dbn - Draw By Number
This repo contains code used to analyze architecture prose and construct generative models used to create synthetic architecture schematics.
With all colab notebooks, make sure that you're connected to a GPU runtime. If you have issues with installation, then restart the runtime and re-run the notebook cells.
- Use text to generate new architecture imagery via a CLIP + genetic algorithms
- Explore fixed latent directions in a StyleGAN2 model trained trained on the ArchML dataset
- Discover new StyleGAN2 controls
- Visualize the principal components of a StyleGAN2 latent space
- Generate some static figures from a StyleGAN2
Visualize the ArchML dataset interactively. Explore the data used to train the models
Basic topic models and clustering of architecture lectures and a cohort of architecture project descriptions.
- host img datasets on ee site
- clean up generative
- add docs to datsets
We'd like to thank:
- Federico Galatolo and the authors of CLIP-GLASS,
for providing open source implementations of their methods and guidance. (
generative/styleclip) - Erik Härkönen and the authors of ganspace (
generative/ganspace) - The authors of the following PyTorch implementation of StyleGAN2 (
generative/styleclip) - The authors of pix-plot for their interactive data visualizer.
All of their work should be distributed following the terms of their original licenses.
The original code of this repository is released under the BSD 3.0-Clause License. Modifications, adaptations and derivative work is encouraged!
Jesse Bassett, Anna Konvicka, Armaan Kohli