LAION-SG: An Enhanced Large-Scale Dataset for Training Complex
  Image-Text Models with Structural Annotations
Authors: name: Zejian Li, name: Chenye Meng, name: Yize Li, name: Ling Yang, name: Shengyuan Zhang, name: Jiarui Ma, name: Jiayi Li, name: Guang Yang, name: Changyuan Yang, name: Zhiyuan Yang, name: Jinxiong Chang, name: Lingyun Sun

Recent advances in text-to-image (T2I) generation have shown remarkable
success in producing high-quality images from text. However, existing T2I
models show decayed performance in compositional image generation involving
multiple objects and intricate relationships. We attribute this problem to
limitations in existing datasets of image-text pairs, which lack precise
inter-object relationship annotations with prompts only. To address this
problem, we construct LAION-SG, a large-scale dataset with high-quality
structural annotations of scene graphs (SG), which precisely describe
attributes and relationships of multiple objects, effectively representing the
semantic structure in complex scenes. Based on LAION-SG, we train a new
foundation model SDXL-SG to incorporate structural annotation information into
the generation process. Extensive experiments show advanced models trained on
our LAION-SG boast significant performance improvements in complex scene
generation over models on existing datasets. We also introduce CompSG-Bench, a
benchmark that evaluates models on compositional image generation, establishing
a new standard for this domain.