luma/ray-3.2/text-to-video

text-to-video

Luma Ray 3.2 Text-to-Video API by LUMA

luma/ray-3.2/text-to-video

Text-to-video

Luma Ray 3.2 text-to-video (Luma AI generations API): generate a cinematic clip from a text prompt. Supports 540p/720p/1080p, 5s/10s, and common aspect ratios.

Compare models

Luma Ray 3.2 Text-to-Video is developed by a third-party provider. 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 *

Duration

Resolution

Aspect Ratio

Loop

Hdr

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.06 per run. For $10 you can run this model approximately 166 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": "luma/ray-3.2/text-to-video",  # Required. Model name. options: luma/ray-3.2/text-to-video
    "prompt": "A majestic eagle soaring through golden clouds at sunset, slow cinematic dolly-in, warm light.",  # Required. Scene description including desired content, camera movement and atmosphere
    "duration": 5,  # Video length in seconds. options: 5 | 10
    "resolution": "720p",  # Output resolution tier. options: 540p | 720p | 1080p
    "aspect_ratio": "16:9",  # Frame aspect ratio (width:height)
    "loop": False,  # Generate a seamlessly looping video
    "hdr": False,  # Generate HDR video
}

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": "luma/ray-3.2/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: 7Required: 2Optional: 5

modelstringrequired

Model name.

Default: "luma/ray-3.2/text-to-video"

luma/ray-3.2/text-to-video

promptstringrequired

Scene description including desired content, camera movement and atmosphere.

Default: "A majestic eagle soaring through golden clouds at sunset, slow cinematic dolly-in, warm light."

durationinteger

Video length in seconds.

Default: 5

510

resolutionstring

Output resolution tier.

Default: "720p"

540p720p1080p

aspect_ratiostring

Frame aspect ratio (width:height).

Default: "16:9"

16:99:161:14:33:421:99:21

loopboolean

Generate a seamlessly looping video. Only valid for type=video; not supported with duration=10s or hdr=true.

Default: false

hdrboolean

Generate HDR video. Requires HDR access. Only valid for 5s clips (not with duration=10s).Only 720p and 1080p are supported.

Default: false

Example Request Body

{
  "model": "luma/ray-3.2/text-to-video",
  "prompt": "A majestic eagle soaring through golden clouds at sunset, slow cinematic dolly-in, warm light.",
  "duration": 5,
  "resolution": "720p",
  "aspect_ratio": "16:9",
  "loop": false,
  "hdr": false
}

Output Schema

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

idstring

Unique identifier for the prediction.

modelstring

Model ID used for the prediction.

statusstring

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

outputsarray

Array of URLs to the generated video. Empty when status is not completed.

created_atstring

ISO timestamp of when the request was created.

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": {
    "title": "AtlasCloud API",
    "version": "1.0.0",
    "description": "Luma Ray 3.2 text-to-video (Luma AI generations API)."
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "paths": {
    "/api/v1/model/generateVideo": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    }
  },
  "components": {
    "schemas": {
      "Input": {
        "type": "object",
        "required": [
          "model",
          "prompt"
        ],
        "properties": {
          "model": {
            "type": "string",
            "description": "Model name.",
            "default": "luma/ray-3.2/text-to-video",
            "enum": [
              "luma/ray-3.2/text-to-video"
            ]
          },
          "prompt": {
            "type": "string",
            "default": "A majestic eagle soaring through golden clouds at sunset, slow cinematic dolly-in, warm light.",
            "description": "Scene description including desired content, camera movement and atmosphere."
          },
          "duration": {
            "type": "integer",
            "default": 5,
            "enum": [
              5,
              10
            ],
            "description": "Video length in seconds.",
            "x-ui-component": "slider"
          },
          "resolution": {
            "type": "string",
            "default": "720p",
            "enum": [
              "540p",
              "720p",
              "1080p"
            ],
            "description": "Output resolution tier.",
            "x-ui-component": "select"
          },
          "aspect_ratio": {
            "type": "string",
            "default": "16:9",
            "enum": [
              "16:9",
              "9:16",
              "1:1",
              "4:3",
              "3:4",
              "21:9",
              "9:21"
            ],
            "description": "Frame aspect ratio (width:height).",
            "x-ui-component": "select"
          },
          "loop": {
            "type": "boolean",
            "default": false,
            "description": "Generate a seamlessly looping video. Only valid for type=video; not supported with duration=10s or hdr=true."
          },
          "hdr": {
            "type": "boolean",
            "default": false,
            "description": "Generate HDR video. Requires HDR access. Only valid for 5s clips (not with duration=10s).Only 720p and 1080p are supported."
          }
        },
        "x-order-properties": [
          "model",
          "prompt",
          "duration",
          "resolution",
          "aspect_ratio",
          "loop",
          "hdr"
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "description": "Unique identifier for the prediction."
          },
          "urls": {
            "type": "object",
            "description": "Object containing related API endpoints."
          },
          "model": {
            "type": "string",
            "description": "Model ID used for the prediction."
          },
          "status": {
            "type": "string",
            "description": "Status of the task: created, processing, completed, or failed."
          },
          "outputs": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "description": "Array of URLs to the generated video. Empty when status is not completed."
          },
          "created_at": {
            "type": "string",
            "format": "date-time",
            "description": "ISO timestamp of when the request was created."
          }
        }
      }
    }
  }
}

LLM-Friendly Prompt Template

# luma/ray-3.2/text-to-video

> Luma Ray 3.2 text-to-video (Luma AI generations API): generate a cinematic clip from a text prompt. Supports 540p/720p/1080p, 5s/10s, and common aspect ratios.


## 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**: `luma/ray-3.2/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: `"luma/ray-3.2/text-to-video"`
  - Options: "luma/ray-3.2/text-to-video"

- **`prompt`** (`string`, _required_):
  Scene description including desired content, camera movement and atmosphere.
  - Default: `"A majestic eagle soaring through golden clouds at sunset, slow cinematic dolly-in, warm light."`

- **`duration`** (`integer`, _optional_):
  Video length in seconds.
  - Default: `5`
  - Options: 5, 10

- **`resolution`** (`string`, _optional_):
  Output resolution tier.
  - Default: `"720p"`
  - Options: "540p", "720p", "1080p"

- **`aspect_ratio`** (`string`, _optional_):
  Frame aspect ratio (width:height).
  - Default: `"16:9"`
  - Options: "16:9", "9:16", "1:1", "4:3", "3:4", "21:9", "9:21"

- **`loop`** (`boolean`, _optional_):
  Generate a seamlessly looping video. Only valid for type=video; not supported with duration=10s or hdr=true.
  - Default: `false`

- **`hdr`** (`boolean`, _optional_):
  Generate HDR video. Requires HDR access. Only valid for 5s clips (not with duration=10s).Only 720p and 1080p are supported.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "luma/ray-3.2/text-to-video",
  "prompt": "A majestic eagle soaring through golden clouds at sunset, slow cinematic dolly-in, warm light."
}
```


**Full Example**:

```json
{
  "model": "luma/ray-3.2/text-to-video",
  "prompt": "A majestic eagle soaring through golden clouds at sunset, slow cinematic dolly-in, warm light.",
  "duration": 5,
  "resolution": "720p",
  "aspect_ratio": "16:9",
  "loop": false,
  "hdr": false
}
```


### Output Schema

The API returns the following output format:


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

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

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

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

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

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created.



**Example Response**:

```json
{
  "id": "",
  "urls": {},
  "model": "",
  "status": "",
  "outputs": [
    ""
  ],
  "created_at": ""
}
```


## 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": "luma/ray-3.2/text-to-video",
  "prompt": "A majestic eagle soaring through golden clouds at sunset, slow cinematic dolly-in, warm light.",
  "duration": 5,
  "resolution": "720p",
  "aspect_ratio": "16:9",
  "loop": false,
  "hdr": false
}'

# 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/luma/ray-3.2/text-to-video)

Three enormous smooth white spheres hover silently above a vast mountain forest. The camera slowly pushes forward through the foreground trees, gradually revealing the gigantic spheres floating above the misty valley. The spheres remain almost perfectly motionless while thin clouds drift slowly around them. Wind gently moves the grass and treetops below. Soft overcast daylight, subtle atmospheric haze, immense scale contrast, quiet mysterious atmosphere, surreal realism, cinematic slow camera movement, photorealistic landscape, natural motion, subtle film grain, 4K.