the Breaking Point of Flow Matching
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Shifting the Breaking Point of Flow Matching for Multi-Instance Editing
arXiv:2602.08749v4 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 input must be edited independently without semantic interference. We...
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 input must be edited independently without semantic interference.
MT-EditFlow: Reinforcement Learning for Multi-Turn Image Editing with Flow Matching
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