Efficient text-to-image generation remains a challenging task due to the high
computational costs associated with the multi-step sampling in diffusion
models. Although distillation of pre-trained diffusion models has been
successful in reducing sampling steps, low-step image generation often falls
short in terms of quality. In this study, we propose a novel sampling design to
achieve high-quality one-step image generation aligning with human preferences,
particularly focusing on exploring the impact of the prior noise distribution.
Our approach, Prompt Adaptive Human Preference Inversion (PAHI), optimizes the
noise distributions for each prompt based on human preferences without the need
for fine-tuning diffusion models. Our experiments showcase that the tailored
noise distributions significantly improve image quality with only a marginal
increase in computational cost. Our findings underscore the importance of noise
optimization and pave the way for efficient and high-quality text-to-image
synthesis.

提出一种新的采样设计方法，通过优化噪音分布以满足人类偏好，而不需要微调扩散模型，从而实现高质量的一步图像生成；实验证明，定制的噪音分布显著提高图像质量，并在计算成本仅略微增加的情况下取得了重要的突破，同时凸显了噪音优化的重要性，为高效和高质量的文本到图像合成铺平了道路。