Skip to main content

Desktop app for local video generation using SANA-WM

Project description

Local Video Gen Studio

Generate stunning AI video clips from text—no subscriptions, no cloud fees, no waiting. Just your GPU and total creative control.

What is this?

Local Video Gen Studio is a cross-platform desktop application that brings professional video generation to your machine. Built around SANA-WM (a 2.6B open-source video model), it lets creators generate 720p video clips from text prompts in minutes. Process batches overnight, export to your editing timeline, and keep everything private—no cloud, no surveillance, no vendor lock-in.

Perfect for YouTubers building B-roll libraries, course creators illustrating concepts, and indie game developers prototyping visual effects without licensing costs.

Features

  • Text-to-Video Generation – Convert detailed prompts into 720p video clips up to 60 seconds
  • Batch Processing – Queue multiple videos and render overnight while you sleep
  • Local-Only Inference – Your GPU, your data, your privacy. No cloud uploads
  • GPU Auto-Detection – Automatically optimizes for NVIDIA/AMD/Metal
  • Prompt Templates – Pre-built templates for common use cases (product demos, tutorials, game footage)
  • One-Click Model Download – Automatic SANA-WM model initialization on first launch
  • Export Ready – Direct MP4 output compatible with any video editor
  • Cross-Platform – macOS, Windows, and Linux via Tauri

Quick Start

Requirements

  • 12GB+ VRAM (NVIDIA RTX 3060 Ti / RTX 4070 or equivalent)
  • Python 3.10+
  • Node.js 16+
  • 50GB free disk space (for model + cache)

Installation

  1. Clone and install dependencies

    git clone https://github.com/yourusername/local-video-gen-studio.git
    cd local-video-gen-studio
    
    # Frontend
    npm install
    
    # Backend
    python -m venv venv
    source venv/bin/activate  # Windows: venv\Scripts\activate
    pip install -r backend/requirements.txt
    
  2. Configure environment

    cp .env.example .env
    # Edit .env with your NVIDIA_API_KEY (optional) and output paths
    
  3. Launch the app

    npm run tauri dev
    

The app will download SANA-WM (~3.2GB) on first run and auto-detect your GPU.

Usage

Generate a Single Video

  1. Open Local Video Gen Studio
  2. Enter a detailed text prompt (e.g., "A silver MacBook Pro sliding across a wooden desk, warm lighting, cinematic")
  3. Select resolution (480p free tier, 720p Pro)
  4. Click Generate
  5. Monitor progress in the queue panel
  6. Export to MP4 when complete

Batch Generate Variations

  1. Create a prompt template with variables: "A {color} {object} {action}, {lighting}"
  2. Upload a CSV with variations
  3. Click Queue All – generates overnight
  4. Retrieve finished videos in the exports folder

Example Prompts

"Overhead shot of coffee pouring into a white ceramic mug, steam rising, warm sunlight"

"Low-angle tracking shot through a forest canopy, autumn leaves, misty morning light"

"Screen recording aesthetic: cursor clicking through a minimalist dashboard, smooth transitions"

Tech Stack

Frontend:

  • TypeScript + React (Vite)
  • Tauri (lightweight Rust native shell)
  • CSS3 for responsive UI

Backend:

  • Python 3.10+
  • SANA-WM (2.6B parameter open-source video model)
  • PyTorch/CUDA for inference
  • FastAPI for IPC
  • SQLite for job queue persistence

Infrastructure:

  • Stripe (optional monetization integration)
  • Local GPU orchestration (no external services)

Development

# Run development server (hot reload)
npm run tauri dev

# Build production binaries
npm run tauri build

# Backend development (watch mode)
cd backend && python main.py --dev

# Type checking
npm run type-check

# Lint
npm run lint

Roadmap

  • Frame interpolation (60fps output)
  • LoRA fine-tuning for style consistency
  • Realtime preview in editor
  • Audio sync generation
  • Commercial license watermark removal

Licensing

MIT License – See LICENSE file for details.


Questions? Open an issue or check out OVERVIEW.md for deeper architecture docs.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

local_video_gen_studio-0.1.0.tar.gz (17.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

local_video_gen_studio-0.1.0-py3-none-any.whl (13.1 kB view details)

Uploaded Python 3

File details

Details for the file local_video_gen_studio-0.1.0.tar.gz.

File metadata

  • Download URL: local_video_gen_studio-0.1.0.tar.gz
  • Upload date:
  • Size: 17.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for local_video_gen_studio-0.1.0.tar.gz
Algorithm Hash digest
SHA256 c29a5f1856d03473210461f17499c41d7cdc62786d64f4a4255882aeb72e5d22
MD5 efdbd782209893eda9ae6256ed4c8d8a
BLAKE2b-256 ad2bfb99205bf0153648cceb16a4a6d09a6137399db7cd5de15251c8133df9d7

See more details on using hashes here.

File details

Details for the file local_video_gen_studio-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for local_video_gen_studio-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 88646bac82f0ad84587fa7a4c59ce4b8bd82c6980a1de5d94e8c36f11a8f544f
MD5 21b6788ba7135dd18911337e64b24938
BLAKE2b-256 3a3356c4c2ab51a03c415b3d0ab254e4e5ee9d8db43529cb088bfcc8c564fa2b

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page