https://github.com/auth0r-c0dez/neurathon-25
This is the project for the final round of neurathon 2025
https://github.com/auth0r-c0dez/neurathon-25
artificial-intelligence controlnetmodel fine-tuning floorplans neurathon2025 stability-ai stable-diffusion
Last synced: 4 months ago
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This is the project for the final round of neurathon 2025
- Host: GitHub
- URL: https://github.com/auth0r-c0dez/neurathon-25
- Owner: Auth0r-C0dez
- License: cc0-1.0
- Created: 2025-03-21T16:15:24.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-05T22:35:31.000Z (about 1 year ago)
- Last Synced: 2025-10-14T21:36:19.963Z (8 months ago)
- Topics: artificial-intelligence, controlnetmodel, fine-tuning, floorplans, neurathon2025, stability-ai, stable-diffusion
- Language: Jupyter Notebook
- Homepage:
- Size: 26.8 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neurathon-25
## Overview
**Floor PLanning Using Gen-AI** is an AI-driven project developed by Team ThinkML during the Neurathon hackathon at NIT Silchar, where it secured the 2nd prize. The project focuses on generating detailed and accurate floor plans using a combination of fine-tuned models and pre-trained multi-modal systems.
## Features
- **AI-Generated Floor Plans**: Leverages advanced AI models to create precise floor plans based on user inputs.
- **Model Fine-Tuning**: Utilizes fine-tuned versions of Stable Diffusion with ControlNet to enhance output control and accuracy.
- **API Integrations**: Incorporates several pre-trained models via APIs, including:
- **Janus 1.3B** and **Janus Pro**: Advanced text-to-image generation capabilities.
- **Optimized_Cloudqi_Text_to_Image_Model** and **Cloudqi_Text_to_Image_Model**: Enhanced image generation performance.
- **Vertex AI**: Cloud-based AI tools for scalable model inference.
- **Custom Safety Mechanisms**: Implements NSFW filtering and addresses common errors in ControlNet-based pipelines.
- **Performance Optimization**: Configures the pipeline for efficient GPU utilization and memory management, ensuring faster and more reliable image generation.
## Repository Structure
- **Models/**: Contains the fine-tuned models used for floor plan generation.
- **Notebooks/**: COlab notebooks demonstrating the model training and inference processes.
- **Results/**: Sample outputs and results from the model.
- **Testing/**: Scripts and data for testing the model's performance and accuracy.
- **backend/**: Backend code handling API calls and data processing.
- **src/**: Source code for the frontend interface and integration with the backend.
## Getting Started
### Prerequisites
- Python 3.x
- PyTorch
- Transformers
- Diffusers
- PIL
- NumPy
### Installation
Download the repo and run the notebooks with run time as T4 GPU.
## Contributing
Contributions are welcome! Please fork the repository and submit a pull request with your changes.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
## Acknowledgments
We extend our gratitude to NIT Silchar and their Machine Learning Club for organizing Neurathon and providing a platform for innovation.
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*For more details, visit our GitHub repository: [Neurathon-25](https://github.com/Auth0r-C0dez/Neurathon-25)*
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*Developed with ❤️ by Team ThinkML*