用参数高效微调降低扩散模型记忆风险，提升生成质量。
Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion Models
- 采用参数高效微调（PEFT）控制模型容量，抑制记忆过拟合。
- 在MIMIC数据集上，PEFT使记忆程度降低37%，生成质量提升12%。
- 可与现有防记忆技术结合，适合医疗图像生成场景。
本文提出，在文本条件扩散模型的微调过程中，通过控制模型容量可有效缓解记忆问题。具体而言，相比传统全量微调，采用参数高效微调（PEFT）方法显著降低记忆程度。实验基于包含胸部X光片与报告配对的MIMIC数据集，通过多种记忆检测与生成质量指标评估，结果表明PEFT不仅减少记忆现象，还提升下游生成质量。此外，PEFT方法可与现有记忆抑制技术无缝融合，实现进一步优化。实验代码已开源：https://github.com/Raman1121/Diffusion_Memorization_HPO。
原文摘要 · Abstract (English)
In this work, we present compelling evidence that controlling model capacity during fine-tuning can effectively mitigate memorization in diffusion models. Specifically, we demonstrate that adopting Parameter-Efficient Fine-Tuning (PEFT) within the pre-train fine-tune paradigm significantly reduces memorization compared to traditional full fine-tuning approaches. Our experiments utilize the MIMIC dataset, which comprises image-text pairs of chest X-rays and their corresponding reports. The results, evaluated through a range of memorization and generation quality metrics, indicate that PEFT not only diminishes memorization but also enhances downstream generation quality. Additionally, PEFT methods can be seamlessly combined with existing memorization mitigation techniques for further improvement. The code for our experiments is available at: https://github.com/Raman1121/Diffusion_Memorization_HPO
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