Remove AI provenance marks from images and video you generated yourself:
- known visible labels such as the Google Gemini sparkle watermark and vendor text marks;
- invisible pixel watermarks through direct local-format disruption or diffusion regeneration;
- C2PA, EXIF, XMP, IPTC, and related AI metadata.
| Before | After |
|---|---|
 |
 |
uv tool install "remove-ai-watermarks[visible]" # CLI for visible marks and metadata
npx skills add wiltodelta/remove-ai-watermarks # agent skill for Claude Code, Codex, Cursor, and other agentsVideo support covers provenance identification, complete visible-plus-metadata cleaning, directory batches, visible Sora, Veo, Seedance, Doubao, Dola, Hailuo AI, and Kling AI mark removal, and oracle-certified VAE regeneration for video SynthID removal.
raiw.cc runs this library as a hosted service, with the GPU included and nothing to install. Visible mark and metadata removal at Standard output up to 12 MP are free there; original resolution above 12 MP and invisible watermark removal are paid.
This is a research project on AI provenance signals, for lawful use on content you own. It is provided as is, without warranty or liability, and each user is responsible for how they use it. It does not target stock agency previews or other watermarks that protect third party paid content. Some jurisdictions, including China, restrict removing AI labels or supplying tools that do. See scope, safety, and legal notes.
| Goal | Command | GPU |
|---|---|---|
| Find provenance signals and watermarks | identify |
No |
| Classify a photograph from pixels (opt-in, not provenance) | classify |
No |
| Remove known visible AI marks | visible |
No |
| Erase a region you select | erase |
No |
| Strip AI metadata | metadata |
No |
| Identify supported video provenance | video identify |
No |
| Remove visible marks and AI metadata from video | video all |
No |
| Strip AI metadata from video | video metadata |
No |
| Remove a registered visible AI mark from video | video visible |
No |
| Process a directory of videos | video batch |
Depends on mode |
| Apply the calibrated video-pixel SynthID-removal profile | video invisible |
Recommended |
| Regenerate an image to disrupt invisible watermarks | invisible |
Required (CUDA) |
| Run visible, invisible, and metadata removal | all |
Recommended |
| Process a directory | batch |
Depends on mode |
Microsoft Paint and Photos InvisMark declarations are routed automatically to
pixel regeneration. The all command removes both the hidden pixel watermark and
its linked C2PA manifest; metadata stripping alone removes only the manifest. A
specialized Python API can inspect and disrupt the validated local
Watermarker.dll payload without diffusion.
| Need | Install |
|---|---|
| Metadata inspection and stripping | remove-ai-watermarks |
| Photograph AI-versus-camera classification | remove-ai-watermarks[classify] |
| 10-provider source-export & model-version classification | remove-ai-watermarks[source-classify] |
| Visible detection and removal | remove-ai-watermarks[visible] |
| Visible video processing | remove-ai-watermarks[video] |
| Video SynthID removal | remove-ai-watermarks[video,diffusion] |
| Torch-free DWT-DCT detection | remove-ai-watermarks[detect] |
| Direct local Paint InvisMark disruption | remove-ai-watermarks[pixels] |
| Invisible image removal (needs CUDA) | remove-ai-watermarks[qwen-zimage] |
| Every production feature available on the active Python | remove-ai-watermarks[all] |
Lower-level and specialized extras include pixels, heif, trustmark,
migan, lama, diffusion, classify-onnx, and source-classify. The
installation guide documents their exact
dependency composition, Python compatibility, and model requirements.
Install the metadata-focused default CLI:
uv tool install remove-ai-watermarksInspect an image:
remove-ai-watermarks identify image.pngTo classify a photograph from pixels (AI versus camera, optional provider),
install the extra and call classify. identify never starts it:
uv tool install --force "remove-ai-watermarks[classify]"
remove-ai-watermarks classify image.pngGuide: photo pixel classification.
For a lightweight, abstaining 10-provider source-export and model-version
signal after metadata removal (classify_source and CLI classify-source),
use the separate API or CLI command. It is not a SynthID detector:
remove-ai-watermarks classify-source image.pngimport remove_ai_watermarks as raiw
result = raiw.classify_source("image.png")
print(result.label, result.version_subfamily, result.reason)Install remove-ai-watermarks[source-classify]. Guide:
source-pipeline classification.
Signed provenance is the supported route for SynthID and identify reads it.
There is no local SynthID pixel detector in the package. Research on a
periodic lattice expert is in scripts/synthid_runtime/ and
synthid-detector-research.md.
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.pngStrip metadata without running visible inpainting or diffusion:
remove-ai-watermarks metadata image.png --remove -o clean.pngWithout -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.mp4The 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,
plus the QuickTime-form meta variants Doubao's iOS export writes (a bare
meta box as a direct moov child, and a keyless hdlr=mdir metadata list).
Keyed workflow and prompt entries in the same metadata-list structure are
also detected and removed, including ComfyUI exports.
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.mp4video 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 allRemove 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.mp4This 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. Valid Google AI-video provenance breaks a
Sora/Veo cross-match in favor of Veo when the diamond is nested in the Sora
region or Veo persists through the complete clip while Sora does not.
Pass an explicit mark to restrict detection to one provider.
Sora covers the moving Sora 2 mascot, with or without the wordmark; mascot-only
matches also require the complete two-eye shape. 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 AI covers the composite
MINIMAX | hailuo AI label, and Kling AI covers the bottom-right KLING AI or
KlingAI 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.
Apply the calibrated video-pixel SynthID-removal profile:
uv tool install --force "remove-ai-watermarks[video,diffusion]"
remove-ai-watermarks video invisible input.mp4 -o clean.mp4This 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. The CLI therefore describes video-pixel
regeneration instead of claiming a per-file SynthID verdict, and reports the
copied audio watermark as UNVERIFIED.
For invisible watermark removal through the CLI or high-level API, install the
qwen-zimage extra. An NVIDIA GPU is required: all diffusion profiles are
CUDA-only, and there is no CPU or MPS fallback. The specialized local Paint API
uses the pixels extra without CUDA.
uv tool install --force "remove-ai-watermarks[qwen-zimage]"
remove-ai-watermarks invisible image.png -o clean.pngIf 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 --forceTypography-heavy images can opt into the experimental verified-text post-pass. It accepts manually reviewed strings and line boxes or an operator-verified geometry-only manifest; it never treats raw OCR output as ground truth or runs automatically:
uv tool install --force "remove-ai-watermarks[text-restoration]"
remove-ai-watermarks invisible image.png -o clean.png \
--text-manifest verified-lines.json --forceSee the CLI guide for the manifest schema, compatibility restrictions, and oracle caveats.
See the installation guide for Homebrew, uv, optional features, and development setup.
The after raster at the top of this page is generated from the tracked before
raster by the public path:
uv run remove-ai-watermarks visible demo_banana_before.png \
--backend cv2 -o demo_banana_after.pngqwen-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. chroma-zimage uses the Apache-2.0 Chroma1 global pass with its own flat
vendor floors; see docs/chroma1-engine-research.md for the calibration. An opt-in
fourth profile, qwen21-zimage, swaps the global stage for Qwen-Image-2.1 (with
madebyollin/texture-fix-vae-for-qwen-image-2.1 and Qwen-Image-2.1-Fun-Controlnet-Union
Canny); because Qwen/Qwen-Image-2.1 is released under the Qwen Research License,
qwen21-zimage is for non-commercial research, evaluation, and personal use only
(not for commercial use). --pipeline auto picks sdxl-zimage for Google,
chroma-zimage for Microsoft, and qwen-zimage otherwise, keeping auto commercial-safe.
All 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.
remove-ai-watermarks visible image.png -o clean.pngThe 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 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 miganuv tool install --force "remove-ai-watermarks[lama]"
remove-ai-watermarks visible image.png -o clean.png --backend lamaremove-ai-watermarks invisible image.png -o clean.png \
--cpu-offload --forceCPU offload lowers CUDA memory pressure by moving model components between CPU and GPU, at the cost of speed.
remove-ai-watermarks batch ./images --mode visible
remove-ai-watermarks batch ./images --mode allVisible mark support includes:
- Google Gemini and Nano Banana visible sparkle watermark;
- Doubao, the Jimeng wordmark and top-left
AI生成layouts, Qwen, Kling AI, Yuanbao, Dola, WorkBuddy, Baidu, LiblibAI's bottom-center wordmark and compact top-left pill, and RunningHub labels; - calibrated Microsoft top-right white and beige AI-badge layouts;
- calibrated Italian and Korean Samsung Galaxy AI disclosures;
- a generic brand-less bottom-right
AI生成fallback for OS/gallery-level AI-edit stamps that carry no vendor wordmark of their own (vivo and Xiaomi Gallery confirmed); synthetic-only calibration, weaker evidence than the vendor-tuned detectors above; - the OpenArt wordmark, placed at the frame center rather than a corner; uncalibrated (one confirmed real case, no captured corpus).
Metadata and provenance inspection covers C2PA, EXIF, XMP, IPTC, common generator parameters in image and video containers, 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. The synthetic example gallery carries one canonical sample for every registered image and video mark.
Visible removal follows three steps:
- Detect a registered mark in its expected area.
- Build a mask around the mark.
- 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 and keyed generation parameters are blanked without changing box sizes or media offsets. Other supported containers use their corresponding metadata path.
Invisible removal through the CLI and high-level API regenerates the image through a diffusion pipeline. Specialized callers can instead decode and disrupt the positively identified local Microsoft Paint format with much smaller pixel changes. Neither path can guarantee that every proprietary verifier will reject every output.
See supported signals and known limitations for the full technical boundary.
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)
report = raiw.remove_visible_detailed("watermarked.png", "clean.png")
print(report.status) # cleaned | partial | unvalidated | no_watermark
complete = raiw.remove_all("watermarked.png", "clean.png")
print(complete.visible_status, complete.visible_marks)
images = raiw.remove_batch("images", "images_clean", mode="visible")
for item in images.items:
print(item.source, item.visible_status, item.visible_marks)
provenance = raiw.identify_video("input.mp4")
report = raiw.inspect_video_metadata("input.mp4")
complete = raiw.remove_video_all("input.mp4", "clean.mp4")
print(complete.visual_invisible_action, complete.audio.watermark_status)
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")
print(synthid_cleaned.visual_invisible_action, synthid_cleaned.audio.watermark_status)
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.
Coding agents can install the published skill and drive this CLI:
npx skills add wiltodelta/remove-ai-watermarksClaude Code can add this repository as a plugin marketplace:
/plugin marketplace add wiltodelta/remove-ai-watermarks
/plugin install remove-ai-watermarks@remove-ai-watermarks
Codex reads the same skill through .codex-plugin/plugin.json.
The skill refuses stock-agency and other third-party paid-asset marks. See
the agent skill guide.
remove-ai-watermarks mcp serves identify, remove and batch as Model
Context Protocol tools over stdio, each a thin wrapper around one CLI command:
{"mcpServers": {"remove-ai-watermarks": {"command": "uvx", "args": ["remove-ai-watermarks", "mcp"]}}}See the MCP server guide.
The separate ComfyUI Remove AI Watermarks package provides nodes for visible removal, detection, region erasing, and invisible removal.