Exploring Chinese Text Generation And Typography Using Diffusion Models

With the widespread application of diffusion models in image restoration, font generation, and super-resolution, the demand for text generation capabilities within these models has significantly increased. However, most existing image generation approaches primarily focus on visual content, such as posters, without fully optimizing text generation, often resulting in inaccurate stroke structures and poor text layout. In this work, we propose a domain-specific large-scale model tailored for Chinese text generation, addressing both single-character and text layout generation tasks. Our approach leverages a diffusion-based architecture trained on high-quality Chinese text data, ensuring improved fidelity in character generation, stroke completeness, and layout accuracy. Experimental results demonstrate that our model effectively generates structured and visually coherent Chinese text, offering a novel solution to the challenges of Chinese text generation within diffusion models.