How to Turn Text into a Series with AI Fusion Video

We've all been there: you have a great video idea or even a finished script, but everything stalls at the production stage. You need to find footage, generate images, somehow stitch it all together, and then realize that the visual style of the shots is drifting in different directions. I recently came across the Stonewuu/ai-fusion-video project, which tries to turn this chaos into a structured pipeline.

This isn't just another "wrapper" around ChatGPT. The developers set out to create a full-fledged platform for managing video production based on AI agents. The project is fresh, and you can feel the drive of the Chinese open-source community behind it. It already knows how to do things that used to require a dozen different browser tabs.

What this powerhouse can do

The main highlight of the project is agent-based workflow. You're not just asking "make it pretty for me" — you go through a chain of stages that the system helps you control.

Script management

Instead of storing text in Google Docs, you work on it directly in the platform's interface. The system supports structure by episodes and scenes. This is handy if you're planning to make not just one video, but a series of short clips for social media.

Automatic storyboarding

The most tedious stage — turning text into shot descriptions. AI Fusion Video takes your script and breaks it down into visual blocks on its own. It writes out image descriptions and even suggests "camera language" (angles, movement). If you don't like how the agent interpreted a scene, you can manually edit the description before generation starts.

Content generation in one place

The system has built-in support for a bunch of models. Want to use OpenAI or Claude for text? Go ahead. Want DeepSeek (which is currently crushing the charts)? Sure. For images and video, the corresponding engines are plugged in. The main thing is that all source materials, prompts, and results live in one project. You don't need to download an image from Midjourney just to upload it to Runway afterward.

Tech stack

For those who like to peek under the hood, it's a fairly modern set. The backend is written in Java 21 using Spring Boot 3.5. The choice of Java for an AI project might seem unusual (everyone's used to Python), but using Spring AI lets you manage data streams from different LLMs quite elegantly.

The frontend is built with Next.js 16 and React 19. The interface looks clean, without unnecessary visual noise — a rarity for tools like this.

How to get it running

The project supports Docker, which makes life much easier. No need to mess with installing JDK or Node.js if you just want to poke around the functionality.

Just run the standard sequence:

git clone https://github.com/Stonewuu/ai-fusion-video.git
cd ai-fusion-video
cp .env.example .env
docker compose up -d

After that, the platform will be available on port 8080. If you're planning to customize the code for yourself, though, you'll need to set up MySQL and Redis separately (there's a ready-made config docker-compose-middleware.yml) and launch the backend via Maven.

Who will find this useful

I see several scenarios where AI Fusion Video really saves time:

1. Content creators for TikTok/Reels. When you need to publish a video a day, automated storyboarding is a lifesaver.
2. Marketers creating quick ad prototypes. You can put together a draft video in half an hour to show a client the idea.
3. Developers who want to learn how to build complex systems based on AI agents using Spring AI.

The project still lacks proper team collaboration and flexible pipeline customization (both are on the roadmap), but the current foundation already lets you produce a video from text "turnkey."

Stonewuu/ai-fusion-video is a solid tool for anyone who wants to structure their neural network workflow. It doesn't replace creativity, but it takes on all the routine work of passing data between models. If you're tired of copy-pasting prompts from one window to another, it's definitely worth trying to deploy this project yourself.

By the way, the project is actively updated, so check their GitHub — they frequently roll out fixes and support for new models.