diff --git a/ldm/invoke/ckpt_to_diffuser.py b/ldm/invoke/ckpt_to_diffuser.py
index 8db2ec8366..1d41fa5bd1 100644
--- a/ldm/invoke/ckpt_to_diffuser.py
+++ b/ldm/invoke/ckpt_to_diffuser.py
@@ -327,10 +327,10 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
     unet_key = "model.diffusion_model."
     # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
     if sum(k.startswith("model_ema") for k in keys) > 100:
-        print(f"  | Checkpoint {path} has both EMA and non-EMA weights.")
+        print(f"   | Checkpoint {path} has both EMA and non-EMA weights.")
         if extract_ema:
             print(
-                '  | Extracting EMA weights (usually better for inference)'
+                '   | Extracting EMA weights (usually better for inference)'
             )
             for key in keys:
                 if key.startswith("model.diffusion_model"):
@@ -338,7 +338,7 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
                     unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
         else:
             print(
-                '  | Extracting only the non-EMA weights (usually better for fine-tuning)'
+                '   | Extracting only the non-EMA weights (usually better for fine-tuning)'
             )
 
     for key in keys:
@@ -809,6 +809,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         vae:AutoencoderKL=None,
         precision:torch.dtype=torch.float32,
         return_generator_pipeline:bool=False,
+        scan_needed:bool=True,
 )->Union[StableDiffusionPipeline,StableDiffusionGeneratorPipeline]:
     '''
     Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`
@@ -844,7 +845,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         dlogging.set_verbosity_error()
 
         if Path(checkpoint_path).suffix == '.ckpt':
-            ModelManager.scan_model(checkpoint_path,checkpoint_path)
+            if scan_needed:
+                ModelManager.scan_model(checkpoint_path,checkpoint_path)
             checkpoint = torch.load(checkpoint_path)
         else:
             checkpoint = load_file(checkpoint_path)
@@ -855,7 +857,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         if "global_step" in checkpoint:
             global_step = checkpoint["global_step"]
         else:
-            print("  | global_step key not found in model")
+            print("   | global_step key not found in model")
             global_step = None
 
         # sometimes there is a state_dict key and sometimes not
@@ -957,14 +959,14 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
 
         # Convert the VAE model, or use the one passed
         if not vae:
-            print('  | Using checkpoint model\'s original VAE')
+            print('   | Using checkpoint model\'s original VAE')
             vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
             converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
 
             vae = AutoencoderKL(**vae_config)
             vae.load_state_dict(converted_vae_checkpoint)
         else:
-            print('  | Using VAE specified in config')
+            print('   | Using VAE specified in config')
 
         # Convert the text model.
         model_type = pipeline_type
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 8bed8a1be2..214ef022bb 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -282,13 +282,13 @@ class ModelManager(object):
             self.stack.remove(model_name)
         if delete_files:
             if weights:
-                print(f"** deleting file {weights}")
+                print(f"** Deleting file {weights}")
                 Path(weights).unlink(missing_ok=True)
             elif path:
-                print(f"** deleting directory {path}")
+                print(f"** Deleting directory {path}")
                 rmtree(path, ignore_errors=True)
             elif repo_id:
-                print(f"** deleting the cached model directory for {repo_id}")
+                print(f"** Deleting the cached model directory for {repo_id}")
                 self._delete_model_from_cache(repo_id)
 
     def add_model(
@@ -420,11 +420,6 @@ class ModelManager(object):
                 "NOHASH",
             )
 
-        # scan model
-        self.scan_model(model_name, weights)
-
-        print(f">> Loading {model_name} from {weights}")
-
         # for usage statistics
         if self._has_cuda():
             torch.cuda.reset_peak_memory_stats()
@@ -440,6 +435,7 @@ class ModelManager(object):
         sd = None
         
         if weights.endswith(".ckpt"):
+            self.scan_model(model_name, weights)
             sd = torch.load(io.BytesIO(weight_bytes), map_location="cpu")
         else:
             sd = safetensors.torch.load(weight_bytes)
@@ -466,18 +462,12 @@ class ModelManager(object):
                 vae = os.path.normpath(os.path.join(Globals.root, vae))
             if os.path.exists(vae):
                 print(f"   | Loading VAE weights from: {vae}")
-                vae_ckpt = None
-                vae_dict = None
-                if vae.endswith(".safetensors"):
-                    vae_ckpt = safetensors.torch.load_file(vae)
-                    vae_dict = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss"}
-                else:
+                if vae.endswith((".ckpt",".pt")):
+                    self.scan_model(vae,vae)
                     vae_ckpt = torch.load(vae, map_location="cpu")
-                    vae_dict = {
-                        k: v
-                        for k, v in vae_ckpt["state_dict"].items()
-                        if k[0:4] != "loss"
-                    }
+                else:
+                    vae_ckpt = safetensors.torch.load_file(vae)
+                vae_dict = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss"}
                 model.first_stage_model.load_state_dict(vae_dict, strict=False)
             else:
                 print(f"   | VAE file {vae} not found. Skipping.")
@@ -499,9 +489,9 @@ class ModelManager(object):
 
         print(f">> Loading diffusers model from {name_or_path}")
         if using_fp16:
-            print("  | Using faster float16 precision")
+            print("   | Using faster float16 precision")
         else:
-            print("  | Using more accurate float32 precision")
+            print("   | Using more accurate float32 precision")
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
@@ -553,7 +543,7 @@ class ModelManager(object):
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
 
-        print(f"  | Default image dimensions = {width} x {height}")
+        print(f"   | Default image dimensions = {width} x {height}")
 
         return pipeline, width, height, model_hash
 
@@ -600,7 +590,7 @@ class ModelManager(object):
         and option to exit if an infected file is identified.
         """
         # scan model
-        print(f">> Scanning Model: {model_name}")
+        print(f"   | Scanning Model: {model_name}")
         scan_result = scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
             if scan_result.infected_files == 1:
@@ -623,7 +613,7 @@ class ModelManager(object):
                     print("### Exiting InvokeAI")
                     sys.exit()
         else:
-            print(">> Model scanned ok")
+            print("   | Model scanned ok")
 
     def import_diffuser_model(
         self,
@@ -803,19 +793,20 @@ class ModelManager(object):
         print(f">> Probing {thing} for import")
 
         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
             is_temporary = True
+
         elif Path(thing).is_file() and thing.endswith((".ckpt", ".safetensors")):
             if Path(thing).stem in ["model", "diffusion_pytorch_model"]:
                 print(
-                    f"  | {Path(thing).name} appears to be part of a diffusers model. Skipping import"
+                    f"   | {Path(thing).name} appears to be part of a diffusers model. Skipping import"
                 )
                 return
             else:
-                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():
@@ -872,11 +863,12 @@ class ModelManager(object):
             return model_path.stem
 
         # another round of heuristics to guess the correct config file.
-        checkpoint = (
-            safetensors.torch.load_file(model_path)
-            if model_path.suffix == ".safetensors"
-            else torch.load(model_path)
-        )
+        checkpoint = None
+        if model_path.suffix.endswith((".ckpt",".pt")):
+            self.scan_model(model_path,model_path)
+            checkpoint = torch.load(model_path)
+        else:
+            checkpoint = safetensors.torch.load_file(model_path)
         # additional probing needed if no config file provided
         if model_config_file is None:
             model_type = self.probe_model_type(checkpoint)
@@ -921,7 +913,7 @@ class ModelManager(object):
         if model_config_file.name.startswith('v2'):
             convert = True
             print(
-                "  | This SD-v2 model will be converted to diffusers format for use"
+                "   | This SD-v2 model will be converted to diffusers format for use"
             )
 
         if convert:
@@ -936,6 +928,7 @@ class ModelManager(object):
                 model_description=description,
                 original_config_file=model_config_file,
                 commit_to_conf=commit_to_conf,
+                scan_needed=False,
             )
             # in the event that this file was downloaded automatically prior to conversion
             # we do not keep the original .ckpt/.safetensors around
@@ -960,14 +953,15 @@ class ModelManager(object):
         return model_name
 
     def convert_and_import(
-        self,
-        ckpt_path: Path,
-        diffusers_path: Path,
-        model_name=None,
-        model_description=None,
-        vae=None,
-        original_config_file: Path = None,
-        commit_to_conf: Path = None,
+            self,
+            ckpt_path: Path,
+            diffusers_path: Path,
+            model_name=None,
+            model_description=None,
+            vae=None,
+            original_config_file: Path = None,
+            commit_to_conf: Path = None,
+            scan_needed: bool=True,
     ) -> str:
         """
         Convert a legacy ckpt weights file to diffuser model and import
@@ -1002,11 +996,12 @@ class ModelManager(object):
                 extract_ema=True,
                 original_config_file=original_config_file,
                 vae=vae_model,
+                scan_needed=scan_needed,
             )
             print(
-                f"  | Success. Optimized model is now located at {str(diffusers_path)}"
+                f"   | Success. Optimized model is now located at {str(diffusers_path)}"
             )
-            print(f"  | Writing new config file entry for {model_name}")
+            print(f"   | Writing new config file entry for {model_name}")
             new_config = dict(
                 path=str(diffusers_path),
                 description=model_description,
@@ -1296,7 +1291,7 @@ class ModelManager(object):
             with open(hashpath) as f:
                 hash = f.read()
             return hash
-        print("  | Calculating sha256 hash of model files")
+        print("   | Calculating sha256 hash of model files")
         tic = time.time()
         sha = hashlib.sha256()
         count = 0
@@ -1308,7 +1303,7 @@ class ModelManager(object):
                         sha.update(chunk)
         hash = sha.hexdigest()
         toc = time.time()
-        print(f"  | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
+        print(f"   | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
         with open(hashpath, "w") as f:
             f.write(hash)
         return hash
@@ -1353,12 +1348,12 @@ class ModelManager(object):
             local_files_only=not Globals.internet_available,
         )
 
-        print(f"  | Loading diffusers VAE from {name_or_path}")
+        print(f"   | Loading diffusers VAE from {name_or_path}")
         if using_fp16:
             vae_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
         else:
-            print("  | Using more accurate float32 precision")
+            print("   | Using more accurate float32 precision")
             fp_args_list = [{}]
 
         vae = None
@@ -1399,7 +1394,7 @@ class ModelManager(object):
                     hashes_to_delete.add(revision.commit_hash)
         strategy = cache_info.delete_revisions(*hashes_to_delete)
         print(
-            f"** deletion of this model is expected to free {strategy.expected_freed_size_str}"
+            f"** Deletion of this model is expected to free {strategy.expected_freed_size_str}"
         )
         strategy.execute()