Add files

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import io
+import os
+import pathlib
+import tarfile
+
+import gradio as gr
+import numpy as np
+import PIL.Image
+from huggingface_hub import hf_hub_download
+
+TITLE = 'TADNE (This Anime Does Not Exist) Image Selector'
+DESCRIPTION = '''The original TADNE site is https://thisanimedoesnotexist.ai/.
+
+You can view images generated by the TADNE model with seed 0-99999.
+You can filter images based on predictions by the [DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) model.
+The original images are 512x512 in size, but here they are resized to 128x128.
+
+Known issues:
+- The `Seed` table in the output doesn't refresh properly in gradio 2.9.1. https://github.com/gradio-app/gradio/issues/921
+'''
+ARTICLE = None
+
+TOKEN = os.environ['TOKEN']
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--theme', type=str)
+    parser.add_argument('--live', action='store_true')
+    parser.add_argument('--share', action='store_true')
+    parser.add_argument('--port', type=int)
+    parser.add_argument('--disable-queue',
+                        dest='enable_queue',
+                        action='store_false')
+    parser.add_argument('--allow-flagging', type=str, default='never')
+    parser.add_argument('--allow-screenshot', action='store_true')
+    return parser.parse_args()
+
+
+def download_image_tarball(size: int, dirname: str) -> pathlib.Path:
+    path = hf_hub_download('hysts/TADNE-sample-images',
+                           f'{size}/{dirname}.tar',
+                           repo_type='dataset',
+                           use_auth_token=TOKEN)
+    return path
+
+
+def load_deepdanbooru_tag_dict() -> dict[str, int]:
+    path = hf_hub_download('hysts/DeepDanbooru',
+                           'tags.txt',
+                           use_auth_token=TOKEN)
+    with open(path) as f:
+        tags = [line.strip() for line in f.readlines()]
+    return {tag: i for i, tag in enumerate(tags)}
+
+
+def load_deepdanbooru_predictions(dirname: str) -> np.ndarray:
+    path = hf_hub_download('hysts/TADNE-sample-images',
+                           f'prediction_results/deepdanbooru/{dirname}.npy',
+                           repo_type='dataset',
+                           use_auth_token=TOKEN)
+    return np.load(path)
+
+
+def run(
+    general_tags: list[str],
+    hair_color_tags: list[str],
+    hair_style_tags: list[str],
+    image_color_tags: list[str],
+    score_threshold: float,
+    start_index: int,
+    nrows: int,
+    ncols: int,
+    image_size: int,
+    min_seed: int,
+    max_seed: int,
+    dirname: str,
+    tarball_path: pathlib.Path,
+    deepdanbooru_tag_dict: dict[str, int],
+    deepdanbooru_predictions: np.ndarray,
+) -> np.ndarray:
+    hair_color_tags = [f'{color}_hair' for color in hair_color_tags]
+
+    tags = general_tags + hair_color_tags + hair_style_tags + image_color_tags
+    tag_indices = [deepdanbooru_tag_dict[tag] for tag in tags]
+
+    conditions = deepdanbooru_predictions[:, tag_indices] > score_threshold
+    image_indices = np.arange(len(deepdanbooru_predictions))
+    image_indices = image_indices[conditions.all(axis=1)]
+
+    start_index = int(start_index)
+    num = nrows * ncols
+    seeds = []
+    images = []
+    dummy = np.ones((image_size, image_size, 3), dtype=np.uint8) * 255
+    with tarfile.TarFile(tarball_path) as tar_file:
+        for index in range(start_index, start_index + num):
+            if index >= len(image_indices):
+                seeds.append(-1)
+                images.append(dummy)
+                continue
+            image_index = image_indices[index]
+            seeds.append(image_index)
+            member = tar_file.getmember(f'{dirname}/{image_index:07d}.jpg')
+            with tar_file.extractfile(member) as f:
+                data = io.BytesIO(f.read())
+            image = PIL.Image.open(data)
+            image = np.asarray(image)
+            images.append(image)
+    res = np.asarray(images).reshape(nrows, ncols, image_size, image_size,
+                                     3).transpose(0, 2, 1, 3, 4).reshape(
+                                         nrows * image_size,
+                                         ncols * image_size, 3)
+    seeds = np.asarray(seeds).reshape(nrows, ncols)
+
+    return len(image_indices), res, seeds
+
+
+def main():
+    gr.close_all()
+
+    args = parse_args()
+
+    image_size = 128
+    min_seed = 0
+    max_seed = 99999
+    dirname = '0-99999'
+    tarball_path = download_image_tarball(image_size, dirname)
+
+    deepdanbooru_tag_dict = load_deepdanbooru_tag_dict()
+    deepdanbooru_predictions = load_deepdanbooru_predictions(dirname)
+
+    func = functools.partial(
+        run,
+        image_size=image_size,
+        min_seed=min_seed,
+        max_seed=max_seed,
+        dirname=dirname,
+        tarball_path=tarball_path,
+        deepdanbooru_tag_dict=deepdanbooru_tag_dict,
+        deepdanbooru_predictions=deepdanbooru_predictions,
+    )
+    func = functools.update_wrapper(func, run)
+
+    gr.Interface(
+        func,
+        [
+            gr.inputs.CheckboxGroup([
+                '1girl',
+                '1boy',
+                'multiple_girls',
+                'multiple_boys',
+            ],
+                                    label='General'),
+            gr.inputs.CheckboxGroup([
+                'aqua',
+                'black',
+                'blonde',
+                'blue',
+                'brown',
+                'green',
+                'grey',
+                'orange',
+                'pink',
+                'purple',
+                'red',
+                'silver',
+                'white',
+            ],
+                                    label='Hair Color'),
+            gr.inputs.CheckboxGroup([
+                'bangs',
+                'curly_hair',
+                'long_hair',
+                'medium_hair',
+                'messy_hair',
+                'short_hair',
+                'straight_hair',
+                'twintails',
+            ],
+                                    label='Hair Style'),
+            gr.inputs.CheckboxGroup([
+                'greyscale',
+                'monochrome',
+            ],
+                                    label='Image Color'),
+            gr.inputs.Slider(0,
+                             1,
+                             step=0.1,
+                             default=0.5,
+                             label='DeepDanbooru Score Threshold'),
+            gr.inputs.Number(default=0, label='Start Index'),
+            gr.inputs.Slider(1, 10, step=1, default=2, label='Number of Rows'),
+            gr.inputs.Slider(
+                1, 10, step=1, default=5, label='Number of Columns'),
+        ],
+        [
+            gr.outputs.Textbox(type='number', label='Number of Found Images'),
+            gr.outputs.Image(type='numpy', label='Output'),
+            gr.outputs.Dataframe(type='numpy', label='Seed'),
+        ],
+        title=TITLE,
+        description=DESCRIPTION,
+        article=ARTICLE,
+        theme=args.theme,
+        allow_screenshot=args.allow_screenshot,
+        allow_flagging=args.allow_flagging,
+        live=args.live,
+    ).launch(
+        enable_queue=args.enable_queue,
+        server_port=args.port,
+        share=args.share,
+    )
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+numpy==1.22.3
+Pillow==9.0.1