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EVF-SAM2 Segmentation Image to Image

fal-ai/evf-sam
EVF-SAM2 combines natural language understanding with advanced segmentation capabilities, allowing you to precisely mask image regions using intuitive positive and negative text prompts.
Inference
Commercial use
Schema
LLMs
PlaygroundAPI

About

Process 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

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/evf-sam", {
  input: {
    prompt: "Cat in the middle of the image",
    image_url: "https://storage.googleapis.com/falserverless/web-examples/evf-sam2/evfsam2-cat.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 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/evf-sam", {
  input: {
    prompt: "Cat in the middle of the image",
    image_url: "https://storage.googleapis.com/falserverless/web-examples/evf-sam2/evfsam2-cat.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("fal-ai/evf-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/evf-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#

prompt string* required

The prompt to generate segmentation from.

negative_prompt string

Areas to exclude from segmentation (will be subtracted from prompt results)

semantic_type boolean

Enable semantic level segmentation for body parts, background or multi objects

image_url string* required

URL of the input image

mask_only boolean

Output only the binary mask instead of masked image Default value: true

use_grounding_dino boolean

Use GroundingDINO instead of SAM for segmentation

revert_mask boolean

Invert the mask (background becomes foreground and vice versa)

blur_mask integer

Apply Gaussian blur to the mask. Value determines kernel size (must be odd number)

expand_mask integer

Expand/dilate the mask by specified pixels

fill_holes boolean

Fill holes in the mask using morphological operations

{
  "prompt": "Cat in the middle of the image",
  "image_url": "https://storage.googleapis.com/falserverless/web-examples/evf-sam2/evfsam2-cat.png",
  "mask_only": true
}

Output#

image File* required

The segmented output image

{
  "image": {
    "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.

file_data string

File data