# ltx-2.3-text-to-video

> LTX-2.3 Text-to-Video generates cinematic video clips directly from text prompts. Built on an upgraded 2.3B architecture, it delivers sharper temporal consistency, faster synthesis, and more precise motion control than previous LTX versions. Ideal for concept visualization, story beats, and prompt-driven animation.

## Overview

- **Endpoint**: `POST https://api.muapi.ai/api/v1/ltx-2.3-text-to-video`
- **Model ID**: `ltx-2.3-text-to-video`
- **Category**: text to video
- **Variant**: Text to Video
- **Family**: ltx2.3
- **Cost**: 0.104 credits per call (some models compute cost dynamically based on params)

## API Usage

MuApi uses a **submit-then-poll** pattern: submit a job, get a `request_id`, then poll the predictions endpoint until `status` is `completed`. Optionally pass `?webhook=YOUR_URL` on the submit call to receive a POST callback when the job finishes (skip polling).

**Authentication**: send your MuApi key in the `x-api-key` header. Get one at https://muapi.ai/access-keys.

### 1. Submit a job

```http
POST https://api.muapi.ai/api/v1/ltx-2.3-text-to-video
Content-Type: application/json
x-api-key: YOUR_API_KEY
```

**Minimum (required only):**

```json
{
  "prompt": "<prompt>"
}
```

**Full example (all params):**

```json
{
  "prompt": "<prompt>",
  "duration": 5,
  "aspect_ratio": "16:9",
  "resolution": "720p",
  "seed": -1
}
```

**Response:**

```json
{
  "request_id": "abc123",
  "status": "processing"
}
```

### 2. Poll for the result

```http
GET https://api.muapi.ai/api/v1/predictions/{request_id}/result
x-api-key: YOUR_API_KEY
```

Possible `status` values: `queued`, `pending`, `processing`, `completed`, `failed`, `cancelled`. Poll every 2-5 seconds until terminal. When `completed`, the result URLs are in the `outputs` array.

**Example response when `completed`:**

```json
{
  "id": "abc123",
  "status": "completed",
  "outputs": [
    "https://cdn.muapi.ai/.../output.png"
  ],
  "urls": {
    "get": "https://api.muapi.ai/api/v1/predictions/abc123/result"
  },
  "created_at": "2026-05-08T12:34:56Z",
  "has_nsfw_contents": []
}
```

### cURL

```bash
# 1. Submit
REQUEST_ID=$(curl -s -X POST https://api.muapi.ai/api/v1/ltx-2.3-text-to-video \
  -H "x-api-key: $MUAPI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"prompt":"<prompt>"}' | jq -r .request_id)

# 2. Poll until completed
while :; do
  RESP=$(curl -s https://api.muapi.ai/api/v1/predictions/$REQUEST_ID/result -H "x-api-key: $MUAPI_API_KEY")
  STATUS=$(echo "$RESP" | jq -r .status)
  [ "$STATUS" = "completed" ] && echo "$RESP" | jq .outputs && break
  [ "$STATUS" = "failed" ] && echo "$RESP" && exit 1
  sleep 3
done
```

### Python

```python
import os, time, requests

API = "https://api.muapi.ai/api/v1"
headers = {"x-api-key": os.environ["MUAPI_API_KEY"]}

r = requests.post(f"{API}/ltx-2.3-text-to-video", headers=headers, json={"prompt":"<prompt>"})
request_id = r.json()["request_id"]

while True:
    res = requests.get(f"{API}/predictions/{request_id}/result", headers=headers).json()
    if res["status"] == "completed":
        print(res["outputs"]); break
    if res["status"] == "failed":
        raise RuntimeError(res.get("error")); 
    time.sleep(3)
```

## Input Schema

The API accepts the following input parameters:

- **`prompt`** (`string`, _required_):
  Text prompt describing the video.

- **`duration`** (`int`, _optional_):
  Duration of the generated video in seconds.
  - Default: `5`

- **`aspect_ratio`** (`string`, _optional_):
  Aspect ratio of the generated video.
  - Default: `"16:9"`
  - Options: `"16:9"`, `"9:16"`

- **`resolution`** (`string`, _optional_):
  The resolution of the generated video.
  - Default: `"720p"`
  - Options: `"480p"`, `"720p"`, `"1080p"`

- **`seed`** (`int`, _optional_):
  Random seed. -1 for random.
  - Default: `-1`

## Output Schema

The polling endpoint returns the following fields:

- **`id`** (`string`): The request ID.
- **`status`** (`string`): One of `queued`, `pending`, `processing`, `completed`, `failed`, `cancelled`.
- **`outputs`** (`array`): URLs to generated images/videos/audio. Empty until `status` is `completed`.
- **`urls.get`** (`string`): Self-link to re-fetch this prediction.
- **`error`** (`string` | `null`): Error message if `status` is `failed`.
- **`created_at`** (`string`): ISO-8601 timestamp of when the request was created.
- **`has_nsfw_contents`** (`array of boolean`): Per-output NSFW detection flags.

## Webhooks (optional)

Append `?webhook=https://your-server/path` to the submit URL. When the job reaches a terminal state, MuApi will POST the same shape as the polling response to your URL — no polling needed.

## Agent Integration

MuApi ships an MCP server and CLI so agents (Claude Code, Cursor, custom) can call this endpoint without writing HTTP code:

```bash
# Install the CLI
npm install -g muapi-cli

# Authenticate once
muapi auth login

# Expose all MuApi models as MCP tools to your agent
muapi mcp serve
```

The MCP server exposes tools that wrap submit + poll for every model, including `ltx-2.3-text-to-video`. See `muapi --help` for category-specific shortcuts (`muapi image generate`, `muapi video from-image`, etc.).

## Related Models
- [Text to Video](https://muapi.ai/playground/ltx-2.3-text-to-video)
- [Audio to Video](https://muapi.ai/playground/ltx-2.3-lipsync)
- [Video Extend](https://muapi.ai/playground/ltx-2.3-video-extend)
- [Image to Video](https://muapi.ai/playground/ltx-2.3-image-to-video)

## Resources
- [Playground Page](https://muapi.ai/playground/ltx-2.3-text-to-video)
- [API Reference](https://muapi.ai/playground/ltx-2.3-text-to-video?tab=2)
- [Global llms.txt](https://muapi.ai/llms.txt)