README.md

---

license: cc-by-nc-sa-4.0
task_categories:
  - image-to-image
  - unconditional-image-generation
language:
  - en
size_categories:
  - 1K<n<10K
pretty_name: FFHQ-2048 (NanoPocket Enhanced) — First 1,000
tags:
  - faces
  - face-dataset
  - ffhq
  - super-resolution
  - face-enhancement
  - face-restoration
  - 2k
  - high-resolution
  - generative-models
  - nanopocket
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.png
---


# FFHQ-2048 — NanoPocket Enhanced (First 1,000)

> **The first new high-quality public face dataset since 2019.**
> 1,000 sharp, artifact-free **2048×2048** portraits, derived from FFHQ and enhanced with the NanoPocket Face Enhance model.

<p align="center">
  <a href="https://huggingface.co/spaces/Nanopocket-ai/FFHQ-2048-demo">
    <img alt="Open the interactive before/after demo on Hugging Face Spaces"

         src="https://img.shields.io/badge/Try%20the%20interactive%20before%2Fafter%20slider-%F0%9F%A4%97%20Spaces-blue?style=for-the-badge">

  </a>

</p>


---

## Why this dataset exists

FFHQ (NVIDIA, 2019) has been the gold-standard face dataset for the past five years — but the field has moved on. Modern generators (Flux, SD3 / SDXL, StyleGAN-T, portrait restoration nets) train at **1024² and above**, and they expose every soft pixel, every JPEG ghost, every out-of-focus eyelash that the original FFHQ contains. Yet **no comparable public face dataset has been released since FFHQ**.

We built **FFHQ-2048** to fill that gap:

- **2× spatial resolution** — 1024×1024 → **2048×2048**.
- **Sharper, more detailed faces** — pores, hair strands, iris texture, fabric weave.
- **Artifact-free** — no over-sharpening halos, no plastic skin, no identity drift.
- **Filename-compatible with original FFHQ** — `00000.png` here corresponds to FFHQ index `0`, so you can swap it in to existing pipelines without rewriting code.

This release contains the **first 1,000 images** as a free, public preview. Scroll down for how to request the full set.

---

## Interactive before / after — six samples

All six samples are shown below as **drag-to-compare sliders with mouse-wheel zoom**, served live from the [Nanopocket-ai/FFHQ-2048-demo](https://huggingface.co/spaces/Nanopocket-ai/FFHQ-2048-demo) Space.

**Controls:** drag the purple divider to compare · scroll wheel to zoom in / out · `Shift` + drag to pan when zoomed in · double-click to reset · `+` / `−` / `⟲` buttons in each card · two-finger pinch on touch devices.

<iframe

  src="https://nanopocket-ai-ffhq-2048-demo.static.hf.space"

  frameborder="0"

  width="100%"

  height="2400"

  style="border-radius:12px;"

></iframe>

If the iframe is blocked, open the demo in a new tab: **[Nanopocket-ai/FFHQ-2048-demo](https://huggingface.co/spaces/Nanopocket-ai/FFHQ-2048-demo)**.

---

## Dataset summary

| Field | Value |
| --- | --- |
| Number of images | **1,000** |
| Resolution | **2048 × 2048** |
| Format | PNG, lossless |
| Total size | ~5.4 GB |
| Filename pattern | `data/{index:05d}.png` (e.g. `data/00042.png`) |
| Index range | `00000` – `00999` (matches original FFHQ indices) |
| Source | NVIDIA FFHQ `images1024x1024` first 1,000 |
| Enhancement | NanoPocket Face Enhance |

```

Nanopocket-ai/FFHQ-2048

├── README.md

└── data/

    ├── metadata.csv          # file_name, ffhq_index, original_split

    ├── 00000.png

    ├── 00001.png

    └── ... 998 more

```

`data/metadata.csv` lives next to the images, so the standard `datasets` ImageFolder loader picks it up automatically.

---

## Quick start

Install the libraries you need:

```bash

pip install -U datasets huggingface_hub pillow

```

### Option 1 — `datasets` library (with metadata)

```python

from datasets import load_dataset



ds = load_dataset("Nanopocket-ai/FFHQ-2048", split="train")

print(ds)                       # 1000 rows: image, ffhq_index, original_split

print(ds[0]["image"].size)      # (2048, 2048)

print(ds[0]["ffhq_index"])      # 0

ds[0]["image"].save("sample.png")

```

### Option 2 — `huggingface_hub` snapshot (full local copy)



```python

from huggingface_hub import snapshot_download



local_dir = snapshot_download(

    repo_id="Nanopocket-ai/FFHQ-2048",
    repo_type="dataset",

    allow_patterns=["data/*", "README.md"],

)

print(local_dir)                # contains data/00000.png ... data/00999.png + data/metadata.csv

```


### Option 3 — Single image via raw URL

```python

import io

import requests

from PIL import Image



url = "https://huggingface.co/datasets/Nanopocket-ai/FFHQ-2048/resolve/main/data/00042.png"

img = Image.open(io.BytesIO(requests.get(url).content))

img.show()

```

---

## Want the full enhanced FFHQ?

This repo is a **public preview**. We have enhanced the **entire 70,000-image FFHQ** to 2048² with the same pipeline. If the previewed quality fits your research or product, get in touch:

> **Email: [marketing@nanopocket.ai](mailto:marketing@nanopocket.ai)**
>

> Tell us briefly:
> 1. Who you are (lab / company / individual).
> 2. What you plan to use the data for.
> 3. Whether the use is non-commercial (FFHQ's CC BY-NC-SA 4.0 inheritance applies).
>

> We will respond with a delivery method (LFS bundle, S3 link, or a private HF dataset invite).

---

## About NanoPocket Face Enhance

NanoPocket Face Enhance is our in-house face restoration / super-resolution model, optimised to (a) preserve identity, (b) recover micro-detail (skin pores, hair, iris, lip texture) and (c) avoid the typical pitfalls of SR networks — over-sharpened halos, plastic skin, and waxy artifacts.

If you would like to use this model locally, please also contact: **[marketing@nanopocket.ai](mailto:marketing@nanopocket.ai)**.

---

## License & attribution

This dataset is a derivative work of **NVIDIA's Flickr-Faces-HQ (FFHQ)** dataset. Per FFHQ's license terms we **inherit the same license** and clearly **indicate the changes** we made.

- **Dataset license:** [Creative Commons BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) — free use, redistribution and adaptation **for non-commercial purposes**, with attribution and share-alike.
- **Per-image licenses:** the underlying photographs were originally collected from Flickr under one of:
  - [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/)
  - [CC BY-NC 2.0](https://creativecommons.org/licenses/by-nc/2.0/)
  - [Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/)
  - [Public Domain CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)
  - [U.S. Government Works](http://www.usa.gov/copyright.shtml)
  Per-image author and license are recorded in the original [`ffhq-dataset-v2.json`](https://github.com/NVlabs/ffhq-dataset) metadata, indexed by the same numeric IDs used in this repo.
- **Indicated changes:** every image in this repo has been **upscaled 2×** (1024×1024 → 2048×2048) and **detail-enhanced** by NanoPocket Face Enhance. No re-cropping, re-alignment or content edits beyond enhancement were performed.

### Important — Not for facial recognition

> Reproducing NVIDIA's explicit clause: **this dataset is not intended for, and should not be used for, the development or improvement of facial recognition technologies.**

---

## Privacy & removal requests

We respect the same privacy / opt-out process as the upstream FFHQ dataset. To request removal of a photo of yourself:

1. On Flickr, do **one** of: tag the photo with `no_cv`, change the licence to All Rights Reserved or any CC `NoDerivs` variant, set the photo to private, or delete it.
2. Email **[researchinquiries@nvidia.com](mailto:researchinquiries@nvidia.com)** (the upstream maintainers) with your Flickr username.
3. **Also** email us at **[marketing@nanopocket.ai](mailto:marketing@nanopocket.ai)** so we can remove the corresponding enhanced image from this repo and any future releases.

---

## Citation

If you use this dataset, please cite **both** the original FFHQ paper and this release:

```bibtex

@inproceedings{karras2019stylebased,

  title     = {A Style-Based Generator Architecture for Generative Adversarial Networks},

  author    = {Tero Karras and Samuli Laine and Timo Aila},

  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

  year      = {2019},

  url       = {https://arxiv.org/abs/1812.04948}

}



@misc{nanopocket2026ffhq2048,

  title        = {FFHQ-2048: A 2K Re-master of Flickr-Faces-HQ via NanoPocket Face Enhance},

  author       = {NanoPocket},

  year         = {2026},

  howpublished = {\url{https://huggingface.co/datasets/Nanopocket-ai/FFHQ-2048}},

  note         = {Public preview release. Full 70k set available on request.}

}

```

---

## Acknowledgements

- **NVIDIA / NVlabs** for releasing the original FFHQ dataset.
- **Tero Karras, Samuli Laine, Timo Aila** for the StyleGAN paper that introduced FFHQ.
- **Vahid Kazemi & Josephine Sullivan** for the face-alignment work that made the original collection possible.
- The Flickr photographers who shared their work under permissive licences.

---

## Changelog

- **v1.0 (2026-04)** — Initial public release: first 1,000 images, 2048×2048, NanoPocket Face Enhance v1.