XVerse: Consistent Multi-Subject Control of Identity and Semantic
  Attributes via DiT Modulation
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.
685e084071131fa43be08acc
Bowen Chen
claimed_verified
2025-06-30T06:22:45.351Z
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/avatars/b3ae6888a41aab8c2a7ef9f7320565c4.svg
Bowen Chen 
user
chenbowen
685e084071131fa43be08acd
Mengyi Zhao
685e084071131fa43be08ace
Haomiao Sun
685e084071131fa43be08acf
Li Chen
685e084071131fa43be08ad0
Xu Wang
685e084071131fa43be08ad1
Kang Du
685e084071131fa43be08ad2
Xinglong Wu
XVerse: Consistent Multi-Subject Control of Identity and Semantic
  Attributes via DiT Modulation
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.
XVerse enhances text-to-image generation by enabling precise and independent control over multiple subjects using token-specific text-stream modulation, improving image coherence and fidelity.
Diffusion Transformers DiTs text-to-image generation multi-subject controlled generation reference images token-specific text-stream modulation image latents multi-subject image synthesis semantic attributes