AnyMS: Training-Free Multi-Subject Customization with Layout Guidance
Paper#Image Generation, AI, Computer Vision🔬 Research|Analyzed: Jan 3, 2026 18:41•
Published: Dec 29, 2025 15:26
•1 min read
•ArXivAnalysis
This paper introduces AnyMS, a novel training-free framework for multi-subject image synthesis. It addresses the challenges of text alignment, subject identity preservation, and layout control by using a bottom-up dual-level attention decoupling mechanism. The key innovation is the ability to achieve high-quality results without requiring additional training, making it more scalable and efficient than existing methods. The use of pre-trained image adapters further enhances its practicality.
Key Takeaways
- •Proposes AnyMS, a training-free framework for multi-subject image customization.
- •Employs a bottom-up dual-level attention decoupling mechanism for text alignment, identity preservation, and layout control.
- •Utilizes pre-trained image adapters, eliminating the need for subject learning or adapter tuning.
- •Achieves state-of-the-art performance and supports complex compositions.
Reference / Citation
View Original"AnyMS leverages a bottom-up dual-level attention decoupling mechanism to harmonize the integration of text prompt, subject images, and layout constraints."