diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index a7cafe7571..2bfd29581d 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -142,12 +142,11 @@ def main():
         report_model_error(opt, e)
 
     # try to autoconvert new models
-    # autoimport new .ckpt files
+    if path := opt.autoimport:
+        gen.model_manager.heuristic_import(str(path), convert=False, commit_to_conf=opt.conf)
+        
     if path := opt.autoconvert:
-        gen.model_manager.autoconvert_weights(
-            conf_path=opt.conf,
-            weights_directory=path,
-        )
+        gen.model_manager.heuristic_import(str(path), convert=True, commit_to_conf=opt.conf)
 
     # web server loops forever
     if opt.web or opt.gui:
@@ -581,7 +580,7 @@ def import_model(model_path: str, gen, opt, completer):
     (3) a huggingface repository id; or (4) a local directory containing a
     diffusers model.
     """
-    model.path = model_path.replace('\\','/') # windows
+    model_path = model_path.replace('\\','/') # windows
     model_name = None
 
     if model_path.startswith(('http:','https:','ftp:')):
@@ -653,7 +652,7 @@ def import_checkpoint_list(models: List[Path], gen, opt, completer)->List[str]:
                     print(f'>> Model {model.stem} imported successfully')
                     model_names.append(model_name)
                 else:
-                    printf('** Model {model} failed to import')
+                    print(f'** Model {model} failed to import')
                 print()
     return model_names
 
@@ -709,7 +708,8 @@ def import_ckpt_model(
         vae = input('VAE file for this model (leave blank for none): ').strip() or None
         done = (not vae) or os.path.exists(vae)
     completer.complete_extensions(None)
-
+    config_file = _ask_for_config_file(path_or_url, completer)
+    
     if not manager.import_ckpt_model(
             path_or_url,
             config = config_file,
@@ -786,7 +786,8 @@ def optimize_model(model_name_or_path: Union[Path,str], gen, opt, completer):
     model_name_or_path = model_name_or_path.replace('\\','/') # windows
     manager = gen.model_manager
     ckpt_path = None
-
+    original_config_file = None
+    
     if model_name_or_path == gen.model_name:
         print("** Can't convert the active model. !switch to another model first. **")
         return
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index d81de4f1ca..e79db94b68 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -527,11 +527,17 @@ class Args(object):
             default=False,
             help='Check for and blur potentially NSFW images. Use --no-nsfw_checker to disable.',
         )
+        model_group.add_argument(
+            '--autoimport',
+            default=None,
+            type=str,
+            help='Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly',
+        )
         model_group.add_argument(
             '--autoconvert',
             default=None,
             type=str,
-            help='Check the indicated directory for .ckpt weights files at startup and import as optimized diffuser models',
+            help='Check the indicated directory for .ckpt/.safetensors weights files at startup and import as optimized diffuser models',
         )
         model_group.add_argument(
             '--patchmatch',
diff --git a/ldm/invoke/config/invokeai_configure.py b/ldm/invoke/config/invokeai_configure.py
index 3ff555881c..93bf73c3d7 100755
--- a/ldm/invoke/config/invokeai_configure.py
+++ b/ldm/invoke/config/invokeai_configure.py
@@ -31,10 +31,8 @@ from transformers import (
 )
 
 import invokeai.configs as configs
-from ldm.invoke.config.model_install import (
-    download_from_hf,
-    select_and_download_models,
-)
+from ldm.invoke.config.model_install_backend import download_from_hf
+from ldm.invoke.config.model_install import select_and_download_models
 from ldm.invoke.globals import Globals, global_config_dir
 from ldm.invoke.readline import generic_completer
 
diff --git a/ldm/invoke/config/model_install.py b/ldm/invoke/config/model_install.py
index 9c4a4d0e61..43b6aed1dd 100644
--- a/ldm/invoke/config/model_install.py
+++ b/ldm/invoke/config/model_install.py
@@ -100,7 +100,7 @@ class addModelsForm(npyscreen.FormMultiPageAction):
             )
         self.add_widget_intelligent(
             npyscreen.TitleFixedText,
-            name="== UNINSTALLED STARTER MODELS (recommended models selected) ==",
+            name="== STARTER MODELS (recommended ones selected) ==",
             value="Select from a starter set of Stable Diffusion models from HuggingFace:",
             begin_entry_at=2,
             editable=False,
@@ -221,6 +221,7 @@ class addModelsForm(npyscreen.FormMultiPageAction):
         '''
         # starter models to install/remove
         starter_models = dict(map(lambda x: (self.starter_model_list[x], True), self.models_selected.value))
+        self.parentApp.purge_deleted_models=False
         if hasattr(self,'previously_installed_models'):
             unchecked = [
                 self.previously_installed_models.values[x]
@@ -243,7 +244,7 @@ class addModelsForm(npyscreen.FormMultiPageAction):
 
         # URLs and the like
         self.parentApp.import_model_paths = self.import_model_paths.value.split()
-        self.parentApp.convert_to_diffusers = self.convert_models.value == 1
+        self.parentApp.convert_to_diffusers = self.convert_models.value[0] == 1
 
 # big chunk of dead code
 # was intended to be a status area in which output of installation steps (including tqdm) was logged in real time
diff --git a/ldm/invoke/config/model_install_backend.py b/ldm/invoke/config/model_install_backend.py
index 451e9b7e2b..cdcef3bb6c 100644
--- a/ldm/invoke/config/model_install_backend.py
+++ b/ldm/invoke/config/model_install_backend.py
@@ -69,6 +69,9 @@ def install_requested_models(
         config_file_path: Path = None,
 ):
     config_file_path=config_file_path or default_config_file()
+    if not config_file_path.exists():
+        open(config_file_path,'w')
+            
     model_manager= ModelManager(OmegaConf.load(config_file_path),precision=precision)
     
     if remove_models and len(remove_models) > 0:
@@ -84,12 +87,20 @@ def install_requested_models(
             models=install_initial_models,
             access_token=None,
             precision=precision,
-        )  # for historical reasons, we don't use model manager here
+        )  # FIX: for historical reasons, we don't use model manager here
         update_config_file(successfully_downloaded, config_file_path)
         if len(successfully_downloaded) < len(install_initial_models):
             print("** Some of the model downloads were not successful")
 
-    if external_models and len(external_models)>0:
+    # due to above, we have to reload the model manager because conf file
+    # was changed behind its back
+    model_manager= ModelManager(OmegaConf.load(config_file_path),precision=precision)
+
+    external_models = external_models or list()
+    if scan_directory:
+        external_models.append(str(scan_directory))
+
+    if len(external_models)>0:
         print("== INSTALLING EXTERNAL MODELS ==")
         for path_url_or_repo in external_models:
             try:
@@ -102,6 +113,18 @@ def install_requested_models(
                 sys.exit(-1)
             except Exception:
                 pass
+
+    if scan_at_startup and scan_directory.is_dir():
+        argument = '--autoconvert' if convert_to_diffusers else '--autoimport'
+        initfile = Path(Globals.root, Globals.initfile)
+        replacement = Path(Globals.root, f'{Globals.initfile}.new')
+        with open(initfile,'r') as input:
+            with open(replacement,'w') as output:
+                while line := input.readline():
+                    if not line.startswith(argument):
+                        output.writelines([line])
+                output.writelines([f'{argument} {str(scan_directory)}'])
+        os.replace(replacement,initfile)
             
 # -------------------------------------
 def yes_or_no(prompt: str, default_yes=True):
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 49213c29c7..b46ba4d90a 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -707,21 +707,19 @@ class ModelManager(object):
             convert: bool= False,
             commit_to_conf: Path=None,
     ):
-        model_path = None
+        model_path: Path = None
         thing = path_url_or_repo  # to save typing
 
-        print(f'here i am; thing={thing}, convert={convert}')
-        
         if thing.startswith(('http:','https:','ftp:')):
-            print(f'* {thing} appears to be a URL')
+            print(f'>> {thing} appears to be a URL')
             model_path = self._resolve_path(thing, 'models/ldm/stable-diffusion-v1')  # _resolve_path does a download if needed
 
         elif Path(thing).is_file() and thing.endswith(('.ckpt','.safetensors')):
-            print(f'* {thing} appears to be a checkpoint file on disk')
+            print(f'>> {thing} appears to be a checkpoint file on disk')
             model_path = self._resolve_path(thing, 'models/ldm/stable-diffusion-v1')
             
         elif Path(thing).is_dir() and Path(thing, 'model_index.json').exists():
-            print(f'* {thing} appears to be a diffusers file on disk')
+            print(f'>> {thing} appears to be a diffusers file on disk')
             model_name = self.import_diffusers_model(
                 thing,
                 vae=dict(repo_id='stabilityai/sd-vae-ft-mse'),                
@@ -729,39 +727,44 @@ class ModelManager(object):
             )
 
         elif Path(thing).is_dir():
-            print(f'* {thing} appears to be a directory. Will scan for models to import')
+            print(f'>> {thing} appears to be a directory. Will scan for models to import')
             for m in list(Path(thing).rglob('*.ckpt')) + list(Path(thing).rglob('*.safetensors')):
                 print('***',m)
                 self.heuristic_import(str(m), convert, commit_to_conf=commit_to_conf)
             return
 
         elif re.match(r'^[\w.+-]+/[\w.+-]+$', thing):
-            print(f'* {thing} appears to be a HuggingFace diffusers repo_id')
+            print(f'>> {thing} appears to be a HuggingFace diffusers repo_id')
             model_name = self.import_diffuser_model(thing, commit_to_conf=commit_to_conf)
             pipeline,_,_,_ = self._load_diffusers_model(self.config[model_name])
 
         else:
-            print(f"* {thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id")
+            print(f">> {thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id")
 
         # Model_path is set in the event of a legacy checkpoint file.
         # If not set, we're all done
         if not model_path:
             return
 
+        if model_path.stem in self.config:  #already imported
+            return
+
         # another round of heuristics to guess the correct config file.
-        model_config_file = Path(Globals.root,'configs/stable-diffusion/v1-inpainting-inference.yaml')
+        model_config_file = Path(Globals.root,'configs/stable-diffusion/v1-inference.yaml')
         
         checkpoint = safetensors.torch.load_file(model_path) if model_path.suffix == '.safetensors' else torch.load(model_path)
         key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024:
-            print(f'* {thing} appears to be an SD-v2 model; model will be converted to diffusers format')
+            print(f'>> {thing} appears to be an SD-v2 model; model will be converted to diffusers format')
             model_config_file = Path(Globals.root,'configs/stable-diffusion/v2-inference-v.yaml')
             convert = True
+            
         elif re.search('inpaint', str(model_path), flags=re.IGNORECASE):
-            print(f'* {thing} appears to be an SD-v1 inpainting model')
+            print(f'>> {thing} appears to be an SD-v1 inpainting model')
             model_config_file = Path(Globals.root,'configs/stable-diffusion/v1-inpainting-inference.yaml')
+            
         else:
-            print(f'* {thing} appears to be an SD-v1 model')
+            print(f'>> {thing} appears to be an SD-v1 model')
 
         if convert:
             diffuser_path = Path(Globals.root, 'models',Globals.converted_ckpts_dir, model_path.stem)
@@ -776,10 +779,12 @@ class ModelManager(object):
             self.import_ckpt_model(
                 model_path,
                 config=model_config_file,
-                vae=Path(Globals.root,'models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt'),
+                vae=str(Path(Globals.root,'models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt')),
                 commit_to_conf=commit_to_conf,
             )
-        
+
+    # this is a defunct method, superseded by heuristic_import()
+    # left here during transition
     def autoconvert_weights (
         self,
         conf_path: Path,
@@ -799,7 +804,7 @@ class ModelManager(object):
         ckpt_files = dict()
         for root, dirs, files in os.walk(weights_directory):
             for f in files:
-                if not f.endswith(".ckpt"):
+                if not f.endswith((".ckpt",".safetensors")):
                     continue
                 basename = Path(f).stem
                 dest = Path(dest_directory, basename)
diff --git a/ldm/util.py b/ldm/util.py
index 1bdcaba0d3..a60106967d 100644
--- a/ldm/util.py
+++ b/ldm/util.py
@@ -306,8 +306,12 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
                  dest/filename
     :param access_token: Access token to access this resource
     '''
-    resp = requests.get(url, stream=True)
-    total = int(resp.headers.get("content-length", 0))
+    header = {"Authorization": f"Bearer {access_token}"} if access_token else {}
+    open_mode = "wb"
+    exist_size = 0
+
+    resp = requests.get(url, header, stream=True)
+    content_length = int(resp.headers.get("content-length", 0))
 
     if dest.is_dir():
         try:
@@ -318,39 +322,42 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
     else:
         dest.parent.mkdir(parents=True, exist_ok=True)
 
-    header = {"Authorization": f"Bearer {access_token}"} if access_token else {}
-    open_mode = "wb"
-    exist_size = 0
 
     if dest.exists():
         exist_size = dest.stat().st_size
         header["Range"] = f"bytes={exist_size}-"
         open_mode = "ab"
+        resp = requests.get(url, headers=header, stream=True) # new request with range
+
+    if exist_size > content_length:
+        print('* corrupt existing file found. re-downloading')
+        os.remove(dest)
+        exist_size = 0
 
     if (
-        resp.status_code == 416
-    ):  # "range not satisfiable", which means nothing to return
+        resp.status_code == 416 or exist_size == content_length
+    ):
         print(f"* {dest}: complete file found. Skipping.")
         return dest
+    elif resp.status_code == 206 or exist_size > 0:
+        print(f"* {dest}: partial file found. Resuming...")
     elif resp.status_code != 200:
         print(f"** An error occurred during downloading {dest}: {resp.reason}")
-    elif exist_size > 0:
-        print(f"* {dest}: partial file found. Resuming...")
     else:
         print(f"* {dest}: Downloading...")
 
     try:
-        if total < 2000:
+        if content_length < 2000:
             print(f"*** ERROR DOWNLOADING {url}: {resp.text}")
             return None
 
         with open(dest, open_mode) as file, tqdm(
-            desc=str(dest),
-            initial=exist_size,
-            total=total + exist_size,
-            unit="iB",
-            unit_scale=True,
-            unit_divisor=1000,
+                desc=str(dest),
+                initial=exist_size,
+                total=content_length,
+                unit="iB",
+                unit_scale=True,
+                unit_divisor=1000,
         ) as bar:
             for data in resp.iter_content(chunk_size=1024):
                 size = file.write(data)