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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],
eye_color_tags: list[str],
image_color_tags: list[str],
other_tags: list[str],
additional_tags: 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,
) -> tuple[int, np.ndarray, np.ndarray]:
hair_color_tags = [f'{color}_hair' for color in hair_color_tags]
eye_color_tags = [f'{color}_eyes' for color in eye_color_tags]
additional_tags = additional_tags.split(',')
tags = general_tags + hair_color_tags + hair_style_tags + \
eye_color_tags + image_color_tags + other_tags + additional_tags
tag_indices = [
deepdanbooru_tag_dict[tag] for tag in tags
if tag in deepdanbooru_tag_dict
]
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(np.nan)
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',
'looking_at_viewer',
],
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',
'ponytail',
'short_hair',
'straight_hair',
'twintails',
],
label='Hair Style'),
gr.inputs.CheckboxGroup([
'aqua',
'black',
'blue',
'brown',
'green',
'grey',
'orange',
'pink',
'purple',
'red',
'white',
'yellow',
],
label='Eye Color'),
gr.inputs.CheckboxGroup([
'greyscale',
'monochrome',
],
label='Image Color'),
gr.inputs.CheckboxGroup([
'animal_ears',
'closed_eyes',
'full_body',
'hat',
'smile',
],
label='Others'),
gr.inputs.Textbox(label='Additional Tags'),
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()
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