DNA-Diffusion is a generative deep learning model that adapts the diffusion probabilistic framework (similar to Stable Diffusion) for the domain of genomics. It trains on genomic data to generate novel regulatory DNA sequences conditioned on cell type, enabling researchers to design synthetic regulatory elements. The model handles sequence generation as a denoising process and can be used for applications in synthetic biology and gene regulation research.