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https://github.com/swiftuiux/openai-async-image-swiftui
OpenAI swift async text to image for SwiftUI app using open ai api Just type in a text description, diffusion model, swift image chat gpt generator chat gpt async image openai client swift asyncimage from openai url ios macos
https://github.com/swiftuiux/openai-async-image-swiftui
asyncimage
Last synced: 4 days ago
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OpenAI swift async text to image for SwiftUI app using open ai api Just type in a text description, diffusion model, swift image chat gpt generator chat gpt async image openai client swift asyncimage from openai url ios macos
- Host: GitHub
- URL: https://github.com/swiftuiux/openai-async-image-swiftui
- Owner: swiftuiux
- License: mit
- Created: 2023-02-28T12:20:58.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-12T16:24:58.000Z (25 days ago)
- Last Synced: 2024-12-26T14:03:47.740Z (11 days ago)
- Topics: asyncimage
- Language: Swift
- Homepage:
- Size: 8.74 MB
- Stars: 56
- Watchers: 6
- Forks: 12
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# OpenAI DALL·E AsyncImage SwiftUI
SwiftUI view that asynchronously loads and displays an OpenAI image from open API
### Please star the repository if you believe continuing the development of this package is worthwhile. This will help me understand which package deserves more effort.
[](https://swiftpackageindex.com/swiftuiux/openai-async-image-swiftui)
## [Example for the package](https://github.com/swiftuiux/openai-async-image-swiftui-example)
## [Documentation(API)](https://swiftpackageindex.com/swiftuiux/openai-async-image-swiftui/main/documentation/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
## How to use
### 1. Get your API key from OpenAI
[Where do I find my Secret API Key?](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key)
### 2. Override the default loader at Environment with you apiKey
```swift
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
```swift
OpenAIAsyncImage(prompt: .constant("sun"))
```
or with custom **ViewBuilder**
```swift
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|
## 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**
## 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](https://github.com/swiftuiux/replicate-kit-swift)
### CoreML Stable Diffusion
[The example app](https://github.com/swiftuiux/coreml-stable-diffusion-swift-example) for running text-to-image or image-to-image models to generate images using Apple's Core ML Stable Diffusion implementation
