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[Partner Nodes] feat(MiniMax): add ContextIR and Regenerate nodes (#15471) 

Signed-off-by: bigcat88 <bigcat88@icloud.com>
This commit is contained in:
Alexander Piskun 2026-08-13 17:46:08 +03:00 committed by GitHub
parent b323a345bb
commit 12666983cb
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2 changed files with 616 additions and 4 deletions
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21
comfy_api_nodes/apis/minimax.py
View File

@ -161,12 +161,30 @@ class Hailuo03TaskCreationRequest(BaseModel):
..., min_length=1
)
resolution: str = Field(...)
duration: int = Field(..., ge=5, le=15)
duration: int = Field(..., ge=4, le=15)
ratio: str | None = Field(None)
seed: int | None = Field(None, ge=0, le=4294967295)
aigc_watermark: bool | None = Field(None)
class Hailuo03ContextIRRequest(BaseModel):
model: str = Field(...)
content: list[Hailuo03TextContent | Hailuo03ImageContent | Hailuo03VideoContent | Hailuo03AudioContent] = Field(
..., min_length=1
)
duration: int = Field(..., ge=4, le=15)
ratio: str | None = Field(None)
class Hailuo03RegenerationRequest(BaseModel):
model: str = Field(...)
content: list[Hailuo03TextContent | Hailuo03ImageContent | Hailuo03VideoContent | Hailuo03AudioContent] = Field(
..., min_length=1
)
resolution: str = Field(...)
aigc_watermark: bool | None = Field(None)
class Hailuo03TaskCreationResponse(BaseModel):
task_id: str = Field(...)
@ -178,6 +196,7 @@ class Hailuo03TaskError(BaseModel):
class Hailuo03TaskContent(BaseModel):
url: str | None = Field(None)
prompt: str | None = Field(None)
class Hailuo03TaskUsage(BaseModel):

599
comfy_api_nodes/nodes_minimax.py
View File

@ -3,12 +3,14 @@ from typing import Optional
import torch
from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.apis.minimax import (
Hailuo03AudioContent,
Hailuo03AudioContentUrl,
Hailuo03ContextIRRequest,
Hailuo03ImageContent,
Hailuo03ImageContentUrl,
Hailuo03RegenerationRequest,
Hailuo03TaskCreationRequest,
Hailuo03TaskCreationResponse,
Hailuo03TaskQueryResponse,
@ -456,6 +458,9 @@ HAILUO_03_QUERY_ENDPOINT = "/proxy/minimax/v2/query/video_generation" # + /{tas
HAILUO_03_MODELS = {"MiniMax H3": "MiniMax-H3"}
HAILUO_03_FAILED_STATUSES = ["failed", "cancelled", "expired"]
HAILUO_03_CONTEXT_IR_ENDPOINT = "/proxy/minimax/v2/h3_context_ir"
HAILUO_03_REGENERATION_ENDPOINT = "/proxy/minimax/v2/video_regeneration"
def _hailuo03_model_inputs(include_ratio: bool = True, allow_adaptive: bool = True):
inputs = [
@ -487,10 +492,10 @@ def _hailuo03_model_inputs(include_ratio: bool = True, allow_adaptive: bool = Tr
IO.Int.Input(
"duration",
default=5,
min=5,
min=4,
max=15,
step=1,
tooltip="Duration of the output video in seconds (5-15).",
tooltip="Duration of the output video in seconds (4-15).",
display_mode=IO.NumberDisplay.slider,
)
)
@ -939,6 +944,592 @@ class MinimaxHailuo03ReferenceNode(IO.ComfyNode):
)
class MinimaxHailuo03ContextIRNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MinimaxHailuo03ContextIRNode",
display_name="MiniMax H3 Context IR (Prompt Enhancer)",
category="partner/video/MiniMax",
description="Analyze text and media context with MiniMax H3 Context IR and produce an enhanced, "
"structured video prompt. Feed the output into the prompt of a MiniMax H3 video node and attach "
"the same media there in the same order, because the enhanced prompt refers to the attached "
"media by position.",
inputs=[
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option(
"MiniMax H3",
[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Description of the video you intend to generate.",
),
IO.Int.Input(
"duration",
default=5,
min=4,
max=15,
step=1,
tooltip="Duration of the video you intend to generate, in seconds (4-15).",
display_mode=IO.NumberDisplay.slider,
),
IO.Combo.Input(
"ratio",
options=["adaptive", "16:9", "4:3", "1:1", "3:4", "9:16", "21:9"],
default="adaptive",
tooltip="Aspect ratio of the video you intend to generate. 'adaptive' "
"requires at least one image, video, or audio input.",
),
IO.Autogrow.Input(
"reference_images",
template=IO.Autogrow.TemplateNames(
IO.Image.Input("reference_image"),
names=[
"image_1",
"image_2",
"image_3",
"image_4",
"image_5",
"image_6",
"image_7",
"image_8",
"image_9",
],
min=0,
),
tooltip="Subject or style reference images, referred to in the prompt "
"as 'Image 1'..'Image 9' in connection order. Up to 9 images.",
),
IO.Autogrow.Input(
"reference_videos",
template=IO.Autogrow.TemplateNames(
IO.Video.Input("reference_video"),
names=["video_1", "video_2", "video_3"],
min=0,
),
tooltip="Motion or scene reference videos, referred to in the prompt "
"as 'Video 1'..'Video 3' in connection order. Up to 3 videos, "
"2-15 seconds each, 15 seconds in total.",
),
IO.Autogrow.Input(
"reference_audios",
template=IO.Autogrow.TemplateNames(
IO.Audio.Input("reference_audio"),
names=["audio_1", "audio_2", "audio_3"],
min=0,
),
tooltip="Audio references, referred to in the prompt as "
"'Audio 1'..'Audio 3' in connection order. Up to 3 clips, "
"2-15 seconds each, 15 seconds in total. Cannot be used without "
"a reference image or video.",
),
],
)
],
tooltip="Model to use for prompt enhancement.",
),
IO.Image.Input(
"first_frame",
tooltip="First frame of the video you intend to generate. Cannot be combined with "
"reference media.",
optional=True,
),
IO.Image.Input(
"last_frame",
tooltip="Last frame of the video you intend to generate. Cannot be combined with "
"reference media.",
optional=True,
),
],
outputs=[
IO.String.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(
inputs=["first_frame", "last_frame"],
input_groups=["model.reference_images", "model.reference_videos", "model.reference_audios"],
),
expr="""
(
$imgsRaw := $lookup(inputGroups, "model.reference_images");
$imgs := $imgsRaw ? $imgsRaw : 0;
$vidsRaw := $lookup(inputGroups, "model.reference_videos");
$vids := $vidsRaw ? $vidsRaw : 0;
$audsRaw := $lookup(inputGroups, "model.reference_audios");
$auds := $audsRaw ? $audsRaw : 0;
$frames := (inputs.first_frame.connected ? 1 : 0) + (inputs.last_frame.connected ? 1 : 0);
($imgs + $vids + $auds) > 0
? {"type": "range_usd", "min_usd": 0.06, "max_usd": 0.11, "format": {"approximate": true}}
: $frames > 0
? {"type": "usd", "usd": 0.05, "format": {"approximate": true}}
: {"type": "usd", "usd": 0.02, "format": {"approximate": true}}
)
""",
),
)
@classmethod
async def execute(
cls,
model: dict,
first_frame: torch.Tensor | None = None,
last_frame: torch.Tensor | None = None,
) -> IO.NodeOutput:
validate_string(model["prompt"], strip_whitespace=True, min_length=1)
reference_images = {k: v for k, v in (model.get("reference_images") or {}).items() if v is not None}
reference_videos = {k: v for k, v in (model.get("reference_videos") or {}).items() if v is not None}
reference_audios = {k: v for k, v in (model.get("reference_audios") or {}).items() if v is not None}
has_frames = first_frame is not None or last_frame is not None
has_references = bool(reference_images) or bool(reference_videos) or bool(reference_audios)
if has_frames and has_references:
raise ValueError(
"First/last frame and reference media are mutually exclusive. Use frames for an "
"image-to-video prompt, or reference media for a reference-to-video prompt."
)
if reference_audios and not reference_images and not reference_videos:
raise ValueError("Reference audio cannot be used without a reference image or video.")
if not has_frames and not has_references and model["ratio"] == "adaptive":
raise ValueError(
"Ratio 'adaptive' is not supported for text-only requests; select an explicit aspect ratio."
)
for frame in (first_frame, last_frame):
if frame is not None:
validate_image_aspect_ratio(frame, (2, 5), (5, 2), strict=False) # 0.4 to 2.5
validate_image_dimensions(frame, min_width=256, min_height=256)
for image in reference_images.values():
validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False) # 0.4 to 2.5
validate_image_dimensions(image, min_width=256, min_height=256)
total_video_duration = 0.0
for i, video in enumerate(reference_videos.values(), 1):
try:
fps = float(video.get_frame_rate())
except Exception:
fps = 0.0
if fps and not (23.9 <= fps <= 60.5):
raise ValueError(f"Reference video {i} is {fps:.2f} FPS. Supported range is 23.976-60 FPS.")
try:
dur = video.get_duration()
except Exception:
continue
if dur < 1.8:
raise ValueError(f"Reference video {i} is too short: {dur:.1f}s. Minimum duration is 2 seconds.")
total_video_duration += dur
if total_video_duration > 15.1:
raise ValueError(
f"Total reference video duration is {total_video_duration:.1f}s. Maximum is 15 seconds."
)
total_audio_duration = 0.0
for i, audio in enumerate(reference_audios.values(), 1):
dur = int(audio["waveform"].shape[-1]) / int(audio["sample_rate"])
if dur < 1.8:
raise ValueError(f"Reference audio {i} is too short: {dur:.1f}s. Minimum duration is 2 seconds.")
total_audio_duration += dur
if total_audio_duration > 15.1:
raise ValueError(
f"Total reference audio duration is {total_audio_duration:.1f}s. Maximum is 15 seconds."
)
content: list = [Hailuo03TextContent(text=model["prompt"])]
if first_frame is not None:
content.append(
Hailuo03ImageContent(
image_url=Hailuo03ImageContentUrl(
url=(
await upload_images_to_comfyapi(
cls, first_frame, max_images=1, wait_label="Uploading first frame"
)
)[0],
),
role="first_frame",
)
)
if last_frame is not None:
content.append(
Hailuo03ImageContent(
image_url=Hailuo03ImageContentUrl(
url=(
await upload_images_to_comfyapi(
cls, last_frame, max_images=1, wait_label="Uploading last frame"
)
)[0],
),
role="last_frame",
)
)
for i, image in enumerate(reference_images.values(), 1):
content.append(
Hailuo03ImageContent(
image_url=Hailuo03ImageContentUrl(
url=(
await upload_images_to_comfyapi(
cls, image, max_images=1, wait_label=f"Uploading image {i}"
)
)[0],
),
role="reference_image",
)
)
for i, video in enumerate(reference_videos.values(), 1):
content.append(
Hailuo03VideoContent(
video_url=Hailuo03VideoContentUrl(
url=await upload_video_to_comfyapi(cls, video, wait_label=f"Uploading video {i}"),
),
)
)
for audio in reference_audios.values():
content.append(
Hailuo03AudioContent(
audio_url=Hailuo03AudioContentUrl(
url=await upload_audio_to_comfyapi(
cls,
audio,
container_format="mp3",
codec_name="libmp3lame",
mime_type="audio/mpeg",
),
),
)
)
response = await sync_op(
cls,
ApiEndpoint(path=HAILUO_03_CONTEXT_IR_ENDPOINT, method="POST"),
response_model=Hailuo03TaskCreationResponse,
data=Hailuo03ContextIRRequest(
model=HAILUO_03_MODELS[model["model"]],
content=content,
duration=model["duration"],
ratio=None if model["ratio"] == "adaptive" else model["ratio"],
),
)
task_result = await poll_op(
cls,
ApiEndpoint(path=f"{HAILUO_03_QUERY_ENDPOINT}/{response.task_id}"),
response_model=Hailuo03TaskQueryResponse,
status_extractor=lambda r: r.task.status,
failed_statuses=HAILUO_03_FAILED_STATUSES,
poll_interval=5,
)
prompt = task_result.task.content.prompt if task_result.task.content else None
if not prompt:
raise Exception(f"No enhanced prompt in the response: {task_result.model_dump()}")
return IO.NodeOutput(prompt)
class MinimaxHailuo03RegenerateNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MinimaxHailuo03RegenerateNode",
display_name="MiniMax H3 Regenerate to 2K",
category="partner/video/MiniMax",
description="Re-render a MiniMax H3 768P output at 2K resolution. Connect the unmodified 768P "
"video and the exact prompt used to generate it; if the original generation used first/last "
"frames or reference media, attach the same inputs.",
inputs=[
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option(
"MiniMax H3",
[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="The exact prompt used to generate the source video.",
),
IO.Combo.Input(
"resolution",
options=["2K"],
tooltip="Resolution to re-render the source video at.",
),
IO.Autogrow.Input(
"reference_images",
template=IO.Autogrow.TemplateNames(
IO.Image.Input("reference_image"),
names=[
"image_1",
"image_2",
"image_3",
"image_4",
"image_5",
"image_6",
"image_7",
"image_8",
"image_9",
],
min=0,
),
tooltip="Reference images from the original generation, in the same "
"order. Up to 9 images.",
),
IO.Autogrow.Input(
"reference_videos",
template=IO.Autogrow.TemplateNames(
IO.Video.Input("reference_video"),
names=["video_1", "video_2", "video_3"],
min=0,
),
tooltip="Reference videos from the original generation, in the same "
"order. Up to 3 videos, 2-15 seconds each, 15 seconds in total.",
),
IO.Autogrow.Input(
"reference_audios",
template=IO.Autogrow.TemplateNames(
IO.Audio.Input("reference_audio"),
names=["audio_1", "audio_2", "audio_3"],
min=0,
),
tooltip="Audio references from the original generation, in the same "
"order. Up to 3 clips, 2-15 seconds each, 15 seconds in total. "
"Cannot be used without a reference image or video.",
),
],
)
],
tooltip="Model to use for video regeneration.",
),
IO.Video.Input(
"video",
tooltip="The MiniMax H3 768P output video to re-render. Connect the unmodified output "
"of a MiniMax H3 video node (24 FPS, 4-15 seconds). 2K outputs cannot be used.",
),
IO.Image.Input(
"first_frame",
tooltip="First frame image from the original generation, if one was used.",
optional=True,
),
IO.Image.Input(
"last_frame",
tooltip="Last frame image from the original generation, if one was used.",
optional=True,
),
IO.Boolean.Input(
"watermark",
default=False,
tooltip="Whether to add an AIGC watermark to the video.",
advanced=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=IO.PriceBadge(
expr="""{"type": "usd", "usd": 0.0715, "format": {"suffix": "/second"}}""",
),
)
@classmethod
async def execute(
cls,
model: dict,
video: Input.Video,
watermark: bool,
first_frame: torch.Tensor | None = None,
last_frame: torch.Tensor | None = None,
) -> IO.NodeOutput:
validate_string(model["prompt"], strip_whitespace=True, min_length=1)
try:
fps = float(video.get_frame_rate())
except Exception:
fps = 0.0
if fps and not (23.9 <= fps <= 24.1):
raise ValueError(
f"The source video is {fps:.2f} FPS. Regeneration accepts unmodified MiniMax H3 768P "
"outputs, which are 24 FPS."
)
try:
width, height = video.get_dimensions()
except Exception:
width = height = 0
if width and height and (width % 32 or height % 32 or width * height > 1_032_192):
raise ValueError(
f"The source video is {width}x{height}. Regeneration accepts MiniMax H3 768P outputs "
"(width and height divisible by 32, at most 1,032,192 total pixels); 2K outputs cannot "
"be used as a source."
)
try:
frame_count = video.get_frame_count()
except Exception:
frame_count = 0
if frame_count and (frame_count < 107 or frame_count > 362 or (frame_count - 107) % 17):
raise ValueError(
f"The source video has {frame_count} frames. Regeneration accepts unmodified "
"MiniMax H3 outputs, whose length is 107 to 362 frames in steps of 17 "
"(4 to 15 seconds at 24 FPS)."
)
reference_images = {k: v for k, v in (model.get("reference_images") or {}).items() if v is not None}
reference_videos = {k: v for k, v in (model.get("reference_videos") or {}).items() if v is not None}
reference_audios = {k: v for k, v in (model.get("reference_audios") or {}).items() if v is not None}
if (first_frame is not None or last_frame is not None) and (
reference_images or reference_videos or reference_audios
):
raise ValueError(
"First/last frame and reference media are mutually exclusive. Use frames for an "
"image-to-video prompt, or reference media for a reference-to-video prompt."
)
if reference_audios and not reference_images and not reference_videos:
raise ValueError("Reference audio cannot be used without a reference image or video.")
for frame in (first_frame, last_frame):
if frame is not None:
validate_image_aspect_ratio(frame, (2, 5), (5, 2), strict=False) # 0.4 to 2.5
validate_image_dimensions(frame, min_width=256, min_height=256)
for image in reference_images.values():
validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False) # 0.4 to 2.5
validate_image_dimensions(image, min_width=256, min_height=256)
total_video_duration = 0.0
for i, ref_video in enumerate(reference_videos.values(), 1):
try:
ref_fps = float(ref_video.get_frame_rate())
except Exception:
ref_fps = 0.0
if ref_fps and not (23.9 <= ref_fps <= 60.5):
raise ValueError(f"Reference video {i} is {ref_fps:.2f} FPS. Supported range is 23.976-60 FPS.")
try:
dur = ref_video.get_duration()
except Exception:
continue
if dur < 1.8:
raise ValueError(f"Reference video {i} is too short: {dur:.1f}s. Minimum duration is 2 seconds.")
total_video_duration += dur
if total_video_duration > 15.1:
raise ValueError(
f"Total reference video duration is {total_video_duration:.1f}s. Maximum is 15 seconds."
)
total_audio_duration = 0.0
for i, audio in enumerate(reference_audios.values(), 1):
dur = int(audio["waveform"].shape[-1]) / int(audio["sample_rate"])
if dur < 1.8:
raise ValueError(f"Reference audio {i} is too short: {dur:.1f}s. Minimum duration is 2 seconds.")
total_audio_duration += dur
if total_audio_duration > 15.1:
raise ValueError(
f"Total reference audio duration is {total_audio_duration:.1f}s. Maximum is 15 seconds."
)
content: list = [
Hailuo03VideoContent(
video_url=Hailuo03VideoContentUrl(
url=await upload_video_to_comfyapi(cls, video, wait_label="Uploading source video"),
),
role="base_video",
),
Hailuo03TextContent(text=model["prompt"]),
]
if first_frame is not None:
content.append(
Hailuo03ImageContent(
image_url=Hailuo03ImageContentUrl(
url=(
await upload_images_to_comfyapi(
cls, first_frame, max_images=1, wait_label="Uploading first frame"
)
)[0],
),
role="first_frame",
)
)
if last_frame is not None:
content.append(
Hailuo03ImageContent(
image_url=Hailuo03ImageContentUrl(
url=(
await upload_images_to_comfyapi(
cls, last_frame, max_images=1, wait_label="Uploading last frame"
)
)[0],
),
role="last_frame",
)
)
for i, image in enumerate(reference_images.values(), 1):
content.append(
Hailuo03ImageContent(
image_url=Hailuo03ImageContentUrl(
url=(
await upload_images_to_comfyapi(
cls, image, max_images=1, wait_label=f"Uploading image {i}"
)
)[0],
),
role="reference_image",
)
)
for i, ref_video in enumerate(reference_videos.values(), 1):
content.append(
Hailuo03VideoContent(
video_url=Hailuo03VideoContentUrl(
url=await upload_video_to_comfyapi(cls, ref_video, wait_label=f"Uploading video {i}"),
),
)
)
for audio in reference_audios.values():
content.append(
Hailuo03AudioContent(
audio_url=Hailuo03AudioContentUrl(
url=await upload_audio_to_comfyapi(
cls,
audio,
container_format="mp3",
codec_name="libmp3lame",
mime_type="audio/mpeg",
),
),
)
)
response = await sync_op(
cls,
ApiEndpoint(path=HAILUO_03_REGENERATION_ENDPOINT, method="POST"),
response_model=Hailuo03TaskCreationResponse,
data=Hailuo03RegenerationRequest(
model=HAILUO_03_MODELS[model["model"]],
content=content,
resolution=model["resolution"],
aigc_watermark=watermark,
),
)
task_result = await poll_op(
cls,
ApiEndpoint(path=f"{HAILUO_03_QUERY_ENDPOINT}/{response.task_id}"),
response_model=Hailuo03TaskQueryResponse,
status_extractor=lambda r: r.task.status,
failed_statuses=HAILUO_03_FAILED_STATUSES,
poll_interval=10,
)
video_url = task_result.task.content.url if task_result.task.content else None
if not video_url:
raise Exception(f"No video URL in the response: {task_result.model_dump()}")
return IO.NodeOutput(await download_url_to_video_output(video_url))
class MinimaxExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@ -950,6 +1541,8 @@ class MinimaxExtension(ComfyExtension):
MinimaxHailuo03TextToVideoNode,
MinimaxHailuo03FirstLastFrameNode,
MinimaxHailuo03ReferenceNode,
MinimaxHailuo03ContextIRNode,
MinimaxHailuo03RegenerateNode,
]