Papers › ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation

ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation

12 Apr 2023NeurIPS 2023 11arXiv:2304.05977archive 2025-07-28

Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, Yuxiao Dong

We present a comprehensive solution to learn and improve text-to-image models from human preference feedback. To begin with, we build ImageReward -- the first general-purpose text-to-image human preference reward model -- to effectively encode human preferences. Its training is based on our systematic annotation pipeline including rating and ranking, which collects 137k expert comparisons to date. In human evaluation, ImageReward outperforms existing scoring models and metrics, making it a promising automatic metric for evaluating text-to-image synthesis. On top of it, we propose Reward Feedback Learning (ReFL), a direct tuning algorithm to optimize diffusion models against a scorer. Both automatic and human evaluation support ReFL's advantages over compared methods. All code and datasets are provided at \url{https://github.com/THUDM/ImageReward}.

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thudm/imagereward officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
AMD-AIG-AIMA/ReNeg mentioned on GitHubpytorchMIT report
LemonTwoL/ReNeg mentioned on GitHubpytorch report

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1ran · violated contract
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Tasks

Image GenerationPreference MappingText to Image GenerationText-to-Image Generation

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Methods

Diffusion

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