Source-Prior-Driven Selective Adaptation for Efficient Diffusion Model Finetuning

Fine-tuning large diffusion models for new domains or styles involves a trade-off: improving target-specific generation often degrades the pretrained model's broad generative capability.

Existing full and parameter-efficient fine-tuning methods typically handle this trade-off only implicitly.

In this work, we propose a novel source-prior-driven selective adaptation method to efficiently fine-tune diffusion models, achieving a favorable trade-off.

Our method relies on two key observations: (1) the loss of general generative capability is highly inconsistent across pretrained parameters, and (2) parameters that have a relatively small impact on the model's general generative capability remain structurally inconsistent across layers and parameter types.

Motivated by these observations, we first learn a static mask to explicitly identify parameters better suited for downstream adaptation, and then construct structured update strategies for the selected subset.

Experiments show that our method achieves a better adaptation-retention trade-off than existing strong baselines.