# Multimodal Generation

Multimodal generation has largely relied on jointly training a shared latent variable from scratch, which scales poorly to high-resolution images. SnB instead couples separately pretrained unimodal diffusion models at sampling time: the modalities share one reverse trajectory up to a split point, then a bridge module routes each into its own branch. On CelebAMask-HQ, SnB reaches the lowest FID across all reported shared-fraction settings while keeping strong coherence; on PolyMNIST it trades wins with top-down methods.

Multimodal generation has largely relied on jointly training a shared latent variable from scratch, which scales poorly to high-resolution images. SnB instead couples separately pretrained unimodal diffusion models at sampling time: the modalities share one reverse trajectory up to a split point, then a bridge module routes each into its own branch. On CelebAMask-HQ, SnB reaches the lowest FID across all reported shared-fraction settings while keeping strong coherence; on PolyMNIST it trades wins with top-down methods.

Continuous bitstream diffusion writes images, speech and proteins as binary codes and, with one training and sampling protocol, reaches 3.5% word error rate in joint speech-text generation, while the fold-level agreement of generated protein sequence-structure pairs is reported as 75% in the results section and 69% in the conclusion.

Continuous bitstream diffusion writes images, speech and proteins as binary codes and, with one training and sampling protocol, reaches 3.5% word error rate in joint speech-text generation, while the fold-level agreement of generated protein sequence-structure pairs is reported as 75% in the results section and 69% in the conclusion.