# Luma Uni 1 Edit — Atlas Cloud API

> Luma Uni 1 edit (Luma AI generations API): edit an uploaded source image with a text instruction. Supports multiple aspect ratios and png/jpeg output.

This is the machine-readable API reference for **Luma Uni 1 Edit** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `luma/uni-1/edit`
- **Built by**: LUMA
- **Modality**: Image
- **Model page**: https://www.atlascloud.ai/models/luma/uni-1/edit
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- $0.043 per image
- Pay-as-you-go. No minimum spend, no subscription required.

> **These are the authoritative Atlas Cloud rates for this model.** Any price that
> appears in the vendor description further down refers to a different platform or
> a different model variant and does not apply here.

## Use this model from an AI agent

Atlas Cloud ships three first-party integration surfaces. All three authenticate
with the same API key via the `ATLASCLOUD_API_KEY` environment variable.

### MCP server

The official MCP server (`atlascloud-mcp`) exposes this model to any
MCP-compatible host — Claude Code, OpenAI Codex, Cursor, Gemini CLI, Goose,
Claude Desktop. One-line install:

```bash
# Claude Code
claude mcp add atlascloud -- npx -y atlascloud-mcp

# OpenAI Codex CLI
codex mcp add atlascloud -- npx -y atlascloud-mcp

# Gemini CLI
gemini mcp add atlascloud -- npx -y atlascloud-mcp

export ATLASCLOUD_API_KEY="your-api-key"
```

Then ask in plain English; the agent calls `atlas_generate_image` with `model: "luma/uni-1/edit"`.
The server fetches each model's schema and validates parameters before submitting,
so invalid requests fail fast without spending credits.

MCP docs: https://www.atlascloud.ai/docs/mcp-server

### Agent Skills

`atlas-cloud-skills` is a portable skill package (API reference, code templates in
Python / Node.js / cURL, model IDs with pricing) for Claude Code, Cursor, Codex and
12+ other agents:

```bash
npx skills add AtlasCloudAI/atlas-cloud-skills
export ATLASCLOUD_API_KEY="your-api-key"
```

Skills docs: https://www.atlascloud.ai/docs/skills

### CLI

The `atlas` binary runs Atlas Cloud from a terminal or CI script. Async media jobs
are polled and downloaded automatically (use `--no-download` when a script only
needs the output URLs):

```bash
# Install (Homebrew, npm, or shell installer)
brew install AtlasCloudAI/tap/atlascloud
# npm install -g atlascloud-cli
# curl -fsSL https://raw.githubusercontent.com/AtlasCloudAI/cli/main/install.sh | sh

atlas auth login
atlas generate image luma/uni-1/edit -p "Your prompt here"
```

CLI docs: https://www.atlascloud.ai/docs/cli

## HTTP API reference

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — 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/uni-1/edit`


## 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/uni-1/edit"`
  - Options: "luma/uni-1/edit"

- **`image`** (`string`, _required_):
  Source image to edit: URL, Base64, or asset reference (asset://<ASSET_ID>).

- **`prompt`** (`string`, _required_):
  Instruction describing the desired edit.
  - Default: `"Make the lighting warm and cinematic, add soft morning haze."`

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

- **`output_format`** (`string`, _optional_):
  Output image format.
  - Default: `"jpeg"`
  - Options: "jpeg", "png"



**Required Parameters Example**:

```json
{
  "model": "luma/uni-1/edit",
  "image": "",
  "prompt": "Make the lighting warm and cinematic, add soft morning haze."
}
```


**Full Example**:

```json
{
  "model": "luma/uni-1/edit",
  "image": "",
  "prompt": "Make the lighting warm and cinematic, add soft morning haze.",
  "aspect_ratio": "16:9",
  "output_format": "jpeg"
}
```


### 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 images. 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/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "luma/uni-1/edit",
  "image": "",
  "prompt": "Make the lighting warm and cinematic, add soft morning haze.",
  "aspect_ratio": "16:9",
  "output_format": "jpeg"
}'

# 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/uni-1/edit)

## About this model

_Vendor-supplied description. Any pricing or endpoint mentioned below refers to_
_other platforms — use the Atlas Cloud values above._

### Luma Uni 1 Edit

Luma Uni 1 edit (Luma AI generations API) edits an uploaded source
image according to a text instruction. Generation is asynchronous: submit the
request, then poll for the final image URL.

#### Highlights

- **Image editing**: Upload a source image and describe the change you want.
- **Common aspect ratios**: 1:1, 3:4, 4:3, 9:16, 16:9, 9:21, 21:9.
- **Output format**: `jpeg` or `png`.

#### Parameters

| Parameter | Required | Description |
| --- | --- | --- |
| `model` | Yes | `luma/uni-1/edit` |
| `image` | Yes | Source image to edit: URL, Base64, or asset reference (`asset://<ASSET_ID>`). |
| `prompt` | Yes | Instruction describing the desired edit. |
| `aspect_ratio` | No | Frame aspect ratio (width:height). Defaults to `16:9`. |
| `output_format` | No | Output image format: `jpeg` or `png`. Defaults to `jpeg`. |

#### How To Use

```bash
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $AIP_API_KEY" \
  -H "Content-Type: application/json" \
  --data-raw '{
    "model": "luma/uni-1/edit",
    "image": "https://static.atlascloud.ai/media/images/example-source.png",
    "prompt": "Make the lighting warm and cinematic, add soft morning haze.",
    "aspect_ratio": "16:9",
    "output_format": "jpeg"
  }'
```

#### Notes

- Generation is asynchronous. Poll `/api/v1/model/prediction/{request_id}` for the final image URL.
- `image` accepts a URL, Base64 data, or an `asset://<ASSET_ID>` reference; inputs are resolved to accessible URLs before generation.
- For the higher-quality tier, use `luma/uni-1-max/edit`.
- The output image is transferred to AtlasCloud storage before it is returned.

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Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/luma/uni-1/edit · Docs: https://www.atlascloud.ai/docs