Jellyfish README

The project's README from github.com/Forget-C/Jellyfish. As one markdown file: README.md.

Jellyfish — AI Short Drama Studio

Jellyfish Logo

License Frontend Backend Deploy Site Build and push images

English · 日本語

An end-to-end production workspace for AI-generated short dramas.
From script input to structured storyboarding, consistency management, shot preparation, video generation, and export.

📷 Screenshots

Project overview Asset management
Project overview Asset management

✨ Core Value

✨ Core Capabilities

Jellyfish is not just a single “AI image/video” utility. It is a production workspace built around:

1. AI script understanding and storyboard breakdown

2. Shot preparation and confirmation workflow

The main workflow is:

script breakdown → shot preparation → candidate confirmation → shot ready → generation workspace

Preparation currently supports:

3. Asset consistency and reuse

The system maintains a shared entity model across:

This supports asset reuse across shots and helps stabilize style and identity.

4. Shot-level image and video orchestration

Once a shot is ready, the generation workspace supports:

5. Unified async task center

Current task infrastructure supports:

6. Model, prompt, and generation infrastructure

Supporting capabilities include:

🚀 Feature Overview

Project and chapter management

AI script processing

Shot preparation workflow

Asset and entity management

Shot generation workspace

Task center

Model and prompt infrastructure

File and media management

🎯 Use Cases

🔁 Frontend OpenAPI client and type generation

Frontend request helpers and types are generated from the backend OpenAPI spec. Output directory:

Cached spec file:

With the backend dev server running at http://127.0.0.1:8000, run:

cd front
pnpm run openapi:update

🐳 Docker Compose

The repository includes a ready-to-run compose setup under deploy/compose/.

Ports

Start

cp deploy/compose/.env.example deploy/compose/.env
docker compose --env-file deploy/compose/.env -f deploy/compose/docker-compose.yml up --build

🧑‍💻 Local Development

Backend

cd backend
cp .env.example .env
uv sync
uv run uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Frontend

cd front
pnpm install
pnpm dev

📄 License

This project is licensed under Apache-2.0.

💬 Community & Feedback

Jellyfish (Forget-C/Jellyfish)

multi-model
Searching Database…

What is this?Jellyfish is an end-to-end production workspace for making AI-generated short dramas, covering everything from script input to storyboarding, asset consistency, shot preparation, video generation, and export.
Is this a single AI video generator or a full production pipeline?It's a full production workspace, not a one-shot image/video tool: it tracks projects, chapters, shots, characters, scenes, props, and generation tasks as a connected pipeline.
How do I get it running?The repo ships a ready-to-run Docker Compose setup under deploy/compose/ that starts the frontend, backend, MySQL, Redis and RustFS together; you can also run the backend and frontend separately for local development.
What backend services does it depend on?It needs MySQL for data, Redis for caching/task state, and RustFS for file storage, all wired up in the compose setup alongside the frontend and backend containers.
Do I have to connect my own AI model providers?Yes — Jellyfish manages providers and models itself but doesn't bundle generation engines; you configure multi-provider/multi-model settings and default models per category to actually run image and video generation.
Who is this built for?It targets short/micro-drama creators, AI studios producing video at scale, solo vertical-drama creators, education teams making lesson videos, and brands doing story-driven promos.
How does Jellyfish keep characters and scenes consistent across shots?It maintains a shared entity model for characters/actors, scenes, props, and costumes that can be linked and reused across shots, reducing visual drift between generated shots.
What does the shot preparation workflow look like?Scripts are broken into shots, candidate assets and dialogue are extracted, you confirm or ignore candidates and link existing entities, then the shot moves through a readiness state to the generation workspace.
What is the task center for?It's a unified async job tracker for text, image, and video generation tasks, showing status, progress, elapsed time and results, with cancellation and context-aware navigation back to the related project or shot.
How are frontend and backend kept in sync?Frontend request helpers and TypeScript types are auto-generated from the backend's OpenAPI spec, keeping the API contract consistent between front/src/services/generated/ and the backend.
Creator
Forget-C
Size
8.7 MB
Stars
6.8K
Age
7mo
Commits · 90 Days