import { type VideoClientParams, type VideoStats } from "../../schemas/index";
export interface VideoProgressTick {
    step: number;
    totalSteps: number;
    elapsedMs: number;
}
export interface VideoResult {
    requestId: string;
    progressStream: AsyncGenerator<VideoProgressTick>;
    outputs: Promise<Uint8Array[]>;
    stats: Promise<VideoStats | undefined>;
}
/**
 * Generates a video using a loaded video diffusion model.
 *
 * @param params - Video request parameters (model, prompt, dimensions, frame count, fps, sampler, seed, etc.).
 * @returns A result object exposing `requestId` (stable identifier for this in-flight generation), `progressStream` (async iterator of `{ step, totalSteps, elapsedMs }`), `outputs` (promise of the generated video buffers, typically a single AVI file), and `stats` (promise of generation statistics).
 *
 * Supports `txt2vid` (text-to-video) and `img2vid` (image-to-video). For `img2vid`,
 * load the Wan pipeline with `modelConfig.clipVisionModelSrc` set to
 * `clip_vision_h.safetensors`. On React Native, prefer a `modelId` loaded with a
 * `delegate` since the bundled video diffusion models are too large for typical mobile devices.
 *
 * @example Basic txt2vid generation
 * ```typescript
 * const { outputs, stats } = video({
 *   modelId,
 *   mode: "txt2vid",
 *   prompt: "a cat surfing a wave at sunset",
 *   width: 480,
 *   height: 832,
 *   video_frames: 17, // must satisfy (4*k + 1)
 *   fps: 16,
 * });
 * const buffers = await outputs;
 * fs.writeFileSync("output.avi", buffers[0]);
 * ```
 *
 * @example With progress tracking
 * ```typescript
 * const { progressStream, outputs } = video({
 *   modelId,
 *   mode: "txt2vid",
 *   prompt: "a sunset over the ocean",
 * });
 * for await (const { step, totalSteps } of progressStream) {
 *   console.log(`${step}/${totalSteps}`);
 * }
 * const buffers = await outputs;
 * ```
 *
 * @example Image-to-video (first frame + motion prompt)
 * ```typescript
 * const firstFrame = fs.readFileSync("portrait.png");
 * const { outputs } = video({
 *   modelId,
 *   mode: "img2vid",
 *   prompt: "the subject slowly turns and smiles, cinematic lighting",
 *   init_image: firstFrame,
 *   strength: 0.85,
 * });
 * ```
 *
 * @example With control frames (e.g. for guided generation)
 * ```typescript
 * const frameA = fs.readFileSync("frame-a.png");
 * const frameB = fs.readFileSync("frame-b.png");
 * const { outputs } = video({
 *   modelId,
 *   mode: "txt2vid",
 *   prompt: "smooth transition between scenes",
 *   control_frames: [frameA, frameB],
 * });
 * ```
 *
 * @example Cancellation via requestId
 * ```typescript
 * const { requestId, outputs } = video({ modelId, mode: "txt2vid", prompt: "..." });
 * // ...later
 * await cancel(requestId);
 * ```
 */
export declare function video(params: VideoClientParams): VideoResult;
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