ContextGen: Contextual Layout Anchoring for Identity-Consistent
  Multi-Instance Generation
Authors: name: Ruihang Xu, name: Dewei Zhou, name: Fan Ma, name: Yi Yang

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),
an innovative attention mechanism that 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.