Video Watermark Remover Github New 【RECOMMENDED × 2026】

The surge of AI-generated content from platforms like Sora, Kling, and Seedance has led to a new wave of open-source projects on GitHub designed specifically to strip "Made with AI" watermarks and logos. These tools leverage advanced deep learning models such as LaMA inpainting and Florence-2 to erase overlays while preserving the original video quality.

Key Features: It combines Florence-2 for smart detection with LaMA inpainting for seamless visual results. video watermark remover github new

VeoWatermarkRemover: A March 2026 release that uses "mathematically precise reverse alpha blending" specifically for Google Veo video watermarks. The surge of AI-generated content from platforms like

| Tool Type | Example | Time (RTX 3060) | Visual Artifact | AI Required? | | :--- | :--- | :--- | :--- | :--- | | Old (FFmpeg) | ffmpeg -i in.mp4 -vf delogo | 3 seconds | Blurry smudge | No | | Middle (Basic AI) | DeepRemaster | 45 seconds | Flickering edge | Yes | | New (GitHub 2025) | ProPainter v2 | 90 seconds | Virtually invisible | Yes (Diffusion) | Frame-based inpainting: simple

. It allows for seamless removal of "Made with Sora" tags through manual mask editing and AI-driven detection. Seedance Watermark Remover

4) Strengths and limitations

• Frame-based inpainting: simple, many implementations, but often produces temporal flicker and inconsistent textures.
• Temporal-aware models: much better coherence, but require more compute, training data, and complex implementation.
• Template matching + filtering: fast for static logos but fails for transparent or moving watermarks.
• Diffusion/transformer methods: high quality for complex scenes but currently experimental and resource-intensive.
• Automatic mask detection: quality depends on watermark contrast/visibility; false positives/negatives common.