flutter_litert #

A Flutter plugin for on-device ML inference using LiteRT (formerly TensorFlow Lite), with native libraries bundled automatically on every platform.

Background #

This project started as a fork of tflite_flutter, the official TensorFlow Lite plugin for Flutter. TensorFlow Lite has since been discontinued by Google and rebranded as LiteRT.

flutter_litert picks up where tflite_flutter left off. It maintains the same Dart API while migrating the underlying native libraries from TensorFlow Lite to LiteRT. Android has already moved to LiteRT. Other platforms are in the process of being migrated.

Why this package? #

The biggest pain point with tflite_flutter was native library setup. You had to manually build .so, .dll, or .dylib files and place them in the right directories for each platform. This was tedious, error-prone, and easy to get wrong.

flutter_litert bundles all native libraries automatically. Add the dependency, and it works. No manual downloads, no copying files around, no platform-specific setup steps.

Other improvements over tflite_flutter:

• Native libraries are kept up to date across all platforms
• Custom ops are built and bundled automatically (e.g. MediaPipe model support)
• GPU delegate libraries are included where available

Installation #

dependencies:
  flutter_litert: ^0.1.7

That's it. No additional setup required on any platform.

Usage #

import 'package:flutter_litert/flutter_litert.dart';

final interpreter = await Interpreter.fromAsset('model.tflite');

// Prepare input and output buffers
var input = [/* your input data */];
var output = List.filled(outputSize, 0.0).reshape([1, outputSize]);

interpreter.run(input, output);

For inference off the main thread:

final interpreter = await Interpreter.fromAsset('model.tflite');
final isolateInterpreter = await IsolateInterpreter.create(address: interpreter.address);

await isolateInterpreter.run(input, output);

Platform support #

iOS and macOS will be migrated to LiteRT as official CocoaPods artifacts become available.

Features #

• Same API as tflite_flutter. Drop-in replacement with no code changes needed.
• Auto-bundled native libraries. Works out of the box on Android, iOS, macOS, Windows, and Linux.
• GPU acceleration. Metal delegate on iOS, GPU delegate on Android, XNNPACK on all platforms.
• CoreML delegate. Available on iOS and macOS for Neural Engine acceleration.
• Custom ops. MediaPipe's Convolution2DTransposeBias op is built and included on all platforms.
• Isolate support. Run inference on a background thread with IsolateInterpreter.

Credits #

Based on tflite_flutter by the TensorFlow team and contributors.