Progressive Denoising Model for Fine-Grained Text-to-Image Generation

Proposes a progressive model for text-to-image generation that creates new image tokens from coarse to fine based on existing context, resulting in significantly better results compared to previous methods in FID score.

Can improve the quality and fidelity of generated images using text descriptions, which can be useful for businesses that rely on visual representations in their operations, such as e-commerce or advertising.