ComfyUI/comfy_api_nodes/apis/bytedance.py
Jedrzej Kosinski 1b96430c60
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Merge master into worksplit-multigpu (#13546)
* fix: pin SQLAlchemy>=2.0 in requirements.txt (fixes #13036) (#13316)

* Refactor io to IO in nodes_ace.py (#13485)

* Bump comfyui-frontend-package to 1.42.12 (#13489)

* Make the ltx audio vae more native. (#13486)

* feat(api-nodes): add automatic downscaling of videos for ByteDance 2 nodes (#13465)

* Support standalone LTXV audio VAEs (#13499)

* [Partner Nodes]  added 4K resolution for Veo models; added Veo 3 Lite model (#13330)

* feat(api nodes): added 4K resolution for Veo models; added Veo 3 Lite model

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

* increase poll_interval from 5 to 9

---------

Signed-off-by: bigcat88 <bigcat88@icloud.com>
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>

* Bump comfyui-frontend-package to 1.42.14 (#13493)

* Add gpt-image-2 as version option (#13501)

* Allow logging in comfy app files. (#13505)

* chore: update workflow templates to v0.9.59 (#13507)

* fix(veo): reject 4K resolution for veo-3.0 models in Veo3VideoGenerationNode (#13504)

The tooltip on the resolution input states that 4K is not available for
veo-3.1-lite or veo-3.0 models, but the execute guard only rejected the
lite combination. Selecting 4K with veo-3.0-generate-001 or
veo-3.0-fast-generate-001 would fall through and hit the upstream API
with an invalid request.

Broaden the guard to match the documented behavior and update the error
message accordingly.

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>

* feat: RIFE and FILM frame interpolation model support (CORE-29) (#13258)

* initial RIFE support

* Also support FILM

* Better RAM usage, reduce FILM VRAM peak

* Add model folder placeholder

* Fix oom fallback frame loss

* Remove torch.compile for now

* Rename model input

* Shorter input type name

---------

* fix: use Parameter assignment for Stable_Zero123 cc_projection weights (fixes #13492) (#13518)

On Windows with aimdo enabled, disable_weight_init.Linear uses lazy
initialization that sets weight and bias to None to avoid unnecessary
memory allocation. This caused a crash when copy_() was called on the
None weight attribute in Stable_Zero123.__init__.

Replace copy_() with direct torch.nn.Parameter assignment, which works
correctly on both Windows (aimdo enabled) and other platforms.

* Derive InterruptProcessingException from BaseException (#13523)

* bump manager version to 4.2.1 (#13516)

* ModelPatcherDynamic: force cast stray weights on comfy layers (#13487)

the mixed_precision ops can have input_scale parameters that are used
in tensor math but arent a weight or bias so dont get proper VRAM
management. Treat these as force-castable parameters like the non comfy
weight, random params are buffers already are.

* Update logging level for invalid version format (#13526)

* [Partner Nodes] add SD2 real human support (#13509)

* feat(api-nodes): add SD2 real human support

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

* fix: add validation before uploading Assets

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

* Add asset_id and group_id displaying on the node

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

* extend poll_op to use instead of custom async cycle

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

* added the polling for the "Active" status after asset creation

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

* updated tooltip for group_id

* allow usage of real human in the ByteDance2FirstLastFrame node

* add reference count limits

* corrected price in status when input assets contain video

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

---------

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

* feat: SAM (segment anything) 3.1 support (CORE-34) (#13408)

* [Partner Nodes] GPTImage: fix price badges, add new resolutions (#13519)

* fix(api-nodes): fixed price badges, add new resolutions

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

* proper calculate the total run cost when "n > 1"

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

---------

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

* chore: update workflow templates to v0.9.61 (#13533)

* chore: update embedded docs to v0.4.4 (#13535)

* add 4K resolution to Kling nodes (#13536)

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

* Fix LTXV Reference Audio node (#13531)

* comfy-aimdo 0.2.14: Hotfix async allocator estimations (#13534)

This was doing an over-estimate of VRAM used by the async allocator when lots
of little small tensors were in play.

Also change the versioning scheme to == so we can roll forward aimdo without
worrying about stable regressions downstream in comfyUI core.

* Disable sageattention for SAM3 (#13529)

Causes Nans

* execution: Add anti-cycle validation (#13169)

Currently if the graph contains a cycle, the just inifitiate recursions,
hits a catch all then throws a generic error against the output node
that seeded the validation. Instead, fail the offending cycling mode
chain and handlng it as an error in its own right.

Co-authored-by: guill <jacob.e.segal@gmail.com>

* chore: update workflow templates to v0.9.62 (#13539)

---------

Signed-off-by: bigcat88 <bigcat88@icloud.com>
Co-authored-by: Octopus <liyuan851277048@icloud.com>
Co-authored-by: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Co-authored-by: Comfy Org PR Bot <snomiao+comfy-pr@gmail.com>
Co-authored-by: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Co-authored-by: Jukka Seppänen <40791699+kijai@users.noreply.github.com>
Co-authored-by: AustinMroz <austin@comfy.org>
Co-authored-by: Daxiong (Lin) <contact@comfyui-wiki.com>
Co-authored-by: Matt Miller <matt@miller-media.com>
Co-authored-by: blepping <157360029+blepping@users.noreply.github.com>
Co-authored-by: Dr.Lt.Data <128333288+ltdrdata@users.noreply.github.com>
Co-authored-by: rattus <46076784+rattus128@users.noreply.github.com>
Co-authored-by: guill <jacob.e.segal@gmail.com>
2026-04-23 19:20:14 -07:00

237 lines
6.7 KiB
Python

from typing import 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)
class Seedream4TaskCreationRequest(BaseModel):
model: str = Field(...)
prompt: str = Field(...)
response_format: str = Field("url")
image: list[str] | None = Field(None, description="Image URLs")
size: str = Field(...)
seed: int = Field(..., ge=0, le=2147483647)
sequential_image_generation: str = Field("disabled")
sequential_image_generation_options: Seedream4Options = Field(Seedream4Options(max_images=15))
watermark: bool = Field(False)
output_format: str | None = None
class ImageTaskCreationResponse(BaseModel):
model: str = Field(...)
created: int = Field(..., description="Unix timestamp (in seconds) indicating time when the request was created.")
data: list = Field([], description="Contains information about the generated image(s).")
error: dict = Field({}, description="Contains `code` and `message` fields in case of error.")
class TaskTextContent(BaseModel):
type: str = Field("text")
text: str = Field(...)
class TaskImageContentUrl(BaseModel):
url: str = Field(...)
class TaskImageContent(BaseModel):
type: str = Field("image_url")
image_url: TaskImageContentUrl = Field(...)
role: Literal["first_frame", "last_frame", "reference_image"] | None = Field(None)
class TaskVideoContentUrl(BaseModel):
url: str = Field(...)
class TaskVideoContent(BaseModel):
type: str = Field("video_url")
video_url: TaskVideoContentUrl = Field(...)
role: str = Field("reference_video")
class TaskAudioContentUrl(BaseModel):
url: str = Field(...)
class TaskAudioContent(BaseModel):
type: str = Field("audio_url")
audio_url: TaskAudioContentUrl = Field(...)
role: str = Field("reference_audio")
class Text2VideoTaskCreationRequest(BaseModel):
model: str = Field(...)
content: list[TaskTextContent] = Field(..., min_length=1)
generate_audio: bool | None = Field(...)
class Image2VideoTaskCreationRequest(BaseModel):
model: str = Field(...)
content: list[TaskTextContent | TaskImageContent] = Field(..., min_length=2)
generate_audio: bool | None = Field(...)
class Seedance2TaskCreationRequest(BaseModel):
model: str = Field(...)
content: list[TaskTextContent | TaskImageContent | TaskVideoContent | TaskAudioContent] = Field(..., min_length=1)
generate_audio: bool | None = Field(None)
resolution: str | None = Field(None)
ratio: str | None = Field(None)
duration: int | None = Field(None, ge=4, le=15)
seed: int | None = Field(None, ge=0, le=2147483647)
watermark: bool | None = Field(None)
class TaskCreationResponse(BaseModel):
id: str = Field(...)
class TaskStatusError(BaseModel):
code: str = Field(...)
message: str = Field(...)
class TaskStatusResult(BaseModel):
video_url: str = Field(...)
class TaskStatusUsage(BaseModel):
completion_tokens: int = Field(0)
total_tokens: int = Field(0)
class TaskStatusResponse(BaseModel):
id: str = Field(...)
model: str = Field(...)
status: Literal["queued", "running", "cancelled", "succeeded", "failed"] = Field(...)
error: TaskStatusError | None = Field(None)
content: TaskStatusResult | None = Field(None)
usage: TaskStatusUsage | None = Field(None)
class GetAssetResponse(BaseModel):
id: str = Field(...)
name: str | None = Field(None)
url: str | None = Field(None)
asset_type: str = Field(...)
group_id: str = Field(...)
status: str = Field(...)
error: TaskStatusError | None = Field(None)
class SeedanceCreateVisualValidateSessionResponse(BaseModel):
session_id: str = Field(...)
h5_link: str = Field(...)
class SeedanceGetVisualValidateSessionResponse(BaseModel):
session_id: str = Field(...)
status: str = Field(...)
group_id: str | None = Field(None)
error_code: str | None = Field(None)
error_message: str | None = Field(None)
class SeedanceCreateAssetRequest(BaseModel):
group_id: str = Field(...)
url: str = Field(...)
asset_type: str = Field(...)
name: str | None = Field(None, max_length=64)
project_name: str | None = Field(None)
class SeedanceCreateAssetResponse(BaseModel):
asset_id: str = Field(...)
# Dollars per 1K tokens, keyed by (model_id, has_video_input).
SEEDANCE2_PRICE_PER_1K_TOKENS = {
("dreamina-seedance-2-0-260128", False): 0.007,
("dreamina-seedance-2-0-260128", True): 0.0043,
("dreamina-seedance-2-0-fast-260128", False): 0.0056,
("dreamina-seedance-2-0-fast-260128", True): 0.0033,
}
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),
("1728x2304 (3:4)", 1728, 2304),
("2560x1440 (16:9)", 2560, 1440),
("1440x2560 (9:16)", 1440, 2560),
("2496x1664 (3:2)", 2496, 1664),
("1664x2496 (2:3)", 1664, 2496),
("3024x1296 (21:9)", 3024, 1296),
("3072x3072 (1:1)", 3072, 3072),
("4096x4096 (1:1)", 4096, 4096),
("Custom", None, None),
]
# Seedance 2.0 reference video pixel count limits per model and output resolution.
SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = {
"dreamina-seedance-2-0-260128": {
"480p": {"min": 409_600, "max": 927_408},
"720p": {"min": 409_600, "max": 927_408},
"1080p": {"min": 409_600, "max": 2_073_600},
},
"dreamina-seedance-2-0-fast-260128": {
"480p": {"min": 409_600, "max": 927_408},
"720p": {"min": 409_600, "max": 927_408},
},
}
# 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,
"1080p": 115,
},
"seedance-1-0-pro-fast-251015": {
"480p": 50,
"720p": 65,
"1080p": 100,
},
"seedance-1-5-pro-251215": {
"480p": 80,
"720p": 100,
"1080p": 150,
},
}