Images from a Field of Oscillators

Unconventional AI released Un-0, an image generator built not on diffusion or adversarial training but on Kuramoto coupled-oscillator dynamics. The learned parameters are coupling strengths between oscillators; the image emerges from a physical simulation rather than a stack of nonlinear layers. FID 6.74 on ImageNet-64 won't unseat SOTA, but the architecture is genuinely different and the code is MIT-licensed.

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Image Generation at 1 Bit

PrismML's Bonsai Image 4B applies 1-bit and ternary quantization to a FLUX.2 Klein diffusion transformer, compressing it 8.3× to 0.93 GB — small enough to generate images on an iPhone in under 10 seconds. It's the first demonstration that extreme quantization techniques developed for language models transfer cleanly to diffusion architectures.

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