remove-ai-watermarks/docs/cli.md

CLI guide

The command line interface is organized around the type of work you want to do.

remove-ai-watermarks [OPTIONS] COMMAND [ARGS]

Run remove-ai-watermarks COMMAND --help for the complete option list and defaults. This page focuses on choosing the right command.

Command dependency map

Command or signal	Required installation
metadata and metadata-only identify	Default package
Visible signals in identify	remove-ai-watermarks[visible] (pixels is the minimal runtime)
Open DWT-DCT signals in identify	remove-ai-watermarks[detect]
Adobe TrustMark signals in identify	remove-ai-watermarks[trustmark]
visible and erase with OpenCV	remove-ai-watermarks[visible] (pixels is the minimal runtime)
visible or erase with MI-GAN	remove-ai-watermarks[migan]
visible or erase with big-LaMa	remove-ai-watermarks[lama]
invisible and all (needs CUDA)	remove-ai-watermarks[qwen-zimage]
video metadata and video identify --no-visible	Default package
video identify	remove-ai-watermarks[video]
video visible, video all, and visible/all batch modes	remove-ai-watermarks[video] plus ffmpeg on PATH
video invisible and video all --invisible	remove-ai-watermarks[video,diffusion] plus ffmpeg on PATH
HEIC/HEIF/AVIF pixel input	Add remove-ai-watermarks[heif]
Every production command and backend	remove-ai-watermarks[all]

batch requires the same extra as its selected mode. Extras can be combined in one installation, for example remove-ai-watermarks[visible,detect,heif].

Inspect an image

remove-ai-watermarks identify image.png

identify always inspects supported metadata. When pixel extras are installed, it also evaluates supported visible and invisible pixel signals. When no signal is found, it reports the origin as unknown. It does not claim the image is clean.

Machine readable output:

remove-ai-watermarks identify image.png --json

Metadata only inspection:

remove-ai-watermarks identify image.png --no-visible

Despite the historical option name, --no-visible skips both visible and open invisible pixel detectors. Metadata inspection still runs.

Remove known visible marks

Install remove-ai-watermarks[visible] before using visible or erase.

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

The default behavior:

• checks every registered visible mark;
• removes every detected match, except the weakly detected Jimeng label pill, which needs corroboration (see supported signals);
• selects the best installed fill backend;
• strips AI metadata from the output.

Use a specific mark:

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

Available mark names are printed by:

remove-ai-watermarks visible --help

Keep metadata:

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

Use the strict visual gate without metadata or sibling corroboration:

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

When no known mark is detected, the command does not write a new output. Use erase if you can identify the affected region yourself.

Erase a region

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

The region format is x,y,width,height. Repeat --region to erase more than one box:

remove-ai-watermarks erase image.png \
  --region 20,20,180,60 \
  --region 1640,1930,400,100 \
  -o clean.png

Choose the fill backend:

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

erase accepts cv2, migan, and lama. The corresponding optional extra must be installed for a learned backend.

Two more knobs tune the fill. --dilate N (default 3) grows every box by N pixels before inpainting, which helps when a mark has a soft edge or a drop shadow just outside the box you measured; it applies to every backend because it shapes the mask. --inpaint-method telea|ns selects the classical algorithm and only affects the cv2 backend. Like visible, erase strips AI metadata from the output by default; pass --keep-metadata to retain it.

Strip AI metadata

Inspect metadata:

remove-ai-watermarks metadata image.png --check

Remove AI metadata and write a new file:

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

When -o is omitted, removal overwrites the source. Standard metadata is kept unless you pass --remove-all.

The command also supports the audio and video containers listed in supported signals. ffmpeg must be available for the non-ISOBMFF audio and video path.

Identify and clean video

Install the video pixel and timestamp runtime for visible identification, removal, and the complete pipeline:

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

Inspect every locally supported video signal:

remove-ai-watermarks video identify input.mp4
remove-ai-watermarks video identify input.mp4 --json
remove-ai-watermarks video identify input.mp4 --no-visible

The default scans the complete clip for stable registered visible marks and inspects supported metadata. A result with no signals is reported as unknown, not clean, because proprietary pixel watermarks have no public local decoder. --no-visible performs metadata-only inspection.

Use the complete locally verifiable cleaning path:

remove-ai-watermarks video all input.mp4 -o clean.mp4

It removes a stable supported visible mark when found and always strips verified AI metadata. When neither signal is found, it writes a same-container passthrough instead of returning a missing output. The source is never overwritten.

Invisible regeneration is deliberately opt-in:

remove-ai-watermarks video all input.mp4 -o clean.mp4 --invisible

That option is supported only for MP4, MOV, and M4V. It is lossy and uses the same oracle-certified profile as video invisible.

Process all supported files in a top-level directory:

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

The batch runs sequentially, preserves successful outputs when another file fails, and exits nonzero if any item failed. Visible no-op files are copied byte-for-byte so the output directory remains complete. --invisible is available only with --mode all.

Strip AI metadata from video

Metadata inspection and removal are also available as an isolated operation:

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

Supported containers are MP4, MOV, M4V, WebM, MKV, AVI, and FLV. The operation delegates to the same verified metadata scanner and stripper as the generic metadata command, so detection and removal stay in parity. Video and audio streams are not transcoded. For MP4 and MOV, this includes the native TC260 AIGC key and JSON value stored in moov.udta.meta.keys/ilst. The inspector seeks past a large mdat to find a tail moov. Removal stream-copies the container in bounded chunks, converts supported top-level provenance boxes to same-size free boxes, and blanks the TC260 key/value in place. Box sizes, media offsets, and encoded stream bytes do not move; the result is atomically published only after the complete copy succeeds.

For MKV and WebM, the inspector reads the native TC260 Segment.Tags.Tag.SimpleTag entry. Removal uses ffmpeg stream copying to discard container tags and chapters without transcoding the streams. AVI uses the normative LIST/INFO/AIGC chunk, while FLV uses the script.onMetaData.AIGC AMF0 string. Their bounded readers skip media payloads, and removal also uses ffmpeg stream copying.

When -o is omitted, the command writes <source>_clean with the same extension. It never overwrites the source, and it rejects an output with a different container extension.

Visible video labels and invisible video watermarks are not handled by this command.

Remove video SynthID

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

The command supports MP4, MOV, and M4V. It samples the complete sequence at the configured frame rate, resizes frames to the configured long side, regenerates them through a VAE, and applies one deterministic latent-noise field to every frame. Reusing one spatial field avoids the unnecessary flicker caused by independent per-frame noise. Frames are regenerated in bounded batches and streamed directly to ffmpeg, which encodes the result, copies audio, and drops source metadata.

The default noise_std=0.15 profile is oracle-certified. The project has no local video SynthID decoder, so an optional per-file recheck is still useful for unusually important files or after provider changes. In a new Gemini chat, upload the original first, invoke the built-in verifier with @synthid, and ask:

For the video attached to this message, was it created or edited by Google AI? Use the built-in SynthID content verification result.

The source must be positive. Then upload the processed result in a separate new chat and repeat the same built-in check. Only a source-positive, output-negative pair is a fresh per-file verification. Do not ask an adversarial follow-up that tells the chat model to ignore the verifier and reason about raw pixels: that is ordinary Gemini reasoning, not a second oracle check.

The default output is <source>_clean in the same container. The source is never overwritten. Use --noise-std, --long-side, --fps, --batch-size, --seed, and --device to control the regeneration. The default noise level is 0.15. It cleared both carriers in the 2026-07-29 short-clip calibration and the complete public eight-second Veo carrier in the 2026-07-31 full-clip check; 0.10 remained detected on that complete clip. This calibration certifies the shipped operating point; the paired check above is an optional runtime audit, not a separate result state.

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

The command supports the moving Sora mascot and wordmark, two Veo corner variants, the Seedance boxed AI label, the Dola AI text label, the composite MINIMAX | hailuo AI label, and the bottom-right Kling label. Sora searches the whole frame at multiple scales. The other detectors search bounded lower-frame regions with separate synthetic silhouettes. Kling additionally requires its bright low-saturation label near the frame edge. Every mark requires a spatially recurring candidate across adjacent frames. Fixed marks must also remain anchored instead of drifting with a scene object. Matching provider provenance may relax the visual score only for registered provenance-aware marks; metadata alone never creates a detection.

--mark auto is the default. It evaluates all providers in one decode pass and selects the first stable match in specificity order: Sora, Veo, Seedance, Dola, Hailuo, then Kling. Their confidence scores are independently calibrated and are not compared across providers. Pass an explicit --mark to scan only that provider.

The video stream is transcoded and the complete original audio stream is copied without truncating an audio tail that extends beyond the final video frame. The encoder probes the source stream and preserves supported 8-bit chroma sampling, color range/matrix/transfer/primaries tags, and MP4/MOV track timescale. For a variable-frame-rate source, decoded PTS are carried through a timestamped in-memory NUT bridge so the output retains the source frame intervals instead of flattening them to a constant rate. A non-zero source start PTS and the copied audio start offset are preserved as well. Supported input and output containers are MP4, MOV, M4V, WebM, MKV, AVI, and FLV; the output extension must match the input. The default cv2 backend is fast but can smear structured backgrounds. Select --backend migan or --backend lama for a learned fill, or --backend auto to choose the best installed backend.

--temporal-consistency is enabled by default. It motion-aligns the preceding accepted fill, requires overlapping removal masks and matching source context, and blends only the safely covered pixels. Scene cuts, disjoint moving marks, or a poor motion match keep the independent current-frame fill. Use --no-temporal-consistency for an exact frame-local baseline.

The pixel path is intentionally limited to SDR 8-bit video. A high-bit-depth, PQ, or HLG source is rejected before ffmpeg starts, preserving any existing output instead of silently downconverting it through OpenCV's 8-bit boundary. On CPU, MI-GAN is the practical learned tier. LaMa remains an explicit offline quality option because full-sequence inference is too slow and memory-heavy for an online worker.

AI metadata is stripped from the encoded output by default. Use --keep-metadata to retain mapped container metadata. When no temporally stable mark is found, the command writes no output and exits with the no-visible-mark status. The final path is replaced atomically only after ffmpeg completes, so a failed encode does not overwrite an existing result.

Remove invisible watermarks

Install the removal dependencies first. Both profiles are CUDA-only and both run the DiffSynth Z-Image face stage, so this is the extra either one needs:

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

Then run:

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

The command normally skips regeneration when no supported local signal is detected. Use --force when you know the image should be processed:

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

Choose a pipeline

Pipeline	When to use it
qwen-zimage	Default. Qwen-Image-2512 global pass plus a SAM-masked Z-Image face stage
sdxl-zimage	The same recipe and face stage on an SDXL global pass, at a higher denoise

Both are CUDA-only. There is no CPU or MPS profile for invisible-watermark removal. The former controlnet, sdxl, qwen and default profiles were removed rather than kept as a CPU path: none of them matched this recipe's face preservation, so offering them implied a quality the library no longer delivers. Passing a retired name is rejected at parse time rather than remapped. Visible-mark removal and every identify path still run anywhere.

Example:

remove-ai-watermarks invisible image.png -o clean.png \
  --pipeline qwen-zimage --force

There is no --model, --steps, --guidance-scale or --device option, and the deprecated --auto is gone. Each profile pins its model stack, its per-stage schedule, CFG 1.0 and CUDA, so every one of those flags existed only to be refused several layers down. They are not parsed at all now, which fails at the point the user can act on rather than after a model load.

Work with limited memory

Lower CUDA memory pressure:

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

Keep large images at native resolution while processing them in overlapping tiles:

remove-ai-watermarks invisible image.png -o clean.png \
  --tile --max-resolution 0 --force

Or set a resolution cap:

remove-ai-watermarks invisible image.png -o clean.png \
  --max-resolution 2048 --force

Tiling avoids the explicit downscale but each tile is regenerated separately. It is a memory strategy, not a guarantee of better quality.

Run the full pipeline

The all command and the all installation extra are separate concepts. The command runs every applicable stage. Installing remove-ai-watermarks[all] makes every production backend available; a smaller installation such as remove-ai-watermarks[visible,qwen-zimage] can also run the command with fewer optional backends.

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

The command runs:

1. visible mark removal;
2. invisible watermark removal when available and applicable;
3. AI metadata stripping.

The visible options and diffusion options are also available on all.

When the qwen-zimage extra is unavailable, all still writes the result of the visible and metadata stages, prints a prominent warning, and exits with code

1. That happens on every run without the extra: the skipped stage is what would have decided whether a signal was there. This prevents a partial result from being reported as complete.

If the extra is installed but the machine has no CUDA, which is the usual macOS case, the run fails at engine construction instead: all prints Error: Invisible-watermark removal is CUDA-only ..., writes no output at all, and exits with code 1.

Process a directory

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

Modes:

• visible;
• invisible;
• metadata;
• all.

Set an output directory:

remove-ai-watermarks batch ./images \
  --mode all \
  --output-dir ./clean

The invisible and full modes accept the same main diffusion controls as their single image counterparts. Run batch --help for the authoritative option list.

Exit behavior

The CLI uses nonzero exit codes for meaningful incomplete outcomes, including no detected target on commands that would otherwise regenerate or create a misleading unchanged result, processing errors, and a required invisible step that could not run.

Scripts should check the process exit code and the output path. The detailed per-command contract is maintained in module internals.