CatGen v2 - 128px DCGAN
This model is a Deep Convolutional Generative Adversarial Network (DCGAN) trained to generate high-quality 128x128 images of cats. It was trained for 165 epochs on a curated dataset of feline images, pushing the boundaries of traditional GAN architectures at this resolution.
Sample
Here's a sample after epoch 165:
Best of - Cat Images
Model Details
- Architecture: DCGAN (Deep Convolutional GAN)
- Resolution: 128x128 pixels (RGB)
- Parameters: ~186M (Generator)
- Training Duration: ~5 hours on NVIDIA T4 GPU
- Framework: PyTorch with Mixed Precision (AMP)
Training Hyperparameters
- Batch Size: 128
- Learning Rate: 0.0002
- Optimizer: Adam (Beta1: 0.5, Beta2: 0.999)
- Latent Vector (Z): 128 dimensions
Training details
The full training code can be found as catgen-v2.ipynb in this repo.
The training data we used was from HF: huggan/cats
Intended Use
This model is intended for artistic and research purposes. It demonstrates how GANs can capture complex textures like fur and eye reflections at medium resolutions.
How to use
To use this model, clone this repository and run the provided inference script. Ensure you have matplotlib, torch and torchvision installed.
python3 inference.py
--> Sample output:
Limitations & Bias
As a GAN, the model might occasionally produce "dream-like" artifacts or distorted anatomy (e.g., extra ears or eyes). It is not a diffusion model and generates images in a single forward pass.