import { type ControlNetType as ArchControlNetType } from "../architectures/types.js";
export interface LoraConfig {
    name: string;
    strength_model?: number;
    strength_clip?: number;
}
export interface BaseSamplerParams {
    prompt: string;
    negativePrompt?: string;
    steps?: number;
    cfgScale?: number;
    sampler?: string;
    scheduler?: string;
    model: string;
    seed?: number;
    filenamePrefix?: string;
    loras?: LoraConfig[];
}
export interface Txt2ImgParams extends BaseSamplerParams {
    width?: number;
    height?: number;
}
export interface Img2ImgParams extends BaseSamplerParams {
    inputImage: string;
    denoise?: number;
}
export interface UpscaleParams {
    inputImage: string;
    upscaleModel?: string;
    targetWidth?: number;
    targetHeight?: number;
    filenamePrefix?: string;
}
export interface InpaintParams extends BaseSamplerParams {
    sourceImage: string;
    maskImage: string;
    denoise?: number;
    growMaskBy?: number;
}
export interface OutpaintParams extends BaseSamplerParams {
    sourceImage: string;
    extendLeft?: number;
    extendRight?: number;
    extendTop?: number;
    extendBottom?: number;
    feathering?: number;
    denoise?: number;
}
export type IPAdapterWeightType = "linear" | "ease in" | "ease out" | "ease in-out" | "reverse in-out" | "weak input" | "weak output" | "weak middle" | "strong middle" | "style transfer" | "composition" | "strong style transfer";
export type IPAdapterCombineEmbeds = "concat" | "add" | "subtract" | "average" | "norm average";
export interface IPAdapterParams extends BaseSamplerParams {
    width?: number;
    height?: number;
    referenceImage: string;
    referenceImages?: string[];
    weight?: number;
    weightType?: IPAdapterWeightType;
    startAt?: number;
    endAt?: number;
    combineEmbeds?: IPAdapterCombineEmbeds;
    ipadapterModel?: string;
    clipVisionModel?: string;
}
export declare function buildTxt2ImgWorkflow(params: Txt2ImgParams): Record<string, any>;
export declare function buildImg2ImgWorkflow(params: Img2ImgParams): Record<string, any>;
export declare function buildInpaintWorkflow(params: InpaintParams): Record<string, any>;
export declare function buildOutpaintWorkflow(params: OutpaintParams): Record<string, any>;
/**
 * Build an IP-Adapter workflow for identity preservation
 *
 * IP-Adapter allows using reference images to guide generation,
 * preserving character identity, style, or composition.
 */
export declare function buildIPAdapterWorkflow(params: IPAdapterParams): Record<string, any>;
export declare function buildUpscaleWorkflow(params: UpscaleParams): Record<string, any>;
export type ControlNetType = ArchControlNetType;
export interface PreprocessorOptions {
    lowThreshold?: number;
    highThreshold?: number;
    detectBody?: boolean;
    detectFace?: boolean;
    detectHands?: boolean;
    objectMinSize?: number;
}
export interface ControlNetConfig {
    type: ControlNetType;
    image: string;
    controlNetModel: string;
    strength?: number;
    startPercent?: number;
    endPercent?: number;
    preprocessorOptions?: PreprocessorOptions;
}
export interface ControlNetParams extends BaseSamplerParams {
    width?: number;
    height?: number;
    controlNet: ControlNetConfig;
    preprocess?: boolean;
}
export interface MultiControlNetParams extends BaseSamplerParams {
    width?: number;
    height?: number;
    controlNets: ControlNetConfig[];
    preprocess?: boolean;
}
/**
 * Build a single ControlNet workflow
 */
export declare function buildControlNetWorkflow(params: ControlNetParams): Record<string, any>;
/**
 * Build a multi-ControlNet workflow (chain multiple control conditions)
 */
export declare function buildMultiControlNetWorkflow(params: MultiControlNetParams): Record<string, any>;
/**
 * Build a preprocessing-only workflow (to see what the control signal looks like)
 */
export declare function buildPreprocessorWorkflow(params: {
    inputImage: string;
    type: ControlNetType;
    options?: PreprocessorOptions;
    filenamePrefix?: string;
}): Record<string, any>;
export type MaskPreset = "hands" | "face" | "eyes" | "body" | "background" | "foreground";
export interface MaskRegion {
    x: number;
    y: number;
    width: number;
    height: number;
}
export interface CreateMaskParams {
    sourceImage: string;
    preset?: MaskPreset;
    textPrompt?: string;
    region?: MaskRegion;
    expandPixels?: number;
    featherPixels?: number;
    invert?: boolean;
    samModel?: string;
    groundingDinoModel?: string;
    threshold?: number;
    filenamePrefix?: string;
}
/**
 * Build an intelligent mask generation workflow using GroundingDINO + SAM.
 * GroundingDINO detects objects from text prompts, SAM generates precise masks.
 * Requires: comfyui_segment_anything extension
 *
 * Based on research from:
 * - https://github.com/storyicon/comfyui_segment_anything
 * - https://stable-diffusion-art.com/sam3-comfyui-image/
 */
export declare function buildMaskWorkflow(params: CreateMaskParams): Record<string, any>;
export interface TTSParams {
    text: string;
    voiceReference: string;
    voiceReferenceText?: string;
    speed?: number;
    seed?: number;
    model?: string;
    vocoder?: "auto" | "vocos" | "bigvgan";
    filenamePrefix?: string;
}
/**
 * Build a TTS workflow using F5-TTS
 */
export declare function buildTTSWorkflow(params: TTSParams): Record<string, any>;
export type LipSyncModel = "sonic" | "dice-talk" | "hallo2" | "sadtalker";
export interface LipSyncParams {
    portraitImage: string;
    audio: string;
    model?: LipSyncModel;
    svdCheckpoint?: string;
    sonicUnet?: string;
    ipAudioScale?: number;
    useInterframe?: boolean;
    dtype?: "fp16" | "fp32" | "bf16";
    minResolution?: number;
    duration?: number;
    expandRatio?: number;
    inferenceSteps?: number;
    dynamicScale?: number;
    fps?: number;
    seed?: number;
    filenamePrefix?: string;
}
/**
 * Build a SONIC lip-sync workflow
 * Uses ImageOnlyCheckpointLoader with SVD model which provides MODEL, CLIP_VISION, and VAE
 */
export declare function buildLipSyncWorkflow(params: LipSyncParams): Record<string, any>;
/**
 * Build a combined TTS + LipSync workflow (full pipeline)
 * This chains TTS output directly into SONIC
 * Uses ImageOnlyCheckpointLoader with SVD model for SONIC
 */
export declare function buildTalkingAvatarWorkflow(params: {
    text: string;
    voiceReference: string;
    voiceReferenceText?: string;
    portraitImage: string;
    speed?: number;
    ttsSeed?: number;
    svdCheckpoint?: string;
    sonicUnet?: string;
    inferenceSteps?: number;
    fps?: number;
    lipSyncSeed?: number;
    filenamePrefix?: string;
}): Record<string, any>;
export interface I2VWorkflowParams {
    sourceImage: string;
    backend?: string;
    checkpoint?: string;
    prompt?: string;
    negativePrompt?: string;
    width?: number;
    height?: number;
    frames?: number;
    fps?: number;
    steps?: number;
    cfgScale?: number;
    seed?: number;
    motionScale?: number;
    filenamePrefix?: string;
}
/**
 * Build an AnimateDiff image-to-video workflow.
 * Uses ADE_LoadAnimateDiffModel + ADE_UseEvolvedSampling for v3,
 * or ADE_ApplyAnimateLCMI2VModel for LCM backend.
 */
export declare function buildI2VWorkflow(params: I2VWorkflowParams): Record<string, any>;