MAST is a mask-guided attention allocation method that enables artifact-free multi-style transfer in diffusion models by anchoring layout, distributing attention mass, scaling sharpness, and injecting details.
A learned representation for artistic style
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Diffusion models with architecture improvements and classifier guidance achieve superior FID scores to GANs on unconditional and conditional ImageNet image synthesis.
Knowledge conflicts in hypernetwork LLM adaptation stem from constant adapter margins losing to frequency-dependent pretrained margins; selective layer boosting and conflict-aware triggering raise deep-conflict accuracy to 71-72.5% on Gemma-2B and Mistral-7B.
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MAST: Mask-Guided Attention Mass Allocation for Training-Free Multi-Style Transfer
MAST is a mask-guided attention allocation method that enables artifact-free multi-style transfer in diffusion models by anchoring layout, distributing attention mass, scaling sharpness, and injecting details.
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Diffusion Models Beat GANs on Image Synthesis
Diffusion models with architecture improvements and classifier guidance achieve superior FID scores to GANs on unconditional and conditional ImageNet image synthesis.
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The Override Gap: A Magnitude Account of Knowledge Conflict Failure in Hypernetwork-Based Instant LLM Adaptation
Knowledge conflicts in hypernetwork LLM adaptation stem from constant adapter margins losing to frequency-dependent pretrained margins; selective layer boosting and conflict-aware triggering raise deep-conflict accuracy to 71-72.5% on Gemma-2B and Mistral-7B.