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app.py
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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