Gelbooru2026: A Deduplicated Anime Illustration Dataset

Dataset Description

Gelbooru2026 is a large-scale, community-tagged anime illustration dataset for research and development in image generation, image classification, multimodal learning, and related tasks.

This release contains 3,784,478 static images distributed across 1,000 tar shards. It was frozen in August 2026 and was processed using the same image release format as Danbooru2026.

Key specifications:

• Shared by: Nyanko Devs
• Source: Gelbooru community
• Language(s): English, Japanese, mixed
• Dataset license: MIT; original images retain their respective copyrights
• Image format: WebP, quality 95, method 6
• Image count: 3,784,478
• Shards: 1,000 deterministic tar files

The dataset may contain adult or otherwise sensitive material. Users are responsible for applying filtering appropriate to their use case.

Deduplication Against Danbooru2026

This release is content-deduplicated against the released Danbooru2026 dataset. Deduplication used two exact-content stages:

1. 9,950,543 Gelbooru candidates were excluded by matching their original file MD5 against a released Danbooru2026 image.
2. 25,474 additional candidates were excluded by matching the processed WebP SHA-256 against a released Danbooru2026 WebP.

In total, 9,976,017 candidates were removed by cross-dataset deduplication. The final validation found zero known processed SHA-256 intersections with the released Danbooru2026 images.

This is exact byte/content deduplication, not perceptual or semantic deduplication. Near-duplicates, crops, edits, recompressions, and visually similar images can therefore remain.

Image Processing

Gelbooru2026 contains static images only. Animations, videos, corrupted files, unsupported media, and source rows that could not be safely resolved are not included in the image tars.

All included images are:

• fully decoded from audited source bytes and encoded as WebP at quality 95;
• resized only when necessary to a maximum of 4,000,000 pixels while preserving aspect ratio;
• never upscaled;
• stored with alpha transparency preserved when present.

Dataset Structure and Format

Images are distributed across 1,000 zero-padded buckets named 0000 through 0999. The bucket for an image is determined by its Gelbooru post ID modulo 1,000:

BUCKET=$(printf "%04d" $((ID % 1000)))

The tar shard and member path are:

original/data-$BUCKET.tar
$BUCKET/$ID.webp

For example, image ID 210001 is stored in:

original/data-0001.tar
0001/210001.webp

File Tree

/
├── README.md
├── metadata/
│   └── posts.parquet
└── original/
    ├── data-0000.tar
    ├── data-0001.tar
    └── ... (through data-0999.tar)

metadata/posts.parquet contains the original metadata fields plus the release fields bucket, path, width, height, file_size, sha256, has_alpha, resized, and source_file_size. Each metadata row corresponds to exactly one released WebP member.

Minimal Access Example

import io
import tarfile

import requests
from PIL import Image

url = (
    "https://huggingface.co/datasets/nyanko-devs/gelbooru2026/"
    "resolve/main/original/data-0001.tar"
)
response = requests.get(url, timeout=120)
response.raise_for_status()

with tarfile.open(fileobj=io.BytesIO(response.content), mode="r:") as archive:
    member = archive.getmember("0001/210001.webp")
    with archive.extractfile(member) as handle:
        image = Image.open(handle)
        image.load()

For bulk training, stream or download shards rather than loading an entire tar into memory as in this small example.

Notes

The processed release does not preserve original source encodings. Use the metadata sha256 field to verify a released WebP member and file_size to verify its length. Exact cross-dataset deduplication applies to this frozen release and the frozen Danbooru2026 release used during construction.