Florence-2 Large | Image to Image | API Documentation

fal-ai/florence-2-large/region-proposal

Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks

Inference

Commercial use

About

Region Proposal

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. Please check the migration guide for more information.

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("fal-ai/florence-2-large/region-proposal", {
  input: {
    image_url: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
  },
  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 case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.

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("fal-ai/florence-2-large/region-proposal", {
  input: {
    image_url: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
  },
  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("fal-ai/florence-2-large/region-proposal", {
  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("fal-ai/florence-2-large/region-proposal", {
  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 URL of the image to be processed.

{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
}

Output#

results BoundingBoxes* required

Results from the model

image Image

Processed image

{
  "results": {
    "bboxes": [
      {
        "label": ""
      }
    ]
  }
}

Other types#

PolygonOutputWithLabels#

results PolygonOutput* required

Results from the model

image Image

Processed image

OCRBoundingBoxSingle#

x float* required

X-coordinate of the top-left corner

y float* required

Y-coordinate of the top-left corner

w float* required

Width of the bounding box

h float* required

Height of the bounding box

label string* required

Label of the bounding box

BoundingBox#

x float* required

X-coordinate of the top-left corner

y float* required

Y-coordinate of the top-left corner

w float* required

Width of the bounding box

h float* required

Height of the bounding box

label string* required

Label of the bounding box

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.

file_data string

File data

width integer

The width of the image in pixels.

height integer

The height of the image in pixels.

OCRBoundingBoxOutputWithLabels#

results OCRBoundingBox* required

Results from the model

image Image

Processed image

BoundingBoxes#

bboxes list<BoundingBox>* required

List of bounding boxes

OCRBoundingBox#

quad_boxes list<OCRBoundingBoxSingle>* required

List of quadrilateral boxes

Polygon#

points list<object>* required

List of points

label string* required

Label of the polygon

Region#

x1 integer* required

X-coordinate of the top-left corner

y1 integer* required

Y-coordinate of the top-left corner

x2 integer* required

X-coordinate of the bottom-right corner

y2 integer* required

Y-coordinate of the bottom-right corner

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Florence-2 Large Image to Image

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#

PolygonOutputWithLabels#

OCRBoundingBoxSingle#

BoundingBox#

Image#

OCRBoundingBoxOutputWithLabels#

BoundingBoxes#

OCRBoundingBox#

Polygon#

Region#

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