Settings cleanup

extras/expansion.py

@@ -12,7 +12,7 @@
 
 from transformers.generation.logits_process import LogitsProcessorList
 from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
-from modules.config import path_fooocus_expansion
+from modules.settings import settings
 from ldm_patched.modules.model_patcher import ModelPatcher
 
 
@@ -36,9 +36,9 @@ def remove_pattern(x, pattern):
 
 class FooocusExpansion:
     def __init__(self):
-        self.tokenizer = AutoTokenizer.from_pretrained(path_fooocus_expansion)
+        self.tokenizer = AutoTokenizer.from_pretrained(settings.path_fooocus_expansion)
 
-        positive_words = open(os.path.join(path_fooocus_expansion, 'positive.txt'),
+        positive_words = open(os.path.join(settings.path_fooocus_expansion, 'positive.txt'),
                               encoding='utf-8').read().splitlines()
         positive_words = ['Ġ' + x.lower() for x in positive_words if x != '']
 

extras/face_crop.py

@@ -1,6 +1,6 @@
 import cv2
 import numpy as np
-import modules.config
+from modules.settings import settings
 
 
 faceRestoreHelper = None
@@ -28,7 +28,7 @@ def crop_image(img_rgb):
         from extras.facexlib.utils.face_restoration_helper import FaceRestoreHelper
         faceRestoreHelper = FaceRestoreHelper(
             upscale_factor=1,
-            model_rootpath=modules.config.path_controlnet,
+            model_rootpath=settings.path_controlnet,
             device='cpu'  # use cpu is safer since we are out of memory management
         )
 

extras/interrogate.py

@@ -5,7 +5,7 @@
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 from modules.model_loader import load_file_from_url
-from modules.config import path_clip_vision
+from modules.settings import settings
 from ldm_patched.modules.model_patcher import ModelPatcher
 from extras.BLIP.models.blip import blip_decoder
 
@@ -27,7 +27,7 @@ def interrogate(self, img_rgb):
         if self.blip_model is None:
             filename = load_file_from_url(
                 url='https://huggingface.co/lllyasviel/misc/resolve/main/model_base_caption_capfilt_large.pth',
-                model_dir=path_clip_vision,
+                model_dir=settings.path_clip_vision,
                 file_name='model_base_caption_capfilt_large.pth',
             )
 

extras/vae_interpose.py

@@ -7,7 +7,7 @@
 import ldm_patched.modules.model_management
 
 from ldm_patched.modules.model_patcher import ModelPatcher
-from modules.config import path_vae_approx
+from modules.settings import settings
 
 
 class Block(nn.Module):
@@ -63,7 +63,7 @@ def forward(self, x):
 
 
 vae_approx_model = None
-vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v3.1.safetensors')
+vae_approx_filename = os.path.join(settings.path_vae_approx, 'xl-to-v1_interposer-v3.1.safetensors')
 
 
 def parse(x):

extras/wd14tagger.py

@@ -16,7 +16,7 @@
 
 from PIL import Image
 from onnxruntime import InferenceSession
-from modules.config import path_clip_vision
+from modules.settings import settings
 from modules.model_loader import load_file_from_url
 
 
@@ -31,13 +31,13 @@ def default_interrogator(image_rgb, threshold=0.35, character_threshold=0.85, ex
 
     model_onnx_filename = load_file_from_url(
         url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.onnx',
-        model_dir=path_clip_vision,
+        model_dir=settings.path_clip_vision,
         file_name=f'{model_name}.onnx',
     )
 
     model_csv_filename = load_file_from_url(
         url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.csv',
-        model_dir=path_clip_vision,
+        model_dir=settings.path_clip_vision,
         file_name=f'{model_name}.csv',
     )
 

launch.py

@@ -84,15 +84,15 @@ def ini_args():
     print("Set device to:", args.gpu_device_id)
 
 
-from modules import config
+from modules.settings import settings
 
 def download_models():
     for file_name, url in vae_approx_filenames:
-        load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name)
+        load_file_from_url(url=url, model_dir=settings.path_vae_approx, file_name=file_name)
 
     load_file_from_url(
         url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin',
-        model_dir=config.path_fooocus_expansion,
+        model_dir=settings.path_fooocus_expansion,
         file_name='pytorch_model.bin'
     )
 
@@ -101,23 +101,23 @@ def download_models():
         return
 
     if not args.always_download_new_model:
-        if not os.path.exists(os.path.join(config.path_checkpoints, config.default_base_model_name)):
-            for alternative_model_name in config.previous_default_models:
-                if os.path.exists(os.path.join(config.path_checkpoints, alternative_model_name)):
-                    print(f'You do not have [{config.default_base_model_name}] but you have [{alternative_model_name}].')
+        if not os.path.exists(os.path.join(settings.path_checkpoints, settings.default_base_model_name)):
+            for alternative_model_name in settings.previous_default_models:
+                if os.path.exists(os.path.join(settings.path_checkpoints, alternative_model_name)):
+                    print(f'You do not have [{settings.default_base_model_name}] but you have [{alternative_model_name}].')
                     print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, '
                           f'but you are not using latest models.')
                     print('Use --always-download-new-model to avoid fallback and always get new models.')
-                    config.checkpoint_downloads = {}
-                    config.default_base_model_name = alternative_model_name
+                    settings.checkpoint_downloads = {}
+                    settings.default_base_model_name = alternative_model_name
                     break
 
-    for file_name, url in config.checkpoint_downloads.items():
-        load_file_from_url(url=url, model_dir=config.path_checkpoints, file_name=file_name)
-    for file_name, url in config.embeddings_downloads.items():
-        load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name)
-    for file_name, url in config.lora_downloads.items():
-        load_file_from_url(url=url, model_dir=config.path_loras, file_name=file_name)
+    for file_name, url in settings.checkpoint_downloads.items():
+        load_file_from_url(url=url, model_dir=settings.path_checkpoints, file_name=file_name)
+    for file_name, url in settings.embeddings_downloads.items():
+        load_file_from_url(url=url, model_dir=settings.path_embeddings, file_name=file_name)
+    for file_name, url in settings.lora_downloads.items():
+        load_file_from_url(url=url, model_dir=settings.path_loras, file_name=file_name)
 
     return
 

modules/async_worker.py

@@ -25,7 +25,7 @@ def worker():
     import modules.default_pipeline as pipeline
     import modules.core as core
     import modules.flags as flags
-    import modules.config
+    import modules.settings
     import modules.patch
     import ldm_patched.modules.model_management
     import extras.preprocessors as preprocessors
@@ -180,7 +180,7 @@ def handler(async_task):
         if performance_selection == 'Extreme Speed':
             print('Enter LCM mode.')
             progressbar(async_task, 1, 'Downloading LCM components ...')
-            loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
+            loras += [(modules.settings.downloading_sdxl_lcm_lora(), 1.0)]
 
             if refiner_model_name != 'None':
                 print(f'Refiner disabled in LCM mode.')
@@ -269,7 +269,7 @@ def handler(async_task):
                             steps = 8
 
                     progressbar(async_task, 1, 'Downloading upscale models ...')
-                    modules.config.downloading_upscale_model()
+                    modules.settings.downloading_upscale_model()
             if (current_tab == 'inpaint' or (
                     current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \
                     and isinstance(inpaint_input_image, dict):
@@ -295,10 +295,10 @@ def handler(async_task):
                 if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
                         and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0):
                     progressbar(async_task, 1, 'Downloading upscale models ...')
-                    modules.config.downloading_upscale_model()
+                    modules.settings.downloading_upscale_model()
                     if inpaint_parameterized:
                         progressbar(async_task, 1, 'Downloading inpainter ...')
-                        inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
+                        inpaint_head_model_path, inpaint_patch_model_path = modules.settings.downloading_inpaint_models(
                             advanced_parameters.inpaint_engine)
                         base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
                         print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
@@ -320,13 +320,13 @@ def handler(async_task):
                 goals.append('cn')
                 progressbar(async_task, 1, 'Downloading control models ...')
                 if len(cn_tasks[flags.cn_canny]) > 0:
-                    controlnet_canny_path = modules.config.downloading_controlnet_canny()
+                    controlnet_canny_path = modules.settings.downloading_controlnet_canny()
                 if len(cn_tasks[flags.cn_cpds]) > 0:
-                    controlnet_cpds_path = modules.config.downloading_controlnet_cpds()
+                    controlnet_cpds_path = modules.settings.downloading_controlnet_cpds()
                 if len(cn_tasks[flags.cn_ip]) > 0:
-                    clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip')
+                    clip_vision_path, ip_negative_path, ip_adapter_path = modules.settings.downloading_ip_adapters('ip')
                 if len(cn_tasks[flags.cn_ip_face]) > 0:
-                    clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters(
+                    clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.settings.downloading_ip_adapters(
                         'face')
                 progressbar(async_task, 1, 'Loading control models ...')