  Recent text-to-image (T2I) models have benefited from large-scale and
high-quality data, demonstrating impressive performance. However, these T2I
models still struggle to produce images that are aesthetically pleasing,
geometrically accurate, faithful to text, and of good low-level quality. We
present VersaT2I, a versatile training framework that can boost the performance
with multiple rewards of any T2I model. We decompose the quality of the image
into several aspects such as aesthetics, text-image alignment, geometry,
low-level quality, etc. Then, for every quality aspect, we select high-quality
images in this aspect generated by the model as the training set to finetune
the T2I model using the Low-Rank Adaptation (LoRA). Furthermore, we introduce a
gating function to combine multiple quality aspects, which can avoid conflicts
between different quality aspects. Our method is easy to extend and does not
require any manual annotation, reinforcement learning, or model architecture
changes. Extensive experiments demonstrate that VersaT2I outperforms the
baseline methods across various quality criteria.
