app.py

app.py

@@ -6,7 +6,7 @@ from PIL import Image
 import os
 import time
 
-from utils.dl_utils import load_cn_model, load_cn_config, load_tagger_model, load_lora_model
+from utils.dl_utils import dl_cn_model, dl_cn_config, dl_tagger_model, dl_lora_model
 from utils.image_utils import resize_image_aspect_ratio, base_generation
 
 from utils.prompt_utils import execute_prompt, remove_color, remove_duplicates
@@ -22,10 +22,10 @@ os.makedirs(cn_dir, exist_ok=True)
 os.makedirs(tagger_dir, exist_ok=True)
 os.makedirs(lora_dir, exist_ok=True)
 
-load_cn_model(cn_dir)
-load_cn_config(cn_dir)
-load_tagger_model(tagger_dir)
-load_lora_model(lora_dir)
+dl_cn_model(cn_dir)
+dl_cn_config(cn_dir)
+dl_tagger_model(tagger_dir)
+dl_lora_model(lora_dir)
 
 def load_model(lora_dir, cn_dir):
     device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -35,8 +35,8 @@ def load_model(lora_dir, cn_dir):
     pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
         "cagliostrolab/animagine-xl-3.1", controlnet=controlnet, vae=vae, torch_dtype=torch.float16
     )
-    pipe.load_lora_weights(lora_dir, weight_name="sdxl_BW_bold_Line.safetensors")
-    pipe.set_adapters(["sdxl_BW_bold_Line"], adapter_weights=[1.2])
+    pipe.load_lora_weights(lora_dir, weight_name="sdxl_BW_Line.safetensors")
+    pipe.set_adapters(["sdxl_BW_Line"], adapter_weights=[1.4])
     pipe.fuse_lora()
     pipe = pipe.to(device)
     return pipe

utils/dl_utils.py

@@ -8,7 +8,7 @@ from PIL import Image, ImageOps
 import numpy as np
 import cv2
 
-def load_cn_model(model_dir):
+def dl_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"
@@ -24,7 +24,7 @@ def load_cn_model(model_dir):
     else:
         print(f'{file_name} already exists.')
 
-def load_cn_config(model_dir):
+def dl_cn_config(model_dir):
   folder = model_dir
   file_name = 'config.json'
   file_path = os.path.join(folder, file_name)
@@ -32,7 +32,7 @@ def load_cn_config(model_dir):
      config_path = os.path.join(os.getcwd(), file_name)
      shutil.copy(config_path, file_path)
 
-def load_tagger_model(model_dir):
+def dl_tagger_model(model_dir):
     model_id = 'SmilingWolf/wd-swinv2-tagger-v3'
     files = [
         'config.json', 'model.onnx', 'selected_tags.csv', 'sw_jax_cv_config.json'
@@ -56,11 +56,11 @@ def load_tagger_model(model_dir):
             print(f'{file} already exists.')
 
 
-def load_lora_model(model_dir):
-    file_name = 'sdxl_BW_bold_Line.safetensors'
+def dl_lora_model(model_dir):
+    file_name = 'sdxl_BW_Line.safetensors'
     file_path = os.path.join(model_dir, file_name)
     if not os.path.exists(file_path):
-        url = "https://huggingface.co/tori29umai/lineart/resolve/main/sdxl_BW_bold_Line.safetensors"
+        url = "https://huggingface.co/tori29umai/lineart/resolve/main/sdxl_BW_Line.safetensors"
         response = requests.get(url, allow_redirects=True)
         if response.status_code == 200:
             with open(file_path, 'wb') as f:
@@ -70,40 +70,3 @@ def load_lora_model(model_dir):
             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