This repository implements a multi-modal diffusion model that unifies marginal, conditional, and joint distributions for image-text data. The approach perturbs data across all modalities simultaneously and uses a transformer backbone to predict noise for each modality with individual timesteps. The model is trained on large-scale paired image-text data and can perform diverse generation tasks by setting appropriate timesteps without architectural changes.