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266 lines
9.2 KiB
Markdown
266 lines
9.2 KiB
Markdown
# Remove AI Watermarks
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Remove AI provenance marks from images you generated yourself:
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- known visible labels such as the Gemini sparkle and vendor text marks;
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- invisible pixel watermarks through diffusion regeneration;
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- C2PA, EXIF, XMP, IPTC, and related AI metadata.
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> Try it online at [raiw.cc](https://raiw.cc) if you do not want to install Python
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> or run diffusion models locally.
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[](https://pypi.org/project/remove-ai-watermarks/)
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[](https://pypi.org/project/remove-ai-watermarks/)
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[](https://pepy.tech/project/remove-ai-watermarks)
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[](LICENSE)
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[](https://github.com/wiltodelta/remove-ai-watermarks/actions/workflows/test.yml)
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[](https://github.com/sponsors/wiltodelta)
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> This project is for lawful use on content you own. It does not target stock
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> agency previews or other watermarks that protect third party paid content.
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> See [scope, safety, and legal notes](docs/legal-and-safety.md).
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## Choose what you want to do
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| Goal | Command | GPU |
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| --- | --- | --- |
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| Find provenance signals and watermarks | `identify` | No |
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| Remove known visible AI marks | `visible` | No |
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| Erase a region you select | `erase` | No |
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| Strip AI metadata | `metadata` | No |
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| Regenerate an image to disrupt invisible watermarks | `invisible` | Recommended |
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| Run visible, invisible, and metadata removal | `all` | Recommended |
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| Process a directory | `batch` | Depends on mode |
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## Quick start
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Install the core CLI:
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```bash
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uv tool install remove-ai-watermarks
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```
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Inspect an image:
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```bash
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remove-ai-watermarks identify image.png
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```
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Remove a known visible mark and AI metadata:
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```bash
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remove-ai-watermarks visible image.png -o clean.png
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```
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Strip metadata without running visible inpainting or diffusion:
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```bash
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remove-ai-watermarks metadata image.png --remove -o clean.png
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```
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For invisible watermark removal, install the diffusion dependencies:
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```bash
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uv tool install --force "remove-ai-watermarks[gpu]"
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remove-ai-watermarks invisible image.png -o clean.png
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```
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If the local detectors cannot confirm an invisible watermark but you know the
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image came from an AI generator, add `--force`:
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```bash
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remove-ai-watermarks invisible image.png -o clean.png --force
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```
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See the [installation guide](docs/installation.md) for Homebrew, uv, optional
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features, and development setup.
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## Examples
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### Visible Gemini mark
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| Before | After |
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| --- | --- |
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|  |  |
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### High quality invisible removal
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The `qwen-zimage` profile is the highest fidelity option for face heavy images.
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It is CUDA only and uses a much larger model stack than the default ControlNet
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profile.
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```bash
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uv tool install --force "remove-ai-watermarks[qwen-zimage]"
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remove-ai-watermarks invisible image.png -o clean.png \
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--pipeline qwen-zimage --force
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```
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| OpenAI example before | OpenAI example after |
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| --- | --- |
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| [](data/synthid/originals/ChatGPT%20Image%20May%2030,%202026,%2010_31_08%20AM.png) | [](docs/images/qwen-zimage/ChatGPT/ChatGPT%20Image%20May%2030,%202026,%2010_31_08%20AM_full_clean.png) |
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| Gemini example before | Gemini example after |
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| --- | --- |
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| [](data/synthid/originals/Gemini_Generated_Image_633uuy633uuy633u.png) | [](docs/images/qwen-zimage/Gemini/Gemini_Generated_Image_633uuy633uuy633u_full_clean.png) |
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These exact output files were checked with the matching provider verifiers. That
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result applies to these files, not to every seed, image, or future watermark
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version.
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## Common recipes
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### Remove every detected visible mark
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```bash
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remove-ai-watermarks visible image.png -o clean.png
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```
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The default `--mark auto` checks all registered visible marks and removes every
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match. If the mark is visible to you but the detector misses it, select its
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region explicitly:
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```bash
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remove-ai-watermarks erase image.png \
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--region 1640,1930,400,100 \
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-o clean.png
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```
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`--region` uses `x,y,width,height` and may be repeated.
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### Use a learned fill backend
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The core install uses OpenCV inpainting when no learned backend is installed.
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For more difficult backgrounds:
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```bash
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uv tool install --force "remove-ai-watermarks[migan]"
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remove-ai-watermarks visible image.png -o clean.png --backend migan
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```
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```bash
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uv tool install --force "remove-ai-watermarks[lama]"
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remove-ai-watermarks visible image.png -o clean.png --backend lama
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```
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### Reduce CUDA memory use
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```bash
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remove-ai-watermarks invisible image.png -o clean.png \
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--cpu-offload --force
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```
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CPU offload lowers CUDA memory pressure by moving model components between CPU
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and GPU. It is slower and has no effect on CPU or MPS.
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### Process a directory
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```bash
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remove-ai-watermarks batch ./images --mode visible
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remove-ai-watermarks batch ./images --mode all
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```
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## What the tool can recognize
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Visible mark support includes:
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- Google Gemini and Nano Banana sparkle;
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- Doubao, Jimeng, Qwen, Kling, Baidu, LibLibAI, and RunningHub labels;
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- one calibrated Samsung Galaxy AI label variant.
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Metadata and provenance inspection covers C2PA, EXIF, XMP, IPTC, common
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generator parameters, China TC260 AIGC labels, and several vendor specific
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signals. Optional decoders add support for open DWT-DCT watermarks and Adobe
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TrustMark.
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The exact support matrix, including important locale and detector limits, lives
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in [supported signals](docs/supported-signals.md).
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## How it works
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Visible removal follows three steps:
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1. Detect a registered mark in its expected area.
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2. Build a mask around the mark.
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3. Fill only the masked region with OpenCV, MI-GAN, or LaMa.
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Metadata removal uses format aware stripping. JPEG metadata removal preserves
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the encoded image scan instead of recompressing it. Other supported containers
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use their corresponding metadata path.
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Invisible removal is different. It regenerates the image through a diffusion
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pipeline to disrupt pixel and frequency domain watermarks. This changes the
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image and cannot guarantee that a proprietary verifier will reject every
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output.
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See [supported signals](docs/supported-signals.md) and
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[known limitations](docs/known-limitations.md) for the full technical boundary.
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## Python API
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```python
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import remove_ai_watermarks as raiw
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result, removed = raiw.remove_visible("watermarked.png", "clean.png")
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print(removed)
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```
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The high level API accepts a file path or a BGR NumPy array. For path inputs it
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also reads provenance metadata, preserves alpha, and can strip AI metadata from
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the written result.
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See the [Python API guide](docs/python-api.md) for visible removal, provenance
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inspection, metadata stripping, and diffusion usage.
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## ComfyUI
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The separate
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[ComfyUI Remove AI Watermarks](https://github.com/wiltodelta/ComfyUI-remove-ai-watermarks)
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package provides nodes for visible removal, detection, region erasing, and
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invisible removal.
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## Important limitations
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- A missing local signal means unknown, not clean. Proprietary pixel
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watermarks may remain after metadata has been stripped.
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- Visible removal reconstructs a small region. Results depend on the background
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and selected fill backend.
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- Invisible removal changes the whole image and may alter faces, text, or fine
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detail.
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- `qwen-zimage` requires CUDA. The other diffusion profiles also support the
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devices listed by `remove-ai-watermarks invisible --help`.
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- Provider watermark systems can change. Validate important outputs with the
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provider's own verifier when one is available.
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## Documentation
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Start with the [documentation index](docs/index.md).
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- [Installation](docs/installation.md)
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- [CLI guide](docs/cli.md)
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- [Python API](docs/python-api.md)
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- [Supported signals](docs/supported-signals.md)
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- [Known limitations](docs/known-limitations.md)
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- [Scope, safety, and legal notes](docs/legal-and-safety.md)
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- [Module internals](docs/module-internals.md)
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- [Release and distribution](docs/release-and-distribution.md)
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Research notes and historical experiments are listed separately in the
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[documentation index](docs/index.md). They explain past decisions but do not
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define the current public API.
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## Contributing
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Install the development environment and run the project gate:
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```bash
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uv sync --frozen --extra dev
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bash maintain.sh
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```
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See [module internals](docs/module-internals.md) before changing a subsystem
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with documented invariants.
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## License
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[Apache 2.0](LICENSE). Copyright 2025-2026 wiltodelta.
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