Remove the Seedance 2.0 watermark ("AI生成" / AI-Generated) from videos automatically — no GPU required, no paid tools.
Works on all videos generated by Seedance 2.0, Seedance Pro, and Seedance Lite video generation models. Also removes watermarks from any corner-positioned logo or text overlay.
Free • Open Source • No GPU Needed • Works on CPU
Don't want to run code locally? Use the free cloud version at https://muapi.ai/playground/seedance-v2.0-watermark-remover — upload your video and get a clean version instantly.
| Model | Image-to-Video | Text-to-Video |
|---|---|---|
| Seedance 2.0 | Playground | — |
| Seedance 2.0 Mini (coming soon) | Playground | Playground |
| Seedance 2.1 (coming soon) | Playground | Playground |
| Seedance 2.5 (coming soon) | Playground | Playground |
- Seedance-2.0-API — Python wrapper to generate Seedance 2 videos — then remove watermarks with this tool
- seedance2-comfyui — Generate Seedance 2 videos inside ComfyUI
- n8n-nodes-seedance2 — Automate Seedance 2 generation in n8n workflows
- awesome-seedance-2.5-api-prompts — Curated Seedance 2.5 API guide, prompts, camera controls, and video generation examples
Seedance 2.0 (by ByteDance) adds a small "AI生成" (AI-Generated) badge to the corner of every generated video. This tool detects and removes that watermark automatically, restoring a clean video without artifacts.
- Sample & average ~60 frames — the static Seedance watermark becomes clearly visible in the mean frame while moving content blurs away
- Auto-detect corner using Canny edge density × temporal stability score — the watermark is static and structured; moving content (people, water, foliage) scores low and is ignored
- Build a precise mask via Canny edge detection on the watermark region — traces only the text strokes
- Inpaint each frame with OpenCV TELEA (default, CPU-only) or optionally LaMa AI inpainting (
--lama) - Reassemble all frames with the original audio using ffmpeg
# Core dependencies (required)
pip install opencv-python-headless numpy
# ffmpeg (required for video reassembly)
# macOS
brew install ffmpeg
# Ubuntu / Debian
sudo apt install ffmpeg
# Optional: LaMa AI inpainting (--lama flag)
pip install torch iopaint# Remove Seedance 2.0 watermark — auto-detects corner
python watermark_remover.py input.mp4
# Save to custom output path
python watermark_remover.py input.mp4 -o clean.mp4
# Manual region if auto-detection fails (x, y, width, height in pixels)
python watermark_remover.py input.mp4 -r 10,5,120,60
# Use LaMa AI inpainting for higher quality output (requires torch + iopaint)
python watermark_remover.py input.mp4 --lama- Removes Seedance 2.0 watermark ("AI生成") automatically
- Works on portrait and landscape video orientations
- Detects watermark in any of the four corners
- Handles videos where people or content move near the watermark corner
- Supports both opaque and semi-transparent watermarks
- No GPU required — runs entirely on CPU with OpenCV TELEA inpainting
- Preserves original audio in output
- Optional LaMa AI inpainting for higher quality results
Each corner region (8% height × 12% width) is scored:
score = edge_density × (1 / (1 + temporal_std))
| Term | Meaning |
|---|---|
edge_density |
Fraction of Canny edge pixels in the mean frame — watermark text has crisp, consistent edges |
temporal_std |
Pixel variation across frames — moving content scores high (bad), static watermark scores low (good) |
Using tight corner regions prevents a moving person near the corner from masking the watermark signal.
| Flag | Description |
|---|---|
input |
Path to input video |
-o, --output |
Output file path (default: <input>_clean.mp4) |
-r, --region |
Manual watermark region x,y,w,h — skips auto-detection |
--lama |
Use LaMa AI inpainting (requires torch + iopaint) |
- Python 3.8+
opencv-pythonoropencv-python-headlessnumpyffmpeg(system install)torch+iopaint(only for--lama)
- Free Cloud Version — remove Seedance watermarks online, no setup required
- muapi.ai — API platform for Seedance 2.0, Seedance Pro, and other generative media models
- Seedance 2.0 — ByteDance video generation model
- iopaint — LaMa inpainting library
MIT