remove-ai-watermarks/docs/cli.md

# CLI guide

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

```text
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 |
| `detect-synthid` and the calibrated-size SynthID pixel signal in `identify` | `remove-ai-watermarks[pixels]` |
| `verify-openai-synthid` | `remove-ai-watermarks[verify]`, API access, and `OPENAI_API_KEY` |
| 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

```bash
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:

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

Metadata only inspection:

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

Despite the historical option name, `--no-visible` skips all pixel detectors,
including the pipeline lattice described below, visible marks, open DWT-DCT, and
TrustMark. Metadata inspection still runs.

## Detect the generation-pipeline pixel lattice (experimental)

```bash
remove-ai-watermarks detect-synthid image.png
remove-ai-watermarks detect-synthid image.png --json
remove-ai-watermarks detect-synthid native-period.png --fixed-period
```

This route is experimental. Signed provenance, read by `identify` and confirmed
against the provider by `verify-openai-synthid`, remains the supported way to
establish SynthID. The command returns one of `detected`, `indeterminate`, or
`unsupported`, and it does not detect the SynthID watermark: its statistic disappears when the image
is cropped off the tile grid, and it changes when the generator's pipeline
changes, so read a positive as evidence about the pipeline and never as a
watermark claim. The JSON carries `identifies_watermark` and
`tile_aligned_crop_required` for exactly this reason. The
runtime detector covers one frozen periodic lattice family in the
[calibrated image-size range](synthid.md#32-how-our-tool-detects-the-supported-carrier)
and needs the `pixels` extra. The production default uses registered-v3 from
250,000 through 10,000,000 decoded pixels with both sides at least 256 pixels.
An opponent-registered-v1 fallback covers 1 through 10 megapixels, both sides
at least 768 pixels, and selected carrier periods 7.9 through 12.0. Period-8
candidates must also pass an opponent-color block-edge veto for the native JPEG
lattice. The
separately challenged opponent-color large-v1 branch covers above 10,000,000
through 18,000,000 pixels when both sides are at least 2,048 pixels.
Registered-v3 performs a bounded carrier-period search and independent split-
patch confirmation. Its measured positive
scale range is approximately 0.65 through 1.5. The fallback recovered 49/49
lossless 0.5x-0.75x views from seven official positives. Its period-8 veto
rejected 1,790 codec-lattice crossings, and 350 matched 0.5x controls produced no
base crossing. The earlier period-band rule accepted 0/1,000 post-freeze Picsum
controls. `identify` uses this production router.

`--fixed-period` explicitly selects the faster legacy fixed-v2 diagnostic below
10 megapixels. It does not resize or register the carrier and is not a
production positive route. `--register-scale` forces the registered-v3 cascade,
including its opponent fallback, on geometry where the default would select
large-v1.
The native large and opponent-registered branches are codec-sensitive. The
large branch fell from 7/7 to 0/7 after same-size JPEG-95 or JPEG-90; the
fallback retained 0/63 JPEG-95, JPEG-85, and WebP-95 views. A miss on a lossy
re-encode is therefore inconclusive.
It is positive-only: `indeterminate` means the score stayed below this
detector's threshold, while `unsupported` means the image geometry is outside
its scope. Neither result proves that another SynthID epoch or payload is
absent.
JSON output includes the exact reason plus provider scope, backend, pixel
preservation, and metadata-use audit fields.

## Verify OpenAI SynthID from pixels

```bash
uv tool install --force "remove-ai-watermarks[verify]"
remove-ai-watermarks verify-openai-synthid image.png --acknowledge-upload
remove-ai-watermarks verify-openai-synthid image.png --acknowledge-upload --json
```

This is an explicit remote check against OpenAI's official Content Provenance
API, not the incomplete local OpenAI carrier research model. Before upload, the
command writes a temporary copy with AI provenance metadata removed and aborts
unless the decoded RGBA pixels are identical to the source. It then reads only
the API's independent `synthid` entry; a C2PA-only response cannot become a
SynthID detection. The source is never modified.

The API supports PNG, JPEG, and WebP files up to 50 MiB. The command requires
`OPENAI_API_KEY` and an organization with endpoint access. Because the sanitized
raster is uploaded to OpenAI and the endpoint is not eligible for Zero Data
Retention, `--acknowledge-upload` is mandatory. This command is never called by
`identify`. `not_detected` means only that OpenAI's verifier did not recognize a
supported watermark in this file; it is not proof of human authorship.

The built-in client bounds the request at 120 seconds and disables automatic
SDK retries, so one acknowledgement cannot silently upload the media multiple
times. A timeout, disconnect, malformed response, access failure, or rate limit
is an error, never a negative watermark verdict. API failures expose status,
error code, request id, `Retry-After`, and whether an explicit caller-controlled
retry is appropriate through `OpenAIProvenanceError`; the verifier itself never
retries an upload.
The JSON result uses the same provider-scope, backend, pixel-preservation, and
metadata-use audit fields as the local detector.

The Python API enforces the same boundary with the required explicit intent
flag `verify_openai_synthid(path, acknowledge_upload=True)`.

## Remove known visible marks

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

```bash
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](supported-signals.md));
- selects the best installed fill backend;
- strips AI metadata from the output.

Use a specific mark:

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

Available mark names are printed by:

```bash
remove-ai-watermarks visible --help
```

Keep metadata:

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

Use the strict visual gate without metadata or sibling corroboration:

```bash
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

```bash
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:

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

Choose the fill backend:

```bash
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:

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

Remove AI metadata and write a new file:

```bash
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](supported-signals.md). 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:

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

Inspect every locally supported video signal:

```bash
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:

```bash
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:

```bash
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:

```bash
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:

```bash
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

```bash
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

```bash
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:

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

Then run:

```bash
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:

```bash
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:

```bash
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:

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

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

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

Or set a resolution cap:

```bash
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.

```bash
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

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

Modes:

- `visible`;
- `invisible`;
- `metadata`;
- `all`.

Set an output directory:

```bash
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](module-internals.md#command-line-interface).