swiftuiux/openai-async-image-swiftui

Features

• [x] Supports multiple platforms: iOS, macOS, watchOS, and tvOS
• [x] Customizable with SwiftUI Image properties (e.g., renderingMode, resizable, antialiased)
• [x] Configurable transport layer via custom Loader
• [x] Designed with interfaces, not implementations
• [x] Fully leverages Swift's new concurrency model

[OpenAI AsyncImage SwiftUI]

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()
        }
    }

| Param | Description | | --- | --- | | prompt | A text description of the desired image(s). The maximum length is 1000 characters | | size | The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024 | | tpl | Custom view builder tpl | | loader | Custom loader if you need something specific|

[OpenAI AsyncImage SwiftUI]

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

[OpenAI AsyncImage SwiftUI]

More Stable Diffusion examples

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

[The concept]

CoreML Stable Diffusion

The example app for running text-to-image or image-to-image models to generate images using Apple's Core ML Stable Diffusion implementation

[The concept]

Package Metadata

Repository: swiftuiux/openai-async-image-swiftui

Default branch: main

README: README.md