further improvements to preload_models.py

- Faster startup for command line switch processing
- Specify configuration file to modify using --config option:

  ./scripts/preload_models.ply --config models/my-models-file.yaml

scripts/preload_models.py

@@ -6,31 +6,30 @@
 #
 # Coauthor: Kevin Turner http://github.com/keturn
 #
-print('Loading Python libraries...\n')
 import argparse
-import clip
 import sys
-import transformers
 import os
 import warnings
-import torch
-import zipfile
-import traceback
-import getpass
-import requests
 from urllib import request
 from tqdm import tqdm
 from omegaconf import OmegaConf
 from pathlib import Path
-from transformers import CLIPTokenizer, CLIPTextModel
-from transformers import BertTokenizerFast, AutoFeatureExtractor
-from huggingface_hub import hf_hub_download, HfFolder, hf_hub_url
+import traceback
+import getpass
+import requests
 
-transformers.logging.set_verbosity_error()
+# deferred loading so that help message can be printed quickly
+def load_libs():
+    print('Loading Python libraries...\n')
+    import clip
+    import transformers
+    import torch
+    import zipfile
+    transformers.logging.set_verbosity_error()
 
 #--------------------------globals--
 Model_dir = './models/ldm/stable-diffusion-v1/'
-Config_file = './configs/models.yaml'
+Default_config_file = './configs/models.yaml'
 SD_Configs = './configs/stable-diffusion'
 Datasets = {
     'stable-diffusion-1.5':  {
@@ -221,6 +220,8 @@ This involves a few easy steps.
 '''
     )
     input('Press <enter> when you are ready to continue:')
+
+    from huggingface_hub import HfFolder
     access_token = HfFolder.get_token()
     if access_token is None:
         print('''
@@ -273,6 +274,7 @@ def download_weight_datasets(models:dict, access_token:str):
     
 #---------------------------------------------
 def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool:
+    from huggingface_hub import hf_hub_url
 
     model_dest = os.path.join(Model_dir, model_name)
     os.makedirs(os.path.dirname(model_dest), exist_ok=True)
@@ -320,8 +322,10 @@ def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool:
     return True
                              
 #---------------------------------------------
-def update_config_file(successfully_downloaded:dict):
-    yaml = new_config_file_contents(successfully_downloaded)
+def update_config_file(successfully_downloaded:dict,opt:dict):
+    Config_file = opt.config_file or Default_config_file
+    
+    yaml = new_config_file_contents(successfully_downloaded,Config_file)
 
     try:
         if os.path.exists(Config_file):
@@ -341,7 +345,7 @@ def update_config_file(successfully_downloaded:dict):
 
     
 #---------------------------------------------    
-def new_config_file_contents(successfully_downloaded:dict)->str:
+def new_config_file_contents(successfully_downloaded:dict, Config_file:str)->str:
     if os.path.exists(Config_file):
         conf = OmegaConf.load(Config_file)
     else:
@@ -379,6 +383,7 @@ def new_config_file_contents(successfully_downloaded:dict)->str:
 # this will preload the Bert tokenizer fles
 def download_bert():
     print('Installing bert tokenizer (ignore deprecation errors)...', end='')
+    from transformers import BertTokenizerFast, AutoFeatureExtractor
     with warnings.catch_warnings():
         warnings.filterwarnings('ignore', category=DeprecationWarning)
         tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
@@ -396,9 +401,10 @@ def download_kornia():
 
 #---------------------------------------------
 def download_clip():
-    version = 'openai/clip-vit-large-patch14'
-    sys.stdout.flush()
     print('Loading CLIP model...',end='')
+    from transformers import CLIPTokenizer, CLIPTextModel
+    sys.stdout.flush()
+    version = 'openai/clip-vit-large-patch14'
     tokenizer = CLIPTokenizer.from_pretrained(version)
     transformer = CLIPTextModel.from_pretrained(version)
     print('...success')
@@ -498,6 +504,7 @@ def download_safety_checker():
     print('Installing safety model for NSFW content detection...',end='')
     try:
         from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+        from transformers import AutoFeatureExtractor
     except ModuleNotFoundError:
         print('Error installing safety checker model:')
         print(traceback.format_exc())
@@ -515,7 +522,14 @@ if __name__ == '__main__':
                         action=argparse.BooleanOptionalAction,
                         default=True,
                         help='run in interactive mode (default)')
+    parser.add_argument('--config_file',
+                        '-c',
+                        dest='config_file',
+                        type=str,
+                        default='./configs/models.yaml',
+                        help='path to configuration file to create')
     opt = parser.parse_args()
+    load_libs()
     
     try:
         if opt.interactive:
@@ -531,7 +545,7 @@ if __name__ == '__main__':
                 access_token = authenticate()
                 print('\n** DOWNLOADING WEIGHTS **')
                 successfully_downloaded = download_weight_datasets(models, access_token)
-                update_config_file(successfully_downloaded)
+                update_config_file(successfully_downloaded,opt)
         print('\n** DOWNLOADING SUPPORT MODELS **')
         download_bert()
         download_kornia()