Shifting the Breaking Point of Flow Matching for Multi-Instance Editing 事件

Shifting the Breaking Point of Flow Matching for Multi-Instance Editing arXiv:2602.08749v3 Announce Type: replace Abstract: Flow matching models have recently emerged as an efficient alternative to diffusion, especially for text-guided image generation and editing, offering faster inference through continuous-time dynamics. However, existing flow-based editors predominantly support global or single-instruction edits and struggle with multi-instance scenarios, where multiple parts of a reference