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

• Watermark removed

• Original

⭐️: Yolo weights has been updated, try the new version watermark detect model, it should works better.

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.

We highly recommend using the uv to install the environments:

1. installation:

uv sync

now the envs will be installed at the .ven, 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.

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

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.

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

Apache License

If you use this project, please cite:

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

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