In this work, we share the insights for achieving state-of-the-art quality in our text-to-image anime image generative model, called Illustrious. To achieve high resolution, dynamic color range images, and high restoration ability, we focus on three critical approaches for model improvement. First, we delve into the significance of the batch size and dropout control, which enables faster learning of controllable token based concept activations. Second, we increase the training resolution of images, affecting the accurate depiction of character anatomy in much higher resolution, extending its generation capability over 20MP with proper methods. Finally, we propose the refined multi-level captions, covering all tags and various natural language captions as a critical factor for model development. Through extensive analysis and experiments, Illustrious demonstrates state-of-the-art performance in terms of animation style, outperforming widely-used models in illustration domains, propelling easier customization and personalization with nature of open source. We plan to publicly release updated Illustrious model series sequentially as well as sustainable plans for improvements.

本研究解决了文本转图像的动漫图像生成模型中的高质量图像生成问题。通过批量大小和 dropout 控制、提高图像训练分辨率以及改进多层次标签的使用，提出了新的模型改进方法。最终，Illustrious 在动画风格上表现出色，具有更高的定制化和个性化能力，预示着其在插图领域的广泛应用潜力。

杰出：一个开源高级插图模型