# A Study on Generating Webtoons Using Multilingual Text-to-Image Models
## 4 Experiment
### Webtoon Generation Using DCGAN  
To generate webtoons by inputting multilingual text into a deep learning-based text-to-image model, the multilingual webtoon dataset was trained using the DCGAN model. As training a DCGAN with a real-time pre-trained multilingual BERT to extract feature vectors for multilingual text takes a longer time, the feature vectors were extracted and stored in advance and then loaded and used during training. The experiment was conducted using 820,752 multilingual text webtoon data pairs for each style, excluding incomplete data. The validation data consisted of 405,040 data points, which were divided into validation and test datasets at an 8:2 ratio. The size of the images used in the experiment was 3 \(\times\) 64 \(\times\) 64, the noise dimension was 100, the batch size was 32, the learning rate was set to 0.0002 for both the generator and discriminator, and the optimizer used was Adam. The experiment was conducted using four A5000 GPUs, and the deep learning framework used for the experiment was PyTorch. Figure 4 shows the images generated by the generator when the validation data text was input as the number of training epochs increased. As shown in Figure 4a, when the number of training epochs was one, the generated images were vague. As the training progressed, the generator began drawing the shape of the objects, as shown in Figure 4e, for up to 100 epochs. However, the generator could not generate further images after a certain point. Therefore, our experiments suggest that the model trained for 75 epochs is suitable for the webtoon dataset constructed in this study, based on the loss of the generator and discriminator as well as the image output using the validation data. \begin{table}
\begin{tabular}{c c} \hline
**Train Image** & **Caption** \\ \hline \multicolumn{3}{c}{A tennis player in action on the court.} \\ \hline a male in a white shirt is playing tennis \\ \hline a man that is playing tennis on a court \\ \hline \multicolumn{3}{c}{A male tennis player hits the ball on a grass court.} \\ \hline \multicolumn{3}{c}{A man in motion hitting a tennis ball with a tennis racket on a tennis court.} \\ \hline \multicolumn{3}{c}{\(\boxed{\boxed{\boxed{\boxed{\boxed{\boxed{\boxed{\boxed{\boxed{\boxed{\boxed{\boxed{\boxed{\boxed{\boxed{\box  
Figure 5-7 show the images generated by inputting the test data into the generator model trained for 75 epochs. Figures 5 and 6 show the webtoons generated when Korean and English treatments were input, respectively, and Figure 7 shows the webtoons generated when treatments with the same meaning in Korean and English were input. Evidently, although the similarity with the original images was not high, the generator was still able to produce some shapes that were expressed in the text. When analyzing objects, the experimental results suggest that the generator does not express objects such as humans and animals well but does express objects such as chairs relatively well. Figure 7 shows the images generated by inputting the same meaning of Korean, English, Japanese, and Chinese into the DCGAN trained in this research. Although the generated webtoons may be different from each other, the semantic shapes of the words expressed in the treatments are similarly represented. Similar to the previous experiments, it can be observed that the generator is drawing shapes related to the keywords in the sentences. Figure 4: Image generation at different epochs on validation data. Figure 5: _Cont._  
Figure 5: Webtoon generated from Korean treatment input. The Korean sentences (a–e) have the following English equivalents with the same meaning: (a) A room with chairs, tables, and a woman. (b) A living space with a television and a table. (c) A white plate with cheese, broccoli, and chicken. (d) A bathroom sink below the mirror. (e) A man prepares to serve a tennis ball in a game. Figure 6: Webtoon generated from English treatment input. Figure 7: Webtoon generated from multilingual treatment input with the same meaning.