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About this paper
Yutong He, Ruslan Salakhutdinov, J. Zico Kolter
Cross Attention Control (CAC) is introduced as a training-free method for localized text-to-image generation, which manipulates cross-attention maps during inference. This approach successfully places objects within user-specified regions, improving spatial control while maintaining overall image quality across various diffusion models.
Key insights
- Cross-attention maps within pre-trained diffusion models inherently contain spatial information that can be directly manipulated to control object placement.
- By constraining cross-attention maps to specific spatial regions during inference, localized generation can be achieved without requiring any additional training or model modifications.
- The method demonstrates that precise spatial control can be integrated into existing text-to-image pipelines, leveraging the models' existing capabilities.
Summary by alphaXiv.
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