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https://github.com/wiltodelta/remove-ai-watermarks.git
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137 lines
2.9 KiB
Markdown
137 lines
2.9 KiB
Markdown
# Installation
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Python 3.10.1 or newer is required.
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## Core install
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The core package provides:
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- provenance inspection;
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- visible watermark removal with OpenCV;
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- manual region erasing with OpenCV;
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- AI metadata inspection and removal.
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Install it as an isolated command with uv:
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```bash
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uv tool install remove-ai-watermarks
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```
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Or with pipx:
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```bash
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pipx install remove-ai-watermarks
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```
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You can also install the Homebrew package on macOS or Linux:
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```bash
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brew install wiltodelta/tap/remove-ai-watermarks
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```
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## Invisible watermark removal
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Diffusion based removal needs the `gpu` extra:
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```bash
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uv tool install --force "remove-ai-watermarks[gpu]"
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```
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The code supports CUDA, XPU, MPS, and CPU devices. A GPU is recommended because
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CPU inference is slow.
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For the CUDA only Qwen Image plus Z-Image profile:
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```bash
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uv tool install --force "remove-ai-watermarks[qwen-zimage]"
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```
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The `qwen-zimage` extra includes the normal `gpu` dependencies.
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## Optional features
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Install only what you need:
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| Extra | Adds |
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| --- | --- |
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| `migan` | MI-GAN ONNX fill backend |
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| `lama` | big-LaMa ONNX fill backend |
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| `detect` | Open DWT-DCT watermark decoder used by `identify` |
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| `trustmark` | Adobe TrustMark decoder |
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| `esrgan` | Real-ESRGAN upscaling before diffusion |
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| `qwen-zimage` | CUDA only Qwen Image plus Z-Image pipeline |
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Example:
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```bash
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uv tool install --force "remove-ai-watermarks[migan,detect]"
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```
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Some optional models download their weights on first use.
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## Install from the repository
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```bash
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git clone https://github.com/wiltodelta/remove-ai-watermarks.git
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cd remove-ai-watermarks
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uv sync --frozen
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```
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Add the feature groups required for your work:
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```bash
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uv sync --frozen --extra dev
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uv sync --frozen --extra dev --extra gpu
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```
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Run commands from the repository root:
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```bash
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uv run remove-ai-watermarks --help
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```
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## Development setup
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Install development dependencies:
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```bash
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uv sync --frozen --extra dev
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```
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Run the complete project gate:
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```bash
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bash maintain.sh
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```
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The script runs dependency checks, linting, formatting checks, type checking,
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and the test suite.
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## Hugging Face authentication
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Pass a Hugging Face token directly when the selected model or account requires
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one:
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```bash
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remove-ai-watermarks invisible image.png --hf-token "$HF_TOKEN"
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```
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The CLI also loads `HF_TOKEN` from the environment and from a local `.env`
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file. The same name is documented in `.env.example`.
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## Troubleshooting
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### The first model run is slow
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Diffusion and learned fill backends may download model weights on first use.
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Later runs reuse their caches.
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### The command skips invisible removal
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The normal behavior is to skip diffusion when no supported local signal is
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found. A missing signal does not prove that the image is clean. If you know the
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image came from a relevant generator, use `--force`.
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If the CLI reports that diffusion dependencies are unavailable, install the
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`gpu` extra.
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