Project overview
Open Generative AI is an AI media generation application project, rather than a resource directory. It brings image generation, video generation, audio creation, lip sync, and visual workflows into one interface, with hosted web, Electron desktop, and source-development options.
The project uses Next.js, React, Tailwind CSS, and npm workspaces, with Muapi providing access to cloud models. The desktop app also offers local-inference configuration for sd.cpp and Wan2GP. The repository lists the MIT license.
Use cases
It is intended for creators who want to generate images or videos from text, turn existing images into videos, or combine portraits or videos with audio to produce lip-sync content.
For tasks that connect image, video, and audio processing across multiple steps, Workflow Studio provides templates and a node editor. Desktop local inference is an option for running selected models on your own equipment; requirements depend on the engine and model.
What it does and problems it solves
The project organizes models and operations for different media types in a shared interface, reducing the work of handling separate model entry points, input assets, and parameters. Image Studio and Video Studio switch model categories depending on whether a reference image or starting frame is uploaded, and display controls such as aspect ratio, resolution, or duration according to model capabilities.
Workflow Studio provides templates, node connections, and an interactive execution interface for multi-step generation tasks. Generation history supports revisiting and downloading outputs, while upload history retains reference-image records for reuse.
A self-hosted app can still call cloud APIs, so self-hosting does not establish that assets always remain on the device. The README states that cloud file uploads return hosted addresses. The hosted web version always uses cloud APIs, and local inference is available only in the desktop app. The supplied material does not establish actual API charges, quotas, or service availability.
How to use
1. Find the hosted web entry in the original repository below this page, or prepare the desktop app using the next section. For the hosted version, follow its account registration prompts. When running the app yourself with cloud models, enter the actual Muapi API key value when prompted on first use, rather than its name or label. You can skip the key when using only local models.
2. To generate images, open Image Studio. Enter a text prompt without a reference image, or upload a reference image to switch to image-editing models. Select a model, adjust its supported parameters, and generate.
3. To generate videos, open Video Studio. Enter a text prompt for text-to-video models, or upload a starting frame for image-to-video models. Set available options such as aspect ratio and duration, then generate.
4. For lip-sync content, open Lip Sync Studio and select portrait-image or video mode. Upload the corresponding asset and an audio file. Optionally enter a motion prompt, select a model and its supported resolution, and click Generate.
5. For multi-step tasks, open Workflow Studio. Select an existing template or connect models and outputs in the node editor, then enter inputs and run the workflow in Playground.
6. For desktop local image generation, open Settings → Local Models, install the sd.cpp engine, and download a model. Z-Image also requires its auxiliary files. Then open Image Studio, enable Local, and select the local model. The README recommends 16 GB RAM for Z-Image and warns that using it on an 8 GB M-series Mac may cause the system to hang.
7. To run local video models through Wan2GP, install it separately using its original repository. Then set its installation folder in Settings → Local Models → Wan2GP and click Save and Check. Enable Local in Video Studio and select a model. The Wan2GP machine requires a CUDA or ROCm GPU; this project does not bundle its Python environment or model weights.
Installation
1. For ordinary desktop use, find the Releases download entry in the original repository below this page and select an installer. The README lists files for macOS Apple Silicon, macOS Intel, Windows x64, and Ubuntu x64 in AppImage and DEB formats. Prebuilt desktop installation does not require Node.js. These packages were not downloaded or tested as part of this listing.
2. The basic macOS installation step is to open the DMG and drag the app into `/Applications`. The Windows instructions state that installation creates a Start Menu shortcut. The README says the macOS app is not notarized by Apple and the Windows installer is not code-signed. If the operating system blocks execution, first check the source and system message; this introduction does not provide steps to disable or bypass system protections.
3. To run from source, prepare Node.js v18 or later. Cloud generation also requires a Muapi access key; the key can be skipped for local-only use. The supplied Quick Start does not separately explain how to install Git or npm. If either is missing, consult the original repository and the relevant tool documentation.
4. Obtain the clone address from the original repository below this page and clone with `--recurse-submodules` enabled. Enter the project directory with `cd Open-Generative-AI`. If an earlier clone omitted submodules, run `git submodule update --init --recursive`.
5. Run `npm run setup` from the repository root to install dependencies and build workspace packages. The README explicitly states that `npm install` alone is insufficient.
6. Choose one startup command: `npm run electron:dev` for desktop development, or `npm run dev` for web development. Run it from the repository root.
7. For a production-mode build, run `npm run build` followed by `npm run start`. To build desktop installers, use `npm run electron:build`, `npm run electron:build:win`, or `npm run electron:build:linux` for the relevant target. Output goes into `release/`. The supplied README does not fully list cross-platform packaging prerequisites; consult the original repository for those details.
8. This listing includes only the repository link. Installers, source startup, and core generation workflows have not been validated. These steps summarize the README and do not establish successful installation or operation.
Source and usage notes
- Original repository: https://github.com/Anil-matcha/Open-Generative-AI
- Source revision: 84b29a55ff8bd536c7fdda5d80e2f120cac9d6e1
This listing links to the upstream repository. It does not provide a verified download package, and the complete workflow has not been independently validated.
This description was organized and translated with AI from public upstream material. Refer to the original repository for capabilities, setup requirements, costs, and license terms. 
