ltx-2.3-quality/text-to-video

text-to-video

Ltx 2.3 Quality Text-to-Video API by LIGHTRICKS

ltx-2.3-quality/text-to-video

Text-to-video

Generate high-quality video with audio from images using LTX-2.3

Compare models

Ltx 2.3 Quality Text-to-Video is developed by Lightricks. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.

INPUT

Prompt *

Num frames

Resolution

Generate Audio

Seed

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.002 per run. For $10 you can run this model approximately 5000 times.

Here's what you can do next:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameters

Code Example
import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "ltx-2.3-quality/text-to-video",  # Required. model name
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. Text description of the video to generate
    "num_frames": 121,  # Number of frames to generate. (min: 25, max: 257)
    "resolution": "landscape_16_9",  # Output video resolution preset. options: square_hd | square | portrait_3_4 | portrait_9_16 | landscape_4_3 | landscape_16_9
    "generate_audio": False,  # Whether to generate accompanying audio for the video
    "seed": 0,  # Random seed for reproducibility. (min: 0)
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated video:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

video_url = check_status()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateVideo

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "ltx-2.3-quality/text-to-video",
    "prompt": "A beautiful sunset over the ocean with gentle waves"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

Response

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Check Status

Poll the prediction endpoint to check the current status of your request.

GET/api/v1/model/prediction/{prediction_id}

Polling Example

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Response

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

The following parameters are accepted in the request body.

Total: 6Required: 2Optional: 4

modelstringrequired

model name

Default: "ltx-2.3-quality/text-to-video"

promptstringrequired

Text description of the video to generate. Maximum 2,000 characters.

num_framesinteger

Number of frames to generate. Must be a multiple of 8 plus 1 (e.g. 25, 41, 81, 121, 161, 201, 241, 257).

Default: 121Min: 25Max: 257

resolutionstring

Output video resolution preset.

Default: "landscape_16_9"

square_hdsquareportrait_3_4portrait_9_16landscape_4_3landscape_16_9

generate_audioboolean

Whether to generate accompanying audio for the video.

Default: false

seedinteger

Random seed for reproducibility. Use the same seed and prompt to reproduce results.

Min: 0

Example Request Body

{
  "model": "ltx-2.3-quality/text-to-video",
  "prompt": "A beautiful landscape",
  "num_frames": 121,
  "resolution": "landscape_16_9",
  "generate_audio": false
}

Output Schema

The API returns a prediction response with the generated output URLs.

modelstring

model name

Default: "ltx-2.3-quality/text-to-video"

created_atstring

ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

idstring

Unique identifier for the prediction.

outputsarray

Array of URLs to the generated videos. Null when status is not completed.

statusstring

Status of the task: created, processing, completed, or failed.

Example Response

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrates 400+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

export ATLASCLOUD_API_KEY="your-api-key-here"

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 400+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 400+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "paths": {
    "/api/v1/model/generateImage": {
      "post": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        },
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        },
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "schema": {
              "type": "string",
              "description": "Request ID"
            },
            "required": true
          }
        ]
      },
      "x-api-name": "model_result"
    }
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "ltx-2.3-quality/text-to-video"
          },
          "prompt": {
            "description": "Text description of the video to generate. Maximum 2,000 characters.",
            "type": "string",
            "minLength": 1,
            "maxLength": 2000
          },
          "num_frames": {
            "description": "Number of frames to generate. Must be a multiple of 8 plus 1 (e.g. 25, 41, 81, 121, 161, 201, 241, 257).",
            "type": "integer",
            "default": 121,
            "minimum": 25,
            "maximum": 257,
            "x-ui-component": "slider"
          },
          "resolution": {
            "description": "Output video resolution preset.",
            "type": "string",
            "default": "landscape_16_9",
            "enum": [
              "square_hd",
              "square",
              "portrait_3_4",
              "portrait_9_16",
              "landscape_4_3",
              "landscape_16_9"
            ],
            "x-ui-component": "select",
            "x-enum-labels": {
              "square_hd": "Square HD (1024×1024)",
              "square": "Square (512×512)",
              "portrait_3_4": "Portrait 3:4",
              "portrait_9_16": "Portrait 9:16",
              "landscape_4_3": "Landscape 4:3",
              "landscape_16_9": "Landscape 16:9"
            },
            "x-enum-mapping": {
              "portrait_3_4": "portrait_4_3"
            }
          },
          "generate_audio": {
            "description": "Whether to generate accompanying audio for the video.",
            "type": "boolean",
            "default": false,
            "x-ui-component": "switch"
          },
          "seed": {
            "description": "Random seed for reproducibility. Use the same seed and prompt to reproduce results.",
            "type": "integer",
            "minimum": 0
          }
        },
        "required": [
          "model",
          "prompt"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "num_frames",
          "resolution",
          "generate_audio",
          "seed"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "ltx-2.3-quality/text-to-video"
          },
          "created_at": {
            "description": "ISO timestamp of when the request was created (e.g., \"2023-04-01T12:34:56.789Z\").",
            "format": "date-time",
            "type": "string"
          },
          "id": {
            "description": "Unique identifier for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the generated videos. Null when status is not completed.",
            "items": {
              "type": "string"
            },
            "nullable": true,
            "type": "array"
          },
          "status": {
            "description": "Status of the task: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          }
        },
        "type": "object"
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  }
}

LLM-Friendly Prompt Template

# ltx-2.3-quality/text-to-video

> Generate high-quality video with audio from images using LTX-2.3


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateVideo` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `ltx-2.3-quality/text-to-video`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"ltx-2.3-quality/text-to-video"`

- **`prompt`** (`string`, _required_):
  Text description of the video to generate. Maximum 2,000 characters.

- **`num_frames`** (`integer`, _optional_):
  Number of frames to generate. Must be a multiple of 8 plus 1 (e.g. 25, 41, 81, 121, 161, 201, 241, 257).
  - Default: `121`
  - Min: 25
  - Max: 257

- **`resolution`** (`string`, _optional_):
  Output video resolution preset.
  - Default: `"landscape_16_9"`
  - Options: "square_hd", "square", "portrait_3_4", "portrait_9_16", "landscape_4_3", "landscape_16_9"

- **`generate_audio`** (`boolean`, _optional_):
  Whether to generate accompanying audio for the video.
  - Default: `false`

- **`seed`** (`integer`, _optional_):
  Random seed for reproducibility. Use the same seed and prompt to reproduce results.
  - Min: 0



**Required Parameters Example**:

```json
{
  "model": "ltx-2.3-quality/text-to-video",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "ltx-2.3-quality/text-to-video",
  "prompt": "",
  "num_frames": 121,
  "resolution": "landscape_16_9",
  "generate_audio": false,
  "seed": 0
}
```


### Output Schema

The API returns the following output format:


- **`model`** (`string`, _optional_):
  model name
  - Default: `"ltx-2.3-quality/text-to-video"`

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated videos. Null when status is not completed.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example Response**:

```json
{
  "model": "ltx-2.3-quality/text-to-video",
  "created_at": "",
  "id": "",
  "outputs": [
    ""
  ],
  "status": "",
  "urls": {}
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "ltx-2.3-quality/text-to-video",
  "prompt": "",
  "num_frames": 121,
  "resolution": "landscape_16_9",
  "generate_audio": false,
  "seed": 0
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/ltx-2.3-quality/text-to-video)

A lone figure wearing a flowing desert cloak walks slowly across endless golden dunes beneath a colossal blazing sun. Powerful winds sweep across the landscape, sending fine sand spiraling into the air. The camera begins with an ultra-wide aerial shot, emphasizing the immense scale of the desert, then slowly descends into a cinematic tracking shot behind the traveler. Heat haze distorts the distant horizon while colossal dunes stretch endlessly into the distance. Monumental atmosphere, slow cinematic pacing, dramatic volumetric lighting, Denis Villeneuve style, IMAX scale, ultra realistic, masterpiece, 8K.

LTX 2.3 Quality Text-to-Video

LTX 2.3 Quality is a high-quality text-to-video model that generates cinematic videos from natural language prompts, with optional audio generation. Built on the LTX-Video architecture, it delivers smooth motion, strong prompt adherence, and flexible resolution presets.

🌟 Key Features

🎬 Cinematic Video Generation

Generates high-fidelity videos from descriptive text prompts with precise motion control, realistic lighting, and consistent style.

🔊 Optional Audio Generation

Optionally produce synchronized audio alongside the video in a single request — no separate audio pipeline required.

📐 Multiple Resolution Presets

Six aspect ratio presets cover every common layout, from square HD to widescreen landscape and vertical portrait formats.

🔁 Reproducible Results

Pin a seed value to reproduce or iterate on a specific generation, enabling systematic prompt and parameter refinement.

⚙️ Parameters

Parameter	Required	Description
`prompt`	✅	Text description of the video to generate. Maximum 2,000 characters.
`num_frames`	❌	Number of frames to generate (default: `121`). Must be a multiple of 8 plus 1.
`resolution`	❌	Output video resolution preset (default: `landscape_16_9`). See options below.
`generate_audio`	❌	Whether to generate accompanying audio (default: `false`).
`seed`	❌	Random seed for reproducibility. Omit for random results.

Resolution Options

Value	Description
`square_hd`	Square HD (1024×1024)
`square`	Square (512×512)
`portrait_3_4`	Portrait 3:4
`portrait_9_16`	Portrait 9:16
`landscape_4_3`	Landscape 4:3
`landscape_16_9`	Landscape 16:9

Frame Count Guide

`num_frames`	Approximate Duration (24fps)
25	~1 second
81	~3.4 seconds
121	~5 seconds
161	~6.7 seconds
257	~10.7 seconds

💲 Pricing

Billed at $0.0024075 per megapixel of generated video data (width × height × num_frames), rounded upward.

Example: landscape_16_9 (1280×720) at 121 frames ≈ 112 MP ≈ $0.27

💡 Prompt Tips

A drone shot sweeping over a misty mountain valley at dawn, golden light breaking through the clouds, cinematic slow motion, ultra-detailed.

Describe motion explicitly: "slowly panning left", "camera zooms in", "waves crashing".
Include lighting and atmosphere cues: "golden hour", "neon-lit rain", "overcast fog".
Reference a visual style: "cinematic 4K", "anime style", "photorealistic".
Use seed when iterating on prompt wording to isolate the effect of each change.

🎯 Use Cases

Creative Storytelling — Generate cinematic sequences and short films from script descriptions.
Marketing & Advertising — Produce dynamic video assets for campaigns without filming.
Social Media Content — Create engaging short-form video content at any aspect ratio.
Prototyping & Storyboarding — Rapidly visualize scene concepts before full production.
Music Visualization — Generate visuals to accompany audio tracks with generate_audio: false.

📝 Notes

prompt is the only required field.
num_frames must satisfy: (num_frames - 1) % 8 == 0 (e.g. 25, 33, 41, 81, 121, 161, 257).
Task status values: created, processing, completed, failed.
Generated video URLs are returned in outputs once status is completed.

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