Alignment-Guided Score Matching for Text-to-Image Alignment in Diffusion Models 事件

Alignment-Guided Score Matching for Text-to-Image Alignment in Diffusion Models arXiv:2605.30038v1 Announce Type: cross Abstract: Diffusion models generate highly realistic images but often struggle with precise text-image alignment. While recent post-training methods improve alignment using external rewards or human preference signals, their performance heavily depends on reward quality and does not directly address alignment within the diffusion process itself. Recent reward-free approaches s