Luma Ray 3.2 Video Reframing

Luma Ray 3.2 Video Reframing

Playground

Luma Ray 3.2 Video Reframing is a fast AI video reframing model that changes an existing video’s aspect ratio and fills the newly exposed canvas from a prompt while preserving the source footage. Ready-to-use REST inference API for aspect ratio conversion, social media resizing, cinematic reframing, video outpainting, creator content, advertising creatives, and professional video editing workflows with simple integration, no coldstarts, and affordable pricing.

Features

Luma Ray 3.2 Video Reframing

Luma Ray 3.2 Video Reframing changes the aspect ratio of an existing video while preserving the original footage. Upload a source clip, choose a target size, and describe what should appear in the newly exposed canvas area.

Why Choose This?

• Aspect-ratio conversion for existing videos
  Reframe source footage into a new layout without rebuilding the video from scratch.

• Prompt-guided canvas expansion
  Use a text prompt to describe how the newly exposed area should be filled.

• Multiple output ratios
  Supports common landscape, portrait, square, and cinematic aspect ratios.

• Flexible resolution options
  Choose 540p, 720p, or 1080p depending on quality and budget needs.

• Production-ready workflow
  Useful for social repurposing, vertical edits, widescreen conversion, and creative reframing tasks.

Parameters

How to Use

1. Upload your source video — provide the clip you want to reframe.
2. Write your prompt — describe what should appear in the newly exposed canvas area.
3. Choose the target size — select the new aspect ratio for the output video.
4. Choose resolution — use 540p for lower cost, 720p for balanced output, or 1080p for higher quality.
5. Submit — run the model and download the reframed video.

Example Prompt

Extend the sides with a realistic rainy city street, blurred traffic lights, and soft reflections that match the original nighttime mood.

Pricing

Pricing is based on source video duration and output resolution.

Billing Rules

• Source duration is rounded up to the next whole second
• Minimum billed duration is 1 second
• Maximum billed duration is 30 seconds
• size does not affect pricing
• Source position is handled automatically and is not exposed in the form

Best Use Cases

• Vertical conversion — Turn horizontal footage into 9:16 clips for short-form platforms.
• Social media repurposing — Reframe one source video for multiple channels and formats.
• Cinematic expansion — Extend footage into wider formats such as 21:9.
• Creative canvas filling — Add scene-consistent content around the original video.
• Marketing adaptation — Resize existing ad clips for different placements without reshooting.

Pro Tips

• Use clear, stable source videos for more consistent reframing results.
• Keep prompts focused on the newly exposed canvas area, not the original content.
• Match the prompt to the source scene’s lighting, mood, and environment for more natural results.
• Start with 540p or 720p when testing, then switch to 1080p for higher-end final delivery.
• Shorter clips are usually easier to validate before reframing longer source footage.

Notes

• video and prompt are required.
• Source videos must be 30 seconds or less.
• Pricing depends on billed source duration and selected resolution.
• Better prompts usually improve the realism of the expanded canvas.

Related Models

• Luma image-to-video workflows — Useful when you want to animate a still image instead of reframing existing footage.
• Luma text-to-video workflows — Useful when you want to generate a new clip from scratch.
• Luma video editing workflows — Useful when you want to transform motion or style rather than only change framing.

<ApiPage model={model}>
  ## Authentication

  For authentication details, please refer to the [Authentication Guide](/docs-authentication).

  ## API Endpoints

  ### Submit Task & Query Result

  ## Parameters

  ### Task Submission Parameters

  #### Request Parameters

  #### Response Parameters

  <SubmitResponse />

  #### Result Request Parameters

  | Parameter | Type | Required | Default | Description |
  |-----------|------|----------|---------|-------------|
  | id | string | Yes | - | Task ID |

  #### Result Response Parameters

  | Parameter | Type | Description |
  |-----------|------|-------------|
  | code | integer | HTTP status code (e.g., 200 for success) |
  | message | string | Status message (e.g., "success") |
  | data | object | The prediction data object containing all details |
  | data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
  | data.model | string | Model ID used for the prediction |
  | data.outputs | string | Array of URLs to the generated content. |
  | data.urls | object | Object containing related API endpoints |
  | data.urls.get | string | URL to retrieve the prediction result |
  | data.status | string | Status of the task: `created`, `processing`, `completed`, or `failed` |
  | data.created_at | string | ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z") |
  | data.error | string | Error message (empty if no error occurred) |
  | data.timings | object | Object containing timing details |
  | data.timings.inference | integer | Inference time in milliseconds |

</ApiPage>