Jellyfish — AI Short Drama Studio

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

✨ Core Value

Connect the full production flow: Move from script input to storyboard preparation, image/video generation, and task tracking in one place.
Turn AI output into reusable production assets: Shots, candidate assets, dialogue, prompts, and generation tasks can all be reviewed and reused.
Treat consistency as a first-class problem: Centralized character, scene, prop, and costume management reduces drift across shots.
Handle long-running generation as trackable tasks: Text, image, and video jobs all go through one async task system with status, cancel, and recovery.
Build AI capability as infrastructure: Model management, prompt templates, files, and OpenAPI-based collaboration make the system extensible.

✨ Core Capabilities

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

script understanding
shot preparation
asset consistency
generation execution
task tracking

1. AI script understanding and storyboard breakdown

Split chapter scripts into shots
Extract characters, scenes, props, costumes, and dialogue
Run script optimization, simplification, and consistency checks
Support targeted analysis such as character portraits or scene details

2. Shot preparation and confirmation workflow

The main workflow is:

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

Preparation currently supports:

extracting and refreshing shot candidates
accepting or ignoring asset candidates
accepting or ignoring dialogue candidates
linking existing characters, scenes, props, and costumes
correcting shot-level basic information
using a unified readiness state to decide whether a shot is prepared

3. Asset consistency and reuse

The system maintains a shared entity model across:

characters / actors
scenes
props
costumes

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:

keyframe and reference image management
shot-level video prompt preview
image and video generation tasks
single-shot and batch pre-checks
writing generation outputs back into the shot/media system

5. Unified async task center

Current task infrastructure supports:

async text-processing tasks
async image and video generation tasks
unified task status, result, and elapsed-time tracking
task cancellation
a global task center with context-aware navigation back to project/chapter/shot

6. Model, prompt, and generation infrastructure

Supporting capabilities include:

multi-provider / multi-model management
default model settings by category
prompt template management
file and generated media management
OpenAPI-driven frontend/backend contracts

🚀 Feature Overview

Project and chapter management

Create and manage projects and chapters
Use chapters as the unit for scripts, shots, and generation
Provide dashboard-style entry points and aggregated stats

AI script processing

Break chapter scripts into shots
Extract characters, scenes, props, costumes, and dialogue
Support optimization, simplification, and consistency checks
Support focused analysis such as character portraits or scene information

Shot preparation workflow

Edit shot title, summary, and basic information
Refresh extracted asset and dialogue candidates
Confirm, ignore, or link candidate items
Use preparation state to determine shot readiness
Keep “prepared” distinct from “currently generating”

Asset and entity management

Manage characters, actors, scenes, props, and costumes
Link and reuse them at shot level
Manage entity images
Check name existence to encourage reuse of existing assets

Shot generation workspace

Manage keyframes, reference images, and video prompts
Check video readiness before generation
Launch image/video generation tasks
Support both single-shot and batch generation workflows

Task center

View active and recently finished tasks
Track status, progress, elapsed time, and results
Cancel tasks
Jump back to the related project, chapter, or shot

Model and prompt infrastructure

Manage providers, models, and default settings
Manage prompt templates for images, video, and shots
Generate frontend request helpers and types from OpenAPI
Provide a stable base for future AI workflow expansion

File and media management

Manage uploads and generated outputs
Preview, link, and reuse image/video assets
Preserve shot and entity context around generated media

🎯 Use Cases

Short / micro-drama creators
AI studios producing video content in batches
Solo creators exploring vertical drama production
Education and training teams making lesson videos
Brands and e-commerce teams producing story-driven promos

🔁 Frontend OpenAPI client and type generation

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

front/src/services/generated/

Cached spec file:

front/openapi.json

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

Frontend: http://localhost:7788
Backend: http://localhost:8000 (/docs for Swagger)
MySQL: localhost:${MYSQL_PORT:-3306}
Redis: localhost:${REDIS_PORT:-6379}
RustFS: http://localhost:${RUSTFS_PORT:-9000}

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

GitHub Issues