Model-Agnostic Human Preference Inversion in Diffusion Models

• URL: http://arxiv.org/abs/2404.00879v1
• Date: Mon, 1 Apr 2024 03:18:12 GMT
• Title: Model-Agnostic Human Preference Inversion in Diffusion Models
• Authors: Jeeyung Kim, Ze Wang, Qiang Qiu,
• Abstract summary: We propose a novel sampling design to achieve high-quality one-step image generation aligning with human preferences. Our approach, Prompt Adaptive Human Preference Inversion (PAHI), optimize the noise distributions for each prompt based on human preferences. Our experiments showcase that the tailored noise distributions significantly improve image quality with only a marginal increase in computational cost.
• Score: 31.992947353231564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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