Transformer Image Generation is a project that explores the application of Transformer Neural Networks for image generation tasks. Leveraging the self-attention mechanism inherent in Transformers, this approach aims to capture complex dependencies in image data, facilitating the generation of coherent and high-quality images.
This project implements a Transformer-based model tailored for image generation. By treating images as sequences of patches, the model learns to generate images by predicting pixel values in an autoregressive manner, capturing both local and global structures effectively.
Transformer-image-generation/
├── transformers.py # Core implementation of the Transformer model
Note: The repository currently contains the primary model implementation. Additional scripts for training, evaluation, and data preprocessing may be added in future updates.
- Python 3.6 or higher
- PyTorch
- NumPy
- Matplotlib
- Clone the repository:
git clone https://github.com/Qadeer2syed/Transformer-image-generation.git
cd Transformer-image-generation- Install dependencies:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txtAs the repository currently contains only the model implementation, usage instructions for training and inference will be provided once the corresponding scripts are available.
Results and sample generated images will be showcased here in future updates.