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README.md

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+---
+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-FFHQ-2048-demo.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.

data/metadata.csv

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