Title: ContextGen: Contextual Layout Anchoring for Identity-Consistent Multi-Instance Generation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Diffusion Models, Controllable Generation, Multi-Instance Generation, Identity Preservation, Attention Mechanisms

Abstract: Multi-instance image generation (MIG) remains a significant challenge for modern diffusion models due to key limitations in achieving precise control over object layout and preserving the identity of multiple distinct subjects. To address these limitations, we introduce **ContextGen**, a novel Diffusion Transformer framework for multi-instance generation that is guided by both layout and reference images. Our approach integrates two key technical contributions: a **Contextual Layout Anchoring (CLA)** mechanism that incorporates the composite layout image into the generation context to robustly anchor the objects in their desired positions, and **Identity Consistency Attention (ICA)**, a novel attention mechanism which leverages contextual reference images to ensure the identity consistency of multiple instances. Recognizing the lack of large-scale, hierarchically-structured datasets for this task, we introduce **IMIG-100K**, the first dataset with detailed layout and identity annotations. Extensive experiments demonstrate that ContextGen sets a new state-of-the-art, outperforming existing methods in control precision, identity fidelity, and overall visual quality.