Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+- gan
+- pytorch
+- vision
+- dcgan
+- faces
+- humans
+- face
+metrics:
+- loss
+datasets:
+- stable-bias/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:
+![image](https://cdn-uploads.huggingface.co/production/uploads/697f2832c2c5e4daa93cece7/xeVq-Mv_NSjbRNWOp6jMK.png)
+
+## 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:**
+![image](https://cdn-uploads.huggingface.co/production/uploads/697f2832c2c5e4daa93cece7/zi_BYFDeK0x1aTEYpSLCj.png)