Getting it Right: Improving Spatial Consistency in Text-to-Image Models

Agneet Chatterjee; Gabriela Ben Melech Stan; Estelle Aflalo; Sayak Paul; Dhruba Ghosh; Tejas Gokhale; Ludwig Schmidt; Hannaneh Hajishirzi; Vasudev Lal; Chitta Baral; Yezhou Yang

arXiv · 2404.01197

Getting it Right: Improving Spatial Consistency in Text-to-Image Models

Agneet Chatterjee·Gabriela Ben Melech Stan·Estelle Aflalo·Sayak Paul·Dhruba Ghosh·Tejas Gokhale·Ludwig Schmidt·Hannaneh Hajishirzi·Vasudev Lal·Chitta Baral·Yezhou Yang

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Abstract

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 support algorithmic solutions to improve spatial reasoning in T2I models. We find that spatial relationships are under-represented in the image descriptions found in current vision-language datasets. To alleviate this data bottleneck, we create SPRIGHT, the first spatially focused, large-scale dataset, by re-captioning 6 million images from 4 widely used vision datasets and through a 3-fold evaluation and analysis pipeline, show that SPRIGHT improves the proportion of spatial relationships in existing datasets. We show the efficacy of SPRIGHT data by showing that using only $\sim$0.25% of SPRIGHT results in a 22% improvement in generating spatially accurate images while also improving FID and CMMD scores. We also find that training on images containing a larger number of objects leads to substantial improvements in spatial consistency, including state-of-the-art results on T2I-CompBench with a spatial score of 0.2133, by fine-tuning on <500 images. Through a set of controlled experiments and ablations, we document additional findings that could support future work that seeks to understand factors that affect spatial consistency in text-to-image models.

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Agneet Chatterjee, Gabriela Ben Melech Stan, Estelle Aflalo, Sayak Paul, Dhruba Ghosh, Tejas Gokhale, Ludwig Schmidt, Hannaneh Hajishirzi, Vasudev Lal, Chitta Baral, Yezhou Yang. 2024-08-06. Getting it Right: Improving Spatial Consistency in Text-to-Image Models. https://arxiv.org/abs/2404.01197

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