metadata
license: mit
pipeline_tag: text-to-image
PixelModel 🖼️
A neural network where the weights are the image.
📌 What is this?
model.png is not a picture — it is the model.
Every pixel encodes neural network weights. At inference, the PNG is decoded into weight matrices forming a tiny MLP. The prompt is embedded into a vector, and the model generates a 32×32 image.
Training directly optimizes pixel values via gradient descent until the PNG becomes the model itself.
🎨 Weight Encoding
- R channel → weight magnitude (0–255 → 0.0–1.0)
- B channel → weight sign (<128 = negative, ≥128 = positive)
- G channel → unused / reserved
🧠 Architecture
prompt string
→ char embedding → 32-dim vector
→ W1 (64×32) → tanh
→ W2 (64×64) → tanh
→ W3 (3072×64) → sigmoid
→ reshape → 32×32×3 image
All weights live inside model.png.
🧪 Dataset vs Outputs
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📁 Files
model.png ← THE MODEL (64×3200 px)
main.py ← inference
train.py ← training
model.py ← architecture
dataset/
red.png
red.txt ← prompt: "red"
...
⚙️ Usage
python train.py
python train.py --epochs 500 --lr 0.05
python main.py "red"
python main.py "a cat" --out cat.png --scale 8
📊 Tips
- 6–20 samples are enough
- Simple patterns converge fastest
- 200–500 epochs typical
- Loss < 0.001 is strong for toy datasets
It’s a toy. It’s not useful. But it works.
Bench Labs · Simple, Reliable, Open sourced