Negation without Confusion: Decomposing Bias and Recomposing Style in Text Embeddings for Negation-aware Image Generation
Negation-aware image generation remains challenging for text-to-image diffusion models, which often struggle with negation due to implicit linguistic biases that are deeply entangled within text representations. As a result, bias-aligned semantics frequently dominate over intended negative constraints. To overcome this limitation, we propose a simple training-free method of Decompose and Recompose text embedding (Dero), which constructs a semantic-preserving subspace and a bias subspace using auxiliary prompts and performs semantic disentanglement based on bidirectional SVD. By decomposing entangled representations and selectively recomposing disentangled semantic components, Dero realign...
| Authors | Sooyeon Park, Hemraj Singh, Kyunghyun Min et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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