import { type VideoClientParams, type VideoStats } from '../schemas/index.js';
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 the Wan,
 * LTX-2, and MiniMax-H3 layouts. For Wan `img2vid`, load the pipeline with
 * `modelConfig.clipVisionModelSrc` set to `clip_vision_h.safetensors`; LTX-2
 * `img2vid` conditions on the first frame through its video VAE and needs no
 * CLIP vision weights (the same LTX-2 model loaded for txt2vid also does
 * img2vid). MiniMax-H3 also conditions through its video VAE and can optionally
 * load `modelConfig.audioVaeModelSrc` for synchronized audio. On React Native,
 * 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 LTX Ingredients reference conditioning
 * ```typescript
 * const referenceSheet = fs.readFileSync("reference-sheet.png");
 * const { outputs } = video({
 *   modelId,
 *   mode: "txt2vid",
 *   prompt: "Reference sheet: a red-haired explorer. Generated video: the explorer crosses a snowy ridge.",
 *   lora: "/absolute/path/to/ltx-2-ingredients.safetensors",
 *   lora_strength: 1.37,
 *   stg_scale: 1,
 *   stg_block: 29,
 *   reference_images: [referenceSheet],
 *   reference_attention_strength: 1,
 *   reference_downscale_factor: 1,
 *   video_frames: 217,
 *   scheduler: "ltx2",
 * });
 * const buffers = await outputs;
 * ```
 *
 * @example Cancellation via requestId
 * ```typescript
 * const { requestId, outputs } = video({ modelId, mode: "txt2vid", prompt: "..." });
 * // ...later
 * await cancel(requestId);
 * ```
 */
export declare function video(params: VideoClientParams): VideoResult;