fixes to env parsing, textual inversion & help text

- Make environment variable settings case InSenSiTive:
  INVOKEAI_MAX_LOADED_MODELS and InvokeAI_Max_Loaded_Models
  environment variables will both set `max_loaded_models`

- Updated realesrgan to use new config system.

- Updated textual_inversion_training to use new config system.

- Discovered a race condition when InvokeAIAppConfig is created
  at module load time, which makes it impossible to customize
  or replace the help message produced with --help on the command
  line. To fix this, moved all instances of get_invokeai_config()
  from module load time to object initialization time. Makes code
  cleaner, too.

- Added `--from_file` argument to `invokeai-node-cli` and changed
  github action to match. CI tests will hopefully work now.

Author: Lincoln Stein

.github/workflows/test-invoke-pip.yml

@@ -80,12 +80,7 @@ jobs:
         uses: actions/checkout@v3
 
       - name: set test prompt to main branch validation
-        if: ${{ github.ref == 'refs/heads/main' }}
-        run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> ${{ matrix.github-env }}
-
-      - name: set test prompt to Pull Request validation
-        if: ${{ github.ref != 'refs/heads/main' }}
-        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
+        run:echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
 
       - name: setup python
         uses: actions/setup-python@v4
@@ -131,7 +126,7 @@ jobs:
           --precision=float32
           --always_use_cpu
           --outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
-          < ${{ env.TEST_PROMPTS }}
+          --from_file ${{ env.TEST_PROMPTS }}
 
       - name: Archive results
         id: archive-results

invokeai/app/cli_app.py

@@ -4,6 +4,7 @@
 import os
 import re
 import shlex
+import sys
 import time
 from typing import (
     Union,
@@ -195,6 +196,11 @@ def invoke_cli():
     parser = config.get_parser()
     parser.add_argument('commands',nargs='*')
     invocation_commands = parser.parse_args().commands
+
+    # get the optional file to read commands from.
+    # Simplest is to use it for STDIN
+    if infile := config.from_file:
+        sys.stdin = open(infile,"r")
 
     model_manager = get_model_manager(config,logger=logger)
 

invokeai/app/services/config.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein)
+# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
 
 '''Invokeai configuration system.
 
@@ -206,8 +206,16 @@ def add_parser_arguments(cls, parser):
             if cls.initconf and settings_stanza in cls.initconf \
                else OmegaConf.create()
 
+        # create an upcase version of the environment in
+        # order to achieve case-insensitive environment
+        # variables (the way Windows does)
+        upcase_environ = dict()
+        for key,value in os.environ.items():
+            upcase_environ[key.upper()] = value
+        
         fields = cls.__fields__
         cls.argparse_groups = {}
+        
         for name, field in fields.items():
             if name not in cls._excluded():
                 current_default = field.default
@@ -216,8 +224,8 @@ def add_parser_arguments(cls, parser):
                 env_name = env_prefix + '_' + name
                 if category in initconf and name in initconf.get(category):
                     field.default = initconf.get(category).get(name)
-                if env_name in os.environ:
-                    field.default = os.environ[env_name]
+                if env_name.upper() in upcase_environ:
+                    field.default = upcase_environ[env_name.upper()]
                 cls.add_field_argument(parser, name, field)
 
                 field.default = current_default
@@ -353,6 +361,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     legacy_conf_dir     : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
     lora_dir            : Path = Field(default='loras', description='Path to InvokeAI LoRA model directory', category='Paths')
     outdir              : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
+    from_file           : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
 
     model               : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models')
     embeddings          : bool = Field(default=True, description='Load contents of embeddings directory', category='Models')
@@ -502,11 +511,11 @@ def print_help(self, file=None):
         text = self.format_help()
         pydoc.pager(text)
 
-def get_invokeai_config(cls:Type[InvokeAISettings]=InvokeAIAppConfig)->InvokeAISettings:
+def get_invokeai_config(cls:Type[InvokeAISettings]=InvokeAIAppConfig,**kwargs)->InvokeAISettings:
     '''
     This returns a singleton InvokeAIAppConfig configuration object.
     '''
     global global_config
     if global_config is None or type(global_config)!=cls:
-        global_config = cls()
+        global_config = cls(**kwargs)
     return global_config

invokeai/backend/config/invokeai_configure.py

@@ -389,8 +389,8 @@ def create(self):
         )
         self.nextrely += 1
         for i in [
-            "If you have an account at HuggingFace you may paste your access token here",
-            'to allow InvokeAI to download styles & subjects from the "Concept Library".',
+            "If you have an account at HuggingFace you may optionally paste your access token here",
+            'to allow InvokeAI to download restricted styles & subjects from the "Concept Library".',
             "See https://huggingface.co/settings/tokens",
         ]:
             self.add_widget_intelligent(
@@ -593,6 +593,9 @@ def marshall_arguments(self):
         new_opts.hf_token = self.hf_token.value
         new_opts.license_acceptance = self.license_acceptance.value
         new_opts.precision = PRECISION_CHOICES[self.precision.value[0]]
+        
+        # widget library workaround to make max_loaded_models an int rather than a float
+        new_opts.max_loaded_models = int(new_opts.max_loaded_models)
 
         return new_opts
 

invokeai/backend/image_util/patchmatch.py

@@ -8,8 +8,6 @@
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import get_invokeai_config
 
-config = get_invokeai_config()
-
 class PatchMatch:
     """
     Thin class wrapper around the patchmatch function.
@@ -23,6 +21,7 @@ def __init__(self):
 
     @classmethod
     def _load_patch_match(self):
+        config = get_invokeai_config()
         if self.tried_load:
             return
         if config.try_patchmatch:

invokeai/backend/image_util/txt2mask.py

@@ -37,7 +37,6 @@
 
 CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
 CLIPSEG_SIZE = 352
-config = get_invokeai_config()
 
 class SegmentedGrayscale(object):
     def __init__(self, image: Image, heatmap: torch.Tensor):
@@ -84,6 +83,7 @@ class Txt2Mask(object):
 
     def __init__(self, device="cpu", refined=False):
         logger.info("Initializing clipseg model for text to mask inference")
+        config = get_invokeai_config()
 
         # BUG: we are not doing anything with the device option at this time
         self.device = device

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -74,8 +74,6 @@
 
 from ..stable_diffusion import StableDiffusionGeneratorPipeline
 
-config = get_invokeai_config()
-
 def shave_segments(path, n_shave_prefix_segments=1):
     """
     Removes segments. Positive values shave the first segments, negative shave the last segments.
@@ -844,7 +842,7 @@ def _copy_layers(hf_layers, pt_layers):
 
 def convert_ldm_clip_checkpoint(checkpoint):
     text_model = CLIPTextModel.from_pretrained(
-        "openai/clip-vit-large-patch14", cache_dir=config.cache_dir
+        "openai/clip-vit-large-patch14", cache_dir=get_invokeai_config().cache_dir
     )
 
     keys = list(checkpoint.keys())
@@ -899,7 +897,7 @@ def convert_ldm_clip_checkpoint(checkpoint):
 
 
 def convert_paint_by_example_checkpoint(checkpoint):
-    cache_dir = config.cache_dir
+    cache_dir = get_invokeai_config().cache_dir
     config = CLIPVisionConfig.from_pretrained(
         "openai/clip-vit-large-patch14", cache_dir=cache_dir
     )
@@ -971,7 +969,7 @@ def convert_paint_by_example_checkpoint(checkpoint):
 
 
 def convert_open_clip_checkpoint(checkpoint):
-    cache_dir = config.cache_dir
+    cache_dir = get_invokeai_config().cache_dir
     text_model = CLIPTextModel.from_pretrained(
         "stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir
     )
@@ -1094,7 +1092,7 @@ 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()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
         verbosity = dlogging.get_verbosity()

invokeai/backend/model_management/model_manager.py

@@ -68,7 +68,6 @@ class SDModelComponent(Enum):
     feature_extractor="feature_extractor"
 
 DEFAULT_MAX_MODELS = 2
-config = get_invokeai_config()
 
 class ModelManager(object):
     """
@@ -99,6 +98,7 @@ def __init__(
         if not isinstance(config, DictConfig):
             config = OmegaConf.load(config)
         self.config = config
+        self.globals = get_invokeai_config()
         self.precision = precision
         self.device = torch.device(device_type)
         self.max_loaded_models = max_loaded_models
@@ -291,7 +291,7 @@ def is_legacy(self, model_name: str) -> bool:
         """
         # if we are converting legacy files automatically, then
         # there are no legacy ckpts!
-        if config.ckpt_convert:
+        if self.globals.ckpt_convert:
             return False
         info = self.model_info(model_name)
         if "weights" in info and info["weights"].endswith((".ckpt", ".safetensors")):
@@ -501,13 +501,13 @@ def _load_diffusers_model(self, mconfig):
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
-            safety_checker=None, local_files_only=not config.internet_available
+            safety_checker=None, local_files_only=not self.globals.internet_available
         )
         if "vae" in mconfig and mconfig["vae"] is not None:
             if vae := self._load_vae(mconfig["vae"]):
                 pipeline_args.update(vae=vae)
         if not isinstance(name_or_path, Path):
-            pipeline_args.update(cache_dir=config.cache_dir)
+            pipeline_args.update(cache_dir=self.globals.cache_dir)
         if using_fp16:
             pipeline_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
@@ -559,10 +559,9 @@ def _load_ckpt_model(self, model_name, mconfig):
         width = mconfig.width
         height = mconfig.height
 
-        if not os.path.isabs(config):
-            config = os.path.join(config.root, config)
-        if not os.path.isabs(weights):
-            weights = os.path.normpath(os.path.join(config.root, weights))
+        root_dir = self.globals.root_dir
+        config = str(root_dir / config)
+        weights = str(root_dir / weights)
 
         # Convert to diffusers and return a diffusers pipeline
         self.logger.info(f"Converting legacy checkpoint {model_name} into a diffusers model...")
@@ -577,11 +576,7 @@ def _load_ckpt_model(self, model_name, mconfig):
 
         vae_path = None
         if vae:
-            vae_path = (
-                vae
-                if os.path.isabs(vae)
-                else os.path.normpath(os.path.join(config.root, vae))
-            )
+            vae_path = str(root_dir / vae)
         if self._has_cuda():
             torch.cuda.empty_cache()
         pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
@@ -613,9 +608,7 @@ def model_name_or_path(self, model_name: Union[str, DictConfig]) -> str | Path:
             )
 
         if "path" in mconfig and mconfig["path"] is not None:
-            path = Path(mconfig["path"])
-            if not path.is_absolute():
-                path = Path(config.root, path).resolve()
+            path = self.globals.root_dir / Path(mconfig["path"])
             return path
         elif "repo_id" in mconfig:
             return mconfig["repo_id"]
@@ -863,16 +856,16 @@ def heuristic_import(
                 model_type = self.probe_model_type(checkpoint)
                 if model_type == SDLegacyType.V1:
                     self.logger.debug("SD-v1 model detected")
-                    model_config_file = config.legacy_conf_path / "v1-inference.yaml"
+                    model_config_file = self.globals.legacy_conf_path / "v1-inference.yaml"
                 elif model_type == SDLegacyType.V1_INPAINT:
                     self.logger.debug("SD-v1 inpainting model detected")
-                    model_config_file = config.legacy_conf_path / "v1-inpainting-inference.yaml",
+                    model_config_file = self.globals.legacy_conf_path / "v1-inpainting-inference.yaml",
                 elif model_type == SDLegacyType.V2_v:
                     self.logger.debug("SD-v2-v model detected")
-                    model_config_file = config.legacy_conf_path / "v2-inference-v.yaml"
+                    model_config_file = self.globals.legacy_conf_path / "v2-inference-v.yaml"
                 elif model_type == SDLegacyType.V2_e:
                     self.logger.debug("SD-v2-e model detected")
-                    model_config_file = config.legacy_conf_path / "v2-inference.yaml"
+                    model_config_file = self.globals.legacy_conf_path / "v2-inference.yaml"
                 elif model_type == SDLegacyType.V2:
                     self.logger.warning(
                         f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
@@ -899,7 +892,7 @@ def heuristic_import(
                 self.logger.debug(f"Using VAE file {vae_path.name}")
         vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
 
-        diffuser_path = config.root / "models/converted_ckpts" / model_path.stem
+        diffuser_path = self.globals.root_dir / "models/converted_ckpts" / model_path.stem
         model_name = self.convert_and_import(
             model_path,
             diffusers_path=diffuser_path,
@@ -1032,7 +1025,7 @@ def commit(self, config_file_path: str) -> None:
         """
         yaml_str = OmegaConf.to_yaml(self.config)
         if not os.path.isabs(config_file_path):
-            config_file_path = config.model_conf_path
+            config_file_path = self.globals.model_conf_path
         tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp")
         with open(tmpfile, "w", encoding="utf-8") as outfile:
             outfile.write(self.preamble())
@@ -1064,7 +1057,8 @@ def migrate_models(cls):
         """
         # Three transformer models to check: bert, clip and safety checker, and
         # the diffusers as well
-        models_dir = config.root / "models"
+        config = get_invokeai_config()
+        models_dir = config.root_dir / "models"
         legacy_locations = [
             Path(
                 models_dir,
@@ -1138,13 +1132,12 @@ def _resolve_path(
         if str(source).startswith(("http:", "https:", "ftp:")):
             dest_directory = Path(dest_directory)
             if not dest_directory.is_absolute():
-                dest_directory = config.root / dest_directory
+                dest_directory = self.globals.root_dir / dest_directory
             dest_directory.mkdir(parents=True, exist_ok=True)
             resolved_path = download_with_resume(str(source), dest_directory)
         else:
-            if not os.path.isabs(source):
-                source = config.root / source
-            resolved_path = Path(source)
+            source = self.globals.root_dir / source
+            resolved_path = source
         return resolved_path
 
     def _invalidate_cached_model(self, model_name: str) -> None:
@@ -1194,7 +1187,7 @@ def _diffuser_sha256(
             path = name_or_path
         else:
             owner, repo = name_or_path.split("/")
-            path = Path(config.cache_dir / f"models--{owner}--{repo}")
+            path = self.globals.cache_dir / f"models--{owner}--{repo}"
         if not path.exists():
             return None
         hashpath = path / "checksum.sha256"
@@ -1255,8 +1248,8 @@ def _load_vae(self, vae_config) -> AutoencoderKL:
         using_fp16 = self.precision == "float16"
 
         vae_args.update(
-            cache_dir=config.cache_dir,
-            local_files_only=not config.internet_available,
+            cache_dir=self.globals.cache_dir,
+            local_files_only=not self.globals.internet_available,
         )
 
         self.logger.debug(f"Loading diffusers VAE from {name_or_path}")
@@ -1294,7 +1287,7 @@ def _load_vae(self, vae_config) -> AutoencoderKL:
 
     @classmethod
     def _delete_model_from_cache(cls,repo_id):
-        cache_info = scan_cache_dir(config.cache_dir)
+        cache_info = scan_cache_dir(get_invokeai_config().cache_dir)
 
         # I'm sure there is a way to do this with comprehensions
         # but the code quickly became incomprehensible!
@@ -1311,9 +1304,10 @@ def _delete_model_from_cache(cls,repo_id):
 
     @staticmethod
     def _abs_path(path: str | Path) -> Path:
+        globals = get_invokeai_config()
         if path is None or Path(path).is_absolute():
             return path
-        return Path(config.root, path).resolve()
+        return Path(globals.root_dir, path).resolve()
 
     @staticmethod
     def _is_huggingface_hub_directory_present() -> bool: