Invisible hints such as logos embedded in images are re-rendered by diffusion models during text-guided editing, enabling phishing and model-poisoning attacks with average success rates of 44.4% and 32.2%.
MaskAttn-SDXL: Controllable region-level text-to-image generation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
Diffusion models have achieved strong results in text-to-image generation, but important limitations remain as prompts become more structured and multi-object. On the architecture side, U-Net backbones are efficient and stable, yet their locality makes global coordination harder, while Transformer-based diffusion models improve global interactions but at substantially higher compute and memory cost. In parallel, compositional reliability remains weak: models often mix attributes across objects, violate spatial relations, or omit requested entities, and these errors are not reliably reflected by global metrics such as FID or CLIP-based scores. To address these issues without changing the SDXL pipeline, we propose MaskAttn-SDXL, a plug-in module that injects token-conditioned spatial gating into cross-attention logits before softmax. The gating sparsifies token-to-location interactions to suppress irrelevant bindings while preserving the pretrained backbone and standard sampling process, requiring no external supervision or inference-time editing.
verdicts
UNVERDICTED 3representative citing papers
MaskAttn-SDXL adds token-conditioned spatial gating to SDXL cross-attention to sparsify irrelevant token-to-location bindings and improve region-level controllability without retraining or inference edits.
A training-free method with time-dependent attention gating and trajectory pruning enhances object-background balance in diffusion-based image synthesis.
citing papers explorer
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Generate "Normal", Edit Poisoned: Branding Injection via Hint Embedding in Image Editing
Invisible hints such as logos embedded in images are re-rendered by diffusion models during text-guided editing, enabling phishing and model-poisoning attacks with average success rates of 44.4% and 32.2%.
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MaskAttn-SDXL: Controllable Region-Level Text-To-Image Generation
MaskAttn-SDXL adds token-conditioned spatial gating to SDXL cross-attention to sparsify irrelevant token-to-location bindings and improve region-level controllability without retraining or inference edits.
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Training-Free Object-Background Compositional T2I via Dynamic Spatial Guidance and Multi-Path Pruning
A training-free method with time-dependent attention gating and trajectory pruning enhances object-background balance in diffusion-based image synthesis.