Plain text has become a prevalent interface for text-to-image synthesis. However, its limited customization options hinder users from accurately describing desired outputs. For example, plain text makes it hard to specify continuous quantities, such as the precise RGB color value or importance of each word. Furthermore, creating detailed text prompts for complex scenes is tedious for humans to write and challenging for text encoders to interpret. To address these challenges, we propose using a rich-text editor supporting formats such as font style, size, color, and footnote. We extract each word's attributes from rich text to enable local style control, explicit token reweighting, precise color rendering, and detailed region synthesis. We achieve these capabilities through a region-based diffusion process. We first obtain each word's region based on cross-attention maps of a vanilla diffusion process using plain text. For each region, we enforce its text attributes by creating region-specific detailed prompts and applying region-specific guidance. We present various examples of image generation from rich text and demonstrate that our method outperforms strong baselines with quantitative evaluations.

翻译：纯文本已成为文本到图像合成的主流交互方式，但其有限的定制化能力阻碍了用户精确描述期望输出。例如，纯文本难以指定连续量参数，如精确的RGB颜色值或各单词的重要程度。此外，针对复杂场景编写详细文本提示对人工编写而言冗长乏味，对文本编码器而言难以解析。为应对这些挑战，我们提出利用支持字体样式、字号、颜色及脚注等格式的富文本编辑器。通过提取富文本中各单词的属性，我们实现了局部风格控制、显式token重加权、精准色彩渲染及区域细节合成等能力。这些功能通过基于区域的扩散过程达成：首先利用纯文本的标准扩散过程交叉注意力图获取各单词对应区域，随后针对每个区域生成区域特异性详细提示并施加区域特异性引导，以强制执行其文本属性。我们展示了从富文本生成图像的多种示例，并通过定量评估证明该方法优于强基线模型。