Markup-to-Image Diffusion Models with Scheduled Sampling
This work addresses the markup-to-image task for applications like document rendering and scientific visualization, but it is incremental as it adapts an existing method (scheduled sampling) to diffusion models.
The authors tackled the problem of generating images from markup (e.g., LaTeX, HTML) using diffusion models, and found that scheduled sampling mitigates compounding errors, improving generation quality across four datasets.
Building on recent advances in image generation, we present a fully data-driven approach to rendering markup into images. The approach is based on diffusion models, which parameterize the distribution of data using a sequence of denoising operations on top of a Gaussian noise distribution. We view the diffusion denoising process as a sequential decision making process, and show that it exhibits compounding errors similar to exposure bias issues in imitation learning problems. To mitigate these issues, we adapt the scheduled sampling algorithm to diffusion training. We conduct experiments on four markup datasets: mathematical formulas (LaTeX), table layouts (HTML), sheet music (LilyPond), and molecular images (SMILES). These experiments each verify the effectiveness of the diffusion process and the use of scheduled sampling to fix generation issues. These results also show that the markup-to-image task presents a useful controlled compositional setting for diagnosing and analyzing generative image models.