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"

• 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

Where do I find my Secret API Key?

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

    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()
        }
    }

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

OpenAI AsyncImage SwiftUI example

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