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About this paper
Yuchao Gu, Yipin Zhou, Yunfan Ye, Yixin Nie, Licheng Yu, Pingchuan Ma, Kevin Qinghong Lin, Mike Zheng Shou
ROICtrl introduces an adapter for text-to-image diffusion models, enabling precise control over multiple instances within a generated image by leveraging a novel ROI-Unpool operation. This approach achieves up to 9.8x faster inference and quantitatively outperforms prior methods in spatial and regional text alignment across various benchmarks.
Key insights
- Explicit ROI manipulation via the ROI-Align/Unpool mechanism offers superior efficiency and accuracy for handling variable-sized regions compared to implicit encoding or attention masking.
- Reusing the pretrained cross-attention within the adapter ensures broad compatibility with existing diffusion models and their extensive ecosystem of add-ons, including spatial and embedding-based controls.
- A regularization loss, combined with global coordinate conditioning, is crucial for minimizing global attention influence within ROIs, thereby enhancing regional text alignment and preventing attribute leakage.
Summary by alphaXiv.
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