ASSERT: Adaptive Stochastic Sampling for Robust Diffusion Models on Analog Compute-in-Memory Hardware
This work investigates diffusion inference using a noise model calibrated and validated against measurements collected from multiple physical CIM chips, and proposes ASSERT, a training-free sampler that uses higher stochasticity early and smoothly transitions to deterministic denoising.