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ARotting/
glyph-forge-cvae-model

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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

powershell

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.

ARotting/glyph-forge-cvae-model

GlyphForge CVAE

Reproduce

Verified results

ARotting/
glyph-forge-cvae-model