Modelslab Video Generation

ModelsLab Video Generation

Generate AI videos from text descriptions, animate static images, or transform existing videos using state-of-the-art video generation models.

When to Use This Skill

• Generate videos from text descriptions
• Animate static images
• Transform existing videos (video-to-video)
• Lip-sync audio to video
• Apply motion control from reference videos
• Create short-form content
• Build video marketing materials

Available APIs (v7)

Video Fusion Endpoints

• Text to Video: POST https://modelslab.com/api/v7/video-fusion/text-to-video
• Image to Video: POST https://modelslab.com/api/v7/video-fusion/image-to-video
• Video to Video: POST https://modelslab.com/api/v7/video-fusion/video-to-video
• Lip Sync: POST https://modelslab.com/api/v7/video-fusion/lip-sync
• Motion Control: POST https://modelslab.com/api/v7/video-fusion/motion-control
• Fetch Result: POST https://modelslab.com/api/v7/video-fusion/fetch/{id}

Note: v6 endpoints (/api/v6/video/text2video, etc.) still work but v7 is the current version.

Discovering Video Models

# Search all video models
modelslab models search --feature video_fusion

# Search by name
modelslab models search --search "seedance"
modelslab models search --search "wan"
modelslab models search --search "veo"

# Get model details
modelslab models detail --id seedance-t2v

Text to Video

import requests
import time

def generate_video(prompt, api_key, model_id="seedance-t2v"):
    """Generate a video from a text prompt.

    Args:
        prompt: Text description of the video
        api_key: Your ModelsLab API key
        model_id: Video model to use
    """
    response = requests.post(
        "https://modelslab.com/api/v7/video-fusion/text-to-video",
        json={
            "key": api_key,
            "model_id": model_id,
            "prompt": prompt,
            "negative_prompt": "low quality, blurry, static, distorted"
        }
    )

    data = response.json()

    if data["status"] == "error":
        raise Exception(f"Error: {data['message']}")

    if data["status"] == "success":
        return data["output"][0]

    # Video generation is async - poll for results
    request_id = data["id"]
    print(f"Video processing... Request ID: {request_id}")
    print(f"Estimated time: {data.get('eta', 'unknown')} seconds")

    return poll_video_result(request_id, api_key)

def poll_video_result(request_id, api_key, timeout=600):
    """Poll for video generation results."""
    start_time = time.time()

    while time.time() - start_time < timeout:
        fetch = requests.post(
            f"https://modelslab.com/api/v7/video-fusion/fetch/{request_id}",
            json={"key": api_key}
        )
        result = fetch.json()

        if result["status"] == "success":
            return result["output"][0]
        elif result["status"] == "failed":
            raise Exception(result.get("message", "Generation failed"))

        print(f"Status: processing... ({int(time.time() - start_time)}s elapsed)")
        time.sleep(10)

    raise Exception("Timeout waiting for video generation")

# Usage
video_url = generate_video(
    "A spaceship flying through an asteroid field, cinematic, 4K",
    "your_api_key",
    model_id="seedance-t2v"
)
print(f"Video ready: {video_url}")

Image to Video (Animate Images)

def animate_image(image_url, prompt, api_key, model_id="seedance-i2v"):
    """Animate a static image based on a motion prompt.

    Args:
        image_url: URL of the image to animate
        prompt: Description of desired motion/animation
        model_id: Video model for image-to-video
    """
    response = requests.post(
        "https://modelslab.com/api/v7/video-fusion/image-to-video",
        json={
            "key": api_key,
            "model_id": model_id,
            "init_image": [image_url],  # v7 expects array
            "prompt": prompt,
            "negative_prompt": "static, still, low quality, blurry"
        }
    )

    data = response.json()

    if data["status"] == "success":
        return data["output"][0]
    elif data["status"] == "processing":
        return poll_video_result(data["id"], api_key)
    else:
        raise Exception(data.get("message", "Unknown error"))

# Animate a landscape
video = animate_image(
    "https://example.com/landscape.jpg",
    "The clouds moving slowly across the sky, birds flying in the distance",
    "your_api_key",
    model_id="seedance-i2v"
)
print(f"Animated video: {video}")

Video to Video

def transform_video(video_url, prompt, api_key, model_id="wan2.1"):
    """Transform an existing video with a new style or content.

    Args:
        video_url: URL of the source video
        prompt: Description of desired transformation
    """
    response = requests.post(
        "https://modelslab.com/api/v7/video-fusion/video-to-video",
        json={
            "key": api_key,
            "model_id": model_id,
            "init_video": [video_url],  # v7 expects array
            "prompt": prompt
        }
    )

    data = response.json()
    if data["status"] == "processing":
        return poll_video_result(data["id"], api_key)
    elif data["status"] == "success":
        return data["output"][0]

Lip Sync

def lip_sync(video_url, audio_url, api_key, model_id="lipsync-2"):
    """Sync lip movements to audio.

    Args:
        video_url: URL of the video with a face
        audio_url: URL of the audio to sync to
    """
    response = requests.post(
        "https://modelslab.com/api/v7/video-fusion/lip-sync",
        json={
            "key": api_key,
            "model_id": model_id,
            "init_video": video_url,
            "init_audio": audio_url
        }
    )

    data = response.json()
    if data["status"] == "processing":
        return poll_video_result(data["id"], api_key)
    elif data["status"] == "success":
        return data["output"][0]

Popular Video Model IDs

Text to Video

• seedance-t2v - Seedance text-to-video (BytePlus)
• seedance-1.0-pro-fast-t2v - Seedance Pro Fast
• wan2.6-t2v - Wan 2.6 text-to-video (Alibaba)
• wan2.1 - Wan 2.1 (ModelsLab in-house)
• veo2 - Google Veo 2
• veo3 - Google Veo 3
• sora-2 - OpenAI Sora 2
• Hailuo-2.3-t2v - Hailuo 2.3 (MiniMax)
• kling-v2-5-turbo-t2v - Kling V2.5 Turbo

Image to Video

• seedance-i2v - Seedance image-to-video
• seedance-1.0-pro-i2v - Seedance Pro
• wan2.6-i2v - Wan 2.6 image-to-video
• Hailuo-2.3-i2v - Hailuo 2.3
• kling-v2-1-i2v - Kling V2.1

Lip Sync

• lipsync-2 - Sync Labs Lipsync 2

Motion Control

• kling-motion-control - Kling Motion Control
• omni-human - OmniHuman (BytePlus)

Browse all models: https://modelslab.com/models

Key Parameters

Best Practices

1. Write Motion-Focused Prompts

Bad: "A cat"
Good: "A cat walking through a garden, looking around curiously, sunlight filtering through trees"

Include: Action, movement, camera motion, atmosphere

2. Set Realistic Expectations

• Videos are 4-30 seconds typically
• Generation takes 30 seconds to several minutes depending on model
• Best for short clips, not full productions

3. Handle Async Operations

# Video generation is ALWAYS async
# Always implement polling or use webhooks
if data["status"] == "processing":
    video = poll_video_result(data["id"], api_key)

4. Use Webhooks

payload = {
    "key": api_key,
    "model_id": "seedance-t2v",
    "prompt": "...",
    "webhook": "https://yourserver.com/webhook/video",
    "track_id": "video_001"
}

Error Handling

try:
    video = generate_video(prompt, api_key, model_id="seedance-t2v")
    print(f"Video generated: {video}")
except Exception as e:
    print(f"Video generation failed: {e}")

Resources

• API Documentation: https://docs.modelslab.com/video-api/overview
• Model Browser: https://modelslab.com/models
• Model Selection Guide: https://docs.modelslab.com/guides/model-selection
• Get API Key: https://modelslab.com/dashboard
• Webhooks Guide: See modelslab-webhooks skill

Related Skills

• modelslab-model-discovery - Find and filter models
• modelslab-image-generation - Generate images for img2video
• modelslab-audio-generation - Generate audio for lip-sync
• modelslab-chat-generation - Chat with LLM models
• modelslab-webhooks - Handle async operations efficiently