// Step 1: Submit generation request
const response = await fetch('https://api.flaq.ai/api/v1/video/task', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_API_KEY'
  },
  body: JSON.stringify({
    model_name: 'wan-2.7-reference-to-video',
    prompt: 'Use image one for the hero and video one for the motion; keep the hero voice consistent with audio one',
    resolution: '1080p',
    duration: 8,
    aspect_ratio: '16:9',
    images: ['https://example.com/hero-reference.jpg'],
    videos: ['https://example.com/motion-reference.mp4'],
    audios: ['https://example.com/voice-reference.mp3'],
    negative_prompt: 'flicker, distorted hands, duplicate subjects',
    seed: 42
  })
});

const { data } = await response.json();
const taskId = data.task_id;

// Use the @ (AT) reference feature in prompt through <<<...>>> placeholders.
// Placeholder numbering is 1-based for each media array:
// <<<image_1>>> = images[0], <<<image_2>>> = images[1]
// <<<video_1>>> = videos[0], <<<audio_1>>> = audios[0]
const mediaReferenceResponse = await fetch('https://api.flaq.ai/api/v1/video/task', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_API_KEY'
  },
  body: JSON.stringify({
    model_name: 'wan-2.7-reference-to-video',
    prompt: 'Place the presenter from <<<image_1>>> in the studio from <<<image_2>>>, follow the camera path in <<<video_1>>>, and use the reference voice from <<<audio_1>>>',
    resolution: '1080p',
    duration: 8,
    aspect_ratio: '16:9',
    images: [
      'https://example.com/presenter-reference.jpg',
      'https://example.com/studio-reference.jpg'
    ],
    videos: ['https://example.com/camera-path-reference.mp4'],
    audios: ['https://example.com/voice-reference.mp3'],
    negative_prompt: 'flicker, distorted hands, duplicate subjects',
    seed: 42
  })
});

const { data: mediaReferenceData } = await mediaReferenceResponse.json();
const mediaReferenceTaskId = mediaReferenceData.task_id;

# Step 1: Submit generation request
import requests

response = requests.post(
    'https://api.flaq.ai/api/v1/video/task',
    headers={
        'Content-Type': 'application/json',
        'Authorization': 'Bearer YOUR_API_KEY'
    },
    json={
        'model_name': 'wan-2.7-reference-to-video',
        'prompt': 'Use image one for the hero and video one for the motion; keep the hero voice consistent with audio one',
        'resolution': '1080p',
        'duration': 8,
        'aspect_ratio': '16:9',
        'images': ['https://example.com/hero-reference.jpg'],
        'videos': ['https://example.com/motion-reference.mp4'],
        'audios': ['https://example.com/voice-reference.mp3'],
        'negative_prompt': 'flicker, distorted hands, duplicate subjects',
        'seed': 42
    }
)

result = response.json()
task_id = result['data']['task_id']

# Use the @ (AT) reference feature in prompt through <<<...>>> placeholders.
# Placeholder numbering is 1-based for each media array:
# <<<image_1>>> = images[0], <<<image_2>>> = images[1]
# <<<video_1>>> = videos[0], <<<audio_1>>> = audios[0]
media_reference_response = requests.post(
    'https://api.flaq.ai/api/v1/video/task',
    headers={
        'Content-Type': 'application/json',
        'Authorization': 'Bearer YOUR_API_KEY'
    },
    json={
        'model_name': 'wan-2.7-reference-to-video',
        'prompt': 'Place the presenter from <<<image_1>>> in the studio from <<<image_2>>>, follow the camera path in <<<video_1>>>, and use the reference voice from <<<audio_1>>>',
        'resolution': '1080p',
        'duration': 8,
        'aspect_ratio': '16:9',
        'images': [
            'https://example.com/presenter-reference.jpg',
            'https://example.com/studio-reference.jpg'
        ],
        'videos': ['https://example.com/camera-path-reference.mp4'],
        'audios': ['https://example.com/voice-reference.mp3'],
        'negative_prompt': 'flicker, distorted hands, duplicate subjects',
        'seed': 42
    }
)

media_reference_result = media_reference_response.json()
media_reference_task_id = media_reference_result['data']['task_id']

# Step 1: Submit generation request
curl -X POST https://api.flaq.ai/api/v1/video/task \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model_name": "wan-2.7-reference-to-video",
    "prompt": "Use image one for the hero and video one for the motion; keep the hero voice consistent with audio one",
    "resolution": "1080p",
    "duration": 8,
    "aspect_ratio": "16:9",
    "images": ["https://example.com/hero-reference.jpg"],
    "videos": ["https://example.com/motion-reference.mp4"],
    "audios": ["https://example.com/voice-reference.mp3"],
    "negative_prompt": "flicker, distorted hands, duplicate subjects",
    "seed": 42
  }'

# Use the @ (AT) reference feature in prompt through <<<...>>> placeholders.
# Placeholder numbering is 1-based for each media array:
# <<<image_1>>> = images[0], <<<image_2>>> = images[1]
# <<<video_1>>> = videos[0], <<<audio_1>>> = audios[0]
curl -X POST https://api.flaq.ai/api/v1/video/task \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model_name": "wan-2.7-reference-to-video",
    "prompt": "Place the presenter from <<<image_1>>> in the studio from <<<image_2>>>, follow the camera path in <<<video_1>>>, and use the reference voice from <<<audio_1>>>",
    "resolution": "1080p",
    "duration": 8,
    "aspect_ratio": "16:9",
    "images": [
      "https://example.com/presenter-reference.jpg",
      "https://example.com/studio-reference.jpg"
    ],
    "videos": ["https://example.com/camera-path-reference.mp4"],
    "audios": ["https://example.com/voice-reference.mp3"],
    "negative_prompt": "flicker, distorted hands, duplicate subjects",
    "seed": 42
  }'