Aligning Diffusion Models by Optimizing Human Utility

• URL: http://arxiv.org/abs/2404.04465v2
• Date: Fri, 11 Oct 2024 19:43:11 GMT
• Title: Aligning Diffusion Models by Optimizing Human Utility
• Authors: Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Yusuke Kato, Kazuki Kozuka,
• Abstract summary: Diffusion-KTO is a novel approach for aligning text-to-image diffusion models with human preferences. Our objective requires simple per-image binary feedback signals, e.g. likes or dislikes, which are abundantly available.
• Score: 1.6166249658374658
• License:
• Abstract: We present Diffusion-KTO, a novel approach for aligning text-to-image diffusion models by formulating the alignment objective as the maximization of expected human utility. Since this objective applies to each generation independently, Diffusion-KTO does not require collecting costly pairwise preference data nor training a complex reward model. Instead, our objective requires simple per-image binary feedback signals, e.g. likes or dislikes, which are abundantly available. After fine-tuning using Diffusion-KTO, text-to-image diffusion models exhibit superior performance compared to existing techniques, including supervised fine-tuning and Diffusion-DPO, both in terms of human judgment and automatic evaluation metrics such as PickScore and ImageReward. Overall, Diffusion-KTO unlocks the potential of leveraging readily available per-image binary signals and broadens the applicability of aligning text-to-image diffusion models with human preferences.

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