通过操控注意力图，让艺术家探索视频生成模型的内部机制。
AttentionBender: Manipulating Cross-Attention in Video Diffusion Transformers as a Creative Probe
- 用2D变换修改跨注意力图，实现对生成过程的干预
- 4500+次实验显示注意力高度耦合，局部控制难实现
- 既可解释模型机制，也能生成独特视觉风格
我们提出 AttentionBender，一个用于操纵视频扩散变换器中跨注意力机制的工具，帮助艺术家探究黑箱视频生成模型的内部运作。尽管生成结果日益逼真，但仅靠提示词控制限制了艺术家对模型生成过程的理解，也难以突破其默认倾向。基于一种自传式的设计研究方法，我们在 Network Bending 基础上构建 AttentionBender，通过对跨注意力图施加二维变换（如旋转、缩放、平移等）来调节生成效果。我们通过在不同提示词、操作类型和层目标下可视化超过4500个视频生成结果进行评估。结果显示，跨注意力高度耦合：针对性的修改往往无法实现干净的局部控制，反而引发分布式的失真与故障美学，而非线性编辑。AttentionBender 不仅作为可解释AI的注意力机制探针，还可作为一种创造新视觉风格的技术，拓展模型学习表征空间之外的表现力。
原文摘要 · Abstract (English)
We present AttentionBender, a tool that manipulates cross-attention in Video Diffusion Transformers to help artists probe the internal mechanics of black-box video generation. While generative outputs are increasingly realistic, prompt-only control limits artists' ability to build intuition for the model's material process or to work beyond its default tendencies. Using an autobiographical research-through-design approach, we built on Network Bending to design AttentionBender, which applies 2D transforms (rotation, scaling, translation, etc.) to cross-attention maps to modulate generation. We assess AttentionBender by visualizing 4,500+ video generations across prompts, operations, and layer targets. Our results suggest that cross-attention is highly entangled: targeted manipulations often resist clean, localized control, producing distributed distortions and glitch aesthetics over linear edits. AttentionBender contributes a tool that functions both as an Explainable AI style probe of transformer attention mechanisms, and as a creative technique for producing novel aesthetics beyond the model's learned representational space.
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