Stable Diffusion with Memristive Cellular Neural Networks

Stable diffusion models mark a pivotal shift in image generation algorithms, currently leading with outstanding performance. These models, rooted in diffusion principles, leverage neural networks operating in continuous time for enhanced accuracy and energy efficiency. This paper focuses on showcasing the potential of memristive cellular neural networks in implementing stable diffusion models for image generation. My findings demonstrate their superior performance in generating high-quality images compared to discrete-time counterparts, validated on the MNIST and CIFAR-10 datasets.