API MiniMax H3 แปลงข้อมูลอ้างอิงเป็นวิดีโอระดับมืออาชีพ (การกำกับภาพแบบมัลติโหมด)

# Step 1: Submit generation request with image, video, and audio references
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': 'minimax-h3-reference-to-video',
        'prompt': 'Use image one for the subject, video one for the movement, and audio one for the atmosphere',
        'resolution': '2k',
        'duration': 8,
        'aspect_ratio': '16:9',
        'images': ['https://example.com/subject-reference.jpg'],
        'videos': ['https://example.com/motion-reference.mp4'],
        'audios': ['https://example.com/atmosphere-reference.mp3']
    }
)

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': 'minimax-h3-reference-to-video',
        'prompt': 'Place the explorer from <<<image_1>>> in the environment from <<<image_2>>>, following the camera movement in <<<video_1>>> and speaking with the reference voice from <<<audio_1>>>',
        'resolution': '2k',
        'duration': 10,
        'aspect_ratio': '16:9',
        'images': [
            'https://example.com/explorer-reference.jpg',
            'https://example.com/environment-reference.jpg'
        ],
        'videos': ['https://example.com/camera-movement-reference.mp4'],
        'audios': ['https://example.com/voice-reference.mp3']
    }
)

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

# Step 1: Submit generation request with image, video, and audio references
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": "minimax-h3-reference-to-video",
    "prompt": "Use image one for the subject, video one for the movement, and audio one for the atmosphere",
    "resolution": "2k",
    "duration": 8,
    "aspect_ratio": "16:9",
    "images": ["https://example.com/subject-reference.jpg"],
    "videos": ["https://example.com/motion-reference.mp4"],
    "audios": ["https://example.com/atmosphere-reference.mp3"]
  }'

# 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": "minimax-h3-reference-to-video",
    "prompt": "Place the explorer from <<<image_1>>> in the environment from <<<image_2>>>, following the camera movement in <<<video_1>>> and speaking with the reference voice from <<<audio_1>>>",
    "resolution": "2k",
    "duration": 10,
    "aspect_ratio": "16:9",
    "images": [
      "https://example.com/explorer-reference.jpg",
      "https://example.com/environment-reference.jpg"
    ],
    "videos": ["https://example.com/camera-movement-reference.mp4"],
    "audios": ["https://example.com/voice-reference.mp3"]
  }'