ROICtrl: Boosting Instance Control for Visual Generation
Authors: name: Yuchao Gu, name: Yipin Zhou, name: Yunfan Ye, name: Yixin Nie, name: Licheng Yu, name: Pingchuan Ma, name: Kevin Qinghong Lin, name: Mike Zheng Shou

Natural language often struggles to accurately associate positional and
attribute information with multiple instances, which limits current text-based
visual generation models to simpler compositions featuring only a few dominant
instances. To address this limitation, this work enhances diffusion models by
introducing regional instance control, where each instance is governed by a
bounding box paired with a free-form caption. Previous methods in this area
typically rely on implicit position encoding or explicit attention masks to
separate regions of interest (ROIs), resulting in either inaccurate coordinate
injection or large computational overhead. Inspired by ROI-Align in object
detection, we introduce a complementary operation called ROI-Unpool. Together,
ROI-Align and ROI-Unpool enable explicit, efficient, and accurate ROI
manipulation on high-resolution feature maps for visual generation. Building on
ROI-Unpool, we propose ROICtrl, an adapter for pretrained diffusion models that
enables precise regional instance control. ROICtrl is compatible with
community-finetuned diffusion models, as well as with existing spatial-based
add-ons (\eg, ControlNet, T2I-Adapter) and embedding-based add-ons (\eg,
IP-Adapter, ED-LoRA), extending their applications to multi-instance
generation. Experiments show that ROICtrl achieves superior performance in
regional instance control while significantly reducing computational costs.