About Local-AI-Image-Generator

A fully self-contained, offline AI image generation studio for Windows. Runs Stable Diffusion (Safetensors/GGUF) locally with zero manual setup. Auto-configures CUDA for Nvidia GPUs and Vulkan for AMD/Intel Arc cards. Zero system-wide dependencies required.

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README.md

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🖼️ 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

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

📖 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.

⚡ Quick Start

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

✨ 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/.

📁 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

🖥️ 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).

⏱️ 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).

🛠️ 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.

📝 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.

Local-AI-Image-Generator