Enhancing Oracle Character Generation with Controllable Styles and Content through Diffusion Models

In this article, we propose a novel approach to generating oracle characters, which are images used in various applications such as computer vision and machine learning. Our method, called Diff-Oracle, leverages the power of diffusion models to create high-quality oracle characters with desired styles and content.
Diffusion models have shown great potential in generating realistic images, but they lack control over the generated images’ styles and contents. To address this challenge, we introduce two scales, s1 and s2, to govern the generation of style and content, respectively. By adjusting these scales, we can generate oracle characters with desired styles and contents.
To evaluate the effectiveness of Diff-Oracle, we conduct a series of experiments using a subset of 3,000 samples from Oracle-241. Our results show that by combining content and style guidance with appropriate scales, Diff-Oracle can generate higher-quality oracle characters than traditional text-to-image diffusion models.
In summary, Diff-Oracle is a novel approach to generating oracle characters that combines the strengths of diffusion models and GANs. By leveraging two scales to govern the generation of style and content, Diff-Oracle can generate high-quality oracle characters with desired styles and contents. Our experiments demonstrate the effectiveness of Diff-Oracle in improving the quality of generated oracle characters compared to traditional text-to-image diffusion models.