🖼️ Local AI Image Generator

An easy, zero-setup Stable Diffusion GUI for Windows. Run GGUF & Safetensors models offline without Python configuration.

Generation Workspace	Model Library	Image Constraints

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🎥 Watch the Setup & Demo Video: https://youtu.be/ESELhY-G_9w

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📖 Overview

Local AI Image Generator is a zero-configuration, portable desktop environment for running Stable Diffusion (Safetensors/GGUF/CKPT) offline on Windows. Double-clicking start.bat automatically handles dependency setup, GPU backend matching (CUDA/Vulkan), and launches a high-performance local web workspace.

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⚡ Quick Start

1. Launch: Double-click start.bat (downloads portable Node.js and pre-compiled GPU backend binaries on first run).
2. Add Models: Drop .safetensors, .gguf, or .ckpt weights into app/models/ (or download them via the Model Manager tab in the UI).
3. Generate: Open http://localhost:1420 in your browser, select your model, and write a prompt.

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✨ Features

• 100% Offline & Private: Inference runs completely locally on your hardware.
• Auto-Detected GPU Acceleration: Configures CUDA for Nvidia cards, and Vulkan for AMD or Intel Arc GPUs.
• Zero System Footprint: Node.js is sandboxed inside the folder. No global environment paths are altered.
• Integrated Model Manager: Paste a Hugging Face URL to download weights directly, or drag-and-drop local weight files to import them.
• Real-time Telemetry: Monitor RAM, VRAM, CPU, and GPU load directly in the UI.
• Local Gallery: Saves generated PNGs alongside prompt metadata JSONs to app/outputs/.

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📁 Repository Structure

local-ai-image-generator/
├── start.bat                  # Main double-click entrypoint
├── LICENSE                    # MIT Open Source license
├── .gitignore
├── README.md                  
├── scripts/
│   ├── setup.ps1              # Automated GPU-detect and environment installer
│   ├── reset.ps1              # Cleans runtime environments (keeps models & outputs)
│   └── serve.cjs              # UI web server and backend lifecycle manager
└── app/
    ├── frontend/              # UI source code (Vite + React)
    ├── models/                # Place weights here (.safetensors, .gguf, .ckpt)
    └── outputs/               # Saved images and parameters metadata

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🖥️ GPU Compatibility Matrix

GPU Vendor	Tech	Status	Notes
Nvidia	CUDA	✅ Native	Maps sd-cuda.exe with Nvidia SDK 12 optimizations.
AMD Radeon	Vulkan	✅ Native	Maps sd-vulkan.exe with Vulkan API acceleration.
Intel Arc	Vulkan	✅ Native	Maps sd-vulkan.exe for Intel hardware.
Integrated / None	CPU	⚠️ Fallback	Runs on logical CPU threads (slow).

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⏱️ Performance Benchmarks

Typical generation times for an image with 20 steps (e.g. 512x512 resolution; actual times can vary depending on specific hardware specifications, clock speeds, and system load):

• CUDA GPU (Nvidia RTX): ~10 seconds.
• Vulkan GPU (AMD / Intel Arc): ~89 seconds.
• GTX Vulkan Fallback (Nvidia GTX): ~30 seconds (Vulkan runs significantly faster on legacy GTX series cards since they lack Tensor Cores).
• CPU (Fallback): ~150 - 300+ seconds (highly dependent on processor core count, speed, and AVX instruction sets).

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🛠️ Troubleshooting

• Reset Environment: If a build fails or you want to clear dependencies, run scripts/reset.ps1. (This preserves your models and generated images).
• Port Conflicts: The frontend uses 1420 by default. The backend tries 8080 first, then automatically falls back to a free port if 8080 is already busy.

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📝 License

This project is licensed under the MIT License - see the LICENSE file. Bundles stable-diffusion.cpp (MIT License). Model weights are subject to their respective creators' licenses.