Control Net Hand Refiner pruned

Introduction

The ControlNet-HandRefiner-pruned is a refined version of the ControlNet model, aimed at enhancing the quality of generated images, specifically focusing on correcting malformed hands through diffusion-based conditional inpainting.

Architecture

The model utilizes a pruned floating-point 16 (fp16) architecture to optimize performance and resource usage while maintaining the capability to refine and enhance hand depictions in generated images.

Training

The model was trained using a specialized dataset targeting the inpainting of hands in digital images. This training involved a diffusion-based approach to conditionally refine and improve the visual representation of hands, addressing common malformations found in AI-generated imagery.

Guide: Running Locally

1. Clone the Repository

   • Begin by cloning the repository from its GitHub source.

2. Install Dependencies

   • Ensure all necessary libraries and dependencies are installed, possibly using a package manager like pip.

3. Configure Environment

   • Set up the environment, ensuring compatible versions of Python and any other software are in use.

4. Run the Model

   • Execute the model using provided scripts or instructions, typically involving command-line execution.

5. Utilize Cloud GPUs

   • For optimal performance, especially with large datasets or complex images, consider using cloud GPU services like AWS, Google Cloud, or Azure.

License

The model is released under the Apache 2.0 license, allowing for free use, modification, and distribution with proper attribution.