🖼️ Local AI Image Generator

An easy, zero-setup Stable Diffusion GUI for Windows and Linux. 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 and Linux. Double-clicking start.bat (Windows) or running ./start.sh (Linux) automatically handles dependency setup, GPU backend matching, and launches a high-performance local web workspace.

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⚡ Quick Start

Windows

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.

Linux

1. Check compatibility: Prebuilt Linux backends are built on Ubuntu 24.04 and require glibc 2.38+. Run ldd --version to verify.
2. Launch: Open a terminal in the project folder and run ./start.sh (downloads portable Node.js and pre-compiled GPU backend binaries on first run).
   • For maximum AMD GPU performance, use ./start.sh --max-perf on first setup (adds the ROCm backend).
3. Add Models: Drop .safetensors, .gguf, or .ckpt weights into app/models/ (or download them via the Model Manager tab in the UI).
4. 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, ROCm for AMD cards, and Vulkan for AMD/Intel/NVIDIA fallback.
• 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 (Windows)
├── start.sh                   # Main terminal entrypoint (Linux)
├── PLAN.md                    # Linux port implementation plan
├── LICENSE                    # MIT Open Source license
├── .gitignore
├── README.md                  
├── scripts/
│   ├── setup.ps1              # Automated GPU-detect and environment installer (Windows)
│   ├── setup.sh               # Automated GPU-detect and environment installer (Linux)
│   ├── reset.ps1              # Cleans runtime environments (Windows)
│   ├── reset.sh               # Cleans runtime environments (Linux)
│   └── 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

Windows

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).

Linux

GPU Vendor	Primary	Fallback	Notes
NVIDIA	Vulkan	CPU	No reliable prebuilt Linux CUDA binary is currently available. NVIDIA GPUs use Vulkan.
AMD Radeon	ROCm	Vulkan	ROCm provides best AMD performance when host ROCm drivers are available.
Intel Arc / integrated	Vulkan	CPU	Cross-vendor Vulkan support.
Integrated / None	CPU	—	Runs on logical CPU threads (slow).

System requirements:

• glibc 2.38 or newer is required for the prebuilt Linux backends (Ubuntu 24.04, Fedora 40+, etc.).
• The setup script will warn you if your glibc is older. You can still run setup, but the prebuilt backends will not start.

Linux setup modes:

• Default (./start.sh): Downloads CPU + Vulkan backends (~120–150 MB).
• Maximum Performance (./start.sh --max-perf): Also downloads the ROCm backend. Total download ~1.3 GB.

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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.
• ROCm GPU (AMD RDNA2/RDNA3): ~15–30 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 (Windows) or scripts/reset.sh (Linux). (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.
• Linux backends fail to start with GLIBC_2.38' not found: The prebuilt binaries require glibc 2.38+ (Ubuntu 24.04). Upgrade your distribution or build stable-diffusion.cpp from source (see below).
• Linux ROCm not loading: Make sure your AMD GPU and kernel are compatible with ROCm 7.13. The app will automatically fall back to Vulkan if ROCm cannot initialize.
• Generation shows "server is not responding or crashed": The backend process exited. Check the terminal where you ran ./start.sh for the exact error (common causes are glibc mismatch, missing Vulkan drivers, or out-of-memory).

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🔨 Building Linux Backends From Source

If your distribution has an older glibc than 2.38, or you want a CUDA backend on Linux, you can build stable-diffusion.cpp directly on your machine. The resulting binary will be linked against your system's glibc and will not have the compatibility issues of the prebuilt releases.

Requirements

• git, cmake, make (or ninja), and a C++17 compiler (g++ / clang++).
• For CUDA: the NVIDIA CUDA toolkit (nvcc) must be on your PATH.
• For Vulkan: the Vulkan SDK / loader and a compatible driver.
• For ROCm: AMD ROCm development libraries.

Build commands

# 1. Clone upstream
git clone https://github.com/leejet/stable-diffusion.cpp.git
cd stable-diffusion.cpp
mkdir build && cd build

# 2. Configure for your backend (pick ONE)
# CPU only
cmake .. -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release

# CUDA
cmake .. -DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release

# Vulkan
cmake .. -DSD_VULKAN=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release

# ROCm
cmake .. -DSD_HIPBLAS=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release

# 3. Build
cmake --build . --config Release -j$(nproc)

# 4. Copy the binaries into this project
cp bin/sd* /path/to/Local-AI-Image-Generator/app/backend/linux/<backend>/

After copying, rename the server binary to match what scripts/serve.cjs expects:

• CPU: sd → sd-cpu
• Vulkan: sd → sd-vulkan
• ROCm: sd → sd-rocm

Then restart the app with ./start.sh.

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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.