Selective Aggregation of Attention Maps Improves Diffusion-Based Visual Interpretation

1. 1Identify relevant attention heads for a target concept
2. 2Selectively aggregate cross-attention maps from these heads
3. 3Apply diffusion-based visual interpretation to the aggregated maps
4. 4Evaluate the improvement in visual interpretability

AI researchers and engineers working on text-to-image generative models can benefit from this study to improve model interpretability, and software engineers can apply these findings to develop more efficient models

💡 Selective aggregation of attention maps from relevant heads improves diffusion-based visual interpretation

🔍 Selective aggregation of attention maps boosts visual interpretability in T2I models