Flux Hands Stabilizer

Introduction

The Flux Hands Stabilizer is a model developed to enhance the stability of hand movements in video or real-time applications. This model is available on the Hugging Face platform, where users can access and utilize it for various hand stabilization tasks.

Architecture

The model is designed to process hand movement data effectively, stabilizing shaky or jittery input signals. Specific architectural details are not provided, but it likely involves machine learning techniques tailored for motion correction.

Training

Details about the training process, including datasets used or specific methodologies, are not provided in the available documentation. Users interested in these specifics would need to examine the model's code or reach out to the developer community for more insights.

Guide: Running Locally

To run the Flux Hands Stabilizer locally, follow these general steps:

1. Clone the repository from Hugging Face:

   git clone https://huggingface.co/Lingyuzhou/Flux_Hands_stabilizer
   

2. Navigate to the project directory.
3. Install necessary dependencies, which might include Python libraries such as torch or tensorflow.
4. Execute the model script with sample input data for testing.

For enhanced performance, especially with large datasets or real-time applications, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The Flux Hands Stabilizer is released under the MIT License, allowing for flexible use, modification, and distribution.