# A Study on Generating Webtoons Using Multilingual Text-to-Image Models
## 1 Introduction  
With the advancement of deep learning techniques, they have been widely utilized in various fields, demonstrating high performance [1; 2; 3]. Image generation technology has progressed rapidly, starting with generative adversarial networks (GANs), which generate fake images similar to the original ones through an adversarial learning process between the generator and discriminator networks. Currently, they can generate photorealistic images that are difficult to distinguish from those drawn by humans. Text-to-image technology refers to the process of enabling computers to generate images based on input text [1; 4]. Deep learning-based text-to-image technology creates images that reflect the contextual features of the text used to condition an image generator [5]. Traditional text-to-image approaches extract keywords from sentences, synthesize images corresponding to those keywords, and require human intervention. However, deep-learning-based text-to-image models extract features from the input text and generate images based on these features. Text-to-image models trained on GANs create images similar to the input text by conditioning the image generator to the feature vector of the sentence. However, this method fails to reflect the contextual meaning of individual words in a sentence, thus generating low-quality images. To overcome this limitation, attention mechanisms have been introduced in AttnGAN [6] to improve the quality of the generated images. Since then, text-to-image models such as StackGAN [7], MirrorGAN [8], R-GAN [9], Stacking VAE and GAN [10], and Dcfgan [11] have been developed. Despitethe development of GAN-based text-to-image models, they suffer from unstable image generation due to the training imbalance between the generator and discriminator. To overcome these limitations, text-to-image technology has recently focused on multimodal learning and diffusion models. Multimodal learning involves representing information in various forms, such as images, sounds, and text, and clustering similar data using contrastive learning [12]. Dalle-2 [13] developed by OpenAI, and DreamBooth [14], developed by Google, utilize multimodal space as an encoder and a diffusion model as a decoder to generate images based on the predicted image embedding from the input text. Currently, most deep learning-based text-to-image methods couple English-language text with image data, which limits their accessibility to countries where English is not spoken as the primary language. Furthermore, translating the native text into English as a preprocessing step incurs additional complexity and may not achieve the same efficiency. Therefore, a multilingual text-to-image technology that can generate images of similar quality, regardless of the input language, must be developed. A deep learning-based text-to-image model that generates images similar to those drawn by humans can be used in the entertainment industry, for example, to create virtual characters and animations. Webtoons, a portmanteau of \"web\" and \"cartoon,\" are comics that are published on the internet. The webtoon creation process involves story planning, storyboarding/sketching, coloring, and background drawing. As each stage of webtoon production requires human involvement, it is time-consuming and costly. To minimize human intervention, artificial intelligence-based methods, such as automatic coloring, automatic line drawing, and style transfer techniques, are typically utilized in different stages of webtoon creation. For webtoons to attract readers, their overall stories are important. To keep readers interested and immersed as webtoons progress, tension and resolution must be provided. To ensure that the readers' interest and immersion are not lost, the author plans the story before creating the webtoon. In the planning stage, the author sets the genre, characters, and worldviews. Before drawing the webtoon, the author describes the scene in detail, which is called a treatment.