WatermarkRemoverLiteVideo

For Windows · Your media stays on your PC

Remove watermarks from video and image with high-quality inpainting

WatermarkRemoverLiteVideo clears logos, stamps, and burned-in captions from video and still images with natural, convincing fills powered by LaMa. Edit on a full timeline, refine masks by hand or with smart assist, then export your clip—processing stays local, so your files are not sent to the cloud for this workflow.

Engine: LaMa (big-lama) UI: PySide6 (Qt) Languages: ES / EN
Demo video Add a demo.mp4 file in the assets/ folder to show your own demo here, or check the browser console if the file fails to load.

Demo video showing what WatermarkRemoverLiteVideo can do — before/after results, mask refinement, and clean exports.

How the application works

The core is an inpainting pipeline: a mask is generated over the watermark region and the LaMa model fills that area by predicting texture and color consistent with the surroundings. For video, processing runs frame by frame (with quality options and temporal smoothing to reduce flicker).

1

Load media

Open a video (e.g. MP4, MKV, AVI) or an image (PNG, JPG, WebP, BMP). FFmpeg and OpenCV prepare frames and metadata.

2

Define the mask

Manual: draw an ROI rectangle over the watermark; choose a solid mask, adaptive text detection, or soft alpha. Auto: corner heuristics (top-hat, Canny, Otsu) for typical edge watermarks.

3

LaMa AI

The inpainting engine reconstructs the patch. You can set the maximum patch side (512–1024 px or unlimited) and passes for opaque logos.

4

Export

The output video is encoded with FFmpeg (CRF, preset, copied or AAC audio). By default output goes to output/videos next to the executable or project root.

Key features

Real capabilities from the app: single LaMa engine, video and image workflows, and fine-grained quality controls.

Video mode with a timeline

Temporal editor: multiple watermark clips, drag ranges, resize edges, double-click to jump to clip start. Ideal when the mark appears only in segments or moves.

Image mode: before / after

Load an image, process, and compare before/after previews. Export to PNG or JPG when you are happy with the result.

Manual: fixed ROI or multiple over time

Fixed ROI: one region for the whole video. Multi + time: different rectangles per segment; the mask can be re-estimated if the ROI changes (motion or scene variation).

Auto detection (corners)

Heuristic detection aimed at corner watermarks: fine text, high contrast, or semi-transparent overlay, using classical vision (no extra ML for the mask).

Adjustable inpainting quality

Maximum patch size to the model (512 px fast, 768 balanced for 1080p, 1024 for fine logos/4K, or unlimited for best quality with more VRAM). Optional multiple passes for opaque logos. Temporal patch smoothing to reduce flicker between frames.

Diagnostics console

Progress messages, device (CPU/GPU), PyTorch backend, resolution and FPS of the loaded video, and export paths for troubleshooting.

Spanish and English UI

Built-in translations for toolbars, dialogs, and tooltips in both languages.

Executable and local dependencies

Project ready for build_exe.bat (PyInstaller). Models and FFmpeg can be bundled; rebuild the .exe after changes to models or paths.

Privacy by design

Models and processing run locally. You do not need to upload your videos to a server to use LaMa in this app.

What kinds of watermarks you can treat

LaMa is strong at filling regions, but results depend on background complexity and whether the mask fully covers the mark. Automatic detection targets corners; for central or moving marks, manual mode or time-based clips usually work better.

Often works well

  • Logos or text in corners (auto or manual).
  • Semi-transparent marks over textured backgrounds (alpha / detect mode).
  • Static text or ROI adjusted frame-by-frame on the timeline.
  • Small channel badges or “subscribe” overlays in a fixed area.
  • 1080p video with 768–1024 px patches for fine detail.

Limitations and good practices

  • Very busy backgrounds behind the mark may show artifacts; try more passes or a larger patch.
  • Marks covering a very large area are harder: reconstruction has less visible context.
  • Auto-detect does not guarantee marks outside corners; use manual in those cases.
  • Respect copyright and licenses for the content you process.

System requirements for the downloadable app

These notes are for end users of the Windows build from WRLV.rar. You normally do not install Python, pip, or FFmpeg yourself.

Operating system
Windows 10 or Windows 11, 64-bit.
Memory (RAM)
8 GB minimum; 16 GB+ recommended for HD/4K.
Graphics (GPU)
NVIDIA GPU with enough VRAM recommended; CPU-only is much slower.
Disk space
Several GB free for app, models, and videos.
Extract and run
Keep folder layout after unzip; run the .exe from the extracted folder.
Exported videos
Default: output/videos near the app.

Other software developed by AmericaPixelGames

If you want to explore more tools from the same studio, here are two additional projects published by AmericaPixelGames.

FFStudio

FFStudio is a desktop video editor created for creators who need a clear workflow for clip selection, crop editing, reframing, and export.

ffstudio.americapixelart.com

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These titles come from the public metadata of AmericaPixelGames game pages and show another side of the studio's work.

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Download for Windows

Want the Windows program ready to run? Get WRLV.rar from the download page.