Remove AI provenance marks from images you generated yourself:

• known visible labels such as the Gemini sparkle and vendor text marks;
• invisible pixel watermarks through diffusion regeneration;
• C2PA, EXIF, XMP, IPTC, and related AI metadata.

Try it online at raiw.cc if you do not want to install Python or run diffusion models locally.

This project is for lawful use on content you own. It does not target stock agency previews or other watermarks that protect third party paid content. See scope, safety, and legal notes.

Install the core CLI:

uv tool install remove-ai-watermarks

Inspect an image:

remove-ai-watermarks identify image.png

Remove a known visible mark and AI metadata:

remove-ai-watermarks visible image.png -o clean.png

Strip metadata without running visible inpainting or diffusion:

remove-ai-watermarks metadata image.png --remove -o clean.png

For invisible watermark removal, install the diffusion dependencies:

uv tool install --force "remove-ai-watermarks[gpu]"
remove-ai-watermarks invisible image.png -o clean.png

If the local detectors cannot confirm an invisible watermark but you know the image came from an AI generator, add --force:

remove-ai-watermarks invisible image.png -o clean.png --force

See the installation guide for Homebrew, uv, optional features, and development setup.

Before	After

The qwen-zimage profile is the highest fidelity option for face heavy images. It is CUDA only and uses a much larger model stack than the default ControlNet profile.

uv tool install --force "remove-ai-watermarks[qwen-zimage]"
remove-ai-watermarks invisible image.png -o clean.png \
  --pipeline qwen-zimage --force

OpenAI example before	OpenAI example after

Gemini example before	Gemini example after

These exact output files were checked with the matching provider verifiers. That result applies to these files, not to every seed, image, or future watermark version.

remove-ai-watermarks visible image.png -o clean.png

The default --mark auto checks all registered visible marks and removes every match. If the mark is visible to you but the detector misses it, select its region explicitly:

remove-ai-watermarks erase image.png \
  --region 1640,1930,400,100 \
  -o clean.png

--region uses x,y,width,height and may be repeated.

The core install uses OpenCV inpainting when no learned backend is installed. For more difficult backgrounds:

uv tool install --force "remove-ai-watermarks[migan]"
remove-ai-watermarks visible image.png -o clean.png --backend migan

uv tool install --force "remove-ai-watermarks[lama]"
remove-ai-watermarks visible image.png -o clean.png --backend lama

remove-ai-watermarks invisible image.png -o clean.png \
  --cpu-offload --force

CPU offload lowers CUDA memory pressure by moving model components between CPU and GPU. It is slower and has no effect on CPU or MPS.

remove-ai-watermarks batch ./images --mode visible
remove-ai-watermarks batch ./images --mode all

Visible mark support includes:

• Google Gemini and Nano Banana sparkle;
• Doubao, Jimeng, Qwen, Kling, Baidu, LibLibAI, and RunningHub labels;
• one calibrated Samsung Galaxy AI label variant.

Metadata and provenance inspection covers C2PA, EXIF, XMP, IPTC, common generator parameters, China TC260 AIGC labels, and several vendor specific signals. Optional decoders add support for open DWT-DCT watermarks and Adobe TrustMark.

The exact support matrix, including important locale and detector limits, lives in supported signals.

Visible removal follows three steps:

1. Detect a registered mark in its expected area.
2. Build a mask around the mark.
3. Fill only the masked region with OpenCV, MI-GAN, or LaMa.

Metadata removal uses format aware stripping. JPEG metadata removal preserves the encoded image scan instead of recompressing it. Other supported containers use their corresponding metadata path.

Invisible removal is different. It regenerates the image through a diffusion pipeline to disrupt pixel and frequency domain watermarks. This changes the image and cannot guarantee that a proprietary verifier will reject every output.

See supported signals and known limitations for the full technical boundary.

import remove_ai_watermarks as raiw

result, removed = raiw.remove_visible("watermarked.png", "clean.png")
print(removed)

The high level API accepts a file path or a BGR NumPy array. For path inputs it also reads provenance metadata, preserves alpha, and can strip AI metadata from the written result.

See the Python API guide for visible removal, provenance inspection, metadata stripping, and diffusion usage.

The separate ComfyUI Remove AI Watermarks package provides nodes for visible removal, detection, region erasing, and invisible removal.

• A missing local signal means unknown, not clean. Proprietary pixel watermarks may remain after metadata has been stripped.
• Visible removal reconstructs a small region. Results depend on the background and selected fill backend.
• Invisible removal changes the whole image and may alter faces, text, or fine detail.
• qwen-zimage requires CUDA. The other diffusion profiles also support the devices listed by remove-ai-watermarks invisible --help.
• Provider watermark systems can change. Validate important outputs with the provider's own verifier when one is available.

Start with the documentation index.

• Installation
• CLI guide
• Python API
• Supported signals
• Known limitations
• Scope, safety, and legal notes
• Module internals
• Release and distribution

Research notes and historical experiments are listed separately in the documentation index. They explain past decisions but do not define the current public API.

Install the development environment and run the project gate:

uv sync --frozen --extra dev
bash maintain.sh

See module internals before changing a subsystem with documented invariants.

Apache 2.0. Copyright 2025-2026 wiltodelta.