Luma Uni-1 turns a text prompt into a single high-fidelity image, with control over aspect ratio and visual style, plus optional web-sourced and reference-image guidance for sharper grounding.

luma/agent/uni-1/v1/text-to-image

Luma Uni-1 turns a text prompt into a single high-fidelity image, with control over aspect ratio and visual style, plus optional web-sourced and reference-image guidance for sharper grounding.

Inference

Commercial use

Partner

Schema

Playground API Examples

SandboxOpen in Sandbox

luma/agent/uni-1/v1/text-to-image

Schema

SandboxOpen in Sandbox

Luma Uni-1 turns a text prompt into a single high-fidelity image, with control over aspect ratio and visual style, plus optional web-sourced and reference-image guidance for sharper grounding.

luma/agent/uni-1/v1/text-to-image

Luma Uni-1 turns a text prompt into a single high-fidelity image, with control over aspect ratio and visual style, plus optional web-sourced and reference-image guidance for sharper grounding.

Inference

Commercial use

Partner

Schema

Playground API Examples

SandboxOpen in Sandbox

About

Text To Image

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("luma/agent/uni-1/v1/text-to-image", {
  input: {
    prompt: "A manga panel of a stoic samurai overlooking a neon cyberpunk city at dusk."
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In an environment where you cannot set environment variables, you can configure the API key manually on the client.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("luma/agent/uni-1/v1/text-to-image", {
  input: {
    prompt: "A manga panel of a stoic samurai overlooking a neon cyberpunk city at dusk."
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("luma/agent/uni-1/v1/text-to-image", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("luma/agent/uni-1/v1/text-to-image", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

prompt string* required

Text prompt describing the image to generate.

aspect_ratio Enum

Aspect ratio of the generated image.

Possible enum values: 3:1, 2:1, 16:9, 3:2, 1:1, 2:3, 9:16, 1:2, 1:3

style StyleEnum

Visual style of the generated image. Default value: "auto"

Possible enum values: auto, manga

output_format Enum

Encoding format of the generated image.

Possible enum values: png, jpeg

enable_web_search boolean

When true, the model may consult the web for references.

reference_image_urls list<string>

Optional list of reference image URLs used to guide the generation.

enable_safety_checker boolean

Authorized callers may set false to request relaxed screening. Default value: true

{
  "prompt": "A manga panel of a stoic samurai overlooking a neon cyberpunk city at dusk.",
  "aspect_ratio": "16:9",
  "style": "auto",
  "enable_safety_checker": true
}

Output#

images list<File>* required

The generated image(s).

{
  "images": [
    {
      "url": "",
      "content_type": "image/png",
      "file_name": "z9RV14K95DvU.png",
      "file_size": 4404019
    }
  ]
}

Other types#

File#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

Related Models

About

Text To Image

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("luma/agent/uni-1/v1/text-to-image", {
  input: {
    prompt: "A manga panel of a stoic samurai overlooking a neon cyberpunk city at dusk."
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In an environment where you cannot set environment variables, you can configure the API key manually on the client.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("luma/agent/uni-1/v1/text-to-image", {
  input: {
    prompt: "A manga panel of a stoic samurai overlooking a neon cyberpunk city at dusk."
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("luma/agent/uni-1/v1/text-to-image", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("luma/agent/uni-1/v1/text-to-image", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

prompt string* required

Text prompt describing the image to generate.

aspect_ratio Enum

Aspect ratio of the generated image.

Possible enum values: 3:1, 2:1, 16:9, 3:2, 1:1, 2:3, 9:16, 1:2, 1:3

style StyleEnum

Visual style of the generated image. Default value: "auto"

Possible enum values: auto, manga

output_format Enum

Encoding format of the generated image.

Possible enum values: png, jpeg

enable_web_search boolean

When true, the model may consult the web for references.

reference_image_urls list<string>

Optional list of reference image URLs used to guide the generation.

enable_safety_checker boolean

Authorized callers may set false to request relaxed screening. Default value: true

{
  "prompt": "A manga panel of a stoic samurai overlooking a neon cyberpunk city at dusk.",
  "aspect_ratio": "16:9",
  "style": "auto",
  "enable_safety_checker": true
}

Output#

images list<File>* required

The generated image(s).

{
  "images": [
    {
      "url": "",
      "content_type": "image/png",
      "file_name": "z9RV14K95DvU.png",
      "file_size": 4404019
    }
  ]
}

Other types#

File#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

Related Models

Luma Uni-1 Text to Image API Docs | fal

luma/agent/uni-1/v1/text-to-image

Table of contents

Table of contents

luma/agent/uni-1/v1/text-to-image

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

File#

Related Models

Table of contents

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

File#

Related Models