| --- |
| license: other |
| license_name: non-commercial |
| license_link: LICENSE |
| tags: |
| - image-to-image |
| - watermark-removal |
| - remove-watermark |
| - watermark |
| - torchscript |
| - computer-vision |
| - image-processing |
| - image-restoration |
| - image-cleaning |
| pipeline_tag: image-to-image |
| library_name: pytorch |
| --- |
| |
| # Fast Watermark Removal |
|
|
| A high-performance TorchScript model for removing watermarks from images. This model uses a dual-stage architecture optimized for speed and quality. |
|
|
| ## Test the Model |
|
|
| Try the model instantly in your browser — no setup required: |
|
|
| **[Remove Watermarks → clearpics.ai](https://clearpics.ai/remove-watermarks)** |
|
|
| ## Features |
|
|
| - **Fast inference**: ~500ms per image (RTX 4090) |
| - **High quality**: Preserves image details while effectively removing watermarks |
| - **Production-ready**: Compiled TorchScript model, no training code needed |
| - **Memory efficient**: Requires 11.5GB VRAM |
|
|
| ## Technical Details |
|
|
| - **Architecture**: Dual-stage with Swin2 Transformers |
| - **Format**: TorchScript (.ts) compiled model |
| - **Input**: RGB images (any resolution) |
| - **Output**: RGB images (max 768px, aspect ratio preserved) |
| - **Precision**: FP32 with TensorFloat32 matmul on Ampere+ GPUs |
| - **Batch size**: 1 |
|
|
| ## Limitations |
|
|
| - **Output resolution**: Limited to 768px maximum dimension (aspect ratio preserved) |
|
|
| ## Commercial License |
|
|
| A commercial license with **1536px maximum output resolution** is available for production use. The 1536px model maintains identical: |
|
|
| - VRAM requirements (11.5GB) |
| - Inference times (~500ms) |
| - Image Output |
|
|
| **Contact**: contact by email for commercial licensing inquiries |
|
|
| ## Installation |
|
|
| ### Requirements |
|
|
| - Python 3.10+ |
| - CUDA-capable GPU with 11.5GB+ VRAM |
| - PyTorch 2.0+ |
|
|
| ### Setup |
|
|
| ```bash |
| # Install dependencies |
| pip install -r requirements.txt |
| ``` |
|
|
| ## Usage |
|
|
| ### Single Image |
|
|
| ```bash |
| python inference.py -i /path/to/watermarked/image.jpg -m model.ts -o output_folder |
| ``` |
|
|
| ### Batch Processing |
|
|
| ```bash |
| python inference.py -f /path/to/images/folder -m model.ts |
| ``` |
|
|
| ### Arguments |
|
|
| - `-i, --image`: Path to single input watermarked image |
| - `-f, --folder`: Path to folder containing watermarked images (processes all .jpg and .webp files) |
| - `-m, --model_path`: Path to TorchScript model file (required) |
| - `-o, --output_folder`: Output folder for results (default: `tests`) |
|
|
| ### Output |
|
|
| The script saves two files per input: |
|
|
| 1. **Original image**: Copied to output folder with original filename |
| 2. **Clean image**: Saved as WebP with `-clean.webp` suffix |
|
|
| Images are automatically resized to maintain aspect ratio while respecting the 768px maximum dimension. |
|
|
| ## How It Works |
|
|
| The model uses a two-stage pipeline: |
|
|
| 1. **Stage 1**: Removes 90-95% of watermarks |
| 2. **Stage 2**: Removes remaining watermarks |
| 3. **Post-processing**: Automatic resizing to original aspect ratio (capped at 768px) |
|
|
| All processing (including resizing and normalization) is performed within the compiled TorchScript model for optimal performance. |
|
|
| ## Future Improvements |
|
|
| I'm actively exploring ways to enhance this model's capabilities. If you have suggestions, encounter issues, or are interested in collaborating on improvements, please reach out! |
|
|
|
|
| ## License |
|
|
| This model is provided for **non-commercial research and personal use only**. For commercial applications, please contact by email for licensing options. |
|
|
| ## Support |
|
|
| - **Issues**: Open an issue on the HuggingFace repository |
| - **Questions**: jason@engageify.com |
| - **Commercial licensing**: jason@engageify.com |
|
|
