README.md

---
title: StyleGAN v1
emoji: 🎨
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: "5.49.0"
app_file: app.py
python_version: "3.11"
pinned: false
license: mit
---

# StyleGAN v1 – Image generation

Generate images with your trained StyleGAN v1 generator.

## How to use

1. **Number of images**: Choose how many images to generate (1–16).
2. **Resolution**: Select output resolution (4 up to 256).
3. **Random seed** (optional): Set a seed for reproducible samples.
4. Click **Generate**.

## Adding your weights to this Space

Your trained weights must be in this repo so the app can load them.

### Option A: Keras weights (recommended)

1. Save your generator in training with:
   ```python
   generator.save_weights("weights/generator.weights.h5")
   ```
2. Create a `weights` folder in this repo and upload `generator.weights.h5` there.
3. Or push the file with Git LFS if it is large:
   ```bash
   git lfs install
   git lfs track "weights/*.h5"
   git add weights/generator.weights.h5 .gitattributes
   git commit -m "Add generator weights"
   git push
   ```

### Option B: State checkpoint (gen_state)

If your training script saves a checkpoint with `ema_trainable` and `non_trainable` (e.g. with `pickle`):

1. Save it as `weights/gen_state.pkl`.
2. Add the `weights` folder and `gen_state.pkl` to this repo (or use Git LFS for large files).

## Run locally

```bash
pip install -r requirements.txt
# Add your weights to weights/generator.weights.h5 (or weights/gen_state.pkl)
gradio app.py
```

Open http://localhost:7860 in your browser.

## Project structure

- `app.py` – Gradio UI and entrypoint for the Space
- `inference.py` – Loads weights and runs generation
- `loading_model.py` – Builds the StyleGAN generator
- `layers.py` – Generator/discriminator layers
- `configuration.py` – Resolutions and hyperparameters
- `weights/` – Put your generator weights here