Diffusion models for document synthesis

Gretel explores state-of-the-art image synthetics for business documents using diffusion models, a potential replacement for Generative Adversarial Networks (GANs). Diffusion Models have shown to generate high-quality images with great diversity and perform well on various data types. The company recently explored how well Diffusion Models perform at document synthesis, particularly in generating realistic-looking receipts from a small dataset of 300 images. They found that the model not only captured shapes, lighting, and variations but also wrote coherent text. This technology can be used for important business applications such as creating synthetic images of documents for analysis or training downstream machine learning systems.