Cross-Attention Steering
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CASteer: Cross-Attention Steering for Controllable Concept Erasure
arXiv:2503.09630v5 Announce Type: replace Abstract: Diffusion models have transformed image generation, yet controlling their outputs to reliably erase undesired concepts remains challenging. Existing approaches usually require task-specific training and struggle to generalize across both concrete (e.g., objects) and abstract (e.g., styles) concepts. We propose CASteer (Cross-Attention Steering), a training-free framework for concept erasure in diffusion models using steering vectors to...
You Don't Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models
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Conditional Graph Diffusion for Negotiation Support: Overcoming Discrete Infeasibility and Preference Elicitation Gaps
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Magenta RealTime 2: Open and Local Live Music Models
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