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MANTA -- Model Adapter Native generations that's Affordable

arXiv 2409.14363 ↗First posted 22 Sep 2024cs.AI eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 22 Sep 2024.

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19 of 34 references.

References · 19

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  5. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  6. 2023Diffusion Model Alignment Using Direct Preference Optimizationcited by 790
  7. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 434
  8. 2023Aligning Text-to-Image Diffusion Models with Reward Backpropagationcited by 288
  9. 2024A Survey on Efficient Inference for Large Language Modelscited by 205
  10. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documentscited by 49
  11. 2024Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategiescited by 45
  12. 2023Fast and Accurate Factual Inconsistency Detection Over Long Documentscited by 23
7 more
  1. 2023Diffusion Model with Perceptual Losscited by 21
  2. 2024Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?cited by 21
  3. 2024Stylus: Automatic Adapter Selection for Diffusion Modelscited by 18
  4. 2024Explaining generative diffusion models via visual analysis for interpretable decision-making processcited by 17
  5. 2024Exploring the Impact of AI-generated Image Tools on Professional and Non-professional Users in the Art and Design Fieldscited by 10
  6. 2023Increased Compute Efficiency and the Diffusion of AI Capabilitiescited by 8
  7. 2024Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Modelcited by 6

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