# Inv-ReVersion: Enhanced Relation Inversion Based on Text-to-Image Diffusion Models
## 4 Experiments
### Ablation Study  
To verify the effectiveness of each module we proposed, we conducted the following ablative experiments: \((1)\) Remove the \"Inverse Relation Text Expansion\" module; \((2)\) Replace the \"POS Weighted Control Loss\" with Steer Loss from ReVersion; \((3)\) Remove the \"High-Frequency Suppressor\" module. From Table 2, we can observe that the absence of these modules leads to a significant decrease in \"Entity Accuracy\" and \"Relation Accuracy\".  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Concepts**} & \multicolumn{2}{c}{**Stable Diffusion**} & \multicolumn{2}{c}{**Textual Inversion**} & \multicolumn{2}{c}{**ReVersion**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-9}  & **Entity** & **Relation** & **Entity** & **Relation** & **Entity** & **Relation** & **Entity** & **Relation** \\ \hline carved by & 1.48 & 1.49 & 2.03 & 1.69 & 2.76 & 2.89 & 3.71 & 3.88 \\ hanging & 1.68 & 1.67 & 2.06 & 1.85 & 2.63 & 2.76 & 3.61 & 3.68 \\ shake hand & 1.83 & 2.13 & 2.04 & 2.04 & 2.76 & 2.81 & 3.32 & 3.04 \\ inside & 1.98 & 1.53 & 2.16 & 1.98 & 2.59 & 3.17 & 3.22 & 3.28 \\ on & 1.87 & 1.72 & 2.25 & 2.16 & 2.64 & 3.03 & 3.19 & 3.07 \\ back2 back & 2.03 & 1.89 & 2.05 & 1.73 & 2.32 & 2.96 & 3.59 & 3.38 \\ painted on & 1.73 & 1.56 & 2.12 & 1.71 & 2.41 & 3.12 & 3.68 & 3.56 \\ hug & 1.93 & 2.14 & 1.97 & 2.01 & 2.56 & 2.58 & 3.52 & 3.24 \\ ride on & 1.69 & 2.03 & 1.74 & 2.14 & 2.92 & 2.78 & 3.63 & 3.03 \\ wrapped in & 1.73 & 1.58 & 2.01 & 1.83 & 2.87 & 3.14 & 3.37 & 3.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison results: average user ranking (AUR) for “Entity Accuracy” and “Relation Accuracy”. We tested the user preference ranking (1–4, from worst to best) of different methods.  
Without Inverse Relation Text Expansion. As described in Section 3.2, the \"Inverse Relation Text Expansion\" was proposed to solve the feature fusion problem between multiple entities in generated images. Based on the finding that the order of entity appearance affects model preference, we designed inverse relation text to allow entities in the text prompt to swap positions, thereby solving this problem. As can be clearly seen from the second row of Figure 7, not using inverse relation text expansion leads to the fusion of features between two entities, turning the pointers on the clock into branches.  
Without POS Weighted Control Loss. Prepositions activate spatial relations well, whereas verbs have an advantage for behavioral relations. In the first row of Figure 7, the \"shake hands\" behavioral relation, the group without POS weighted control failed to generate images with this relation concept well. This indicates that prepositions have poor capturing ability for higher-level relations such as behavioral relations, and adding verbs can expand the relations learned in the relation inversion task.  
Without High-Frequency Suppressor. Learning high-level semantic concepts like relation concepts require the model to reduce attention to low-frequency detail features. The high-frequency suppressor can solve this problem well. However, generated images without the high-frequency suppressor often carry obvious low-level features like textures, making them unable to reconstruct relation concepts well. As seen in the second row of Figure 7, the group without the high-frequency suppressor can generate detailed textures of tree bark well but failed to correctly generate the \"hanging from\" relation concept.