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<!doctype html>
<html lang="en">
<head>
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<title>Pocket Diffusion</title>
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<main>
  <div class="eyebrow">Jacob Garcia · Hugging Face Model Foundry</div>
  <h1>Pocket Diffusion</h1>
  <p class="lead">Interactive classifier-free guided digit diffusion. This showcase backs up the
  trained artifacts, measured evaluation, and complete runnable source.</p>
  <div class="actions">
    <a class="button" href="https://huggingface.co/spaces/ARotting/pocket-diffusion/tree/main">Explore every file</a>
    <a class="button alt" href="https://huggingface.co/ARotting">View the full foundry</a>
  </div>
  <div class="grid">
    <section class="card">
      <h2>Verified project card</h2>
      <pre># 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

```powershell
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&#x27;s 0.0066 to 0.0168 range under the same 100-samples-per-class protocol.
</pre>
      <h2>Evaluation snapshot</h2>
      <pre>{
  &quot;model&quot;: &quot;PocketDiffusion&quot;,
  &quot;parameters&quot;: 55608,
  &quot;diffusion_steps&quot;: 50,
  &quot;epochs&quot;: 300,
  &quot;guidance_search&quot;: {
    &quot;1.0&quot;: 0.460999995470047,
    &quot;1.5&quot;: 0.7149999737739563,
    &quot;2.0&quot;: 0.8560000061988831,
    &quot;2.5&quot;: 0.9359999895095825,
    &quot;3.0&quot;: 0.9620000123977661
  },
  &quot;generation&quot;: {
    &quot;judge_accuracy&quot;: 0.9620000123977661,
    &quot;judge_accuracy_by_class&quot;: {
      &quot;0&quot;: 1.0,
      &quot;1&quot;: 0.9200000166893005,
      &quot;2&quot;: 0.9900000095367432,
      &quot;3&quot;: 0.9800000190734863,
      &quot;4&quot;: 0.9599999785423279,
      &quot;5&quot;: 0.9800000190734863,
      &quot;6&quot;: 1.0,
      &quot;7&quot;: 0.9900000095367432,
      &quot;8&quot;: 0.8299999833106995,
      &quot;9&quot;: 0.9700000286102295
    },
    &quot;mean_pixel_variance_by_class&quot;: {
      &quot;0&quot;: 0.020940322428941727,
      &quot;1&quot;: 0.03187673166394234,
      &quot;2&quot;: 0.03836727514863014,
      &quot;3&quot;: 0.03142565116286278,
      &quot;4&quot;: 0.04158321022987366,
      &quot;5&quot;: 0.034002821892499924,
      &quot;6&quot;: 0.022990796715021133,
      &quot;7&quot;: 0.036991652101278305,
      &quot;8&quot;: 0.04558330774307251,
      &quot;9&quot;: 0.03457583114504814
    },
    &quot;samples&quot;: 1000,
    &quot;guidance&quot;: 3.0
  },
  &quot;judge&quot;: &quot;Tiny Vision labels-only student, 98.52% real-image test accuracy&quot;
}</pre>
    </section>
    <section class="card">
      <h2>Backed-up artifact tree</h2>
      <input id="filter" placeholder="Filter files…" autocomplete="off">
      <ul id="files"><li><code>README.md</code></li>
<li><code>__pycache__/app.cpython-311.pyc</code></li>
<li><code>__pycache__/model.cpython-311.pyc</code></li>
<li><code>app.py</code></li>
<li><code>artifacts/pocket-diffusion/evaluation.json</code></li>
<li><code>artifacts/pocket-diffusion/model.safetensors</code></li>
<li><code>artifacts/pocket-diffusion/samples.png</code></li>
<li><code>model.py</code></li>
<li><code>requirements.txt</code></li>
<li><code>train.py</code></li></ul>
    </section>
  </div>
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