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Segment Anything Model Image to Image

fal-ai/imageutils/sam
SAM.
Inference
Commercial use
Schema
LLMs
PlaygroundAPI

About

Sam

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

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/imageutils/sam", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/model_tests/remove_background/elephant.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"
});

3. Queue#

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/imageutils/sam", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/model_tests/remove_background/elephant.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/imageutils/sam", {
  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/imageutils/sam", {
  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);

Read more about file handling in our file upload guide.

5. Schema#

Input#

image_url string* required

Url to input image

text_prompt string

The prompt to use when generating masks

size integer

Image size Default value: 1024

iou float

IOU threshold for filtering the annotations Default value: 0.9

retina boolean

Draw high-resolution segmentation masks Default value: true

confidence float

Object confidence threshold Default value: 0.4

box_prompt list<list<void>>

Coordinates for multiple boxes, e.g. [[x,y,w,h],[x2,y2,w2,h2]]

point_prompt list<list<void>>

Coordinates for multiple points [[x1,y1],[x2,y2]]

point_label list<integer>

Label for point, [1,0], 0 = background, 1 = foreground

with_contours boolean

Draw the edges of the masks

better_quality boolean

Attempt better quality output using morphologyEx

black_white boolean

Output black and white, multiple masks will be combined into one mask

invert boolean

Invert mask colors

{
  "image_url": "https://storage.googleapis.com/falserverless/model_tests/remove_background/elephant.jpg",
  "text_prompt": "a photo of elephant",
  "size": 1024,
  "iou": 0.9,
  "retina": true,
  "confidence": 0.4,
  "box_prompt": [
    [
      0,
      0,
      0,
      0
    ]
  ],
  "point_prompt": [
    [
      0,
      0
    ]
  ],
  "point_label": [
    0
  ]
}

Output#

image Image

Combined image of all detected masks

{}

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.

file_data string

File data

width integer

The width of the image in pixels.

height integer

The height of the image in pixels.