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October 20, 2025Open Access

DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models

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DEFT enhances personalization using text-to-image models by efficiently fine-tuning with low-rank techniques.
The framework uses two trainable matrices to adapt the model while maintaining flexibility and editability.
Extensive experiments on datasets like Dreambooth and InsDet show DEFT's robust performance improvements.
Results indicate efficient fine-tuning can balance model adaptation and task instruction capabilities.

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Proposed framework DEFT achieves state-of-the-art personalization and adaptation in T2I models.

Kumar et al. (2025) studied this question.

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