OpenAI DALL·E AsyncImage SwiftUI

SwiftUI view that asynchronously loads and displays an OpenAI image from open API

You just type in any 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"

Features

• ☑ Multiplatform iOS, macOS, watchOS and tvOS
• ☑ Customizable in term of SwiftUI Image specs [renderingMode, resizable, antialiased...]
• ☑ Customizable in term of the transport layer [Loader]
• ☑ Based on interfaces not implementations

How to use

1. Get your API key from OpenAI

Where do I find my Secret API Key?

2. Override the default loader at Environment with you apiKey

    let apiKey = "your API KEY"
    let endpoint = OpenAIImageEndpoint.get(with: apiKey)
    let loader = OpenAIDefaultLoader(endpoint: endpoint)
    OpenAIDefaultLoaderKey.defaultValue = loader

3. Add OpenAIAsyncImage to your code

    OpenAIAsyncImage(prompt: .constant("sun"))

or with custom ViewBuilder

    OpenAIAsyncImage(prompt: $imageText, size: .dpi1024){ state in
        switch state{
            case .loaded(let image) :
            image
                .resizable()
                .scaledToFill()
            case .loadError(let error) : Text(error.localizedDescription)
            case .loading : ProgressView()
        }
    }

Documentation(API)

• You need to have Xcode 13 installed in order to have access to Documentation Compiler (DocC)
• Go to Product > Build Documentation or ⌃⇧⌘ D

SwiftUI example for the package

OpenAI AsyncImage SwiftUI example

Replicate toolkit for swift. Set of diffusion models

Announced in 2022, 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. Diffusion models can be applied to a variety of tasks, including image denoising, inpainting, super-resolution, and image generation. For example, an image generation model would start with a random noise image and then, after having been trained reversing the diffusion process on natural images, the model would be able to generate new natural images. Replicate kit