# A Study on Generating Webtoons Using Multilingual Text-to-Image Models
## 5 Conclusions  
In this study, we proposed a multilingual text-to-image model that can generate webtoons when multilingual inputs are provided. To construct a multilingual webtoon dataset, we used the Korean MSCOCO dataset from the AI Hub and transformed it into webtoon images using CartoonGAN. The resulting webtoon dataset consisted of Korean and English treatments in four art styles, and we constructed 1,232,879 pairs of multilingual text webtoon data. We used a GAN-based DCGAN model as the text-to-image model and trained it on a dataset of 820,752 pairs from one style of a multilingual webtoon dataset. The DCGAN used for training consists of a generator and discriminator. The generator uses the feature vector of the treatment and noise as input to generate a fake image, which is then evaluated by the discriminator to update the network weights and ultimately generate an image similar to the input webtoon image.  
We validated the model by generating images using validation data and found that for up to 100 epochs of training, the model could accurately represent the shapes present in the treatments; however, the model stopped learning after this point. Therefore, we considered training the generator for 75 rounds, which produces high-quality images suitable for our multilingual webtoon dataset. When the generated images were evaluated using the test data, an inception score of 4.992 and a FID score of 22.212 were obtained. Although the generated images generally did not accurately represent the shapes of the original data, we confirmed that the shapes present in the input treatments could be expressed in the generated webtoons. We observed that the DCGAN trained on the webtoon dataset performed worse than that trained on the MSCOCO dataset. We speculate that this result was due to the distortion of the shapes in the images when the MSCOCO data were cartoonized, which affected the training. However, when we input the same multilingual sentences, the generated images represented the semantic shapes of the words expressed in the text.  
In future studies, we plan on training a multilingual webtoon dataset using the latest text-to-image models to generate high-quality webtoons. This will help webtoons preview the scenes they envision in advance, which can aid the overall storytelling process. In addition, generating webtoons from treatments allows authors to modify and use them, thus leading to time and cost savings in webtoon production.  
[PERSON] conducted the deep learning model experiments and manuscript writing for this paper. H.K. and J.K. handled the data set collection and transformation tasks. [PERSON] contributed to the experimental design and writing of the manuscript. [PERSON] supervised the development of the idea as well as overseeing the funding acquisition. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Inception Score** & **FID Score** \\ \hline Multilingual Webtoon & & \\
1,620,160 image & 4.992 & 22.212 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation of the proposed multilingual text-to-image model.  
**Funding:** This research was supported as a 'Technology Commercialization Collaboration Platform Construction' project of the INNOPOLIS FOUNDATION (Project Number: 1711177250).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.