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The FalClient is a robust and user-friendly Swift package designed for seamless integration of fal serverless functions into Swift projects. This library, developed in pure Swift, provides developers with simple APIs to interact with AI models, suitable for both iOS and macOS platforms.

The FalClient library serves as a client for fal serverless Python functions. Before using this library, ensure you've set up your serverless functions as per the quickstart guide.

This Swift client library is crafted as a lightweight layer atop Swift's networking standards like URLSession. It ensures hassle-free integration into your existing Swift codebase. The library is designed to address the nuances of Swift and Apple's ecosystem, ensuring smooth operation across different Apple platforms.

Note: Make sure to review the fal-serverless getting started guide to acquire your credentials and register your functions.

1. Add FalClient as a dependency in your Swift Package Manager.

2. Set up the client instance:

   import FalClient
   let fal = FalClient.withCredentials(.keyPair("FAL_KEY_ID:FAL_KEY_SECRET"))
   
   // You can also use a proxy to protect your credentials
   // let fal = FalClient.withProxy("http://localhost:3333/api/fal/proxy")

3. Use fal.subscribe to dispatch requests to the model API:

   let result = try await fal.subscribe(to: "text-to-image",
       input: [
           "prompt": "a cute shih-tzu puppy",
           "model_name": "stabilityai/stable-diffusion-xl-base-1.0",
           "image_size": "square_hd"
       ]) { update in
           print(update)
       }

Notes:

• Replace text-to-image with a valid model id. Check fal.ai/models for all available models.
• It fully relies on async/await for asynchronous programming.
• The result type in Swift will be a [String: Any] and the entries depend on the API output schema.
• The Swift client also supports typed inputs and outputs through Codable.

The client supports real-time model APIs. Checkout the FalRealtimeSampleApp for more details.

let connection = try fal.realtime.connect(
    to: OptimizedLatentConsistency,
    connectionKey: "PencilKitDemo",
    throttleInterval: .milliseconds(128)
) { (result: Result<LcmResponse, Error>)  in
    if case let .success(data) = result,
        let image = data.images.first {
        let data = try? Data(contentsOf: URL(string: image.url)!)
        DispatchQueue.main.async {
            self.currentImage = data
        }
    }
}

try connection.send(LcmInput(
    prompt: prompt,
    imageUrl: "data:image/jpeg;base64,\(drawing.base64EncodedString())",
    seed: 6_252_023,
    syncMode: true
))

Check the Sources/Samples folder for a handful of sample applications using the FalClient.

Open them with xed to quickly start playing with

xed Sources/Samples/FalSampleApp

See the open feature requests for a list of proposed features and join the discussion.

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make to the Swift version of the client are greatly appreciated.

Distributed under the MIT License. See LICENSE for more information.