MaPO
Collection
This collection includes the models and datasets as a part of the MaPO release.
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9 items
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Margin-aware Preference Optimization for Aligning Diffusion Models without Reference
We propose MaPO, a reference-free, sample-efficient, memory-friendly alignment technique for text-to-image diffusion models. For more details on the technique, please refer to our paper here.
Developed by
- Jiwoo Hong* (KAIST AI)
- Sayak Paul* (Hugging Face)
- Noah Lee (KAIST AI)
- Kashif Rasul (Hugging Face)
- James Thorne (KAIST AI)
- Jongheon Jeong (Korea University)
Dataset
This dataset is cartoon split of Pick-Style, self-curated with Stable Diffusion XL. Using the context prompts (i.e., without stylistic specifications), we generate (1) cartoon style generation with stylistic prefix prompt and (2) normal generation with context prompt. Then, (1) is used as the chosen image, and (2) as the rejected image.
Citation
@misc{hong2024marginaware,
title={Margin-aware Preference Optimization for Aligning Diffusion Models without Reference},
author={Jiwoo Hong and Sayak Paul and Noah Lee and Kashif Rasul and James Thorne and Jongheon Jeong},
year={2024},
eprint={2406.06424},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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