Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+tags:
+- gan
+- pytorch
+- vision
+- cats
+- dcgan
+metrics:
+- loss
+datasets:
+- huggan/cats
+---
+
+# 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:
+![__results___8_0](https://cdn-uploads.huggingface.co/production/uploads/697f2832c2c5e4daa93cece7/VV8AhZgJFA_dvsV1-ul7P.png)
+
+## 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
+
+## 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.
+
+```bash
+python3 inference.py
+```
+
+## 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.