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Segment Video Rle
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @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.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"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/sam-3-1/video-rle", {
input: {
video_url: "https://v3b.fal.media/files/b/elephant/NQdDxB0Ddfo82SPLbhYDp_bedroom.mp4"
},
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);This model has a real-time mode via WebSockets, supported by fal.realtime.connect.
import { fal } from "@fal-ai/client";
const connection = fal.realtime.connect("fal-ai/sam-3-1/video-rle", {
onResult: (result) => {
console.log(result);
},
onError: (error) => {
console.error(error);
},
// Fetch short-lived JWT token from your backend
tokenProvider: async (app) => {
const response = await fetch("/api/fal/realtime-token", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ app }),
});
return response.text();
},
tokenExpirationSeconds: 10,
});
connection.send({
video_url: "https://v3b.fal.media/files/b/elephant/NQdDxB0Ddfo82SPLbhYDp_bedroom.mp4"
});The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_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.
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/sam-3-1/video-rle", {
input: {
video_url: "https://v3b.fal.media/files/b/elephant/NQdDxB0Ddfo82SPLbhYDp_bedroom.mp4"
},
webhookUrl: "https://optional.webhook.url/for/results",
});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/sam-3-1/video-rle", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
logs: true,
});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/sam-3-1/video-rle", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);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.
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.
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.
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);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.
video_url string* requiredThe URL of the video to be segmented.
mask_url stringThe URL of the mask to be applied initially.
prompt stringText prompt for segmentation. Use commas to track multiple objects (e.g., 'person, cloth'). Default value: ""
List of point prompts with frame indices.
List of box prompts with optional frame_index.
apply_mask booleanApply the mask on the video.
boundingbox_zip booleanReturn per-frame bounding box overlays as a zip archive.
detection_threshold floatDetection confidence threshold (0.0-1.0). Lower = more detections but less precise. Defaults: 0.5 for existing, 0.7 for new objects. Try 0.2-0.3 if text prompts fail. Default value: 0.5
frame_index integerFrame index used for initial interaction when mask_url is provided.
max_num_objects integerMaximum number of objects to track in the video. Default value: 16
return_object_rles booleanAlso return every tracked object's own mask in each frame, with an id the object keeps for the whole video. The merged rle is returned as before.
{
"video_url": "https://v3b.fal.media/files/b/elephant/NQdDxB0Ddfo82SPLbhYDp_bedroom.mp4",
"prompt": "person",
"point_prompts": [],
"box_prompts": [],
"detection_threshold": 0.5,
"max_num_objects": 16
}The segmented video.
Zip file containing per-frame bounding box overlays.
{
"video": "https://fal.media/files/lion/gr-RSvy2e7_AWbjxAq4mJ_output.mp4"
}Run Length Encoding of the mask.
Zip file containing per-frame bounding box overlays.
Per-mask metadata when multiple RLEs are returned.
Per-mask confidence scores when requested.
Per-mask normalized bounding boxes [cx, cy, w, h] when requested.
With return_object_rles, each frame's tracked objects, each with its own mask and id.
Run Length Encoding of the mask.
Zip file containing per-frame bounding box overlays.
Per-mask metadata when multiple RLEs are returned.
Per-mask confidence scores when requested.
Per-mask normalized bounding boxes [cx, cy, w, h] when requested.
With return_object_rles, a JSON file of each frame's tracked objects, each with its own mask and id.
x integerX Coordinate of the prompt
y integerY Coordinate of the prompt
label Enum1 for foreground, 0 for background
Possible enum values: 0, 1
object_id integerOptional object identifier. Prompts sharing an object id refine the same object. When a text prompt is also given, the id selects which detected object the points refine.
frame_index integerThe frame index to interact with.
x_min integerX Min Coordinate of the box
y_min integerY Min Coordinate of the box
x_max integerX Max Coordinate of the box
y_max integerY Max Coordinate of the box
object_id integerOptional object identifier. Boxes sharing an object id refine the same object.
frame_index integerThe frame index to interact with.
url string* requiredThe URL where the file can be downloaded from.
content_type stringThe mime type of the file.
file_name stringThe name of the file. It will be auto-generated if not provided.
file_size integerThe size of the file in bytes.
index integer* requiredIndex of the mask inside the model output.
score floatScore for this mask.
Bounding box for the mask in normalized cxcywh coordinates.
object_id integer* requiredThe object's id, the same in every frame the object appears in.
rle string* requiredRun Length Encoding of this object's mask.
Bounding box of this object's mask in normalized cxcywh coordinates.