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[Partner Nodes] deprecate retired models (#16121)

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* [Partner Nodes] chore(OpenAI): remove the DALL·E 2 and DALL·E 3 nodes, OpenAI shut both models down

Signed-off-by: Alexander Piskun <bigcat88@icloud.com>

* [Partner Nodes] chore(LTX): remove the LTX-2 nodes, the vendor no longer serves ltx-2-fast and ltx-2-pro

Signed-off-by: Alexander Piskun <bigcat88@icloud.com>

* [Partner Nodes] chore(ByteDance): remove the Seedream 3.0 node and the Seedance 1.0 Lite models, BytePlus deactivated them

Signed-off-by: Alexander Piskun <bigcat88@icloud.com>

* [Partner Nodes] chore(Kling): remove the Video Extend node, video-extend only accepted videos from the retired 1.x models

Signed-off-by: Alexander Piskun <bigcat88@icloud.com>

* [Partner Nodes] chore(ByteDance): remove the Reference Images to Video node, no Seedance 1.0 model accepts reference images

Signed-off-by: Alexander Piskun <bigcat88@icloud.com>

---------

Signed-off-by: Alexander Piskun <bigcat88@icloud.com>
This commit is contained in:
Alexander Piskun
2026-09-06 01:12:49 +04:00
committed by GitHub
parent 8a43c6bd92
commit f00bfd610c
6 changed files with 4 additions and 859 deletions
Download Patch File Download Diff File Expand all files Collapse all files
comfy_api_nodes/apis/bytedance.py
-33
View File
@@ -3,16 +3,6 @@ from typing import Any, Literal
from pydantic import BaseModel, Field
class Text2ImageTaskCreationRequest(BaseModel):
model: str = Field(...)
prompt: str = Field(...)
response_format: str | None = Field("url")
size: str | None = Field(None)
seed: int | None = Field(0, ge=0, le=2147483647)
guidance_scale: float | None = Field(..., ge=1.0, le=10.0)
watermark: bool | None = Field(False)
class Seedream4Options(BaseModel):
max_images: int = Field(15)
@@ -188,19 +178,6 @@ class SeedanceVirtualLibraryCreateAssetRequest(BaseModel):
asset_type: str | None = Field(None, description="BytePlus asset type. Defaults to Image server-side when omitted.")
RECOMMENDED_PRESETS = [
("1024x1024 (1:1)", 1024, 1024),
("864x1152 (3:4)", 864, 1152),
("1152x864 (4:3)", 1152, 864),
("1280x720 (16:9)", 1280, 720),
("720x1280 (9:16)", 720, 1280),
("832x1248 (2:3)", 832, 1248),
("1248x832 (3:2)", 1248, 832),
("1512x648 (21:9)", 1512, 648),
("2048x2048 (1:1)", 2048, 2048),
("Custom", None, None),
]
RECOMMENDED_PRESETS_SEEDREAM_4 = [
("2048x2048 (1:1)", 2048, 2048),
("2304x1728 (4:3)", 2304, 1728),
@@ -327,16 +304,6 @@ def seedance2_reference_limits(model_id: str) -> dict:
# The time in this dictionary are given for 10 seconds duration.
VIDEO_TASKS_EXECUTION_TIME = {
"seedance-1-0-lite-t2v-250428": {
"480p": 40,
"720p": 60,
"1080p": 90,
},
"seedance-1-0-lite-i2v-250428": {
"480p": 40,
"720p": 60,
"1080p": 90,
},
"seedance-1-0-pro-250528": {
"480p": 70,
"720p": 85,
comfy_api_nodes/apis/openai.py
-1
View File
@@ -49,7 +49,6 @@ class OpenAIImageGenerationRequest(BaseModel):
prompt: str = Field(...)
quality: str | None = Field(None, description="The quality of the generated image")
size: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
style: str | None = Field(None, description="Style of the image (only for dall-e-3)")
class Reasoning(BaseModel):
comfy_api_nodes/nodes_bytedance.py
+3 -294
View File
@@ -12,7 +12,6 @@ from typing_extensions import override
from comfy.utils import common_upscale
from comfy_api.latest import IO, ComfyExtension, Input, Types
from comfy_api_nodes.apis.bytedance import (
RECOMMENDED_PRESETS,
RECOMMENDED_PRESETS_SEEDREAM_4,
RECOMMENDED_PRESETS_SEEDREAM_4_0,
RECOMMENDED_PRESETS_SEEDREAM_4_5,
@@ -50,7 +49,6 @@ from comfy_api_nodes.apis.bytedance import (
TaskTextContent,
TaskVideoContent,
TaskVideoContentUrl,
Text2ImageTaskCreationRequest,
Text2VideoTaskCreationRequest,
seedance2_reference_limits,
)
@@ -124,9 +122,6 @@ SEEDANCE_MODEL_TOOLTIP = (
"Mini for the fastest, lowest-cost generation."
)
DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-250428"}
logger = logging.getLogger(__name__)
@@ -419,130 +414,6 @@ def get_image_url_from_response(response: ImageTaskCreationResponse) -> str:
return response.data[0]["url"]
class ByteDanceImageNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="ByteDanceImageNode",
display_name="ByteDance Image",
category="partner/image/ByteDance",
description="Generate images using ByteDance models via api based on prompt",
inputs=[
IO.Combo.Input("model", options=["seedream-3-0-t2i-250415"]),
IO.String.Input(
"prompt",
multiline=True,
tooltip="The text prompt used to generate the image",
),
IO.Combo.Input(
"size_preset",
options=[label for label, _, _ in RECOMMENDED_PRESETS],
tooltip="Pick a recommended size. Select Custom to use the width and height below",
),
IO.Int.Input(
"width",
default=1024,
min=512,
max=2048,
step=64,
tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
),
IO.Int.Input(
"height",
default=1024,
min=512,
max=2048,
step=64,
tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed to use for generation",
optional=True,
),
IO.Float.Input(
"guidance_scale",
default=2.5,
min=1.0,
max=10.0,
step=0.01,
display_mode=IO.NumberDisplay.number,
tooltip="Higher value makes the image follow the prompt more closely",
optional=True,
),
IO.Boolean.Input(
"watermark",
default=False,
tooltip='Whether to add an "AI generated" watermark to the image',
optional=True,
advanced=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(
expr="""{"type":"usd","usd":0.03}""",
),
is_deprecated=True,
)
@classmethod
async def execute(
cls,
model: str,
prompt: str,
size_preset: str,
width: int,
height: int,
seed: int,
guidance_scale: float,
watermark: bool,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1)
w = h = None
for label, tw, th in RECOMMENDED_PRESETS:
if label == size_preset:
w, h = tw, th
break
if w is None or h is None:
w, h = width, height
if not (512 <= w <= 2048) or not (512 <= h <= 2048):
raise ValueError(
f"Custom size out of range: {w}x{h}. " "Both width and height must be between 512 and 2048 pixels."
)
payload = Text2ImageTaskCreationRequest(
model=model,
prompt=prompt,
size=f"{w}x{h}",
seed=seed,
guidance_scale=guidance_scale,
watermark=watermark,
)
response = await sync_op(
cls,
ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
data=payload,
response_model=ImageTaskCreationResponse,
)
return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
class ByteDanceSeedreamNode(IO.ComfyNode):
@classmethod
@@ -1578,7 +1449,6 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
options=[
"seedance-1-5-pro-251215",
"seedance-1-0-pro-250528",
"seedance-1-0-lite-t2v-250428",
"seedance-1-0-pro-fast-251015",
],
default="seedance-1-0-pro-fast-251015",
@@ -1706,7 +1576,6 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
options=[
"seedance-1-5-pro-251215",
"seedance-1-0-pro-250528",
"seedance-1-0-lite-i2v-250428",
"seedance-1-0-pro-fast-251015",
],
default="seedance-1-0-pro-fast-251015",
@@ -1840,8 +1709,8 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
inputs=[
IO.Combo.Input(
"model",
options=["seedance-1-5-pro-251215", "seedance-1-0-pro-250528", "seedance-1-0-lite-i2v-250428"],
default="seedance-1-0-lite-i2v-250428",
options=["seedance-1-5-pro-251215", "seedance-1-0-pro-250528"],
default="seedance-1-5-pro-251215",
),
IO.String.Input(
"prompt",
@@ -1976,152 +1845,6 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
)
class ByteDanceImageReferenceNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="ByteDanceImageReferenceNode",
display_name="ByteDance Reference Images to Video",
category="partner/video/ByteDance",
description="Generate video using prompt and reference images.",
inputs=[
IO.Combo.Input(
"model",
options=["seedance-1-0-pro-250528", "seedance-1-0-lite-i2v-250428"],
default="seedance-1-0-lite-i2v-250428",
),
IO.String.Input(
"prompt",
multiline=True,
tooltip="The text prompt used to generate the video.",
),
IO.Image.Input(
"images",
tooltip="One to four images.",
),
IO.Combo.Input(
"resolution",
options=["480p", "720p"],
tooltip="The resolution of the output video.",
),
IO.Combo.Input(
"aspect_ratio",
options=["adaptive", "16:9", "4:3", "1:1", "3:4", "9:16", "21:9"],
tooltip="The aspect ratio of the output video.",
),
IO.Int.Input(
"duration",
default=5,
min=3,
max=12,
step=1,
tooltip="The duration of the output video in seconds.",
display_mode=IO.NumberDisplay.slider,
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed to use for generation.",
optional=True,
),
IO.Boolean.Input(
"watermark",
default=False,
tooltip='Whether to add an "AI generated" watermark to the video.',
optional=True,
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(
depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"]),
expr="""
(
$priceByModel := {
"seedance-1-0-pro": {
"480p":[0.23,0.24],
"720p":[0.51,0.56]
},
"seedance-1-0-lite": {
"480p":[0.17,0.18],
"720p":[0.37,0.41]
}
};
$model := widgets.model;
$modelKey :=
$contains($model, "seedance-1-0-pro") ? "seedance-1-0-pro" :
"seedance-1-0-lite";
$resolution := widgets.resolution;
$resKey :=
$contains($resolution, "720") ? "720p" :
"480p";
$modelPrices := $lookup($priceByModel, $modelKey);
$baseRange := $lookup($modelPrices, $resKey);
$min10s := $baseRange[0];
$max10s := $baseRange[1];
$scale := widgets.duration / 10;
$minCost := $min10s * $scale;
$maxCost := $max10s * $scale;
($minCost = $maxCost)
? {"type":"usd","usd": $minCost}
: {"type":"range_usd","min_usd": $minCost, "max_usd": $maxCost}
)
""",
),
)
@classmethod
async def execute(
cls,
model: str,
prompt: str,
images: Input.Image,
resolution: str,
aspect_ratio: str,
duration: int,
seed: int,
watermark: bool,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1)
raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "watermark"])
for image in images:
validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000)
validate_image_aspect_ratio(image, (2, 5), (5, 2), strict=False) # 0.4 to 2.5
image_urls = await upload_images_to_comfyapi(cls, images, max_images=4, mime_type="image/png")
prompt = (
f"{prompt} "
f"--resolution {resolution} "
f"--ratio {aspect_ratio} "
f"--duration {duration} "
f"--seed {seed} "
f"--watermark {str(watermark).lower()}"
)
x = [
TaskTextContent(text=prompt),
*[TaskImageContent(image_url=TaskImageContentUrl(url=str(i)), role="reference_image") for i in image_urls],
]
return await process_video_task(
cls,
payload=Image2VideoTaskCreationRequest(model=model, content=x, generate_audio=None),
estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
)
def raise_if_text_params(prompt: str, text_params: list[str]) -> None:
for i in text_params:
if f"--{i} " in prompt:
@@ -2149,19 +1872,13 @@ PRICE_BADGE_VIDEO = IO.PriceBadge(
"480p":[0.09,0.1],
"720p":[0.21,0.23],
"1080p":[0.47,0.49]
},
"seedance-1-0-lite": {
"480p":[0.17,0.18],
"720p":[0.37,0.41],
"1080p":[0.85,0.88]
}
};
$model := widgets.model;
$modelKey :=
$contains($model, "seedance-1-5-pro") ? "seedance-1-5-pro" :
$contains($model, "seedance-1-0-pro-fast") ? "seedance-1-0-pro-fast" :
$contains($model, "seedance-1-0-pro") ? "seedance-1-0-pro" :
"seedance-1-0-lite";
"seedance-1-0-pro";
$resolution := widgets.resolution;
$resKey :=
$contains($resolution, "1080") ? "1080p" :
@@ -3175,12 +2892,6 @@ async def process_video_task(
payload: Text2VideoTaskCreationRequest | Image2VideoTaskCreationRequest,
estimated_duration: int | None,
) -> IO.NodeOutput:
if payload.model in DEPRECATED_MODELS:
logger.warning(
"Model '%s' is deprecated and will be deactivated on May 13, 2026. "
"Please switch to a newer model. Recommended: seedance-1-0-pro-fast-251015.",
payload.model,
)
initial_response = await sync_op(
cls,
ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"),
@@ -3884,7 +3595,6 @@ class ByteDanceExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
ByteDanceImageNode,
ByteDanceSeedreamNode,
ByteDanceSeedreamNodeV2,
ByteDanceSeedreamNodeV3,
@@ -3892,7 +3602,6 @@ class ByteDanceExtension(ComfyExtension):
ByteDanceTextToVideoNode,
ByteDanceImageToVideoNode,
ByteDanceFirstLastFrameNode,
ByteDanceImageReferenceNode,
ByteDance2TextToVideoNode,
ByteDance2FirstLastFrameNode,
ByteDance2ReferenceNode,
comfy_api_nodes/nodes_kling.py
-84
View File
@@ -20,8 +20,6 @@ from comfy_api_nodes.apis import (
KlingText2VideoResponse,
KlingImage2VideoRequest,
KlingImage2VideoResponse,
KlingVideoExtendRequest,
KlingVideoExtendResponse,
KlingLipSyncVoiceLanguage,
KlingLipSyncInputObject,
KlingLipSyncRequest,
@@ -102,7 +100,6 @@ def _generate_storyboard_inputs(count: int) -> list:
KLING_API_VERSION = "v1"
PATH_TEXT_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/text2video"
PATH_IMAGE_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/image2video"
PATH_VIDEO_EXTEND = f"/proxy/kling/{KLING_API_VERSION}/videos/video-extend"
PATH_LIP_SYNC = f"/proxy/kling/{KLING_API_VERSION}/videos/lip-sync"
PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations"
@@ -116,7 +113,6 @@ AVERAGE_DURATION_T2V = 319
AVERAGE_DURATION_I2V = 164
AVERAGE_DURATION_LIP_SYNC = 455
AVERAGE_DURATION_IMAGE_GEN = 32
AVERAGE_DURATION_VIDEO_EXTEND = 320
MODE_TEXT2VIDEO = {
@@ -1629,85 +1625,6 @@ class KlingStartEndFrameNode(IO.ComfyNode):
)
class KlingVideoExtendNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="KlingVideoExtendNode",
display_name="Kling Video Extend",
category="partner/video/Kling",
description="Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes.",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
tooltip="Positive text prompt for guiding the video extension",
),
IO.String.Input(
"negative_prompt",
multiline=True,
tooltip="Negative text prompt for elements to avoid in the extended video",
),
IO.Float.Input("cfg_scale", default=0.5, min=0.0, max=1.0),
IO.String.Input(
"video_id",
force_input=True,
tooltip="The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.",
),
],
outputs=[
IO.Video.Output(),
IO.String.Output(display_name="video_id"),
IO.String.Output(display_name="duration"),
],
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":0.28}""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
negative_prompt: str,
cfg_scale: float,
video_id: str,
) -> IO.NodeOutput:
validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
task_creation_response = await sync_op(
cls,
ApiEndpoint(path=PATH_VIDEO_EXTEND, method="POST"),
response_model=KlingVideoExtendResponse,
data=KlingVideoExtendRequest(
prompt=prompt if prompt else None,
negative_prompt=negative_prompt if negative_prompt else None,
cfg_scale=cfg_scale,
video_id=video_id,
),
)
validate_task_creation_response(task_creation_response)
task_id = task_creation_response.data.task_id
final_response = await poll_op(
cls,
ApiEndpoint(path=f"{PATH_VIDEO_EXTEND}/{task_id}"),
response_model=KlingVideoExtendResponse,
estimated_duration=AVERAGE_DURATION_VIDEO_EXTEND,
status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None),
)
validate_video_result_response(final_response)
video = get_video_from_response(final_response)
return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration))
class KlingLipSyncAudioToVideoNode(IO.ComfyNode):
"""Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file."""
@@ -2740,7 +2657,6 @@ class KlingExtension(ComfyExtension):
KlingTextToVideoNode,
KlingImage2VideoNode,
KlingStartEndFrameNode,
KlingVideoExtendNode,
KlingLipSyncAudioToVideoNode,
KlingLipSyncTextToVideoNode,
KlingImageGenerationNode,
comfy_api_nodes/nodes_ltxv.py
+1 -186
View File
@@ -1,26 +1,19 @@
from io import BytesIO
from pydantic import BaseModel, Field
from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.util import (
ApiEndpoint,
download_url_to_video_output,
get_number_of_images,
poll_op,
sync_op,
sync_op_raw,
upload_audio_to_comfyapi,
upload_images_to_comfyapi,
validate_string,
)
MODELS_MAP = {
"LTX-2 (Pro)": "ltx-2-pro",
"LTX-2 (Fast)": "ltx-2-fast",
}
V25_MODELS_MAP = {
"LTX-2.5 (Fast)": "ltx-2-5-fast",
"LTX-2.5 (Pro)": "ltx-2-5-pro",
@@ -79,21 +72,6 @@ async def _v25_submit_and_poll(cls: type[IO.ComfyNode], route: str, data: BaseMo
return IO.NodeOutput(await download_url_to_video_output(job.result.video_url, cls=cls))
PRICE_BADGE = IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"]),
expr="""
(
$prices := {
"ltx-2 (pro)": {"1920x1080":0.06,"2560x1440":0.12,"3840x2160":0.24},
"ltx-2 (fast)": {"1920x1080":0.04,"2560x1440":0.08,"3840x2160":0.16}
};
$modelPrices := $lookup($prices, $lowercase(widgets.model));
$pps := $lookup($modelPrices, widgets.resolution);
{"type":"usd","usd": $pps * widgets.duration}
)
""",
)
V25_PRICE_BADGE = IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
expr="""
@@ -217,167 +195,6 @@ def _v25_validate_settings(model: dict) -> None:
raise ValueError("Durations over 10s require a 720p or 1080p resolution and 24/25 FPS.")
class TextToVideoNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="LtxvApiTextToVideo",
display_name="LTXV Text To Video",
category="partner/video/LTXV",
description="Professional-quality videos with customizable duration and resolution.",
inputs=[
IO.Combo.Input("model", options=list(MODELS_MAP.keys())),
IO.String.Input(
"prompt",
multiline=True,
default="",
),
IO.Combo.Input("duration", options=[6, 8, 10, 12, 14, 16, 18, 20], default=8),
IO.Combo.Input(
"resolution",
options=[
"1920x1080",
"2560x1440",
"3840x2160",
],
),
IO.Combo.Input("fps", options=[25, 50], default=25),
IO.Boolean.Input(
"generate_audio",
default=False,
optional=True,
tooltip="When true, the generated video will include AI-generated audio matching the scene.",
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,
is_deprecated=True,
price_badge=PRICE_BADGE,
)
@classmethod
async def execute(
cls,
model: str,
prompt: str,
duration: int,
resolution: str,
fps: int = 25,
generate_audio: bool = False,
) -> IO.NodeOutput:
validate_string(prompt, min_length=1, max_length=10000)
if duration > 10 and (model != "LTX-2 (Fast)" or resolution != "1920x1080" or fps != 25):
raise ValueError(
"Durations over 10s are only available for the Fast model at 1920x1080 resolution and 25 FPS."
)
response = await sync_op_raw(
cls,
ApiEndpoint("/proxy/ltx/v1/text-to-video", "POST"),
data=ExecuteTaskRequest(
prompt=prompt,
model=MODELS_MAP[model],
duration=duration,
resolution=resolution,
fps=fps,
generate_audio=generate_audio,
),
as_binary=True,
max_retries=1,
)
return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(response)))
class ImageToVideoNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="LtxvApiImageToVideo",
display_name="LTXV Image To Video",
category="partner/video/LTXV",
description="Professional-quality videos with customizable duration and resolution based on start image.",
inputs=[
IO.Image.Input("image", tooltip="First frame to be used for the video."),
IO.Combo.Input("model", options=list(MODELS_MAP.keys())),
IO.String.Input(
"prompt",
multiline=True,
default="",
),
IO.Combo.Input("duration", options=[6, 8, 10, 12, 14, 16, 18, 20], default=8),
IO.Combo.Input(
"resolution",
options=[
"1920x1080",
"2560x1440",
"3840x2160",
],
),
IO.Combo.Input("fps", options=[25, 50], default=25),
IO.Boolean.Input(
"generate_audio",
default=False,
optional=True,
tooltip="When true, the generated video will include AI-generated audio matching the scene.",
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,
is_deprecated=True,
price_badge=PRICE_BADGE,
)
@classmethod
async def execute(
cls,
image: Input.Image,
model: str,
prompt: str,
duration: int,
resolution: str,
fps: int = 25,
generate_audio: bool = False,
) -> IO.NodeOutput:
validate_string(prompt, min_length=1, max_length=10000)
if duration > 10 and (model != "LTX-2 (Fast)" or resolution != "1920x1080" or fps != 25):
raise ValueError(
"Durations over 10s are only available for the Fast model at 1920x1080 resolution and 25 FPS."
)
if get_number_of_images(image) != 1:
raise ValueError("Currently only one input image is supported.")
response = await sync_op_raw(
cls,
ApiEndpoint("/proxy/ltx/v1/image-to-video", "POST"),
data=ExecuteTaskRequest(
image_uri=(await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png"))[0],
prompt=prompt,
model=MODELS_MAP[model],
duration=duration,
resolution=resolution,
fps=fps,
generate_audio=generate_audio,
),
as_binary=True,
max_retries=1,
)
return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(response)))
class Ltx25TextToVideoNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
@@ -584,8 +401,6 @@ class LtxvApiExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
TextToVideoNode,
ImageToVideoNode,
Ltx25TextToVideoNode,
Ltx25ImageToVideoNode,
Ltx25AudioToVideoNode,
comfy_api_nodes/nodes_openai.py
-261
View File
@@ -131,265 +131,6 @@ async def validate_and_cast_response(response, timeout: int = None) -> torch.Ten
return torch.stack(image_tensors, dim=0)
class OpenAIDalle2(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="OpenAIDalle2",
display_name="OpenAI DALL·E 2",
category="partner/image/OpenAI",
description="Generates images synchronously via OpenAI's DALL·E 2 endpoint.",
inputs=[
IO.String.Input(
"prompt",
default="",
multiline=True,
tooltip="Text prompt for DALL·E",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2**31 - 1,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="not implemented yet in backend",
optional=True,
),
IO.Combo.Input(
"size",
default="1024x1024",
options=["256x256", "512x512", "1024x1024"],
tooltip="Image size",
optional=True,
),
IO.Int.Input(
"n",
default=1,
min=1,
max=8,
step=1,
tooltip="How many images to generate",
display_mode=IO.NumberDisplay.number,
optional=True,
),
IO.Image.Input(
"image",
tooltip="Optional reference image for image editing.",
optional=True,
),
IO.Mask.Input(
"mask",
tooltip="Optional mask for inpainting (white areas will be replaced)",
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=["size", "n"]),
expr="""
(
$size := widgets.size;
$nRaw := widgets.n;
$n := ($nRaw != null and $nRaw != 0) ? $nRaw : 1;
$base :=
$contains($size, "256x256") ? 0.016 :
$contains($size, "512x512") ? 0.018 :
0.02;
{"type":"usd","usd": $round($base * $n, 3)}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt,
seed=0,
image=None,
mask=None,
n=1,
size="1024x1024",
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False)
model = "dall-e-2"
path = "/proxy/openai/images/generations"
content_type = "application/json"
request_class = OpenAIImageGenerationRequest
img_binary = None
if image is not None and mask is not None:
path = "/proxy/openai/images/edits"
content_type = "multipart/form-data"
request_class = OpenAIImageEditRequest
input_tensor = image.squeeze().cpu()
height, width, channels = input_tensor.shape
rgba_tensor = torch.ones(height, width, 4, device="cpu")
rgba_tensor[:, :, :channels] = input_tensor
if mask.shape[1:] != image.shape[1:-1]:
raise Exception("Mask and Image must be the same size")
rgba_tensor[:, :, 3] = 1 - mask.squeeze().cpu()
rgba_tensor = downscale_image_tensor(rgba_tensor.unsqueeze(0)).squeeze()
image_np = (rgba_tensor.numpy() * 255).astype(np.uint8)
img = Image.fromarray(image_np)
img_byte_arr = BytesIO()
img.save(img_byte_arr, format="PNG")
img_byte_arr.seek(0)
img_binary = img_byte_arr # .getvalue()
img_binary.name = "image.png"
elif image is not None or mask is not None:
raise Exception("Dall-E 2 image editing requires an image AND a mask")
response = await sync_op(
cls,
ApiEndpoint(path=path, method="POST"),
response_model=OpenAIImageGenerationResponse,
data=request_class(
model=model,
prompt=prompt,
n=n,
size=size,
seed=seed,
),
files=(
{
"image": ("image.png", img_binary, "image/png"),
}
if img_binary
else None
),
content_type=content_type,
)
return IO.NodeOutput(await validate_and_cast_response(response))
class OpenAIDalle3(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="OpenAIDalle3",
display_name="OpenAI DALL·E 3",
category="partner/image/OpenAI",
description="Generates images synchronously via OpenAI's DALL·E 3 endpoint.",
inputs=[
IO.String.Input(
"prompt",
default="",
multiline=True,
tooltip="Text prompt for DALL·E",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2**31 - 1,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="not implemented yet in backend",
optional=True,
),
IO.Combo.Input(
"quality",
default="standard",
options=["standard", "hd"],
tooltip="Image quality",
optional=True,
),
IO.Combo.Input(
"style",
default="natural",
options=["natural", "vivid"],
tooltip="Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.",
optional=True,
),
IO.Combo.Input(
"size",
default="1024x1024",
options=["1024x1024", "1024x1792", "1792x1024"],
tooltip="Image size",
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=["size", "quality"]),
expr="""
(
$size := widgets.size;
$q := widgets.quality;
$hd := $contains($q, "hd");
$price :=
$contains($size, "1024x1024")
? ($hd ? 0.08 : 0.04)
: (($contains($size, "1792x1024") or $contains($size, "1024x1792"))
? ($hd ? 0.12 : 0.08)
: 0.04);
{"type":"usd","usd": $price}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt,
seed=0,
style="natural",
quality="standard",
size="1024x1024",
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False)
model = "dall-e-3"
# build the operation
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/openai/images/generations", method="POST"),
response_model=OpenAIImageGenerationResponse,
data=OpenAIImageGenerationRequest(
model=model,
prompt=prompt,
quality=quality,
size=size,
style=style,
seed=seed,
),
)
return IO.NodeOutput(await validate_and_cast_response(response))
class OpenAIGPTImage1(IO.ComfyNode):
@classmethod
@@ -1360,8 +1101,6 @@ class OpenAIExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
OpenAIDalle2,
OpenAIDalle3,
OpenAIGPTImage1,
OpenAIGPTImageNodeV2,
OpenAIChatNode,