luma/ray-3.2/image-to-video

image-to-video

Luma Ray 3.2 Image-to-Video API by LUMA

luma/ray-3.2/image-to-video

Image-to-video

Luma Ray 3.2 image-to-video (Luma AI generations API): animate a first-frame image (optionally interpolating to a last frame) driven by a text prompt. Supports 540p/720p/1080p, 5s/10s.

Compare models

Luma Ray 3.2 Image-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

Image *

You can drag and drop a file or click to upload

MAX:1

Last Image

You can drag and drop a file or click to upload

MAX:1

Duration

Resolution

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/image-to-video",  # Required. Model name. options: luma/ray-3.2/image-to-video
    "image": "example_value",  # Required. First-frame image: URL, Base64, or asset reference (asset://<ASSET_ID>)
    "last_image": "example_value",  # Optional last-frame image (URL / Base64 / asset://)
    "prompt": "The scene comes alive with gentle, natural motion and cinematic lighting.",  # Text prompt describing the desired motion, camera movement and atmosphere
    "duration": 5,  # Video length in seconds. options: 5 | 10
    "resolution": "720p",  # Output resolution tier. options: 540p | 720p | 1080p
    "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/image-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: 8Required: 2Optional: 6

modelstringrequired

Model name.

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

luma/ray-3.2/image-to-video

imagestringrequired

First-frame image: URL, Base64, or asset reference (asset://<ASSET_ID>). The video starts from this image.

last_imagestring

Optional last-frame image (URL / Base64 / asset://). When set, the clip interpolates from the first frame to this frame.

promptstring

Text prompt describing the desired motion, camera movement and atmosphere. Optional but recommended.

Default: "The scene comes alive with gentle, natural motion and cinematic lighting."

durationinteger

Video length in seconds.

Default: 5

510

resolutionstring

Output resolution tier.

Default: "720p"

540p720p1080p

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/image-to-video",
  "image": "example_image",
  "prompt": "The scene comes alive with gentle, natural motion and cinematic lighting.",
  "duration": 5,
  "resolution": "720p",
  "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 image-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",
          "image"
        ],
        "properties": {
          "model": {
            "type": "string",
            "description": "Model name.",
            "default": "luma/ray-3.2/image-to-video",
            "enum": [
              "luma/ray-3.2/image-to-video"
            ]
          },
          "image": {
            "type": "string",
            "description": "First-frame image: URL, Base64, or asset reference (asset://<ASSET_ID>). The video starts from this image."
          },
          "last_image": {
            "type": "string",
            "description": "Optional last-frame image (URL / Base64 / asset://). When set, the clip interpolates from the first frame to this frame."
          },
          "prompt": {
            "type": "string",
            "default": "The scene comes alive with gentle, natural motion and cinematic lighting.",
            "description": "Text prompt describing the desired motion, camera movement and atmosphere. Optional but recommended."
          },
          "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"
          },
          "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",
          "image",
          "last_image",
          "prompt",
          "duration",
          "resolution",
          "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/image-to-video

> Luma Ray 3.2 image-to-video (Luma AI generations API): animate a first-frame image (optionally interpolating to a last frame) driven by a text prompt. Supports 540p/720p/1080p, 5s/10s.


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

- **`image`** (`string`, _required_):
  First-frame image: URL, Base64, or asset reference (asset://<ASSET_ID>). The video starts from this image.

- **`last_image`** (`string`, _optional_):
  Optional last-frame image (URL / Base64 / asset://). When set, the clip interpolates from the first frame to this frame.

- **`prompt`** (`string`, _optional_):
  Text prompt describing the desired motion, camera movement and atmosphere. Optional but recommended.
  - Default: `"The scene comes alive with gentle, natural motion and cinematic lighting."`

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

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

- **`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/image-to-video",
  "image": ""
}
```


**Full Example**:

```json
{
  "model": "luma/ray-3.2/image-to-video",
  "image": "",
  "last_image": "",
  "prompt": "The scene comes alive with gentle, natural motion and cinematic lighting.",
  "duration": 5,
  "resolution": "720p",
  "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/image-to-video",
  "image": "",
  "last_image": "",
  "prompt": "The scene comes alive with gentle, natural motion and cinematic lighting.",
  "duration": 5,
  "resolution": "720p",
  "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/image-to-video)

A lone hooded traveler stands motionless in the middle of an endless pale desert, facing a gigantic futuristic spacecraft resting silently on the sand dunes. The enormous spacecraft begins to slowly hover above the ground, its massive shadow gradually separating from the sand as fine dust and grains of sand are lifted into the air beneath it. The traveler’s long robe gently moves in the desert wind. The camera slowly pushes forward from behind the traveler, emphasizing the extreme scale contrast between the tiny human figure and the colossal spacecraft. Subtle atmospheric haze moves across the distant dunes, soft sunlight diffuses through the pale sky, creating long delicate shadows. The spacecraft remains sleek, minimalist and almost completely silent, with subtle mechanical details and faint reflections on its matte surface. The ship slowly rises higher into the air, then pauses above the desert, creating a mysterious sense of anticipation. Epic cinematic science fiction, restrained visual effects, monumental scale, minimal color palette, soft golden-white tones, atmospheric depth, realistic sand movement, natural physics, photorealistic, cinematic lighting, slow controlled camera movement, 70mm cinematography, subtle film grain, ultra-detailed, 4K.