README.md

---
configs:
- config_name: stylegan2-ffhq-256
  data_files:
  - viewer-stylegan2-ffhq-256.parquet
- config_name: stylegan3-ffhq-256
  data_files:
  - viewer-stylegan3-ffhq-256.parquet
- config_name: r3gan-ffhq-256
  data_files:
  - viewer-r3gan-ffhq-256.parquet
- config_name: cips-ffhq-256
  data_files:
  - viewer-cips-ffhq-256.parquet
- config_name: ganformer-ffhq-256
  data_files:
  - viewer-ganformer-ffhq-256.parquet
- config_name: styleswin-ffhq-256
  data_files:
  - viewer-styleswin-ffhq-256.parquet
- config_name: vqvae-ffhq-256
  data_files:
  - viewer-vqvae-ffhq-256.parquet
- config_name: nvae-ffhq-256
  data_files:
  - viewer-nvae-ffhq-256.parquet
- config_name: vdvae-ffhq-256
  data_files:
  - viewer-vdvae-ffhq-256.parquet
- config_name: adm-ffhq-256
  data_files:
  - viewer-adm-ffhq-256.parquet
- config_name: ldm-ffhq-256
  data_files:
  - viewer-ldm-ffhq-256.parquet
- config_name: ncsnpp-ffhq-256
  data_files:
  - viewer-ncsnpp-ffhq-256.parquet
license: other
language:
- en
task_categories:
- image-classification
tags:
- ffhq
- model-attribution
- generated-images
- face-generation
- gan
- vae
- diffusion
- benchmark
---

# FFHQ Image Attribution

[![Images](https://img.shields.io/badge/images-24,000-blue)](https://huggingface.co/datasets/kaikaiyao/ffhq-image-attribution)
[![Models](https://img.shields.io/badge/models-12-2ea44f)](https://huggingface.co/datasets/kaikaiyao/ffhq-image-attribution)
[![Families](https://img.shields.io/badge/families-3-8250df)](https://huggingface.co/datasets/kaikaiyao/ffhq-image-attribution)
[![Version](https://img.shields.io/badge/version-v1-111827)](https://huggingface.co/datasets/kaikaiyao/ffhq-image-attribution)

A public benchmark for **FFHQ model attribution**, built from twelve face generators spanning GAN, VAE, and diffusion families.

![Showcase overview](preview/showcase-hero.png)

> Version `v1` includes `2,000` images from each of `12` FFHQ-trained generators for a total of `24,000` images.

## Why this dataset

- Same image domain across multiple FFHQ generators makes source attribution cleaner and easier to study.
- Public metadata links each image to its source model, family, release, seed, and file integrity hash.
- The dataset viewer uses lightweight embedded thumbnails so you can browse each model subset quickly.
- The release is deliberately simple: only model identity varies, without prompt or text metadata.

## At a glance

| Images | Models | Families | Version |
|---:|---:|---:|---|
| **24,000** | **12** | **3** | **v1** |

## Coverage

All images are `256x256` RGB face images from FFHQ-trained generators. Each subset corresponds to one model family member.

| Model | Family | Checkpoint |
|---|---|---|
| `stylegan2-ffhq-256` | `gan` | `https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/paper-fig7c-training-set-sweeps/ffhq140k-paper256-noaug.pkl` |
| `stylegan3-ffhq-256` | `gan` | `https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhqu-256x256.pkl` |
| `r3gan-ffhq-256` | `gan` | `brownvc/R3GAN-FFHQ-256x256` |
| `cips-ffhq-256` | `gan` | `https://drive.google.com/file/d/1JRd4ZpMDmlkbNlxnVvZx77Eyfac53KSq/view?usp=sharing` |
| `ganformer-ffhq-256` | `gan` | `https://drive.google.com/uc?id=1-b0vwevUQs6LI_EybdO8XJ5uYSx63vEa` |
| `styleswin-ffhq-256` | `gan` | `https://drive.google.com/file/d/1OjYZ1zEWGNdiv0RFKv7KhXRmYko72LjO/view` |
| `vqvae-ffhq-256` | `vae` | `kohido/ffhq256_vqvae_mhvq` |
| `nvae-ffhq-256` | `vae` | `https://drive.google.com/uc?id=1lQzywY5O71Z5NqAUJUcWy2Q1K2hPFO6j` |
| `vdvae-ffhq-256` | `vae` | `https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/ffhq256-iter-1700000-model-ema.th` |
| `adm-ffhq-256` | `diffusion` | `xutongda/adm_ffhq_256x256` |
| `ldm-ffhq-256` | `diffusion` | `kaayaanil/ldm-ffhq-256` |
| `ncsnpp-ffhq-256` | `diffusion` | `https://drive.google.com/uc?id=1-mtdSwuefIZA0n85QWScQo2WRvJNWwUy` |

## Gallery

![Overview grid](preview/overview-grid.png)

## How to use

Load a viewer subset with `datasets`:

```python
from datasets import load_dataset

ds = load_dataset("kaikaiyao/ffhq-image-attribution", "stylegan2-ffhq-256", split="train")
print(ds[0]["source_id"], ds[0]["seed"])
```

Or read the full metadata table with `pandas`:

```python
import pandas as pd

df = pd.read_parquet(
    "https://huggingface.co/datasets/kaikaiyao/ffhq-image-attribution/resolve/main/metadata/all.parquet"
)
print(df[["source_id", "seed", "image_path"]].head())
```

## Metadata

`metadata/all.parquet` is the main table for the release.

- Identity: `source_id`, `family`, `seed`
- Image and integrity: `image_path`, `image_size`, `sha256`
- Release: `release`

Each subset also has:

- `metadata/by_model/<source_id>.parquet`
- `viewer-<source_id>.parquet`

## Uses and limitations

- Intended for research on model attribution, image provenance, model fingerprinting, and generated-face forensics.
- All images are `256x256` and come from FFHQ-trained generators.
- `vqvae-ffhq-256` is reconstruction-based, unlike the other models that directly sample generated images.
- This public release includes `2,000` images per model; the internal bank is larger.
- Use of this dataset remains subject to the licenses and terms of the upstream model checkpoints and FFHQ-related resources.

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{yao2026ffhq_image_attribution,
  author = {{Kai Yao}},
  title = {{FFHQ Image Attribution}},
  year = {2026},
  publisher = {{Hugging Face}},
  url = {https://huggingface.co/datasets/kaikaiyao/ffhq-image-attribution},
  note = {Version: v1}
}
```