I've been trying to get the bash/script version of DeepFaceLab to work with Apple Silicon Macs, but this was original a Windows project that even now has non-existent support for MacOs/Apple Silicon. I am thinking of converting everything into a native macOS app using Swift, specifically optimized for Apple Silicon GPUs.
Here's what I got from ChatGPT. Any help/advice on how to do this would be greatly appreciated. I don't have any Swift programming experience, but I have experience with some coding and can generally figure things out. I know that this is probably not feasible for a single individual with little programming experience, but I wanted to throw this out there to see what others think. Thank you
Here's a high-level overview of the steps involved in porting DeepFaceLab to Swift with a graphical UI:
Understand DeepFaceLab: Thoroughly study the DeepFaceLab project, its Python scripts, and the overall architecture to grasp its functionalities and dependencies.
Choose a Swift Framework: Decide on the UI framework you want to use for the macOS app. SwiftUI is Apple's latest UI framework that works across all Apple platforms, including macOS. Alternatively, you can use AppKit for a more traditional approach.
Rewrite Python to Swift: Convert the Python code from DeepFaceLab into Swift. You'll need to rewrite all the image processing, deep learning, and video manipulation code in Swift, potentially using third-party Swift libraries or native macOS frameworks.
Deep Learning Integration: Replace the Python-based deep learning library used in DeepFaceLab with an appropriate Swift-compatible deep learning framework. TensorFlow and PyTorch both offer Swift APIs, but you may need to adapt the specific model implementation to Swift.
Image Processing: Find equivalent Swift libraries or frameworks for image processing tasks used in DeepFaceLab.
UI Development: Design and implement the graphical user interface using SwiftUI or AppKit. You'll need to create views, controls, and navigation elements to interact with the underlying Swift code.
Integration: Connect the Swift code with the UI components, ensuring that actions in the GUI trigger the appropriate Swift functions and display results back to the user.
Testing and Debugging: Rigorously test the Swift application and debug any issues that arise during the porting process.
Optimization: Ensure that the Swift app performs efficiently and effectively on macOS devices.