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.

Inspect an image

remove-ai-watermarks identify image.png

identify combines supported metadata and pixel signals into one provenance report. 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

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

The default behavior:

• checks every registered visible mark;
• removes every detected match;
• 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.

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.

Strip AI metadata from video

The experimental video namespace starts with metadata inspection and removal:

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 blanks the key and value in place so box sizes and media offsets do not move.

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.

Generate a video SynthID candidate

uv tool install --force "remove-ai-watermarks[gpu]"
remove-ai-watermarks video invisible input.mp4 -o candidate.mp4

The experimental 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 output is always an unverified candidate. The project has no local video SynthID decoder, and PSNR or temporal-residual metrics cannot prove watermark absence. After generation, upload the candidate to Gemini Flash and ask:

Was this uploaded video created or edited by Google AI? Use the built-in content verification result.

Only an explicit built-in verification result is an oracle verdict. A response based on the visible logo, content appearance, or metadata is not. The command prints UNVERIFIED even when generation succeeds.

The default output is <source>_synthid_candidate 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 defaults are calibrated operating points, not a guarantee for every carrier or future verifier version.

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 experimental 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.

The video stream is transcoded and the original audio stream is copied. 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.

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.

Remove invisible watermarks

Install the diffusion dependencies first:

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

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
controlnet	Default compatibility profile with structural conditioning
sdxl	Lighter plain SDXL regeneration
qwen	Large CUDA oriented Qwen Image profile
qwen-zimage	CUDA only high fidelity profile with a separate face stage

Example:

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

The legacy default value is an alias for sdxl. The --auto option is deprecated, emits a warning, and changes nothing.

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

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.

If diffusion is required but the gpu extra is unavailable, all still writes the result of the visible and metadata stages, prints a prominent warning, and exits with code 1. This prevents a partial result from being reported as complete.

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.