Recently, the application of modern diffusion-based text-to-image generation
models for creating artistic fonts, traditionally the domain of professional
designers, has garnered significant interest. Diverging from the majority of
existing studies that concentrate on generating artistic typography, our
research aims to tackle a novel and more demanding challenge: the generation of
text effects for multilingual fonts. This task essentially requires generating
coherent and consistent visual content within the confines of a font-shaped
canvas, as opposed to a traditional rectangular canvas. To address this task,
we introduce a novel shape-adaptive diffusion model capable of interpreting the
given shape and strategically planning pixel distributions within the irregular
canvas. To achieve this, we curate a high-quality shape-adaptive image-text
dataset and incorporate the segmentation mask as a visual condition to steer
the image generation process within the irregular-canvas. This approach enables
the traditionally rectangle canvas-based diffusion model to produce the desired
concepts in accordance with the provided geometric shapes. Second, to maintain
consistency across multiple letters, we also present a training-free,
shape-adaptive effect transfer method for transferring textures from a
generated reference letter to others. The key insights are building a font
effect noise prior and propagating the font effect information in a
concatenated latent space. The efficacy of our FontStudio system is confirmed
through user preference studies, which show a marked preference (78% win-rates
on aesthetics) for our system even when compared to the latest unrivaled
commercial product, Adobe Firefly.

我们引入一种新颖的形状自适应扩散模型，能够解释给定形状并在非规则画布内策划像素分布，用于生成多语言字体的文本效果；此外，我们还提出了一种训练免费的、形状自适应的效果转移方法，用于在生成的参考字母和其他字母之间传递纹理和字体效果信息。使用用户偏好研究验证了我们的 FontStudio 系统的有效性。