arXiv:2606.14756v1 Announce Type: cross Abstract: The abundance of pre-trained diffusion models provides an opportunity for composition. Combining several models, however, runs the risk of one model dominating or models disagreeing with each other. Here, we propose Divide-and-Denoise, a method for coordinating multiple pre-trained diffusion models during sampling. Much like managing a specialized workforce, our method creates a fair but efficient division of labor across models. Central to our method is the notion of an allocation which defines the responsibility of each model to every region
The rapid proliferation of diverse pre-trained diffusion models creates an immediate need for effective methods to combine them without issues of model domination or disagreement.
This research addresses a core technical challenge in advancing AI capabilities by enabling more effective and fair composition of specialized models, potentially leading to more sophisticated and nuanced generative AI outputs.
The ability to coordinate multiple diffusion models fairly and efficiently will likely lead to a new paradigm in generative AI development, moving beyond single-model applications to integrated, specialized systems.
- · AI developers
- · Generative AI platforms
- · Creative industries relying on AI art
- · Research institutions in AI
- · Monolithic, single-model generative AI approaches
Improved quality and versatility of AI-generated content through compositional models.
Acceleration of multi-modal generative AI which combines different specialized models.
Potential for more democratized and accessible advanced generative AI by leveraging diverse pre-trained components.
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Read at arXiv cs.AI