Datasets:

Junjun2333
/

HPDv3-PlusPlus

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++, 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

pip install -U "huggingface_hub[cli]"
hf download Junjun2333/HPDv3-PlusPlus --repo-type dataset --local-dir HPDv3pp
cd HPDv3pp
# Reassemble and extract our image pool (split tar parts -> images/qwen_image, images/rollout, images/thumbs):
cat images.tar.part* | tar -xf -

The split tar contains only the images we generated (images/qwen_image/, images/rollout/, images/thumbs/). The stage1_ref.json reference pairs point to the original HPDv3 images (images/hpdv3/...), which we do not re-host here. If you need them (only required to reproduce HPSv3++ Stage 1 with the original HPDv3 reference set), download the HPDv3 images from the official repo and place them under images/hpdv3/:

# Original HPDv3 images (only needed for stage1_ref.json)
hf download MizzenAI/HPDv3 --repo-type dataset --include "images.tar.gz.*" --local-dir hpdv3_src
cat hpdv3_src/images.tar.gz.* | gunzip | tar -xv   # then move/symlink the resulting images into images/hpdv3/

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. The four ready-to-use train/test files (train_aes, train_tf, test_aes, test_tf) reference only our own images and need no HPDv3 download.

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 OUR images (~268 GB; qwen_image + rollout + thumbs)
|-- 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: qwen_image/, rollout/, thumbs/ (ours);
                              #   hpdv3/ must be downloaded separately from MizzenAI/HPDv3 (only for stage1_ref)

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 images we host here (qwen_image + rollout + thumbs) are ~268 GB. The original HPDv3 images (hpdv3/, ~60 GB, referenced only by stage1_ref.json) are not re-hosted -- download them from MizzenAI/HPDv3 if needed (see Quick start).
The ready-to-use train/test files reference only our own images, so they work with just the split tar above (no HPDv3 download needed).
For the full two-stage training / evaluation pipeline (which additionally uses rollout.json, stage1_ref.json, etc.), see the HPSv3++ code repository.

Citation

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