Reverse-Engineered Stable Diffusion: Image-Based Prompt Generation

This repository showcases a project where stable diffusion techniques are reverse-engineered to generate image-based prompts. By integrating advanced deep learning models such as ViT, CLIP, and BLIP, the project demonstrates a robust framework for leveraging these models collaboratively. The work also explores loss computation techniques to refine the generated outputs.

Features

• Reverse Engineering Stable Diffusion: Explores the mechanics of stable diffusion and attempts to reverse it for generating image prompts.
• Model Integration: Combines Vision Transformer (ViT), CLIP, and BLIP for feature extraction and semantic analysis.
• Loss Computation: Implements loss functions to optimize the reverse-generation process.
• Advanced Tech Stack: Developed using Python and PyTorch for seamless integration with deep learning models.

Tech Stack

• Programming Language: Python
• Framework: PyTorch
• Models:
  • BLIP: Bootstrapped Language-Image Pretraining for image-caption tasks.
  • ViT: Vision Transformer for image recognition.
  • CLIP: Contrastive Language–Image Pretraining for linking text and images.
• Libraries: Deep learning utilities and stable diffusion libraries.

Requirements

• Python 3.8+
• PyTorch 1.10+
• BLIP, ViT, and CLIP model weights (Download instructions provided below)
• Other dependencies:

  pip install -r requirements.txt

Setup

1. Clone the Repository:

   git clone https://github.com/username/reverse-engineered-stable-diffusion.git  
   cd reverse-engineered-stable-diffusion  

2. Install Dependencies:

   pip install -r requirements.txt  

3. Download Pretrained Models:

   • BLIP: Link to BLIP weights.
   • ViT: Link to ViT weights.
   • CLIP: Link to CLIP weights.

4. Run the Project:

   python main.py  

Project Structure

reverse-engineered-stable-diffusion/  
│  
├── models/                 # Contains model integration scripts  
├── utils/                  # Utility scripts for data preprocessing and loss computation  
├── main.py                 # Entry point for the project  
├── requirements.txt        # List of dependencies  
├── README.md               # Project documentation  
└── results/                # Outputs and visualizations  

How It Works

1. Input Image Processing: The image is preprocessed and passed through the ViT model to extract features.
2. Language-Image Alignment: CLIP links the extracted image features to text embeddings.
3. Prompt Generation: BLIP generates text-based prompts from the image features.
4. Loss Computation: The generated prompts are evaluated, and the model adjusts outputs to minimize loss.

Results

The project successfully demonstrates prompt generation from images using a reverse-engineered stable diffusion pipeline. Further refinements and experiments with loss functions can enhance the model's accuracy.

Future Work

• Experiment with alternative diffusion models.
• Optimize the loss function for improved prompt accuracy.
• Expand the dataset for better generalization.

Contributions

Contributions are welcome! Please fork the repository, create a new branch, and submit a pull request.

License

This project is licensed under the MIT License.