# Kling V2.1  Image To Video 

> Transform static images into smooth, 1080p videos with natural motion, flexible durations (5–10s), and optional text prompts—ideal for content, marketing, and creative storytelling.

## Overview

- **Model ID**: `kling-v2-1-i2v`
- **Category**: video
- **Provider**: tencent
- **Source Type**: Closed Source Model
- **Status**: model_ready
- **Screenshot**: `https://assets.modelslab.ai/generations/17b21170-bd49-4758-b9c0-9475512d71d6.webp`

## API Information

This model can be used via our HTTP API. See the API documentation and usage examples below.

### Endpoint

- **URL**: `https://modelslab.com/api/v7/video-fusion/image-to-video`
- **Method**: POST

### Parameters

- **`init_image`** (required): Upload the reference image
  - Type: file

- **`prompt`** (required): Text description of the video you want to generate
  - Type: textarea
  - Example: Enter prompt for video

- **`duration`** (required): Video Length, unit: s (seconds) values: 5，10 The default is 5.
  - Type: select (options: 5s, 10s)

## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://modelslab.com/api/v7/video-fusion/image-to-video \
  --header "Content-Type: application/json" \
  --data '{
    "key": "YOUR_API_KEY",
    "model_id": "kling-v2-1-i2v",
    "init_image": "https://assets.modelslab.ai/generations/ee582a49-2fb3-4176-be8b-12a5bc1dfdd1.png",
    "prompt": "A cinematic action sequence featuring a team of muscular, yellow-suited tactical soldiers wearing black armor and helmets labeled “NEVER GOON.” They move stealthily through a dimly lit corridor filled with smoke and tension. The camera pans dynamically as the soldiers take sniper positions and begin firing precise shots down the hallway — muzzle flashes lighting up the darkness, bullet sparks against concrete walls, and echoing gunfire. Realistic lighting, cinematic depth, intense slow-motion shots, 4K ultra detail, dramatic atmosphere, tactical teamwork, war-movie style realism.",
    "duration": "5"
  }'
```

### Python

```python
import requests

response = requests.post(
    "https://modelslab.com/api/v7/video-fusion/image-to-video",
    headers={
        "Content-Type": "application/json"
    },
    json={
        "key": "YOUR_API_KEY",
        "model_id": "kling-v2-1-i2v",
        "init_image": "https://assets.modelslab.ai/generations/ee582a49-2fb3-4176-be8b-12a5bc1dfdd1.png",
        "prompt": "A cinematic action sequence featuring a team of muscular, yellow-suited tactical soldiers wearing black armor and helmets labeled “NEVER GOON.” They move stealthily through a dimly lit corridor filled with smoke and tension. The camera pans dynamically as the soldiers take sniper positions and begin firing precise shots down the hallway — muzzle flashes lighting up the darkness, bullet sparks against concrete walls, and echoing gunfire. Realistic lighting, cinematic depth, intense slow-motion shots, 4K ultra detail, dramatic atmosphere, tactical teamwork, war-movie style realism.",
        "duration": "5"
    }
)

print(response.json())
```

### JavaScript

```javascript
fetch("https://modelslab.com/api/v7/video-fusion/image-to-video", {
  method: "POST",
  headers: {
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    "key": "YOUR_API_KEY",
    "model_id": "kling-v2-1-i2v",
    "init_image": "https://assets.modelslab.ai/generations/ee582a49-2fb3-4176-be8b-12a5bc1dfdd1.png",
    "prompt": "A cinematic action sequence featuring a team of muscular, yellow-suited tactical soldiers wearing black armor and helmets labeled “NEVER GOON.” They move stealthily through a dimly lit corridor filled with smoke and tension. The camera pans dynamically as the soldiers take sniper positions and begin firing precise shots down the hallway — muzzle flashes lighting up the darkness, bullet sparks against concrete walls, and echoing gunfire. Realistic lighting, cinematic depth, intense slow-motion shots, 4K ultra detail, dramatic atmosphere, tactical teamwork, war-movie style realism.",
    "duration": "5"
  })
})
.then(response => response.json())
.then(data => console.log(data));
```

## Integration Options

### CLI

Install: `curl -fsSL https://modelslab.sh/install.sh | sh` or `brew install modelslab/tap/modelslab`

```bash
modelslab auth login                                    # Authenticate
modelslab models search "kling-v2-1-i2v"                       # Find this model
modelslab models detail --id kling-v2-1-i2v                    # Get model details
modelslab generate image --prompt "..." --model kling-v2-1-i2v  # Generate with model
modelslab config set generation.default_model kling-v2-1-i2v   # Set as default
```

- Website: https://modelslab.sh
- GitHub: https://github.com/ModelsLab/modelslab-cli

### MCP Servers (Model Context Protocol)

For Claude Code, Cursor, VS Code, Windsurf, and any MCP-compatible agent.

- Generation: `https://modelslab.com/mcp/v7` (API key auth, 23 tools)
- Agent Control Plane: `https://modelslab.com/mcp/agents` (Bearer token auth, 10 tools)

Claude Code config (`~/.claude/settings.json`):

```json
{
  "mcpServers": {
    "modelslab-v7": {
      "url": "https://modelslab.com/mcp/v7",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}
```

- Documentation: https://docs.modelslab.com/mcp-web-api/overview

### Agent Skills

Install skill files directly into AI coding agents:

```bash
npx skills add modelslab/skills --all                          # All skills
npx skills add modelslab/skills --skill image-generation       # Specific skill
npx skills add modelslab/skills --all -a claude-code -a cursor # Target agents
```

- GitHub: https://github.com/ModelsLab/skills
- Documentation: https://docs.modelslab.com/agent-skills

### SDKs

- Python: `pip install modelslab`
- TypeScript: `npm install modelslab`
- PHP: `composer require modelslab/modelslab`
- Go: `go get github.com/modelslab/modelslab-go`
- Dart: `dart pub add modelslab`

## Links

- [Model Playground](https://modelslab.com/models/kling-v2-1-image-to-video/kling-v2-1-i2v)
- [API Documentation](https://docs.modelslab.com)
- [ModelsLab Platform](https://modelslab.com)