The search for privacy, customization, and low cost is increasing interest in AI models that can run on local hardware. [Tim] developed an image-generation model that can run on the RP2350 microcontroller. The system on the Waveshare RP2350 development board generates only human faces; the resolution is 128×128, and the time per image is approximately twenty seconds. The output can be obtained via USB or displayed with a VGA adapter board. Instead of generating the image directly, the model produces a distribution in latent space; the decoder of the variational autoencoder converts this into an image. The latent flow diffusion transformer gradually creates the image, starting from noise. [Tim] quantized two models—one larger and the other faster—with 8-bit integers; both models and the inference program fit into 4 MB of flash.
Why it matters
This work shows that image generation is not limited to powerful graphics cards or remote servers and can also be carried out within a defined scope on resource-constrained microcontrollers. Processing on the device may appeal to users seeking privacy and wanting to operate independently of a connection, since the generated face images do not need to be sent to the cloud. The low memory and storage requirements indicate that a technical barrier has been overcome in adapting the model to small, embedded hardware. However, the system’s ability to generate only faces, its limited resolution, and the approximately twenty seconds required for a single image narrow its potential applications. The open question, therefore, is whether a similar approach can be extended to more diverse content and shorter generation times.