---
license: apache-2.0
task_categories:
- image-classification
- text-to-image
language:
- en
- zh
tags:
- preference
- reward-model
- text-to-image
- human-feedback
pretty_name: HPDv3++
size_categories:
- 100K<n<1M
---

# HPDv3++: A Dual-Dimension Preference Dataset for Text-to-Image Reward Modeling

HPDv3++ is a large-scale human-preference dataset for text-to-image (T2I) generation, built on a frontier generator (Qwen-Image) and annotated along two axes: **text-following (TF)** and **aesthetic quality (Aes)**. It is the dataset used to train [HPSv3++](https://huggingface.co/Junjun2333/HPSv3-PlusPlus), a capability-aware and RL-iteration-aware reward model.

Each preference pair stores a **preferred** image (`path1`) and a **non-preferred** image (`path2`) for the same prompt.

## Quick start

```bash
pip install -U "huggingface_hub[cli]"
huggingface-cli download Junjun2333/HPDv3-PlusPlus --repo-type dataset --local-dir HPDv3pp
cd HPDv3pp
# Reassemble and extract the image pool (split tar parts):
cat images.tar.part* | tar -xf -
```

After extraction you get an `images/` directory. Every path in the JSON files
(`path1` / `path2` / `image_path`) is **relative** and resolves against the
repo root, e.g. `images/qwen_image/prompt_000000/6.jpg`.

## What you can use directly

These four files are ready-to-use, self-contained, and **do not require any
HPSv3++ code or model** -- just images + JSON. Each record is
`{"path1": <preferred>, "path2": <non-preferred>, "prompt": <text>}` (the same
format as HPSv3/HPDv3), with `path1` preferred over `path2`.

| File | Pairs | Use |
|---|---|---|
| `train/train_aes.json` | 100,463 | Training -- aesthetic preference |
| `train/train_tf.json`  | 90,908  | Training -- text-following preference |
| `test/test_aes.json`   | 5,720   | Evaluation -- aesthetic |
| `test/test_tf.json`    | 4,465   | Evaluation -- text-following |

The training and test sets are **disjoint** (no shared pairs), including across
the two axes (aes/tf), so they can be used together without leakage.

## Repository layout

```
HPDv3-PlusPlus/
|-- images.tar.part00, images.tar.part01, ...   # split tar of the image pool (~328 GB total)
|-- train/
|   |-- train_aes.json        # 100,463  ready-to-use aesthetic training pairs
|   |-- train_tf.json         # 90,908   ready-to-use text-following training pairs
|   |-- stage1_labeled.json   # 191,466  labeled pairs (used by HPSv3++ Stage 1)
|   |-- stage1_ref.json       # 284,974  original HPDv3 reference pairs (Stage 1 OGD anti-forgetting)
|   |-- stage2_labeled.json   # 111,650  labeled pairs (used by HPSv3++ Stage 2)
|   |-- rollout.json          # 322,452  unlabeled rollouts, long format, one image per row
|   `-- ogd_std.json          # 58,242   pre-computed per-group std (also embedded in rollout.json)
|-- test/
|   |-- test_aes.json         # 5,720   ready-to-use aesthetic test pairs
|   `-- test_tf.json          # 4,465   ready-to-use text-following test pairs
`-- images/                   # (after extraction) the unified, deduplicated image pool
```

## JSON formats

**Preference pairs** (`train_aes`, `train_tf`, `stage1_labeled`, `stage1_ref`, `stage2_labeled`, `test_aes`, `test_tf`):

| Field | Meaning |
|---|---|
| `path1` / `path2` | Preferred / non-preferred image (relative `images/...` path) |
| `prompt` | Text prompt |
| `choice_dist` / `confidence` / `model1` / `model2` | (where annotated) vote distribution, confidence, generator names; `null` otherwise. The ready-to-use `train_aes`/`train_tf` and `test` files keep only `path1/path2/prompt`. |

**`rollout.json`** (unlabeled rollouts for HPSv3++ Stage 2; long format, one image per row):

| Field | Meaning |
|---|---|
| `group_id` | Group id (same prompt + tier + iter_step form one group) |
| `source` | `capability` or `iteration` |
| `prompt` | Text prompt |
| `tier` | Generator tier |
| `iter_step` / `iter_norm` | Raw / normalized RL iteration |
| `capability` / `level` | Continuous capability score / discrete level |
| `image_path` | Relative image path |
| `ogd_std` | Pre-computed per-group std |

## Notes

- The full image pool is ~328 GB (922,699 deduplicated images). If you only need
  the ready-to-use train/test files, you still need the image pool, since the
  JSONs reference images by relative path.
- For the full two-stage training / evaluation pipeline (which additionally uses
  `rollout.json`, `stage1_ref.json`, etc.), see the HPSv3++ code repository.

## Citation

```bibtex
@misc{hpsv3pp,
  title  = {HPSv3++: Scaling Reward Models Across the Full Spectrum of Diffusion Model Capabilities},
  author = {HPSv3++ Team},
  year   = {2026}
}
```