make InvokeAI package installable

This commit makes InvokeAI 3.0 to be installable via PyPi.org and the
installer script.

Main changes.

1. Move static web pages into `invokeai/frontend/web` and modify the
API to look for them there. This allows pip to copy the files into the
distribution directory so that user no longer has to be in repo root
to launch.

2. Update invoke.sh and invoke.bat to launch the new web application
properly. This also changes the wording for launching the CLI from
"generate images" to "explore the InvokeAI node system," since I would
not recommend using the CLI to generate images routinely.

3. Fix a bug in the checkpoint converter script that was identified
during testing.

4. Better error reporting when checkpoint converter fails.

5. Rebuild front end.

invokeai/backend/config/model_install_backend.py

@@ -123,8 +123,8 @@ def install_requested_models(
     if scan_at_startup and scan_directory.is_dir():
         argument = "--autoconvert"
         print('** The global initfile is no longer supported; rewrite to support new yaml format **')
-        initfile = Path(config.root, 'invokeai.init')
-        replacement = Path(config.root, f"invokeai.init.new")
+        initfile = Path(config.root_dir, 'invokeai.init')
+        replacement = Path(config.root_dir, f"invokeai.init.new")
         directory = str(scan_directory).replace("\\", "/")
         with open(initfile, "r") as input:
             with open(replacement, "w") as output:
@@ -149,10 +149,8 @@ def yes_or_no(prompt: str, default_yes=True):
 def get_root(root: str = None) -> str:
     if root:
         return root
-    elif os.environ.get("INVOKEAI_ROOT"):
-        return os.environ.get("INVOKEAI_ROOT")
     else:
-        return config.root
+        return config.root_dir
 
 
 # ---------------------------------------------
@@ -185,7 +183,7 @@ def all_datasets() -> dict:
 # look for legacy model.ckpt in models directory and offer to
 # normalize its name
 def migrate_models_ckpt():
-    model_path = os.path.join(config.root, Model_dir, Weights_dir)
+    model_path = os.path.join(config.root_dir, Model_dir, Weights_dir)
     if not os.path.exists(os.path.join(model_path, "model.ckpt")):
         return
     new_name = initial_models()["stable-diffusion-1.4"]["file"]
@@ -230,7 +228,7 @@ def _download_repo_or_file(
 def _download_ckpt_weights(mconfig: DictConfig, access_token: str) -> Path:
     repo_id = mconfig["repo_id"]
     filename = mconfig["file"]
-    cache_dir = os.path.join(config.root, Model_dir, Weights_dir)
+    cache_dir = os.path.join(config.root_dir, Model_dir, Weights_dir)
     return hf_download_with_resume(
         repo_id=repo_id,
         model_dir=cache_dir,
@@ -419,7 +417,7 @@ def new_config_file_contents(
             stanza["height"] = mod["height"]
         if "file" in mod:
             stanza["weights"] = os.path.relpath(
-                successfully_downloaded[model], start=config.root
+                successfully_downloaded[model], start=config.root_dir
             )
             stanza["config"] = os.path.normpath(
                 os.path.join(sd_configs(), mod["config"])
@@ -458,7 +456,7 @@ def delete_weights(model_name: str, conf_stanza: dict):
 
     weights = Path(weights)
     if not weights.is_absolute():
-        weights = Path(config.root) / weights
+        weights = config.root_dir / weights
         try:
             weights.unlink()
         except OSError as e:

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -1092,7 +1092,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     :param vae: A diffusers VAE to load into the pipeline.
     :param vae_path: Path to a checkpoint VAE that will be converted into diffusers and loaded into the pipeline.
     """
-    config = get_invokeai_config()
+    invoke_config = get_invokeai_config()
+    cache_dir = invoke_config.cache_dir
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
         verbosity = dlogging.get_verbosity()
@@ -1105,7 +1106,6 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         else:
             checkpoint = load_file(checkpoint_path)
 
-        cache_dir = config.cache_dir
         pipeline_class = (
             StableDiffusionGeneratorPipeline
             if return_generator_pipeline
@@ -1297,7 +1297,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
             )
             safety_checker = StableDiffusionSafetyChecker.from_pretrained(
                 "CompVis/stable-diffusion-safety-checker",
-                cache_dir=config.cache_dir,
+                cache_dir=cache_dir,
             )
             feature_extractor = AutoFeatureExtractor.from_pretrained(
                 "CompVis/stable-diffusion-safety-checker", cache_dir=cache_dir

invokeai/backend/model_management/model_manager.py

@@ -14,6 +14,7 @@ import re
 import sys
 import textwrap
 import time
+import traceback
 import warnings
 from enum import Enum, auto
 from pathlib import Path
@@ -974,6 +975,7 @@ class ModelManager(object):
             self.logger.debug("Conversion succeeded")
         except Exception as e:
             self.logger.warning(f"Conversion failed: {str(e)}")
+            self.logger.warning(traceback.format_exc())
             self.logger.warning(
                 "If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)"
             )

invokeai/backend/util/util.py

@@ -331,7 +331,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
     elif resp.status_code != 200:
         logger.error(f"An error occurred during downloading {dest}: {resp.reason}")
     else:
-        logger.error(f"{dest}: Downloading...")
+        logger.info(f"{dest}: Downloading...")
 
     try:
         if content_length < 2000: