Python API

Use the high level API for normal application integration. Low level detector and pipeline modules are intended for maintainers and specialized workflows.

Remove visible marks

import remove_ai_watermarks as raiw

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

The function returns:

the result as a BGR NumPy array;
a list of labels that were removed.

An empty removed list means that no registered visible mark was selected. It does not prove the image has no metadata or invisible watermark.

Path input

For a path input, remove_visible:

reads metadata provenance for the default auto sensitivity;
preserves a separate alpha channel;
writes the output when an output path is supplied;
strips AI metadata from the written output by default;
preserves the original bytes for a same-format no-op copy.

result, removed = raiw.remove_visible(
    "watermarked.png",
    "clean.png",
    sensitivity="auto",
    backend="auto",
    strip_metadata=True,
)

Set write_noop=False if the output path must remain untouched when nothing is removed:

result, removed = raiw.remove_visible(
    "input.png",
    "clean.png",
    write_noop=False,
)

Array input

Array inputs are BGR NumPy arrays. They do not carry file metadata or a separate alpha plane:

import cv2
import remove_ai_watermarks as raiw

image = cv2.imread("input.png")
result, removed = raiw.remove_visible(image, backend="cv2")

Inspect provenance

Get the vendor keys used by visible removal:

import remove_ai_watermarks as raiw

vendors = raiw.visible_provenance("input.png")

Get the full provenance report:

from pathlib import Path

from remove_ai_watermarks.identify import identify

report = identify(Path("input.png"))
print(report.platform)
print(report.signals)

Use check_visible=False and check_invisible=False for metadata-only inspection through the compatible path-based API:

report = identify(
    Path("input.png"),
    check_visible=False,
    check_invisible=False,
)

Extraction and detection are also available as separate steps. This is useful when a file-reading worker collects the metadata once and another component evaluates the resulting evidence:

from remove_ai_watermarks.identify import (
    extract_provenance_evidence,
    identify_from_evidence,
)

evidence = extract_provenance_evidence(Path("input.png"))
report = identify_from_evidence(evidence)

identify_from_evidence does not reopen the source file. It evaluates metadata only; registered visible marks and pixel-backed invisible watermarks remain in the path-based identify call.

Strip metadata

from pathlib import Path

from remove_ai_watermarks.metadata import has_ai_metadata, strip_and_verify

source = Path("input.png")
output = Path("clean.png")

if has_ai_metadata(source):
    output_path, surviving_markers = strip_and_verify(source, output)
    if surviving_markers:
        raise RuntimeError(
            f"AI metadata remains in {output_path}: {surviving_markers}"
        )

Use strip_and_verify when your application reports that stripping succeeded. It checks the written output and returns (output_path, surviving_markers). When the first strip leaves markers in a malformed but raster-decodable image, it normalizes the container through image_io and checks again. That recovery path preserves the pixels but drops standard metadata. Treat a nonempty surviving_markers mapping as a failure.

remove_ai_metadata is the lower level fail-safe transformer. It may copy an undecodable input through unchanged, so its return alone must not be presented as proof that metadata was removed.

Remove invisible watermarks

from pathlib import Path

from remove_ai_watermarks.invisible_engine import InvisibleEngine

engine = InvisibleEngine(
    pipeline="controlnet",
    device=None,
    cpu_offload=False,
)

engine.remove_watermark(
    Path("watermarked.png"),
    Path("clean.png"),
)

device=None selects the device automatically. Supported explicit values are defined by the CLI and runtime device resolver.

For limited CUDA memory:

engine = InvisibleEngine(
    pipeline="controlnet",
    cpu_offload=True,
)

For the CUDA only high fidelity profile:

engine = InvisibleEngine(pipeline="qwen-zimage")

The qwen-zimage extra must be installed for that profile.

The full remove_watermark signature includes strength, steps, guidance, seeding, tiling, resolution, upscaling, and postprocessing controls. Read the method signature in invisible_engine.py or use the CLI guide for the concepts. Defaults can differ between the Python method and CLI profile resolution, so pass values explicitly when reproducibility matters.

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