Let Triggers Control: Frequency-Aware Dropout for Effective Token Control
Text-to-image models such as Stable Diffusion have achieved unprecedented levels of high-fidelity visual synthesis. As these models advance, personalization of generative models -- commonly facilitated through Low-Rank Adaptation (LoRA) with a dedicated trigger token -- has become a significant area of research. Previous works have naively assumed that fine-tuning with a single trigger token to represent new concepts. However, this often results in poor controllability, where the trigger token alone fails to reliably evoke the intended concept. We attribute this issue to the frequent co-occurrence of the trigger token with the surrounding context during fine-tuning, which entangles their ...
| Authors | Junyoung Koh, Hoyeon Moon, Dongha Kim et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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