Remove-AI-Watermarks

Unified tool for removing visible and invisible AI watermarks from images.

Features

• Visible watermark removal — Gemini sparkle logo via reverse alpha blending (fast, offline, deterministic)
• Invisible watermark removal — SynthID, StableSignature, TreeRing via diffusion-based regeneration
• AI metadata stripping — EXIF, PNG text chunks, C2PA provenance manifests
• Analog Humanizer — film grain and chromatic aberration injection to bypass AI image classifiers
• Smart Face Protection — automatic extraction and blending of human faces to prevent AI distortion
• High-Res Upscaler — prevents resolution degradation during invisible watermark removal
• Batch processing — process entire directories
• Detection — three-stage NCC watermark detection with confidence scoring

Examples

Before (Watermarked)	After (Cleaned)

Installation

Recommended (macOS)

Install as an isolated CLI tool — no need to manage virtual environments:

# Using pipx (brew install pipx)
pipx install git+https://github.com/wiltodelta/remove-ai-watermarks.git

# Or using uv (brew install uv)
uv tool install git+https://github.com/wiltodelta/remove-ai-watermarks.git

Install from repository (macOS)

Prerequisites: Python 3.10+ and pip (or uv).

# 1. Clone the repository
git clone https://github.com/wiltodelta/remove-ai-watermarks.git
cd remove-ai-watermarks

# 2. Install the package in editable mode
pip install -e .

# Or, if you use uv:
uv pip install -e .

After installation the remove-ai-watermarks command is available system-wide.

Invisible watermark removal (optional)

Invisible removal uses diffusion models and requires a HuggingFace token and a decent GPU (CUDA) or Apple Silicon (MPS).

# 1. Create a free token at https://huggingface.co/settings/tokens
# 2. Copy the example env file and paste your token
cp .env.example .env
# Edit .env and set HF_TOKEN=hf_your_token_here

# 3. On first run, the model (~2 GB) will be downloaded automatically.
#    On macOS with Apple Silicon, MPS acceleration is used by default.
#    On macOS without GPU, add --device cpu (inference will be slow).

Developer setup

# Install with dev dependencies (pytest, ruff, pyright)
pip install -e ".[dev]"
# Or with uv:
uv pip install -e ".[dev]"

# Run tests
pytest

# Run linters
./maintain.sh

Usage

CLI

# Remove visible Gemini watermark
remove-ai-watermarks visible image.png -o clean.png

# Remove invisible watermarks (SynthID etc.) with optimal quality retention
remove-ai-watermarks invisible image.png -o clean.png --humanize 4.0

# Strip AI metadata
remove-ai-watermarks metadata image.png --check
remove-ai-watermarks metadata image.png --remove

# Batch processing
remove-ai-watermarks batch ./images/ --mode visible

# Full pipeline: visible + invisible + metadata
remove-ai-watermarks all image.png -o clean.png

Python API

from remove_ai_watermarks.gemini_engine import GeminiEngine
import cv2

engine = GeminiEngine()
image = cv2.imread("watermarked.png")

# Detect
result = engine.detect_watermark(image)
print(f"Detected: {result.detected} (confidence: {result.confidence:.1%})")

# Remove
clean = engine.remove_watermark(image)
cv2.imwrite("clean.png", clean)

Metadata stripping

from remove_ai_watermarks.metadata import has_ai_metadata, remove_ai_metadata
from pathlib import Path

if has_ai_metadata(Path("image.png")):
    remove_ai_metadata(Path("image.png"), Path("clean.png"))

Requirements

• Python ≥ 3.10
• Visible removal / metadata: CPU only, no GPU required
• Invisible removal: GPU recommended (CUDA or MPS), works on CPU (slow)

Credits

• noai-watermark by mertizci — invisible watermark removal
• GeminiWatermarkTool by Allen Kuo — visible watermark removal algorithm

License

MIT