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cs.CV2025
MUSE: Multi-Subject Unified Synthesis via Explicit Layout Semantic Expansion
Fei Peng, Junqiang Wu, Yan Li +3
Existing text-to-image diffusion models have demonstrated remarkable capabilities in generating high-quality images guided by textual prompts. However, achieving multi-subject comp…
cs.CV2025
Diffusion Model as a Noise-Aware Latent Reward Model for Step-Level Preference Optimization
Tao Zhang, Cheng Da, Kun Ding +7
Preference optimization for diffusion models aims to align them with human preferences for images. Previous methods typically use Vision-Language Models (VLMs) as pixel-level rewar…