further improvements to preload_models script

- User can choose to download just recommended models, customize list to download,
  or skip downloading altogether.
- Does direct download to models directory instead of to HuggingFace cache
- Able to resume interrupted downloads

scripts/preload_models.py

@@ -13,10 +13,13 @@ import transformers
 import os
 import warnings
 import torch
-import urllib.request
 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
@@ -119,35 +122,55 @@ def yes_or_no(prompt:str, default_yes=True):
         return response[0] in ('y','Y')
 
 #---------------------------------------------
-def user_wants_to_download_weights():
+def user_wants_to_download_weights()->str:
+    '''
+    Returns one of "skip", "recommended" or "customized"
+    '''
     print('''You can download and configure the weights files manually or let this
 script do it for you. Manual installation is described at:
 
 https://github.com/invoke-ai/InvokeAI/blob/main/docs/installation/INSTALLING_MODELS.md
 
+You may download the recommended models (about 10GB total), select a customized set, or
+completely skip this step.
 '''
     )
-    return yes_or_no('Would you like to download the Stable Diffusion model weights now?')
+    selection = None
+    while selection is None:
+        choice = input('Download <r>ecommended models, <c>ustomize the list, or <s>kip this step? [r]: ')
+        if choice.startswith(('r','R')) or len(choice)==0:
+            selection = 'recommended'
+        elif choice.startswith(('c','C')):
+            selection = 'customized'
+        elif choice.startswith(('s','S')):
+            selection = 'skip'
+    return selection
 
 #---------------------------------------------
-def select_datasets():
+def select_datasets(action:str):
     done = False
     while not done:
+        datasets = dict()
+        dflt = None   # the first model selected will be the default; TODO let user change
+        counter = 1
+
+        if action == 'customized':
             print('''
 Choose the weight file(s) you wish to download. Before downloading you 
 will be given the option to view and change your selections.
 '''
         )
-        datasets = dict()
-
-        counter = 1
-        dflt = None   # the first model selected will be the default; TODO let user change
             for ds in Datasets.keys():
                 recommended = '(recommended)' if Datasets[ds]['recommended'] else ''
                 print(f'[{counter}] {ds}:\n    {Datasets[ds]["description"]} {recommended}')
                 if yes_or_no('    Download?',default_yes=Datasets[ds]['recommended']):
                     datasets[ds]=counter
                     counter += 1
+        else:
+            for ds in Datasets.keys():
+                if Datasets[ds]['recommended']:
+                    datasets[ds]=counter
+                    counter += 1
                 
         print('The following weight files will be downloaded:')
         for ds in datasets:
@@ -157,6 +180,7 @@ will be given the option to view and change your selections.
         ok_to_download = yes_or_no('Ok to download?')
         if not ok_to_download:
             if yes_or_no('Change your selection?'):
+                action = 'customized'
                 pass
             else:
                 done = True
@@ -164,6 +188,7 @@ will be given the option to view and change your selections.
             done = True
     return datasets if ok_to_download else None
 
+
 #-------------------------------Authenticate against Hugging Face
 def authenticate():
     print('''
@@ -180,13 +205,19 @@ This involves a few easy steps.
    You will need to verify your email address as part of the HuggingFace
    registration process.
 
-2. Log into your account Hugging Face:
+2. Log into your Hugging Face account:
 
     https://huggingface.co/login
 
 3. Accept the license terms located here:
 
-   https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
+   https://huggingface.co/runwayml/stable-diffusion-v1-5
+
+   and here:
+
+   https://huggingface.co/runwayml/stable-diffusion-inpainting
+
+    (Yes, you have to accept two slightly different license agreements)
 '''
     )
     input('Press <enter> when you are ready to continue:')
@@ -229,7 +260,7 @@ def download_weight_datasets(models:dict, access_token:str):
     for mod in models.keys():
         repo_id = Datasets[mod]['repo_id']
         filename = Datasets[mod]['file']
-        success = conditional_download(
+        success = download_with_resume(
             repo_id=repo_id,
             model_name=filename,
             access_token=access_token
@@ -241,19 +272,50 @@ def download_weight_datasets(models:dict, access_token:str):
     return successful
     
 #---------------------------------------------
-def conditional_download(repo_id:str, model_name:str, access_token:str):
+def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool:
+
     model_dest = os.path.join(Model_dir, model_name)
-    if os.path.exists(model_dest):
-        print(f' * {model_name}: exists')
-        return True
     os.makedirs(os.path.dirname(model_dest), exist_ok=True)
+    url = hf_hub_url(repo_id, model_name)
+
+    header = {"Authorization": f'Bearer {access_token}'}
+    open_mode = 'wb'
+    exist_size = 0
+    
+    if os.path.exists(model_dest):
+        exist_size = os.path.getsize(model_dest)
+        header['Range'] = f'bytes={exist_size}-'
+        open_mode = 'ab'
+
+    resp = requests.get(url, headers=header, stream=True)
+    total = int(resp.headers.get('content-length', 0))
+    
+    if resp.status_code==416:  # "range not satisfiable", which means nothing to return
+        print(f'* {model_name}: complete file found. Skipping.')
+        return True
+    elif exist_size > 0:
+        print(f'* {model_name}: partial file found. Resuming...')
+    else:
+        print(f'* {model_name}: Downloading...')
 
     try:
-        print(f' * {model_name}: downloading or retrieving from cache...')
+        if total < 2000:
-        path = Path(hf_hub_download(repo_id, model_name, use_auth_token=access_token))
+            print(f'* {model_name}: {resp.text}')
-        path.resolve(strict=True).link_to(model_dest)
+            return False
+
+        with open(model_dest, open_mode) as file, tqdm(
+                desc=model_name,
+                initial=exist_size,
+                total=total+exist_size,
+                unit='iB',
+                unit_scale=True,
+                unit_divisor=1000,
+        ) as bar:
+            for data in resp.iter_content(chunk_size=1024):
+                size = file.write(data)
+                bar.update(size)
     except Exception as e:
-        print(f'** Error downloading {model_name}: {str(e)} **')
+        print(f'An error occurred while downloading {model_name}: {str(e)}')
         return False
     return True
                              
@@ -441,8 +503,9 @@ if __name__ == '__main__':
     try:
         introduction()
         print('** WEIGHT SELECTION **')
-        if user_wants_to_download_weights():
+        choice = user_wants_to_download_weights()
-            models = select_datasets()
+        if choice != 'skip':
+            models = select_datasets(choice)
             if models is None:
                 if yes_or_no('Quit?',default_yes=False):
                     sys.exit(0)