| ---
|
| 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
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| - face-enhancement
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| - face-restoration
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| - 2k
|
| - high-resolution
|
| - generative-models
|
| - nanopocket
|
| configs:
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| - config_name: default
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| data_files:
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| - 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.
|
|
|
