Open-Generative-AI README

The project's README from github.com/Anil-matcha/Open-Generative-AI. As one markdown file: README.md.

Open Generative AI — Unrestricted Open-Source Alternative to AI Video Platforms

Powered by MuAPI

The free, open-source alternative to AI Video Platforms. Generate AI images and videos using 400+ state-of-the-art models across 14 studios — no content filters, no closed ecosystem, no subscription fees.

Community: Join Discord for discussions and support

▶ Watch: Free Unlimited AI Image Generator (Truly no limits, Open Source, No Watermark)

Awesome Generative AI Apps

🎨 Explore 50+ more open-source AI apps →

💰 Turn This Into Your Own Product — White Label & Resell

Want to launch this as your own branded AI studio and charge your own customers for it? MuAPI White Label lets you spin up a fully white-labeled version of this app — your logo, your colors, your custom domain, your own pricing — with zero infra to manage. You keep the markup on every generation; MuAPI handles the models, the queue, and the billing plumbing underneath.

Plans start at $49/mo. Get started with White Label →

What similar AI studios charge their users

Consumer AI image/video platforms almost all run on paid monthly subscriptions — this is the same playbook you'd run under your own brand:

Platform Typical subscription range
Midjourney ~$10–$120/mo (Basic → Mega)
Runway ~$12–$76/mo (Standard → Unlimited), custom Enterprise
Kling AI ~$10–$92/mo across Standard → Premier tiers
Luma Dream Machine ~$10–$100+/mo
Pika ~$8–$58/mo

(Figures are approximate, general-market ranges and change over time — check each platform's current pricing page before quoting them.) With MuAPI White Label, you set these numbers yourself for your own end users — the subscription revenue is yours.


This is a curated set of high-value hubs, popular distribution tools, and model-specific integrations rather than a directory of every related repository.

🌐 Try it Online — No Install Required

Hosted version: https://muapi.ai/open-generative-ai?utm_source=github&utm_medium=readme&utm_campaign=open-generative-ai

Use all studios (Image, Video, Audio, AI Clipping, Vibe Motion, Lip Sync, Cinema, Marketing, Workflows, Agents, Design Agent, Apps, MCP & CLI) 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.

Follow the creator for updates


⬇️ Download Desktop App

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

macOS Installation Guide

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 Installation — SmartScreen warning fix

Windows SmartScreen may show a warning because the installer is not code-signed:

  1. Click More info on the SmartScreen dialog
  2. Click Run anyway

The app will install silently to %LocalAppData% with a Start Menu shortcut.

Ubuntu / Linux Installation

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:

If AppImage fails to start on older systems, install libfuse2:

sudo apt install libfuse2

Ubuntu 24.04+ / AppArmor sandbox restriction

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?

For a deep dive into the technical architecture and the philosophy behind the "Infinite Budget" cinema workflow, see our comprehensive guide and roadmap.

⚡ Local Model Inference (Desktop App Only)

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.

Engine 1 — sd.cpp (bundled)

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:

To 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.

Engine 2 — Wan2GP (remote Gradio server)

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.

Hardware Notes

Verifying the SD 1.5 path (the fastest sanity test on Mac)

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.


✨ Features

🖼️ Image Studio — Dual Mode

The Image Studio automatically switches between two model sets:

Mode Trigger Models Prompt
Text-to-Image Default (no image) 50+ t2i models (Flux, Nano Banana 2, Seedream 5.0, Ideogram, GPT-4o, Midjourney…) Required
Image-to-Image Reference image uploaded 55+ i2i models (Kontext, Nano Banana 2 Edit, Seedream 5.0 Edit, Seededit, Upscaler…) Optional

Newly Added Models

Model Type Key Features
Nano Banana 2 Text-to-Image Google Gemini 3.1 Flash Image · Resolution 1K/2K/4K · Google Search enhancement · aspect ratio auto
Nano Banana 2 Edit Image-to-Image Up to 14 reference images · Resolution 1K/2K/4K · Google Search enhancement
Seedream 5.0 Text-to-Image ByteDance · Quality basic/high · 8 aspect ratios · up to 4K
Seedream 5.0 Edit Image-to-Image ByteDance · Natural language style transfer · Quality basic/high
MiniMax Image 01 Text-to-Image MiniMax · 8 aspect ratios · up to 4 images per request · 1500 char prompt

Multi-Image Input

Models that accept multiple reference images expose a multi-select picker when active:

Model Max Images
Nano Banana 2 Edit 14
Nano Banana Edit 10
Flux Kontext Dev I2I 10
Kling O1 Edit Image 10
GPT-4o Edit / GPT Image 1.5 Edit 10
Bytedance Seedream Edit v4 / v4.5 10
Vidu Q2 Reference to Image 7
Flux 2 Flex/Pro Edit 8
Nano Banana Pro Edit 8
Flux Kontext Pro/Max I2I 2
Wan 2.5/2.6 Image Edit 2–3
Qwen Image Edit Plus / 2511 3
GPT-4o Image to Image 5
Flux 2 Klein 4b/9b Edit 4

When a multi-image model is selected the upload trigger switches to multi-select mode:

🎬 Video Studio — Dual Mode

The Video Studio follows the same pattern:

Mode Trigger Models Prompt
Text-to-Video Default (no image) 40+ t2v models (Kling, Sora, Veo, Wan, Seedance 2.0, Hailuo, Runway…) Required
Image-to-Video Start frame uploaded 60+ i2v models (Kling I2V, Veo3 I2V, Runway I2V, Wan I2V, Seedance 2.0 I2V, Midjourney I2V…) Optional

Newly Added Models

Model Type Key Features
Seedance 2.0 Text-to-Video ByteDance · Aspect ratios 16:9 / 9:16 / 4:3 / 3:4 · Duration 5 / 10 / 15s · Quality basic/high
Seedance 2.0 I2V Image-to-Video ByteDance · Animate images into video · Up to 9 reference images · Aspect ratios 16:9 / 9:16 / 4:3 / 3:4 · Duration 5 / 10 / 15s · Quality basic/high
Seedance 2.0 Extend Video Extension ByteDance · Seamlessly continue any Seedance 2.0 generation · Preserves style, motion & audio · Optional continuation prompt · Duration 5 / 10 / 15s · Quality basic/high
Grok Imagine T2V Text-to-Video xAI · Duration 6 / 10 / 15s · Modes: fun / normal / spicy · Aspect ratios 9:16 / 16:9 / 2:3 / 3:2 / 1:1
Grok Imagine I2V Image-to-Video xAI · Duration 6 / 10 / 15s · Modes: fun / normal / spicy · Cinematic motion from still images
MiniMax Hailuo 02 / 2.3 Standard & Pro Text-to-Video / Image-to-Video MiniMax · Full HD video · Multiple aspect ratios · Fast variant included

🎙️ Lip Sync Studio

The Lip Sync Studio generates audio-driven talking videos using 9 models across two input modes:

Mode Trigger Description
Portrait Image Default Upload a portrait image + audio file → animated talking video
Video Switch to Video mode Upload an existing video + audio file → lipsync video

Image-based Models (Portrait Image + Audio → Video)

Model Endpoint Resolutions Prompt
Infinite Talk infinitetalk-image-to-video 480p, 720p Optional
Wan 2.2 Speech to Video wan2.2-speech-to-video 480p, 720p Optional
LTX 2.3 Lipsync ltx-2.3-lipsync 480p, 720p, 1080p Optional
LTX 2 19B Lipsync ltx-2-19b-lipsync 480p, 720p, 1080p Optional

Video-based Models (Video + Audio → Lipsync Video)

Model Endpoint Resolutions Prompt
Sync Lipsync sync-lipsync — —
LatentSync latentsync-video — —
Creatify Lipsync creatify-lipsync — —
Veed Lipsync veed-lipsync — —
Infinite Talk V2V infinitetalk-video-to-video 480p, 720p Optional

How it works:

  1. Select Portrait Image or Video mode using the toggle
  2. Upload your portrait image (or video) using the image/video upload button
  3. Upload your audio file using the audio upload button
  4. Optionally enter a prompt to guide the motion style
  5. Select a model and resolution (where supported), then click Generate

Generation history is saved separately in lipsync_history and pending jobs resume automatically on page reload.

🔀 Workflow Studio

The Workflow Studio lets you build and run multi-step AI pipelines without writing code.

Key capabilities:

💡 Want to add workflows to your own app? Check out Vibe Workflow — the open-source workflow engine powering this feature. Drop it into any project.

🎥 Cinema Studio Controls

The Cinema Studio offers precise control over the virtual camera, translating your choices into optimized prompt modifiers:

Category Available Options
Cameras Modular 8K Digital, Full-Frame Cine Digital, Grand Format 70mm Film, Studio Digital S35, Classic 16mm Film, Premium Large Format Digital
Lenses Creative Tilt, Compact Anamorphic, Extreme Macro, 70s Cinema Prime, Classic Anamorphic, Premium Modern Prime, Warm Cinema Prime, Swirl Bokeh Portrait, Vintage Prime, Halation Diffusion, Clinical Sharp Prime
Focal Lengths 8mm (Ultra-Wide), 14mm, 24mm, 35mm (Human Eye), 50mm (Portrait), 85mm (Tight Portrait)
Apertures f/1.4 (Shallow DoF), f/4 (Balanced), f/11 (Deep Focus)

📁 Upload History & Picker

Every image you upload is saved locally (URL + thumbnail) so you never upload the same file twice:

🚀 Quick Start

Prerequisites

Setup

Most users want the desktop app, not this dev path. If you just want to run Open Generative AI on your machine, download a prebuilt installer instead — no Node.js required. The instructions below are for contributors building from source.

Pick the entry point that matches your goal:

# Clone the repository (with submodules — required for the workflow + agent packages)
git clone --recurse-submodules https://github.com/Anil-matcha/Open-Generative-AI.git
cd Open-Generative-AI

# If you already cloned without --recurse-submodules, run this once:
# git submodule update --init --recursive

# Install dependencies + build workspace packages (studio, workflow, agents).
# This step is REQUIRED — `npm install` alone is not enough; the workspaces
# need to be built before either dev script will work.
npm run setup

# Then start ONE of:
npm run electron:dev   # Desktop app (Electron + Vite) — recommended
npm run dev            # Hosted web version (Next.js) → http://localhost:3000

You'll be prompted to enter your Muapi API key on first use (skip the key if you only plan to use local models).

Troubleshooting — Couldn't find a 'pages' directory: this means Next.js can't see the app/ folder. Confirm you're running npm run dev from the repo root (the directory that contains app/, package.json, and next.config.mjs), and that you cloned with submodules. Re-run npm run setup if packages/Vibe-Workflow or packages/agents are empty.

Production Build

npm run build
npm run start

Desktop App Build

Build native desktop apps with Electron:

# macOS (DMG — Intel + Apple Silicon)
npm run electron:build

# Windows (NSIS installer — x64 + ARM64)
npm run electron:build:win

# Linux (AppImage + DEB — x64)
npm run electron:build:linux

# Both platforms in one pass
npm run electron:build:all

Installers are output to the release/ folder. Pre-built binaries are also available on the Releases page.

🏗️ Architecture

The app is a Next.js monorepo with a shared packages/studio component library.

Open-Generative-AI/
├── app/                        # Next.js App Router
│   ├── layout.js               # Root layout (Tailwind, fonts)
│   ├── page.js                 # Redirects → /studio
│   └── studio/
│       └── page.js             # Studio page — renders StandaloneShell
├── components/
│   ├── StandaloneShell.js      # Tab nav + BYOK (API key from localStorage)
│   └── ApiKeyModal.js          # API key entry modal
├── packages/
│   └── studio/                 # Shared React component library
│       └── src/
│           ├── index.js        # Exports: ImageStudio, VideoStudio, AudioStudio, ClippingStudio, VibeMotionStudio, LipSyncStudio, RecastStudio, CinemaStudio, MarketingStudio, WorkflowStudio, AgentStudio, DesignAgentStudio, AppsStudio, AiInfluencerStudio, McpCliStudio
│           ├── models.js       # 400+ model definitions (single source of truth)
│           ├── muapi.js        # API client (named exports, apiKey as first param)
│           └── components/
│               ├── ImageStudio.jsx    # Dual-mode t2i/i2i studio
│               ├── VideoStudio.jsx    # Dual-mode t2v/i2v studio
│               ├── LipSyncStudio.jsx  # Portrait/video + audio → talking video
│               ├── CinemaStudio.jsx   # Pro studio with camera controls
│               └── WorkflowStudio.jsx # Multi-step pipeline builder & playground
├── next.config.mjs             # transpilePackages: ['studio']
├── tailwind.config.js
└── package.json                # workspaces: ["packages/studio"]

The packages/studio library is also consumed by the hosted version on muapi.ai — model updates made in packages/studio/src/models.js apply to both the self-hosted app and the hosted version automatically.

🔌 API Integration

The app communicates with Muapi.ai using a two-step pattern:

  1. Submit — POST /api/v1/{model-endpoint} with prompt and parameters
  2. Poll — GET /api/v1/predictions/{request_id}/result until status is completed

Authentication uses the x-api-key header. During development, a Vite proxy handles CORS by routing /api requests to https://api.muapi.ai.

File uploads use POST /api/v1/upload_file (multipart/form-data) and return a hosted URL that is passed to image-conditioned models. For multi-image models the full images_list array is forwarded to the API in one request.

Lip sync jobs use the same two-step pattern: a dedicated processLipSync() method accepts image_url or video_url alongside audio_url, dispatches to the model's endpoint, and polls until the output video URL is available.

🎨 Supported Model Categories

Category Count Examples
Text-to-Image 70+ Flux Dev, Nano Banana 2, Seedream 5.0, Ideogram v3, Midjourney v7, GPT-4o, SDXL
Image-to-Image 70+ Nano Banana 2 Edit (×14), Flux Kontext Pro, GPT-4o Edit, Seededit v3, Upscaler, Background Remover
Text-to-Video 85+ Kling v3, Sora 2, Veo 3, Wan 2.6, Seedance 2.0, Seedance 2.0 Extend, Seedance Pro, Hailuo 2.3, Runway Gen-3
Image-to-Video 120+ Kling v2.1 I2V, Veo3 I2V, Runway I2V, Seedance 2.0 I2V, Midjourney v7 I2V, Hunyuan I2V, Wan2.2 I2V
Video-to-Video 35+ Video effects, AI Clipping, Vibe Motion, video-conditioned edits
Lip Sync 15 Infinite Talk I2V, Wan 2.2 Speech to Video, LTX 2.3 Lipsync, LTX 2 19B Lipsync, Sync, LatentSync, Creatify, Veed, Infinite Talk V2V
Body Swap / Recast 3 Subject/appearance recast across image and video
Audio 15+ Text-to-music, remix, and audio editing models

(Counts verified against packages/studio/src/models.js — total 420+ models across these 8 categories, plus additional models surfaced through Marketing, Agent, and Design Agent studios.)

🛠️ Tech Stack

🤔 How is this different from other AI Video Platforms?

Open Generative AI is a community-driven, open-source alternative that provides similar creative capabilities without the closed ecosystem:

Other providers Open Generative AI
Cost Subscription-based Free (open-source)
Content filters Yes — prompts blocked or altered None
Restrictions Platform guardrails enforced Full creative freedom
Models Proprietary 400+ open & commercial models
Multi-image input Limited Up to 14 images per request
Lip sync No 9 models, image & video modes
Hosted version Subscription Free at muapi.ai/open-generative-ai
Self-hosting No Yes
Customizable No Fully hackable
Data privacy Cloud-based Your data stays local
Source code Closed MIT licensed

📄 License

MIT

🙏 Credits

Built with Muapi.ai — the unified API for AI image and video generation models.


Deep Dive: For more details on the "AI Influencer" engine, upcoming "Popcorn" storyboarding features, and the future of this project, read the full technical overview.


Looking for a free, open-source AI Video Platform? Open Generative AI is an open-source AI image and video generation studio — with no content filters that you can self-host, customize, and extend.

Open-Generative-AI (Anil-matcha/Open-Generative-AI)

Searching Database…

这是什么东西?一个免费开源的 AI 创作工作室,可以生成 AI 图片、视频、电影级画面和对口型视频,定位是付费 AI 视频平台的无限制替代品:内含 400 多个模型(Flux、Midjourney、Kling、Sora、Veo),分布在 14 个工作室中,没有任何内容过滤。
它是我访问的网站、要安装的桌面软件,还是需要自己部署的东西?三种都是:有浏览器直接用的在线网页版,有支持 macOS、Windows 和 Linux 的桌面应用,也有可以自己部署的源代码。本地模型推理只在桌面版可用;网页版始终使用云端 API。
我怎么把它跑起来?最快的方式是用在线版——在浏览器里打开,注册一个免费账号就行。也可以从 GitHub 的 releases 页面下载一键安装包(DMG、EXE、AppImage 或 .deb),不需要装 Node.js,也不用碰命令行。
生成内容要花钱吗——需要 API 密钥或者订阅吗?这个软件本身免费开源;在线版只需要一个免费账号,云端生成通过 Muapi.ai 完成。桌面版自带的本地模型完全不需要 API 密钥。
安装包能顺利打开吗,有没有什么坑?有几个。macOS 的 Gatekeeper 首次启动会拦截未签名的应用(右键点“打开”,或者在“隐私与安全性”里选“仍要打开”)。Windows 的 SmartScreen 需要点“更多信息”再选“仍要运行”。在 Ubuntu 24.04 及以上版本建议用 .deb 包——AppArmor 会拦截 AppImage 的沙箱。
我输入的提示词和生成的文件会存在哪里?这正是自己部署和本地推理的卖点:你的提示词和输出都留在自己的电脑上。在线版和云端模型的生成请求会经过 Muapi.ai 的 API。
真的可以免费使用和二次开发吗?根据仓库的元数据,它采用 MIT 许可证——可以自由使用、修改和自行部署,没有订阅费,也不会被单一厂商绑定。
我能用自己的硬件来生成,而不走云端吗?可以,在桌面版里就行。自带的 sd.cpp 引擎可以在 CPU 上运行图像模型(SD 1.5、SDXL、Z-Image),在 Apple 芯片上用 Metal,也支持 CUDA/Vulkan/ROCm。想本地生成视频的话,把它指向你自己的 Wan2GP 服务器,配一块 CUDA 或 ROCm 显卡即可。
比起付费订阅 Midjourney 或 Runway,选它有什么好处?没有内容过滤和提示词拦截,不被厂商绑定,也没有月费——按 README 自己的对比表,那些平台每月大约要 8 到 120 美元。你还能用上同样的前沿模型(Flux、Kling、Sora、Veo),并且界面可以自行部署、随意修改。
除了图片和视频,它还有哪些工作室?一共十四个:Image、Video、Audio、AI Clipping、Vibe Motion、Lip Sync、Cinema、Marketing、Workflows、Agents、Design Agent、Apps,还有 MCP 和 CLI。光是 Image Studio 就有 50 多个文生图模型和 55 多个图生图模型,最多支持 14 张参考图;Lip Sync 自带 9 个专用模型。
我的 Mac 跑得动本地图像模型吗?像 Dreamshaper 8(2.1 GB)这样的 SD 1.5 模型,在 M2 上用 Metal 大约每步 1 到 2 秒。Z-Image 需要 16 GB 内存,已知会让 8 GB 内存的 M 系列基础款 Mac 卡死,所以在那种机器上还是用 SD 1.5 吧。
我能换个品牌,把它当成自己的产品来卖吗?可以——MuAPI 的白标方案每月 49 美元起:用你自己的 logo、配色、自定义域名和定价,包含全部工作室,模型、队列和账单都由 MuAPI 处理。每一次生成的差价都归你。
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Commits · 90 Days