Diffu-LoRA: A Novel Low-Rank Adaptation for Personalized Diffusion Models

Diffu-LoRA is a parameter‑efficient method for personalizing text‑to‑image diffusion models. It inserts trainable low‑rank components into Transformer linear layers and assigns a learnable gate to each component, allowing the model to learn how to distribute adaptation capacity across layers. Using bilevel optimization and progressive pruning, Diffu-LoRA keeps the pretrained backbone frozen while improving subject fidelity and prompt alignment compared to fine‑tuning baselines.

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• llms
• diffusion
• fine-tuning
• efficiency
• benchmarks
• safety