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

✨ 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