arXiv:2606.16408v1 Announce Type: new Abstract: We introduce MUNI, an end-to-end multimodal latent diffusion framework for any-to-any generation that unifies subset-conditioned cross-modal generation and unconditional joint sampling through a shared stochastic latent. Existing multimodal generative models are largely LLM-based, which limits leveraging modality-specific generators and requires text-paired data for training. Recent diffusion- and flow-based any-to-any extensions take a different direction but still rely on text-aligned embeddings, fully-paired training, or matched-dimensionality
The proliferation of diffusion models and the drive towards more efficient, flexible AI architectures make this development timely.
This framework could significantly advance multimodal AI generation by removing dependencies on text-paired data and specific generator types, opening new applications.
The ability to perform 'any-to-any' generation coherently without full modality pairing or text-aligned embeddings simplifies multimodal AI development and broadens its applicability.
- · AI researchers
- · Generative AI developers
- · Content creation industries
- · Software companies
- · Models requiring extensive paired data
- · LLM-centric multimodal approaches
MUNI directly enables more flexible and efficient cross-modal content generation across various inputs and outputs.
This could lead to new applications in creative fields, data augmentation, and human-computer interaction, reducing current modality-specific constraints.
The reduced need for perfectly paired datasets might accelerate AI development in resource-scarce domains or less common data combinations.
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Read at arXiv cs.LG