# Fast Fourier Asymmetric Context Aggregation Network:
## 4 Experiments
### Comparison with Existing Approaches
#### 4.5.2 Qualitative Comparison  
Figures 12-14 present the qualitative comparison results on the SKFashion dataset. To facilitate comparison, we have zoomed in on certain details in the visual comparisons. Please note that the annotated details are not the only noticeable differences in performance. For better overall comparison, we recommend zooming in on the images. The qualitative comparison results on the VITON-HD and the Fashion-Gen will be presented in the appendix. As shown in Figure 12, under small texture patch sizes, the images generated by our method exhibit significant advantages over the baseline methods in terms of texture details, semantic consistency, and other aspects. The images generated by the baseline methods suffer from issues such as style leakage (e.g., black-and-white texture spillover on the striped top in the first row), color mismatches and artifacts (e.g., color discrepancies and meaningless artifacts on the blue coat in the second row and the denim skirt in the fourth row), as well as structural and semantic loss (e.g., missing waistband on the red sports shorts in the third row). These issues indicate that the baseline methods perform poorly in integrating guidance information and content inference. As shown in Figure 13, as the texture patch size increases, the quality of the images generated by all methods improves to some extent. However, when generating densely repetitive textures and large-scale texture propagation, the baseline methods still encounter \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Parameters** \\ \hline PAINT [56] & 83.56 M \(\Uparrow\) 202\% \\ MST [79] & 54.93 M \(\Uparrow\) 98.5\% \\ Albahar-MT [80] & 57.29 M \(\Uparrow\) 107.0\% \\ EdgeConnect [81] & 53.59 M \(\Uparrow\) 93.7\% \\ ZITS [61] & 56.59 M \(\Uparrow\) 104.5\% \\ FCAN (Our) & **27.67 M** \\ \hline \hline \end{tabular} In the table, a red upward arrow is used to indicate the percentage increase in the number of parameters of the corresponding methods compared to our approach.  
\end{table} 
Table 4: Comparison of the model parameters. \begin{table}
\begin{tabular}{c l l l l l l l} \hline \hline
**Texture Patch Size** & \multicolumn{1}{c}{**FID\(\downarrow\)**} & **PSNR\(\uparrow\)** & **LPIPS\(\downarrow\)** & **SSIM\(\uparrow\)** & **TOPIQ-FR\(\uparrow\)** & **TOPIQ-NR\(\uparrow\)** \\ \hline \multirow{4}{*}{Standard} & PAINT [56] & 23.10 & 29.08 & 0.04544 & 0.942 & 0.655 & 0.493 \\  & MST [79] & 13.61 & 33.35 & 0.02118 & 0.962 & 0.789 & 0.529 \\  & Albahar-MT [80] & 22.96 & 30.33 & 0.02118 & 0.962 & 0.652 & 0.492 \\  & EdgeConnect [81] & 13.84 & 33.29 & 0.02244 & 0.962 & 0.778 & **0.531** \\  & ZITS [61] & 13.80 & 32.91 & 0.02143 & **0.963** & 0.784 & 0.527 \\  & FCAN (Our) & **12.73** & **33.41** & **0.02040** & 0.962 & **0.794** & 0.528 \\ \hline \multirow{4}{*}{Medium} & PAINT [56] & 27.73 & 27.50 & 0.05539 & 0.927 & 0.594 & 0.491 \\  & MST [79] & 16.78 & 32.07 & **0.02597** & 0.953 & **0.744** & **0.526** \\  & Albahar-MT [80] & 32.70 & 28.44 & 0.05120 & 0.931 & 0.573 & 0.484 \\  & EdgeConnect [81] & 18.50 & 30.77 & 0.03073 & 0.948 & 0.697 & 0.524 \\  & ZITS [61] & 17.93 & 31.81 & 0.02639 & **0.954** & 0.743 & 0.524 \\  & FCAN (Our) & **16.53** & **32.05** & 0.02630 & **0.954** & 0.742 & 0.525 \\ \hline \multirow{4}{*}{Small} & PAINT [56] & 37.28 & 24.98 & 0.07366 & 0.907 & 0.526 & 0.466 \\  & MST [79] & 27.63 & 27.05 & 0.07061 & 0.923 & 0.535 & 0.478 \\ \cline{1-1}  & Albahar-MT [80] & 45.37 & 26.60 & 0.06243 & 0.916 & 0.500 & 0.485 \\ \cline{1-1}  & EdgeConnect [81] & 35.38 & 26.40 & 0.05133 & 0.924 & 0.551 & 0.503 \\ \cline{1-1}  & ZITS [61] & 21.92 & 30.71 & 0.03310 & 0.940 & 0.706 & 0.523 \\ \cline{1-1}  & FCAN (Our) & **20.68** & **30.75** & **0.03302** & **0.943** & **0.710** & **0.528** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative experimental results on the Fashion-Gen dataset.