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README.md

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+---
+annotations_creators: []
+language: []
+language_creators: []
+license:
+- cc0-1.0
+multilinguality: []
+pretty_name: Full Body Anime HQ
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+tags: []
+task_categories:
+- unconditional-image-generation
+task_ids: []
+---
+
+## Dataset Description
+
+FBAnimeHQ is a dataset with high-quality full-body anime girl images in a resolution of 1024 × 512.
+
+### Dataset Summary
+
+The dataset contains 112,806 images.
+
+All images are on white background
+
+### Collection Method
+
+#### v1.0
+Collect from danbooru website.
+
+Use yolov5 to detect and clip image.
+
+Use anime-segmentation to remove background.
+
+Use deepdanbooru to filter image.
+
+Finally clean the dataset manually.
+
+#### v2.0
+
+Base on v1.0, use Novelai image-to-image to enhance and expand the dataset.
+
+### Contributions
+
+Thanks to [@SkyTNT](https://github.com/SkyTNT) for adding this dataset.

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fbanimehq.py

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+import os
+import datasets
+from datasets import DownloadManager, DatasetInfo
+
+_DESCRIPTION = """\
+FBAnimeHQ is a dataset with high-quality full-body anime girl images in a resolution of 1024 × 512.
+"""
+_HOMEPAGE = "https://huggingface.co/datasets/skytnt/fbanimehq"
+_URL_BASE = "https://huggingface.co/datasets/skytnt/fbanimehq/resolve/main/data/"
+_EXTENSION = [".png", ".jpg"]
+_URLS_HQ = [f"{_URL_BASE}fbanimehq-{i:02d}.zip" for i in range(12)]
+_URLS_SMALL = [
+    # _URL_BASE + "fbanimesmall.zip",
+]
+
+
+class FBAnimeHQConfig(datasets.BuilderConfig):
+
+    def __init__(self, urls, **kwargs):
+        super(FBAnimeHQConfig, self).__init__(**kwargs)
+        self.urls = urls
+
+
+class FBAnimeHQ(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        FBAnimeHQConfig(name="hq", urls=_URLS_HQ),
+        FBAnimeHQConfig(name="small", urls=_URLS_SMALL)
+    ]
+
+    def _info(self) -> DatasetInfo:
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation="",
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        dirs = dl_manager.download_and_extract(self.config.urls)
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"dirs": dirs})]
+
+    def _generate_examples(self, dirs):
+        for path in dirs:
+            all_fnames = {os.path.relpath(os.path.join(root, fname), start=path)
+                          for root, _dirs, files in os.walk(path) for fname in files}
+            image_fnames = sorted(fname for fname in all_fnames
+                                  if os.path.splitext(fname)[1].lower() in _EXTENSION)
+            for image_fname in image_fnames:
+                yield image_fname, {"image": os.path.join(path, image_fname)}