2 min readfrom Machine Learning

I deployed a GAN on a Raspberry Pi 4 and built a physical NFT minting device [P]

I deployed a GAN on a Raspberry Pi 4 and built a physical NFT minting device [P]
I deployed a GAN on a Raspberry Pi 4 and built a physical NFT minting device [P]

I trained a 128×128 DCGAN on my Macbook M3 and deployed it on a Raspberry Pi 4 connected to a LILYGO TTGO T-Display ESP32. The whole thing runs headlessly as a systemd service and generates hallucinated face hybrids at the press of a button.

It is a 6-block generator (latent → 4×4 → 8×8 → 16×16 → 32×32 → 64×64 → 128×128) with feature maps starting at f×16=1024. Corresponding 6-block discriminator. Trained for 800 epochs on Apple Silicon MPS, 4 hours. Dataset was 2480 images across 11 subjects. One dominant anchor class (2000 images) contaminated with minority classes to produce hybrid outputs. (Can you guess who and what was included?).

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I exported the model from PyTorch to ONNX (float32, 53MB). Inference takes 3 seconds per face on Pi 4.

The Pi generates the face and sends it to the ESP32. The title is generated through a dictionary and a template sentence: "This is a <adjective> NFT and I want to <verb> it."

The device was built as an art piece. I took it to the streets of NYC and let strangers use it. Full video: https://youtu.be/y-S74aoud54?si=yPh5GmCJZFIIzwq6

Happy to discuss the training pipeline, ONNX conversion, or anything you're curious about.

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