diff --git a/ldm/generate.py b/ldm/generate.py
index 5d4b7a736c..06325f3d04 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -27,6 +27,7 @@ from pytorch_lightning import seed_everything, logging
 
 from ldm.invoke.prompt_parser import PromptParser
 from ldm.util import instantiate_from_config
+from ldm.invoke.globals import Globals
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ksampler import KSampler
@@ -220,8 +221,14 @@ class Generate:
                 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
                 from transformers import AutoFeatureExtractor
                 safety_model_id = "CompVis/stable-diffusion-safety-checker"
-                self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id, local_files_only=True)
-                self.safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id, local_files_only=True)
+                self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id,
+                                                                                   local_files_only=True,
+                                                                                   cache_dir=os.path.join(Globals.root,'models',safety_model_id)
+                )
+                self.safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id,
+                                                                                     local_files_only=True,
+                                                                                     cache_dir=os.path.join(Globals.root,'models',safety_model_id)
+                )
                 self.safety_checker.to(self.device)
             except Exception:
                 print('** An error was encountered while installing the safety checker:')
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index 72b785171e..4d01759653 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -389,6 +389,11 @@ class Args(object):
 
         deprecated_group.add_argument('--laion400m')
         deprecated_group.add_argument('--weights') # deprecated
+        model_group.add_argument(
+            '--root_dir',
+            default='.',
+            help='Path to directory containing "models", "outputs" and "configs"'
+        )
         model_group.add_argument(
             '--config',
             '-c',
diff --git a/ldm/invoke/globals.py b/ldm/invoke/globals.py
new file mode 100644
index 0000000000..7901501163
--- /dev/null
+++ b/ldm/invoke/globals.py
@@ -0,0 +1,15 @@
+'''
+ldm.invoke.globals defines a small number of global variables that would
+otherwise have to be passed through long and complex call chains.
+
+It defines a Namespace object named "Globals" that contains
+the attributes:
+
+  - root    - the root directory under which "models" and "outputs" can be found
+'''
+
+from argparse import Namespace
+
+Globals = Namespace()
+Globals.root = '.'
+
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index d4007c46de..5cb644f38c 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -19,6 +19,7 @@ from sys import getrefcount
 from omegaconf import OmegaConf
 from omegaconf.errors import ConfigAttributeError
 from ldm.util import instantiate_from_config
+from ldm.invoke.globals import Globals
 
 DEFAULT_MAX_MODELS=2
 
@@ -200,6 +201,9 @@ class ModelCache(object):
         width = mconfig.width
         height = mconfig.height
 
+        if not os.path.isabs(weights):
+            weights = os.path.normpath(os.path.join(Globals.root,weights))
+
         print(f'>> Loading {model_name} from {weights}')
 
         # for usage statistics
@@ -210,6 +214,8 @@ class ModelCache(object):
         tic = time.time()
 
         # this does the work
+        if not os.path.isabs(config):
+            config = os.path.join(Globals.root,config)
         c     = OmegaConf.load(config)
         with open(weights,'rb') as f:
             weight_bytes = f.read()
@@ -228,6 +234,8 @@ class ModelCache(object):
 
         # look and load a matching vae file. Code borrowed from AUTOMATIC1111 modules/sd_models.py
         if vae:
+            if not os.path.isabs(vae):
+                vae = os.path.normpath(os.path.join(Globals.root,vae))
             if os.path.exists(vae):
                 print(f'   | Loading VAE weights from: {vae}')
                 vae_ckpt = torch.load(vae, map_location="cpu")
diff --git a/ldm/invoke/restoration/codeformer.py b/ldm/invoke/restoration/codeformer.py
index 325f1b1d94..2620e0e8af 100644
--- a/ldm/invoke/restoration/codeformer.py
+++ b/ldm/invoke/restoration/codeformer.py
@@ -3,13 +3,18 @@ import torch
 import numpy as np
 import warnings
 import sys
+from ldm.invoke.globals import Globals
 
 pretrained_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
 
 class CodeFormerRestoration():
     def __init__(self,
-            codeformer_dir='ldm/invoke/restoration/codeformer',
-            codeformer_model_path='weights/codeformer.pth') -> None:
+            codeformer_dir='models/codeformer',
+            codeformer_model_path='codeformer.pth') -> None:
+
+        if not os.path.isabs(codeformer_dir):
+            codeformer_dir = os.path.join(Globals.root, codeformer_dir)
+        
         self.model_path = os.path.join(codeformer_dir, codeformer_model_path)
         self.codeformer_model_exists = os.path.isfile(self.model_path)
 
@@ -33,9 +38,20 @@ class CodeFormerRestoration():
             
             cf_class = CodeFormer
             
-            cf = cf_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(device)
-            
-            checkpoint_path = load_file_from_url(url=pretrained_model_url, model_dir=os.path.abspath('ldm/invoke/restoration/codeformer/weights'), progress=True)
+            cf = cf_class(
+                dim_embd=512,
+                codebook_size=1024,
+                n_head=8,
+                n_layers=9,
+                connect_list=['32', '64', '128', '256']
+            ).to(device)
+
+            # note that this file should already be downloaded and cached at
+            # this point
+            checkpoint_path = load_file_from_url(url=pretrained_model_url,
+                                                 model_dir=os.path.abspath(os.path.dirname(self.model_path)),
+                                                 progress=True
+            )
             checkpoint = torch.load(checkpoint_path)['params_ema']
             cf.load_state_dict(checkpoint)
             cf.eval()
diff --git a/ldm/invoke/restoration/gfpgan.py b/ldm/invoke/restoration/gfpgan.py
index 037b4eed5b..66c22d8133 100644
--- a/ldm/invoke/restoration/gfpgan.py
+++ b/ldm/invoke/restoration/gfpgan.py
@@ -3,6 +3,7 @@ import warnings
 import os
 import sys
 import numpy as np
+from ldm.invoke.globals import Globals
 
 from PIL import Image
 
@@ -10,18 +11,21 @@ from PIL import Image
 class GFPGAN():
     def __init__(
             self,
-            gfpgan_model_path='./models/gfpgan/GFPGANv1.4.pth') -> None:
-
-        self.model_path = os.path.join(gfpgan_model_path)
+            gfpgan_model_path='models/gfpgan/GFPGANv1.4.pth'
+    ) -> None:
+    
+        if not os.path.isabs(gfpgan_model_path):
+            gfpgan_model_path=os.path.join(Globals.root,gfpgan_model_path)
+        self.model_path = gfpgan_model_path
         self.gfpgan_model_exists = os.path.isfile(self.model_path)
-
+        
         if not self.gfpgan_model_exists:
             print('## NOT FOUND: GFPGAN model not found at ' + self.model_path)
             return None
-
+    
     def model_exists(self):
         return os.path.isfile(self.model_path)
-
+    
     def process(self, image, strength: float, seed: str = None):
         if seed is not None:
             print(f'>> GFPGAN - Restoring Faces for image seed:{seed}')
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index cf8644a7fb..317222cc5a 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -1,5 +1,5 @@
 import math
-
+import os.path
 import torch
 import torch.nn as nn
 from functools import partial
@@ -8,6 +8,7 @@ from einops import rearrange, repeat
 from transformers import CLIPTokenizer, CLIPTextModel
 import kornia
 from ldm.invoke.devices import choose_torch_device
+from ldm.invoke.globals import Globals
 
 from ldm.modules.x_transformer import (
     Encoder,
@@ -98,21 +99,19 @@ class BERTTokenizer(AbstractEncoder):
     """Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
 
     def __init__(
-        self, device=choose_torch_device(), vq_interface=True, max_length=77
+            self, device=choose_torch_device(), vq_interface=True, max_length=77
     ):
         super().__init__()
         from transformers import (
             BertTokenizerFast,
-        )  # TODO: add to reuquirements
+        )
 
-        # Modified to allow to run on non-internet connected compute nodes.
-        # Model needs to be loaded into cache from an internet-connected machine
-        # by running:
-        #   from transformers import BertTokenizerFast
-        #   BertTokenizerFast.from_pretrained("bert-base-uncased")
+        cache = os.path.join(Globals.root,'models/bert-base-uncased')
         try:
             self.tokenizer = BertTokenizerFast.from_pretrained(
-                'bert-base-uncased', local_files_only=True
+                'bert-base-uncased',
+                cache_dir=cache,
+                local_files_only=True
             )
         except OSError:
             raise SystemExit(
@@ -150,14 +149,14 @@ class BERTEmbedder(AbstractEncoder):
     """Uses the BERT tokenizr model and add some transformer encoder layers"""
 
     def __init__(
-        self,
-        n_embed,
-        n_layer,
-        vocab_size=30522,
-        max_seq_len=77,
-        device=choose_torch_device(),
-        use_tokenizer=True,
-        embedding_dropout=0.0,
+            self,
+            n_embed,
+            n_layer,
+            vocab_size=30522,
+            max_seq_len=77,
+            device=choose_torch_device(),
+            use_tokenizer=True,
+            embedding_dropout=0.0,
     ):
         super().__init__()
         self.use_tknz_fn = use_tokenizer
@@ -245,10 +244,14 @@ class FrozenCLIPEmbedder(AbstractEncoder):
     ):
         super().__init__()
         self.tokenizer = CLIPTokenizer.from_pretrained(
-            version, local_files_only=True
+            version,
+            cache_dir=os.path.join(Globals.root,'models',version),
+            local_files_only=True
         )
         self.transformer = CLIPTextModel.from_pretrained(
-            version, local_files_only=True
+            version,
+            cache_dir=os.path.join(Globals.root,'models',version),
+            local_files_only=True
         )
         self.device = device
         self.max_length = max_length
diff --git a/models/ldm/stable-diffusion-v1/place-ckpt-files-here.txt b/models/ldm/stable-diffusion-v1/place-ckpt-files-here.txt
deleted file mode 100644
index a174e54540..0000000000
--- a/models/ldm/stable-diffusion-v1/place-ckpt-files-here.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-See docs/features/INSTALLING_MODELS.md for how to populate this
-directory with one or more Stable Diffusion model weight files.
diff --git a/scripts/invoke.py b/scripts/invoke.py
index ac63474d6a..049aca7935 100755
--- a/scripts/invoke.py
+++ b/scripts/invoke.py
@@ -11,9 +11,9 @@ import time
 import traceback
 import yaml
 
-from ldm.invoke.prompt_parser import PromptParser
-
 sys.path.append('.')    # corrects a weird problem on Macs
+from ldm.invoke.globals import Globals
+from ldm.invoke.prompt_parser import PromptParser
 from ldm.invoke.readline import get_completer
 from ldm.invoke.args import Args, metadata_dumps, metadata_from_png, dream_cmd_from_png
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata, write_metadata
@@ -47,6 +47,9 @@ def main():
             print('--max_loaded_models must be >= 1; using 1')
             args.max_loaded_models = 1
 
+    # alert - setting a global here
+    Globals.root=os.path.expanduser(args.root_dir)
+
     from ldm.generate import Generate
 
     # these two lines prevent a horrible warning message from appearing
@@ -57,10 +60,16 @@ def main():
     # Loading Face Restoration and ESRGAN Modules
     gfpgan,codeformer,esrgan = load_face_restoration(opt)
 
-    # make sure the output directory exists
+    # normalize the outdir relative to root and make sure it exists
+    if not os.path.abspath(opt.outdir):
+        opt.outdir=os.path.normpath(os.path.join(Globals.root,opt.outdir))
     if not os.path.exists(opt.outdir):
         os.makedirs(opt.outdir)
 
+    # normalize the config directory relative to root
+    if not os.path.abspath(opt.conf):
+        opt.conf=os.path.normpath(os.path.join(Globals.root,opt.conf))
+
     # load the infile as a list of lines
     if opt.infile:
         try:
@@ -77,7 +86,7 @@ def main():
     # creating a Generate object:
     try:
         gen = Generate(
-            conf = opt.conf,
+            conf = os.path.join(Globals.root,opt.conf),
             model = opt.model,
             sampler_name = opt.sampler_name,
             embedding_path = opt.embedding_path,
@@ -128,6 +137,8 @@ def main_loop(gen, opt):
     doneAfterInFile = infile is not None
     path_filter = re.compile(r'[<>:"/\\|?*]')
     last_results = list()
+    if not os.path.isabs(opt.conf):
+        opt.conf = os.path.join(Globals.root,opt.conf)
     model_config = OmegaConf.load(opt.conf)
 
     # The readline completer reads history from the .dream_history file located in the
diff --git a/scripts/merge_embeddings.py b/scripts/merge_embeddings.py
index 452b27faf4..97bfa6a8c6 100644
--- a/scripts/merge_embeddings.py
+++ b/scripts/merge_embeddings.py
@@ -1,5 +1,6 @@
 from ldm.modules.encoders.modules import FrozenCLIPEmbedder, BERTEmbedder
 from ldm.modules.embedding_manager import EmbeddingManager
+from ldm.modules.globals import Globals
 
 import argparse, os
 from functools import partial
@@ -51,6 +52,13 @@ if __name__ == "__main__":
 
     parser = argparse.ArgumentParser()
 
+    parser.add_argument(
+        "--root_dir", 
+        type=str, 
+        default='.',
+        help="Path to the InvokeAI install directory containing 'models', 'outputs' and 'configs'."
+    )
+
     parser.add_argument(
         "--manager_ckpts", 
         type=str, 
@@ -73,6 +81,7 @@ if __name__ == "__main__":
     )
 
     args = parser.parse_args()
+    Globals.root=args.root_dir
 
     if args.use_bert:
         embedder = BERTEmbedder(n_embed=1280, n_layer=32).cuda()
diff --git a/scripts/preload_models.py b/scripts/preload_models.py
old mode 100644
new mode 100755
index 67b143d82d..dced531277
--- a/scripts/preload_models.py
+++ b/scripts/preload_models.py
@@ -32,7 +32,9 @@ warnings.filterwarnings('ignore')
 #warnings.filterwarnings('ignore',category=UserWarning)
 
 #--------------------------globals--
-Model_dir = './models/ldm/stable-diffusion-v1/'
+Root_dir = '.'
+Model_dir = 'models'
+Weights_dir = 'ldm/stable-diffusion-v1/'
 Default_config_file = './configs/models.yaml'
 SD_Configs = './configs/stable-diffusion'
 Datasets = {
@@ -253,14 +255,15 @@ This involves a few easy steps.
 # look for legacy model.ckpt in models directory and offer to
 # normalize its name
 def migrate_models_ckpt():
-    if not os.path.exists(os.path.join(Model_dir,'model.ckpt')):
+    model_path = os.path.join(Root_dir,Model_dir,Weights_dir)
+    if not os.path.exists(os.path.join(model_path,'model.ckpt')):
         return
     new_name = Datasets['stable-diffusion-1.4']['file']
     print('You seem to have the Stable Diffusion v4.1 "model.ckpt" already installed.')
     rename = yes_or_no(f'Ok to rename it to "{new_name}" for future reference?')
     if rename:
         print(f'model.ckpt => {new_name}')
-        os.rename(os.path.join(Model_dir,'model.ckpt'),os.path.join(Model_dir,new_name))
+        os.rename(os.path.join(model_path,'model.ckpt'),os.path.join(model_path,new_name))
             
 #---------------------------------------------
 def download_weight_datasets(models:dict, access_token:str):
@@ -271,6 +274,7 @@ def download_weight_datasets(models:dict, access_token:str):
         filename = Datasets[mod]['file']
         success = download_with_resume(
             repo_id=repo_id,
+            model_dir=os.path.join(Root_dir,Model_dir,Weights_dir),
             model_name=filename,
             access_token=access_token
         )
@@ -290,12 +294,12 @@ def download_weight_datasets(models:dict, access_token:str):
     return successful
     
 #---------------------------------------------
-def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool:
-    model_dest = os.path.join(Model_dir, model_name)
-    os.makedirs(os.path.dirname(model_dest), exist_ok=True)
+def download_with_resume(repo_id:str, model_dir:str, model_name:str, access_token:str=None)->bool:
+    model_dest = os.path.join(model_dir, model_name)
+    os.makedirs(model_dir, exist_ok=True)
     url = hf_hub_url(repo_id, model_name)
 
-    header = {"Authorization": f'Bearer {access_token}'}
+    header = {"Authorization": f'Bearer {access_token}'} if access_token else {}
     open_mode = 'wb'
     exist_size = 0
     
@@ -399,30 +403,27 @@ def new_config_file_contents(successfully_downloaded:dict, Config_file:str)->str
 #---------------------------------------------
 # this will preload the Bert tokenizer fles
 def download_bert():
-    print('Installing bert tokenizer (ignore deprecation errors)...', end='')
-    sys.stdout.flush()
+    print('Installing bert tokenizer (ignore deprecation errors)...', end='',file=sys.stderr)
     with warnings.catch_warnings():
         warnings.filterwarnings('ignore', category=DeprecationWarning)
         from transformers import BertTokenizerFast, AutoFeatureExtractor
-        tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
-        print('...success')
+        download_from_hf(BertTokenizerFast,'bert-base-uncased')
+        print('...success',file=sys.stderr)
 
 #---------------------------------------------
-# this will download requirements for Kornia
-def download_kornia():
-    print('Installing Kornia requirements (ignore deprecation errors)...', end='')
-    sys.stdout.flush()
-    import kornia
-    print('...success')
+def download_from_hf(model_class:object, model_name:str):
+    return model_class.from_pretrained(model_name,
+                                       cache_dir=os.path.join(Root_dir,Model_dir,model_name),
+                                       resume_download=True
+    )
 
 #---------------------------------------------
 def download_clip():
-    print('Loading CLIP model (ignore deprecation errors)...',end='')
-    sys.stdout.flush()
+    print('Loading CLIP model (ignore deprecation errors)...',end='',file=sys.stderr)
     version = 'openai/clip-vit-large-patch14'
-    tokenizer = CLIPTokenizer.from_pretrained(version)
-    transformer = CLIPTextModel.from_pretrained(version)
-    print('...success')
+    download_from_hf(CLIPTokenizer,version)
+    download_from_hf(CLIPTextModel,version)
+    print('...success',file=sys.stderr)
 
 #---------------------------------------------
 def download_gfpgan():
@@ -464,7 +465,7 @@ def download_gfpgan():
             if not os.path.exists(model_dest):
                 print(f'Downloading gfpgan model file {model_url}...',end='')
                 os.makedirs(os.path.dirname(model_dest), exist_ok=True)
-                request.urlretrieve(model_url,model_dest)
+                request.urlretrieve(model_url,model_dest,ProgressBar(os.path.basename(model_dest)))
                 print('...success')
         except Exception:
             print('Error loading GFPGAN:')
@@ -472,18 +473,18 @@ def download_gfpgan():
 
 #---------------------------------------------
 def download_codeformer():
-    print('Installing CodeFormer model file...',end='')
+    print('Installing CodeFormer model file...',end='',file=sys.stderr)
     try:
-            model_url  = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-            model_dest = 'ldm/invoke/restoration/codeformer/weights/codeformer.pth'
-            if not os.path.exists(model_dest):
-                print('Downloading codeformer model file...')
-                os.makedirs(os.path.dirname(model_dest), exist_ok=True)
-                request.urlretrieve(model_url,model_dest)
+        model_url  = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
+        model_dest = os.path.join(Root_dir,'models/codeformer/codeformer.pth')
+        if not os.path.exists(model_dest):
+            print('Downloading codeformer model file...')
+            os.makedirs(os.path.dirname(model_dest), exist_ok=True)
+            request.urlretrieve(model_url,model_dest,ProgressBar(os.path.basename(model_dest)))
     except Exception:
         print('Error loading CodeFormer:')
         print(traceback.format_exc())
-    print('...success')
+    print('...success',file=sys.stderr)
     
 #---------------------------------------------
 def download_clipseg():
@@ -497,7 +498,7 @@ def download_clipseg():
         if not os.path.exists(model_dest):
             os.makedirs(os.path.dirname(model_dest), exist_ok=True)
         if not os.path.exists(f'{model_dest}/rd64-uni-refined.pth'):
-            request.urlretrieve(model_url,weights_zip)
+            request.urlretrieve(model_url,weights_zip,ProgressBar(os.path.basename(model_dest)))
             with zipfile.ZipFile(weights_zip,'r') as zip:
                 zip.extractall('models/clipseg')
             os.remove(weights_zip)
@@ -519,7 +520,7 @@ def download_clipseg():
 
 #-------------------------------------
 def download_safety_checker():
-    print('Installing safety model for NSFW content detection...',end='')
+    print('Installing safety model for NSFW content detection...',end='',file=sys.stderr)
     try:
         from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
         from transformers import AutoFeatureExtractor
@@ -528,9 +529,25 @@ def download_safety_checker():
         print(traceback.format_exc())
         return
     safety_model_id = "CompVis/stable-diffusion-safety-checker"
-    safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
-    safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
-    print('...success')
+    download_from_hf(AutoFeatureExtractor,safety_model_id)
+    download_from_hf(StableDiffusionSafetyChecker,safety_model_id)
+    print('...success',file=sys.stderr)
+
+#-------------------------------------
+class ProgressBar():
+    def __init__(self,model_name='file'):
+        self.pbar = None
+        self.name = model_name
+
+    def __call__(self, block_num, block_size, total_size):
+        if not self.pbar:
+            self.pbar=tqdm(desc=self.name,
+                           initial=0,
+                           unit='iB',
+                           unit_scale=True,
+                           total=total_size)
+        downloaded = block_num * block_size
+        self.pbar.update(downloaded)
 
 #-------------------------------------
 if __name__ == '__main__':
@@ -540,13 +557,26 @@ if __name__ == '__main__':
                         action=argparse.BooleanOptionalAction,
                         default=True,
                         help='run in interactive mode (default)')
+    parser.add_argument('--yes','-y',
+                        dest='yes_to_all',
+                        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')
+    parser.add_argument('--root',
+                        dest='root',
+                        type=str,
+                        default='.',
+                        help='path to root of install directory')
     opt = parser.parse_args()
+
+    # setting a global here
+    Root_dir = os.path.expanduser(opt.root)
     
     try:
         if opt.interactive:
@@ -565,7 +595,6 @@ if __name__ == '__main__':
                 update_config_file(successfully_downloaded,opt)
         print('\n** DOWNLOADING SUPPORT MODELS **')
         download_bert()
-        download_kornia()
         download_clip()
         download_gfpgan()
         download_codeformer()