Uncensored-Local-Studio

Portable AI Studio

A premium, zero-configuration local AI studio and offline GUI for Stable Diffusion (Image Generation), LLMs (Chat), Whisper (Speech-to-Text), and Kokoro (Text-to-Speech). Powered by hardware-accelerated GPU and NPU execution on Windows, Linux, and macOS.

Watch the Setup & Demo Video: https://youtu.be/yeFvP3SWMak

Table of Contents

• What is Portable AI Studio?
• Key Features
• Workspace & Engine Architecture
• Supported Models
• Folder Architecture
• Getting Started
  • Windows Setup
  • Linux Setup
  • macOS Setup
• Hardware Compatibility & Acceleration
• Troubleshooting & FAQ
• Building From Source
• Licensing

What is Portable AI Studio?

Portable AI Studio is a completely offline, zero-setup, self-contained AI studio for Windows, Linux, and macOS. Unlike cloud-based AI systems, it runs entirely on your own hardware with no censorship, tracking, subscriptions, or login requirements.

It unifies four major local AI capabilities into one high-performance desktop interface:

1. ** Image Generation (Stable Diffusion):** Generate and edit high-quality images offline using .safetensors, .gguf, or .ckpt model weights.
2. ** Text Chat (LLMs):** Converse privately with open-source language models (GGUF format) powered by official, high-performance llama.cpp backends.
3. ️ Speech-to-Text (Whisper): Transcribe voice recordings and speech to text in real-time with an integrated whisper.cpp engine.
4. ️ Text-to-Speech (Kokoro TTS): Convert text outputs into highly natural, lifelike vocal audio offline using the Kokoro-82M ONNX model.

Key Features

• 100% Offline & Private: Run inferences locally. No internet, telemetry, cloud logging, or API keys required.
• Zero-Install Portability: Entire runtime (Node.js, models, GPU backends) is self-contained. Zero global system environment changes.
• Auto-Configured Acceleration: Auto-detects hardware specs to load CUDA (Nvidia), ROCm (AMD), Vulkan (Intel/AMD/NVIDIA), Metal (macOS), or OpenVINO (Intel NPU) backends.
• Integrated Model Manager: Paste Hugging Face URLs to download weights directly, or drag-and-drop local weights to import them.
• Live Performance Monitor: Track CPU, RAM, GPU, and VRAM utilization in real-time directly inside the web UI.
• Local Output Gallery: Saves generated images side-by-side with prompt parameters and metadata JSON files.

⚙️ Workspace & Engine Architecture

To avoid exhausting system RAM or VRAM, text and image engines are mutually exclusive by default. You can switch between workspaces inside the UI:

• Image Generation Workspace: Uses a dedicated stable-diffusion.cpp backend node. Model weights are stored in app/models/.
• Text Chat Workspace: Uses a portable llama.cpp server backend. Model weights (.gguf) are stored in app/llm-models/. A small Qwen2.5 Coder starter model can be downloaded directly from the Text Chat panel.
• Speech Worker (Whisper): Runs a localized whisper-cli process to convert your vocal input to text.
• Audio Output (Kokoro TTS): Utilizes kokoro-js locally on the server side to read responses in natural voices.

Supported Models

The app is designed around single-file local models that can be loaded directly by the bundled backend engines.

Image generation

Known-good image models available from the Model Manager:

Text, speech, and TTS

[!NOTE] Linux release binaries are built for Ubuntu 24.04-era systems and require glibc 2.38+ plus GLIBCXX_3.4.32+. On older Ubuntu/Debian VMs, a model such as CyberRealistic may be valid but the backend can still fail before loading it. Upgrade the VM OS or build the backend from source.

Folder Architecture

…

Getting Started

Ensure you have a modern web browser installed. Follow the quick guide below for your platform:

Windows Setup

1. Launch: Double-click windows.bat.

   [!NOTE] On the first run, the script will automatically download a portable Node.js runtime and configure pre-compiled GPU/CPU backend binaries.

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 Setup

1. Make executable: Open a terminal in the project folder and make the script executable:
   bash复制

   chmod +x linux.sh

2. Launch: Run ./linux.sh.
   • NVIDIA GPU Users: You will be prompted to set up the high-performance CUDA backend (downloads prebuilt or automatically compiles from source as a fallback).
   • AMD Radeon Performance: Run with ./linux.sh --max-perf to add the ROCm backend (~1.3 GB download).
   • Intel Core Ultra NPU: Run with ./linux.sh --setup-openvino to configure Intel NPU support (requires Intel Linux NPU driver).
3. Add Models: Drop your weights into app/models/ or download them via the Model Manager tab.
4. Generate: Open http://localhost:1420 in your browser.

macOS Setup

1. Make executable: Open a terminal in the project folder and make the script executable:
   bash复制

   chmod +x mac.sh

2. Launch: Run ./mac.sh.

   [!IMPORTANT] The prebuilt macOS backend is optimized for Apple Silicon (M1 or newer) and uses Metal GPU acceleration. (macOS Intel hardware is completely unsupported).

3. Add Models: Drop your weights into app/models/ or download them via the Model Manager tab.
4. Generate: Open http://localhost:1420 in your browser.

️ Hardware Compatibility & Acceleration

Windows

Linux

macOS

[!IMPORTANT] System Requirements & Notes:

• 64-bit Windows 10 or Windows 11 is required for the portable Node.js 22 runtime used by the Windows launcher.
• 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.
• Linux runtime libraries: The prebuilt backends require libgomp.so.1; Vulkan additionally requires libvulkan.so.1 and a working GPU driver. The setup script now checks these before installing a backend and prints the exact distro package command when one is missing.
• Linux OpenVINO NPU: Intel Core Ultra, x86_64 Linux, kernel 6.6+, a working /dev/accel/accel0 device, Python 3 with venv, and the Intel Linux NPU driver are required.

️ Troubleshooting & FAQ

Reset Environment: If a build fails or you want to clear dependencies

Run scripts/reset/reset.ps1 (Windows) or scripts/reset/reset.sh (Linux/macOS). This will clear temporary compilation and package caches to repair your environment. (Note: This preserves your model weights and generated output images).

Linux backends fail to start with GLIBC_2.38 not found

The prebuilt binaries require glibc 2.38+ (e.g. Ubuntu 24.04). If your distribution uses an older glibc version, you can upgrade your operating system or compile the backend from source (see the Building From Source guide below).

Port Conflicts: Default port address already busy

The web user interface runs on port 1420 by default. The GPU backend manager attempts to bind to port 8080 first, then automatically detects and falls back to a free system port if 8080 is already occupied.

Linux ROCm not loading for AMD Radeon GPUs

Ensure your AMD GPU hardware and host kernel are fully compatible with ROCm 7.13. If ROCm fails to initialize correctly, the application will automatically fall back to Vulkan acceleration.

Linux uses the integrated GPU instead of the discrete GPU

On dual-GPU Linux systems, Vulkan device order can put the integrated Intel GPU at vulkan0 and the discrete AMD/NVIDIA GPU at vulkan1. The launcher now tri