Hugging Face's logo

Spaces:
tori29umai
/
sketch2lineart
Running on Zero

App Files Community
1
sketch2lineart
File size: 3,558 Bytes 
3542be4
 
 
 
 
 
 
 
 
 
 
5836c22
 
 
 
 
 
 
 
 
 
 
 
 
 
3542be4
 
 
 
 
 
 
 
 
63144e1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3542be4
 
c689a76
d5b61f5
5836c22
 
d5b61f5
5836c22
 
 
 
 
 
 
 
 
c689a76
5836c22
3542be4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
72dca33
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
 
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, allow_redirects=True)
        if response.status_code == 200:
            with open(file_path, 'wb') as f:
                f.write(response.content)
            print(f'Downloaded {file_name}')
        else:
            print(f'Failed to download {file_name}')
    else:
        print(f'{file_name} already exists.')

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):
    file_name = 'sdxl_BW_bold_Line.safetensors'
    file_path = os.path.join(model_dir, file_name)
    if not os.path.exists(file_path):
        url = "https://huggingface.co/tori29umai/lineart/blob/main/sdxl_BW_bold_Line.safetensors"
        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_name}')
        else:
            print(f'Failed to download {file_name}')
    else:
        print(f'{file_name} already exists.')

 
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.LANCZOS)

    return resized_image


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