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.

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -19,14 +19,14 @@ from huggingface_hub import (
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import get_invokeai_config
-config = get_invokeai_config()
 
 class HuggingFaceConceptsLibrary(object):
     def __init__(self, root=None):
         """
         Initialize the Concepts object. May optionally pass a root directory.
         """
-        self.root = root or config.root
+        self.config = get_invokeai_config()
+        self.root = root or self.config.root
         self.hf_api = HfApi()
         self.local_concepts = dict()
         self.concept_list = None
@@ -58,7 +58,7 @@ class HuggingFaceConceptsLibrary(object):
                 self.concept_list.extend(list(local_concepts_to_add))
                 return self.concept_list
             return self.concept_list
-        elif config.internet_available is True:
+        elif self.config.internet_available is True:
             try:
                 models = self.hf_api.list_models(
                     filter=ModelFilter(model_name="sd-concepts-library/")

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -43,8 +43,6 @@ from .diffusion import (
 from .offloading import FullyLoadedModelGroup, LazilyLoadedModelGroup, ModelGroup
 from .textual_inversion_manager import TextualInversionManager
 
-config = get_invokeai_config()
-
 @dataclass
 class PipelineIntermediateState:
     run_id: str
@@ -348,6 +346,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         """
         if xformers is available, use it, otherwise use sliced attention.
         """
+        config = get_invokeai_config()
         if (
             torch.cuda.is_available()
             and is_xformers_available()

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -32,8 +32,6 @@ ModelForwardCallback: TypeAlias = Union[
     Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],
 ]
 
-config = get_invokeai_config()
-
 @dataclass(frozen=True)
 class PostprocessingSettings:
     threshold: float
@@ -74,6 +72,7 @@ class InvokeAIDiffuserComponent:
         :param model: the unet model to pass through to cross attention control
         :param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
         """
+        config = get_invokeai_config()
         self.conditioning = None
         self.model = model
         self.is_running_diffusers = is_running_diffusers