  A good Text-to-Image model should not only generate high quality images, but
also ensure the consistency between the text and the generated image. Previous
models failed to simultaneously fix both sides well. This paper proposes a
Gradual Refinement Generative Adversarial Network (GR-GAN) to alleviates the
problem efficiently. A GRG module is designed to generate images from low
resolution to high resolution with the corresponding text constraints from
coarse granularity (sentence) to fine granularity (word) stage by stage, a ITM
module is designed to provide image-text matching losses at both sentence-image
level and word-region level for corresponding stages. We also introduce a new
metric Cross-Model Distance (CMD) for simultaneously evaluating image quality
and image-text consistency. Experimental results show GR-GAN significant
outperform previous models, and achieve new state-of-the-art on both FID and
CMD. A detailed analysis demonstrates the efficiency of different generation
stages in GR-GAN.
