Build a video from text

Now that we have a video model in memory, let's make our first clip.

video() returns the same return type as diffusion(): outputs is a Promise<Uint8Array[]>, and outputs is AVI bytes instead of PNG. We save it to disk with the same fs.writeFileSync pattern.

Frame count and framerate together determine video length. video_frames: 17, fps: 16 is roughly one second of video. We bump video_frames and we get longer clips at proportionally higher inference cost.

Wan 2.1 T2V is three files: T5-XXL (text encoder), VAE (decoder), diffusion model. The loadModel() call mirrors I2V's, but T5-XXL replaces the vision tower:

const videoId = await loadModel({
  modelSrc: WAN2_1_T2V_1_3B_FP16,
  modelType: "sdcpp-generation",
  modelConfig: {
    mode: "video",
    t5XxlModelSrc: UMT5_XXL_FP16,
    vaeModelSrc: WAN_2_1_COMFYUI_REPACKAGED_VAE,
  },
});

The txt2vid video({ ... }) matches img2vid's, with mode: "txt2vid" and no init_image:

const result = video({
  modelId: videoId,
  mode: "txt2vid",
  prompt: "a colorful bird flapping its wings",
  width: 480,
  height: 832,
  video_frames: 17,
  fps: 16,
});

Once await result.outputs resolves, write outputs[0] to a .avi file with fs.writeFileSync, the extension picks the container. Note that the if (!firstClip) guard throws if the result is empty as follows:

const outputs = await result.outputs;
const firstClip = outputs[0];
if (!firstClip) throw new Error("No video returned from video()");
fs.writeFileSync("../../apps/desktop/output/video-gen/bird.avi", firstClip);
console.log(`Generated ${outputs.length} video`);

Open the AVI in any player. That's our one-second text-prompted clip. The next lesson animates a still image instead of starting from text.

Note: width and height on video calls are smaller than image calls. 480×832 keeps the per-frame memory budget reasonable on a 1.3B video model.

Put it to the test

1. Call loadModel with modelSrc: WAN2_1_T2V_1_3B_FP16, modelType: "sdcpp-generation", and modelConfig.mode: "video" (plus t5XxlModelSrc and vaeModelSrc).
2. Call video({ modelId, mode: "txt2vid", prompt, width, height, video_frames: 17, fps: 16 }) and await result.outputs.
3. Write outputs[0] to a .avi file and console.log the count.