SoraWatermarkCleaner

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This project provides an elegant way to remove the sora watermark in the sora2 generated videos.

Watermark removed sora_watermark_removed.mp4

Original sora_watermark_original.mp4

⭐️:

1. **Yolo weights has been updated, try the new version watermark detect model, it should work better. **

2. We have uploaded the labelled datasets into huggingface, check this dataset out. Free free to train your custom detector model or improve our model!

3. One-click portable build is available — Download here for Windows users! No installation required.

1. Method

The SoraWatermarkCleaner(we call it SoraWm later) is composed of two parsts:

• SoraWaterMarkDetector: We trained a yolov11s version to detect the sora watermark. (Thank you yolo!)

• WaterMarkCleaner: We refer iopaint's implementation for watermark removal using the lama model.

  (This codebase is from https://github.com/Sanster/IOPaint#, thanks for their amazing work!)

Our SoraWm is purely deeplearning driven and yields good results in many generated videos.

2. Installation

FFmpeg is needed for video processing, please install it first. We highly recommend using the uv to install the environments:

1. installation:

uv sync

now the envs will be installed at the .venv, you can activate the env using:

source .venv/bin/activate

2. Downloaded the pretrained models:

The trained yolo weights will be stored in the resources dir as the best.pt. And it will be automatically download from https://github.com/linkedlist771/SoraWatermarkCleaner/releases/download/V0.0.1/best.pt . The Lama model is downloaded from https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt, and will be stored in the torch cache dir. Both downloads are automatic, if you fail, please check your internet status.

3. One-Click Portable Version

For users who prefer a ready-to-use solution without manual installation, we provide a one-click portable distribution that includes all dependencies pre-configured.

Download Links

Google Drive:

• Download from Google Drive

Baidu Pan (百度网盘) - For users in China:

• Link: https://pan.baidu.com/s/1i4exYsPvXv0evnGs5MWcYA?pwd=3jr6
• Extract Code (提取码): 3jr6

Features

• ✅ No installation required
• ✅ All dependencies included
• ✅ Pre-configured environment
• ✅ Ready to use out of the box

Simply download, extract, and run!

4. Demo

To have a basic usage, just try the example.py:

from pathlib import Path
from sorawm.core import SoraWM


if __name__ == "__main__":
    input_video_path = Path(
        "resources/dog_vs_sam.mp4"
    )
    output_video_path = Path("outputs/sora_watermark_removed.mp4")
    sora_wm = SoraWM()
    sora_wm.run(input_video_path, output_video_path)

We also provide you with a streamlit based interactive web page, try it with:

streamlit run app.py

5. WebServer

Here, we provide a FastAPI-based web server that can quickly turn this watermark remover into a service.

Simply run:

python start_server.py

The web server will start on port 5344.

You can view the FastAPI documentation for more details.

There are three routes available:

1. submit_remove_task

   After uploading a video, a task ID will be returned, and the video will begin processing immediately.

2. get_results

You can use the task ID obtained above to check the task status.

It will display the percentage of video processing completed.

Once finished, the returned data will include a download URL.

3. download

You can use the download URL from step 2 to retrieve the cleaned video.

6. Datasets

We have uploaded the labelled datasets into huggingface, check this out https://huggingface.co/datasets/LLinked/sora-watermark-dataset. Free free to train your custom detector model or improve our model!

7. API

Packaged as a Cog and published to Replicate for simple API based usage.

8. License

Apache License

9. Citation

If you use this project, please cite:

@misc{sorawatermarkcleaner2025,
  author = {linkedlist771},
  title = {SoraWatermarkCleaner},
  year = {2025},
  url = {https://github.com/linkedlist771/SoraWatermarkCleaner}
}

10. Acknowledgments

• IOPaint for the LAMA implementation
• Ultralytics YOLO for object detection