# LTX 2.5 Pro Text To Video

> AI model for generating video content

## Overview

- **Model ID**: `ltx-2-5-pro-t2v`
- **Category**: video
- **Provider**: ltx
- **Source Type**: Closed Source Model
- **Status**: model_ready
- **Screenshot**: `https://assets.modelslab.ai/generations/863ee57d-5f3c-4738-ae95-30081e502b49.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/text-to-video`
- **Method**: POST

### Parameters

- **`prompt`** (required): Text prompt describing the desired video content
  - Type: textarea

- **`resolution`** (optional): Output video resolution
  - Type: select (options: Landscape FHD, Protrait FHD, Landscape HD, Protrait HD)

- **`duration`** (required): Video duration (in seconds): Supported durations depend on the model and include 6s, 8s, and 10s.
  - Type: select (options: 6s, 8s, 10s)

- **`generate_audio`** (optional): Generate audio for the video. When true, the generated video will include AI-generated audio matching the scene. When false, only silent video is generated.
  - Type: toggle

- **`fps`** (optional): **Frame rate (frames per second):** Supported frame rates depend on the model and resolution, with available options **25 FPS** and **50 FPS**. This parameter is optional and defaults to **25 FPS**.
  - Type: select (options: 25, 50)

## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://modelslab.com/api/v7/video-fusion/text-to-video \
  --header "Content-Type: application/json" \
  --data '{
    "key": "YOUR_API_KEY",
    "model_id": "ltx-2-5-pro-t2v",
    "prompt": "A wide shot frames a rainy city intersection at dusk, neon signs reflecting on wet asphalt. A young woman in a yellow raincoat walks toward camera, gripping a folded newspaper, while cars hiss past behind her. Soft synth music and distant traffic fill the air. A hard cut transitions to a medium close-up of her face under the hood, raindrops catching the neon as she looks off-screen left; the synth score continues across the cut, traffic muffled. She whispers, “He’s late.” Another hard cut jumps to a low-angle shot of a man’s scuffed boots stepping into a puddle at the curb; the music drops to a low drone. He lifts his head into frame — short dark hair, soaked jacket — and smiles toward her off-screen as a bus rumbles past.",
    "resolution": "1920x1080",
    "duration": "6",
    "generate_audio": false,
    "fps": "25"
  }'
```

### Python

```python
import requests

response = requests.post(
    "https://modelslab.com/api/v7/video-fusion/text-to-video",
    headers={
        "Content-Type": "application/json"
    },
    json={
        "key": "YOUR_API_KEY",
        "model_id": "ltx-2-5-pro-t2v",
        "prompt": "A wide shot frames a rainy city intersection at dusk, neon signs reflecting on wet asphalt. A young woman in a yellow raincoat walks toward camera, gripping a folded newspaper, while cars hiss past behind her. Soft synth music and distant traffic fill the air. A hard cut transitions to a medium close-up of her face under the hood, raindrops catching the neon as she looks off-screen left; the synth score continues across the cut, traffic muffled. She whispers, “He’s late.” Another hard cut jumps to a low-angle shot of a man’s scuffed boots stepping into a puddle at the curb; the music drops to a low drone. He lifts his head into frame — short dark hair, soaked jacket — and smiles toward her off-screen as a bus rumbles past.",
        "resolution": "1920x1080",
        "duration": "6",
        "generate_audio": False,
        "fps": "25"
    }
)

print(response.json())
```

### JavaScript

```javascript
fetch("https://modelslab.com/api/v7/video-fusion/text-to-video", {
  method: "POST",
  headers: {
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    "key": "YOUR_API_KEY",
    "model_id": "ltx-2-5-pro-t2v",
    "prompt": "A wide shot frames a rainy city intersection at dusk, neon signs reflecting on wet asphalt. A young woman in a yellow raincoat walks toward camera, gripping a folded newspaper, while cars hiss past behind her. Soft synth music and distant traffic fill the air. A hard cut transitions to a medium close-up of her face under the hood, raindrops catching the neon as she looks off-screen left; the synth score continues across the cut, traffic muffled. She whispers, “He’s late.” Another hard cut jumps to a low-angle shot of a man’s scuffed boots stepping into a puddle at the curb; the music drops to a low drone. He lifts his head into frame — short dark hair, soaked jacket — and smiles toward her off-screen as a bus rumbles past.",
    "resolution": "1920x1080",
    "duration": "6",
    "generate_audio": false,
    "fps": "25"
  })
})
.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 "ltx-2-5-pro-t2v"                       # Find this model
modelslab models detail --id ltx-2-5-pro-t2v                    # Get model details
modelslab generate image --prompt "..." --model ltx-2-5-pro-t2v  # Generate with model
modelslab config set generation.default_model ltx-2-5-pro-t2v   # 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/ltx/ltx-2-5-pro-t2v)
- [API Documentation](https://docs.modelslab.com)
- [ModelsLab Platform](https://modelslab.com)