metadata

license: openrail++
library_name: diffusers
dataset_info:
  features:
    - name: caption
      dtype: int64
  splits:
    - name: train
      num_bytes: 2929653589
      num_examples: 1000
  download_size: 2929757570
  dataset_size: 2929653589
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

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] (TODO).

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 pixel art 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. The chosen field comprises pixel art style generations from SDXL, while the rejected field comprises the ordinary generations from SDXL.

Citation

@misc{todo,
    title={Margin-aware Preference Optimization for Aligning Diffusion Models without Reference}, 
    author={Jiwoo Hong and Sayak Paul and Noah Lee and Kashif Rasuland James Thorne and Jongheon Jeong},
    year={2024},
    eprint={todo},
    archivePrefix={arXiv},
    primaryClass={cs.CV,cs.LG}
}