Hugging Face's logo

Spaces:
tori29umai
/
Photo2ShojoManga
Running on Zero

App Files Community
Photo2ShojoManga
File size: 3,721 Bytes 
3542be4
 
 
 
 
 
 
 
 
 
 
 
 
be7cf46
3542be4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
63144e1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3542be4
 
c689a76
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3542be4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
 
import os

import requests
from tqdm import tqdm
import shutil

from PIL import Image, ImageOps
import numpy as np
import cv2

def load_cn_model(model_dir):
  folder = model_dir
  file_name = 'diffusion_pytorch_model.safetensors'
  url = "  https://huggingface.co/2vXpSwA7/iroiro-lora/resolve/main/test_controlnet2/CN-anytest_v3-50000_fp16.safetensors"
  file_path = os.path.join(folder, file_name)
  if not os.path.exists(file_path):
    response = requests.get(url, stream=True)

    total_size = int(response.headers.get('content-length', 0))
    with open(file_path, 'wb') as f, tqdm(
            desc=file_name,
            total=total_size,
            unit='iB',
            unit_scale=True,
            unit_divisor=1024,
        ) as bar:
        for data in response.iter_content(chunk_size=1024):
            size = f.write(data)
            bar.update(size)

def load_cn_config(model_dir):
  folder = model_dir
  file_name = 'config.json'
  file_path = os.path.join(folder, file_name)
  if not os.path.exists(file_path):
     config_path = os.path.join(os.getcwd(), file_name)
     shutil.copy(config_path, file_path)

def load_tagger_model(model_dir):
    model_id = 'SmilingWolf/wd-swinv2-tagger-v3'
    files = [
        'config.json', 'model.onnx', 'selected_tags.csv', 'sw_jax_cv_config.json'
    ]

    if not os.path.exists(model_dir):
        os.makedirs(model_dir)
    
    for file in files:
        file_path = os.path.join(model_dir, file)
        if not os.path.exists(file_path):
            url = f'https://huggingface.co/{model_id}/resolve/main/{file}'
            response = requests.get(url, allow_redirects=True)
            if response.status_code == 200:
                with open(file_path, 'wb') as f:
                    f.write(response.content)
                print(f'Downloaded {file}')
            else:
                print(f'Failed to download {file}')
        else:
            print(f'{file} already exists.')


def load_lora_model(model_dir):
  folder = model_dir
  file_name = 'sdxl_BWLine.safetensors'
  url = "https://huggingface.co/tori29umai/lineart/resolve/main/sdxl_BWLine.safetensors"

  file_path = os.path.join(folder, file_name)
  if not os.path.exists(file_path):
    response = requests.get(url, stream=True)

    total_size = int(response.headers.get('content-length', 0))
    with open(file_path, 'wb') as f, tqdm(
            desc=file_name,
            total=total_size,
            unit='iB',
            unit_scale=True,
            unit_divisor=1024,
        ) as bar:
        for data in response.iter_content(chunk_size=1024):
            size = f.write(data)
            bar.update(size)            


def resize_image_aspect_ratio(image):
    # 元の画像サイズを取得
    original_width, original_height = image.size

    # アスペクト比を計算
    aspect_ratio = original_width / original_height

    # 標準のアスペクト比サイズを定義
    sizes = {
        1: (1024, 1024),  # 正方形
        4/3: (1152, 896),  # 横長画像
        3/2: (1216, 832),
        16/9: (1344, 768),
        21/9: (1568, 672),
        3/1: (1728, 576),
        1/4: (512, 2048),  # 縦長画像
        1/3: (576, 1728),
        9/16: (768, 1344),
        2/3: (832, 1216),
        3/4: (896, 1152)
    }

    # 最も近いアスペクト比を見つける
    closest_aspect_ratio = min(sizes.keys(), key=lambda x: abs(x - aspect_ratio))
    target_width, target_height = sizes[closest_aspect_ratio]

    # リサイズ処理
    resized_image = image.resize((target_width, target_height), Image.ANTIALIAS)

    return resized_image


def base_generation(size, color):
    canvas = Image.new("RGBA", size, color) 
    return canvas