Client Library for Swift (iOS)

Introduction

The client for Swift (iOS) provides a seamless interface to interact with fal.

Installation

First, add the client as a dependency in your project:

.package(url: "https://github.com/fal-ai/fal-swift.git", from: "0.5.6")

Features

1. Call an endpoint

Endpoints requests are managed by a queue system. This allows fal to provide a reliable and scalable service.

The subscribe method allows you to submit a request to the queue and wait for the result.

import FalClient

let result = try await fal.subscribe(
    to: "fal-ai/flux/dev",
    input: [
        "prompt": "a cat",
        "seed": 6252023,
        "image_size": "landscape_4_3",
        "num_images": 4
    ],
    includeLogs: true
) { update in
    if case let .inProgress(logs) = update {
        print(logs)
    }
}

2. Queue Management

You can manage the queue using the following methods:

Submit a Request

Submit a request to the queue using the queue.submit method.

import FalClient

let job = try await fal.queue.submit(
    "fal-ai/flux/dev",
    input: [
        "prompt": "a cat",
        "seed": 6252023,
        "image_size": "landscape_4_3",
        "num_images": 4
    ],
    webhookUrl: "https://optional.webhook.url/for/results"
)

This is useful when you want to submit a request to the queue and retrieve the result later. You can save the request_id and use it to retrieve the result later.

Webhooks

For long-running requests, such as training jobs, you can use webhooks to receive the result asynchronously. You can specify the webhook URL when submitting a request.

Check Request Status

Retrieve the status of a specific request in the queue:

import FalClient

let status = try await fal.queue.status(
    "fal-ai/flux/dev",
    of: "764cabcf-b745-4b3e-ae38-1200304cf45b",
    includeLogs: true
)

Retrieve Request Result

Get the result of a specific request from the queue:

import FalClient

let result = try await fal.queue.response(
    "fal-ai/flux/dev",
    of: "764cabcf-b745-4b3e-ae38-1200304cf45b"
)

3. File Uploads

Some endpoints require files as input. However, since the endpoints run asynchronously, processed by the queue, you will need to provide URLs to the files instead of the actual file content.

Luckily, the client library provides a way to upload files to the server and get a URL to use in the request.

import FalClient

let data = try await Data(contentsOf: URL(fileURLWithPath: "/path/to/file"))
let url = try await fal.storage.upload(data)

4. Streaming

Some endpoints support streaming:

Not implemented yet

This functionality is not available on this client yet.

5. Realtime Communication

For the endpoints that support real-time inference via WebSockets, you can use the realtime client that abstracts the WebSocket connection, re-connection, serialization, and provides a simple interface to interact with the endpoint:

import FalClient

let connection = try fal.realtime.connect(to: "fal-ai/flux/dev") { result in
    switch result {
    case let .success(data):
        print(data)
    case let .failure(error):
        print(error)
    }
}

connection.send([
    "prompt": "a cat",
    "seed": 6252023,
    "image_size": "landscape_4_3",
    "num_images": 4
])

6. Run

The endpoints can also be called directly instead of using the queue system.

Prefer the queue

We do not recommend this use most use cases as it will block the client until the response is received. Moreover, if the connection is closed before the response is received, the request will be lost.

import FalClient

let result = try await fal.run(
    "fal-ai/flux/dev",
    input: [
        "prompt": "a cat",
        "seed": 6252023,
        "image_size": "landscape_4_3",
        "num_images": 4
    ])

API Reference

For a complete list of available methods and their parameters, please refer to Swift (iOS) API Reference documentation.

Support

If you encounter any issues or have questions, please visit the GitHub repository or join our Discord Community.