aiqbench analysis
This research addresses the critical challenge in diffusion model fine-tuning where improving domain-specific generation often degrades a model's general capabilities. By proposing a source-prior-driven selective adaptation method that masks parameters with minimal impact on broad generative ability, this work offers an efficient strategy to optimize the trade-off between specialization and retention of pretrained knowledge. The approach provides a structured update mechanism for selected parameter subsets, demonstrating superior performance over existing baselines in maintaining general utility while adapting to new tasks.
Source-Prior-Driven Selective Adaptation for Efficient Diffusion Model Finetuning
arXiv:2607.20913v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI)
arXiv:2607.20913 [cs.AI]
(or arXiv:2607.20913v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.20913 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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