Orthogonal Negative Guidance subtracts only the orthogonal component of negative-prompt attention features from positive ones in FLUX models to suppress concepts while preserving semantics and quality.
arXiv preprint arXiv:2402.05375 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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AdaEraser introduces token-wise adaptive attention suppression in diffusion denoising to enable high-quality training-free object removal by modulating suppression according to evolving self-attention maps.
STEDiff improves semantic alignment in text-to-image diffusion models via training-free embedding strengthening with the [EOT] token and a spatial semantic loss, showing gains on T2I-CompBench.
Implicit generative choices in diffusion models concentrate in self-attention layers; targeted ICM interventions there outperform broader debiasing methods with fewer artifacts.
citing papers explorer
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Orthogonal Negative Guidance in Attention Feature Space for Text-to-Image Generation
Orthogonal Negative Guidance subtracts only the orthogonal component of negative-prompt attention features from positive ones in FLUX models to suppress concepts while preserving semantics and quality.
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AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression
AdaEraser introduces token-wise adaptive attention suppression in diffusion denoising to enable high-quality training-free object removal by modulating suppression according to evolving self-attention maps.
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STEDiff: Strengthening Text Embedding for Text-to-Image Alignment in Diffusion Model
STEDiff improves semantic alignment in text-to-image diffusion models via training-free embedding strengthening with the [EOT] token and a spatial semantic loss, showing gains on T2I-CompBench.
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Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models
Implicit generative choices in diffusion models concentrate in self-attention layers; targeted ICM interventions there outperform broader debiasing methods with fewer artifacts.