XVerse: Consistent Multi-Subject Control of Identity and Semantic
  Attributes via DiT Modulation
Authors: name: Bowen Chen, name: Mengyi Zhao, name: Haomiao Sun, name: Li Chen, name: Xu Wang, name: Kang Du, name: Xinglong Wu

Achieving fine-grained control over subject identity and semantic attributes
(pose, style, lighting) in text-to-image generation, particularly for multiple
subjects, often undermines the editability and coherence of Diffusion
Transformers (DiTs). Many approaches introduce artifacts or suffer from
attribute entanglement. To overcome these challenges, we propose a novel
multi-subject controlled generation model XVerse. By transforming reference
images into offsets for token-specific text-stream modulation, XVerse allows
for precise and independent control for specific subject without disrupting
image latents or features. Consequently, XVerse offers high-fidelity, editable
multi-subject image synthesis with robust control over individual subject
characteristics and semantic attributes. This advancement significantly
improves personalized and complex scene generation capabilities.