Video Generation OpenAI Compatible (Legacy)

curl -X POST https://aihubmix.com/v1/videos \
  -H "Authorization: Bearer $AIHUBMIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sora-2",
    "prompt": "A cinematic drone shot soaring over a misty mountain range at sunrise, golden light filtering through the clouds",
    "seconds": "8",
    "size": "1280x720"
  }'

curl -X POST https://aihubmix.com/v1/videos \
  -H "Authorization: Bearer $AIHUBMIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sora-2-pro",
    "prompt": "A person walking through a neon-lit city street at night, rain reflecting on the pavement, cinematic lighting",
    "seconds": "12",
    "size": "720x1280"
  }'

curl -X POST https://aihubmix.com/v1/videos \
  -H "Authorization: Bearer $AIHUBMIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "veo-3.1-generate-preview",
    "prompt": "A tranquil Japanese garden, cherry blossom petals slowly drifting down, koi swimming in the pond, with the melodious sound of wind chimes in the background",
    "seconds": "8",
    "size": "1280x720"
  }'

curl -X POST https://aihubmix.com/v1/videos \
  -H "Authorization: Bearer $AIHUBMIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "veo-3.1-fast-generate-preview",
    "prompt": "Ocean waves crashing on rocky cliffs at sunset, seagulls flying overhead",
    "seconds": "8",
    "size": "1280x720"
  }'

curl -X POST https://aihubmix.com/v1/videos \
  -H "Authorization: Bearer $AIHUBMIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "veo-3.1-generate-preview",
    "prompt": "The camera slowly pushes in from a valley at dawn, transitioning naturally into sunset",
    "seconds": "8",
    "size": "1280x720",
    "first_frame": "https://example.com/first.jpg",
    "last_frame": "https://example.com/last.jpg"
  }'

curl -X POST https://aihubmix.com/v1/videos \
  -H "Authorization: Bearer $AIHUBMIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "veo-3.1-generate-preview",
    "prompt": "The character from the reference images walks through a forest, cinematic shot",
    "seconds": "8",
    "size": "1280x720",
    "reference_images": [
      "https://example.com/ref1.jpg",
      "https://example.com/ref2.jpg"
    ]
  }'

curl -X POST https://aihubmix.com/v1/videos \
  -H "Authorization: Bearer $AIHUBMIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "wan2.6-t2v",
    "prompt": "A winding stream flows through an autumn forest, golden fallen leaves drifting on the water surface, sunlight casting dappled light and shadow through the leaves",
    "seconds": "5",
    "size": "1920x1080"
  }'

curl -X POST https://aihubmix.com/v1/videos \
  -H "Authorization: Bearer $AIHUBMIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "wan2.6-i2v",
    "prompt": "The character in the frame slowly turns their head and smiles, the camera slowly pushes in",
    "seconds": "5",
    "size": "1280x720",
    "input_reference": "https://example.com/my-image.jpg"
  }'

import requests
import time

API_KEY = "AIHUBMIX_API_KEY"
BASE_URL = "https://aihubmix.com"
HEADERS = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# Step 1: Create the video generation task
response = requests.post(
    f"{BASE_URL}/v1/videos",
    headers=HEADERS,
    json={
        "model": "wan2.6-t2v",
        "prompt": "A desert under a starry sky, a meteor streaking across the night sky, the glow of a distant campfire flickering in the breeze",
        "seconds": "5",
        "size": "1920x1080"
    }
)
result = response.json()
video_id = result["id"]
print(f"Task created, video_id: {video_id}")

# Step 2: Poll the status
while True:
    status_response = requests.get(
        f"{BASE_URL}/v1/videos/{video_id}",
        headers=HEADERS
    )
    status_data = status_response.json()
    current_status = status_data["status"]
    print(f"Current status: {current_status}")

    if current_status == "completed":
        print("Video generation complete!")
        break
    elif current_status == "failed":
        error_msg = status_data.get("error", {})
        if isinstance(error_msg, dict):
            error_msg = error_msg.get("message", "Unknown error")
        print(f"Generation failed: {error_msg}")
        break

    time.sleep(15)  # Query every 15 seconds

# Step 3: Download the video
video_response = requests.get(
    f"{BASE_URL}/v1/videos/{video_id}/content",
    headers=HEADERS
)
with open("output.mp4", "wb") as f:
    f.write(video_response.content)
print(f"Video saved as output.mp4 ({len(video_response.content) / 1024 / 1024:.1f} MB)")

import requests
import time

API_KEY = "AIHUBMIX_API_KEY"
BASE_URL = "https://aihubmix.com"
HEADERS = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# Create the video generation task
response = requests.post(
    f"{BASE_URL}/v1/videos",
    headers=HEADERS,
    json={
        "model": "sora-2",
        "prompt": "A cinematic shot of a futuristic city at sunset, flying cars in the background",
        "seconds": "8",       # Options "4"/"8"/"12"
        "size": "1280x720"    # Supports 1280x720, 720x1280, 1024x1792, 1792x1024
    }
)
result = response.json()
video_id = result["id"]
print(f"Task created, video_id: {video_id}")

# Sora status polling (may go through queued -> in_progress -> completed)
while True:
    status_response = requests.get(
        f"{BASE_URL}/v1/videos/{video_id}",
        headers=HEADERS
    )
    status_data = status_response.json()
    current_status = status_data["status"]
    progress = status_data.get("progress", "")
    print(f"Status: {current_status}, Progress: {progress}%")

    if current_status == "completed":
        print("Video generation complete!")
        break
    elif current_status == "failed":
        print(f"Generation failed: {status_data.get('error')}")
        break

    time.sleep(15)

# Download the video
video_response = requests.get(
    f"{BASE_URL}/v1/videos/{video_id}/content",
    headers=HEADERS
)
with open("sora_output.mp4", "wb") as f:
    f.write(video_response.content)
print("Video saved as sora_output.mp4")

const API_KEY = "your_aihubmix_api_key";
const BASE_URL = "https://aihubmix.com";

async function generateVideo() {
  // Step 1: Create the task (using Veo 3.1 as an example)
  const createResponse = await fetch(`${BASE_URL}/v1/videos`, {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${API_KEY}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      model: "veo-3.1-generate-preview",
      prompt: "A desert under a starry sky, a meteor streaking across the night sky",
      seconds: "8",
      size: "1280x720"
    })
  });
  const { id: videoId } = await createResponse.json();
  console.log(`Task created: ${videoId}`);

  // Step 2: Poll the status
  let status = "in_progress";
  while (status !== "completed" && status !== "failed") {
    await new Promise(resolve => setTimeout(resolve, 15000));
    const statusResponse = await fetch(`${BASE_URL}/v1/videos/${videoId}`, {
      headers: { "Authorization": `Bearer ${API_KEY}` }
    });
    const result = await statusResponse.json();
    status = result.status;
    console.log(`Current status: ${status}`);
  }

  if (status === "completed") {
    // Step 3: Download the video
    const videoResponse = await fetch(`${BASE_URL}/v1/videos/${videoId}/content`, {
      headers: { "Authorization": `Bearer ${API_KEY}` }
    });
    const fs = require("fs");
    const buffer = Buffer.from(await videoResponse.arrayBuffer());
    fs.writeFileSync("output.mp4", buffer);
    console.log("Video saved as output.mp4");
  }
}

generateVideo();