--- license: cc-by-nc-sa-4.0 task_categories: - image-to-image - unconditional-image-generation language: - en size_categories: - 1K **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.

Open the interactive before/after demo on Hugging Face Spaces

--- ## 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. 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.