Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Authors: name: Agneet Chatterjee, name: Gabriela Ben Melech Stan, name: Estelle Aflalo, name: Sayak Paul, name: Dhruba Ghosh, name: Tejas Gokhale, name: Ludwig Schmidt, name: Hannaneh Hajishirzi, name: Vasudev Lal, name: Chitta Baral, name: Yezhou Yang

One of the key shortcomings in current text-to-image (T2I) models is their
inability to consistently generate images which faithfully follow the spatial
relationships specified in the text prompt. In this paper, we offer a
comprehensive investigation of this limitation, while also developing datasets
and methods that achieve state-of-the-art performance. First, we find that
current vision-language datasets do not represent spatial relationships well
enough; to alleviate this bottleneck, we create SPRIGHT, the first
spatially-focused, large scale dataset, by re-captioning 6 million images from
4 widely used vision datasets. Through a 3-fold evaluation and analysis
pipeline, we find that SPRIGHT largely improves upon existing datasets in
capturing spatial relationships. To demonstrate its efficacy, we leverage only
~0.25% of SPRIGHT and achieve a 22% improvement in generating spatially
accurate images while also improving the FID and CMMD scores. Secondly, we find
that training on images containing a large number of objects results in
substantial improvements in spatial consistency. Notably, we attain
state-of-the-art on T2I-CompBench with a spatial score of 0.2133, by
fine-tuning on <500 images. Finally, through a set of controlled experiments
and ablations, we document multiple findings that we believe will enhance the
understanding of factors that affect spatial consistency in text-to-image
models. We publicly release our dataset and model to foster further research in
this area.