While diffusion models excel at image generation, their growing adoption raises critical concerns about copyright issues and model transparency. Existing attribution methods identify training examples influencing an entire image, but fall short in isolating contributions to specific elements, such as styles or objects, that are of primary concern to stakeholders. To address this gap, we introduce concept-level attribution through a novel method called Concept-TRAK, which extends influence functions with a key innovation: specialized training and utility loss functions designed to isolate concept-specific influences rather than overall reconstruction quality. We evaluate Concept-TRAK on novel concept attribution benchmarks using Synthetic and CelebA-HQ datasets, as well as the established AbC benchmark, showing substantial improvements over prior methods in concept-level attribution scenarios. We further demonstrate its versatility on real-world text-to-image generation with compositional and multi-concept prompts.
@article{arxiv.2507.06547,
title = {Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attribution},
author = {Yonghyun Park and Chieh-Hsin Lai and Satoshi Hayakawa and Yuhta Takida and Naoki Murata and Wei-Hsiang Liao and Woosung Choi and Kin Wai Cheuk and Junghyun Koo and Yuki Mitsufuji},
journal= {arXiv preprint arXiv:2507.06547},
year = {2026}
}