MANTA -- Model Adapter Native generations that's Affordable
<|reference_start|>MANTA -- Model Adapter Native generations that's Affordable: The presiding model generation algorithms rely on simple, inflexible adapter selection to provide personalized results. We propose the model-adapter composition problem as a generalized problem to past work factoring in practical hardware and affordability constraints, and introduce MANTA as a new approach to the problem. Experiments on COCO 2014 validation show MANTA to be superior in image task diversity and quality at the cost of a modest drop in alignment. Our system achieves a $94\%$ win rate in task diversity and a $80\%$ task quality win rate versus the best known system, and demonstrates strong potential for direct use in synthetic data generation and the creative art domains.<|reference_end|>
@article{chaurasia2024manta,
    title={MANTA -- Model Adapter Native generations that's Affordable},
    author={Ansh Chaurasia},
    journal={arXiv preprint arXiv:2409.14363},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14363},
    primaryClass={cs.AI eess.IV}
}