Un-0: Generating Images with Coupled Oscillators
TL;DR. Executing deep neural networks on GPUs has dominated AI for a decade, but we think the next jump in energy efficiency demands a fundamentally different computer, one where physics does the computing. We built Un-0 , an image generator powered by a simulated system of coupled oscillators , an example of an emerging physical computing substrate. On ImageNet 64ร64 it reaches FID 6.74 , matching the quality of leading conventional image generation methods when they were first published. Weights, training, and ablation code are all open. Join us on an Unconventional journey!
At Unconventional AI, weโre building a new kind of computer, one that harnesses the laws of physics to do the computing. Our goal is to run modern AI on a fraction of the energy todayโs machines need, around 1,000x less. As a first step, we ask: can we train a physical dynamical system to generate images at scale?
The best AI models today are conventional deep networks with transformer backbones. However, there is also a long history of alternatives that seek energy efficiency by leveraging the dynamics of a physical system, such as the noisy, time-varying behavior of analog circuits that compute with analog voltage and current instead of conventional digitized numbers.
These physics-based alternatives include Neuromorphic Computing (Mead, 1990), Hopfield networks (Hopfield, 1982), and reservoir computing (Jaeger, 2001; Maass et al., 2002). Recently the community has also developed Hamiltonian (Greydanus et al., 2019) and Liquid (Hasani et al., 2021) networks, Neural Wave Machines (Keller & Welling, 2023), Thermodynamic Computing (Coles et al., 2023; Jelinฤiฤ, 2025), and Kuramoto Oscillators (Miyato et al., 2025; Song et al., 2025).
To exploit these alternative computing methods, the AI task needs to be mapped efficiently to the dynamics of the physical system. Un-0 validates that modern AI workloads can run more efficiently on physical substrates than on todayโs hardware.
Among a growing community building AI on physical and unconventional substrates [1โ8, and others], Un-0 is, to our knowledge, the most capable image generator to date to use a simulation of a physical dynamical system. Un-0 reaches FID 6.74 on class-conditional ImageNet 64ร64, though there are still opportunities to improve model performance as a function of parameter count towards the conventional frontier.