---
license: cc-by-nc-4.0
task_categories:
  - text-to-image
language:
  - en
tags:
  - preference-alignment
  - diffusion-models
  - text-to-image
  - dpo
  - preference-pairs
pretty_name: RealAlign Preference Dataset
size_categories:
  - 1K<n<10K
---

# RealAlign Preference Dataset

This is the curated preference dataset for the paper
**"When Preference Labels Fall Short: Aligning Diffusion Models from Real Data" (ICML 2026)**.

| Resource | Link |
|---|---|
| 📄 Paper | [arXiv:2605.19839](https://arxiv.org/abs/2605.19839) |
| 🌐 Project page | [cwyxx.github.io/RealAlign](https://cwyxx.github.io/RealAlign/) |
| 🚀 Code | [github.com/Cwyxx/RealAlign](https://github.com/Cwyxx/RealAlign) |

## Dataset summary

RealAlign studies whether **real data** can replace manually annotated preference labels for
aligning text-to-image diffusion models. Instead of comparing two model-generated images and
labelling which is preferred, we treat a **high-quality reference image as the preferred ("win")**
sample — a real photograph (HPDv3) or a curated high-quality generated image (Civitai-top) —
and contrast it with a **perturbed / inpainted version as the non-preferred
("lose")** sample. This yields preference pairs without any human preference annotation, and we
show such reference-based supervision aligns diffusion models comparably to existing
preference-based methods.

This dataset provides the resulting `(real, fake)` preference pairs used to train the RealAlign
SD-1.5 and SD-3.5-M models.

## Sources

This release contains two sources, kept as separate subsets:

| Subset | Reference (win) images | Prompt / image origin |
|---|---|---|
| `HPDv3/` | real photographs | Human Preference Dataset v3 |
| `Civitai-top/` | high-quality generated images | Civitai top SFW images |

> **Note:** The Pick-a-Pic v2 subset used in the paper is **not** included in this release because the
> source data may contain not-safe-for-work (NSFW) content.

## Curation pipeline

The pairs are produced by RealAlign's four-stage data-curation pipeline
(see [`data_curation/`](https://github.com/Cwyxx/RealAlign/tree/main/data_curation) in the code repo):

1. **Extract** — collect `(uid, prompt)` entries for each source.
2. **Construct pairs** — for each reference image, generate the non-preferred ("fake")
   counterpart by computing a **U²-Net saliency mask** and **inpainting** the salient region with a
   text-to-image model (SD / SD-3.5 / PixArt). The real image becomes the "win" sample and the
   inpainted image the "lose" sample for the same prompt.
3. **Score** — score candidates with colorfulness, **PickScore**, and a **Qwen3-VL** anime
   classifier.
4. **Filter** — curate per source. HPDv3 uses `anime drop → color filter → discard negative →
   top-512`; Civitai-top applies top selection only.

## Directory layout

```
HPDv3/
├── HPDv3.csv          # uid, prompt, win_image_path, lose_image_path
├── real/<uid>.png     # reference (preferred / "win") images
└── fake/<uid>.png     # perturbed (non-preferred / "lose") images
Civitai-top/
├── Civitai-top.csv
├── real/<uid>.png
└── fake/<uid>.png
```

Each subset ships **512 final curated preference pairs** as the images under `real/` and `fake/`,
paired by `uid`. The accompanying CSV may list additional candidate rows from earlier curation
stages (HPDv3 in particular), so treat the images present in `real/`/`fake/` as the delivered set
and intersect the CSV by `uid` with those files.

### CSV columns

| Column | Description |
|---|---|
| `uid` | Pair identifier; matches the `<uid>.png` filenames in `real/` and `fake/`. |
| `prompt` | Text prompt for the pair. |
| `win_image_path` | Path to the preferred (real) image. |
| `lose_image_path` | Path to the non-preferred (fake / inpainted) image. |

> **Note:** `win_image_path` / `lose_image_path` in the CSVs are the absolute paths from the
> original training machine (e.g. `/data_center/.../real/<uid>.png`). When using this dataset,
> resolve images by `uid` against the local `real/` and `fake/` folders, or remap the path prefix
> to your download location.

## Usage

The curated CSV is consumed directly by the RealAlign trainers (`csv_file_path_train` for SD-1.5,
`config.{irl,dpo}.csv_file_path` for SD-3.5-M). See the
[GitHub repository](https://github.com/Cwyxx/RealAlign) for full training instructions.

## Citation

If you find this dataset useful, please consider citing:

```bibtex
@article{chen2026preference,
  title={When Preference Labels Fall Short: Aligning Diffusion Models from Real Data},
  author={Chen, Weiyan and Deng, Weijian and Xiao, Yao and Tu, Weijie and Dong, ZiYi and Radwan, Ibrahim and Lin, Liang and Wei, Pengxu},
  journal={arXiv preprint arXiv:2605.19839},
  year={2026}
}
```