I implemented a very tiny image generation model (latent flow transformer) on a RP2350 microcontroller - it can generate 128x128 images of faces [P]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
| Its a 2.4-4 million parameter model, quantized to int8, that can be fully executed on the microcontroller in ~20s with the longest generation. The generated image will then be displayed on a monitor or transferred via usb. Its a latent flow transformer with 12 layers using AdaLN-Zero for conditioning. CFG is also supported and boosted the image quality a lot. The inference engine streams the weight via DMA from the flash while the previous layer is computed. Relu² activation was used to increase sparsity, which the engine can use to skip calculations. Took a lot of ablations to get it right and I am quite astonished I got so far with so few parameters. Will post the repo below [link] [comments] |
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