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arXiv Machine Learning By Yupeng Qiu, Han Fang, Dhananjai Sharma, Ee-Chien Chang

Discovering Undisclosed Concepts in LoRA Adapters for Diffusion-Based AIGC Models

Read the original on arXiv Machine Learning →

The paper introduces a two‑stage method for uncovering hidden concepts encoded in Low‑Rank Adaptation (LoRA) adapters used to personalize diffusion models. First, Subspace‑Guided Evolutionary Search (SGES) identifies promising prompts by evaluating their alignment with the LoRA subspace without rendering images. Second, Paired Visual Identification (PVI) renders only the shortlisted prompts and compares LoRA outputs to the base model to confirm coherent, distinctive generation, thereby efficiently revealing undisclosed concepts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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