luma/ray-3.2/video-to-video

video-to-video

Luma Ray 3.2 Video-to-Video API by LUMA

luma/ray-3.2/video-to-video

Video-to-video

Luma Ray 3.2 video-to-video (Luma AI generations API): restyle/edit a source video with a text instruction. strength controls preserve↔transform. Supports 540p/720p/1080p, 5s/10s, HDR.

Luma Ray 3.2 Video-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 *

Video *

You can drag and drop a file or click to upload

MAX:1

Strength

Duration

Resolution

Hdr

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.54 per run. For $10 you can run this model approximately 18 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/video-to-video",  # Required. Model name. options: luma/ray-3.2/video-to-video
    "video": "example_value",  # Required. Source video to edit (HTTPS URL, mp4)
    "prompt": "Restyle the clip with a warm cinematic look and richer color grading.",  # Required. Instruction describing the desired edit / restyle
    "strength": "flex_2",  # How much to preserve vs
    "duration": 5,  # Output video length in seconds. options: 5 | 10
    "resolution": "720p",  # Output resolution tier. options: 540p | 720p | 1080p
    "hdr": False,  # Generate HDR output
}

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/video-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: 3Optional: 4

modelstringrequired

Model name.

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

luma/ray-3.2/video-to-video

videostringrequired

Source video to edit (HTTPS URL, mp4). The edit is conditioned on this clip.

promptstringrequired

Instruction describing the desired edit / restyle.

Default: "Restyle the clip with a warm cinematic look and richer color grading."

strengthstring

How much to preserve vs. transform the source: adhere_* preserves, flex_* is balanced, reimagine_* transforms.

Default: "flex_2"

adhere_1adhere_2adhere_3flex_1flex_2flex_3reimagine_1reimagine_2reimagine_3

durationinteger

Output video length in seconds. Typically matches the source clip.

Default: 5

510

resolutionstring

Output resolution tier.

Default: "720p"

540p720p1080p

hdrboolean

Generate HDR output. Requires HDR access.Only 720p and 1080p are supported.

Default: false

Example Request Body

{
  "model": "luma/ray-3.2/video-to-video",
  "video": "example_video",
  "prompt": "Restyle the clip with a warm cinematic look and richer color grading.",
  "strength": "flex_2",
  "duration": 5,
  "resolution": "720p",
  "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 video-to-video edit (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",
          "video",
          "prompt"
        ],
        "properties": {
          "model": {
            "type": "string",
            "description": "Model name.",
            "default": "luma/ray-3.2/video-to-video",
            "enum": [
              "luma/ray-3.2/video-to-video"
            ]
          },
          "video": {
            "type": "string",
            "description": "Source video to edit (HTTPS URL, mp4). The edit is conditioned on this clip."
          },
          "prompt": {
            "type": "string",
            "default": "Restyle the clip with a warm cinematic look and richer color grading.",
            "description": "Instruction describing the desired edit / restyle."
          },
          "strength": {
            "type": "string",
            "default": "flex_2",
            "enum": [
              "adhere_1",
              "adhere_2",
              "adhere_3",
              "flex_1",
              "flex_2",
              "flex_3",
              "reimagine_1",
              "reimagine_2",
              "reimagine_3"
            ],
            "description": "How much to preserve vs. transform the source: adhere_* preserves, flex_* is balanced, reimagine_* transforms.",
            "x-ui-component": "select"
          },
          "duration": {
            "type": "integer",
            "default": 5,
            "enum": [
              5,
              10
            ],
            "description": "Output video length in seconds. Typically matches the source clip.",
            "x-ui-component": "slider"
          },
          "resolution": {
            "type": "string",
            "default": "720p",
            "enum": [
              "540p",
              "720p",
              "1080p"
            ],
            "description": "Output resolution tier.",
            "x-ui-component": "select"
          },
          "hdr": {
            "type": "boolean",
            "default": false,
            "description": "Generate HDR output. Requires HDR access.Only 720p and 1080p are supported."
          }
        },
        "x-order-properties": [
          "model",
          "video",
          "prompt",
          "strength",
          "duration",
          "resolution",
          "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/video-to-video

> Luma Ray 3.2 video-to-video (Luma AI generations API): restyle/edit a source video with a text instruction. strength controls preserve↔transform. Supports 540p/720p/1080p, 5s/10s, HDR.


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

- **`video`** (`string`, _required_):
  Source video to edit (HTTPS URL, mp4). The edit is conditioned on this clip.

- **`prompt`** (`string`, _required_):
  Instruction describing the desired edit / restyle.
  - Default: `"Restyle the clip with a warm cinematic look and richer color grading."`

- **`strength`** (`string`, _optional_):
  How much to preserve vs. transform the source: adhere_* preserves, flex_* is balanced, reimagine_* transforms.
  - Default: `"flex_2"`
  - Options: "adhere_1", "adhere_2", "adhere_3", "flex_1", "flex_2", "flex_3", "reimagine_1", "reimagine_2", "reimagine_3"

- **`duration`** (`integer`, _optional_):
  Output video length in seconds. Typically matches the source clip.
  - Default: `5`
  - Options: 5, 10

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

- **`hdr`** (`boolean`, _optional_):
  Generate HDR output. Requires HDR access.Only 720p and 1080p are supported.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "luma/ray-3.2/video-to-video",
  "video": "",
  "prompt": "Restyle the clip with a warm cinematic look and richer color grading."
}
```


**Full Example**:

```json
{
  "model": "luma/ray-3.2/video-to-video",
  "video": "",
  "prompt": "Restyle the clip with a warm cinematic look and richer color grading.",
  "strength": "flex_2",
  "duration": 5,
  "resolution": "720p",
  "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/video-to-video",
  "video": "",
  "prompt": "Restyle the clip with a warm cinematic look and richer color grading.",
  "strength": "flex_2",
  "duration": 5,
  "resolution": "720p",
  "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/video-to-video)

Adjust the video style to a comic book style.