Jellyfish

# 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: ```bash 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 ```bash 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 ```bash cd backend cp .env.example .env uv sync uv run uvicorn app.main:app --reload --host 0.0.0.0 --port 8000 ``` ### Frontend ```bash cd front pnpm install pnpm dev ``` ## License This project is licensed under [Apache-2.0](./LICENSE). ## Community & Feedback - [GitHub Issues](https://github.com/Forget-C/Jellyfish/issues)