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arXiv — cs.AI

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Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning

arXiv:2609.10656v1 Announce Type: new Abstract: Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost. We present a controlled study on CIFAR-10 using a DDPM U-Net with ranks {2,4,8,16,32}, fixed optimization settings, and a reproducible…

arXiv — cs.AIBy: Iman Khazrak, Narges Nejad, Mostafa M. Rezaee, Robert C. Green IISeptember 12, 2026 (Sep 12)
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  • arXiv:2609.10656v1 Announce Type: new Abstract: Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost
  • We present a controlled study on CIFAR-10 using a DDPM U-Net with ranks {2,4,8,16,32}, fixed optimization settings, and a reproducible…
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Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning

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