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"""API Nodes for ByteDance Seed LLM via the BytePlus ModelArk Responses API.

See: https://docs.byteplus.com/en/docs/ModelArk/1585128

"""

from typing_extensions import override

from comfy_api.latest import IO, ComfyExtension, Input

from comfy_api_nodes.apis.bytedance_llm import (

BytePlusInputImage,

BytePlusInputMessage,

BytePlusInputText,

BytePlusInputVideo,

BytePlusMessageContent,

BytePlusResponseCreateRequest,

BytePlusResponseObject,

)

from comfy_api_nodes.util import (

ApiEndpoint,

get_number_of_images,

sync_op,

upload_images_to_comfyapi,

upload_video_to_comfyapi,

validate_string,

)

BYTEPLUS_RESPONSES_ENDPOINT = "/proxy/byteplus/api/v3/responses"

SEED_MAX_IMAGES = 20

SEED_MAX_VIDEOS = 4

SEED_MODELS: dict[str, str] = {

"Seed 2.0 Pro": "seed-2-0-pro-260328",

"Seed 2.0 Lite": "seed-2-0-lite-260228",

"Seed 2.0 Mini": "seed-2-0-mini-260215",

}

def _seed_model_inputs(max_images: int = SEED_MAX_IMAGES, max_videos: int = SEED_MAX_VIDEOS):

return [

IO.Autogrow.Input(

"images",

template=IO.Autogrow.TemplateNames(

IO.Image.Input("image"),

names=[f"image_{i}" for i in range(1, max_images + 1)],

min=0,

tooltip=f"Optional image(s) to use as context for the model. Up to {max_images} images.",

IO.Autogrow.Input(

"videos",

template=IO.Autogrow.TemplateNames(

IO.Video.Input("video"),

names=[f"video_{i}" for i in range(1, max_videos + 1)],

min=0,

tooltip=f"Optional video(s) to use as context for the model. Up to {max_videos} videos.",

IO.Float.Input(

"temperature",

default=1.0,

min=0.0,

max=2.0,

step=0.01,

tooltip="Controls randomness. 0.0 is deterministic, higher values are more random.",

advanced=True,

]

def _get_text_from_response(response: BytePlusResponseObject) -> str:

"""Extract concatenated text from all assistant message output_text blocks."""

if not response.output:

return ""

chunks: list[str] = []

for item in response.output:

if item.type != "message" or not item.content:

continue

for block in item.content:

if block.type == "output_text" and block.text:

chunks.append(block.text)

elif block.type == "refusal" and block.refusal:

raise ValueError(f"Model refused to respond: {block.refusal}")

return "\n".join(chunks)

async def _build_image_content_blocks(

cls: type[IO.ComfyNode],

image_tensors: list[Input.Image],

) -> list[BytePlusInputImage]:

urls = await upload_images_to_comfyapi(

cls,

image_tensors,

max_images=SEED_MAX_IMAGES,

wait_label="Uploading reference images",

)

return [BytePlusInputImage(image_url=url) for url in urls]

async def _build_video_content_blocks(

cls: type[IO.ComfyNode],

videos: list[Input.Video],

) -> list[BytePlusInputVideo]:

blocks: list[BytePlusInputVideo] = []

total = len(videos)

for idx, video in enumerate(videos):

label = "Uploading reference video"

if total > 1:

label = f"{label} ({idx + 1}/{total})"

url = await upload_video_to_comfyapi(cls, video, wait_label=label)

blocks.append(BytePlusInputVideo(video_url=url))

return blocks

class ByteDanceSeedNode(IO.ComfyNode):

"""Generate text responses from a ByteDance Seed 2.0 model."""

@classmethod

def define_schema(cls):

return IO.Schema(

node_id="ByteDanceSeedNode",

display_name="ByteDance Seed",

category="partner/text/ByteDance",

essentials_category="Text Generation",

description="Generate text responses with ByteDance's Seed 2.0 models. "

"Provide a text prompt and optionally one or more images or videos for multimodal context.",

inputs=[

IO.String.Input(

"prompt",

multiline=True,

default="",

tooltip="Text input to the model.",

IO.DynamicCombo.Input(

"model",

options=[IO.DynamicCombo.Option(label, _seed_model_inputs()) for label in SEED_MODELS],

tooltip="The Seed model used to generate the response.",

IO.Int.Input(

"seed",

default=0,

min=0,

max=2147483647,

control_after_generate=True,

tooltip="Seed controls whether the node should re-run; "

"results are non-deterministic regardless of seed.",

IO.String.Input(

"system_prompt",

multiline=True,

default="",

optional=True,

advanced=True,

tooltip="Foundational instructions that dictate the model's behavior.",

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(widgets=["model"]),

expr="""

(

$m := widgets.model;

$contains($m, "mini") ? {

"type": "list_usd",

"usd": [0.00025, 0.0009],

"format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }

}

: $contains($m, "lite") ? {

"type": "list_usd",

"usd": [0.0003, 0.002],

"format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }

}

: $contains($m, "pro") ? {

"type": "list_usd",

"usd": [0.0005, 0.003],

"format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }

}

: {"type":"text", "text":"Token-based"}

)

""",

)

@classmethod

async def execute(

cls,

prompt: str,

model: dict,

seed: int,

system_prompt: str = "",

) -> IO.NodeOutput:

validate_string(prompt, strip_whitespace=True, min_length=1)

model_label = model["model"]

temperature = model["temperature"]

model_id = SEED_MODELS[model_label]

image_tensors: list[Input.Image] = [t for t in (model.get("images") or {}).values() if t is not None]

if sum(get_number_of_images(t) for t in image_tensors) > SEED_MAX_IMAGES:

raise ValueError(f"Up to {SEED_MAX_IMAGES} images are supported per request.")

video_inputs: list[Input.Video] = [v for v in (model.get("videos") or {}).values() if v is not None]

if len(video_inputs) > SEED_MAX_VIDEOS:

raise ValueError(f"Up to {SEED_MAX_VIDEOS} videos are supported per request.")

content: list[BytePlusMessageContent] = []

if image_tensors:

content.extend(await _build_image_content_blocks(cls, image_tensors))

if video_inputs:

content.extend(await _build_video_content_blocks(cls, video_inputs))

content.append(BytePlusInputText(text=prompt))

response = await sync_op(

cls,

ApiEndpoint(path=BYTEPLUS_RESPONSES_ENDPOINT, method="POST"),

response_model=BytePlusResponseObject,

data=BytePlusResponseCreateRequest(

model=model_id,

input=[BytePlusInputMessage(role="user", content=content)],

instructions=system_prompt or None,

temperature=temperature,

store=False,

stream=False,

)

if response.error:

raise ValueError(f"Seed API error ({response.error.code}): {response.error.message}")

result = _get_text_from_response(response)

if not result:

raise ValueError("Empty response from Seed model.")

return IO.NodeOutput(result)

class ByteDanceLLMExtension(ComfyExtension):

@override

async def get_node_list(self) -> list[type[IO.ComfyNode]]:

return [ByteDanceSeedNode]

async def comfy_entrypoint() -> ByteDanceLLMExtension:

return ByteDanceLLMExtension()

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