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

ArXi:2602.08749v3 Announce Type: replace 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 global or single-instruction edits and struggle with multi-instance scenarios, where multiple parts of a reference input must be edited independently without semantic interference.