metadata
license: apache-2.0
task_categories:
- unconditional-image-generation
tags:
- conditional-vae
- generative-model
- pytorch
- gradio
GlyphForge CVAE
GlyphForge is a compact conditional variational autoencoder that generates 8x8 handwritten digits. A requested digit label conditions the decoder while an eight-dimensional Gaussian latent captures style.
The benchmark measures:
- held-out reconstruction MSE;
- KL divergence to the unit-Gaussian prior;
- generated-class fidelity through the frozen Tiny Vision classifier;
- within-class sample diversity.
Reproduce
uv run python projects/tiny-vision-foundry/prepare_data.py
uv run python projects/glyph-forge-cvae/train.py
The saved sample grid and Gradio app are model outputs, not hand-authored examples.
Verified results
- Parameters: 9,504
- Latent dimensions: 8
- Held-out reconstruction MSE: 0.02394
- Mean held-out KL divergence: 0.3978
- Conditional samples evaluated: 1,000
- Frozen-judge class fidelity: 99.90%
Nine digit classes achieved 100% judge fidelity; digit 1 achieved 99%. Every class
had nonzero mean pixel variance across its 100 samples, ranging from 0.0066 to 0.0168.
The judge is the 2,198-parameter Tiny Vision labels-only student with 98.52% accuracy
on real held-out images, so this metric measures recognizability to that specific
classifier rather than human perceptual quality.