ComfyUI/comfy_api_nodes/apis/__init__.py

# generated by datamodel-codegen:
#   filename:  filtered-openapi.yaml
#   timestamp: 2025-04-24T22:29:35+00:00

from __future__ import annotations

from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Optional

from pydantic import BaseModel, Field, conint, constr


class ErrorResponse(BaseModel):
    error: str
    message: str


class ImageRequest(BaseModel):
    aspect_ratio: Optional[str] = Field(
        None,
        description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.",
    )
    color_palette: Optional[Dict[str, Any]] = Field(
        None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.'
    )
    magic_prompt_option: Optional[str] = Field(
        None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')."
    )
    model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')")
    negative_prompt: Optional[str] = Field(
        None,
        description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.',
    )
    num_images: Optional[conint(ge=1, le=8)] = Field(
        1, description='Optional. Number of images to generate (1-8). Defaults to 1.'
    )
    prompt: str = Field(
        ..., description='Required. The prompt to use to generate the image.'
    )
    resolution: Optional[str] = Field(
        None,
        description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.",
    )
    seed: Optional[conint(ge=0, le=2147483647)] = Field(
        None, description='Optional. A number between 0 and 2147483647.'
    )
    style_type: Optional[str] = Field(
        None,
        description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.",
    )


class IdeogramGenerateRequest(BaseModel):
    image_request: ImageRequest = Field(
        ..., description='The image generation request parameters.'
    )


class Datum(BaseModel):
    is_image_safe: Optional[bool] = Field(
        None, description='Indicates whether the image is considered safe.'
    )
    prompt: Optional[str] = Field(
        None, description='The prompt used to generate this image.'
    )
    resolution: Optional[str] = Field(
        None, description="The resolution of the generated image (e.g., '1024x1024')."
    )
    seed: Optional[int] = Field(
        None, description='The seed value used for this generation.'
    )
    style_type: Optional[str] = Field(
        None,
        description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').",
    )
    url: Optional[str] = Field(None, description='URL to the generated image.')


class IdeogramGenerateResponse(BaseModel):
    created: Optional[datetime] = Field(
        None, description='Timestamp when the generation was created.'
    )
    data: Optional[List[Datum]] = Field(
        None, description='Array of generated image information.'
    )


class Code(Enum):
    int_1100 = 1100
    int_1101 = 1101
    int_1102 = 1102
    int_1103 = 1103


class Code1(Enum):
    int_1000 = 1000
    int_1001 = 1001
    int_1002 = 1002
    int_1003 = 1003
    int_1004 = 1004


class KlingErrorResponse(BaseModel):
    code: int = Field(
        ..., description='Error code value as defined in the API documentation'
    )
    message: str = Field(..., description='Human-readable error message')
    request_id: str = Field(
        ..., description='Request ID for tracking and troubleshooting'
    )


class Code2(Enum):
    int_1200 = 1200
    int_1201 = 1201
    int_1202 = 1202
    int_1203 = 1203


class KlingRequestError(KlingErrorResponse):
    code: Optional[Code2] = Field(
        None,
        description='- 1200: Invalid request parameters\n- 1201: Invalid parameters\n- 1202: Invalid request method\n- 1203: Requested resource does not exist\n',
    )


class Code3(Enum):
    int_5000 = 5000
    int_5001 = 5001
    int_5002 = 5002


class KlingServerError(KlingErrorResponse):
    code: Optional[Code3] = Field(
        None,
        description='- 5000: Internal server error\n- 5001: Service temporarily unavailable\n- 5002: Server internal timeout\n',
    )


class Code4(Enum):
    int_1300 = 1300
    int_1301 = 1301
    int_1302 = 1302
    int_1303 = 1303
    int_1304 = 1304


class KlingStrategyError(KlingErrorResponse):
    code: Optional[Code4] = Field(
        None,
        description='- 1300: Trigger platform strategy\n- 1301: Trigger content security policy\n- 1302: API request too frequent\n- 1303: Concurrency/QPS exceeds limit\n- 1304: Trigger IP whitelist policy\n',
    )


class MinimaxBaseResponse(BaseModel):
    status_code: int = Field(
        ...,
        description='Status code. 0 indicates success, other values indicate errors.',
    )
    status_msg: str = Field(
        ..., description='Specific error details or success message.'
    )


class File(BaseModel):
    bytes: Optional[int] = Field(None, description='File size in bytes')
    created_at: Optional[int] = Field(
        None, description='Unix timestamp when the file was created, in seconds'
    )
    download_url: Optional[str] = Field(
        None, description='The URL to download the video'
    )
    file_id: Optional[int] = Field(None, description='Unique identifier for the file')
    filename: Optional[str] = Field(None, description='The name of the file')
    purpose: Optional[str] = Field(None, description='The purpose of using the file')


class MinimaxFileRetrieveResponse(BaseModel):
    base_resp: MinimaxBaseResponse
    file: File


class Status(str, Enum):
    Queueing = 'Queueing'
    Preparing = 'Preparing'
    Processing = 'Processing'
    Success = 'Success'
    Fail = 'Fail'


class MinimaxTaskResultResponse(BaseModel):
    base_resp: MinimaxBaseResponse
    file_id: Optional[str] = Field(
        None,
        description='After the task status changes to Success, this field returns the file ID corresponding to the generated video.',
    )
    status: Status = Field(
        ...,
        description="Task status: 'Queueing' (in queue), 'Preparing' (task is preparing), 'Processing' (generating), 'Success' (task completed successfully), or 'Fail' (task failed).",
    )
    task_id: str = Field(..., description='The task ID being queried.')


class Model(str, Enum):
    T2V_01_Director = 'T2V-01-Director'
    I2V_01_Director = 'I2V-01-Director'
    S2V_01 = 'S2V-01'
    I2V_01 = 'I2V-01'
    I2V_01_live = 'I2V-01-live'
    T2V_01 = 'T2V-01'


class SubjectReferenceItem(BaseModel):
    image: Optional[str] = Field(
        None, description='URL or base64 encoding of the subject reference image.'
    )
    mask: Optional[str] = Field(
        None,
        description='URL or base64 encoding of the mask for the subject reference image.',
    )


class MinimaxVideoGenerationRequest(BaseModel):
    callback_url: Optional[str] = Field(
        None,
        description='Optional. URL to receive real-time status updates about the video generation task.',
    )
    first_frame_image: Optional[str] = Field(
        None,
        description='URL or base64 encoding of the first frame image. Required when model is I2V-01, I2V-01-Director, or I2V-01-live.',
    )
    model: Model = Field(
        ...,
        description='Required. ID of model. Options: T2V-01-Director, I2V-01-Director, S2V-01, I2V-01, I2V-01-live, T2V-01',
    )
    prompt: Optional[constr(max_length=2000)] = Field(
        None,
        description='Description of the video. Should be less than 2000 characters. Supports camera movement instructions in [brackets].',
    )
    prompt_optimizer: Optional[bool] = Field(
        True,
        description='If true (default), the model will automatically optimize the prompt. Set to false for more precise control.',
    )
    subject_reference: Optional[List[SubjectReferenceItem]] = Field(
        None,
        description='Only available when model is S2V-01. The model will generate a video based on the subject uploaded through this parameter.',
    )


class MinimaxVideoGenerationResponse(BaseModel):
    base_resp: MinimaxBaseResponse
    task_id: str = Field(
        ..., description='The task ID for the asynchronous video generation task.'
    )


class Moderation(str, Enum):
    low = 'low'
    auto = 'auto'


class OutputFormat(str, Enum):
    png = 'png'
    webp = 'webp'
    jpeg = 'jpeg'


class OpenAIImageEditRequest(BaseModel):
    background: Optional[str] = Field(
        None, description='Background transparency', examples=['opaque']
    )
    model: str = Field(
        ..., description='The model to use for image editing', examples=['gpt-image-1']
    )
    moderation: Optional[Moderation] = Field(
        None, description='Content moderation setting', examples=['auto']
    )
    n: Optional[int] = Field(
        None, description='The number of images to generate', examples=[1]
    )
    output_compression: Optional[int] = Field(
        None, description='Compression level for JPEG or WebP (0-100)', examples=[100]
    )
    output_format: Optional[OutputFormat] = Field(
        None, description='Format of the output image', examples=['png']
    )
    prompt: str = Field(
        ...,
        description='A text description of the desired edit',
        examples=['Give the rocketship rainbow coloring'],
    )
    quality: Optional[str] = Field(
        None, description='The quality of the edited image', examples=['low']
    )
    size: Optional[str] = Field(
        None, description='Size of the output image', examples=['1024x1024']
    )
    user: Optional[str] = Field(
        None,
        description='A unique identifier for end-user monitoring',
        examples=['user-1234'],
    )


class Background(str, Enum):
    transparent = 'transparent'
    opaque = 'opaque'


class Quality(str, Enum):
    low = 'low'
    medium = 'medium'
    high = 'high'
    standard = 'standard'
    hd = 'hd'


class ResponseFormat(str, Enum):
    url = 'url'
    b64_json = 'b64_json'


class Style(str, Enum):
    vivid = 'vivid'
    natural = 'natural'


class OpenAIImageGenerationRequest(BaseModel):
    background: Optional[Background] = Field(
        None, description='Background transparency', examples=['opaque']
    )
    model: Optional[str] = Field(
        None, description='The model to use for image generation', examples=['dall-e-3']
    )
    moderation: Optional[Moderation] = Field(
        None, description='Content moderation setting', examples=['auto']
    )
    n: Optional[int] = Field(
        None,
        description='The number of images to generate (1-10). Only 1 supported for dall-e-3.',
        examples=[1],
    )
    output_compression: Optional[int] = Field(
        None, description='Compression level for JPEG or WebP (0-100)', examples=[100]
    )
    output_format: Optional[OutputFormat] = Field(
        None, description='Format of the output image', examples=['png']
    )
    prompt: str = Field(
        ...,
        description='A text description of the desired image',
        examples=['Draw a rocket in front of a blackhole in deep space'],
    )
    quality: Optional[Quality] = Field(
        None, description='The quality of the generated image', examples=['high']
    )
    response_format: Optional[ResponseFormat] = Field(
        None, description='Response format of image data', examples=['b64_json']
    )
    size: Optional[str] = Field(
        None,
        description='Size of the image (e.g., 1024x1024, 1536x1024, auto)',
        examples=['1024x1536'],
    )
    style: Optional[Style] = Field(
        None, description='Style of the image (only for dall-e-3)', examples=['vivid']
    )
    user: Optional[str] = Field(
        None,
        description='A unique identifier for end-user monitoring',
        examples=['user-1234'],
    )


class Datum1(BaseModel):
    b64_json: Optional[str] = Field(None, description='Base64 encoded image data')
    revised_prompt: Optional[str] = Field(None, description='Revised prompt')
    url: Optional[str] = Field(None, description='URL of the image')


class OpenAIImageGenerationResponse(BaseModel):
    data: Optional[List[Datum1]] = None


class KlingAccountError(KlingErrorResponse):
    code: Optional[Code] = Field(
        None,
        description='- 1100: Account exception\n- 1101: Account in arrears (postpaid scenario)\n- 1102: Resource pack depleted or expired (prepaid scenario)\n- 1103: Unauthorized access to requested resource\n',
    )


class KlingAuthenticationError(KlingErrorResponse):
    code: Optional[Code1] = Field(
        None,
        description='- 1000: Authentication failed\n- 1001: Authorization is empty\n- 1002: Authorization is invalid\n- 1003: Authorization is not yet valid\n- 1004: Authorization has expired\n',
    )