卓越：一個開放的高級插圖模型

摘要

在這份工作中，我們分享了實現我們的文本到圖像動漫生成模型Illustrious達到最先進品質的見解。為了實現高解析度、動態色彩範圍圖像和高還原能力，我們專注於三個關鍵方法以改進模型。首先，我們深入探討了批次大小和輸出層控制的重要性，這使得可控的基於標記的概念激活更快地學習。其次，我們提高了圖像的訓練解析度，影響了在更高解析度下對角色解剖的準確描述，並通過適當方法將其生成能力擴展到超過20MP。最後，我們提出了精煉的多級標題，涵蓋所有標籤和各種自然語言標題，作為模型發展的關鍵因素。通過廣泛的分析和實驗，Illustrious在動畫風格方面展現了最先進的性能，勝過插畫領域中廣泛使用的模型，推動更容易的定製和個性化，並具有開源性質。我們計劃按順序公開發布更新的Illustrious模型系列，以及持續改進的計劃。

English

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.

卓越：一個開放的高級插圖模型

Illustrious: an Open Advanced Illustration Model

摘要

Summary

Support

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