Remove AI Watermarks

Remove AI provenance marks from images and video 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.

Video support covers provenance identification, complete visible-plus-metadata
cleaning, directory batches, visible Sora, Veo, Seedance, Dola, Hailuo, and
Kling mark removal, and oracle-certified VAE regeneration for video SynthID
removal.

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.

Choose what you want to do

Installation modes

Lower-level and specialized extras include pixels, heif, trustmark,
migan, lama, and diffusion. The
installation guide documents their exact
dependency composition and model requirements.

Quick start

Install the metadata-focused default CLI:

uv tool install remove-ai-watermarks

Inspect an image:

remove-ai-watermarks identify image.png

For visible watermark removal, install the pixel dependencies:

uv tool install --force "remove-ai-watermarks[visible]"

Then 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

Without -o this command overwrites the source in place.

Inspect or remove AI metadata from an MP4, MOV, M4V, WebM, MKV, AVI, or FLV
file:

remove-ai-watermarks video metadata input.mp4 --check
remove-ai-watermarks video metadata input.mp4 --remove -o clean.mp4

The video metadata command does not transcode video or audio streams. Unlike
the image command above, when -o is omitted it writes <source>_clean and
preserves the original. MP4 and MOV
inspection includes the native TC260 AIGC tag in
moov.udta.meta.keys/ilst, including a moov placed after the media payload.
MKV and WebM inspection reads the normative
Segment.Tags.Tag.SimpleTag placement. AVI uses LIST/INFO/AIGC, while FLV
uses script.onMetaData.AIGC. The non-ISOBMFF formats are remuxed with stream
copy for removal.

Use the product-oriented video path to identify or clean a file:

uv tool install --force "remove-ai-watermarks[video]"
remove-ai-watermarks video identify input.mp4
remove-ai-watermarks video all input.mp4 -o clean.mp4

video all removes a stable registered visible mark when present and always
strips verified AI metadata. If neither signal is found, it still writes a
same-container passthrough, so application callers get one predictable output
contract. Proprietary invisible-video removal is excluded by default.
--invisible opts into the lossy, oracle-certified video SynthID profile.

Process a directory with the same contract:

remove-ai-watermarks video batch ./videos --mode all

Remove a supported visible video mark:

remove-ai-watermarks video visible input.mp4 -o clean.mp4
remove-ai-watermarks video visible veo.mp4 --mark veo -o veo_clean.mp4
remove-ai-watermarks video visible seedance.mp4 --mark seedance -o seedance_clean.mp4
remove-ai-watermarks video visible dola.mp4 --mark dola -o dola_clean.mp4
remove-ai-watermarks video visible hailuo.mp4 --mark hailuo -o hailuo_clean.mp4
remove-ai-watermarks video visible kling.mp4 --mark kling -o kling_clean.mp4

This path scans the complete sequence before changing pixels. It accepts only a
mark that repeats at a stable position across adjacent frames, then reuses the
same OpenCV, MI-GAN, or LaMa fill backends as image removal. Audio is copied
without re-encoding and is allowed to reach its natural end; the video stream
is transcoded because its pixels change. By default, a guarded optical-flow
pass motion-aligns the preceding accepted fill and blends it only when the
nearby source context agrees; use --no-temporal-consistency to disable it.
The encoder preserves supported
8-bit source chroma sampling, color tags, and MP4/MOV track timescale instead
of relying on ffmpeg's implicit raw-BGR defaults. Variable frame intervals are
preserved through a timestamped in-memory NUT bridge instead of being flattened
to the average frame rate. Non-zero source start timestamps are retained
together with the copied audio offset. The default --mark auto
scans all providers in one decode pass and selects the first stable match in
the specificity order shown below. Pass an explicit mark to restrict detection
to one provider.
Sora covers the moving Sora 2 mascot and wordmark. Veo covers both the current
four-point diamond and the legacy Veo text. Seedance covers the fixed boxed
AI label, Dola covers the fixed Dola AI text, Hailuo covers the composite
MINIMAX | hailuo AI label, and Kling covers the bottom-right KLING AI
label with its version suffix. A completed encode is published atomically. No
output is written when no stable mark is found.
HDR, PQ/HLG, and greater-than-8-bit inputs are rejected before encoding rather
than silently reduced through OpenCV's 8-bit BGR boundary.

Remove video SynthID:

uv tool install --force "remove-ai-watermarks[video,diffusion]"
remove-ai-watermarks video invisible input.mp4 -o clean.mp4

This path regenerates the complete sequence with one latent-noise field shared
across time, copies complete audio, strips source metadata, and publishes the
completed encode atomically. The default noise_std=0.15 profile passed both
the two-carrier calibration and a complete public eight-second Veo oracle
check. Google does not publish a local decoder, so a fresh provider check
remains useful for unusually important files or after provider changes, but it
is not a product result state.

For invisible watermark removal, install the qwen-zimage extra. An NVIDIA GPU
is required: both profiles are CUDA-only, and there is no CPU or MPS fallback.

uv tool install --force "remove-ai-watermarks[qwen-zimage]"
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.

Examples

Visible Gemini mark

Before	After

High quality invisible removal

qwen-zimage is the default profile: a Qwen-Image-2512 Lightning pass under Canny
ControlNet, followed by SAM-masked Z-Image repair of any detected face. The
alternative, sdxl-zimage, swaps the global stage for SDXL and keeps the same face
stage. Both are CUDA only.

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

Common recipes

Remove every detected visible mark

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.

Use a learned fill backend

The visible extra uses OpenCV inpainting when no learned backend is installed.
For more difficult backgrounds, the learned-backend extras include the same
pixel dependencies automatically:

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

Reduce CUDA memory use

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, at the cost of speed.

Process a directory

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

What the tool can recognize

Visible mark support includes:

• Google Gemini and Nano Banana sparkle;
• Doubao, Jimeng, Qwen, Kling, Yuanbao, 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.

How it works

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. Native MP4/MOV TC260 values
are blanked without changing box sizes or media offsets. 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.

Python API

The visible-removal API requires remove-ai-watermarks[visible].

import remove_ai_watermarks as raiw

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

provenance = raiw.identify_video("input.mp4")
report = raiw.inspect_video_metadata("input.mp4")
complete = raiw.remove_video_all("input.mp4", "clean.mp4")
batch = raiw.remove_video_batch("videos", "videos_clean")
cleaned = raiw.remove_video_metadata("input.mp4")
synthid_cleaned = raiw.remove_video_invisible("input.mp4", "synthid_clean.mp4")
visible = raiw.remove_video_visible("input.mp4", "clean.mp4")
print(visible.mark)
veo = raiw.remove_video_visible("veo.mp4", "veo_clean.mp4", mark="veo")
seedance = raiw.remove_video_visible(
    "seedance.mp4",
    "seedance_clean.mp4",
    mark="seedance",
)
dola = raiw.remove_video_visible("dola.mp4", "dola_clean.mp4", mark="dola")

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, the full
remove_all and remove_batch pipeline, provenance inspection, metadata
stripping, and diffusion usage.

ComfyUI

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

Important limitations

• 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.
• Visible video removal recognizes the moving Sora 2 wordmark, the current Veo
  diamond plus legacy Veo text, the Seedance boxed AI label, and the fixed
  Dola, Hailuo, and Kling labels. It does not recognize the older Sora Turbo
  corner swirl or unregistered layouts from those providers.
  The classical OpenCV backend can smear structured backgrounds; use MI-GAN or
  LaMa when recovery quality matters.
• Video SynthID regeneration changes resolution, frame rate, and image detail.
  The shipped profile is oracle-certified, but no public local decoder can
  certify an arbitrary output at runtime. Recheck unusually important outputs
  after provider changes.
• Invisible-watermark removal requires CUDA. Both profiles refuse any other
  device at construction rather than falling back to one that cannot run them.
  Visible removal, metadata stripping and identify still run anywhere.
• Provider watermark systems can change. Validate important outputs with the
  provider's own verifier when one is available.

The shipped video invisible command uses the certified noise_std=0.15
profile. The companion scripts/video_synthid_sweep.py research harness builds
a matched re-encode control plus VAE-regenerated candidates and leaves the
verifier verdict blank:

uv run --extra video --extra diffusion python scripts/video_synthid_sweep.py input.mp4 -o sweep/

To score what an output actually cost, use scripts/video_fidelity_probe.py:
the engine's own PSNR is measured before the resize and the encode, so only the
probe sees the delivered picture.

uv run --extra video python scripts/video_fidelity_probe.py input.mp4 input_clean.mp4

The control must still be SynthID-positive before a negative candidate can
count as removal evidence. In the 2026-07-29 two-clip calibration, both matched
controls were positive in Gemini's built-in SynthID verifier; the stronger
candidate was negative on both carriers, while a weaker candidate was
negative on one. A later adversarial follow-up that asked ordinary Gemini to
reinterpret the pixel result returned UNAVAILABLE; that follow-up was not a
verifier rerun and does not invalidate the built-in verdicts. A 2026-07-31
full-clip check on a public eight-second Veo sample found 0.10 still detected
and 0.15 not detected, so 0.15 is now the certified default. The
reproducible hashes and verdicts live in
data/evaluations/video-synthid-oracle.csv.

Documentation

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.

Contributing

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.

License

Apache 2.0. Copyright 2025-2026 wiltodelta.