How far can we go with ImageNet for Text-to-Image generation?
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.
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L. Degeorge
claimed_verified
2025-03-03T16:07:10.195Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63bb08b07fd5e883e13efd32/aKAR8alYsYteEQImBrWO7.jpeg
Lucas Degeorge
user
Lucasdegeorge
67c5c13ca10c7059c3d3d4ca
A. Ghosh
extracted_confirmed
2025-03-03T18:07:11.151Z
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Arijit Ghosh
user
arijitghosh
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N. Dufour
claimed_verified
2025-03-03T16:07:14.366Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/630652803aed65d34e98eee3/XG_PuVFA6ziGQZd3UUZSF.jpeg
Nicolas Dufour
user
nicolas-dufour
67c5c13ca10c7059c3d3d4cc
D. Picard
67c5c13ca10c7059c3d3d4cd
V. Kalogeiton
How far can we go with ImageNet for Text-to-Image generation?
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.
Strategic data augmentation of small, well-curated datasets can achieve competitive performance in text-to-image generation with significantly fewer parameters and training images compared to large-scale models.
text-to-image generation ImageNet data augmentation SD-XL GenEval DPGBench