ROICtrl: Boosting Instance Control for Visual Generation (2411.17949v1)

Abstract: 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.

• The paper introduces ROI-Unpool to enable precise spatially aligned control in text-to-image diffusion models.
• It integrates bounding box data with free-form captions to overcome attribute leakage and improve complex scene composition.
• Evaluations on InstDiff-Bench, MIG-Bench, and ROICtrl-Bench demonstrate superior performance in managing small ROIs and intricate visual details.

An Expert Overview of "ROICtrl: Boosting Instance Control for Visual Generation"

The paper "ROICtrl: Boosting Instance Control for Visual Generation" presents a novel approach to address the limitations of current text-based visual generation models in handling multiple instances with precise spatial and attribute control. The authors propose an enhancement to diffusion models through the introduction of an operation named ROI-Unpool. This operation, alongside ROI-Align, facilitates explicit and efficient manipulation of regions of interest (ROIs) on high-resolution feature maps. Building upon this advancement, the authors introduce ROICtrl, an adapter for pretrained diffusion models designed to significantly improve regional instance control while reducing computational costs.

The key contribution of the paper is its ability to perform precise instance control by integrating bounding box information with free-form captions. Traditional models have struggled with accurately associating descriptive attributes and positions, often resulting in suboptimal image composition when dealing with complex multi-instance scenarios. The introduction of ROI-Unpool allows for explicit handling of ROIs, enhancing spatial alignment and mitigating attribute leakage issues that were prevalent in previous methodologies.

The effectiveness of ROICtrl is demonstrated through comprehensive evaluations against existing benchmarks such as InstDiff-Bench and MIG-Bench, as well as a newly introduced benchmark, ROICtrl-Bench. The results indicate that ROICtrl achieves superior performance in terms of spatial and regional text alignment, particularly excelling in scenarios involving small-sized ROIs and free-form instance captions. Quantitatively, ROICtrl surpasses existing methods, suggesting a robust capability to handle intricate visual generation tasks.

Practically, ROICtrl's compatibility with various community-finetuned diffusion models, spatial-based add-ons like ControlNet, and embedding-based add-ons such as IP-Adapter underscores its versatility and potential for wide adoption. Additionally, its application in continuous generation scenarios, where local regions can be modified without affecting previously generated content, opens new avenues for creative and interactive applications in AI-art generation and multimedia content creation.

Theoretically, the introduction of ROI-Unpool into the architecture provides a new perspective on addressing the spatial alignment and attribute binding challenges in text-to-image synthesis. This advancement could stimulate further research into optimizing the computational efficiency and scalability of diffusion models, particularly as they scale with higher resolutions and more complex input specifications.

Looking forward, while the current work primarily focuses on image generation, the techniques and insights provided by ROICtrl may be extended to video instance control, with preliminary results indicating potential benefits in addressing temporal consistency issues in video generation. Thus, ROICtrl not only represents a significant step forward in visual generative modeling but also sets the stage for future explorations into dynamic and interactive media content synthesis.