kaikaiyao/ffhq-image-attribution

FFHQ Image Attribution

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

Version v2 includes 10,000 images from each of 12 FFHQ-trained generators for a total of 120,000 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
120,000	12	3	v2

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

How to use

Load a viewer subset with datasets:

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:

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 10,000 images per model from the current FFHQ bank snapshot.
• 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:

@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: v2}
}