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.

.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/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
-        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 }}
 
       - 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 argparse
 import os
 import re
 import shlex
+import sys
 import time
 from typing import (
     Union,
@@ -196,6 +197,11 @@ def invoke_cli():
     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)
     
     events = EventServiceBase()

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 @@ class InvokeAISettings(BaseSettings):
             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 @@ class InvokeAISettings(BaseSettings):
                 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:
+                if env_name.upper() in upcase_environ:
-                    field.default = os.environ[env_name]
+                    field.default = upcase_environ[env_name.upper()]
                 cls.add_field_argument(parser, name, field)
 
                 field.default = current_default
@@ -353,6 +361,7 @@ setting environment variables INVOKEAI_<setting>.
     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 @@ class PagingArgumentParser(argparse.ArgumentParser):
         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 @@ class editOptsForm(npyscreen.FormMultiPage):
         )
         self.nextrely += 1
         for i in [
-            "If you have an account at HuggingFace you may paste your access token here",
+            "If you have an account at HuggingFace you may optionally paste your access token here",
-            'to allow InvokeAI to download styles & subjects from the "Concept Library".',
+            'to allow InvokeAI to download restricted styles & subjects from the "Concept Library".',
             "See https://huggingface.co/settings/tokens",
         ]:
             self.add_widget_intelligent(
@@ -594,6 +594,9 @@ class editOptsForm(npyscreen.FormMultiPage):
         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 numpy as np
 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 @@ class PatchMatch:
 
     @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 @@ from invokeai.app.services.config import get_invokeai_config
 
 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 transformers import (
 
 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 convert_ldm_bert_checkpoint(checkpoint, config):
 
 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 @@ textenc_pattern = re.compile("|".join(protected.keys()))
 
 
 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 @@ class ModelManager(object):
         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 @@ class ModelManager(object):
         """
         # 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 @@ class ModelManager(object):
 
         # 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 @@ class ModelManager(object):
         width = mconfig.width
         height = mconfig.height
 
-        if not os.path.isabs(config):
+        root_dir = self.globals.root_dir
-            config = os.path.join(config.root, config)
+        config = str(root_dir / config)
-        if not os.path.isabs(weights):
+        weights = str(root_dir / weights)
-            weights = os.path.normpath(os.path.join(config.root, 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 @@ class ModelManager(object):
 
         vae_path = None
         if vae:
-            vae_path = (
+            vae_path = str(root_dir / vae)
-                vae
-                if os.path.isabs(vae)
-                else os.path.normpath(os.path.join(config.root, vae))
-            )
         if self._has_cuda():
             torch.cuda.empty_cache()
         pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
@@ -613,9 +608,7 @@ class ModelManager(object):
             )
 
         if "path" in mconfig and mconfig["path"] is not None:
-            path = Path(mconfig["path"])
+            path = self.globals.root_dir / Path(mconfig["path"])
-            if not path.is_absolute():
-                path = Path(config.root, path).resolve()
             return path
         elif "repo_id" in mconfig:
             return mconfig["repo_id"]
@@ -863,16 +856,16 @@ class ModelManager(object):
                 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 @@ class ModelManager(object):
                 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 @@ class ModelManager(object):
         """
         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 @@ class ModelManager(object):
         """
         # 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 @@ class ModelManager(object):
         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 = self.globals.root_dir / source
-                source = config.root / source
+            resolved_path = source
-            resolved_path = Path(source)
         return resolved_path
 
     def _invalidate_cached_model(self, model_name: str) -> None:
@@ -1194,7 +1187,7 @@ class ModelManager(object):
             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 @@ class ModelManager(object):
         using_fp16 = self.precision == "float16"
 
         vae_args.update(
-            cache_dir=config.cache_dir,
+            cache_dir=self.globals.cache_dir,
-            local_files_only=not config.internet_available,
+            local_files_only=not self.globals.internet_available,
         )
 
         self.logger.debug(f"Loading diffusers VAE from {name_or_path}")
@@ -1294,7 +1287,7 @@ class ModelManager(object):
 
     @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 @@ class ModelManager(object):
 
     @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:

invokeai/backend/prompting/conditioning.py

@@ -25,8 +25,6 @@ from invokeai.app.services.config import get_invokeai_config
 from ..stable_diffusion import InvokeAIDiffuserComponent
 from ..util import torch_dtype
 
-config = get_invokeai_config()
-
 def get_uc_and_c_and_ec(prompt_string,
                         model: InvokeAIDiffuserComponent,
                         log_tokens=False, skip_normalize_legacy_blend=False):
@@ -41,6 +39,8 @@ def get_uc_and_c_and_ec(prompt_string,
                     truncate_long_prompts=False,
                     )
     
+    config = get_invokeai_config()
+
     # get rid of any newline characters
     prompt_string = prompt_string.replace("\n", " ")
     positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)

invokeai/backend/restoration/codeformer.py

@@ -7,7 +7,6 @@ import torch
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import get_invokeai_config
-config = get_invokeai_config()
 
 pretrained_model_url = (
     "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
@@ -18,11 +17,11 @@ class CodeFormerRestoration:
     def __init__(
         self, codeformer_dir="models/codeformer", codeformer_model_path="codeformer.pth"
     ) -> None:
-        if not os.path.isabs(codeformer_dir):
-            codeformer_dir = os.path.join(config.root, codeformer_dir)
 
-        self.model_path = os.path.join(codeformer_dir, codeformer_model_path)
+        self.globals = get_invokeai_config()
-        self.codeformer_model_exists = os.path.isfile(self.model_path)
+        codeformer_dir = self.globals.root_dir / codeformer_dir
+        self.model_path = codeformer_dir / codeformer_model_path
+        self.codeformer_model_exists = self.model_path.exists()
 
         if not self.codeformer_model_exists:
             logger.error("NOT FOUND: CodeFormer model not found at " + self.model_path)
@@ -72,9 +71,7 @@ class CodeFormerRestoration:
                 upscale_factor=1,
                 use_parse=True,
                 device=device,
-                model_rootpath=os.path.join(
+                model_rootpath = self.globals.root_dir / "gfpgan" / "weights"
-                    config.root, "models", "gfpgan", "weights"
-                ),
             )
             face_helper.clean_all()
             face_helper.read_image(bgr_image_array)

invokeai/backend/restoration/gfpgan.py

@@ -8,14 +8,12 @@ from PIL import Image
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import get_invokeai_config
-config = get_invokeai_config()
 
 class GFPGAN:
     def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
+        self.globals = get_invokeai_config()
         if not os.path.isabs(gfpgan_model_path):
-            gfpgan_model_path = os.path.abspath(
+            gfpgan_model_path = self.globals.root_dir / gfpgan_model_path
-                os.path.join(config.root, gfpgan_model_path)
-            )
         self.model_path = gfpgan_model_path
         self.gfpgan_model_exists = os.path.isfile(self.model_path)
 
@@ -34,7 +32,7 @@ class GFPGAN:
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=UserWarning)
             cwd = os.getcwd()
-            os.chdir(os.path.join(config.root, "models"))
+            os.chdir(self.globals.root_dir / 'models')
             try:
                 from gfpgan import GFPGANer
 

invokeai/backend/restoration/realesrgan.py

@@ -1,4 +1,3 @@
-import os
 import warnings
 
 import numpy as np
@@ -7,7 +6,8 @@ from PIL import Image
 from PIL.Image import Image as ImageType
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import Globals
+from invokeai.app.services.config import get_invokeai_config
+config = get_invokeai_config()
 
 class ESRGAN:
     def __init__(self, bg_tile_size=400) -> None:
@@ -30,12 +30,8 @@ class ESRGAN:
             upscale=4,
             act_type="prelu",
         )
-        model_path = os.path.join(
+        model_path = config.root_dir / "models/realesrgan/realesr-general-x4v3.pth"
-            Globals.root, "models/realesrgan/realesr-general-x4v3.pth"
+        wdn_model_path = config.root_dir / "models/realesrgan/realesr-general-wdn-x4v3.pth"
-        )
-        wdn_model_path = os.path.join(
-            Globals.root, "models/realesrgan/realesr-general-wdn-x4v3.pth"
-        )
         scale = 4
 
         bg_upsampler = RealESRGANer(

invokeai/backend/safety_checker.py

@@ -18,8 +18,6 @@ import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import get_invokeai_config
 from .util import CPU_DEVICE
 
-config = get_invokeai_config()
-
 class SafetyChecker(object):
     CAUTION_IMG = "caution.png"
     
@@ -28,6 +26,7 @@ class SafetyChecker(object):
         caution = Image.open(path)
         self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
         self.device = device
+        config = get_invokeai_config()
 
         try:
             safety_model_id = "CompVis/stable-diffusion-safety-checker"

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

invokeai/backend/training/textual_inversion_training.py

@@ -7,7 +7,6 @@
 This is the backend to "textual_inversion.py"
 """
 
-import argparse
 import logging
 import math
 import os
@@ -47,7 +46,7 @@ from tqdm.auto import tqdm
 from transformers import CLIPTextModel, CLIPTokenizer
 
 # invokeai stuff
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config import InvokeAIAppConfig,PagingArgumentParser
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
     PIL_INTERPOLATION = {
@@ -89,10 +88,9 @@ def save_progress(
 
 
 def parse_args():
-    config = InvokeAIAppConfig()
+    config = InvokeAIAppConfig(argv=[])
-    
     parser = PagingArgumentParser(
-        description="Textual inversion training", formatter_class=ArgFormatter
+        description="Textual inversion training"
     )
     general_group = parser.add_argument_group("General")
     model_group = parser.add_argument_group("Models and Paths")
@@ -529,6 +527,7 @@ def get_full_repo_name(
 
 
 def do_textual_inversion_training(
+    config: InvokeAIAppConfig,
     model: str,
     train_data_dir: Path,
     output_dir: Path,
@@ -629,7 +628,7 @@ def do_textual_inversion_training(
         elif output_dir is not None:
             os.makedirs(output_dir, exist_ok=True)
 
-    models_conf = OmegaConf.load(os.path.join(config.root, "configs/models.yaml"))
+    models_conf = OmegaConf.load(config.model_conf_path)
     model_conf = models_conf.get(model, None)
     assert model_conf is not None, f"Unknown model: {model}"
     assert (
@@ -641,7 +640,7 @@ def do_textual_inversion_training(
     assert (
         pretrained_model_name_or_path
     ), f"models.yaml error: neither 'repo_id' nor 'path' is defined for {model}"
-    pipeline_args = dict(cache_dir=config.cache_dir())
+    pipeline_args = dict(cache_dir=config.cache_dir)
 
     # Load tokenizer
     if tokenizer_name:

invokeai/backend/util/devices.py

@@ -9,10 +9,10 @@ from invokeai.app.services.config import get_invokeai_config
 CPU_DEVICE = torch.device("cpu")
 CUDA_DEVICE = torch.device("cuda")
 MPS_DEVICE = torch.device("mps")
-config = get_invokeai_config()
 
 def choose_torch_device() -> torch.device:
     """Convenience routine for guessing which GPU device to run model on"""
+    config = get_invokeai_config()    
     if config.always_use_cpu:
         return CPU_DEVICE
     if torch.cuda.is_available():
@@ -32,6 +32,7 @@ def choose_precision(device: torch.device) -> str:
 
 
 def torch_dtype(device: torch.device) -> torch.dtype:
+    config = get_invokeai_config()
     if config.full_precision:
         return torch.float32
     if choose_precision(device) == "float16":

invokeai/frontend/training/textual_inversion.py

@@ -21,14 +21,17 @@ from npyscreen import widget
 from omegaconf import OmegaConf
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import Globals, global_set_root
 
-from ...backend.training import do_textual_inversion_training, parse_args
+from invokeai.app.services.config import get_invokeai_config
+from ...backend.training import (
+    do_textual_inversion_training,
+    parse_args
+)
 
 TRAINING_DATA = "text-inversion-training-data"
 TRAINING_DIR = "text-inversion-output"
 CONF_FILE = "preferences.conf"
-
+config = None
 
 class textualInversionForm(npyscreen.FormMultiPageAction):
     resolutions = [512, 768, 1024]
@@ -122,7 +125,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             value=str(
                 saved_args.get(
                     "train_data_dir",
-                    Path(Globals.root) / TRAINING_DATA / default_placeholder_token,
+                    config.root_dir / TRAINING_DATA / default_placeholder_token,
                 )
             ),
             scroll_exit=True,
@@ -135,7 +138,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             value=str(
                 saved_args.get(
                     "output_dir",
-                    Path(Globals.root) / TRAINING_DIR / default_placeholder_token,
+                    config.root_dir / TRAINING_DIR / default_placeholder_token,
                 )
             ),
             scroll_exit=True,
@@ -241,9 +244,9 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
         placeholder = self.placeholder_token.value
         self.prompt_token.value = f"(Trigger by using <{placeholder}> in your prompts)"
         self.train_data_dir.value = str(
-            Path(Globals.root) / TRAINING_DATA / placeholder
+            config.root_dir / TRAINING_DATA / placeholder
         )
-        self.output_dir.value = str(Path(Globals.root) / TRAINING_DIR / placeholder)
+        self.output_dir.value = str(config.root_dir / TRAINING_DIR / placeholder)
         self.resume_from_checkpoint.value = Path(self.output_dir.value).exists()
 
     def on_ok(self):
@@ -284,7 +287,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             return True
 
     def get_model_names(self) -> Tuple[List[str], int]:
-        conf = OmegaConf.load(os.path.join(Globals.root, "configs/models.yaml"))
+        conf = OmegaConf.load(config.root_dir / "configs/models.yaml")
         model_names = [
             idx
             for idx in sorted(list(conf.keys()))
@@ -367,7 +370,7 @@ def copy_to_embeddings_folder(args: dict):
     """
     source = Path(args["output_dir"], "learned_embeds.bin")
     dest_dir_name = args["placeholder_token"].strip("<>")
-    destination = Path(Globals.root, "embeddings", dest_dir_name)
+    destination = config.root_dir / "embeddings" / dest_dir_name
     os.makedirs(destination, exist_ok=True)
     logger.info(f"Training completed. Copying learned_embeds.bin into {str(destination)}")
     shutil.copy(source, destination)
@@ -383,7 +386,7 @@ def save_args(args: dict):
     """
     Save the current argument values to an omegaconf file
     """
-    dest_dir = Path(Globals.root) / TRAINING_DIR
+    dest_dir = config.root_dir / TRAINING_DIR
     os.makedirs(dest_dir, exist_ok=True)
     conf_file = dest_dir / CONF_FILE
     conf = OmegaConf.create(args)
@@ -394,7 +397,7 @@ def previous_args() -> dict:
     """
     Get the previous arguments used.
     """
-    conf_file = Path(Globals.root) / TRAINING_DIR / CONF_FILE
+    conf_file = config.root_dir / TRAINING_DIR / CONF_FILE
     try:
         conf = OmegaConf.load(conf_file)
         conf["placeholder_token"] = conf["placeholder_token"].strip("<>")
@@ -420,7 +423,7 @@ def do_front_end(args: Namespace):
         save_args(args)
 
         try:
-            do_textual_inversion_training(**args)
+            do_textual_inversion_training(get_invokeai_config(),**args)
             copy_to_embeddings_folder(args)
         except Exception as e:
             logger.error("An exception occurred during training. The exception was:")
@@ -430,13 +433,20 @@ def do_front_end(args: Namespace):
 
 
 def main():
+    global config
+    
     args = parse_args()
-    global_set_root(args.root_dir or Globals.root)
+    config = get_invokeai_config(argv=[])
+
+    # change root if needed
+    if args.root_dir:
+        config.root = args.root_dir
+    
     try:
         if args.front_end:
             do_front_end(args)
         else:
-            do_textual_inversion_training(**vars(args))
+            do_textual_inversion_training(config,**vars(args))
     except AssertionError as e:
         logger.error(e)
         sys.exit(-1)

tests/preflight_prompts.txt

@@ -1,4 +0,0 @@
-banana sushi -Ak_lms -W640 -H480 -S42 -s20
-banana sushi -Ak_lms -S42 -G1 -U 2 0.5 -s20
-banana sushi -Ak_lms -S42 -v0.2 -n3 -s20
-banana sushi -Ak_lms -S42 -V1349749425:0.1,4145759947:0.1 -s20

tests/test_config.py

@@ -58,6 +58,11 @@ def test_env_override():
     conf = InvokeAIAppConfig(conf=init1,argv=['--max_loaded=10'])
     assert conf.nsfw_checker==True
 
+    # environment variables should be case insensitive
+    os.environ['InvokeAI_Max_Loaded_Models'] = '15'
+    conf = InvokeAIAppConfig(conf=init1)
+    assert conf.max_loaded_models == 15
+
     conf = InvokeAIAppConfig(conf=init1,argv=['--no-nsfw_checker','--max_loaded=10'])
     assert conf.nsfw_checker==False
     assert conf.max_loaded_models==10

tests/validate_pr_prompt.txt

@@ -1,4 +1,3 @@
 t2i --positive_prompt 'banana sushi' --seed 42
 compel --prompt 'strawberry sushi' | compel | noise | t2l --scheduler heun --steps 3 --scheduler ddim --link -3 conditioning positive_conditioning --link -2 conditioning negative_conditioning | l2i
-compel --prompt 'banana sushi' | compel | noise | t2i --scheduler heun --steps 3 --scheduler euler_a --link -3 conditioning positive_conditioning --link -2 conditioning negative_conditioning
+compel --prompt 'banana sushi' | compel | noise | t2l --scheduler heun --steps 3 --scheduler euler_a --link -3 conditioning positive_conditioning --link -2 conditioning negative_conditioning
-