How far can we go with ImageNet for Text-to-Image generation?
Authors: name: L. Degeorge, name: A. Ghosh, name: N. Dufour, name: D. Picard, name: V. Kalogeiton

Recent text-to-image (T2I) generation models have achieved remarkable results
by training on billion-scale datasets, following a `bigger is better' paradigm
that prioritizes data quantity over quality. We challenge this established
paradigm by demonstrating that strategic data augmentation of small,
well-curated datasets can match or outperform models trained on massive
web-scraped collections. Using only ImageNet enhanced with well-designed text
and image augmentations, we achieve a +2 overall score over SD-XL on GenEval
and +5 on DPGBench while using just 1/10th the parameters and 1/1000th the
training images. Our results suggest that strategic data augmentation, rather
than massive datasets, could offer a more sustainable path forward for T2I
generation.