| --- |
| license: mit |
| tags: |
| - gan |
| - pytorch |
| - vision |
| - dcgan |
| - faces |
| - humans |
| - face |
| metrics: |
| - loss |
| datasets: |
| - SDbiaseval/faces |
| --- |
| |
| # FaceGen v1 - 128px DCGAN |
|
|
| This model is a Deep Convolutional Generative Adversarial Network (DCGAN) trained to generate high-quality 128x128 images of human faces. It was trained for 250 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 200: |
|  |
|
|
| ## Model Details |
| - **Architecture:** DCGAN (Deep Convolutional GAN) |
| - **Resolution:** 128x128 pixels (RGB) |
| - **Parameters:** ~186M (Generator) |
| - **Training Duration:** ~22 hours on NVIDIA RTX 5060 Ti 16GB |
| - **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 `train.py` in this repo. |
| The training data we used was from HF: stable-bias/faces |
|
|
| ## Intended Use |
| This model is intended for artistic and research purposes. It demonstrates how GANs can capture complex faces and even 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. |
|
|
| ```bash |
| python3 inference.py |
| ``` |
|
|
| **Sample output:** |
|  |
