Towards Lifelong Few-Shot Customization of Text-to-Image Diffusion

• URL: http://arxiv.org/abs/2411.05544v1
• Date: Fri, 08 Nov 2024 12:58:48 GMT
• Title: Towards Lifelong Few-Shot Customization of Text-to-Image Diffusion
• Authors: Nan Song, Xiaofeng Yang, Ze Yang, Guosheng Lin,
• Abstract summary: Lifelong few-shot customization for text-to-image diffusion aims to continually generalize existing models for new tasks with minimal data. In this study, we identify and categorize the catastrophic forgetting problems into two folds: relevant concepts forgetting and previous concepts forgetting. Unlike existing methods that rely on additional real data or offline replay of original concept data, our approach enables on-the-fly knowledge distillation to retain the previous concepts while learning new ones.
• Score: 50.26583654615212
• License:
• Abstract: Lifelong few-shot customization for text-to-image diffusion aims to continually generalize existing models for new tasks with minimal data while preserving old knowledge. Current customization diffusion models excel in few-shot tasks but struggle with catastrophic forgetting problems in lifelong generations. In this study, we identify and categorize the catastrophic forgetting problems into two folds: relevant concepts forgetting and previous concepts forgetting. To address these challenges, we first devise a data-free knowledge distillation strategy to tackle relevant concepts forgetting. Unlike existing methods that rely on additional real data or offline replay of original concept data, our approach enables on-the-fly knowledge distillation to retain the previous concepts while learning new ones, without accessing any previous data. Second, we develop an In-Context Generation (ICGen) paradigm that allows the diffusion model to be conditioned upon the input vision context, which facilitates the few-shot generation and mitigates the issue of previous concepts forgetting. Extensive experiments show that the proposed Lifelong Few-Shot Diffusion (LFS-Diffusion) method can produce high-quality and accurate images while maintaining previously learned knowledge.

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