Open Source Swift Artificial Intelligence Software
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Swift Artificial Intelligence Software
View 10466 business solutionsBrowse free open source Swift Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source Swift Artificial Intelligence Software by OS, license, language, programming language, and project status.
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1
WhisperKit
On-device Speech Recognition for Apple Silicon
WhisperKit is a Swift package that integrates OpenAI's popular Whisper speech recognition model with Apple's CoreML framework for efficient, local inference on Apple devices. Whisper has pulled the future forward when fast, free and virtually error-free translation and transcription will be ubiquitous. It inspired numerous developers to improve and deploy it with minimal friction and maximum performance. We founded Argmax in November 2023 to empower developers and enterprises everywhere to deploy commercial-scale inference workloads on user devices. The fast-growing need for Whisper inference in production convinced us to take it on as our first project. -
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Mochi Diffusion
Run Stable Diffusion on Mac natively
Run Stable Diffusion on Mac natively. This app uses Apple's Core ML Stable Diffusion implementation to achieve maximum performance and speed on Apple Silicon based Macs while reducing memory requirements. Extremely fast and memory efficient (~150MB with Neural Engine) Runs well on all Apple Silicon Macs by fully utilizing Neural Engine. Generate images locally and completely offline. Generate images based on an existing image (commonly known as Image2Image) Generated images are saved with prompt info inside EXIF metadata (view in Finder's Get Info window) Convert generated images to high resolution (using RealESRGAN) Autosave & restore images. Use custom Stable Diffusion Core ML models. No worries about pickled models. macOS native app using SwiftUI. -
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ImagePicker
Reinventing the way ImagePicker works
ImagePicker is an all-in-one camera solution for your iOS app. It lets your users select images from the library and take pictures at the same time. As a developer you get notified of all the user interactions and get the beautiful UI for free, out of the box, it's just that simple. ImagePicker has been optimized to give a great user experience, it passes around referenced images instead of the image itself which makes it less memory-consuming. This is what makes it smooth as butter. ImagePicker works with referenced images, that is really powerful because it lets you download the asset and choose the size you want. If you want to change the default implementation, just add a variable in your controller. -
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ARKit + CoreLocation
Combines the high accuracy of AR with the scale of GPS data
ARKit uses camera and motion data to map out the local world as you move around. CoreLocation uses wifi and GPS data to determine your global location, with a low degree of accuracy. ARKit + CoreLocation combines the high accuracy of AR with the scale of GPS data. The potential for combining these technologies is huge, with so many potential applications across many different areas. Allow items to be placed within the AR world using real-world coordinates. Dramatically improved location accuracy, using recent location data points combined with knowledge about movement through the AR world. The improved location accuracy is currently in an “experimental” phase, but could be the most important component. The library and demo come with a bunch of additional features for configuration. It’s all fully documented to be sure to have a look around. -
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5
Bender
Easily craft fast Neural Networks on iOS
Bender allows you to easily define and run neural networks on your iOS apps, it uses Apple’s MetalPerformanceShaders under the hood. Bender provides the ease of use of CoreML with the flexibility of a modern ML framework. Bender allows you to run trained models, you can use Tensorflow, Keras, Caffe, the choice is yours. Either freeze the graph or export the weights to files. You can import a frozen graph directly from supported platforms or re-define the network structure and load the weights. Either way, it just takes a few minutes. Bender suports the most common ML nodes and layers but it is also extensible so you can write your own custom functions. With Core ML, you can integrate trained machine learning models into your app, it supports Caffe and Keras 1.2.2+ at the moment. Apple released conversion tools to create CoreML models which then can be run easily. Finally, there is no easy way to add additional pre or post-processing layers to run on the GPU. -
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NSFWDetector
A NSFW detector with CoreML
NSFWDetector is a small (17 kB) CoreML Model to scan images for nudity. It was trained using CreateML to distinguish between porn/nudity and appropriate pictures. With the main focus on distinguishing between Instagram model-like pictures and porn. -
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Yodd's AI Chat
This app uses the OpenAISwift library, ChatGPTSwift library and OpenAI
Welcome to Yodd AI Chat, your new AI-powered chatbot companion! Yodd AI Chat uses the latest OpenAI API to provide you with an unparalleled chat experience. Whether you're looking for a friendly conversation or need some help with a problem, Yodd AI Chat is here to help. Free chatbot app for OpenAI's ChatGPT. Yodd's ChatGPT is a free and open-source implementation of the OpenAI API in Swift for iOS. It uses the OpenAISwift framework, ChatGPTSwift framework and OpenAI framework. Apparenlty you need to have some credits on your OpenAI account, if you don't have them is looks that adding a payment method to your account is enough. If the testflight link is down you can download the source code and build it yourself, or you can install the IPA. With Yodd AI Chat, you can also generate images to accompany your messages, adding a new level of creativity and personalization to your conversations. Plus, you can save, listen to, and delete messages. -
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Elucidate
Straightforward OCR for Mac to create searchable PDFs.
Elucidate is now available on the Mac App Store: https://itunes.apple.com/us/app/elucidate/id1066088407?ls=1&mt=12 -
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LLM.swift
LLM.swift is a simple and readable library
LLM.swift is a Swift package that enables developers to run Large Language Models (LLMs) directly on Apple devices, including iOS, macOS, and watchOS. By leveraging Apple's hardware and software optimizations, LLM.swift facilitates on-device natural language processing tasks, ensuring user privacy and reducing latency associated with cloud-based solutions. -
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10
LlamaChat
Chat with your favourite LLaMA models in a native macOS app
Chat with your favourite LLaMA models, right on your Mac. LlamaChat is a macOS app that allows you to chat with LLaMA, Alpaca, and GPT4All models all running locally on your Mac. -
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OpenAI DALL·E AsyncImage SwiftUI
OpenAI swift async text to image for SwiftUI app using OpenAI
SwiftUI views that asynchronously loads and displays an OpenAI image from open API. You just type in your idea and AI will give you an art solution. DALL-E and DALL-E 2 are deep learning models developed by OpenAI to generate digital images from natural language descriptions, called "prompts". You need to have Xcode 13 installed in order to have access to Documentation Compiler (DocC) OpenAI's text-to-image model DALL-E 2 is a recent example of diffusion models. It uses diffusion models for both the model's prior (which produces an image embedding given a text caption) and the decoder that generates the final image. In machine learning, diffusion models, also known as diffusion probabilistic models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space. -
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Swift AI
The Swift machine learning library
Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. We've created some example projects to demonstrate the usage of Swift AI. Each resides in their own repository and can be built with little or no configuration. Each module now contains its own documentation. We recommend that you read the docs carefully for detailed instructions on using the various components of Swift AI. The example projects are another great resource for seeing real-world usage of these tools. Swift AI currently depends on Apple's Accelerate framework for vector/matrix calculations and digital signal processing. -
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fullmoon
Chat with private and local large language models
Fullmoon is a free, open-source application that enables users to interact with large language models directly on their devices, ensuring privacy and offline accessibility. Optimized for Apple silicon, it operates seamlessly across iOS, iPadOS, macOS, and visionOS platforms. Users can personalize the app by adjusting themes, fonts, and system prompts, and it integrates with Apple's Shortcuts for enhanced functionality. Fullmoon supports models like Llama-3.2-1B-Instruct-4bit and Llama-3.2-3B-Instruct-4bit, facilitating efficient on-device AI interactions without the need for an internet connection.
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