True, tooltip="First frame of the generated video."),
IO.Image.Input("end_frame", optional=True, tooltip="Last frame of the generated video."),
],
outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=_BADGE_RAY32_VIDEO_5S,
)
@classmethod
async def execute(
cls,
prompt: str,
resolution: str,
loop: bool,
seed: int,
start_frame: torch.Tensor | None = None,
end_frame: torch.Tensor | None = None,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
if start_frame is None and end_frame is None:
raise ValueError("Provide at least one of start_frame / end_frame.")
if loop and end_frame is not None:
raise ValueError("Looping is not available when an end_frame is set.")
video = Luma2VideoOptions(resolution=resolution, duration="5s", loop=loop or None)
if start_frame is not None:
url = await upload_image_to_comfyapi(cls, start_frame, mime_type="image/png")
video.start_frame = Luma2ImageRef(url=url)
if end_frame is not None:
url = await upload_image_to_comfyapi(cls, end_frame, mime_type="image/png")
video.end_frame = Luma2ImageRef(url=url)
request = Luma2GenerationRequest(prompt=prompt, model="ray-3.2", type="video", video=video)
return await _ray32_generate(cls, request)
class LumaRay32KeyframeNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaRay32KeyframeNode",
display_name="Luma Ray 3.2 Keyframe",
category="partner/video/Luma",
description="Anchor a guide image to a position on the Ray 3.2 output video timeline. Connect this to "
"the 'keyframes' input of the Luma Ray 3.2 Keyframes to Video node; chain several together via the "
"optional 'keyframes' input below.",
inputs=[
IO.Image.Input("image", tooltip="Guide image to place at the chosen moment of the output video."),
IO.DynamicCombo.Input(
"position",
options=[
IO.DynamicCombo.Option(
"Fraction of duration (0.0-1.0)",
[
IO.Float.Input(
"fraction",
default=0.0,
min=0.0,
max=1.0,
step=0.01,
display_mode=IO.NumberDisplay.number,
tooltip="Where in the output video this image applies " "(0.0 = start, 1.0 = end).",
),
],
),
IO.DynamicCombo.Option(
"Absolute time (seconds)",
[
IO.Float.Input(
"seconds",
default=0.0,
min=0.0,
max=10.0,
step=0.1,
display_mode=IO.NumberDisplay.number,
tooltip="Time in seconds from the start of the output video where this "
"image applies.",
),
],
),
],
tooltip="How to place this image on the output video's timeline.",
),
IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Input(
"keyframes",
optional=True,
tooltip="Optional earlier keyframes to chain with this one.",
),
],
outputs=[IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Output(display_name="keyframes")],
)
@classmethod
def execute(
cls,
image: torch.Tensor,
position: dict,
keyframes: LumaRay32KeyframeChain | None = None,
) -> IO.NodeOutput:
chain = keyframes.clone() if keyframes is not None else LumaRay32KeyframeChain()
if position["position"] == "Absolute time (seconds)":
mode, value = LUMA_KEYFRAME_MODE_SECONDS, float(position["seconds"])
else:
mode, value = LUMA_KEYFRAME_MODE_FRACTION, float(position["fraction"])
chain.add(LumaRay32KeyframeItem(image=image, mode=mode, value=value))
return IO.NodeOutput(chain)
class LumaRay32KeyframesToVideoNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaRay32KeyframesToVideoNode",
display_name="Luma Ray 3.2 Keyframes to Video",
category="partner/video/Luma",
description="Generate a video that interpolates through a sequence of guide images, each anchored to a "
"position on the timeline, using Luma Ray 3.2. Build the sequence with Luma Ray 3.2 Keyframe nodes "
"(at least 2).",
inputs=[
IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."),
IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
IO.Combo.Input("duration", options=["5s", "10s"]),
_ray32_seed_input(),
IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Input(
"keyframes",
tooltip="Keyframe sequence from Luma Ray 3.2 Keyframe nodes (at least 2).",
),
],
outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=_BADGE_RAY32_VIDEO,
)
@classmethod
async def execute(
cls,
prompt: str,
resolution: str,
duration: str,
seed: int,
keyframes: LumaRay32KeyframeChain | None = None,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
items = keyframes.items if keyframes is not None else []
if len(items) < 2:
raise ValueError(
"Connect at least 2 Luma Ray 3.2 Keyframe nodes "
"(use Luma Ray 3.2 Image to Video for a single start/end frame)."
)
if len(items) > 64:
raise ValueError(f"Ray 3.2 supports at most 64 keyframes; got {len(items)}.")
maxframe = 120 if duration == "5s" else 240
duration_seconds = maxframe / 24 # 5.0 or 10.0
# Resolve each keyframe to an output-frame index, then order by position
# (so the user can chain keyframes in any order — the position is what places them)
placed: list[tuple[int, torch.Tensor]] = []
for item in items:
if item.mode == LUMA_KEYFRAME_MODE_SECONDS:
if item.value > duration_seconds:
raise ValueError(
f"Keyframe position {item.value:g}s is past the end of the {duration} video; "
f"use 0-{duration_seconds:g}s (or switch the keyframe to fraction mode)."
)
idx = round(item.value * 24)
else:
idx = round(item.value * maxframe)
placed.append((max(0, min(maxframe, idx)), item.image))
placed.sort(key=lambda p: p[0])
indexes = [idx for idx, _ in placed]
for a, b in zip(indexes, indexes[1:]):
if a == b:
raise ValueError(
f"Two keyframes resolve to the same output frame ({a}) for a {duration} video "
f"(valid range 0-{maxframe}); give each keyframe a distinct position."
)
refs: list[Luma2ImageRef] = []
for _, image in placed:
url = await upload_image_to_comfyapi(cls, image, mime_type="image/png")
refs.append(Luma2ImageRef(url=url))
request = Luma2GenerationRequest(
prompt=prompt,
model="ray-3.2",
type="video",
video=Luma2VideoOptions(resolution=resolution, duration=duration, keyframes=refs, keyframe_indexes=indexes),
)
return await _ray32_generate(cls, request)
class LumaRay32VideoEditNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaRay32VideoEditNode",
display_name="Luma Ray 3.2 Video Edit",
category="partner/video/Luma",
description="Re-render an existing video under a new prompt using Luma Ray 3.2 (restyle, relight, add "
"or remove elements) while keeping the original motion. Source video up to 18 seconds; the edited "
"video keeps the source's length.",
inputs=[
IO.Video.Input("video", tooltip="Source video to edit. Up to 18 seconds."),
IO.String.Input("prompt", multiline=True, default="", tooltip="Describes the desired edit."),
IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
IO.Combo.Input(
"strength",
options=[
"auto",
"adhere_1",
"adhere_2",
"adhere_3",
"flex_1",
"flex_2",
"flex_3",
"reimagine_1",
"reimagine_2",
"reimagine_3",
],
default="auto",
tooltip="How strongly to preserve vs. reimagine the source. 'auto' lets Ray 3.2 choose; "
"adhere_* preserves the most, flex_* is balanced, reimagine_* changes the most.",
),
_ray32_seed_input(),
],
outputs=[
IO.Video.Output(),
IO.String.Output(display_name="generation_id"),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=_BADGE_RAY32_EDIT,
)
@classmethod
async def execute(
cls, video: Input.Video, prompt: str, resolution: str, strength: str, seed: int
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000)
try:
duration = "5s" if video.get_duration() <= 5.0 else "10s"
except Exception:
duration = "10s"
source_url = await upload_video_to_comfyapi(cls, video, max_duration=18)
edit = Luma2VideoEdit(auto_controls=True) if strength == "auto" else Luma2VideoEdit(strength=strength)
request = Luma2GenerationRequest(
prompt=prompt,
model="ray-3.2",
type="video_edit",
source=Luma2ImageRef(url=source_url, media_type="video/mp4"),
video=Luma2VideoOptions(resolution=resolution, duration=duration, edit=edit),
)
return await _ray32_generate(cls, request)
class LumaRay32VideoReframeNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaRay32VideoReframeNode",
display_name="Luma Ray 3.2 Video Reframe",
category="partner/video/Luma",
description="Change the aspect ratio of an existing video, using Luma Ray 3.2 to fill the newly "
"exposed canvas areas. Source video up to 30 seconds. Billed per second of output.",
inputs=[
IO.Video.Input("video", tooltip="Source video to reframe. Up to 30 seconds."),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Describes how the newly exposed canvas areas should be filled.",
),
IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1", "4:3", "3:4", "21:9"]),
IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"),
_ray32_seed_input(),
],
outputs=[
IO.Video.Output(),
IO.String.Output(display_name="generation_id"),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=_BADGE_RAY32_REFRAME,
)
@classmethod
async def execute(
cls, video: Input.Video, prompt: str, aspect_ratio: str, resolution: str, seed: int
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False, min_length=1, max_length=6000)
if resolution == "1080p" and aspect_ratio in {"9:16", "3:4"}:
raise ValueError("1080p is not available for vertical aspect ratios (9:16, 3:4) when reframing.")
source_url = await upload_video_to_comfyapi(cls, video, max_duration=30)
request = Luma2GenerationRequest(
prompt=prompt,
model="ray-3.2",
type="video_reframe",
aspect_ratio=aspect_ratio,
source=Luma2ImageRef(url=source_url, media_type="video/mp4"),
video=Luma2VideoOptions(resolution=resolution),
)
return await _ray32_generate(cls, request)
class LumaRay32ExtendVideoNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="LumaRay32ExtendVideoNode",
display_name="Luma Ray 3.2 Extend Video",
category="partner/video/Luma",
description="Extend a previous Ray 3.2 generation forward (continue after it) or backward (lead-in "
"before it). Connect the generation_id output of a prior Luma Ray 3.2 node."
" Extensions are always 5 seconds.",
inputs=[
IO.String.Input(
"source_generation_id",
default="",
tooltip="generation_id of the prior Ray 3.2 video to extend."
" Connect the generation_id output of another Luma Ray 3.2 node.",
),
IO.DynamicCombo.Input(
"direction",
options=[
IO.DynamicCombo.Option(
"Forward (continue after)",
[
IO.Boolean.Input(
"loop",
default=False,
tooltip="Loop the extended video seamlessly (forward extend only).",
),
],
),
IO.DynamicCombo.Option("Backward (lead-in before)", []),
],
tooltip="Forward continues after the prior clip; backward is prepended before it.",
),
IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the new content."),
IO.Combo.Input("resolution", options=["540p", "720p", "1080p"], default="720p"),
_ray32_seed_input(),
],
outputs=[
IO.Video.Output(),
IO.String.Output(display_name="generation_id"),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=_BADGE_RAY32_VIDEO_5S,
)
@classmethod
async def execute(
cls, source_generation_id: str, direction: dict, prompt: str, resolution: str, seed: int
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False, min_length=1, max_length=6000)
gen_id = (source_generation_id or "").strip()
if not gen_id:
raise ValueError(
"source_generation_id is required (connect the generation_id output of a prior Luma Ray 3.2 node)."
)
video = Luma2VideoOptions(resolution=resolution, duration="5s")
ref = Luma2ImageRef(generation_id=gen_id)
if direction["direction"] == "Forward (continue after)":
video.start_frame = ref
if direction.get("loop"):
video.loop = True
else:
video.end_frame = ref
request = Luma2GenerationRequest(prompt=prompt, model="ray-3.2", type="video", video=video)
return await _ray32_generate(cls, request)
return IO.NodeOutput(await _luma2_submit_and_poll(cls, request))
class LumaExtension(ComfyExtension):
@ -1481,13 +944,6 @@ class LumaExtension(ComfyExtension):
LumaConceptsNode,
LumaImageNode,
LumaImageEditNode,
LumaRay32TextToVideoNode,
LumaRay32ImageToVideoNode,
LumaRay32KeyframeNode,
LumaRay32KeyframesToVideoNode,
LumaRay32VideoEditNode,
LumaRay32VideoReframeNode,
LumaRay32ExtendVideoNode,
]