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

• URL: http://arxiv.org/abs/2602.08749v2
• Date: Tue, 10 Feb 2026 12:18:27 GMT
• Title: Shifting the Breaking Point of Flow Matching for Multi-Instance Editing
• Authors: Carmine Zaccagnino, Fabio Quattrini, Enis Simsar, Marta Tintoré Gazulla, Rita Cucchiara, Alessio Tonioni, Silvia Cascianelli,
• Abstract summary: We introduce Instance-Disentangled Attention, a mechanism that partitions joint attention operations and enforces binding between instance-specific textual instructions and spatial regions.<n>Our approach promotes edit disentanglement and locality while preserving global output coherence, enabling single-pass, instance-level editing.
• Score: 47.32746672482526
• License: http://creativecommons.org/licenses/by/4.0/
• 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 identify this limitation as a consequence of globally conditioned velocity fields and joint attention mechanisms, which entangle concurrent edits. To address this issue, we introduce Instance-Disentangled Attention, a mechanism that partitions joint attention operations, enforcing binding between instance-specific textual instructions and spatial regions during velocity field estimation. We evaluate our approach on both natural image editing and a newly introduced benchmark of text-dense infographics with region-level editing instructions. Experimental results demonstrate that our approach promotes edit disentanglement and locality while preserving global output coherence, enabling single-pass, instance-level editing.

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