implemented the following fixes:

Enhancements:
1. Directory-based imports will not attempt to import components of diffusers models.
2. Diffuser directory imports now supported
3. Files that end with .ckpt that are not Stable Diffusion models (such as VAEs) are
   skipped during import.

Bugs identified in Psychedelicious's review:
1. The invokeai-configure form now tracks the current contents of `invokeai.init` correctly.
2. The autoencoders are no longer treated like installable models, but instead are
   mandatory support models. They will no longer appear in `models.yaml`

Bugs identified in Damian's review:
1. If invokeai-model-install is started before the root directory is initialized, it will
   call invokeai-configure to fix the matter.
2. Fix bug that was causing empty `models.yaml` under certain conditions.
3. Made import textbox smaller
4. Hide the "convert to diffusers" options if nothing to import.

ldm/invoke/config/invokeai_configure.py

@@ -21,7 +21,9 @@ from pathlib import Path
 from urllib import request
 
 import npyscreen
+import torch
 import transformers
+from diffusers import AutoencoderKL
 from huggingface_hub import HfFolder
 from huggingface_hub import login as hf_hub_login
 from omegaconf import OmegaConf
@@ -36,13 +38,14 @@ from transformers import (
 import invokeai.configs as configs
 
 from ..args import PRECISION_CHOICES, Args
-from ..globals import Globals, global_config_dir
+from ..globals import Globals, global_config_dir, global_config_file, global_cache_dir
 from ..readline import generic_completer
 from .model_install import addModelsForm, process_and_execute
 from .model_install_backend import (
     default_dataset,
     download_from_hf,
     recommended_datasets,
+    hf_download_with_resume,
 )
 from .widgets import IntTitleSlider
 
@@ -170,10 +173,9 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
         else:
             print("...exists", file=sys.stderr)
     except Exception:
-        print("...download failed")
-        print(f"Error downloading {label} model")
-        print(traceback.format_exc())
-
+        print("...download failed", file=sys.stderr)
+        print(f"Error downloading {label} model", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
 
 # ---------------------------------------------
 # this will preload the Bert tokenizer fles
@@ -282,6 +284,36 @@ def download_safety_checker():
     download_from_hf(StableDiffusionSafetyChecker, safety_model_id)
     print("...success", file=sys.stderr)
 
+# -------------------------------------
+def download_vaes(precision: str):
+    print("Installing stabilityai VAE...", file=sys.stderr)
+    try:
+        # first the diffusers version
+        repo_id = 'stabilityai/sd-vae-ft-mse'
+        args = dict(
+            cache_dir=global_cache_dir('diffusers'),
+        )
+        if precision=='float16':
+            args.update(
+                torch_dtype=torch.float16,
+                revision='fp16'
+            )
+        if not AutoencoderKL.from_pretrained(repo_id, **args):
+            raise Exception(f'download of {repo_id} failed')
+
+        repo_id = 'stabilityai/sd-vae-ft-mse-original'
+        model_name = 'vae-ft-mse-840000-ema-pruned.ckpt'
+        # next the legacy checkpoint version
+        if not hf_download_with_resume(
+                repo_id = repo_id,
+                model_name = model_name,
+                model_dir = str(Globals.root / Model_dir / Weights_dir)
+        ):
+            raise Exception(f'download of {model_name} failed')
+        print("...downloaded successfully", file=sys.stderr)
+    except Exception as e:
+        print(f"Error downloading StabilityAI standard VAE: {str(e)}", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
 
 # -------------------------------------
 def get_root(root: str = None) -> str:
@@ -297,6 +329,8 @@ class editOptsForm(npyscreen.FormMultiPage):
     def create(self):
         program_opts = self.parentApp.program_opts
         old_opts = self.parentApp.invokeai_opts
+        with open('log.txt','w') as f:
+            f.write(str(old_opts))
         first_time = not (Globals.root / Globals.initfile).exists()
         access_token = HfFolder.get_token()
 
@@ -585,10 +619,13 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
     return editApp.new_opts()
 
 
-def default_startup_options() -> Namespace:
+def default_startup_options(init_file: Path) -> Namespace:
     opts = Args().parse_args([])
-    opts.outdir = str(default_output_dir())
-    opts.safety_checker = True
+    outdir = Path(opts.outdir)
+    if not outdir.is_absolute():
+        opts.outdir = str(Globals.root / opts.outdir)
+    if not init_file.exists():
+        opts.safety_checker = True
     return opts
 
 
@@ -627,9 +664,9 @@ def initialize_rootdir(root: str, yes_to_all: bool = False):
 
 
 # -------------------------------------
-def run_console_ui(program_opts: Namespace) -> (Namespace, Namespace):
+def run_console_ui(program_opts: Namespace, initfile: Path=None) -> (Namespace, Namespace):
     # parse_args() will read from init file if present
-    invokeai_opts = default_startup_options()
+    invokeai_opts = default_startup_options(initfile)
     editApp = EditOptApplication(program_opts, invokeai_opts)
     editApp.run()
     if editApp.user_cancelled:
@@ -766,13 +803,13 @@ def main():
 
         # We check for to see if the runtime directory is correctly initialized.
         init_file = Path(Globals.root, Globals.initfile)
-        if not init_file.exists():
+        if not init_file.exists() or not global_config_file().exists():
             initialize_rootdir(Globals.root, opt.yes_to_all)
 
         if opt.yes_to_all:
             write_default_options(opt, init_file)
         else:
-            init_options, models_to_download = run_console_ui(opt)
+            init_options, models_to_download = run_console_ui(opt, init_file)
             if init_options:
                 write_opts(init_options, init_file)
             else:
@@ -792,6 +829,7 @@ def main():
             download_codeformer()
             download_clipseg()
             download_safety_checker()
+            download_vaes(init_options.precision)
 
         if opt.skip_sd_weights:
             print("\n** SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST **")