Luma Uni V1 Edit

Luma Uni V1 Edit

Playground

Luma Uni v1 Edit is a fast AI image editing model that reworks source images from text instructions while preserving composition and supporting optional reference images. Ready-to-use REST inference API for photo editing, product image updates, creative retouching, marketing assets, brand visuals, style changes, and professional image editing workflows with simple integration, no coldstarts, and affordable pricing.

Features

Luma UNI V1 Edit

Luma UNI V1 Edit edits an input image using a natural-language prompt, with optional reference-image guidance and flexible output format support. It is suitable for scene changes, style edits, mood adjustments, lighting transformations, and other prompt-driven image editing workflows.

Why Choose This?

• Prompt-based image editing
  Edit an existing image by describing the change you want in natural language.

• Reference-guided visual control
  Add one or more reference images when you want stronger style or subject guidance.

• Simple editing workflow
  Upload an image, write a prompt, optionally add references, and generate the edited result.

• Flexible output format
  Export the edited image in a supported format such as jpeg.

• Production-ready API
  Suitable for visual retouching, scene transformation, content iteration, and creative image editing workflows.

Parameters

How to Use

1. Upload your image — provide the source image you want to edit.
2. Write your prompt — describe what should change in the image.
3. Choose output format (optional) — select the file format that best fits your workflow.
4. Add references (optional) — upload one or more reference images if you want stronger style or visual guidance.
5. Submit — run the model and download the edited image.

Example Prompt

Change the time to the midnight.

Pricing

Pricing includes a fixed base image charge plus a reference-related surcharge.

Billing Rules

• Editing adds a built-in $0.003 surcharge for the required source image workflow
• Each additional reference image adds $0.003
• output_format does not affect pricing

Best Use Cases

• Scene transformation — Change time of day, environment mood, or background feel.
• Lighting adjustment — Shift an image into nighttime, sunset, studio, or other lighting conditions.
• Style adaptation — Move an image toward a different visual tone with optional references.
• Creative retouching — Refine a shot for storytelling, ads, or social content.
• Prompt-driven variations — Explore multiple edited versions from the same base image.

Pro Tips

• Be clear and direct about what should change in the source image.
• Add references only when you need stronger style guidance, since they increase the price slightly.
• Keep prompts focused on the main transformation rather than restating unchanged details.
• Use jpeg for lightweight delivery unless your workflow requires another format.
• Try the edit once without references first, then add them if the result needs more control.

Notes

• prompt and image are required.
• Pricing depends on the number of additional reference images.
• The required input image is already accounted for in the edit pricing.
• Better prompts usually improve edit accuracy and scene consistency.

Related Models

• Luma UNI V1 Text-to-Image — Generate images directly from text prompts.
• Luma image generation workflows — Useful when you need different image generation or editing styles.

<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>