The free, open-source alternative to AI Video Platforms. Generate AI images and videos using 200+ state-of-the-art models — no content filters, no closed ecosystem, no subscription fees.
Community: Join Reddit & Discord for discussions and support
Latest Gemini Omni model from Google best prompts and resources: Gemini Omni Resources
🤖 Automate media generations with AI coding agents: Generative-Media-Skills — a library of skills that let agents like Claude Code, Codex, and other coding assistants drive 200+ image/video models end-to-end (prompt → generate → edit → stitch) directly from your terminal. Perfect for building automated media pipelines without touching a UI.
Open-source Node based workflow builder -> https://github.com/SamurAIGPT/Vibe-Workflow
Open-source AI Clipping — turn any long-form YouTube video into viral-ready vertical shorts -> https://github.com/SamurAIGPT/AI-Youtube-Shorts-Generator
Open-source AI Design Agent -> https://github.com/Anil-matcha/Open-AI-Design-Agent
Hosted version: https://muapi.ai/open-generative-ai
Use all four studios (Image, Video, Lip Sync, Cinema) directly in your browser — no Node.js, no setup. Sign up for a free account to start generating. The hosted version is always up to date with the latest models.
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One-click installers — no Node.js or terminal required.
| Platform | Download |
|---|---|
| macOS Apple Silicon (M1/M2/M3/M4) | Open Generative AI-1.0.9-arm64.dmg |
| macOS Intel (x64) | Open Generative AI-1.0.9.dmg |
| Windows (x64) | Open Generative AI Setup 1.0.9.exe |
| Linux (Ubuntu x64) | v1.0.9 release (.AppImage / .deb), or build locally with npm run electron:build:linux. |
All releases: github.com/Anil-matcha/Open-Generative-AI/releases
Because the app is not notarized by Apple, macOS Gatekeeper will block it on first launch. Follow these steps:
Step 1 — Mount the DMG and drag the app to /Applications
Step 2 — Open Terminal and run:
xattr -cr "/Applications/Open Generative AI.app"
Step 3 — Right-click the app in /Applications → click Open → click Open again on the dialog
You only need to do this once. After that, the app opens normally.
Alternative (no Terminal): 1. Try to open the app — macOS will block it 2. Go to System Settings → Privacy & Security 3. Scroll down to find "Open Generative AI was blocked" 4. Click Open Anyway → Open
Windows SmartScreen may show a warning because the installer is not code-signed:
The app will install silently to %LocalAppData% with a Start Menu shortcut.
Linux artifacts are available when building with Electron Builder:
# Build Linux installers (AppImage + .deb)
npm run electron:build:linux
Generated files are written to the release/ folder:
- AppImage — portable, run directly after making executable:
bash
chmod +x "release/Open Generative AI-*.AppImage"
./release/Open\ Generative\ AI-*.AppImage
- .deb — install on Debian/Ubuntu:
bash
sudo apt install ./release/open-generative-ai_*_amd64.deb
If AppImage fails to start on older systems, install libfuse2:
sudo apt install libfuse2
Ubuntu 24.04 and later enable a kernel security policy (apparmor_restrict_unprivileged_userns) that blocks Chromium's user-namespace sandbox. If the app fails to start silently or crashes immediately, you have two options:
Option A — Recommended: install the .deb instead.
The .deb package ships an AppArmor profile that grants the required permission automatically on install with no system-wide changes.
Option B — Temporary system fix (AppImage users):
sudo sysctl -w kernel.apparmor_restrict_unprivileged_userns=0
This lasts until next reboot. To make it permanent:
echo 'kernel.apparmor_restrict_unprivileged_userns=0' | sudo tee /etc/sysctl.d/99-userns.conf
Open Generative AI is a free, open-source AI image, video, cinema, and lip sync studio that brings creative workflows to everyone. No content filters, no prompt rejections, no guardrails — just full creative freedom. Powered by Muapi.ai, it supports text-to-image, image-to-image, text-to-video, image-to-video, and audio-driven lip sync generation across models like Flux, Nano Banana, Midjourney, Kling, Sora, Veo, Seedream, Infinite Talk, LTX Lipsync, Wan 2.2, and more — all from a sleek, modern interface you can self-host and customize.
Why Open Generative AI instead of other AI Video Platforms? - No filters — no content filters, no nanny guardrails, no prompt rejections - Free & open-source — no subscription, no vendor lock-in - Self-hosted — your data stays on your machine, full creative control - 200+ models — text-to-image, image-to-image, text-to-video, image-to-video, lip sync - Multi-image input — feed up to 14 reference images into compatible models - Lip Sync Studio — animate portraits or sync lips to any audio with 9 dedicated models - Extensible — add your own models, modify the UI, build on top of it
For a deep dive into the technical architecture and the philosophy behind the "Infinite Budget" cinema workflow, see our comprehensive guide and roadmap.
The desktop app supports two independent local engines. Pick whichever fits the machine you actually run on:
| Engine | What it is | Best for |
|---|---|---|
| sd.cpp (bundled) | C++ engine from stable-diffusion.cpp, runs on the same machine as the app. Metal GPU on Apple Silicon, CUDA/Vulkan/ROCm on Linux/Windows. | Image-only models. Works on Mac M-series. |
| Wan2GP (BYO server) | HTTP client to a user-run Wan2GP server. The server runs Python + PyTorch on a CUDA/ROCm GPU; the desktop app only sends prompts and receives results. | Video models (Wan 2.2, Hunyuan, LTX) and large image models (Flux, Qwen-Image). NVIDIA/AMD GPU required on the server; the desktop app itself can run on a Mac. |
Both engines share the same UI: open Settings → Local Models to configure each.
| Model | Type | Size | Notes |
|---|---|---|---|
| Z-Image Turbo ⚡ | Diffusion Transformer | 2.5 GB + 2.7 GB aux | 8-step turbo. Heavy on memory. |
| Z-Image Base ⚡ | Diffusion Transformer | 3.5 GB + 2.7 GB aux | 50-step high-quality. Heavy on memory. |
| Dreamshaper 8 | SD 1.5 | 2.1 GB | 20-step versatile. Lightest tested option on Mac. |
| Realistic Vision v5.1 | SD 1.5 | 2.1 GB | 25-step photorealistic |
| Anything v5 | SD 1.5 | 2.1 GB | 20-step anime/illustration |
| SDXL Base 1.0 | SDXL | 6.9 GB | 30-step high-res |
Z-Image models require two shared auxiliary files (downloaded once, shared across both models): - Qwen3-4B Text Encoder — 2.4 GB - FLUX VAE — 335 MB
How to use: 1. Open Settings → Local Models in the desktop app 2. Install the sd.cpp inference engine (one click — auto-downloaded) 3. Download your chosen model (and auxiliary files for Z-Image) 4. In Image Studio, click the ⚡ Local toggle next to the model selector 5. Select your local model and generate — no API key needed
All downloads happen inside the app. Nothing is installed system-wide.
By default, sd.cpp stores the engine, model weights, and temporary downloads under Electron's app data directory. Common paths are:
~/Library/Application Support/open-generative-ai/local-ai%APPDATA%\open-generative-ai\local-ai~/.config/open-generative-ai/local-aiTo keep multi-GB model weights on another drive, set OPEN_GENERATIVE_AI_LOCAL_AI_DIR
before launching the desktop app. The app will create bin/, models/, and tmp/
inside that directory, and Settings -> Local Models shows the resolved model folder.
Local engine output and download errors are written to the app process console, so launch
from Terminal or PowerShell when you need troubleshooting logs.
The app does not bundle Python or model weights for Wan2GP. You run Wan2GP yourself on a machine with a CUDA or ROCm GPU and point the desktop app at its URL.
# On your GPU machine
git clone https://github.com/deepbeepmeep/Wan2GP
cd Wan2GP
./install.sh # or install.bat on Windows
python wgp.py --listen --server-name 0.0.0.0 # binds to all interfaces
Then in the desktop app: Settings → Local Models → Wan2GP server, paste the URL (e.g. http://192.168.1.42:7860), click Test, then Save. The Wan2GP models become available — image models in Image Studio, video models reachable via the same generation API (Image Studio rejects video output explicitly; full Video Studio wiring is on the roadmap).
| Model | Type | Notes |
|---|---|---|
| Flux.1 Dev | Image | 1024px, 28 steps |
| Qwen Image | Image | 1024px, 30 steps |
| Wan 2.2 (T2V / I2V) | Video | Slow on consumer GPUs |
| Hunyuan Video | Video | High-quality T2V |
| LTX Video | Video | Fastest video option |
Why a separate server? Wan2GP's runtime (Sage attention, flash-attn, AWQ/GGUF kernels) is CUDA-only — there is no MPS / Apple Silicon path. Treating it as a remote server lets a Mac-only user keep the desktop app while offloading inference to a Linux/Windows GPU box, a gaming PC on the LAN, or a rented RunPod/vast.ai instance.
Local inference is only available in the desktop app. The hosted web version always uses cloud APIs.
If you want to confirm sd.cpp is installed correctly without going through the UI, you can drive sd-cli directly. This is the same binary the app uses.
# 1. App data layout (created on first app launch)
APP_DATA="${OPEN_GENERATIVE_AI_LOCAL_AI_DIR:-$HOME/Library/Application Support/open-generative-ai/local-ai}"
ls "$APP_DATA/bin" # sd-cli, libstable-diffusion.dylib
ls "$APP_DATA/models" # whatever you've downloaded
# 2. Grab a small SD 1.5 model directly (Dreamshaper 8, ~2 GB)
curl -L --fail --progress-bar \
-o "$APP_DATA/models/DreamShaper_8_pruned.safetensors" \
"https://huggingface.co/Lykon/DreamShaper/resolve/main/DreamShaper_8_pruned.safetensors"
# 3. Run a single 512x512 / 12-step inference
DYLD_LIBRARY_PATH="$APP_DATA/bin" "$APP_DATA/bin/sd-cli" \
-m "$APP_DATA/models/DreamShaper_8_pruned.safetensors" \
-p "a serene mountain lake at sunrise, oil painting" \
-o /tmp/sd15-test.png \
--steps 12 -H 512 -W 512 --cfg-scale 7.5 --seed 42 \
--sampling-method euler_a
A healthy run on Apple Silicon prints total params memory size = 1969.78MB (VRAM 1969.78MB, RAM 0.00MB) (Metal-backed) and produces a coherent 512×512 PNG. If VRAM is 0.00MB instead, the dylib is CPU-only — check otool -L "$APP_DATA/bin/libstable-diffusion.dylib" | grep -i metal and reinstall the engine from Settings → Local Models if Metal is missing.
$ claude mcp add Open-Generative-AI \
-- python -m otcore.mcp_server <graph>