Suppressing attention sinks in Stable Diffusion 3 does not degrade text-image alignment or preference metrics at mild intervention levels, though stronger suppression reveals sink-specific perceptual shifts larger than random masking.
Clipscore: A reference-free evaluation metric for image captioning
4 Pith papers cite this work. Polarity classification is still indexing.
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CLIP models understand 360-degree textual semantics via explicit identifiers but show limited comprehension of visual semantics under horizontal circular shifts, which a LoRA fine-tuning approach improves with a noted trade-off in original task performance.
An ELBO-based likelihood estimator from the final generated sample dominates other RL design factors for diffusion models, raising GenEval from 0.24 to 0.95 in 90 GPU hours with better efficiency than prior methods.
SPOT projects prompts to a tau-safe set via total variation to cut inappropriate content 14-44% relative to baselines while preserving benign prompt behavior in frozen T2I models.
citing papers explorer
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Attention Sinks in Diffusion Transformers: A Causal Analysis
Suppressing attention sinks in Stable Diffusion 3 does not degrade text-image alignment or preference metrics at mild intervention levels, though stronger suppression reveals sink-specific perceptual shifts larger than random masking.
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Probing CLIP's Comprehension of 360-Degree Textual and Visual Semantics
CLIP models understand 360-degree textual semantics via explicit identifiers but show limited comprehension of visual semantics under horizontal circular shifts, which a LoRA fine-tuning approach improves with a noted trade-off in original task performance.
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Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
An ELBO-based likelihood estimator from the final generated sample dominates other RL design factors for diffusion models, raising GenEval from 0.24 to 0.95 in 90 GPU hours with better efficiency than prior methods.
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SPOT: Selective Prompt Projection via Total Variation for Inference-Only Safe Text-to-Image Generation
SPOT projects prompts to a tau-safe set via total variation to cut inappropriate content 14-44% relative to baselines while preserving benign prompt behavior in frozen T2I models.