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Training text-to-image models with web scale image-text pairs enables thegeneration of a wide range of visual concepts from text. However, thesepre-trained models often face challenges when it comes to generating highlyaesthetic images. This creates the need for aesthetic alignment postpre-training. In this paper, we propose quality-tuning to effectively guide apre-trained model to exclusively generate highly visually appealing images,while maintaining generality across visual concepts. Our key insight is thatsupervised fine-tuning with a set of surprisingly small but extremely visuallyappealing images can significantly improve the generation quality. We pre-traina latent diffusion model on $1.1$ billion image-text pairs and fine-tune itwith only a few thousand carefully selected high-quality images. The resultingmodel, Emu, achieves a win rate of $82.9\%$ compared with its pre-trained onlycounterpart. Compared to the state-of-the-art SDXLv1.0, Emu is preferred$68.4\%$ and $71.3\%$ of the time on visual appeal on the standard PartiPromptsand our Open User Input benchmark based on the real-world usage oftext-to-image models. In addition, we show that quality-tuning is a genericapproach that is also effective for other architectures, including pixeldiffusion and masked generative transformer models.

Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack