luma/uni-1/edit

image-to-image

Luma Uni 1 Edit API by LUMA

luma/uni-1/edit

Edit

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.

Luma Uni 1 Edit 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

Aspect Ratio

Output format

OUTPUT

Idle

Your generated images will appear here

Configure your settings and click Run to get started

Your request will cost $0.043 per run. For $10 you can run this model approximately 232 times.

Here's what you can do next:

Image-to-Video Image-to-Image

Parameters

Code Example
import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "luma/uni-1/edit",  # Required. Model name. options: luma/uni-1/edit
    "image": "example_value",  # Required. Source image to edit: URL, Base64, or asset reference (asset://<ASSET_ID>)
    "prompt": "Make the lighting warm and cinematic, add soft morning haze.",  # Required. Instruction describing the desired edit
    "aspect_ratio": "16:9",  # Frame aspect ratio (width:height)
    "output_format": "jpeg",  # Output image format. options: jpeg | png
}

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"] == "completed":
            print("Generated image:", 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)

image_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/generateImage"
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/generateImage

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "luma/uni-1/edit",
    "prompt": "A beautiful landscape with mountains and lake"
}

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.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "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: 5Required: 3Optional: 2

modelstringrequired

Model name.

Default: "luma/uni-1/edit"

luma/uni-1/edit

imagestringrequired

Source image to edit: URL, Base64, or asset reference (asset://<ASSET_ID>).

promptstringrequired

Instruction describing the desired edit.

Default: "Make the lighting warm and cinematic, add soft morning haze."

aspect_ratiostring

Frame aspect ratio (width:height).

Default: "16:9"

1:13:44:39:1616:99:2121:9

output_formatstring

Output image format.

Default: "jpeg"

jpegpng

Example Request Body

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

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 images. 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.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "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 Uni 1 edit (Luma AI generations API)."
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "paths": {
    "/api/v1/model/generateImage": {
      "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",
          "prompt"
        ],
        "properties": {
          "model": {
            "type": "string",
            "description": "Model name.",
            "default": "luma/uni-1/edit",
            "enum": [
              "luma/uni-1/edit"
            ]
          },
          "image": {
            "type": "string",
            "description": "Source image to edit: URL, Base64, or asset reference (asset://<ASSET_ID>)."
          },
          "prompt": {
            "type": "string",
            "default": "Make the lighting warm and cinematic, add soft morning haze.",
            "description": "Instruction describing the desired edit."
          },
          "aspect_ratio": {
            "type": "string",
            "default": "16:9",
            "enum": [
              "1:1",
              "3:4",
              "4:3",
              "9:16",
              "16:9",
              "9:21",
              "21:9"
            ],
            "description": "Frame aspect ratio (width:height).",
            "x-ui-component": "select"
          },
          "output_format": {
            "type": "string",
            "default": "jpeg",
            "enum": [
              "jpeg",
              "png"
            ],
            "description": "Output image format.",
            "x-ui-component": "select"
          }
        },
        "x-order-properties": [
          "model",
          "image",
          "prompt",
          "aspect_ratio",
          "output_format"
        ]
      },
      "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 images. 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/uni-1/edit

> 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.


## Overview

- **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)

Transform the image into a poster with a retro-tech aesthetic.

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

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.

Luma Uni 1 Edit API by LUMA

INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

Response

Input Schema

Example Request Body

Output Schema

Example Response

Atlas Cloud Skills

Supported Clients

Install

Setup API Key

Capabilities

MCP Server

Supported Clients

Install

Configuration

Available Tools

API Schema

LLM-Friendly Prompt Template

Luma Uni 1 Edit

Highlights

Parameters

How To Use

Notes

Explore Similar Models

GPT Image 2.5 Flare Edit

GPT Image 2.5 Sunburst Text-to-Image

GPT Image 2.5 Sunburst Edit

GPT Image 2.5 Flare Text-to-Image

Openai GPT Image 2 Text-to-Image

Openai GPT Image 2 Edit

GPT Image 2.5 Flare Developer Edit

GPT Image 2.5 Flare Developer Text-to-Image

GPT Image 2.5 Sunburst Developer Edit

GPT Image 2.5 Sunburst Developer Text-to-Image

GPT Image 2 Developer Edit

GPT Image 2 Developer Text-to-Image

Seedream v4.7 Edit Sequential

Seedream v4.7 Edit

Seedream v4.7 Sequential

Seedream v4.7 Text-to-Image

One API for All Media AI.

INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

Response

Input Schema

Example Request Body

Output Schema

Example Response