Papers › Low-Rank Continual Personalization of Diffusion Models

Low-Rank Continual Personalization of Diffusion Models

7 Oct 2024arXiv:2410.04891archive 2025-07-28

Łukasz Staniszewski, Katarzyna Zaleska, Kamil Deja

Recent personalization methods for diffusion models, such as Dreambooth, allow fine-tuning pre-trained models to generate new concepts. However, applying these techniques across multiple tasks in order to include, e.g., several new objects or styles, leads to mutual interference between their adapters. While recent studies attempt to mitigate this issue by combining trained adapters across tasks after fine-tuning, we adopt a more rigorous regime and investigate the personalization of large diffusion models under a continual learning scenario, where such interference leads to catastrophic forgetting of previous knowledge. To that end, we evaluate the na\"ive continual fine-tuning of customized models and compare this approach with three methods for consecutive adapters' training: sequentially merging new adapters, merging orthogonally initialized adapters, and updating only relevant parameters according to the task. In our experiments, we show that the proposed approaches mitigate forgetting when compared to the na\"ive approach.

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convert_metrics_to_arrays luk-st/continual-lora/eval/eval_method.py official repository ran · our draft was wrong no licence file found · pointer only · 7f50eff9d670c3d5 · report
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Continual Learning

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Methods

Diffusion

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