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# Datasets for the Direct Preference for Denoising Diffusion Policy Optimization (D3PO)
**Description**: This repository contains the dataset for the D3PO method in this paper [Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model](https://arxiv.org/abs/2311.13231). The *d3po_dataset* file pertains to the image distortion experiment of the [`anything-v5`](https://huggingface.co/stablediffusionapi/anything-v5) model.
The *text2img_dataset* comprises the images generated from the pretrained, preferred image fine-tuned, reward weighted fine-tuned and D3PO fine-tuned models in the prompt-image alignment experiment.
**Source Code**: The code used to generate this data can be found [here](https://github.com/yk7333/D3PO/).
**Directory**
- d3po_dataset
- epoch1
- all_img
- *.png
- deformed_img
- *.png
- json
- data.json (required for training)
- prompt.json
- sample.pkl(required for training)
- epoch2`
- ...
- epoch5
- text2img_dataset:
- img
- data_*.json
- plot.ipynb
- prompt.txt
**Citation**
```
@article{yang2023using,
title={Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model},
author={Yang, Kai and Tao, Jian and Lyu, Jiafei and Ge, Chunjiang and Chen, Jiaxin and Li, Qimai and Shen, Weihan and Zhu, Xiaolong and Li, Xiu},
journal={arXiv preprint arXiv:2311.13231},
year={2023}
}
```
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