Bria Eraser removes what a mask covers and fills in the scene behind it, in one of two modes picked with `composite`. Context aware, the default, also removes the shadow or reflection the object casts outside the mask. Pixel perfect rebuilds only the masked area and keeps everything outside the mask as it was. Built on Bria's FIBO-Edit-1.5, trained on licensed data for commercial use.

bria/fibo-edit-1.5/eraser

Inference

Commercial use

Partner

Schema

LLMs

Playground API Examples

bria/fibo-edit-1.5/eraser

Schema

LLMs

bria/fibo-edit-1.5/eraser

Inference

Commercial use

Partner

Schema

LLMs

Playground API Examples

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Remove what the mask covers and continue the scene behind it, in one of two modes.

composite false (default), context aware: the object goes, and so does the shadow or reflection it casts outside the mask. composite true, pixel perfect: only the masked area is rebuilt, and everything outside the mask stays as it was.

Same request fields as fal-ai/bria/eraser, so an existing request works unchanged.

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("bria/fibo-edit-1.5/eraser", {
  input: {
    image_url: "https://labs-assets.bria.ai/api-examples/fibo-edit-1.5-eraser/coffee_cup_input.jpg",
    mask_url: "https://labs-assets.bria.ai/api-examples/fibo-edit-1.5-eraser/coffee_cup_mask.png"
  },
  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("bria/fibo-edit-1.5/eraser", {
  input: {
    image_url: "https://labs-assets.bria.ai/api-examples/fibo-edit-1.5-eraser/coffee_cup_input.jpg",
    mask_url: "https://labs-assets.bria.ai/api-examples/fibo-edit-1.5-eraser/coffee_cup_mask.png"
  },
  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("bria/fibo-edit-1.5/eraser", {
  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("bria/fibo-edit-1.5/eraser", {
  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#

image_url string* required

The image to erase from.

mask_url string* required

A binary mask the same size as the image. White marks what to remove, black what to keep. Send it lossless -- a JPEG mask's compression noise on nominally black pixels reads as more to erase.

mask_type MaskTypeEnum

How the mask was made. Accepted for compatibility with the SDXL eraser, where it selects how far the mask is grown; this model is trained on the exact silhouette and does not grow it. Default value: "manual"

Possible enum values: manual, automatic

composite boolean

Pick the erase mode. False (default), context aware: removes the object and also the shadow or reflection it casts outside the mask. True, pixel perfect: only the masked area is rebuilt, and everything outside the mask stays as it was, at full resolution.

sync_mode boolean

If true, returns the image directly in the response (increases latency).

preserve_alpha boolean

Keep the input's transparency. The model works in RGB, so the alpha channel is not regenerated -- the original's own is carried through onto the result.

seed integer

Reuse the seed from a previous response to recreate that result. Omit it for a random one, which is reported in the response either way.

{
  "image_url": "https://labs-assets.bria.ai/api-examples/fibo-edit-1.5-eraser/coffee_cup_input.jpg",
  "mask_url": "https://labs-assets.bria.ai/api-examples/fibo-edit-1.5-eraser/coffee_cup_mask.png",
  "mask_type": "manual"
}

Output#

image Image* required

The image with the masked region erased.

seed integer* required

The seed used, so the result can be reproduced.

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  }
}

Other types#

Image#

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.

width integer

The width of the image in pixels.

height integer

The height of the image in pixels.

bria/fibo-edit-1.5/eraser

Table of contents

Table of contents

bria/fibo-edit-1.5/eraser

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

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#

Image#

Related Models

Table of contents

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

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#

Image#

Related Models