  Diffusion models have become a new generative paradigm for text generation.
Considering the discrete categorical nature of text, in this paper, we propose
GlyphDiffusion, a novel diffusion approach for text generation via text-guided
image generation. Our key idea is to render the target text as a glyph image
containing visual language content. In this way, conditional text generation
can be cast as a glyph image generation task, and it is then natural to apply
continuous diffusion models to discrete texts. Specially, we utilize a cascaded
architecture (ie a base and a super-resolution diffusion model) to generate
high-fidelity glyph images, conditioned on the input text. Furthermore, we
design a text grounding module to transform and refine the visual language
content from generated glyph images into the final texts. In experiments over
four conditional text generation tasks and two classes of metrics (ie quality
and diversity), GlyphDiffusion can achieve comparable or even better results
than several baselines, including pretrained language models. Our model also
makes significant improvements compared to the recent diffusion model.
