WatermarkRemover-AI
AI-Powered Watermark Removal Tool using Florence-2 and LaMA Models
Overview
WatermarkRemover-AI is a cutting-edge application that leverages AI models for precise watermark detection and seamless removal. Perfect for removing watermarks from AI-generated videos like Sora, Sora 2, Runway, and others.
It uses Florence-2 from Microsoft for watermark identification and LaMA for inpainting to fill in the removed regions naturally. The software features a modern GUI built with PyWebview for an accessible and intuitive experience.
Screenshot
Demo
https://github.com/user-attachments/assets/505be2a8-8eda-4def-90b6-5a4ceefee456
Features
- Smart Detection - AI-powered watermark detection using Florence-2
- Seamless Removal - LaMA inpainting for natural-looking results
- Video Support - Process videos with two-pass detection and audio preservation
- AI Video Ready - Remove watermarks from Sora, Sora 2, Runway, and other AI-generated videos
- Batch Processing - Handle entire folders at once
- Preview Mode - Preview detected watermarks before processing
- Fade In/Out Handling - Extend masks for watermarks that fade in/out
- GPU Acceleration - CUDA support for faster processing
Installation
Windows
The setup script downloads a portable Python environment automatically - no system Python required.
git clone https://github.com/D-Ogi/WatermarkRemover-AI.git
cd WatermarkRemover-AI
.\setup.ps1
After setup, double-click run.bat to launch the app.
Linux / macOS
Requires Python 3.10+ installed on your system.
git clone https://github.com/D-Ogi/WatermarkRemover-AI.git
cd WatermarkRemover-AI
chmod +x setup.sh
./setup.sh
After setup:
source venv/bin/activate
python remwmgui.py
Optional: FFmpeg
Install FFmpeg to preserve audio when processing videos:
- Windows: Download from ffmpeg.org and add to PATH
- Linux:
sudo apt install ffmpeg - macOS:
brew install ffmpeg
Usage
GUI Mode
- Run the app (
run.baton Windows, orpython remwmgui.py) - Select your mode (Single File or Batch)
- Set input and output paths
- Configure settings as needed
- Hit LET HIM COOK
CLI Mode
# Basic usage
python remwm.py input.png output_folder/
# With options
python remwm.py ./images ./output --overwrite --max-bbox-percent=15 --force-format=PNG
# Process video with two-pass detection
python remwm.py video.mp4 ./output --detection-skip=3 --fade-in=0.5 --fade-out=0.5
# Preview mode (detect without processing)
python remwm.py input.png --preview
CLI Options
| Option | Description |
|---|---|
--overwrite |
Overwrite existing files |
--transparent |
Make watermark regions transparent (images only) |
--max-bbox-percent |
Max detection size as % of image (default: 10) |
--force-format |
Force output format (PNG, WEBP, JPG, MP4, AVI) |
--detection-prompt |
Custom detection prompt (default: "watermark") |
--detection-skip |
Detect every N frames for videos (1-10, default: 1) |
--fade-in |
Extend mask backwards by N seconds (for fade-in watermarks) |
--fade-out |
Extend mask forwards by N seconds (for fade-out watermarks) |
--preview |
Preview detected watermarks without processing |
Video Processing
- Supported formats: MP4, AVI, MOV, MKV, FLV, WMV, WEBM
- Audio preservation: Requires FFmpeg installed
- Two-pass mode: Faster processing with
--detection-skip> 1 - Fade handling: Use
--fade-in/--fade-outfor watermarks that appear/disappear gradually
Tech Stack
- Florence-2 - Microsoft's vision model for watermark detection
- LaMA - Large Mask Inpainting model
- PyWebview - Cross-platform webview wrapper
- Alpine.js - Lightweight JavaScript framework for UI
- PyTorch - Deep learning backend
Contributing
Contributions are welcome! Feel free to:
- Fork the repository
- Create a feature branch
- Submit a pull request
License
This project is licensed under the MIT License. See the LICENSE file for details.