Analog Diffusion Models

As generative artificial intelligence (GenAI) drives computational demands to unprecedented scales, digital hardware is approaching fundamental limits. Analog and optical systems promise orders-of-magnitude efficiency gains, but translating these to application-level gains is challenging due to the mismatch between hardware primitives and algorithmic requirements. Here, we introduce Analog Diffusion Models (ADMs) which implement diffusion inference with an implicit integration scheme, formulating each diffusion step as a fixed-point problem amenable for acceleration by efficient analog hardware. At the same time, training remains identical to that of conventional diffusion models, allowing adoption of established scalable training approaches with no additional overhead. We validate ADMs on analog hardware using three-dimensional optics with 2,304 programmable weights. On hardware, we generate two-dimensional distributions and latent-space distributions for MNIST, FashionMNIST, and ExtendedMNIST, demonstrating the feasibility of executing multi-layer diffusion processes entirely on noisy, non-traditional hardware. The current prototype reaches fixed-point convergence in 10–15 µs per diffusion step, with projections to nanosecond-scale convergence with miniaturization. In simulation, across multiple datasets, backbone architectures, and model sizes ranging from 32 million to 13 billion parameters, ADMs match the sample quality of standard methods with up to 16× fewer diffusion steps. Most importantly, they could achieve efficiency gains of more than 100× at the application level without sacrificing generation quality, 100× from hardware acceleration, and an additional 1-2× from algorithmic improvement, highlighting the multiplicative benefit of hardware–algorithm co-design. Together, these results establish ADMs as a scalable and general, hardware-aligned framework for low-latency and energy-efficient generative modeling on analog computing platforms.