remove-ai-watermarks/docs/installation.md

Installation

Python 3.11 through 3.14 are supported.

Default metadata mode

The default package provides:

• provenance inspection;
• AI metadata inspection and removal.

It installs Pillow, piexif, and c2pa-python for reading metadata directly from files. It does not install NumPy, OpenCV, pillow-heif, Torch, diffusion models, or invisible-watermark decoders.

Install it as an isolated command with uv:

uv tool install remove-ai-watermarks

Or with pipx:

pipx install remove-ai-watermarks

You can also install the Homebrew package on macOS or Linux:

brew install wiltodelta/tap/remove-ai-watermarks

Visible watermark removal

Visible mark detection, OpenCV inpainting, and manual region erasing need the visible extra:

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

Add heif only when the pixel path must decode HEIC, HEIF, or AVIF:

uv tool install --force "remove-ai-watermarks[visible,heif]"

Video processing

Video metadata inspection works with the default package, and MP4 and MOV stripping uses the in-tree ISOBMFF box walker. Stripping the non-ISOBMFF containers (MKV, WebM, AVI, FLV, and the audio formats) and writing any cleaned video need ffmpeg on PATH, for example brew install ffmpeg. Stable visible-mark identification and removal, full video cleaning, and visible/all batch modes need the video extra:

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

The extra includes the visible pixel runtime and PyAV for preserving variable frame timestamps. Video SynthID regeneration also needs the diffusion stack:

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

Invisible watermark removal

Install the qwen-zimage extra:

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

Both remaining profiles run a Z-Image face stage on the DiffSynth runtime, so both need this extra. It includes the diffusion dependencies; diffusion on its own covers the torch and diffusers imports but not the face stage, so it is not enough to run a removal.

An NVIDIA GPU is required. qwen-zimage and sdxl-zimage are CUDA-only, and construction refuses any other device rather than falling back to a slow or broken one. There is no CPU, MPS or XPU path for invisible-watermark removal. Visible-mark removal, metadata stripping and every identify command still run anywhere.

Video SynthID regeneration is a separate VAE path and does still run on CPU or MPS; it needs the diffusion extra, not this one.

Feature extras

Extras are composable. Install only the capabilities and file formats the application actually uses:

Extra	Capability	Automatically includes	Torch or model download
pixels	Shared BGR array and image-processing runtime	NumPy, headless OpenCV	No
heif	HEIC, HEIF, and AVIF pixel decoding	pillow-heif	No
visible	Visible mark detection, OpenCV inpainting, and manual erasing	pixels	No
video	Visible video identification/removal and timestamp preservation	visible, PyAV	No
detect	Open DWT-DCT detection for Stable Diffusion, SDXL, and FLUX	pixels, PyWavelets	No
trustmark	Adobe TrustMark detection on Python 3.11-3.12	trustmark	Yes
diffusion	Torch and Diffusers runtime; video SynthID regeneration	pixels, Torch, Diffusers	Yes
migan	MI-GAN ONNX fill backend	visible, ONNX Runtime	Model download, no Torch
lama	big-LaMa ONNX fill backend	visible, ONNX Runtime	Model download, no Torch
qwen-zimage	Invisible image-watermark removal, both CUDA-only profiles	diffusion, DiffSynth	Yes
all	Every production feature available on the active Python	All compatible rows above	Yes
dev	Tests, linting, typing, and upstream parity checks	video, detect, upstream invisible-watermark	Yes, for parity tests

Dependency composition:

flowchart LR
    visible --> pixels
    video --> visible
    detect --> pixels
    diffusion --> pixels
    migan --> visible
    lama --> visible
    qwen["qwen-zimage"] --> diffusion
    heif
    trustmark

heif and trustmark are independent branches. Combine them explicitly with another feature when required. TrustMark requires NumPy 1.x, which has no CPython 3.13 or 3.14 wheels, so that branch is available only on Python 3.11-3.12. The all bundle contains every production branch compatible with the active Python and never includes dev.

Examples:

# Metadata plus torch-free DWT-DCT detection
uv tool install --force "remove-ai-watermarks[detect]"

# Visible removal with HEIC/AVIF support and MI-GAN
uv tool install --force "remove-ai-watermarks[migan,heif]"

# Visible video removal with preserved timestamps
uv tool install --force "remove-ai-watermarks[video]"

# DWT-DCT and TrustMark detection without diffusion removal
uv tool install --force "remove-ai-watermarks[detect,trustmark]"

# Every production capability compatible with this Python
uv tool install --force "remove-ai-watermarks[all]"

# An arbitrary minimal combination
uv tool install --force "remove-ai-watermarks[migan,detect]"

heif stays independent so applications that only process PNG, JPEG, or WebP do not install libheif. detect uses the in-tree torch-free decoder and does not install the upstream invisible-watermark package. Optional models download their weights on first use.

The old gpu and remove aliases are intentionally not provided. Use diffusion and visible respectively.

Install from the repository

git clone https://github.com/wiltodelta/remove-ai-watermarks.git
cd remove-ai-watermarks
uv sync --frozen

Add the feature groups required for your work:

uv sync --frozen --extra dev
uv sync --frozen --extra dev --extra diffusion

Run commands from the repository root:

uv run remove-ai-watermarks --help

Development setup

Install development dependencies:

uv sync --frozen --extra dev

Run the complete project gate:

bash maintain.sh

The script syncs every optional backend on top of the dev environment above, then runs dependency checks, linting, formatting, type checking, and the test suite. It applies Ruff fixes and formatting in place rather than only reporting them.

Hugging Face authentication

Pass a Hugging Face token directly when the selected model or account requires one:

remove-ai-watermarks invisible image.png --hf-token "$HF_TOKEN"

The CLI also loads HF_TOKEN from the environment and from a local .env file. The same name is documented in .env.example.

Troubleshooting

The first model run is slow

Diffusion and learned fill backends may download model weights on first use. Later runs reuse their caches.

The command skips invisible removal

The normal behavior is to skip diffusion when no supported local signal is found. A missing signal does not prove that the image is clean. If you know the image came from a relevant generator, use --force.

If the CLI reports that the removal dependencies are unavailable, install the qwen-zimage extra. diffusion alone covers Torch and Diffusers but not the DiffSynth face stage that both profiles run. Video SynthID removal is a separate path and needs video and diffusion.