LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized
  Text-Guided Image Editing
Text-guided image editing aims to modify specific regions of an image
according to natural language instructions while maintaining the general
structure and the background fidelity. Existing methods utilize masks derived
from cross-attention maps generated from diffusion models to identify the
target regions for modification. However, since cross-attention mechanisms
focus on semantic relevance, they struggle to maintain the image integrity. As
a result, these methods often lack spatial consistency, leading to editing
artifacts and distortions. In this work, we address these limitations and
introduce LOCATEdit, which enhances cross-attention maps through a graph-based
approach utilizing self-attention-derived patch relationships to maintain
smooth, coherent attention across image regions, ensuring that alterations are
limited to the designated items while retaining the surrounding structure.
\method consistently and substantially outperforms existing baselines on
PIE-Bench, demonstrating its state-of-the-art performance and effectiveness on
various editing tasks. Code can be found on
https://github.com/LOCATEdit/LOCATEdit/
67e68802327f995244ac2e45
Achint Soni
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2025-03-28T12:25:43.793Z
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Achint Soni
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Trickyjustice
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Meet Soni
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2025-03-31T08:21:44.872Z
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Meet soni
user
Meet8494
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Sirisha Rambhatla
LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized
  Text-Guided Image Editing
Text-guided image editing aims to modify specific regions of an image
according to natural language instructions while maintaining the general
structure and the background fidelity. Existing methods utilize masks derived
from cross-attention maps generated from diffusion models to identify the
target regions for modification. However, since cross-attention mechanisms
focus on semantic relevance, they struggle to maintain the image integrity. As
a result, these methods often lack spatial consistency, leading to editing
artifacts and distortions. In this work, we address these limitations and
introduce LOCATEdit, which enhances cross-attention maps through a graph-based
approach utilizing self-attention-derived patch relationships to maintain
smooth, coherent attention across image regions, ensuring that alterations are
limited to the designated items while retaining the surrounding structure.
\method consistently and substantially outperforms existing baselines on
PIE-Bench, demonstrating its state-of-the-art performance and effectiveness on
various editing tasks. Code can be found on
https://github.com/LOCATEdit/LOCATEdit/
LOCATEdit enhances text-guided image editing by improving cross-attention maps with a graph-based approach to maintain spatial consistency and reduce editing artifacts.
text-guided image editing cross-attention maps diffusion models semantic relevance spatial consistency editing artifacts self-attention patch relationships PIE-Bench