HappyHorse-1.1-I2V

import os
import time

from openai import OpenAI

client = OpenAI(
    base_url="https://api.llmapi.ai/v1",
    api_key=os.environ["LLMAPI_KEY"],
)

video = client.videos.create(
    model="happyhorse-1.1-i2v",
    prompt="The paper boat drifts down the rainy street",
    extra_body={"image_url": "https://llmapi.ai/samples/paper-boat.jpg"},
)
while video.status in ("queued", "in_progress"):
    time.sleep(10)
    video = client.videos.retrieve(video.id)
if video.status == "failed":
    raise RuntimeError(video.error)
client.videos.download_content(video.id).write_to_file("video.mp4")

import fs from "node:fs";
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.llmapi.ai/v1",
  apiKey: process.env.LLMAPI_KEY,
});

let video = await client.videos.create({
  model: "happyhorse-1.1-i2v",
  prompt: "The paper boat drifts down the rainy street",
  // @ts-expect-error LLM API parameter
  image_url: "https://llmapi.ai/samples/paper-boat.jpg",
});
while (video.status === "queued" || video.status === "in_progress") {
  await new Promise((r) => setTimeout(r, 10_000));
  video = await client.videos.retrieve(video.id);
}
if (video.status === "failed") throw new Error(video.error?.message);
const content = await client.videos.downloadContent(video.id);
fs.writeFileSync("video.mp4", Buffer.from(await content.arrayBuffer()));

curl https://api.llmapi.ai/v1/videos \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "happyhorse-1.1-i2v",
    "prompt": "The paper boat drifts down the rainy street",
    "image_url": "https://llmapi.ai/samples/paper-boat.jpg"
  }'

# Poll GET https://api.llmapi.ai/v1/videos/{id} until "completed",
# then download GET https://api.llmapi.ai/v1/videos/{id}/content