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🎯 Why RustScale?

Most desktop upscalers are built on outdated technology—heavy Electron frameworks consuming hundreds of megabytes of RAM, clunky CLI wrappers, and rigid model support. RustScale breaks this mold:

Legacy Apps	RustScale
🐌 ~400MB RAM (Electron)	⚡ ~30MB RAM (Tauri v2)
🔧 CLI wrapper processes	🧠 In-process AI runtime
📦 Proprietary formats	🌐 Industry-standard ONNX
🎲 Manual configuration	🤖 Auto-detects hardware
🔲 Visible grid artifacts	✨ Seamless tiling

✨ Key Features

🎨 Universal Model Support Drag and drop any ONNX super-resolution model—no compilation needed. ⚙️ Intelligent Hardware Optimization Automatically detects and optimizes for GPU, NPU, or CPU. 📦 Batch Processing Queue multiple images or entire folders with real-time progress.

🔍 Real-Time Preview Compare before/after with pan, zoom, and slider controls. 👤 Face Recovery Optional facial enhancement using CodeFormer integration. 🎯 Zero Configuration Smart defaults that just work, with advanced controls when needed.

🤖 Built with AI Assistance

This project was built using AI coding agents: Google Antigravity, Gemini 3, and Claude Sonnet 4.5.

I don't have extensive coding experience, but I had a clear vision for an upscaler that respects both user hardware and modern AI capabilities. Agentic AI coding tools helped me turn that vision into reality—an app I genuinely want to use.

If you have reservations about AI-assisted development, that's completely valid, and you're free not to use this app. I believe in transparency about the tools used in creation. What matters most is whether the end result serves users well.

I'm always open to feedback, suggestions, and contributions that can make RustScale even better. 💡

🏗️ How It Works

RustScale uses a "Native Integration Architecture" instead of spawning external processes:

┌─────────────┐      ┌──────────────┐      ┌─────────────┐
│   Svelte    │ ───> │  Rust Core   │ ───> │ ONNX Runtime│
│   UI Layer  │ IPC  │  + HAL       │ Zero │  (In-Memory)│
└─────────────┘      └──────────────┘ Copy └─────────────┘
                            │
                    ┌───────┴───────┐
                    │ GPU/NPU/CPU   │
                    └───────────────┘

Benefits:

• ⚡ Zero-latency communication between UI and AI
• 💾 Direct VRAM management
• 📊 Real-time progress tracking
• ⏹️ Instant cancellation

📦 Included Models

Model	Speed	Quality	Best For
🏃 SPAN	⚡⚡⚡	⭐⭐⭐	Fast upscaling for low-res images
🚀 SAFMN-V3	⚡⚡⚡	⭐⭐⭐	Next-gen efficient model
🎨 NomosUni SPAN	⚡⚡	⭐⭐⭐	Digital art and illustrations
💎 Real-PLSKR	⚡	⭐⭐⭐⭐	Maximum quality for photos
🎭 RealPLSKR Anime	⚡	⭐⭐⭐⭐	Best quality for anime & digital art
🔧 Real-ESRGAN-V3	⚡⚡	⭐⭐	Legacy model for compatibility

💻 System Requirements

🪟 Windows	🍎 macOS	🐧 Linux
Windows 10/11 DirectML support	macOS 13+ Apple Silicon or Intel	Ubuntu 20.04+ or equivalent

📥 Installation

Installation Files:

• 🪟 Windows: .exe installer
• 🐧 Linux: .AppImage

🎬 Usage

1. Launch RustScale
2. Select your scenario (Fast, Balanced, Quality, or Digital Art)
3. Drag & drop images or folders
4. Adjust settings if needed (repair strength, face recovery)
5. Click upscale and watch the magic happen ✨

💡 Performance Tips

Scenario	Tip
💻 Laptop users	App automatically enables Eco Mode on battery power
🎮 Limited VRAM	Engine adjusts tile sizes based on available memory
🚀 Batch processing	Higher batch sizes improve throughput on powerful GPUs

🤝 Contributing

Contributions are welcome! Whether it's:

• 🐛 Bug reports
• 💡 Feature requests
• 🔧 Code improvements
• 📖 Documentation

I'd love to hear from you!

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Built with modern, powerful tools:

• 🦀 Tauri v2 - Lightweight desktop framework
• 🧠 ONNX Runtime - Cross-platform AI inference
• ⚡ Svelte 5 - Reactive UI framework
• 🎨 TailwindCSS v4 - Utility-first styling
• 🤖 AI Coding Agents: Google Antigravity, Gemini 3, Claude Sonnet 4.5

📊 Project Stats

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