Emu: Enhancing Image Generation Models Using Photogenic Needles in a
  Haystack
Xiaoliang Dai Ji Hou Chih-Yao Ma Sam Tsai Jialiang Wang Rui Wang Peizhao Zhang Simon Vandenhende Xiaofang Wang Abhimanyu Dubey Matthew Yu Abhishek Kadian Filip Radenovic Dhruv Mahajan Kunpeng Li Yue Zhao Vladan Petrovic Mitesh Kumar Singh Simran Motwani Yi Wen Yiwen Song Roshan Sumbaly Vignesh Ramanathan Zijian He Peter Vajda Devi Parikh
Training text-to-image models with web scale image-text pairs enables the
generation of a wide range of visual concepts from text. However, these
pre-trained models often face challenges when it comes to generating highly
aesthetic images. This creates the need for aesthetic alignment post
pre-training. In this paper, we propose quality-tuning to effectively guide a
pre-trained model to exclusively generate highly visually appealing images,
while maintaining generality across visual concepts. Our key insight is that
supervised fine-tuning with a set of surprisingly small but extremely visually
appealing images can significantly improve the generation quality. We pre-train
a latent diffusion model on $1.1$ billion image-text pairs and fine-tune it
with only a few thousand carefully selected high-quality images. The resulting
model, Emu, achieves a win rate of $82.9\%$ compared with its pre-trained only
counterpart. 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 PartiPrompts
and our Open User Input benchmark based on the real-world usage of
text-to-image models. In addition, we show that quality-tuning is a generic
approach that is also effective for other architectures, including pixel
diffusion and masked generative transformer models.