README.md

metadata

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:

   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:

   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

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