base on AI-Powered Watermark Remover using Florence-2 and LaMA Models: A Python application leveraging state-of-the-art deep learning models to effectively remove watermarks from images with a user-friendly PyQt6 interface. # WatermarkRemover-AI **AI-Powered Watermark Removal Tool using Florence-2 and LaMA Models** 🇬🇧 English | 🇫🇷 Français | 🇨🇳 中文 | 🇯🇵 日本語 | 🇧🇷 Português | 🧠 Brainrot [](https://opensource.org/licenses/MIT) --- ## 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 - **Multi-Language UI** - Available in English, French, Chinese, Japanese, Portuguese, and more - **Themes** - Multiple UI themes to choose from --- ## Installation ### Windows The setup script downloads a portable Python environment automatically - no system Python required. ```powershell 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. ```bash git clone https://github.com/D-Ogi/WatermarkRemover-AI.git cd WatermarkRemover-AI chmod +x setup.sh ./setup.sh ``` After setup, run `./run.sh` to launch the app. ### Optional: FFmpeg Install FFmpeg to preserve audio when processing videos: - **Windows**: Download from [ffmpeg.org](https://ffmpeg.org/download.html) and add to PATH - **Linux**: `sudo apt install ffmpeg` - **macOS**: `brew install ffmpeg` --- ## Usage ### GUI Mode 1. Run the app (`run.bat` on Windows, `./run.sh` on macOS/Linux) 2. Select your preferred language and theme from the top-right corner 3. Select your mode (Single File or Batch) 4. Set input and output paths 5. Configure settings as needed 6. Hit **Start Processing** Your settings are automatically saved and restored on next launch. ### CLI Mode ```bash # 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-out` for 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: 1. Fork the repository 2. Create a feature branch 3. Submit a pull request --- ## License This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. ", Assign "at most 3 tags" to the expected json: {"id":"13555","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"