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5 changed files with 66 additions and 842 deletions

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@ -140,7 +140,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
- Commits outside of the stable release tags may be very unstable and break many custom nodes.
- Serves as the foundation for the desktop release
2. **[Comfy Desktop](https://github.com/Comfy-Org/Comfy-Desktop)**
2. **[ComfyUI Desktop](https://github.com/Comfy-Org/Comfy-Desktop)**
- Builds a new release using the latest stable core version
3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**

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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

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@ -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,
]

View File

@ -20,8 +20,6 @@ from PIL.PngImagePlugin import PngInfo
import numpy as np
import safetensors.torch
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
import comfy.diffusers_load
import comfy.samplers
import comfy.sample
@ -2299,6 +2297,9 @@ async def init_external_custom_nodes():
Returns:
None
"""
# TODO: remove at some point when custom nodes don't break.
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
base_node_names = set(NODE_CLASS_MAPPINGS.keys())
node_paths = folder_paths.get_folder_paths("custom_nodes")
node_import_times = []

View File

@ -673,35 +673,6 @@ components:
- created_at
- updated_at
type: object
JobsCancelRequest:
additionalProperties: false
description: Request to cancel multiple jobs by ID.
properties:
job_ids:
description: Job identifiers (UUIDs) to cancel.
items:
format: uuid
type: string
maxItems: 100
minItems: 1
type: array
required:
- job_ids
type: object
JobsCancelResponse:
description: Response for POST /api/jobs/cancel.
properties:
cancelled:
description: |
Job IDs for which a cancel event was successfully dispatched by this
call. Jobs already in a terminal or cancelling state are idempotently
skipped and will not appear here.
items:
type: string
type: array
required:
- cancelled
type: object
JobsListResponse:
description: Paginated list of jobs for the authenticated user.
properties:
@ -1035,7 +1006,7 @@ components:
description: If true, clear all pending jobs from the queue
type: boolean
delete:
description: Array of job IDs to cancel; pending and running jobs transition to cancelled
description: Array of PENDING job IDs to cancel
items:
type: string
type: array
@ -1851,83 +1822,6 @@ paths:
summary: Update asset metadata
tags:
- file
/api/assets/{id}/content:
get:
description: |
Returns the binary content of an asset by ID.
The contract is the same across runtimes — "GET this path and you
receive the asset's bytes" — but the mechanism differs:
- **Local ComfyUI** streams the bytes directly (`200`,
`application/octet-stream`).
- **Cloud** does not proxy large files; it responds `302` with a
`Location` redirect to a short-lived signed storage URL. Clients that
follow redirects (browsers, `fetch`/XHR, `<img>`/`<video>`) receive
the bytes transparently.
Prefer this over the filename-addressed `/api/view` when you have an
asset ID.
operationId: getAssetContent
parameters:
- description: Asset ID
in: path
name: id
required: true
schema:
type: string
- description: |
Content-Disposition for the response: `attachment` (download) or
`inline` (render in browser). Defaults to `attachment`.
in: query
name: disposition
schema:
default: attachment
enum:
- inline
- attachment
type: string
responses:
"200":
content:
application/octet-stream:
schema:
format: binary
type: string
description: Asset content stream (local runtime streams the bytes directly)
"302":
description: Redirect to a signed storage URL (cloud runtime)
headers:
Cache-Control:
description: Private caching directive scoped to the signed URL lifetime
schema:
type: string
Location:
description: Short-lived signed URL to the asset content in storage
schema:
type: string
Vary:
description: Partitions any cached redirect by auth credentials so a private redirect is not reused across users
schema:
type: string
"404":
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
description: Asset not found
"500":
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
description: Internal server error
security:
- ApiKeyAuth: []
- BearerAuth: []
- CookieAuth: []
summary: Get asset content
tags:
- file
/api/assets/{id}/tags:
delete:
description: Removes one or more tags from an existing asset
@ -2781,20 +2675,14 @@ paths:
summary: Get internationalisation translation strings
/api/interrupt:
post:
deprecated: true
description: |
Deprecated. Prefer the jobs-namespace cancel endpoints:
POST /api/jobs/{job_id}/cancel for a single job, or
POST /api/jobs/cancel to cancel jobs by ID.
Cancels the first active job for the authenticated user (the currently
running job if there is one, otherwise the next pending job). Takes no
body and cannot target a specific job — use the jobs-namespace endpoints
for that.
Cancel all currently RUNNING jobs for the authenticated user.
This will interrupt any job that is currently in 'in_progress' status.
Note: This endpoint only affects running jobs. To cancel pending jobs, use /api/queue.
operationId: interruptJob
responses:
"200":
description: Success - first active job cancelled, or no active job found
description: Success - Job interrupted or no running job found
"401":
content:
application/json:
@ -2807,7 +2695,7 @@ paths:
schema:
$ref: '#/components/schemas/ErrorResponse'
description: Internal server error
summary: Interrupt the first active job
summary: Interrupt currently running jobs
tags:
- queue
/api/job/{job_id}/status:
@ -3066,64 +2954,6 @@ paths:
summary: Cancel a job
tags:
- workflow
/api/jobs/cancel:
post:
description: |
Cancel one or more jobs for the authenticated user in a single request.
State-agnostic: cancels both pending and running jobs (both transition to
the cancelled state via the same mechanism as the single-job endpoint).
Idempotent per job: a job already in a terminal or cancelling state is a
no-op and simply will not appear in the returned `cancelled` list.
Fail-fast on unknown IDs: if any provided job ID does not exist for this
user, the request returns 404 and no jobs are cancelled. This surfaces
bad IDs to the caller rather than silently dropping them.
This is the canonical batch-cancel endpoint. The delete operation on
POST /api/queue is deprecated in favour of this.
operationId: cancelJobs
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/JobsCancelRequest'
required: true
responses:
"200":
content:
application/json:
schema:
$ref: '#/components/schemas/JobsCancelResponse'
description: Success - cancel requests dispatched (or jobs were already terminal)
"400":
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
description: Bad Request - job_ids is missing, empty, exceeds the maximum count, or contains an invalid UUID
"401":
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
description: Unauthorized - Authentication required
"404":
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
description: One or more job IDs not found for this user (no jobs cancelled)
"500":
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
description: Internal server error - cancellation failed
summary: Cancel multiple jobs
tags:
- workflow
/api/node_replacements:
get:
description: |
@ -3274,18 +3104,9 @@ paths:
tags:
- queue
post:
deprecated: true
description: |
Deprecated. Prefer the jobs-namespace cancel endpoints:
POST /api/jobs/cancel for cancelling jobs by ID, and
POST /api/jobs/{job_id}/cancel for a single job.
Cancel specific jobs by ID (the `delete` field) or clear all pending
jobs in the queue (the `clear` field). Despite the `delete` naming, this
does not delete anything — listed jobs transition to the cancelled state,
and `delete` cancels both pending and running jobs (not pending-only as
previously documented). Job-by-ID cancellation is superseded by
POST /api/jobs/cancel; `clear` has no jobs-namespace replacement yet.
Cancel specific PENDING jobs by ID or clear all pending jobs in the queue.
Note: This endpoint only affects pending jobs. To cancel running jobs, use /api/interrupt.
operationId: manageQueue
requestBody:
content: