  Photonic computing, with potentials of high parallelism, low latency and high
energy efficiency, have gained progressive interest at the forefront of neural
network (NN) accelerators. However, most existing photonic computing
accelerators concentrate on discriminative NNs. Large-scale generative photonic
computing machines remain largely unexplored, partly due to poor data
accessibility, accuracy and hardware feasibility. Here, we harness random light
scattering in disordered media as a native noise source and leverage
large-scale diffractive optical computing to generate images from above noise,
thereby achieving hardware consistency by solely pursuing the spatial
parallelism of light. To realize experimental data accessibility, we design two
encoding strategies between images and optical noise latent space that
effectively solves the training problem. Furthermore, we utilize advanced
photonic NN architectures including cascaded and parallel configurations of
diffraction layers to enhance the image generation performance. Our results
show that the photonic generator is capable of producing clear and meaningful
synthesized images across several standard public datasets. As a photonic
generative machine, this work makes an important contribution to photonic
computing and paves the way for more sophisticated applications such as real
world data augmentation and multi modal generation.
