ARotting/pocket-diffusion-model

PocketDiffusion

PocketDiffusion is a compact class-conditional denoising diffusion model for 8x8 handwritten digits. It learns to predict Gaussian noise over 50 diffusion steps and uses classifier-free guidance during sampling.

The same frozen Tiny Vision classifier used for GlyphForge evaluates conditional recognizability, making the VAE and diffusion results directly comparable under one judge.

Reproduce

uv run python projects/tiny-vision-foundry/prepare_data.py
uv run python projects/pocket-diffusion/train.py

Verified results

• Parameters: 55,608
• Diffusion steps: 50
• Training epochs: 300
• Generated samples: 1,000
• Selected classifier-free guidance: 3.0
• Frozen-judge class fidelity: 96.20%

Guidance search improved fidelity monotonically from 46.10% at 1.0 to 96.20% at 3.0. Per-class fidelity ranged from 83% for digit 8 to 100% for digits 0 and 6. Mean within-class pixel variance ranged from 0.0209 to 0.0456, noticeably higher than the CVAE's 0.0066 to 0.0168 range under the same 100-samples-per-class protocol.