resolve some undefined symbols in model_cache

.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
@@ -105,12 +100,6 @@ jobs:
         id: run-pytest
         run: pytest
 
-      - name: set INVOKEAI_OUTDIR
-        run: >
-          python -c
-          "import os;from invokeai.backend.globals import Globals;OUTDIR=os.path.join(Globals.root,str('outputs'));print(f'INVOKEAI_OUTDIR={OUTDIR}')"
-          >> ${{ matrix.github-env }}
-
       - name: run invokeai-configure
         id: run-preload-models
         env:
@@ -129,15 +118,20 @@ jobs:
           HF_HUB_OFFLINE: 1
           HF_DATASETS_OFFLINE: 1
           TRANSFORMERS_OFFLINE: 1
+          INVOKEAI_OUTDIR: ${{ github.workspace }}/results
         run: >
           invokeai
           --no-patchmatch
           --no-nsfw_checker
-          --from_file ${{ env.TEST_PROMPTS }}
+          --precision=float32
+          --always_use_cpu
           --outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
+          --from_file ${{ env.TEST_PROMPTS }}
 
       - name: Archive results
         id: archive-results
+        env:
+          INVOKEAI_OUTDIR: ${{ github.workspace }}/results
         uses: actions/upload-artifact@v3
         with:
           name: results

.gitignore

@@ -201,6 +201,8 @@ checkpoints
 # If it's a Mac
 .DS_Store
 
+invokeai/frontend/web/dist/*
+
 # Let the frontend manage its own gitignore
 !invokeai/frontend/web/*
 

invokeai/app/api/dependencies.py

@@ -7,7 +7,6 @@ from typing import types
 
 from ..services.default_graphs import create_system_graphs
 from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
-from ...backend import Globals
 from ..services.restoration_services import RestorationServices
 from ..services.graph import GraphExecutionState, LibraryGraph
 from ..services.image_storage import DiskImageStorage
@@ -42,17 +41,8 @@ class ApiDependencies:
 
     invoker: Invoker = None
 
-    @staticmethod
     def initialize(config, event_handler_id: int, logger: types.ModuleType=logger):
-        Globals.try_patchmatch = config.patchmatch
+        logger.info(f"Internet connectivity is {config.internet_available}")
-        Globals.always_use_cpu = config.always_use_cpu
-        Globals.internet_available = config.internet_available and check_internet()
-        Globals.disable_xformers = not config.xformers
-        Globals.ckpt_convert = config.ckpt_convert
-
-        # TO DO: Use the config to select the logger rather than use the default
-        # invokeai logging module
-        logger.info(f"Internet connectivity is {Globals.internet_available}")
 
         events = FastAPIEventService(event_handler_id)
 
@@ -72,7 +62,6 @@ class ApiDependencies:
         services = InvocationServices(
             model_manager=ModelManagerService(config,logger),
             events=events,
-            logger=logger,
             latents=latents,
             images=images,
             metadata=metadata,
@@ -85,6 +74,8 @@ class ApiDependencies:
             ),
             processor=DefaultInvocationProcessor(),
             restoration=RestorationServices(config,logger),
+            configuration=config,
+            logger=logger,
         )
 
         create_system_graphs(services.graph_library)

invokeai/app/api_app.py

@@ -13,11 +13,11 @@ from fastapi_events.handlers.local import local_handler
 from fastapi_events.middleware import EventHandlerASGIMiddleware
 from pydantic.schema import schema
 
-from ..backend import Args
 from .api.dependencies import ApiDependencies
 from .api.routers import images, sessions, models
 from .api.sockets import SocketIO
 from .invocations.baseinvocation import BaseInvocation
+from .services.config import InvokeAIAppConfig
 
 # Create the app
 # TODO: create this all in a method so configuration/etc. can be passed in?
@@ -33,30 +33,25 @@ app.add_middleware(
     middleware_id=event_handler_id,
 )
 
-# Add CORS
-# TODO: use configuration for this
-origins = []
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=origins,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
 socket_io = SocketIO(app)
 
-config = {}
+# initialize config
-
+# this is a module global
+app_config = InvokeAIAppConfig()
 
 # Add startup event to load dependencies
 @app.on_event("startup")
 async def startup_event():
-    config = Args()
+    app.add_middleware(
-    config.parse_args()
+        CORSMiddleware,
+        allow_origins=app_config.allow_origins,
+        allow_credentials=app_config.allow_credentials,
+        allow_methods=app_config.allow_methods,
+        allow_headers=app_config.allow_headers,
+    )
 
     ApiDependencies.initialize(
-        config=config, event_handler_id=event_handler_id, logger=logger
+        config=app_config, event_handler_id=event_handler_id, logger=logger
     )
 
 
@@ -148,14 +143,11 @@ app.mount("/", StaticFiles(directory="invokeai/frontend/web/dist", html=True), n
 
 def invoke_api():
     # Start our own event loop for eventing usage
-    # TODO: determine if there's a better way to do this
     loop = asyncio.new_event_loop()
-    config = uvicorn.Config(app=app, host="0.0.0.0", port=9090, loop=loop)
+    config = uvicorn.Config(app=app, host=app_config.host, port=app_config.port, loop=loop)
     # Use access_log to turn off logging
-
     server = uvicorn.Server(config)
     loop.run_until_complete(server.serve())
 
-
 if __name__ == "__main__":
     invoke_api()

invokeai/app/cli/commands.py

@@ -285,3 +285,19 @@ class DrawExecutionGraphCommand(BaseCommand):
         nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
         plt.axis("off")
         plt.show()
+
+class SortedHelpFormatter(argparse.HelpFormatter):
+    def _iter_indented_subactions(self, action):
+        try:
+            get_subactions = action._get_subactions
+        except AttributeError:
+            pass
+        else:
+            self._indent()
+            if isinstance(action, argparse._SubParsersAction):
+                for subaction in sorted(get_subactions(), key=lambda x: x.dest):
+                    yield subaction
+            else:
+                for subaction in get_subactions():
+                    yield subaction
+                self._dedent()

invokeai/app/cli/completer.py

@@ -11,9 +11,10 @@ from pathlib import Path
 from typing import List, Dict, Literal, get_args, get_type_hints, get_origin
 
 import invokeai.backend.util.logging as logger
-from ...backend import ModelManager, Globals
+from ...backend import ModelManager
 from ..invocations.baseinvocation import BaseInvocation
 from .commands import BaseCommand
+from ..services.invocation_services import InvocationServices
 
 # singleton object, class variable
 completer = None
@@ -131,13 +132,13 @@ class Completer(object):
             readline.redisplay()
             self.linebuffer = None
     
-def set_autocompleter(model_manager: ModelManager) -> Completer:
+def set_autocompleter(services: InvocationServices) -> Completer:
     global completer
     
     if completer:
         return completer
     
-    completer = Completer(model_manager)
+    completer = Completer(services.model_manager)
 
     readline.set_completer(completer.complete)
     # pyreadline3 does not have a set_auto_history() method
@@ -153,7 +154,7 @@ def set_autocompleter(model_manager: ModelManager) -> Completer:
     readline.parse_and_bind("set skip-completed-text on")
     readline.parse_and_bind("set show-all-if-ambiguous on")
 
-    histfile = Path(Globals.root, ".invoke_history")
+    histfile = Path(services.configuration.root_dir / ".invoke_history")
     try:
         readline.read_history_file(histfile)
         readline.set_history_length(1000)

invokeai/app/cli_app.py

@@ -4,13 +4,14 @@ import argparse
 import os
 import re
 import shlex
+import sys
 import time
 from typing import (
     Union,
     get_type_hints,
 )
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ValidationError
 from pydantic.fields import Field
 
 import invokeai.backend.util.logging as logger
@@ -20,10 +21,7 @@ from invokeai.app.services.metadata import PngMetadataService
 from .services.default_graphs import create_system_graphs
 from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
 
-from ..backend import Args
+from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers, SortedHelpFormatter
-from ..backend import Globals   # this should go when pr 3340 merged
-
-from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers
 from .cli.completer import set_autocompleter
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
@@ -37,6 +35,7 @@ from .services.invoker import Invoker
 from .services.processor import DefaultInvocationProcessor
 from .services.sqlite import SqliteItemStorage
 from .services.model_manager_service import ModelManagerService
+from .services.config import get_invokeai_config
 
 class CliCommand(BaseModel):
     command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type")  # type: ignore
@@ -66,7 +65,7 @@ def add_invocation_args(command_parser):
 
 def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
     # Create invocation parser
-    parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser(formatter_class=SortedHelpFormatter)
 
     def exit(*args, **kwargs):
         raise InvalidArgs
@@ -191,28 +190,26 @@ def invoke_all(context: CliContext):
 
 
 def invoke_cli():
-    config = Args()
+    # this gets the basic configuration
-    config.parse_args()
+    config = get_invokeai_config()
 
-    logger.info(f"{invokeai.version.__app_name__}, version {invokeai.version.__version__}")
+    # get the optional list of invocations to execute on the command line
-    logger.info(f'InvokeAI runtime directory is "{Globals.root}"')
+    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 = ModelManagerService(config,logger)
-
-    # This initializes the autocompleter and returns it.
-    # Currently nothing is done with the returned Completer
-    # object, but the object can be used to change autocompletion
-    # behavior on the fly, if desired.
     set_autocompleter(model_manager)
 
     events = EventServiceBase()
-
+    output_folder = config.output_path
     metadata = PngMetadataService()
 
-    output_folder = os.path.abspath(
-        os.path.join(os.path.dirname(__file__), "../../../outputs")
-    )
-
     # TODO: build a file/path manager?
     db_location = os.path.join(output_folder, "invokeai.db")
 
@@ -232,6 +229,7 @@ def invoke_cli():
         processor=DefaultInvocationProcessor(),
         restoration=RestorationServices(config,logger=logger),
         logger=logger,
+        configuration=config,
     )
 
     system_graphs = create_system_graphs(services.graph_library)
@@ -247,9 +245,17 @@ def invoke_cli():
     # print(services.session_manager.list())
 
     context = CliContext(invoker, session, parser)
+    set_autocompleter(services)
 
-    while True:
+    command_line_args_exist = len(invocation_commands) > 0
+    done = False
+    
+    while not done:
         try:
+            if command_line_args_exist:
+                cmd_input = invocation_commands.pop(0)
+                done = len(invocation_commands) == 0
+            else:
                 cmd_input = input("invoke> ")
         except (KeyboardInterrupt, EOFError):
             # Ctrl-c exits
@@ -374,6 +380,9 @@ def invoke_cli():
             invoker.services.logger.warning('Invalid command, use "help" to list commands')
             continue
 
+        except ValidationError:
+            invoker.services.logger.warning('Invalid command arguments, run "<command> --help" for summary')
+
         except SessionError:
             # Start a new session
             invoker.services.logger.warning("Session error: creating a new session")

invokeai/app/invocations/compel.py

@@ -5,10 +5,8 @@ from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationCont
 
 from .model import ClipField
 
-from ...backend.util.devices import choose_torch_device, torch_dtype
+from ...backend.util.devices import torch_dtype
 from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
-from ...backend.stable_diffusion.textual_inversion_manager import TextualInversionManager
-from ...backend.model_management import SDModelType
 
 from compel import Compel
 from compel.prompt_parser import (
@@ -18,8 +16,6 @@ from compel.prompt_parser import (
     Fragment,
 )
 
-from invokeai.backend.globals import Globals
-
 
 class ConditioningField(BaseModel):
     conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
@@ -91,7 +87,7 @@ class CompelInvocation(BaseInvocation):
 
             prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(self.prompt)
 
-            if getattr(Globals, "log_tokenization", False):
+            if context.services.configuration.log_tokenization:
                 log_tokenization_for_prompt_object(prompt, tokenizer)
 
             c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)

invokeai/app/invocations/math.py

@@ -5,7 +5,12 @@ from typing import Literal
 from pydantic import BaseModel, Field
 import numpy as np
 
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    InvocationContext,
+    InvocationConfig,
+)
 
 
 class MathInvocationConfig(BaseModel):
@@ -22,19 +27,21 @@ class MathInvocationConfig(BaseModel):
 
 class IntOutput(BaseInvocationOutput):
     """An integer output"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["int_output"] = "int_output"
     a: int = Field(default=None, description="The output integer")
-    #fmt: on
+    # fmt: on
 
 
 class AddInvocation(BaseInvocation, MathInvocationConfig):
     """Adds two numbers"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["add"] = "add"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a + self.b)
@@ -42,11 +49,12 @@ class AddInvocation(BaseInvocation, MathInvocationConfig):
 
 class SubtractInvocation(BaseInvocation, MathInvocationConfig):
     """Subtracts two numbers"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["sub"] = "sub"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a - self.b)
@@ -54,11 +62,12 @@ class SubtractInvocation(BaseInvocation, MathInvocationConfig):
 
 class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
     """Multiplies two numbers"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["mul"] = "mul"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a * self.b)
@@ -66,11 +75,12 @@ class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
 
 class DivideInvocation(BaseInvocation, MathInvocationConfig):
     """Divides two numbers"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["div"] = "div"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=int(self.a / self.b))
@@ -78,8 +88,13 @@ class DivideInvocation(BaseInvocation, MathInvocationConfig):
 
 class RandomIntInvocation(BaseInvocation):
     """Outputs a single random integer."""
-    #fmt: off
+
+    # fmt: off
     type: Literal["rand_int"] = "rand_int"
-    #fmt: on
+    low: int = Field(default=0, description="The inclusive low value")
+    high: int = Field(
+        default=np.iinfo(np.int32).max, description="The exclusive high value"
+    )
+    # fmt: on
     def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=np.random.randint(0, np.iinfo(np.int32).max))
+        return IntOutput(a=np.random.randint(self.low, self.high))

invokeai/app/services/config.py

@@ -0,0 +1,528 @@
+# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
+
+'''Invokeai configuration system.
+
+Arguments and fields are taken from the pydantic definition of the
+model.  Defaults can be set by creating a yaml configuration file that
+has a top-level key of "InvokeAI" and subheadings for each of the
+categories returned by `invokeai --help`. The file looks like this:
+
+[file: invokeai.yaml]
+
+InvokeAI:
+  Paths:
+    root: /home/lstein/invokeai-main
+    conf_path: configs/models.yaml
+    legacy_conf_dir: configs/stable-diffusion
+    outdir: outputs
+    embedding_dir: embeddings
+    lora_dir: loras
+    autoconvert_dir: null
+    gfpgan_model_dir: models/gfpgan/GFPGANv1.4.pth
+  Models:
+    model: stable-diffusion-1.5
+    embeddings: true
+  Memory/Performance:
+    xformers_enabled: false
+    sequential_guidance: false
+    precision: float16
+    max_loaded_models: 4
+    always_use_cpu: false
+    free_gpu_mem: false
+  Features:
+    nsfw_checker: true
+    restore: true
+    esrgan: true
+    patchmatch: true
+    internet_available: true
+    log_tokenization: false
+  Web Server:
+    host: 127.0.0.1
+    port: 8081
+    allow_origins: []
+    allow_credentials: true
+    allow_methods:
+    - '*'
+    allow_headers:
+    - '*'
+
+The default name of the configuration file is `invokeai.yaml`, located
+in INVOKEAI_ROOT. You can replace supersede this by providing any
+OmegaConf dictionary object initialization time:
+
+ omegaconf = OmegaConf.load('/tmp/init.yaml')
+ conf = InvokeAIAppConfig(conf=omegaconf)
+
+By default, InvokeAIAppConfig will parse the contents of `sys.argv` at
+initialization time. You may pass a list of strings in the optional
+`argv` argument to use instead of the system argv:
+
+ conf = InvokeAIAppConfig(arg=['--xformers_enabled'])
+
+It is also possible to set a value at initialization time. This value
+has highest priority.
+
+ conf = InvokeAIAppConfig(xformers_enabled=True)
+
+Any setting can be overwritten by setting an environment variable of
+form: "INVOKEAI_<setting>", as in:
+
+  export INVOKEAI_port=8080
+
+Order of precedence (from highest):
+   1) initialization options
+   2) command line options
+   3) environment variable options
+   4) config file options
+   5) pydantic defaults
+
+Typical usage:
+
+ from invokeai.app.services.config import InvokeAIAppConfig
+ from invokeai.invocations.generate import TextToImageInvocation
+
+ # get global configuration and print its nsfw_checker value
+ conf = InvokeAIAppConfig()
+ print(conf.nsfw_checker)
+
+ # get the text2image invocation and print its step value
+ text2image = TextToImageInvocation()
+ print(text2image.steps)
+
+Computed properties:
+
+The InvokeAIAppConfig object has a series of properties that
+resolve paths relative to the runtime root directory. They each return
+a Path object:
+
+ root_path          - path to InvokeAI root
+ output_path        - path to default outputs directory
+ model_conf_path    - path to models.yaml
+ conf               - alias for the above
+ embedding_path     - path to the embeddings directory
+ lora_path          - path to the LoRA directory
+
+In most cases, you will want to create a single InvokeAIAppConfig
+object for the entire application. The get_invokeai_config() function
+does this:
+
+  config = get_invokeai_config()
+  print(config.root)
+
+# Subclassing
+
+If you wish to create a similar class, please subclass the
+`InvokeAISettings` class and define a Literal field named "type",
+which is set to the desired top-level name.  For example, to create a
+"InvokeBatch" configuration, define like this:
+
+  class InvokeBatch(InvokeAISettings):
+     type: Literal["InvokeBatch"] = "InvokeBatch"
+     node_count : int = Field(default=1, description="Number of nodes to run on", category='Resources')
+     cpu_count  : int = Field(default=8, description="Number of GPUs to run on per node", category='Resources')
+
+This will now read and write from the "InvokeBatch" section of the
+config file, look for environment variables named INVOKEBATCH_*, and
+accept the command-line arguments `--node_count` and `--cpu_count`. The
+two configs are kept in separate sections of the config file:
+
+  # invokeai.yaml
+
+  InvokeBatch:
+     Resources:
+        node_count: 1
+        cpu_count: 8
+
+  InvokeAI:
+     Paths:
+        root: /home/lstein/invokeai-main
+        conf_path: configs/models.yaml
+        legacy_conf_dir: configs/stable-diffusion
+        outdir: outputs
+     ...
+'''
+import argparse
+import pydoc
+import typing
+import os
+import sys
+from argparse import ArgumentParser
+from omegaconf import OmegaConf, DictConfig
+from pathlib import Path
+from pydantic import BaseSettings, Field, parse_obj_as
+from typing import Any, ClassVar, Dict, List, Literal, Type, Union, get_origin, get_type_hints, get_args
+
+INIT_FILE = Path('invokeai.yaml')
+LEGACY_INIT_FILE = Path('invokeai.init')
+
+# This global stores a singleton InvokeAIAppConfig configuration object
+global_config = None
+
+class InvokeAISettings(BaseSettings):
+    '''
+    Runtime configuration settings in which default values are
+    read from an omegaconf .yaml file.
+    '''
+    initconf             : ClassVar[DictConfig] = None
+    argparse_groups      : ClassVar[Dict] = {}
+
+    def parse_args(self, argv: list=sys.argv[1:]):
+        parser = self.get_parser()
+        opt, _ = parser.parse_known_args(argv)
+        for name in self.__fields__:
+            if name not in self._excluded():
+                setattr(self, name, getattr(opt,name))
+
+    def to_yaml(self)->str:
+        """
+        Return a YAML string representing our settings. This can be used
+        as the contents of `invokeai.yaml` to restore settings later.
+        """
+        cls = self.__class__
+        type = get_args(get_type_hints(cls)['type'])[0]
+        field_dict = dict({type:dict()})
+        for name,field in self.__fields__.items():
+            if name in cls._excluded():
+                continue
+            category = field.field_info.extra.get("category") or "Uncategorized"
+            value = getattr(self,name)
+            if category not in field_dict[type]:
+                field_dict[type][category] = dict()
+            # keep paths as strings to make it easier to read
+            field_dict[type][category][name] = str(value) if isinstance(value,Path) else value
+        conf = OmegaConf.create(field_dict)
+        return OmegaConf.to_yaml(conf)
+
+    @classmethod
+    def add_parser_arguments(cls, parser):
+        if 'type' in get_type_hints(cls):
+            settings_stanza = get_args(get_type_hints(cls)['type'])[0]
+        else:
+            settings_stanza = "Uncategorized"
+
+        env_prefix = cls.Config.env_prefix if hasattr(cls.Config,'env_prefix') else settings_stanza.upper()
+
+        initconf = cls.initconf.get(settings_stanza) \
+            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
+
+                category = field.field_info.extra.get("category","Uncategorized")
+                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.upper() in upcase_environ:
+                    field.default = upcase_environ[env_name.upper()]
+                cls.add_field_argument(parser, name, field)
+
+                field.default = current_default
+
+    @classmethod
+    def cmd_name(self, command_field: str='type')->str:
+        hints = get_type_hints(self)
+        if command_field in hints:
+            return get_args(hints[command_field])[0]
+        else:
+            return 'Uncategorized'
+
+    @classmethod
+    def get_parser(cls)->ArgumentParser:
+        parser = PagingArgumentParser(
+            prog=cls.cmd_name(),
+            description=cls.__doc__,
+        )
+        cls.add_parser_arguments(parser)
+        return parser
+
+    @classmethod
+    def add_subparser(cls, parser: argparse.ArgumentParser):
+        parser.add_parser(cls.cmd_name(), help=cls.__doc__)
+
+    @classmethod
+    def _excluded(self)->List[str]:
+        return ['type','initconf']
+
+    class Config:
+        env_file_encoding = 'utf-8'
+        arbitrary_types_allowed = True
+        case_sensitive = True
+
+    @classmethod
+    def add_field_argument(cls, command_parser, name: str, field, default_override = None):
+        field_type = get_type_hints(cls).get(name)
+        default = default_override if default_override is not None else field.default if field.default_factory is None else field.default_factory()
+        if category := field.field_info.extra.get("category"):
+            if category not in cls.argparse_groups:
+                cls.argparse_groups[category] = command_parser.add_argument_group(category)
+            argparse_group = cls.argparse_groups[category]
+        else:
+            argparse_group = command_parser
+
+        if get_origin(field_type) == Literal:
+            allowed_values = get_args(field.type_)
+            allowed_types = set()
+            for val in allowed_values:
+                allowed_types.add(type(val))
+            allowed_types_list = list(allowed_types)
+            field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list]  # type: ignore
+
+            argparse_group.add_argument(
+                f"--{name}",
+                dest=name,
+                type=field_type,
+                default=default,
+                choices=allowed_values,
+                help=field.field_info.description,
+            )
+
+        elif get_origin(field_type) == list:
+            argparse_group.add_argument(
+                f"--{name}",
+                dest=name,
+                nargs='*',
+                type=field.type_,
+                default=default,
+                action=argparse.BooleanOptionalAction if field.type_==bool else 'store',
+                help=field.field_info.description,
+            )
+        else:
+            argparse_group.add_argument(
+                f"--{name}",
+                dest=name,
+                type=field.type_,
+                default=default,
+                action=argparse.BooleanOptionalAction if field.type_==bool else 'store',
+                help=field.field_info.description,
+            )
+def _find_root()->Path:
+    if os.environ.get("INVOKEAI_ROOT"):
+        root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
+    elif (
+            os.environ.get("VIRTUAL_ENV")
+            and (Path(os.environ.get("VIRTUAL_ENV"), "..", INIT_FILE).exists()
+                 or
+                 Path(os.environ.get("VIRTUAL_ENV"), "..", LEGACY_INIT_FILE).exists()
+                 )
+    ):
+        root = Path(os.environ.get("VIRTUAL_ENV"), "..").resolve()
+    else:
+        root = Path("~/invokeai").expanduser().resolve()
+    return root
+
+class InvokeAIAppConfig(InvokeAISettings):
+    '''
+Generate images using Stable Diffusion. Use "invokeai" to launch
+the command-line client (recommended for experts only), or
+"invokeai-web" to launch the web server. Global options
+can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
+setting environment variables INVOKEAI_<setting>.
+     '''
+    #fmt: off
+    type: Literal["InvokeAI"] = "InvokeAI"
+    host                : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
+    port                : int = Field(default=9090, description="Port to bind to", category='Web Server')
+    allow_origins       : List[str] = Field(default=[], description="Allowed CORS origins", category='Web Server')
+    allow_credentials   : bool = Field(default=True, description="Allow CORS credentials", category='Web Server')
+    allow_methods       : List[str] = Field(default=["*"], description="Methods allowed for CORS", category='Web Server')
+    allow_headers       : List[str] = Field(default=["*"], description="Headers allowed for CORS", category='Web Server')
+
+    esrgan              : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
+    internet_available  : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
+    log_tokenization    : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
+    nsfw_checker        : bool = Field(default=True, description="Enable/disable the NSFW checker", category='Features')
+    patchmatch          : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
+    restore             : bool = Field(default=True, description="Enable/disable face restoration code", category='Features')
+
+    always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
+    free_gpu_mem        : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
+    max_loaded_models   : int = Field(default=2, gt=0, description="Maximum number of models to keep in memory for rapid switching", category='Memory/Performance')
+    precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='float16',description='Floating point precision', category='Memory/Performance')
+    sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
+    xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
+
+    root                : Path = Field(default=_find_root(), description='InvokeAI runtime root directory', category='Paths')
+    autoconvert_dir     : Path = Field(default=None, description='Path to a directory of ckpt files to be converted into diffusers and imported on startup.', category='Paths')
+    conf_path           : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
+    embedding_dir       : Path = Field(default='embeddings', description='Path to InvokeAI textual inversion aembeddings directory', category='Paths')
+    gfpgan_model_dir    : Path = Field(default="./models/gfpgan/GFPGANv1.4.pth", description='Path to GFPGAN models directory.', category='Paths')
+    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')
+    #fmt: on
+
+    def __init__(self, conf: DictConfig = None, argv: List[str]=None, **kwargs):
+        '''
+        Initialize InvokeAIAppconfig.
+        :param conf: alternate Omegaconf dictionary object
+        :param argv: aternate sys.argv list
+        :param **kwargs: attributes to initialize with
+        '''
+        super().__init__(**kwargs)
+
+        # Set the runtime root directory. We parse command-line switches here
+        # in order to pick up the --root_dir option.
+        self.parse_args(argv)
+        if conf is None:
+            try:
+                conf = OmegaConf.load(self.root_dir / INIT_FILE)
+            except:
+                pass
+        InvokeAISettings.initconf = conf
+
+        # parse args again in order to pick up settings in configuration file
+        self.parse_args(argv)
+
+        # restore initialization values
+        hints = get_type_hints(self)
+        for k in kwargs:
+            setattr(self,k,parse_obj_as(hints[k],kwargs[k]))
+
+    @property
+    def root_path(self)->Path:
+        '''
+        Path to the runtime root directory
+        '''
+        if self.root:
+            return Path(self.root).expanduser()
+        else:
+            return self.find_root()
+
+    @property
+    def root_dir(self)->Path:
+        '''
+        Alias for above.
+        '''
+        return self.root_path
+
+    def _resolve(self,partial_path:Path)->Path:
+        return (self.root_path / partial_path).resolve()
+
+    @property
+    def output_path(self)->Path:
+        '''
+        Path to defaults outputs directory.
+        '''
+        return self._resolve(self.outdir)
+
+    @property
+    def model_conf_path(self)->Path:
+        '''
+        Path to models configuration file.
+        '''
+        return self._resolve(self.conf_path)
+
+    @property
+    def legacy_conf_path(self)->Path:
+        '''
+        Path to directory of legacy configuration files (e.g. v1-inference.yaml)
+        '''
+        return self._resolve(self.legacy_conf_dir)
+
+    @property
+    def cache_dir(self)->Path:
+        '''
+        Path to the global cache directory for HuggingFace hub-managed models
+        '''
+        return self.models_dir / "hub"
+
+    @property
+    def models_dir(self)->Path:
+        '''
+        Path to the models directory
+        '''
+        return self._resolve("models")
+
+    @property
+    def converted_ckpts_dir(self)->Path:
+        '''
+        Path to the converted models
+        '''
+        return self._resolve("models/converted_ckpts")
+
+    @property
+    def embedding_path(self)->Path:
+        '''
+        Path to the textual inversion embeddings directory.
+        '''
+        return self._resolve(self.embedding_dir) if self.embedding_dir else None
+
+    @property
+    def lora_path(self)->Path:
+        '''
+        Path to the LoRA models directory.
+        '''
+        return self._resolve(self.lora_dir) if self.lora_dir else None
+
+    @property
+    def autoconvert_path(self)->Path:
+        '''
+        Path to the directory containing models to be imported automatically at startup.
+        '''
+        return self._resolve(self.autoconvert_dir) if self.autoconvert_dir else None
+
+    @property
+    def gfpgan_model_path(self)->Path:
+        '''
+        Path to the GFPGAN model.
+        '''
+        return self._resolve(self.gfpgan_model_dir) if self.gfpgan_model_dir else None
+
+    # the following methods support legacy calls leftover from the Globals era
+    @property
+    def full_precision(self)->bool:
+        """Return true if precision set to float32"""
+        return self.precision=='float32'
+
+    @property
+    def disable_xformers(self)->bool:
+        """Return true if xformers_enabled is false"""
+        return not self.xformers_enabled
+
+    @property
+    def try_patchmatch(self)->bool:
+        """Return true if patchmatch true"""
+        return self.patchmatch
+
+    @staticmethod
+    def find_root()->Path:
+        '''
+        Choose the runtime root directory when not specified on command line or
+        init file.
+        '''
+        return _find_root()
+
+
+class PagingArgumentParser(argparse.ArgumentParser):
+    '''
+    A custom ArgumentParser that uses pydoc to page its output.
+    It also supports reading defaults from an init file.
+    '''
+    def print_help(self, file=None):
+        text = self.format_help()
+        pydoc.pager(text)
+
+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(**kwargs)
+    return global_config

invokeai/app/services/graph.py

@@ -135,6 +135,7 @@ class GraphInvocationOutput(BaseInvocationOutput):
 
 # TODO: Fill this out and move to invocations
 class GraphInvocation(BaseInvocation):
+    """Execute a graph"""
     type: Literal["graph"] = "graph"
 
     # TODO: figure out how to create a default here
@@ -162,6 +163,7 @@ class IterateInvocationOutput(BaseInvocationOutput):
 
 # TODO: Fill this out and move to invocations
 class IterateInvocation(BaseInvocation):
+    """Iterates over a list of items"""
     type: Literal["iterate"] = "iterate"
 
     collection: list[Any] = Field(

invokeai/app/services/invocation_services.py

@@ -10,6 +10,7 @@ from .image_storage import ImageStorageBase
 from .restoration_services import RestorationServices
 from .invocation_queue import InvocationQueueABC
 from .item_storage import ItemStorageABC
+from .config import InvokeAISettings
 
 class InvocationServices:
     """Services that can be used by invocations"""
@@ -21,6 +22,7 @@ class InvocationServices:
     queue: InvocationQueueABC
     model_manager: ModelManager
     restoration: RestorationServices
+    configuration: InvokeAISettings
     
     # NOTE: we must forward-declare any types that include invocations, since invocations can use services
     graph_library: ItemStorageABC["LibraryGraph"]
@@ -40,6 +42,7 @@ class InvocationServices:
             graph_execution_manager: ItemStorageABC["GraphExecutionState"],
             processor: "InvocationProcessorABC",
             restoration: RestorationServices,
+            configuration: InvokeAISettings=None,
     ):
         self.model_manager = model_manager
         self.events = events
@@ -52,3 +55,4 @@ class InvocationServices:
         self.graph_execution_manager = graph_execution_manager
         self.processor = processor
         self.restoration = restoration
+        self.configuration = configuration

invokeai/backend/__init__.py

@@ -1,7 +1,6 @@
 """
 Initialization file for invokeai.backend
 """
-from .generate import Generate
 from .generator import (
     InvokeAIGeneratorBasicParams,
     InvokeAIGenerator,
@@ -12,5 +11,3 @@ from .generator import (
 )
 from .model_management import ModelManager, ModelCache, SDModelType, SDModelInfo
 from .safety_checker import SafetyChecker
-from .args import Args
-from .globals import Globals

invokeai/backend/config/invokeai_configure.py

@@ -19,10 +19,10 @@ import warnings
 from argparse import Namespace
 from pathlib import Path
 from shutil import get_terminal_size
+from typing import get_type_hints
 from urllib import request
 
 import npyscreen
-import torch
 import transformers
 from diffusers import AutoencoderKL
 from huggingface_hub import HfFolder
@@ -38,34 +38,40 @@ from transformers import (
 
 import invokeai.configs as configs
 
-from ...frontend.install.model_install import addModelsForm, process_and_execute
+from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
-from ...frontend.install.widgets import (
+from invokeai.frontend.install.widgets import (
     CenteredButtonPress,
     IntTitleSlider,
     set_min_terminal_size,
 )
-from ..args import PRECISION_CHOICES, Args
+from invokeai.backend.config.legacy_arg_parsing import legacy_parser
-from ..globals import Globals, global_cache_dir, global_config_dir, global_config_file
+from invokeai.backend.config.model_install_backend import (
-from .model_install_backend import (
     default_dataset,
     download_from_hf,
     hf_download_with_resume,
     recommended_datasets,
 )
+from invokeai.app.services.config import (
+    get_invokeai_config,
+    InvokeAIAppConfig,
+)
 
 warnings.filterwarnings("ignore")
 
 transformers.logging.set_verbosity_error()
 
+
 # --------------------------globals-----------------------
+config = get_invokeai_config()
+
 Model_dir = "models"
 Weights_dir = "ldm/stable-diffusion-v1/"
 
 # the initial "configs" dir is now bundled in the `invokeai.configs` package
 Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
 
-Default_config_file = Path(global_config_dir()) / "models.yaml"
+Default_config_file = config.model_conf_path
-SD_Configs = Path(global_config_dir()) / "stable-diffusion"
+SD_Configs = config.legacy_conf_path
 
 Datasets = OmegaConf.load(Dataset_path)
 
@@ -73,17 +79,12 @@ Datasets = OmegaConf.load(Dataset_path)
 MIN_COLS = 135
 MIN_LINES = 45
 
+PRECISION_CHOICES = ['auto','float16','float32','autocast']
+
 INIT_FILE_PREAMBLE = """# InvokeAI initialization file
 # This is the InvokeAI initialization file, which contains command-line default values.
 # Feel free to edit. If anything goes wrong, you can re-initialize this file by deleting
 # or renaming it and then running invokeai-configure again.
-# Place  frequently-used startup commands here, one or more per line.
-# Examples:
-# --outdir=D:\data\images
-# --no-nsfw_checker
-# --web --host=0.0.0.0
-# --steps=20
-# -Ak_euler_a -C10.0
 """
 
 
@@ -96,14 +97,13 @@ If you installed manually from source or with 'pip install': activate the virtua
 then run one of the following commands to start InvokeAI.
 
 Web UI:
-   invokeai --web # (connect to http://localhost:9090)
+   invokeai-web
-   invokeai --web --host 0.0.0.0 # (connect to http://your-lan-ip:9090 from another computer on the local network)
 
-Command-line interface:
+Command-line client:
    invokeai
 
 If you installed using an installation script, run:
-  {Globals.root}/invoke.{"bat" if sys.platform == "win32" else "sh"}
+  {config.root}/invoke.{"bat" if sys.platform == "win32" else "sh"}
 
 Add the '--help' argument to see all of the command-line switches available for use.
 """
@@ -216,11 +216,11 @@ def download_realesrgan():
     wdn_model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth"
 
     model_dest = os.path.join(
-        Globals.root, "models/realesrgan/realesr-general-x4v3.pth"
+        config.root, "models/realesrgan/realesr-general-x4v3.pth"
     )
 
     wdn_model_dest = os.path.join(
-        Globals.root, "models/realesrgan/realesr-general-wdn-x4v3.pth"
+        config.root, "models/realesrgan/realesr-general-wdn-x4v3.pth"
     )
 
     download_with_progress_bar(model_url, model_dest, "RealESRGAN")
@@ -243,7 +243,7 @@ def download_gfpgan():
             "./models/gfpgan/weights/parsing_parsenet.pth",
         ],
     ):
-        model_url, model_dest = model[0], os.path.join(Globals.root, model[1])
+        model_url, model_dest = model[0], os.path.join(config.root, model[1])
         download_with_progress_bar(model_url, model_dest, "GFPGAN weights")
 
 
@@ -253,7 +253,7 @@ def download_codeformer():
     model_url = (
         "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
     )
-    model_dest = os.path.join(Globals.root, "models/codeformer/codeformer.pth")
+    model_dest = os.path.join(config.root, "models/codeformer/codeformer.pth")
     download_with_progress_bar(model_url, model_dest, "CodeFormer")
 
 
@@ -295,7 +295,7 @@ def download_vaes():
         # first the diffusers version
         repo_id = "stabilityai/sd-vae-ft-mse"
         args = dict(
-            cache_dir=global_cache_dir("hub"),
+            cache_dir=config.cache_dir,
         )
         if not AutoencoderKL.from_pretrained(repo_id, **args):
             raise Exception(f"download of {repo_id} failed")
@@ -306,7 +306,7 @@ def download_vaes():
         if not hf_download_with_resume(
             repo_id=repo_id,
             model_name=model_name,
-            model_dir=str(Globals.root / Model_dir / Weights_dir),
+            model_dir=str(config.root / Model_dir / Weights_dir),
         ):
             raise Exception(f"download of {model_name} failed")
     except Exception as e:
@@ -321,8 +321,7 @@ def get_root(root: str = None) -> str:
     elif os.environ.get("INVOKEAI_ROOT"):
         return os.environ.get("INVOKEAI_ROOT")
     else:
-        return Globals.root
+        return config.root
-
 
 # -------------------------------------
 class editOptsForm(npyscreen.FormMultiPage):
@@ -332,7 +331,7 @@ class editOptsForm(npyscreen.FormMultiPage):
     def create(self):
         program_opts = self.parentApp.program_opts
         old_opts = self.parentApp.invokeai_opts
-        first_time = not (Globals.root / Globals.initfile).exists()
+        first_time = not (config.root / 'invokeai.yaml').exists()
         access_token = HfFolder.get_token()
         window_width, window_height = get_terminal_size()
         for i in [
@@ -366,7 +365,7 @@ class editOptsForm(npyscreen.FormMultiPage):
         self.outdir = self.add_widget_intelligent(
             npyscreen.TitleFilename,
             name="(<tab> autocompletes, ctrl-N advances):",
-            value=old_opts.outdir or str(default_output_dir()),
+            value=str(old_opts.outdir) or str(default_output_dir()),
             select_dir=True,
             must_exist=False,
             use_two_lines=False,
@@ -381,17 +380,17 @@ class editOptsForm(npyscreen.FormMultiPage):
             editable=False,
             color="CONTROL",
         )
-        self.safety_checker = self.add_widget_intelligent(
+        self.nsfw_checker = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="NSFW checker",
-            value=old_opts.safety_checker,
+            value=old_opts.nsfw_checker,
             relx=5,
             scroll_exit=True,
         )
         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(
@@ -435,17 +434,10 @@ class editOptsForm(npyscreen.FormMultiPage):
             relx=5,
             scroll_exit=True,
         )
-        self.xformers = self.add_widget_intelligent(
+        self.xformers_enabled = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="Enable xformers support if available",
-            value=old_opts.xformers,
+            value=old_opts.xformers_enabled,
-            relx=5,
-            scroll_exit=True,
-        )
-        self.ckpt_convert = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Load legacy checkpoint models into memory as diffusers models",
-            value=old_opts.ckpt_convert,
             relx=5,
             scroll_exit=True,
         )
@@ -480,19 +472,30 @@ class editOptsForm(npyscreen.FormMultiPage):
         self.nextrely += 1
         self.add_widget_intelligent(
             npyscreen.FixedText,
-            value="Directory containing embedding/textual inversion files:",
+            value="Directories containing textual inversion and LoRA models (<tab> autocompletes, ctrl-N advances):",
             editable=False,
             color="CONTROL",
         )
-        self.embedding_path = self.add_widget_intelligent(
+        self.embedding_dir = self.add_widget_intelligent(
             npyscreen.TitleFilename,
-            name="(<tab> autocompletes, ctrl-N advances):",
+            name=" Textual Inversion Embeddings:",
             value=str(default_embedding_dir()),
             select_dir=True,
             must_exist=False,
             use_two_lines=False,
             labelColor="GOOD",
-            begin_entry_at=40,
+            begin_entry_at=32,
+            scroll_exit=True,
+        )
+        self.lora_dir = self.add_widget_intelligent(
+            npyscreen.TitleFilename,
+            name="             LoRA and LyCORIS:",
+            value=str(default_lora_dir()),
+            select_dir=True,
+            must_exist=False,
+            use_two_lines=False,
+            labelColor="GOOD",
+            begin_entry_at=32,
             scroll_exit=True,
         )
         self.nextrely += 1
@@ -559,9 +562,9 @@ class editOptsForm(npyscreen.FormMultiPage):
             bad_fields.append(
                 f"The output directory does not seem to be valid. Please check that {str(Path(opt.outdir).parent)} is an existing directory."
             )
-        if not Path(opt.embedding_path).parent.exists():
+        if not Path(opt.embedding_dir).parent.exists():
             bad_fields.append(
-                f"The embedding directory does not seem to be valid. Please check that {str(Path(opt.embedding_path).parent)} is an existing directory."
+                f"The embedding directory does not seem to be valid. Please check that {str(Path(opt.embedding_dir).parent)} is an existing directory."
             )
         if len(bad_fields) > 0:
             message = "The following problems were detected and must be corrected:\n"
@@ -577,13 +580,13 @@ class editOptsForm(npyscreen.FormMultiPage):
 
         for attr in [
                 "outdir",
-            "safety_checker",
+                "nsfw_checker",
                 "free_gpu_mem",
                 "max_loaded_models",
-            "xformers",
+                "xformers_enabled",
                 "always_use_cpu",
-            "embedding_path",
+                "embedding_dir",
-            "ckpt_convert",
+                "lora_dir",
         ]:
             setattr(new_opts, attr, getattr(self, attr).value)
 
@@ -591,6 +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
 
 
@@ -628,15 +634,14 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
 
 
 def default_startup_options(init_file: Path) -> Namespace:
-    opts = Args().parse_args([])
+    opts = InvokeAIAppConfig(argv=[])
     outdir = Path(opts.outdir)
     if not outdir.is_absolute():
-        opts.outdir = str(Globals.root / opts.outdir)
+        opts.outdir = str(config.root / opts.outdir)
     if not init_file.exists():
-        opts.safety_checker = True
+        opts.nsfw_checker = True
     return opts
 
-
 def default_user_selections(program_opts: Namespace) -> Namespace:
     return Namespace(
         starter_models=default_dataset()
@@ -690,70 +695,61 @@ def run_console_ui(
 # -------------------------------------
 def write_opts(opts: Namespace, init_file: Path):
     """
-    Update the invokeai.init file with values from opts Namespace
+    Update the invokeai.yaml file with values from current settings.
     """
-    # touch file if it doesn't exist
-    if not init_file.exists():
-        with open(init_file, "w") as f:
-            f.write(INIT_FILE_PREAMBLE)
 
-    # We want to write in the changed arguments without clobbering
+    # this will load current settings
-    # any other initialization values the user has entered. There is
+    config = InvokeAIAppConfig()
-    # no good way to do this because of the one-way nature of
+    for key,value in opts.__dict__.items():
-    # argparse: i.e. --outdir could be --outdir, --out, or -o
+        if hasattr(config,key):
-    # initfile needs to be replaced with a fully structured format
+            setattr(config,key,value)
-    # such as yaml; this is a hack that will work much of the time
-    args_to_skip = re.compile(
-        "^--?(o|out|no-xformer|xformer|no-ckpt|ckpt|free|no-nsfw|nsfw|prec|max_load|embed|always|ckpt|free_gpu)"
-    )
-    # fix windows paths
-    opts.outdir = opts.outdir.replace("\\", "/")
-    opts.embedding_path = opts.embedding_path.replace("\\", "/")
-    new_file = f"{init_file}.new"
-    try:
-        lines = [x.strip() for x in open(init_file, "r").readlines()]
-        with open(new_file, "w") as out_file:
-            for line in lines:
-                if len(line) > 0 and not args_to_skip.match(line):
-                    out_file.write(line + "\n")
-            out_file.write(
-                f"""
---outdir={opts.outdir}
---embedding_path={opts.embedding_path}
---precision={opts.precision}
---max_loaded_models={int(opts.max_loaded_models)}
---{'no-' if not opts.safety_checker else ''}nsfw_checker
---{'no-' if not opts.xformers else ''}xformers
---{'no-' if not opts.ckpt_convert else ''}ckpt_convert
-{'--free_gpu_mem' if opts.free_gpu_mem else ''}
-{'--always_use_cpu' if opts.always_use_cpu else ''}
-"""
-            )
-    except OSError as e:
-        print(f"** An error occurred while writing the init file: {str(e)}")
-
-    os.replace(new_file, init_file)
-
-    if opts.hf_token:
-        HfLogin(opts.hf_token)
 
+    with open(init_file,'w', encoding='utf-8') as file:
+        file.write(config.to_yaml())
 
 # -------------------------------------
 def default_output_dir() -> Path:
-    return Globals.root / "outputs"
+    return config.root / "outputs"
-
 
 # -------------------------------------
 def default_embedding_dir() -> Path:
-    return Globals.root / "embeddings"
+    return config.root / "embeddings"
 
+# -------------------------------------
+def default_lora_dir() -> Path:
+    return config.root / "loras"
 
 # -------------------------------------
 def write_default_options(program_opts: Namespace, initfile: Path):
     opt = default_startup_options(initfile)
-    opt.hf_token = HfFolder.get_token()
     write_opts(opt, initfile)
 
+# -------------------------------------
+# Here we bring in
+# the legacy Args object in order to parse
+# the old init file and write out the new
+# yaml format.
+def migrate_init_file(legacy_format:Path):
+    old = legacy_parser.parse_args([f'@{str(legacy_format)}'])
+    new = InvokeAIAppConfig(conf={})
+
+    fields = list(get_type_hints(InvokeAIAppConfig).keys())
+    for attr in fields:
+        if hasattr(old,attr):
+            setattr(new,attr,getattr(old,attr))
+
+    # a few places where the field names have changed and we have to
+    # manually add in the new names/values
+    new.nsfw_checker = old.safety_checker
+    new.xformers_enabled = old.xformers
+    new.conf_path = old.conf
+    new.embedding_dir = old.embedding_path
+
+    invokeai_yaml = legacy_format.parent / 'invokeai.yaml'
+    with open(invokeai_yaml,"w", encoding="utf-8") as outfile:
+        outfile.write(new.to_yaml())
+
+    legacy_format.replace(legacy_format.parent / 'invokeai.init.old')
 
 # -------------------------------------
 def main():
@@ -810,7 +806,8 @@ def main():
     opt = parser.parse_args()
 
     # setting a global here
-    Globals.root = Path(os.path.expanduser(get_root(opt.root) or ""))
+    global config
+    config.root = Path(os.path.expanduser(get_root(opt.root) or ""))
 
     errors = set()
 
@@ -818,19 +815,26 @@ def main():
         models_to_download = default_user_selections(opt)
 
         # We check for to see if the runtime directory is correctly initialized.
-        init_file = Path(Globals.root, Globals.initfile)
+        old_init_file = Path(config.root, 'invokeai.init')
-        if not init_file.exists() or not global_config_file().exists():
+        new_init_file = Path(config.root, 'invokeai.yaml')
-            initialize_rootdir(Globals.root, opt.yes_to_all)
+        if old_init_file.exists() and not new_init_file.exists():
+            print('** Migrating invokeai.init to invokeai.yaml')
+            migrate_init_file(old_init_file)
+            config = get_invokeai_config()  # reread defaults
+
+
+        if not config.model_conf_path.exists():
+            initialize_rootdir(config.root, opt.yes_to_all)
 
         if opt.yes_to_all:
-            write_default_options(opt, init_file)
+            write_default_options(opt, new_init_file)
             init_options = Namespace(
                 precision="float32" if opt.full_precision else "float16"
             )
         else:
-            init_options, models_to_download = run_console_ui(opt, init_file)
+            init_options, models_to_download = run_console_ui(opt, new_init_file)
             if init_options:
-                write_opts(init_options, init_file)
+                write_opts(init_options, new_init_file)
             else:
                 print(
                     '\n** CANCELLED AT USER\'S REQUEST. USE THE "invoke.sh" LAUNCHER TO RUN LATER **\n'

invokeai/backend/config/legacy_arg_parsing.py

@@ -0,0 +1,390 @@
+# Copyright 2023 Lincoln D. Stein and the InvokeAI Team
+
+import argparse
+import shlex
+from argparse import ArgumentParser
+
+SAMPLER_CHOICES = [
+    "ddim",
+    "ddpm",
+    "deis",
+    "lms",
+    "pndm",
+    "heun",
+    "heun_k",
+    "euler",
+    "euler_k",
+    "euler_a",
+    "kdpm_2",
+    "kdpm_2_a",
+    "dpmpp_2s",
+    "dpmpp_2m",
+    "dpmpp_2m_k",
+    "unipc",
+]
+
+PRECISION_CHOICES = [
+    "auto",
+    "float32",
+    "autocast",
+    "float16",
+]
+
+class FileArgumentParser(ArgumentParser):
+    """
+    Supports reading defaults from an init file.
+    """
+    def convert_arg_line_to_args(self, arg_line):
+        return shlex.split(arg_line, comments=True)
+
+
+legacy_parser = FileArgumentParser(
+    description=
+    """
+Generate images using Stable Diffusion.
+    Use --web to launch the web interface.
+    Use --from_file to load prompts from a file path or standard input ("-").
+    Otherwise you will be dropped into an interactive command prompt (type -h for help.)
+    Other command-line arguments are defaults that can usually be overridden
+    prompt the command prompt.
+    """,
+    fromfile_prefix_chars='@',
+)
+general_group    = legacy_parser.add_argument_group('General')
+model_group      = legacy_parser.add_argument_group('Model selection')
+file_group       = legacy_parser.add_argument_group('Input/output')
+web_server_group = legacy_parser.add_argument_group('Web server')
+render_group     = legacy_parser.add_argument_group('Rendering')
+postprocessing_group     = legacy_parser.add_argument_group('Postprocessing')
+deprecated_group = legacy_parser.add_argument_group('Deprecated options')
+
+deprecated_group.add_argument('--laion400m')
+deprecated_group.add_argument('--weights') # deprecated
+general_group.add_argument(
+    '--version','-V',
+    action='store_true',
+    help='Print InvokeAI version number'
+)
+model_group.add_argument(
+    '--root_dir',
+    default=None,
+    help='Path to directory containing "models", "outputs" and "configs". If not present will read from environment variable INVOKEAI_ROOT. Defaults to ~/invokeai.',
+)
+model_group.add_argument(
+    '--config',
+    '-c',
+    '-config',
+    dest='conf',
+    default='./configs/models.yaml',
+    help='Path to configuration file for alternate models.',
+)
+model_group.add_argument(
+    '--model',
+    help='Indicates which diffusion model to load (defaults to "default" stanza in configs/models.yaml)',
+)
+model_group.add_argument(
+    '--weight_dirs',
+    nargs='+',
+    type=str,
+    help='List of one or more directories that will be auto-scanned for new model weights to import',
+)
+model_group.add_argument(
+    '--png_compression','-z',
+    type=int,
+    default=6,
+    choices=range(0,9),
+    dest='png_compression',
+    help='level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.'
+)
+model_group.add_argument(
+    '-F',
+    '--full_precision',
+    dest='full_precision',
+    action='store_true',
+    help='Deprecated way to set --precision=float32',
+)
+model_group.add_argument(
+    '--max_loaded_models',
+    dest='max_loaded_models',
+    type=int,
+    default=2,
+    help='Maximum number of models to keep in memory for fast switching, including the one in GPU',
+)
+model_group.add_argument(
+    '--free_gpu_mem',
+    dest='free_gpu_mem',
+    action='store_true',
+    help='Force free gpu memory before final decoding',
+)
+model_group.add_argument(
+    '--sequential_guidance',
+    dest='sequential_guidance',
+    action='store_true',
+    help="Calculate guidance in serial instead of in parallel, lowering memory requirement "
+         "at the expense of speed",
+)
+model_group.add_argument(
+    '--xformers',
+    action=argparse.BooleanOptionalAction,
+    default=True,
+    help='Enable/disable xformers support (default enabled if installed)',
+)
+model_group.add_argument(
+    "--always_use_cpu",
+    dest="always_use_cpu",
+    action="store_true",
+    help="Force use of CPU even if GPU is available"
+)
+model_group.add_argument(
+    '--precision',
+    dest='precision',
+    type=str,
+    choices=PRECISION_CHOICES,
+    metavar='PRECISION',
+    help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
+    default='auto',
+)
+model_group.add_argument(
+    '--ckpt_convert',
+    action=argparse.BooleanOptionalAction,
+    dest='ckpt_convert',
+    default=True,
+    help='Deprecated option. Legacy ckpt files are now always converted to diffusers when loaded.'
+)
+model_group.add_argument(
+    '--internet',
+    action=argparse.BooleanOptionalAction,
+    dest='internet_available',
+    default=True,
+    help='Indicate whether internet is available for just-in-time model downloading (default: probe automatically).',
+)
+model_group.add_argument(
+    '--nsfw_checker',
+    '--safety_checker',
+    action=argparse.BooleanOptionalAction,
+    dest='safety_checker',
+    default=False,
+    help='Check for and blur potentially NSFW images. Use --no-nsfw_checker to disable.',
+)
+model_group.add_argument(
+    '--autoimport',
+    default=None,
+    type=str,
+    help='Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly',
+)
+model_group.add_argument(
+    '--autoconvert',
+    default=None,
+    type=str,
+    help='Check the indicated directory for .ckpt/.safetensors weights files at startup and import as optimized diffuser models',
+)
+model_group.add_argument(
+    '--patchmatch',
+    action=argparse.BooleanOptionalAction,
+    default=True,
+    help='Load the patchmatch extension for outpainting. Use --no-patchmatch to disable.',
+)
+file_group.add_argument(
+    '--from_file',
+    dest='infile',
+    type=str,
+    help='If specified, load prompts from this file',
+)
+file_group.add_argument(
+    '--outdir',
+    '-o',
+    type=str,
+    help='Directory to save generated images and a log of prompts and seeds. Default: ROOTDIR/outputs',
+    default='outputs',
+)
+file_group.add_argument(
+    '--prompt_as_dir',
+    '-p',
+    action='store_true',
+    help='Place images in subdirectories named after the prompt.',
+)
+render_group.add_argument(
+    '--fnformat',
+    default='{prefix}.{seed}.png',
+    type=str,
+    help='Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png',
+)
+render_group.add_argument(
+    '-s',
+    '--steps',
+    type=int,
+    default=50,
+    help='Number of steps'
+)
+render_group.add_argument(
+    '-W',
+    '--width',
+    type=int,
+    help='Image width, multiple of 64',
+)
+render_group.add_argument(
+    '-H',
+    '--height',
+    type=int,
+    help='Image height, multiple of 64',
+)
+render_group.add_argument(
+    '-C',
+    '--cfg_scale',
+    default=7.5,
+    type=float,
+    help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
+)
+render_group.add_argument(
+    '--sampler',
+    '-A',
+    '-m',
+    dest='sampler_name',
+    type=str,
+    choices=SAMPLER_CHOICES,
+    metavar='SAMPLER_NAME',
+    help=f'Set the default sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
+    default='k_lms',
+)
+render_group.add_argument(
+    '--log_tokenization',
+    '-t',
+    action='store_true',
+    help='shows how the prompt is split into tokens'
+)
+render_group.add_argument(
+    '-f',
+    '--strength',
+    type=float,
+    help='img2img strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
+)
+render_group.add_argument(
+    '-T',
+    '-fit',
+    '--fit',
+    action=argparse.BooleanOptionalAction,
+    help='If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)',
+)
+
+render_group.add_argument(
+    '--grid',
+    '-g',
+    action=argparse.BooleanOptionalAction,
+    help='generate a grid'
+)
+render_group.add_argument(
+    '--embedding_directory',
+    '--embedding_path',
+    dest='embedding_path',
+    default='embeddings',
+    type=str,
+    help='Path to a directory containing .bin and/or .pt files, or a single .bin/.pt file. You may use subdirectories. (default is ROOTDIR/embeddings)'
+)
+render_group.add_argument(
+    '--lora_directory',
+    dest='lora_path',
+    default='loras',
+    type=str,
+    help='Path to a directory containing LoRA files; subdirectories are not supported. (default is ROOTDIR/loras)'
+)
+render_group.add_argument(
+    '--embeddings',
+    action=argparse.BooleanOptionalAction,
+    default=True,
+    help='Enable embedding directory (default). Use --no-embeddings to disable.',
+)
+render_group.add_argument(
+    '--enable_image_debugging',
+    action='store_true',
+    help='Generates debugging image to display'
+)
+render_group.add_argument(
+    '--karras_max',
+    type=int,
+    default=None,
+    help="control the point at which the K* samplers will shift from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts). Set to 0 to use LatentDiffusion for all step values, and to a high value (e.g. 1000) to use Karras for all step values. [29]."
+)
+# Restoration related args
+postprocessing_group.add_argument(
+    '--no_restore',
+    dest='restore',
+    action='store_false',
+    help='Disable face restoration with GFPGAN or codeformer',
+)
+postprocessing_group.add_argument(
+    '--no_upscale',
+    dest='esrgan',
+    action='store_false',
+    help='Disable upscaling with ESRGAN',
+)
+postprocessing_group.add_argument(
+    '--esrgan_bg_tile',
+    type=int,
+    default=400,
+    help='Tile size for background sampler, 0 for no tile during testing. Default: 400.',
+)
+postprocessing_group.add_argument(
+    '--esrgan_denoise_str',
+    type=float,
+    default=0.75,
+    help='esrgan denoise str. 0 is no denoise, 1 is max denoise.  Default: 0.75',
+)
+postprocessing_group.add_argument(
+    '--gfpgan_model_path',
+    type=str,
+    default='./models/gfpgan/GFPGANv1.4.pth',
+    help='Indicates the path to the GFPGAN model',
+)
+web_server_group.add_argument(
+    '--web',
+    dest='web',
+    action='store_true',
+    help='Start in web server mode.',
+)
+web_server_group.add_argument(
+    '--web_develop',
+    dest='web_develop',
+    action='store_true',
+    help='Start in web server development mode.',
+)
+web_server_group.add_argument(
+    "--web_verbose",
+    action="store_true",
+    help="Enables verbose logging",
+)
+web_server_group.add_argument(
+    "--cors",
+    nargs="*",
+    type=str,
+    help="Additional allowed origins, comma-separated",
+)
+web_server_group.add_argument(
+    '--host',
+    type=str,
+    default='127.0.0.1',
+    help='Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.'
+)
+web_server_group.add_argument(
+    '--port',
+    type=int,
+    default='9090',
+    help='Web server: Port to listen on'
+)
+web_server_group.add_argument(
+    '--certfile',
+    type=str,
+    default=None,
+    help='Web server: Path to certificate file to use for SSL. Use together with --keyfile'
+)
+web_server_group.add_argument(
+    '--keyfile',
+    type=str,
+    default=None,
+    help='Web server: Path to private key file to use for SSL. Use together with --certfile'
+)
+web_server_group.add_argument(
+    '--gui',
+    dest='gui',
+    action='store_true',
+    help='Start InvokeAI GUI',
+)

invokeai/backend/config/model_install_backend.py

@@ -19,13 +19,15 @@ from tqdm import tqdm
 
 import invokeai.configs as configs
 
-from ..globals import Globals, global_cache_dir, global_config_dir
+from invokeai.app.services.config import get_invokeai_config
 from ..model_management import ModelManager
 from ..stable_diffusion import StableDiffusionGeneratorPipeline
 
+
 warnings.filterwarnings("ignore")
 
 # --------------------------globals-----------------------
+config = get_invokeai_config()
 Model_dir = "models"
 Weights_dir = "ldm/stable-diffusion-v1/"
 
@@ -47,12 +49,11 @@ Config_preamble = """
 
 
 def default_config_file():
-    return Path(global_config_dir()) / "models.yaml"
+    return config.model_conf_path
 
 
 def sd_configs():
-    return Path(global_config_dir()) / "stable-diffusion"
+    return config.legacy_conf_path
-
 
 def initial_models():
     global Datasets
@@ -121,8 +122,9 @@ def install_requested_models(
 
     if scan_at_startup and scan_directory.is_dir():
         argument = "--autoconvert"
-        initfile = Path(Globals.root, Globals.initfile)
+        print('** The global initfile is no longer supported; rewrite to support new yaml format **')
-        replacement = Path(Globals.root, f"{Globals.initfile}.new")
+        initfile = Path(config.root, 'invokeai.init')
+        replacement = Path(config.root, f"invokeai.init.new")
         directory = str(scan_directory).replace("\\", "/")
         with open(initfile, "r") as input:
             with open(replacement, "w") as output:
@@ -150,7 +152,7 @@ def get_root(root: str = None) -> str:
     elif os.environ.get("INVOKEAI_ROOT"):
         return os.environ.get("INVOKEAI_ROOT")
     else:
-        return Globals.root
+        return config.root
 
 
 # ---------------------------------------------
@@ -183,7 +185,7 @@ 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(Globals.root, Model_dir, Weights_dir)
+    model_path = os.path.join(config.root, 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"]
@@ -228,7 +230,7 @@ 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(Globals.root, Model_dir, Weights_dir)
+    cache_dir = os.path.join(config.root, Model_dir, Weights_dir)
     return hf_download_with_resume(
         repo_id=repo_id,
         model_dir=cache_dir,
@@ -239,9 +241,9 @@ def _download_ckpt_weights(mconfig: DictConfig, access_token: str) -> Path:
 
 # ---------------------------------------------
 def download_from_hf(
-    model_class: object, model_name: str, cache_subdir: Path = Path("hub"), **kwargs
+    model_class: object, model_name: str, **kwargs
 ):
-    path = global_cache_dir(cache_subdir)
+    path = config.cache_dir
     model = model_class.from_pretrained(
         model_name,
         cache_dir=path,
@@ -417,7 +419,7 @@ def new_config_file_contents(
             stanza["height"] = mod["height"]
         if "file" in mod:
             stanza["weights"] = os.path.relpath(
-                successfully_downloaded[model], start=Globals.root
+                successfully_downloaded[model], start=config.root
             )
             stanza["config"] = os.path.normpath(
                 os.path.join(sd_configs(), mod["config"])
@@ -456,7 +458,7 @@ def delete_weights(model_name: str, conf_stanza: dict):
 
     weights = Path(weights)
     if not weights.is_absolute():
-        weights = Path(Globals.root) / weights
+        weights = Path(config.root) / weights
         try:
             weights.unlink()
         except OSError as e:

invokeai/backend/globals.py

@@ -1,126 +0,0 @@
-"""
-invokeai.backend.globals defines a small number of global variables that would
-otherwise have to be passed through long and complex call chains.
-
-It defines a Namespace object named "Globals" that contains
-the attributes:
-
-  - root           - the root directory under which "models" and "outputs" can be found
-  - initfile       - path to the initialization file
-  - try_patchmatch - option to globally disable loading of 'patchmatch' module
-  - always_use_cpu - force use of CPU even if GPU is available
-"""
-
-import os
-import os.path as osp
-from argparse import Namespace
-from pathlib import Path
-from typing import Union
-
-Globals = Namespace()
-
-# Where to look for the initialization file and other key components
-Globals.initfile = "invokeai.init"
-Globals.models_file = "models.yaml"
-Globals.models_dir = "models"
-Globals.config_dir = "configs"
-Globals.autoscan_dir = "weights"
-Globals.converted_ckpts_dir = "converted_ckpts"
-
-# Set the default root directory. This can be overwritten by explicitly
-# passing the `--root <directory>` argument on the command line.
-# logic is:
-# 1) use INVOKEAI_ROOT environment variable (no check for this being a valid directory)
-# 2) use VIRTUAL_ENV environment variable, with a check for initfile being there
-# 3) use ~/invokeai
-
-if os.environ.get("INVOKEAI_ROOT"):
-    Globals.root = osp.abspath(os.environ.get("INVOKEAI_ROOT"))
-elif (
-    os.environ.get("VIRTUAL_ENV")
-    and Path(os.environ.get("VIRTUAL_ENV"), "..", Globals.initfile).exists()
-):
-    Globals.root = osp.abspath(osp.join(os.environ.get("VIRTUAL_ENV"), ".."))
-else:
-    Globals.root = osp.abspath(osp.expanduser("~/invokeai"))
-
-# Try loading patchmatch
-Globals.try_patchmatch = True
-
-# Use CPU even if GPU is available (main use case is for debugging MPS issues)
-Globals.always_use_cpu = False
-
-# Whether the internet is reachable for dynamic downloads
-# The CLI will test connectivity at startup time.
-Globals.internet_available = True
-
-# Whether to disable xformers
-Globals.disable_xformers = False
-
-# Low-memory tradeoff for guidance calculations.
-Globals.sequential_guidance = False
-
-# whether we are forcing full precision
-Globals.full_precision = False
-
-# whether we should convert ckpt files into diffusers models on the fly
-Globals.ckpt_convert = True
-
-# logging tokenization everywhere
-Globals.log_tokenization = False
-
-
-def global_config_file() -> Path:
-    return Path(Globals.root, Globals.config_dir, Globals.models_file)
-
-
-def global_config_dir() -> Path:
-    return Path(Globals.root, Globals.config_dir)
-
-
-def global_models_dir() -> Path:
-    return Path(Globals.root, Globals.models_dir)
-
-
-def global_autoscan_dir() -> Path:
-    return Path(Globals.root, Globals.autoscan_dir)
-
-
-def global_converted_ckpts_dir() -> Path:
-    return Path(global_models_dir(), Globals.converted_ckpts_dir)
-
-
-def global_set_root(root_dir: Union[str, Path]):
-    Globals.root = root_dir
-
-def global_resolve_path(path: Union[str,Path]):
-    if path is None:
-        return None
-    return Path(Globals.root,path).resolve()
-
-def global_cache_dir(subdir: Union[str, Path] = "") -> Path:
-    """
-    Returns Path to the model cache directory. If a subdirectory
-    is provided, it will be appended to the end of the path, allowing
-    for Hugging Face-style conventions. Currently, Hugging Face has
-    moved all models into the "hub" subfolder, so for any pretrained
-    HF model, use:
-         global_cache_dir('hub')
-
-    The legacy location for transformers used to be global_cache_dir('transformers')
-    and global_cache_dir('diffusers') for diffusers.
-    """
-    home: str = os.getenv("HF_HOME")
-
-    if home is None:
-        home = os.getenv("XDG_CACHE_HOME")
-
-        if home is not None:
-            # Set `home` to $XDG_CACHE_HOME/huggingface, which is the default location mentioned in Hugging Face Hub Client Library.
-            # See: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#xdgcachehome
-            home += os.sep + "huggingface"
-
-    if home is not None:
-        return Path(home, subdir)
-    else:
-        return Path(Globals.root, "models", subdir)

invokeai/backend/image_util/patchmatch.py

@@ -6,7 +6,7 @@ be suppressed or deferred
 """
 import numpy as np
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import Globals
+from invokeai.app.services.config import get_invokeai_config
 
 class PatchMatch:
     """
@@ -21,9 +21,10 @@ class PatchMatch:
 
     @classmethod
     def _load_patch_match(self):
+        config = get_invokeai_config()
         if self.tried_load:
             return
-        if Globals.try_patchmatch:
+        if config.try_patchmatch:
             from patchmatch import patch_match as pm
 
             if pm.patchmatch_available:

invokeai/backend/image_util/txt2mask.py

@@ -33,12 +33,11 @@ from PIL import Image, ImageOps
 from transformers import AutoProcessor, CLIPSegForImageSegmentation
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import global_cache_dir
+from invokeai.app.services.config import get_invokeai_config
 
 CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
 CLIPSEG_SIZE = 352
 
-
 class SegmentedGrayscale(object):
     def __init__(self, image: Image, heatmap: torch.Tensor):
         self.heatmap = heatmap
@@ -84,14 +83,15 @@ 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
         self.processor = AutoProcessor.from_pretrained(
-            CLIPSEG_MODEL, cache_dir=global_cache_dir("hub")
+            CLIPSEG_MODEL, cache_dir=config.cache_dir
         )
         self.model = CLIPSegForImageSegmentation.from_pretrained(
-            CLIPSEG_MODEL, cache_dir=global_cache_dir("hub")
+            CLIPSEG_MODEL, cache_dir=config.cache_dir
         )
 
     @torch.no_grad()

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -26,7 +26,7 @@ import torch
 from safetensors.torch import load_file
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import global_cache_dir, global_config_dir
+from invokeai.app.services.config import get_invokeai_config
 
 from .model_manager import ModelManager, SDLegacyType
 from .model_cache import ModelCache
@@ -76,7 +76,6 @@ from transformers import (
 
 from ..stable_diffusion import StableDiffusionGeneratorPipeline
 
-
 def shave_segments(path, n_shave_prefix_segments=1):
     """
     Removes segments. Positive values shave the first segments, negative shave the last segments.
@@ -858,7 +857,7 @@ 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=global_cache_dir("hub")
+        "openai/clip-vit-large-patch14", cache_dir=get_invokeai_config().cache_dir
     )
 
     keys = list(checkpoint.keys())
@@ -913,7 +912,7 @@ textenc_pattern = re.compile("|".join(protected.keys()))
 
 
 def convert_paint_by_example_checkpoint(checkpoint):
-    cache_dir = global_cache_dir("hub")
+    cache_dir = get_invokeai_config().cache_dir
     config = CLIPVisionConfig.from_pretrained(
         "openai/clip-vit-large-patch14", cache_dir=cache_dir
     )
@@ -985,7 +984,7 @@ def convert_paint_by_example_checkpoint(checkpoint):
 
 
 def convert_open_clip_checkpoint(checkpoint):
-    cache_dir = global_cache_dir("hub")
+    cache_dir = get_invokeai_config().cache_dir
     text_model = CLIPTextModel.from_pretrained(
         "stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir
     )
@@ -1121,7 +1120,7 @@ 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()
@@ -1134,7 +1133,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         else:
             checkpoint = load_file(checkpoint_path)
 
-        cache_dir = global_cache_dir("hub")
+        cache_dir = config.cache_dir
         pipeline_class = (
             StableDiffusionGeneratorPipeline
             if return_generator_pipeline
@@ -1158,25 +1157,23 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
 
             if model_type == SDLegacyType.V2_v:
                 original_config_file = (
-                    global_config_dir() / "stable-diffusion" / "v2-inference-v.yaml"
+                    config.legacy_conf_path / "v2-inference-v.yaml"
                 )
                 if global_step == 110000:
                     # v2.1 needs to upcast attention
                     upcast_attention = True
             elif model_type == SDLegacyType.V2_e:
                 original_config_file = (
-                    global_config_dir() / "stable-diffusion" / "v2-inference.yaml"
+                    config.legacy_conf_path / "v2-inference.yaml"
                 )
             elif model_type == SDLegacyType.V1_INPAINT:
                 original_config_file = (
-                    global_config_dir()
+                    config.legacy_conf_path / "v1-inpainting-inference.yaml"
-                    / "stable-diffusion"
-                    / "v1-inpainting-inference.yaml"
                 )
 
             elif model_type == SDLegacyType.V1:
                 original_config_file = (
-                    global_config_dir() / "stable-diffusion" / "v1-inference.yaml"
+                    config.legacy_conf_path / "v1-inference.yaml"
                 )
 
             else:
@@ -1323,7 +1320,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
             )
             safety_checker = StableDiffusionSafetyChecker.from_pretrained(
                 "CompVis/stable-diffusion-safety-checker",
-                cache_dir=global_cache_dir("hub"),
+                cache_dir=config.cache_dir,
             )
             feature_extractor = AutoFeatureExtractor.from_pretrained(
                 "CompVis/stable-diffusion-safety-checker", cache_dir=cache_dir

invokeai/backend/model_management/model_cache.py

@@ -25,27 +25,25 @@ import warnings
 from contextlib import suppress
 from enum import Enum
 from pathlib import Path
-from typing import Dict, Sequence, Union, Tuple, types, Optional, List, Type, Any
+from typing import Dict, Sequence, Union, types, Optional, List, Type, Any
 
 import torch
-import safetensors.torch
 
 from diffusers import DiffusionPipeline, SchedulerMixin, ConfigMixin
 from diffusers import logging as diffusers_logging
 from huggingface_hub import HfApi, scan_cache_dir
-from picklescan.scanner import scan_file_path
-from pydantic import BaseModel
 from transformers import logging as transformers_logging
 
 import invokeai.backend.util.logging as logger
-from ..globals import global_cache_dir
+from invokeai.app.services.config import get_invokeai_config
-
 
 def get_model_path(repo_id_or_path: str):
+    globals = get_invokeai_config()
+    
     if os.path.exists(repo_id_or_path):
         return repo_id_or_path
 
-    cache = scan_cache_dir(global_cache_dir("hub"))
+    cache = scan_cache_dir(globals.cache_dir)
     for repo in cache.repos:
         if repo.repo_id != repo_id_or_path:
             continue
@@ -234,7 +232,7 @@ class DiffusersModelInfo(ModelInfoBase):
                 model = self.child_types[child_type].from_pretrained(
                     self.repo_id_or_path,
                     subfolder=child_type.value,
-                    cache_dir=global_cache_dir('hub'),
+                    cache_dir=get_invokeai_config.cache_dir('hub'),
                     torch_dtype=torch_dtype,
                     variant=variant,
                 )
@@ -248,7 +246,7 @@ class DiffusersModelInfo(ModelInfoBase):
         return model
 
 
-    def get_pipeline(self, **kwrags):
+    def get_pipeline(self, **kwargs):
         return DiffusionPipeline.from_pretrained(
             self.repo_id_or_path,
             **kwargs,
@@ -349,7 +347,7 @@ class ClassifierModelInfo(ModelInfoBase):
         model = self.child_types[child_type].from_pretrained(
             self.repo_id_or_path,
             subfolder=child_type.value,
-            cache_dir=global_cache_dir('hub'),
+            cache_dir=get_invokeai_config().cache_dir('hub'),
             torch_dtype=torch_dtype,
         )
         # calc more accurate size
@@ -394,7 +392,7 @@ class VaeModelInfo(ModelInfoBase):
 
         model = self.vae_type.from_pretrained(
             self.repo_id_or_path,
-            cache_dir=global_cache_dir('hub'),
+            cache_dir=get_invokeai_config().cache_dir('hub'),
             torch_dtype=torch_dtype,
         )
         # calc more accurate size

invokeai/backend/model_management/model_manager.py

@@ -149,8 +149,7 @@ from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import (Globals, global_cache_dir,
+from invokeai.app.services.config import get_invokeai_config
-                                      global_resolve_path)
 from invokeai.backend.util import download_with_resume
 
 from ..util import CUDA_DEVICE
@@ -226,7 +225,8 @@ class ModelManager(object):
 
         # check config version number and update on disk/RAM if necessary
         self._update_config_file_version()
-
+        self.globals = get_invokeai_config()
+        self.logger = logger
         self.cache = ModelCache(
             max_cache_size=max_cache_size,
             execution_device = device_type,
@@ -235,7 +235,6 @@ class ModelManager(object):
             logger = logger,
         )
         self.cache_keys = dict()
-        self.logger = logger
 
     def model_exists(
         self,
@@ -304,12 +303,6 @@ class ModelManager(object):
         # raises an InvalidModelError
 
         """
-        
-        # Commented-out workaround for callers that use "type/name" as the model name
-        # because they haven't adjusted to the new return format of `list_models()`
-        # if "/" in model_name:
-        #     model_key = model_name
-        # else:
         model_key = self.create_key(model_name, model_type)
         if model_key not in self.config:
             raise InvalidModelError(
@@ -326,13 +319,15 @@ class ModelManager(object):
             if mconfig.format in ["ckpt", "safetensors"]:
                 location = self.convert_ckpt_and_cache(mconfig)
             else:
-                location = global_resolve_path(mconfig.get('path')) or mconfig.get('repo_id')
+                location = self.globals.root_dir / mconfig.get('path') or mconfig.get('repo_id')
+        elif p := mconfig.get('path'):
+            location = self.globals.root_dir / p
+        elif r := mconfig.get('repo_id'):
+            location = r
+        elif w := mconfig.get('weights'):
+            location = self.globals.root_dir / w
         else:
-            location = global_resolve_path(
+            location = None
-                mconfig.get('path')) \
-                or mconfig.get('repo_id') \
-                or global_resolve_path(mconfig.get('weights')
-            )
         
         revision = mconfig.get('revision')
         hash = self.cache.model_hash(location, revision)
@@ -423,7 +418,7 @@ class ModelManager(object):
         """
         # if we are converting legacy files automatically, then
         # there are no legacy ckpts!
-        if Globals.ckpt_convert:
+        if self.globals.ckpt_convert:
             return False
         info = self.model_info(model_name, model_type)
         if "weights" in info and info["weights"].endswith((".ckpt", ".safetensors")):
@@ -862,25 +857,16 @@ 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 = Path(
+                    model_config_file = self.globals.legacy_conf_path / "v1-inference.yaml"
-                        Globals.root, "configs/stable-diffusion/v1-inference.yaml"
-                    )
                 elif model_type == SDLegacyType.V1_INPAINT:
                     self.logger.debug("SD-v1 inpainting model detected")
-                    model_config_file = Path(
+                    model_config_file = self.globals.legacy_conf_path / "v1-inpainting-inference.yaml",
-                        Globals.root,
-                        "configs/stable-diffusion/v1-inpainting-inference.yaml",
-                    )
                 elif model_type == SDLegacyType.V2_v:
                     self.logger.debug("SD-v2-v model detected")
-                    model_config_file = Path(
+                    model_config_file = self.globals.legacy_conf_path / "v2-inference-v.yaml"
-                        Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
-                    )
                 elif model_type == SDLegacyType.V2_e:
                     self.logger.debug("SD-v2-e model detected")
-                    model_config_file = Path(
+                    model_config_file = self.globals.legacy_conf_path / "v2-inference.yaml"
-                        Globals.root, "configs/stable-diffusion/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."
@@ -907,9 +893,7 @@ 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 = Path(
+        diffuser_path = self.globals.converted_ckpts_dir / model_path.stem
-            Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
-        )
         with SilenceWarnings():
             model_name = self.convert_and_import(
                 model_path,
@@ -930,9 +914,9 @@ class ModelManager(object):
         diffusers, cache it to disk, and return Path to converted
         file. If already on disk then just returns Path.
         """
-        weights = global_resolve_path(mconfig.weights)
+        weights = self.globals.root_dir / mconfig.weights
-        config_file = global_resolve_path(mconfig.config)
+        config_file = self.globals.root_dir / mconfig.config
-        diffusers_path = global_resolve_path(Path('models',Globals.converted_ckpts_dir)) / weights.stem
+        diffusers_path = self.globals.converted_ckpts_dir / weights.stem
 
         # return cached version if it exists
         if diffusers_path.exists():
@@ -949,7 +933,7 @@ class ModelManager(object):
                 extract_ema=True,
                 original_config_file=config_file,
                 vae=vae_model,
-                vae_path=str(global_resolve_path(vae_ckpt_path)) if vae_ckpt_path else None,
+                vae_path=str(self.globals.root_dir / vae_ckpt_path) if vae_ckpt_path else None,
                 scan_needed=True,
             )
         return diffusers_path
@@ -960,9 +944,10 @@ class ModelManager(object):
         object, cache it to disk, and return Path to converted
         file. If already on disk then just returns Path.
         """
-        weights_file = global_resolve_path(mconfig.weights)
+        root = self.globals.root_dir
-        config_file = global_resolve_path(mconfig.config)
+        weights_file = root / mconfig.weights
-        diffusers_path = global_resolve_path(Path('models',Globals.converted_ckpts_dir)) / weights_file.stem
+        config_file = root / mconfig.config
+        diffusers_path = self.globals.converted_ckpts_dir / weights_file.stem
         image_size = mconfig.get('width') or mconfig.get('height') or 512
         
         # return cached version if it exists
@@ -1018,7 +1003,9 @@ class ModelManager(object):
             
         # 3. If mconfig has a vae dict, then we use it as the diffusers-style vae
         if vae_config and isinstance(vae_config,DictConfig):
-            vae_diffusers_location = global_resolve_path(vae_config.get('path')) or vae_config.get('repo_id')
+            vae_diffusers_location = self.globals.root_dir / vae_config.get('path') \
+                if vae_config.get('path') \
+                   else vae_config.get('repo_id')
 
         # 4. Otherwise, we use stabilityai/sd-vae-ft-mse "because it works"
         else:
@@ -1072,7 +1059,9 @@ class ModelManager(object):
             # will be built into the model rather than tacked on afterward via the config file
             vae_model = None
             if vae:
-                vae_location = global_resolve_path(vae.get('path')) or vae.get('repo_id')
+                vae_location = self.globals.root_dir / vae.get('path') \
+                    if vae.get('path') \
+                       else vae.get('repo_id')
                 vae_model = self.cache.get_model(vae_location, SDModelType.Vae).model
                 vae_path = None
             convert_ckpt_to_diffusers(
@@ -1140,6 +1129,7 @@ class ModelManager(object):
         yaml_str = OmegaConf.to_yaml(self.config)
         config_file_path = conf_file or self.config_path
         assert config_file_path is not None,'no config file path to write to'
+        config_file_path = self.globals.root_dir / config_file_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())
@@ -1160,7 +1150,7 @@ class ModelManager(object):
 
     @classmethod
     def _delete_model_from_cache(cls,repo_id):
-        cache_info =  scan_cache_dir(global_cache_dir("hub"))
+        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!
@@ -1177,9 +1167,10 @@ 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(Globals.root, path).resolve()
+        return Path(globals.root_dir, path).resolve()
 
     # This is not the same as global_resolve_path(), which prepends
     # Globals.root.
@@ -1188,15 +1179,11 @@ class ModelManager(object):
     ) -> Optional[Path]:
         resolved_path = None
         if str(source).startswith(("http:", "https:", "ftp:")):
-            dest_directory = Path(dest_directory)
+            dest_directory = self.globals.root_dir / dest_directory
-            if not dest_directory.is_absolute():
-                dest_directory = Globals.root / 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):
+            resolved_path = self.globals.root_dir / source
-                source = os.path.join(Globals.root, source)
-            resolved_path = Path(source)
         return resolved_path
 
     def _update_config_file_version(self):

invokeai/backend/prompting/conditioning.py

@@ -17,67 +17,59 @@ from compel.prompt_parser import (
     FlattenedPrompt,
     Fragment,
     PromptParser,
+    Conjunction,
 )
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import Globals
 
+from invokeai.app.services.config import get_invokeai_config
 from ..stable_diffusion import InvokeAIDiffuserComponent
 from ..util import torch_dtype
 
-
+def get_uc_and_c_and_ec(prompt_string,
-def get_uc_and_c_and_ec(
+                        model: InvokeAIDiffuserComponent,
-    prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False
+                        log_tokens=False, skip_normalize_legacy_blend=False):
-):
     # lazy-load any deferred textual inversions.
     # this might take a couple of seconds the first time a textual inversion is used.
-    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
+    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(prompt_string)
-        prompt_string
-    )
 
-    tokenizer = model.tokenizer
+    compel = Compel(tokenizer=model.tokenizer,
-    compel = Compel(
-        tokenizer=tokenizer,
                     text_encoder=model.text_encoder,
                     textual_inversion_manager=model.textual_inversion_manager,
                     dtype_for_device_getter=torch_dtype,
-        truncate_long_prompts=False
+                    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)
-        positive_prompt_string,
-        negative_prompt_string,
-    ) = split_prompt_to_positive_and_negative(prompt_string)
-    legacy_blend = try_parse_legacy_blend(
-        positive_prompt_string, skip_normalize_legacy_blend
-    )
-    positive_prompt: Union[FlattenedPrompt, Blend]
-    if legacy_blend is not None:
-        positive_prompt = legacy_blend
-    else:
-        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
-        negative_prompt_string
-    )
 
-    if log_tokens or getattr(Globals, "log_tokenization", False):
+    legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-        log_tokenization(positive_prompt, negative_prompt, tokenizer=tokenizer)
+    positive_conjunction: Conjunction
+    if legacy_blend is not None:
+        positive_conjunction = legacy_blend
+    else:
+        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
+    positive_prompt = positive_conjunction.prompts[0]
+
+    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
+    negative_prompt: FlattenedPrompt | Blend = negative_conjunction.prompts[0]
+
+    tokens_count = get_max_token_count(model.tokenizer, positive_prompt)
+    if log_tokens or config.log_tokenization:
+        log_tokenization(positive_prompt, negative_prompt, tokenizer=model.tokenizer)
 
     c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
     uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
     [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
 
-    tokens_count = get_max_token_count(tokenizer, positive_prompt)
+    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
-
+                                                         cross_attention_control_args=options.get(
-    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+                                                             'cross_attention_control', None))
-        tokens_count_including_eos_bos=tokens_count,
-        cross_attention_control_args=options.get("cross_attention_control", None),
-    )
     return uc, c, ec
 
-
 def get_prompt_structure(
     prompt_string, skip_normalize_legacy_blend: bool = False
 ) -> (Union[FlattenedPrompt, Blend], FlattenedPrompt):
@@ -88,18 +80,17 @@ def get_prompt_structure(
     legacy_blend = try_parse_legacy_blend(
         positive_prompt_string, skip_normalize_legacy_blend
     )
-    positive_prompt: Union[FlattenedPrompt, Blend]
+    positive_prompt: Conjunction
     if legacy_blend is not None:
-        positive_prompt = legacy_blend
+        positive_conjunction = legacy_blend
     else:
-        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
+        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
+    positive_prompt = positive_conjunction.prompts[0]
-        negative_prompt_string
+    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
-    )
+    negative_prompt: FlattenedPrompt|Blend = negative_conjunction.prompts[0]
 
     return positive_prompt, negative_prompt
 
-
 def get_max_token_count(
     tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
 ) -> int:
@@ -246,22 +237,21 @@ def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_t
         logger.info(f"[TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
         logger.debug(f"{discarded}\x1b[0m")
 
-
+def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Conjunction]:
-def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Blend]:
     weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
     if len(weighted_subprompts) <= 1:
         return None
     strings = [x[0] for x in weighted_subprompts]
-    weights = [x[1] for x in weighted_subprompts]
 
     pp = PromptParser()
     parsed_conjunctions = [pp.parse_conjunction(x) for x in strings]
-    flattened_prompts = [x.prompts[0] for x in parsed_conjunctions]
+    flattened_prompts = []
-
+    weights = []
-    return Blend(
+    for i, x in enumerate(parsed_conjunctions):
-        prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize
+        if len(x.prompts)>0:
-    )
+            flattened_prompts.append(x.prompts[0])
-
+            weights.append(weighted_subprompts[i][1])
+    return Conjunction([Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)])
 
 def split_weighted_subprompts(text, skip_normalize=False) -> list:
     """

invokeai/backend/restoration/codeformer.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 
 import invokeai.backend.util.logging as logger
-from ..globals import Globals
+from invokeai.app.services.config import get_invokeai_config
 
 pretrained_model_url = (
     "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
@@ -17,11 +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(Globals.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)
@@ -71,9 +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"
-                    Globals.root, "models", "gfpgan", "weights"
-                ),
             )
             face_helper.clean_all()
             face_helper.read_image(bgr_image_array)

invokeai/backend/restoration/gfpgan.py

@@ -7,14 +7,13 @@ import torch
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import Globals
+from invokeai.app.services.config import 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(Globals.root, gfpgan_model_path)
-            )
         self.model_path = gfpgan_model_path
         self.gfpgan_model_exists = os.path.isfile(self.model_path)
 
@@ -33,7 +32,7 @@ class GFPGAN:
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=UserWarning)
             cwd = os.getcwd()
-            os.chdir(os.path.join(Globals.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

@@ -15,7 +15,7 @@ from transformers import AutoFeatureExtractor
 
 import invokeai.assets.web as web_assets
 import invokeai.backend.util.logging as logger
-from .globals import global_cache_dir
+from invokeai.app.services.config import get_invokeai_config
 from .util import CPU_DEVICE
 
 class SafetyChecker(object):
@@ -26,10 +26,11 @@ 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"
-            safety_model_path = global_cache_dir("hub")
+            safety_model_path = config.cache_dir
             self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(
                 safety_model_id,
                 local_files_only=True,

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -18,15 +18,15 @@ from huggingface_hub import (
 )
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import Globals
+from invokeai.app.services.config import 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 Globals.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 Globals.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

@@ -33,8 +33,7 @@ from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from typing_extensions import ParamSpec
 
-from invokeai.backend.globals import Globals
+from invokeai.app.services.config import get_invokeai_config
-
 from ..util import CPU_DEVICE, normalize_device
 from .diffusion import (
     AttentionMapSaver,
@@ -44,7 +43,6 @@ from .diffusion import (
 from .offloading import FullyLoadedModelGroup, LazilyLoadedModelGroup, ModelGroup
 from .textual_inversion_manager import TextualInversionManager
 
-
 @dataclass
 class PipelineIntermediateState:
     run_id: str
@@ -348,10 +346,11 @@ 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()
-            and not Globals.disable_xformers
+            and not config.disable_xformers
         ):
             self.enable_xformers_memory_efficient_attention()
         else:
@@ -548,6 +547,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             additional_guidance = []
         extra_conditioning_info = conditioning_data.extra
         with self.invokeai_diffuser.custom_attention_context(
+                self.invokeai_diffuser.model,
                 extra_conditioning_info=extra_conditioning_info,
                 step_count=len(self.scheduler.timesteps),
         ):

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -10,6 +10,7 @@ import diffusers
 import psutil
 import torch
 from compel.cross_attention_control import Arguments
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from diffusers.models.attention_processor import AttentionProcessor
 from torch import nn
 
@@ -352,8 +353,7 @@ def restore_default_cross_attention(
     else:
         remove_attention_function(model)
 
-
+def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: Context):
-def override_cross_attention(model, context: Context, is_running_diffusers=False):
     """
     Inject attention parameters and functions into the passed in model to enable cross attention editing.
 
@@ -372,15 +372,13 @@ def override_cross_attention(model, context: Context, is_running_diffusers=False
     indices = torch.arange(max_length, dtype=torch.long)
     for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
         if b0 < max_length:
-            if name == "equal":  # or (name == "replace" and a1 - a0 == b1 - b0):
+            if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
                 # these tokens have not been edited
                 indices[b0:b1] = indices_target[a0:a1]
                 mask[b0:b1] = 1
 
     context.cross_attention_mask = mask.to(device)
     context.cross_attention_index_map = indices.to(device)
-    if is_running_diffusers:
-        unet = model
     old_attn_processors = unet.attn_processors
     if torch.backends.mps.is_available():
         # see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
@@ -388,21 +386,8 @@ def override_cross_attention(model, context: Context, is_running_diffusers=False
     else:
         # try to re-use an existing slice size
         default_slice_size = 4
-            slice_size = next(
+        slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
-                (
-                    p.slice_size
-                    for p in old_attn_processors.values()
-                    if type(p) is SlicedAttnProcessor
-                ),
-                default_slice_size,
-            )
         unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
-        return old_attn_processors
-    else:
-        context.register_cross_attention_modules(model)
-        inject_attention_function(model, context)
-        return None
-
 
 def get_cross_attention_modules(
     model, which: CrossAttentionType

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -5,11 +5,12 @@ from typing import Any, Callable, Dict, Optional, Union
 
 import numpy as np
 import torch
+from diffusers import UNet2DConditionModel
 from diffusers.models.attention_processor import AttentionProcessor
 from typing_extensions import TypeAlias
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import Globals
+from invokeai.app.services.config import get_invokeai_config
 
 from .cross_attention_control import (
     Arguments,
@@ -17,8 +18,8 @@ from .cross_attention_control import (
     CrossAttentionType,
     SwapCrossAttnContext,
     get_cross_attention_modules,
-    override_cross_attention,
     restore_default_cross_attention,
+    setup_cross_attention_control_attention_processors,
 )
 from .cross_attention_map_saving import AttentionMapSaver
 
@@ -31,7 +32,6 @@ ModelForwardCallback: TypeAlias = Union[
     Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],
 ]
 
-
 @dataclass(frozen=True)
 class PostprocessingSettings:
     threshold: float
@@ -72,31 +72,43 @@ 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
         self.model_forward_callback = model_forward_callback
         self.cross_attention_control_context = None
-        self.sequential_guidance = Globals.sequential_guidance
+        self.sequential_guidance = config.sequential_guidance
 
+    @classmethod
     @contextmanager
     def custom_attention_context(
-        self, extra_conditioning_info: Optional[ExtraConditioningInfo], step_count: int
+        cls,
+        unet: UNet2DConditionModel, # note: also may futz with the text encoder depending on requested LoRAs
+        extra_conditioning_info: Optional[ExtraConditioningInfo],
+        step_count: int
     ):
-        do_swap = (
+        old_attn_processors = None
-            extra_conditioning_info is not None
+        if extra_conditioning_info and (
-            and extra_conditioning_info.wants_cross_attention_control
+            extra_conditioning_info.wants_cross_attention_control
+        ):
+            old_attn_processors = unet.attn_processors
+            # Load lora conditions into the model
+            if extra_conditioning_info.wants_cross_attention_control:
+                cross_attention_control_context = Context(
+                    arguments=extra_conditioning_info.cross_attention_control_args,
+                    step_count=step_count,
                 )
-        old_attn_processor = None
+                setup_cross_attention_control_attention_processors(
-        if do_swap:
+                    unet,
-            old_attn_processor = self.override_cross_attention(
+                    cross_attention_control_context,
-                extra_conditioning_info, step_count=step_count
                 )
+
         try:
             yield None
         finally:
-            if old_attn_processor is not None:
+            if old_attn_processors is not None:
-                self.restore_default_cross_attention(old_attn_processor)
+                unet.set_attn_processor(old_attn_processors)
             # TODO resuscitate attention map saving
             # self.remove_attention_map_saving()
 

invokeai/backend/stable_diffusion/schedulers/schedulers.py

@@ -9,7 +9,8 @@ SCHEDULER_MAP = dict(
     deis=(DEISMultistepScheduler, dict()),
     lms=(LMSDiscreteScheduler, dict()),
     pndm=(PNDMScheduler, dict()),
-    heun=(HeunDiscreteScheduler, dict()),
+    heun=(HeunDiscreteScheduler, dict(use_karras_sigmas=False)),
+    heun_k=(HeunDiscreteScheduler, dict(use_karras_sigmas=True)),
     euler=(EulerDiscreteScheduler, dict(use_karras_sigmas=False)),
     euler_k=(EulerDiscreteScheduler, dict(use_karras_sigmas=True)),
     euler_a=(EulerAncestralDiscreteScheduler, dict()),

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
@@ -47,8 +46,7 @@ from tqdm.auto import tqdm
 from transformers import CLIPTextModel, CLIPTokenizer
 
 # invokeai stuff
-from ..args import ArgFormatter, PagingArgumentParser
+from invokeai.app.services.config import InvokeAIAppConfig,PagingArgumentParser
-from ..globals import Globals, global_cache_dir
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
     PIL_INTERPOLATION = {
@@ -90,8 +88,9 @@ def save_progress(
 
 
 def parse_args():
+    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")
@@ -112,7 +111,7 @@ def parse_args():
         "--root_dir",
         "--root",
         type=Path,
-        default=Globals.root,
+        default=config.root,
         help="Path to the invokeai runtime directory",
     )
     general_group.add_argument(
@@ -127,7 +126,7 @@ def parse_args():
     general_group.add_argument(
         "--output_dir",
         type=Path,
-        default=f"{Globals.root}/text-inversion-model",
+        default=f"{config.root}/text-inversion-model",
         help="The output directory where the model predictions and checkpoints will be written.",
     )
     model_group.add_argument(
@@ -528,6 +527,7 @@ def get_full_repo_name(
 
 
 def do_textual_inversion_training(
+    config: InvokeAIAppConfig,
     model: str,
     train_data_dir: Path,
     output_dir: Path,
@@ -580,7 +580,7 @@ def do_textual_inversion_training(
 
     # setting up things the way invokeai expects them
     if not os.path.isabs(output_dir):
-        output_dir = os.path.join(Globals.root, output_dir)
+        output_dir = os.path.join(config.root, output_dir)
 
     logging_dir = output_dir / logging_dir
 
@@ -628,7 +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(Globals.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 (
@@ -640,7 +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=global_cache_dir("hub"))
+    pipeline_args = dict(cache_dir=config.cache_dir)
 
     # Load tokenizer
     if tokenizer_name:

invokeai/backend/util/devices.py

@@ -4,17 +4,16 @@ from contextlib import nullcontext
 
 import torch
 from torch import autocast
-
+from invokeai.app.services.config import get_invokeai_config
-from invokeai.backend.globals import Globals
 
 CPU_DEVICE = torch.device("cpu")
 CUDA_DEVICE = torch.device("cuda")
 MPS_DEVICE = torch.device("mps")
 
-
 def choose_torch_device() -> torch.device:
     """Convenience routine for guessing which GPU device to run model on"""
-    if Globals.always_use_cpu:
+    config = get_invokeai_config()    
+    if config.always_use_cpu:
         return CPU_DEVICE
     if torch.cuda.is_available():
         return torch.device("cuda")
@@ -33,7 +32,8 @@ def choose_precision(device: torch.device) -> str:
 
 
 def torch_dtype(device: torch.device) -> torch.dtype:
-    if Globals.full_precision:
+    config = get_invokeai_config()
+    if config.full_precision:
         return torch.float32
     if choose_precision(device) == "float16":
         return torch.float16

invokeai/backend/util/logging.py

@@ -2,34 +2,37 @@
 
 """invokeai.util.logging
 
-Logging class for InvokeAI that produces console messages that follow
+Logging class for InvokeAI that produces console messages
-the conventions established in InvokeAI 1.X through 2.X.
 
-
+Usage:
-One way to use it:
 
 from invokeai.backend.util.logging import InvokeAILogger
 
-logger = InvokeAILogger.getLogger(__name__)
+logger = InvokeAILogger.getLogger(name='InvokeAI') // Initialization
-logger.critical('this is critical')
+(or)
-logger.error('this is an error')
+logger = InvokeAILogger.getLogger(__name__) // To use the filename
-logger.warning('this is a warning')
+
-logger.info('this is info')
+logger.critical('this is critical') // Critical Message
-logger.debug('this is debugging')
+logger.error('this is an error') // Error Message
+logger.warning('this is a warning') // Warning Message
+logger.info('this is info') // Info Message
+logger.debug('this is debugging') // Debug Message
 
 Console messages:
-     ### this is critical
+    [12-05-2023 20]::[InvokeAI]::CRITICAL --> This is an info message [In Bold Red]
-     *** this is an error ***
+    [12-05-2023 20]::[InvokeAI]::ERROR --> This is an info message [In Red]
-     ** this is a warning
+    [12-05-2023 20]::[InvokeAI]::WARNING --> This is an info message [In Yellow]
-     >> this is info
+    [12-05-2023 20]::[InvokeAI]::INFO --> This is an info message [In Grey]
-        | this is debugging
+    [12-05-2023 20]::[InvokeAI]::DEBUG --> This is an info message [In Grey]
 
-Another way:
+Alternate Method (in this case the logger name will be set to InvokeAI):
-import invokeai.backend.util.logging as ialog
+import invokeai.backend.util.logging as IAILogger
-ialogger.debug('this is a debugging message')
+IAILogger.debug('this is a debugging message')
 """
+
 import logging
 
+
 # module level functions
 def debug(msg, *args, **kwargs):
     InvokeAILogger.getLogger().debug(msg, *args, **kwargs)
@@ -55,49 +58,47 @@ def disable(level=logging.CRITICAL):
 def basicConfig(**kwargs):
     InvokeAILogger.getLogger().basicConfig(**kwargs)
 
-def getLogger(name: str=None)->logging.Logger:
+def getLogger(name: str = None) -> logging.Logger:
     return InvokeAILogger.getLogger(name)
 
+
 class InvokeAILogFormatter(logging.Formatter):
     '''
-    Repurposed from:
+    Custom Formatting for the InvokeAI Logger
-    https://stackoverflow.com/questions/14844970/modifying-logging-message-format-based-on-message-logging-level-in-python3
     '''
-    crit_fmt = "### %(msg)s"
-    err_fmt = "*** %(msg)s"
-    warn_fmt = "** %(msg)s"
-    info_fmt = ">> %(msg)s"
-    dbg_fmt = "   | %(msg)s"
 
-    def __init__(self):
+    # Color Codes
-        super().__init__(fmt="%(levelno)d: %(msg)s", datefmt=None, style='%')
+    grey = "\x1b[38;20m"
+    yellow = "\x1b[33;20m"
+    red = "\x1b[31;20m"
+    cyan = "\x1b[36;20m"
+    bold_red = "\x1b[31;1m"
+    reset = "\x1b[0m"
+
+    # Log Format
+    format = "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
+    ## More Formatting Options: %(pathname)s, %(filename)s, %(module)s, %(lineno)d
+
+    # Format Map
+    FORMATS = {
+        logging.DEBUG: cyan + format + reset,
+        logging.INFO: grey + format + reset,
+        logging.WARNING: yellow + format + reset,
+        logging.ERROR: red + format + reset,
+        logging.CRITICAL: bold_red + format + reset
+    }
 
     def format(self, record):
-        # Remember the format used when the logging module
+        log_fmt = self.FORMATS.get(record.levelno)
-        # was installed (in the event that this formatter is
+        formatter = logging.Formatter(log_fmt, datefmt="%d-%m-%Y %H:%M:%S")
-        # used with the vanilla logging module.
+        return formatter.format(record)
-        format_orig = self._style._fmt
-        if record.levelno == logging.DEBUG:
-            self._style._fmt = InvokeAILogFormatter.dbg_fmt
-        if record.levelno == logging.INFO:
-            self._style._fmt = InvokeAILogFormatter.info_fmt
-        if record.levelno == logging.WARNING:
-            self._style._fmt = InvokeAILogFormatter.warn_fmt
-        if record.levelno == logging.ERROR:
-            self._style._fmt = InvokeAILogFormatter.err_fmt
-        if record.levelno == logging.CRITICAL:
-            self._style._fmt = InvokeAILogFormatter.crit_fmt
 
-        # parent class does the work
-        result = super().format(record)
-        self._style._fmt = format_orig
-        return result
 
 class InvokeAILogger(object):
     loggers = dict()
 
     @classmethod
-    def getLogger(self, name:str='invokeai')->logging.Logger:
+    def getLogger(self, name: str = 'InvokeAI') -> logging.Logger:
         if name not in self.loggers:
             logger = logging.getLogger(name)
             logger.setLevel(logging.DEBUG)

invokeai/backend/web/modules/parameters.py

@@ -9,6 +9,7 @@ SAMPLER_CHOICES = [
     "lms",
     "pndm",
     "heun",
+    'heun_k',
     "euler",
     "euler_k",
     "euler_a",

invokeai/frontend/CLI/readline.py

@@ -1,498 +0,0 @@
-"""
-Readline helper functions for invoke.py.
-You may import the global singleton `completer` to get access to the
-completer object itself. This is useful when you want to autocomplete
-seeds:
-
- from invokeai.frontend.CLI.readline import completer
- completer.add_seed(18247566)
- completer.add_seed(9281839)
-"""
-import atexit
-import os
-import re
-
-from ...backend.args import Args
-from ...backend.globals import Globals
-from ...backend.stable_diffusion import HuggingFaceConceptsLibrary
-
-# ---------------readline utilities---------------------
-try:
-    import readline
-
-    readline_available = True
-except (ImportError, ModuleNotFoundError) as e:
-    print(f"** An error occurred when loading the readline module: {str(e)}")
-    readline_available = False
-
-IMG_EXTENSIONS = (".png", ".jpg", ".jpeg", ".PNG", ".JPG", ".JPEG", ".gif", ".GIF")
-WEIGHT_EXTENSIONS = (".ckpt", ".vae", ".safetensors")
-TEXT_EXTENSIONS = (".txt", ".TXT")
-CONFIG_EXTENSIONS = (".yaml", ".yml")
-COMMANDS = (
-    "--steps",
-    "-s",
-    "--seed",
-    "-S",
-    "--iterations",
-    "-n",
-    "--width",
-    "-W",
-    "--height",
-    "-H",
-    "--cfg_scale",
-    "-C",
-    "--threshold",
-    "--perlin",
-    "--grid",
-    "-g",
-    "--individual",
-    "-i",
-    "--save_intermediates",
-    "--init_img",
-    "-I",
-    "--init_mask",
-    "-M",
-    "--init_color",
-    "--strength",
-    "-f",
-    "--variants",
-    "-v",
-    "--outdir",
-    "-o",
-    "--sampler",
-    "-A",
-    "-m",
-    "--embedding_path",
-    "--device",
-    "--grid",
-    "-g",
-    "--facetool",
-    "-ft",
-    "--facetool_strength",
-    "-G",
-    "--codeformer_fidelity",
-    "-cf",
-    "--upscale",
-    "-U",
-    "-save_orig",
-    "--save_original",
-    "--log_tokenization",
-    "-t",
-    "--hires_fix",
-    "--inpaint_replace",
-    "-r",
-    "--png_compression",
-    "-z",
-    "--text_mask",
-    "-tm",
-    "--h_symmetry_time_pct",
-    "--v_symmetry_time_pct",
-    "!fix",
-    "!fetch",
-    "!replay",
-    "!history",
-    "!search",
-    "!clear",
-    "!models",
-    "!switch",
-    "!import_model",
-    "!optimize_model",
-    "!convert_model",
-    "!edit_model",
-    "!del_model",
-    "!mask",
-    "!triggers",
-)
-MODEL_COMMANDS = (
-    "!switch",
-    "!edit_model",
-    "!del_model",
-)
-CKPT_MODEL_COMMANDS = ("!optimize_model",)
-WEIGHT_COMMANDS = (
-    "!import_model",
-    "!convert_model",
-)
-IMG_PATH_COMMANDS = ("--outdir[=\s]",)
-TEXT_PATH_COMMANDS = ("!replay",)
-IMG_FILE_COMMANDS = (
-    "!fix",
-    "!fetch",
-    "!mask",
-    "--init_img[=\s]",
-    "-I",
-    "--init_mask[=\s]",
-    "-M",
-    "--init_color[=\s]",
-    "--embedding_path[=\s]",
-)
-
-path_regexp = "(" + "|".join(IMG_PATH_COMMANDS + IMG_FILE_COMMANDS) + ")\s*\S*$"
-weight_regexp = "(" + "|".join(WEIGHT_COMMANDS) + ")\s*\S*$"
-text_regexp = "(" + "|".join(TEXT_PATH_COMMANDS) + ")\s*\S*$"
-
-
-class Completer(object):
-    def __init__(self, options, models={}):
-        self.options = sorted(options)
-        self.models = models
-        self.seeds = set()
-        self.matches = list()
-        self.default_dir = None
-        self.linebuffer = None
-        self.auto_history_active = True
-        self.extensions = None
-        self.concepts = None
-        self.embedding_terms = set()
-        return
-
-    def complete(self, text, state):
-        """
-        Completes invoke command line.
-        BUG: it doesn't correctly complete files that have spaces in the name.
-        """
-        buffer = readline.get_line_buffer()
-
-        if state == 0:
-            # extensions defined, so go directly into path completion mode
-            if self.extensions is not None:
-                self.matches = self._path_completions(text, state, self.extensions)
-
-            # looking for an image file
-            elif re.search(path_regexp, buffer):
-                do_shortcut = re.search("^" + "|".join(IMG_FILE_COMMANDS), buffer)
-                self.matches = self._path_completions(
-                    text, state, IMG_EXTENSIONS, shortcut_ok=do_shortcut
-                )
-
-            # looking for a seed
-            elif re.search("(-S\s*|--seed[=\s])\d*$", buffer):
-                self.matches = self._seed_completions(text, state)
-
-            # looking for an embedding concept
-            elif re.search("<[\w-]*$", buffer):
-                self.matches = self._concept_completions(text, state)
-
-            # looking for a model
-            elif re.match("^" + "|".join(MODEL_COMMANDS), buffer):
-                self.matches = self._model_completions(text, state)
-
-            # looking for a ckpt model
-            elif re.match("^" + "|".join(CKPT_MODEL_COMMANDS), buffer):
-                self.matches = self._model_completions(text, state, ckpt_only=True)
-
-            elif re.search(weight_regexp, buffer):
-                self.matches = self._path_completions(
-                    text,
-                    state,
-                    WEIGHT_EXTENSIONS,
-                    default_dir=Globals.root,
-                )
-
-            elif re.search(text_regexp, buffer):
-                self.matches = self._path_completions(text, state, TEXT_EXTENSIONS)
-
-            # This is the first time for this text, so build a match list.
-            elif text:
-                self.matches = [s for s in self.options if s and s.startswith(text)]
-            else:
-                self.matches = self.options[:]
-
-        # Return the state'th item from the match list,
-        # if we have that many.
-        try:
-            response = self.matches[state]
-        except IndexError:
-            response = None
-        return response
-
-    def complete_extensions(self, extensions: list):
-        """
-        If called with a list of extensions, will force completer
-        to do file path completions.
-        """
-        self.extensions = extensions
-
-    def add_history(self, line):
-        """
-        Pass thru to readline
-        """
-        if not self.auto_history_active:
-            readline.add_history(line)
-
-    def clear_history(self):
-        """
-        Pass clear_history() thru to readline
-        """
-        readline.clear_history()
-
-    def search_history(self, match: str):
-        """
-        Like show_history() but only shows items that
-        contain the match string.
-        """
-        self.show_history(match)
-
-    def remove_history_item(self, pos):
-        readline.remove_history_item(pos)
-
-    def add_seed(self, seed):
-        """
-        Add a seed to the autocomplete list for display when -S is autocompleted.
-        """
-        if seed is not None:
-            self.seeds.add(str(seed))
-
-    def set_default_dir(self, path):
-        self.default_dir = path
-
-    def set_options(self, options):
-        self.options = options
-
-    def get_line(self, index):
-        try:
-            line = self.get_history_item(index)
-        except IndexError:
-            return None
-        return line
-
-    def get_current_history_length(self):
-        return readline.get_current_history_length()
-
-    def get_history_item(self, index):
-        return readline.get_history_item(index)
-
-    def show_history(self, match=None):
-        """
-        Print the session history using the pydoc pager
-        """
-        import pydoc
-
-        lines = list()
-        h_len = self.get_current_history_length()
-        if h_len < 1:
-            print("<empty history>")
-            return
-
-        for i in range(0, h_len):
-            line = self.get_history_item(i + 1)
-            if match and match not in line:
-                continue
-            lines.append(f"[{i+1}] {line}")
-        pydoc.pager("\n".join(lines))
-
-    def set_line(self, line) -> None:
-        """
-        Set the default string displayed in the next line of input.
-        """
-        self.linebuffer = line
-        readline.redisplay()
-
-    def update_models(self, models: dict) -> None:
-        """
-        update our list of models
-        """
-        self.models = models
-
-    def _seed_completions(self, text, state):
-        m = re.search("(-S\s?|--seed[=\s]?)(\d*)", text)
-        if m:
-            switch = m.groups()[0]
-            partial = m.groups()[1]
-        else:
-            switch = ""
-            partial = text
-
-        matches = list()
-        for s in self.seeds:
-            if s.startswith(partial):
-                matches.append(switch + s)
-        matches.sort()
-        return matches
-
-    def add_embedding_terms(self, terms: list[str]):
-        self.embedding_terms = set(terms)
-        if self.concepts:
-            self.embedding_terms.update(set(self.concepts.list_concepts()))
-
-    def _concept_completions(self, text, state):
-        if self.concepts is None:
-            # cache Concepts() instance so we can check for updates in concepts_list during runtime.
-            self.concepts = HuggingFaceConceptsLibrary()
-            self.embedding_terms.update(set(self.concepts.list_concepts()))
-        else:
-            self.embedding_terms.update(set(self.concepts.list_concepts()))
-
-        partial = text[1:]  # this removes the leading '<'
-        if len(partial) == 0:
-            return list(self.embedding_terms)  # whole dump - think if user wants this!
-
-        matches = list()
-        for concept in self.embedding_terms:
-            if concept.startswith(partial):
-                matches.append(f"<{concept}>")
-        matches.sort()
-        return matches
-
-    def _model_completions(self, text, state, ckpt_only=False):
-        m = re.search("(!switch\s+)(\w*)", text)
-        if m:
-            switch = m.groups()[0]
-            partial = m.groups()[1]
-        else:
-            switch = ""
-            partial = text
-        matches = list()
-        for s in self.models:
-            name = self.models[s]["model_name"]
-            format = self.models[s]["format"]
-            if format == "vae":
-                continue
-            if ckpt_only and format != "ckpt":
-                continue
-            if name.startswith(partial):
-                matches.append(switch + name)
-        matches.sort()
-        return matches
-
-    def _pre_input_hook(self):
-        if self.linebuffer:
-            readline.insert_text(self.linebuffer)
-            readline.redisplay()
-            self.linebuffer = None
-
-    def _path_completions(
-        self, text, state, extensions, shortcut_ok=True, default_dir: str = ""
-    ):
-        # separate the switch from the partial path
-        match = re.search("^(-\w|--\w+=?)(.*)", text)
-        if match is None:
-            switch = None
-            partial_path = text
-        else:
-            switch, partial_path = match.groups()
-
-        partial_path = partial_path.lstrip()
-
-        matches = list()
-        path = os.path.expanduser(partial_path)
-
-        if os.path.isdir(path):
-            dir = path
-        elif os.path.dirname(path) != "":
-            dir = os.path.dirname(path)
-        else:
-            dir = default_dir if os.path.exists(default_dir) else ""
-            path = os.path.join(dir, path)
-
-        dir_list = os.listdir(dir or ".")
-        if shortcut_ok and os.path.exists(self.default_dir) and dir == "":
-            dir_list += os.listdir(self.default_dir)
-
-        for node in dir_list:
-            if node.startswith(".") and len(node) > 1:
-                continue
-            full_path = os.path.join(dir, node)
-
-            if not (node.endswith(extensions) or os.path.isdir(full_path)):
-                continue
-
-            if path and not full_path.startswith(path):
-                continue
-
-            if switch is None:
-                match_path = os.path.join(dir, node)
-                matches.append(
-                    match_path + "/" if os.path.isdir(full_path) else match_path
-                )
-            elif os.path.isdir(full_path):
-                matches.append(
-                    switch + os.path.join(os.path.dirname(full_path), node) + "/"
-                )
-            elif node.endswith(extensions):
-                matches.append(switch + os.path.join(os.path.dirname(full_path), node))
-
-        return matches
-
-
-class DummyCompleter(Completer):
-    def __init__(self, options):
-        super().__init__(options)
-        self.history = list()
-
-    def add_history(self, line):
-        self.history.append(line)
-
-    def clear_history(self):
-        self.history = list()
-
-    def get_current_history_length(self):
-        return len(self.history)
-
-    def get_history_item(self, index):
-        return self.history[index - 1]
-
-    def remove_history_item(self, index):
-        return self.history.pop(index - 1)
-
-    def set_line(self, line):
-        print(f"# {line}")
-
-
-def generic_completer(commands: list) -> Completer:
-    if readline_available:
-        completer = Completer(commands, [])
-        readline.set_completer(completer.complete)
-        readline.set_pre_input_hook(completer._pre_input_hook)
-        readline.set_completer_delims(" ")
-        readline.parse_and_bind("tab: complete")
-        readline.parse_and_bind("set print-completions-horizontally off")
-        readline.parse_and_bind("set page-completions on")
-        readline.parse_and_bind("set skip-completed-text on")
-        readline.parse_and_bind("set show-all-if-ambiguous on")
-    else:
-        completer = DummyCompleter(commands)
-    return completer
-
-
-def get_completer(opt: Args, models=[]) -> Completer:
-    if readline_available:
-        completer = Completer(COMMANDS, models)
-
-        readline.set_completer(completer.complete)
-        # pyreadline3 does not have a set_auto_history() method
-        try:
-            readline.set_auto_history(False)
-            completer.auto_history_active = False
-        except:
-            completer.auto_history_active = True
-        readline.set_pre_input_hook(completer._pre_input_hook)
-        readline.set_completer_delims(" ")
-        readline.parse_and_bind("tab: complete")
-        readline.parse_and_bind("set print-completions-horizontally off")
-        readline.parse_and_bind("set page-completions on")
-        readline.parse_and_bind("set skip-completed-text on")
-        readline.parse_and_bind("set show-all-if-ambiguous on")
-
-        outdir = os.path.expanduser(opt.outdir)
-        if os.path.isabs(outdir):
-            histfile = os.path.join(outdir, ".invoke_history")
-        else:
-            histfile = os.path.join(Globals.root, outdir, ".invoke_history")
-        try:
-            readline.read_history_file(histfile)
-            readline.set_history_length(1000)
-        except FileNotFoundError:
-            pass
-        except OSError:  # file likely corrupted
-            newname = f"{histfile}.old"
-            print(
-                f"## Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
-            )
-            os.replace(histfile, newname)
-        atexit.register(readline.write_history_file, histfile)
-
-    else:
-        completer = DummyCompleter(COMMANDS)
-    return completer

invokeai/frontend/CLI/sd_metadata.py

@@ -1,30 +0,0 @@
-'''
-This is a modularized version of the sd-metadata.py script,
-which retrieves and prints the metadata from a series of generated png files.
-'''
-import sys
-import json
-from invokeai.backend.image_util import retrieve_metadata
-
-
-def print_metadata():
-    if len(sys.argv) < 2:
-        print("Usage: file2prompt.py <file1.png> <file2.png> <file3.png>...")
-        print("This script opens up the indicated invoke.py-generated PNG file(s) and prints out their metadata.")
-        exit(-1)
-
-    filenames = sys.argv[1:]
-    for f in filenames:
-        try:
-            metadata = retrieve_metadata(f)
-            print(f'{f}:\n',json.dumps(metadata['sd-metadata'], indent=4))
-        except FileNotFoundError:
-            sys.stderr.write(f'{f} not found\n')
-            continue
-        except PermissionError:
-            sys.stderr.write(f'{f} could not be opened due to inadequate permissions\n')
-            continue
-
-if __name__== '__main__':
-    print_metadata()
-

invokeai/frontend/install/model_install.py

@@ -23,7 +23,6 @@ from npyscreen import widget
 from omegaconf import OmegaConf
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.globals import Globals, global_config_dir
 
 from ...backend.config.model_install_backend import (
     Dataset_path,
@@ -41,11 +40,13 @@ from .widgets import (
     TextBox,
     set_min_terminal_size,
 )
+from invokeai.app.services.config import get_invokeai_config
 
 # minimum size for the UI
 MIN_COLS = 120
 MIN_LINES = 45
 
+config = get_invokeai_config()
 
 class addModelsForm(npyscreen.FormMultiPage):
     # for responsive resizing - disabled
@@ -453,9 +454,9 @@ def main():
     opt = parser.parse_args()
 
     # setting a global here
-    Globals.root = os.path.expanduser(get_root(opt.root) or "")
+    config.root = os.path.expanduser(get_root(opt.root) or "")
 
-    if not global_config_dir().exists():
+    if not (config.conf_path / '..' ).exists():
         logger.info(
             "Your InvokeAI root directory is not set up. Calling invokeai-configure."
         )

invokeai/frontend/merge/merge_diffusers.py

@@ -8,7 +8,6 @@ import argparse
 import curses
 import os
 import sys
-import traceback
 import warnings
 from argparse import Namespace
 from pathlib import Path
@@ -20,20 +19,13 @@ from diffusers import logging as dlogging
 from npyscreen import widget
 from omegaconf import OmegaConf
 
-from ...backend.globals import (
-    Globals,
-    global_cache_dir,
-    global_config_file,
-    global_models_dir,
-    global_set_root,
-)
-
 import invokeai.backend.util.logging as logger
+from invokeai.services.config import get_invokeai_config
 from ...backend.model_management import ModelManager
 from ...frontend.install.widgets import FloatTitleSlider
 
 DEST_MERGED_MODEL_DIR = "merged_models"
-
+config = get_invokeai_config()
 
 def merge_diffusion_models(
     model_ids_or_paths: List[Union[str, Path]],
@@ -60,7 +52,7 @@ def merge_diffusion_models(
 
         pipe = DiffusionPipeline.from_pretrained(
             model_ids_or_paths[0],
-            cache_dir=kwargs.get("cache_dir", global_cache_dir()),
+            cache_dir=kwargs.get("cache_dir", config.cache_dir),
             custom_pipeline="checkpoint_merger",
         )
         merged_pipe = pipe.merge(
@@ -94,7 +86,7 @@ def merge_diffusion_models_and_commit(
     **kwargs - the default DiffusionPipeline.get_config_dict kwargs:
          cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map
     """
-    config_file = global_config_file()
+    config_file = config.model_conf_path
     model_manager = ModelManager(OmegaConf.load(config_file))
     for mod in models:
         assert mod in model_manager.model_names(), f'** Unknown model "{mod}"'
@@ -106,7 +98,7 @@ def merge_diffusion_models_and_commit(
     merged_pipe = merge_diffusion_models(
         model_ids_or_paths, alpha, interp, force, **kwargs
     )
-    dump_path = global_models_dir() / DEST_MERGED_MODEL_DIR
+    dump_path = config.models_dir / DEST_MERGED_MODEL_DIR
 
     os.makedirs(dump_path, exist_ok=True)
     dump_path = dump_path / merged_model_name
@@ -126,7 +118,7 @@ def _parse_args() -> Namespace:
     parser.add_argument(
         "--root_dir",
         type=Path,
-        default=Globals.root,
+        default=config.root,
         help="Path to the invokeai runtime directory",
     )
     parser.add_argument(
@@ -398,7 +390,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
 class Mergeapp(npyscreen.NPSAppManaged):
     def __init__(self):
         super().__init__()
-        conf = OmegaConf.load(global_config_file())
+        conf = OmegaConf.load(config.model_conf_path)
         self.model_manager = ModelManager(
             conf, "cpu", "float16"
         )  # precision doesn't really matter here
@@ -429,7 +421,7 @@ def run_cli(args: Namespace):
             f'No --merged_model_name provided. Defaulting to "{args.merged_model_name}"'
         )
 
-        model_manager = ModelManager(OmegaConf.load(global_config_file()))
+        model_manager = ModelManager(OmegaConf.load(config.model_conf_path))
         assert (
             args.clobber or args.merged_model_name not in model_manager.model_names()
         ), f'A model named "{args.merged_model_name}" already exists. Use --clobber to overwrite.'
@@ -440,9 +432,9 @@ def run_cli(args: Namespace):
 
 def main():
     args = _parse_args()
-    global_set_root(args.root_dir)
+    config.root = args.root_dir
 
-    cache_dir = str(global_cache_dir("hub"))
+    cache_dir = config.cache_dir
     os.environ[
         "HF_HOME"
     ] = cache_dir  # because not clear the merge pipeline is honoring cache_dir

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)

invokeai/frontend/web/docs/PACKAGE_SCRIPTS.md

@@ -15,15 +15,3 @@ The `postinstall` script patches a few packages and runs the Chakra CLI to gener
 ### Patch `@chakra-ui/cli`
 
 See: <https://github.com/chakra-ui/chakra-ui/issues/7394>
-
-### Patch `redux-persist`
-
-We want to persist the canvas state to `localStorage` but many canvas operations change data very quickly, so we need to debounce the writes to `localStorage`.
-
-`redux-persist` is unfortunately unmaintained. The repo's current code is nonfunctional, but the last release's code depends on a package that was removed from `npm` for being malware, so we cannot just fork it.
-
-So, we have to patch it directly. Perhaps a better way would be to write a debounced storage adapter, but I couldn't figure out how to do that.
-
-### Patch `redux-deep-persist`
-
-This package makes blacklisting and whitelisting persist configs very simple, but we have to patch it to match `redux-persist` for the types to work.

invokeai/frontend/web/package.json

@@ -62,11 +62,13 @@
     "@dagrejs/graphlib": "^2.1.12",
     "@emotion/react": "^11.10.6",
     "@emotion/styled": "^11.10.6",
+    "@floating-ui/react-dom": "^2.0.0",
     "@fontsource/inter": "^4.5.15",
     "@reduxjs/toolkit": "^1.9.5",
     "@roarr/browser-log-writer": "^1.1.5",
     "chakra-ui-contextmenu": "^1.0.5",
     "dateformat": "^5.0.3",
+    "downshift": "^7.6.0",
     "formik": "^2.2.9",
     "framer-motion": "^10.12.4",
     "fuse.js": "^6.6.2",
@@ -87,18 +89,13 @@
     "react-i18next": "^12.2.2",
     "react-icons": "^4.7.1",
     "react-konva": "^18.2.7",
-    "react-konva-utils": "^1.0.4",
     "react-redux": "^8.0.5",
     "react-resizable-panels": "^0.0.42",
-    "react-rnd": "^10.4.1",
-    "react-transition-group": "^4.4.5",
     "react-use": "^17.4.0",
     "react-virtuoso": "^4.3.5",
     "react-zoom-pan-pinch": "^3.0.7",
     "reactflow": "^11.7.0",
-    "redux-deep-persist": "^1.0.7",
     "redux-dynamic-middlewares": "^2.2.0",
-    "redux-persist": "^6.0.0",
     "redux-remember": "^3.3.1",
     "roarr": "^7.15.0",
     "serialize-error": "^11.0.0",

invokeai/frontend/web/patches/redux-deep-persist+1.0.7.patch

@@ -1,24 +0,0 @@
-diff --git a/node_modules/redux-deep-persist/lib/types.d.ts b/node_modules/redux-deep-persist/lib/types.d.ts
-index b67b8c2..7fc0fa1 100644
---- a/node_modules/redux-deep-persist/lib/types.d.ts
-+++ b/node_modules/redux-deep-persist/lib/types.d.ts
-@@ -35,6 +35,7 @@ export interface PersistConfig<S, RS = any, HSS = any, ESS = any> {
-     whitelist?: Array<string>;
-     transforms?: Array<Transform<HSS, ESS, S, RS>>;
-     throttle?: number;
-+    debounce?: number;
-     migrate?: PersistMigrate;
-     stateReconciler?: false | StateReconciler<S>;
-     getStoredState?: (config: PersistConfig<S, RS, HSS, ESS>) => Promise<PersistedState>;
-diff --git a/node_modules/redux-deep-persist/src/types.ts b/node_modules/redux-deep-persist/src/types.ts
-index 398ac19..cbc5663 100644
---- a/node_modules/redux-deep-persist/src/types.ts
-+++ b/node_modules/redux-deep-persist/src/types.ts
-@@ -91,6 +91,7 @@ export interface PersistConfig<S, RS = any, HSS = any, ESS = any> {
-     whitelist?: Array<string>;
-     transforms?: Array<Transform<HSS, ESS, S, RS>>;
-     throttle?: number;
-+    debounce?: number;
-     migrate?: PersistMigrate;
-     stateReconciler?: false | StateReconciler<S>;
-     /**

invokeai/frontend/web/patches/redux-persist+6.0.0.patch

@@ -1,116 +0,0 @@
-diff --git a/node_modules/redux-persist/es/createPersistoid.js b/node_modules/redux-persist/es/createPersistoid.js
-index 8b43b9a..184faab 100644
---- a/node_modules/redux-persist/es/createPersistoid.js
-+++ b/node_modules/redux-persist/es/createPersistoid.js
-@@ -6,6 +6,7 @@ export default function createPersistoid(config) {
-   var whitelist = config.whitelist || null;
-   var transforms = config.transforms || [];
-   var throttle = config.throttle || 0;
-+  var debounce = config.debounce || 0;
-   var storageKey = "".concat(config.keyPrefix !== undefined ? config.keyPrefix : KEY_PREFIX).concat(config.key);
-   var storage = config.storage;
-   var serialize;
-@@ -28,30 +29,37 @@ export default function createPersistoid(config) {
-   var timeIterator = null;
-   var writePromise = null;
- 
--  var update = function update(state) {
--    // add any changed keys to the queue
--    Object.keys(state).forEach(function (key) {
--      if (!passWhitelistBlacklist(key)) return; // is keyspace ignored? noop
-+  // Timer for debounced `update()`
-+  let timer = 0;
- 
--      if (lastState[key] === state[key]) return; // value unchanged? noop
-+  function update(state) {
-+    // Debounce the update
-+    clearTimeout(timer);
-+    timer = setTimeout(() => {
-+      // add any changed keys to the queue
-+      Object.keys(state).forEach(function (key) {
-+        if (!passWhitelistBlacklist(key)) return; // is keyspace ignored? noop
- 
--      if (keysToProcess.indexOf(key) !== -1) return; // is key already queued? noop
-+        if (lastState[key] === state[key]) return; // value unchanged? noop
- 
--      keysToProcess.push(key); // add key to queue
--    }); //if any key is missing in the new state which was present in the lastState,
--    //add it for processing too
-+        if (keysToProcess.indexOf(key) !== -1) return; // is key already queued? noop
- 
--    Object.keys(lastState).forEach(function (key) {
--      if (state[key] === undefined && passWhitelistBlacklist(key) && keysToProcess.indexOf(key) === -1 && lastState[key] !== undefined) {
--        keysToProcess.push(key);
--      }
--    }); // start the time iterator if not running (read: throttle)
-+        keysToProcess.push(key); // add key to queue
-+      }); //if any key is missing in the new state which was present in the lastState,
-+      //add it for processing too
- 
--    if (timeIterator === null) {
--      timeIterator = setInterval(processNextKey, throttle);
--    }
-+      Object.keys(lastState).forEach(function (key) {
-+        if (state[key] === undefined && passWhitelistBlacklist(key) && keysToProcess.indexOf(key) === -1 && lastState[key] !== undefined) {
-+          keysToProcess.push(key);
-+        }
-+      }); // start the time iterator if not running (read: throttle)
-+
-+      if (timeIterator === null) {
-+        timeIterator = setInterval(processNextKey, throttle);
-+      }
- 
--    lastState = state;
-+      lastState = state;
-+    }, debounce)
-   };
- 
-   function processNextKey() {
-diff --git a/node_modules/redux-persist/es/types.js.flow b/node_modules/redux-persist/es/types.js.flow
-index c50d3cd..39d8be2 100644
---- a/node_modules/redux-persist/es/types.js.flow
-+++ b/node_modules/redux-persist/es/types.js.flow
-@@ -19,6 +19,7 @@ export type PersistConfig = {
-   whitelist?: Array<string>,
-   transforms?: Array<Transform>,
-   throttle?: number,
-+  debounce?: number,
-   migrate?: (PersistedState, number) => Promise<PersistedState>,
-   stateReconciler?: false | Function,
-   getStoredState?: PersistConfig => Promise<PersistedState>, // used for migrations
-diff --git a/node_modules/redux-persist/lib/types.js.flow b/node_modules/redux-persist/lib/types.js.flow
-index c50d3cd..39d8be2 100644
---- a/node_modules/redux-persist/lib/types.js.flow
-+++ b/node_modules/redux-persist/lib/types.js.flow
-@@ -19,6 +19,7 @@ export type PersistConfig = {
-   whitelist?: Array<string>,
-   transforms?: Array<Transform>,
-   throttle?: number,
-+  debounce?: number,
-   migrate?: (PersistedState, number) => Promise<PersistedState>,
-   stateReconciler?: false | Function,
-   getStoredState?: PersistConfig => Promise<PersistedState>, // used for migrations
-diff --git a/node_modules/redux-persist/src/types.js b/node_modules/redux-persist/src/types.js
-index c50d3cd..39d8be2 100644
---- a/node_modules/redux-persist/src/types.js
-+++ b/node_modules/redux-persist/src/types.js
-@@ -19,6 +19,7 @@ export type PersistConfig = {
-   whitelist?: Array<string>,
-   transforms?: Array<Transform>,
-   throttle?: number,
-+  debounce?: number,
-   migrate?: (PersistedState, number) => Promise<PersistedState>,
-   stateReconciler?: false | Function,
-   getStoredState?: PersistConfig => Promise<PersistedState>, // used for migrations
-diff --git a/node_modules/redux-persist/types/types.d.ts b/node_modules/redux-persist/types/types.d.ts
-index b3733bc..2a1696c 100644
---- a/node_modules/redux-persist/types/types.d.ts
-+++ b/node_modules/redux-persist/types/types.d.ts
-@@ -35,6 +35,7 @@ declare module "redux-persist/es/types" {
-     whitelist?: Array<string>;
-     transforms?: Array<Transform<HSS, ESS, S, RS>>;
-     throttle?: number;
-+    debounce?: number;
-     migrate?: PersistMigrate;
-     stateReconciler?: false | StateReconciler<S>;
-     /**

invokeai/frontend/web/public/locales/en.json

@@ -450,7 +450,7 @@
         "cfgScale": "CFG Scale",
         "width": "Width",
         "height": "Height",
-        "sampler": "Sampler",
+        "scheduler": "Scheduler",
         "seed": "Seed",
         "imageToImage": "Image to Image",
         "randomizeSeed": "Randomize Seed",
@@ -540,7 +540,10 @@
         "consoleLogLevel": "Log Level",
         "shouldLogToConsole": "Console Logging",
         "developer": "Developer",
-        "general": "General"
+        "general": "General",
+        "generation": "Generation",
+        "ui": "User Interface",
+        "availableSchedulers": "Available Schedulers"
     },
     "toast": {
         "serverError": "Server Error",
@@ -549,8 +552,8 @@
         "canceled": "Processing Canceled",
         "tempFoldersEmptied": "Temp Folder Emptied",
         "uploadFailed": "Upload failed",
-        "uploadFailedMultipleImagesDesc": "Multiple images pasted, may only upload one image at a time",
         "uploadFailedUnableToLoadDesc": "Unable to load file",
+        "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "downloadImageStarted": "Image Download Started",
         "imageCopied": "Image Copied",
         "imageLinkCopied": "Image Link Copied",

invokeai/frontend/web/src/app/components/App.tsx

@@ -2,14 +2,11 @@ import ImageUploader from 'common/components/ImageUploader';
 import SiteHeader from 'features/system/components/SiteHeader';
 import ProgressBar from 'features/system/components/ProgressBar';
 import InvokeTabs from 'features/ui/components/InvokeTabs';
-
-import useToastWatcher from 'features/system/hooks/useToastWatcher';
-
 import FloatingGalleryButton from 'features/ui/components/FloatingGalleryButton';
 import FloatingParametersPanelButtons from 'features/ui/components/FloatingParametersPanelButtons';
 import { Box, Flex, Grid, Portal } from '@chakra-ui/react';
 import { APP_HEIGHT, APP_WIDTH } from 'theme/util/constants';
-import GalleryDrawer from 'features/gallery/components/ImageGalleryPanel';
+import GalleryDrawer from 'features/gallery/components/GalleryPanel';
 import Lightbox from 'features/lightbox/components/Lightbox';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { memo, ReactNode, useCallback, useEffect, useState } from 'react';
@@ -17,25 +14,28 @@ import { motion, AnimatePresence } from 'framer-motion';
 import Loading from 'common/components/Loading/Loading';
 import { useIsApplicationReady } from 'features/system/hooks/useIsApplicationReady';
 import { PartialAppConfig } from 'app/types/invokeai';
-import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
 import { configChanged } from 'features/system/store/configSlice';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
 import { useLogger } from 'app/logging/useLogger';
 import ParametersDrawer from 'features/ui/components/ParametersDrawer';
 import { languageSelector } from 'features/system/store/systemSelectors';
 import i18n from 'i18n';
+import Toaster from './Toaster';
+import GlobalHotkeys from './GlobalHotkeys';
 
 const DEFAULT_CONFIG = {};
 
 interface Props {
   config?: PartialAppConfig;
   headerComponent?: ReactNode;
+  setIsReady?: (isReady: boolean) => void;
 }
 
-const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
+const App = ({
-  useToastWatcher();
+  config = DEFAULT_CONFIG,
-  useGlobalHotkeys();
+  headerComponent,
-
+  setIsReady,
+}: Props) => {
   const language = useAppSelector(languageSelector);
 
   const log = useLogger();
@@ -61,7 +61,18 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
     setLoadingOverridden(true);
   }, []);
 
+  useEffect(() => {
+    if (isApplicationReady && setIsReady) {
+      setIsReady(true);
+    }
+
+    return () => {
+      setIsReady && setIsReady(false);
+    };
+  }, [isApplicationReady, setIsReady]);
+
   return (
+    <>
       <Grid w="100vw" h="100vh" position="relative" overflow="hidden">
         {isLightboxEnabled && <Lightbox />}
         <ImageUploader>
@@ -121,6 +132,9 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
           <FloatingGalleryButton />
         </Portal>
       </Grid>
+      <Toaster />
+      <GlobalHotkeys />
+    </>
   );
 };
 

invokeai/frontend/web/src/app/components/AuxiliaryProgressIndicator.tsx

@@ -0,0 +1,44 @@
+import { Flex, Spinner, Tooltip } from '@chakra-ui/react';
+import { createSelector } from '@reduxjs/toolkit';
+import { useAppSelector } from 'app/store/storeHooks';
+import { systemSelector } from 'features/system/store/systemSelectors';
+import { memo } from 'react';
+
+const selector = createSelector(systemSelector, (system) => {
+  const { isUploading } = system;
+
+  let tooltip = '';
+
+  if (isUploading) {
+    tooltip = 'Uploading...';
+  }
+
+  return {
+    tooltip,
+    shouldShow: isUploading,
+  };
+});
+
+export const AuxiliaryProgressIndicator = () => {
+  const { shouldShow, tooltip } = useAppSelector(selector);
+
+  if (!shouldShow) {
+    return null;
+  }
+
+  return (
+    <Flex
+      sx={{
+        alignItems: 'center',
+        justifyContent: 'center',
+        color: 'base.600',
+      }}
+    >
+      <Tooltip label={tooltip} placement="right" hasArrow>
+        <Spinner />
+      </Tooltip>
+    </Flex>
+  );
+};
+
+export default memo(AuxiliaryProgressIndicator);

invokeai/frontend/web/src/app/components/GlobalHotkeys.ts

@@ -10,6 +10,7 @@ import {
   togglePinParametersPanel,
 } from 'features/ui/store/uiSlice';
 import { isEqual } from 'lodash-es';
+import React, { memo } from 'react';
 import { isHotkeyPressed, useHotkeys } from 'react-hotkeys-hook';
 
 const globalHotkeysSelector = createSelector(
@@ -27,7 +28,11 @@ const globalHotkeysSelector = createSelector(
 
 // TODO: Does not catch keypresses while focused in an input. Maybe there is a way?
 
-export const useGlobalHotkeys = () => {
+/**
+ * Logical component. Handles app-level global hotkeys.
+ * @returns null
+ */
+const GlobalHotkeys: React.FC = () => {
   const dispatch = useAppDispatch();
   const { shift } = useAppSelector(globalHotkeysSelector);
 
@@ -75,4 +80,8 @@ export const useGlobalHotkeys = () => {
   useHotkeys('4', () => {
     dispatch(setActiveTab('nodes'));
   });
+
+  return null;
 };
+
+export default memo(GlobalHotkeys);

invokeai/frontend/web/src/app/components/InvokeAIUI.tsx

@@ -24,9 +24,16 @@ interface Props extends PropsWithChildren {
   token?: string;
   config?: PartialAppConfig;
   headerComponent?: ReactNode;
+  setIsReady?: (isReady: boolean) => void;
 }
 
-const InvokeAIUI = ({ apiUrl, token, config, headerComponent }: Props) => {
+const InvokeAIUI = ({
+  apiUrl,
+  token,
+  config,
+  headerComponent,
+  setIsReady,
+}: Props) => {
   useEffect(() => {
     // configure API client token
     if (token) {
@@ -55,7 +62,11 @@ const InvokeAIUI = ({ apiUrl, token, config, headerComponent }: Props) => {
       <Provider store={store}>
         <React.Suspense fallback={<Loading />}>
           <ThemeLocaleProvider>
-            <App config={config} headerComponent={headerComponent} />
+            <App
+              config={config}
+              headerComponent={headerComponent}
+              setIsReady={setIsReady}
+            />
           </ThemeLocaleProvider>
         </React.Suspense>
       </Provider>

invokeai/frontend/web/src/app/components/Toaster.ts

@@ -0,0 +1,65 @@
+import { useToast, UseToastOptions } from '@chakra-ui/react';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { toastQueueSelector } from 'features/system/store/systemSelectors';
+import { addToast, clearToastQueue } from 'features/system/store/systemSlice';
+import { useCallback, useEffect } from 'react';
+
+export type MakeToastArg = string | UseToastOptions;
+
+/**
+ * Makes a toast from a string or a UseToastOptions object.
+ * If a string is passed, the toast will have the status 'info' and will be closable with a duration of 2500ms.
+ */
+export const makeToast = (arg: MakeToastArg): UseToastOptions => {
+  if (typeof arg === 'string') {
+    return {
+      title: arg,
+      status: 'info',
+      isClosable: true,
+      duration: 2500,
+    };
+  }
+
+  return { status: 'info', isClosable: true, duration: 2500, ...arg };
+};
+
+/**
+ * Logical component. Watches the toast queue and makes toasts when the queue is not empty.
+ * @returns null
+ */
+const Toaster = () => {
+  const dispatch = useAppDispatch();
+  const toastQueue = useAppSelector(toastQueueSelector);
+  const toast = useToast();
+  useEffect(() => {
+    toastQueue.forEach((t) => {
+      toast(t);
+    });
+    toastQueue.length > 0 && dispatch(clearToastQueue());
+  }, [dispatch, toast, toastQueue]);
+
+  return null;
+};
+
+/**
+ * Returns a function that can be used to make a toast.
+ * @example
+ * const toaster = useAppToaster();
+ * toaster('Hello world!');
+ * toaster({ title: 'Hello world!', status: 'success' });
+ * @returns A function that can be used to make a toast.
+ * @see makeToast
+ * @see MakeToastArg
+ * @see UseToastOptions
+ */
+export const useAppToaster = () => {
+  const dispatch = useAppDispatch();
+  const toaster = useCallback(
+    (arg: MakeToastArg) => dispatch(addToast(makeToast(arg))),
+    [dispatch]
+  );
+
+  return toaster;
+};
+
+export default Toaster;

invokeai/frontend/web/src/app/constants.ts

@@ -1,28 +1,28 @@
 // TODO: use Enums?
 
-export const DIFFUSERS_SCHEDULERS: Array<string> = [
+export const SCHEDULERS = [
   'ddim',
-  'ddpm',
-  'deis',
   'lms',
-  'pndm',
-  'heun',
   'euler',
   'euler_k',
   'euler_a',
-  'kdpm_2',
-  'kdpm_2_a',
   'dpmpp_2s',
   'dpmpp_2m',
   'dpmpp_2m_k',
+  'kdpm_2',
+  'kdpm_2_a',
+  'deis',
+  'ddpm',
+  'pndm',
+  'heun',
+  'heun_k',
   'unipc',
-];
+] as const;
 
-export const IMG2IMG_DIFFUSERS_SCHEDULERS = DIFFUSERS_SCHEDULERS.filter(
+export type Scheduler = (typeof SCHEDULERS)[number];
-  (scheduler) => {
+
-    return scheduler !== 'dpmpp_2s';
+export const isScheduler = (x: string): x is Scheduler =>
-  }
+  SCHEDULERS.includes(x as Scheduler);
-);
 
 // Valid image widths
 export const WIDTHS: Array<number> = Array.from(Array(64)).map(

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -15,6 +15,10 @@ import { addUserInvokedCanvasListener } from './listeners/userInvokedCanvas';
 import { addUserInvokedNodesListener } from './listeners/userInvokedNodes';
 import { addUserInvokedTextToImageListener } from './listeners/userInvokedTextToImage';
 import { addUserInvokedImageToImageListener } from './listeners/userInvokedImageToImage';
+import { addCanvasSavedToGalleryListener } from './listeners/canvasSavedToGallery';
+import { addCanvasDownloadedAsImageListener } from './listeners/canvasDownloadedAsImage';
+import { addCanvasCopiedToClipboardListener } from './listeners/canvasCopiedToClipboard';
+import { addCanvasMergedListener } from './listeners/canvasMerged';
 
 export const listenerMiddleware = createListenerMiddleware();
 
@@ -43,3 +47,8 @@ addUserInvokedCanvasListener();
 addUserInvokedNodesListener();
 addUserInvokedTextToImageListener();
 addUserInvokedImageToImageListener();
+
+addCanvasSavedToGalleryListener();
+addCanvasDownloadedAsImageListener();
+addCanvasCopiedToClipboardListener();
+addCanvasMergedListener();

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasCopiedToClipboard.ts

@@ -0,0 +1,33 @@
+import { canvasCopiedToClipboard } from 'features/canvas/store/actions';
+import { startAppListening } from '..';
+import { log } from 'app/logging/useLogger';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { addToast } from 'features/system/store/systemSlice';
+import { copyBlobToClipboard } from 'features/canvas/util/copyBlobToClipboard';
+
+const moduleLog = log.child({ namespace: 'canvasCopiedToClipboardListener' });
+
+export const addCanvasCopiedToClipboardListener = () => {
+  startAppListening({
+    actionCreator: canvasCopiedToClipboard,
+    effect: async (action, { dispatch, getState }) => {
+      const state = getState();
+
+      const blob = await getBaseLayerBlob(state);
+
+      if (!blob) {
+        moduleLog.error('Problem getting base layer blob');
+        dispatch(
+          addToast({
+            title: 'Problem Copying Canvas',
+            description: 'Unable to export base layer',
+            status: 'error',
+          })
+        );
+        return;
+      }
+
+      copyBlobToClipboard(blob);
+    },
+  });
+};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasDownloadedAsImage.ts

@@ -0,0 +1,33 @@
+import { canvasDownloadedAsImage } from 'features/canvas/store/actions';
+import { startAppListening } from '..';
+import { log } from 'app/logging/useLogger';
+import { downloadBlob } from 'features/canvas/util/downloadBlob';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { addToast } from 'features/system/store/systemSlice';
+
+const moduleLog = log.child({ namespace: 'canvasSavedToGalleryListener' });
+
+export const addCanvasDownloadedAsImageListener = () => {
+  startAppListening({
+    actionCreator: canvasDownloadedAsImage,
+    effect: async (action, { dispatch, getState }) => {
+      const state = getState();
+
+      const blob = await getBaseLayerBlob(state);
+
+      if (!blob) {
+        moduleLog.error('Problem getting base layer blob');
+        dispatch(
+          addToast({
+            title: 'Problem Downloading Canvas',
+            description: 'Unable to export base layer',
+            status: 'error',
+          })
+        );
+        return;
+      }
+
+      downloadBlob(blob, 'mergedCanvas.png');
+    },
+  });
+};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasGraphBuilt.ts

@@ -1,31 +0,0 @@
-import { canvasGraphBuilt } from 'features/nodes/store/actions';
-import { startAppListening } from '..';
-import {
-  canvasSessionIdChanged,
-  stagingAreaInitialized,
-} from 'features/canvas/store/canvasSlice';
-import { sessionInvoked } from 'services/thunks/session';
-
-export const addCanvasGraphBuiltListener = () =>
-  startAppListening({
-    actionCreator: canvasGraphBuilt,
-    effect: async (action, { dispatch, getState, take }) => {
-      const [{ meta }] = await take(sessionInvoked.fulfilled.match);
-      const { sessionId } = meta.arg;
-      const state = getState();
-
-      if (!state.canvas.layerState.stagingArea.boundingBox) {
-        dispatch(
-          stagingAreaInitialized({
-            sessionId,
-            boundingBox: {
-              ...state.canvas.boundingBoxCoordinates,
-              ...state.canvas.boundingBoxDimensions,
-            },
-          })
-        );
-      }
-
-      dispatch(canvasSessionIdChanged(sessionId));
-    },
-  });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasMerged.ts

@@ -0,0 +1,88 @@
+import { canvasMerged } from 'features/canvas/store/actions';
+import { startAppListening } from '..';
+import { log } from 'app/logging/useLogger';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { addToast } from 'features/system/store/systemSlice';
+import { imageUploaded } from 'services/thunks/image';
+import { v4 as uuidv4 } from 'uuid';
+import { deserializeImageResponse } from 'services/util/deserializeImageResponse';
+import { setMergedCanvas } from 'features/canvas/store/canvasSlice';
+import { getCanvasBaseLayer } from 'features/canvas/util/konvaInstanceProvider';
+
+const moduleLog = log.child({ namespace: 'canvasCopiedToClipboardListener' });
+
+export const addCanvasMergedListener = () => {
+  startAppListening({
+    actionCreator: canvasMerged,
+    effect: async (action, { dispatch, getState, take }) => {
+      const state = getState();
+
+      const blob = await getBaseLayerBlob(state, true);
+
+      if (!blob) {
+        moduleLog.error('Problem getting base layer blob');
+        dispatch(
+          addToast({
+            title: 'Problem Merging Canvas',
+            description: 'Unable to export base layer',
+            status: 'error',
+          })
+        );
+        return;
+      }
+
+      const canvasBaseLayer = getCanvasBaseLayer();
+
+      if (!canvasBaseLayer) {
+        moduleLog.error('Problem getting canvas base layer');
+        dispatch(
+          addToast({
+            title: 'Problem Merging Canvas',
+            description: 'Unable to export base layer',
+            status: 'error',
+          })
+        );
+        return;
+      }
+
+      const baseLayerRect = canvasBaseLayer.getClientRect({
+        relativeTo: canvasBaseLayer.getParent(),
+      });
+
+      const filename = `mergedCanvas_${uuidv4()}.png`;
+
+      dispatch(
+        imageUploaded({
+          imageType: 'intermediates',
+          formData: {
+            file: new File([blob], filename, { type: 'image/png' }),
+          },
+        })
+      );
+
+      const [{ payload }] = await take(
+        (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
+          imageUploaded.fulfilled.match(action) &&
+          action.meta.arg.formData.file.name === filename
+      );
+
+      const mergedCanvasImage = deserializeImageResponse(payload.response);
+
+      dispatch(
+        setMergedCanvas({
+          kind: 'image',
+          layer: 'base',
+          image: mergedCanvasImage,
+          ...baseLayerRect,
+        })
+      );
+
+      dispatch(
+        addToast({
+          title: 'Canvas Merged',
+          status: 'success',
+        })
+      );
+    },
+  });
+};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery.ts

@@ -0,0 +1,40 @@
+import { canvasSavedToGallery } from 'features/canvas/store/actions';
+import { startAppListening } from '..';
+import { log } from 'app/logging/useLogger';
+import { imageUploaded } from 'services/thunks/image';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { addToast } from 'features/system/store/systemSlice';
+
+const moduleLog = log.child({ namespace: 'canvasSavedToGalleryListener' });
+
+export const addCanvasSavedToGalleryListener = () => {
+  startAppListening({
+    actionCreator: canvasSavedToGallery,
+    effect: async (action, { dispatch, getState }) => {
+      const state = getState();
+
+      const blob = await getBaseLayerBlob(state);
+
+      if (!blob) {
+        moduleLog.error('Problem getting base layer blob');
+        dispatch(
+          addToast({
+            title: 'Problem Saving Canvas',
+            description: 'Unable to export base layer',
+            status: 'error',
+          })
+        );
+        return;
+      }
+
+      dispatch(
+        imageUploaded({
+          imageType: 'results',
+          formData: {
+            file: new File([blob], 'mergedCanvas.png', { type: 'image/png' }),
+          },
+        })
+      );
+    },
+  });
+};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageUploaded.ts

@@ -3,6 +3,10 @@ import { startAppListening } from '..';
 import { uploadAdded } from 'features/gallery/store/uploadsSlice';
 import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { imageUploaded } from 'services/thunks/image';
+import { addToast } from 'features/system/store/systemSlice';
+import { initialImageSelected } from 'features/parameters/store/actions';
+import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
+import { resultAdded } from 'features/gallery/store/resultsSlice';
 
 export const addImageUploadedListener = () => {
   startAppListening({
@@ -11,15 +15,32 @@ export const addImageUploadedListener = () => {
       action.payload.response.image_type !== 'intermediates',
     effect: (action, { dispatch, getState }) => {
       const { response } = action.payload;
+      const { imageType } = action.meta.arg;
 
       const state = getState();
       const image = deserializeImageResponse(response);
 
+      if (imageType === 'uploads') {
         dispatch(uploadAdded(image));
 
+        dispatch(addToast({ title: 'Image Uploaded', status: 'success' }));
+
         if (state.gallery.shouldAutoSwitchToNewImages) {
           dispatch(imageSelected(image));
         }
+
+        if (action.meta.arg.activeTabName === 'img2img') {
+          dispatch(initialImageSelected(image));
+        }
+
+        if (action.meta.arg.activeTabName === 'unifiedCanvas') {
+          dispatch(setInitialCanvasImage(image));
+        }
+      }
+
+      if (imageType === 'results') {
+        dispatch(resultAdded(image));
+      }
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/initialImageSelected.ts

@@ -2,11 +2,11 @@ import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import { Image, isInvokeAIImage } from 'app/types/invokeai';
 import { selectResultsById } from 'features/gallery/store/resultsSlice';
 import { selectUploadsById } from 'features/gallery/store/uploadsSlice';
-import { makeToast } from 'features/system/hooks/useToastWatcher';
 import { t } from 'i18next';
 import { addToast } from 'features/system/store/systemSlice';
 import { startAppListening } from '..';
 import { initialImageSelected } from 'features/parameters/store/actions';
+import { makeToast } from 'app/components/Toaster';
 
 export const addInitialImageSelectedListener = () => {
   startAppListening({

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedCanvas.ts

@@ -1,6 +1,6 @@
 import { startAppListening } from '..';
 import { sessionCreated, sessionInvoked } from 'services/thunks/session';
-import { buildCanvasGraphAndBlobs } from 'features/nodes/util/graphBuilders/buildCanvasGraph';
+import { buildCanvasGraphComponents } from 'features/nodes/util/graphBuilders/buildCanvasGraph';
 import { log } from 'app/logging/useLogger';
 import { canvasGraphBuilt } from 'features/nodes/store/actions';
 import { imageUploaded } from 'services/thunks/image';
@@ -11,9 +11,17 @@ import {
   stagingAreaInitialized,
 } from 'features/canvas/store/canvasSlice';
 import { userInvoked } from 'app/store/actions';
+import { getCanvasData } from 'features/canvas/util/getCanvasData';
+import { getCanvasGenerationMode } from 'features/canvas/util/getCanvasGenerationMode';
+import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
+import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
 
 const moduleLog = log.child({ namespace: 'invoke' });
 
+/**
+ * This listener is responsible for building the canvas graph and blobs when the user invokes the canvas.
+ * It is also responsible for uploading the base and mask layers to the server.
+ */
 export const addUserInvokedCanvasListener = () => {
   startAppListening({
     predicate: (action): action is ReturnType<typeof userInvoked> =>
@@ -21,25 +29,49 @@ export const addUserInvokedCanvasListener = () => {
     effect: async (action, { getState, dispatch, take }) => {
       const state = getState();
 
-      const data = await buildCanvasGraphAndBlobs(state);
+      // Build canvas blobs
+      const canvasBlobsAndImageData = await getCanvasData(state);
 
-      if (!data) {
+      if (!canvasBlobsAndImageData) {
+        moduleLog.error('Unable to create canvas data');
+        return;
+      }
+
+      const { baseBlob, baseImageData, maskBlob, maskImageData } =
+        canvasBlobsAndImageData;
+
+      // Determine the generation mode
+      const generationMode = getCanvasGenerationMode(
+        baseImageData,
+        maskImageData
+      );
+
+      if (state.system.enableImageDebugging) {
+        const baseDataURL = await blobToDataURL(baseBlob);
+        const maskDataURL = await blobToDataURL(maskBlob);
+        openBase64ImageInTab([
+          { base64: maskDataURL, caption: 'mask b64' },
+          { base64: baseDataURL, caption: 'image b64' },
+        ]);
+      }
+
+      moduleLog.debug(`Generation mode: ${generationMode}`);
+
+      // Build the canvas graph
+      const graphComponents = await buildCanvasGraphComponents(
+        state,
+        generationMode
+      );
+
+      if (!graphComponents) {
         moduleLog.error('Problem building graph');
         return;
       }
 
-      const {
+      const { rangeNode, iterateNode, baseNode, edges } = graphComponents;
-        rangeNode,
-        iterateNode,
-        baseNode,
-        edges,
-        baseBlob,
-        maskBlob,
-        generationMode,
-      } = data;
 
+      // Upload the base layer, to be used as init image
       const baseFilename = `${uuidv4()}.png`;
-      const maskFilename = `${uuidv4()}.png`;
 
       dispatch(
         imageUploaded({
@@ -66,6 +98,9 @@ export const addUserInvokedCanvasListener = () => {
         };
       }
 
+      // Upload the mask layer image
+      const maskFilename = `${uuidv4()}.png`;
+
       if (baseNode.type === 'inpaint') {
         dispatch(
           imageUploaded({
@@ -103,9 +138,12 @@ export const addUserInvokedCanvasListener = () => {
       dispatch(canvasGraphBuilt(graph));
       moduleLog({ data: graph }, 'Canvas graph built');
 
+      // Actually create the session
       dispatch(sessionCreated({ graph }));
 
+      // Wait for the session to be invoked (this is just the HTTP request to start processing)
       const [{ meta }] = await take(sessionInvoked.fulfilled.match);
+
       const { sessionId } = meta.arg;
 
       if (!state.canvas.layerState.stagingArea.boundingBox) {

invokeai/frontend/web/src/app/types/invokeai.ts

@@ -52,6 +52,7 @@ export type CommonGeneratedImageMetadata = {
     | 'lms'
     | 'pndm'
     | 'heun'
+    | 'heun_k'
     | 'euler'
     | 'euler_k'
     | 'euler_a'

invokeai/frontend/web/src/common/components/IAICustomSelect.tsx

@@ -0,0 +1,172 @@
+import { CheckIcon } from '@chakra-ui/icons';
+import {
+  Box,
+  Flex,
+  FlexProps,
+  FormControl,
+  FormControlProps,
+  FormLabel,
+  Grid,
+  GridItem,
+  List,
+  ListItem,
+  Select,
+  Text,
+  Tooltip,
+  TooltipProps,
+} from '@chakra-ui/react';
+import { autoUpdate, offset, shift, useFloating } from '@floating-ui/react-dom';
+import { useSelect } from 'downshift';
+import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
+
+import { memo } from 'react';
+
+type IAICustomSelectProps = {
+  label?: string;
+  items: string[];
+  selectedItem: string;
+  setSelectedItem: (v: string | null | undefined) => void;
+  withCheckIcon?: boolean;
+  formControlProps?: FormControlProps;
+  buttonProps?: FlexProps;
+  tooltip?: string;
+  tooltipProps?: Omit<TooltipProps, 'children'>;
+};
+
+const IAICustomSelect = (props: IAICustomSelectProps) => {
+  const {
+    label,
+    items,
+    setSelectedItem,
+    selectedItem,
+    withCheckIcon,
+    formControlProps,
+    tooltip,
+    buttonProps,
+    tooltipProps,
+  } = props;
+
+  const {
+    isOpen,
+    getToggleButtonProps,
+    getLabelProps,
+    getMenuProps,
+    highlightedIndex,
+    getItemProps,
+  } = useSelect({
+    items,
+    selectedItem,
+    onSelectedItemChange: ({ selectedItem: newSelectedItem }) =>
+      setSelectedItem(newSelectedItem),
+  });
+
+  const { refs, floatingStyles } = useFloating<HTMLButtonElement>({
+    whileElementsMounted: autoUpdate,
+    middleware: [offset(4), shift({ crossAxis: true, padding: 8 })],
+  });
+
+  return (
+    <FormControl sx={{ w: 'full' }} {...formControlProps}>
+      {label && (
+        <FormLabel
+          {...getLabelProps()}
+          onClick={() => {
+            refs.floating.current && refs.floating.current.focus();
+          }}
+        >
+          {label}
+        </FormLabel>
+      )}
+      <Tooltip label={tooltip} {...tooltipProps}>
+        <Select
+          {...getToggleButtonProps({ ref: refs.setReference })}
+          {...buttonProps}
+          as={Flex}
+          sx={{
+            alignItems: 'center',
+            userSelect: 'none',
+            cursor: 'pointer',
+          }}
+        >
+          <Text sx={{ fontSize: 'sm', fontWeight: 500, color: 'base.100' }}>
+            {selectedItem}
+          </Text>
+        </Select>
+      </Tooltip>
+      <Box {...getMenuProps()}>
+        {isOpen && (
+          <List
+            as={Flex}
+            ref={refs.setFloating}
+            sx={{
+              ...floatingStyles,
+              width: 'max-content',
+              top: 0,
+              left: 0,
+              flexDirection: 'column',
+              zIndex: 1,
+              bg: 'base.800',
+              borderRadius: 'base',
+              border: '1px',
+              borderColor: 'base.700',
+              shadow: 'dark-lg',
+              py: 2,
+              px: 0,
+              h: 'fit-content',
+              maxH: 64,
+            }}
+          >
+            <OverlayScrollbarsComponent>
+              {items.map((item, index) => (
+                <ListItem
+                  sx={{
+                    bg: highlightedIndex === index ? 'base.700' : undefined,
+                    py: 1,
+                    paddingInlineStart: 3,
+                    paddingInlineEnd: 6,
+                    cursor: 'pointer',
+                    transitionProperty: 'common',
+                    transitionDuration: '0.15s',
+                  }}
+                  key={`${item}${index}`}
+                  {...getItemProps({ item, index })}
+                >
+                  {withCheckIcon ? (
+                    <Grid gridTemplateColumns="1.25rem auto">
+                      <GridItem>
+                        {selectedItem === item && <CheckIcon boxSize={2} />}
+                      </GridItem>
+                      <GridItem>
+                        <Text
+                          sx={{
+                            fontSize: 'sm',
+                            color: 'base.100',
+                            fontWeight: 500,
+                          }}
+                        >
+                          {item}
+                        </Text>
+                      </GridItem>
+                    </Grid>
+                  ) : (
+                    <Text
+                      sx={{
+                        fontSize: 'sm',
+                        color: 'base.100',
+                        fontWeight: 500,
+                      }}
+                    >
+                      {item}
+                    </Text>
+                  )}
+                </ListItem>
+              ))}
+            </OverlayScrollbarsComponent>
+          </List>
+        )}
+      </Box>
+    </FormControl>
+  );
+};
+
+export default memo(IAICustomSelect);

invokeai/frontend/web/src/common/components/IAIInput.tsx

@@ -5,6 +5,7 @@ import {
   Input,
   InputProps,
 } from '@chakra-ui/react';
+import { stopPastePropagation } from 'common/util/stopPastePropagation';
 import { ChangeEvent, memo } from 'react';
 
 interface IAIInputProps extends InputProps {
@@ -31,7 +32,7 @@ const IAIInput = (props: IAIInputProps) => {
       {...formControlProps}
     >
       {label !== '' && <FormLabel>{label}</FormLabel>}
-      <Input {...rest} />
+      <Input {...rest} onPaste={stopPastePropagation} />
     </FormControl>
   );
 };

invokeai/frontend/web/src/common/components/IAINumberInput.tsx

@@ -14,6 +14,7 @@ import {
   Tooltip,
   TooltipProps,
 } from '@chakra-ui/react';
+import { stopPastePropagation } from 'common/util/stopPastePropagation';
 import { clamp } from 'lodash-es';
 
 import { FocusEvent, memo, useEffect, useState } from 'react';
@@ -125,6 +126,7 @@ const IAINumberInput = (props: Props) => {
           onChange={handleOnChange}
           onBlur={handleBlur}
           {...rest}
+          onPaste={stopPastePropagation}
         >
           <NumberInputField {...numberInputFieldProps} />
           {showStepper && (

invokeai/frontend/web/src/common/components/IAITextarea.tsx

@@ -0,0 +1,9 @@
+import { Textarea, TextareaProps, forwardRef } from '@chakra-ui/react';
+import { stopPastePropagation } from 'common/util/stopPastePropagation';
+import { memo } from 'react';
+
+const IAITextarea = forwardRef((props: TextareaProps, ref) => {
+  return <Textarea ref={ref} onPaste={stopPastePropagation} {...props} />;
+});
+
+export default memo(IAITextarea);

invokeai/frontend/web/src/common/components/ImageUploader.tsx

@@ -1,4 +1,4 @@
-import { Box, useToast } from '@chakra-ui/react';
+import { Box } from '@chakra-ui/react';
 import { ImageUploaderTriggerContext } from 'app/contexts/ImageUploaderTriggerContext';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import useImageUploader from 'common/hooks/useImageUploader';
@@ -10,12 +10,33 @@ import {
   ReactNode,
   useCallback,
   useEffect,
+  useMemo,
+  useRef,
   useState,
 } from 'react';
 import { FileRejection, useDropzone } from 'react-dropzone';
 import { useTranslation } from 'react-i18next';
 import { imageUploaded } from 'services/thunks/image';
 import ImageUploadOverlay from './ImageUploadOverlay';
+import { useAppToaster } from 'app/components/Toaster';
+import { filter, map, some } from 'lodash-es';
+import { createSelector } from '@reduxjs/toolkit';
+import { systemSelector } from 'features/system/store/systemSelectors';
+import { ErrorCode } from 'react-dropzone';
+
+const selector = createSelector(
+  [systemSelector, activeTabNameSelector],
+  (system, activeTabName) => {
+    const { isConnected, isUploading } = system;
+
+    const isUploaderDisabled = !isConnected || isUploading;
+
+    return {
+      isUploaderDisabled,
+      activeTabName,
+    };
+  }
+);
 
 type ImageUploaderProps = {
   children: ReactNode;
@@ -24,38 +45,49 @@ type ImageUploaderProps = {
 const ImageUploader = (props: ImageUploaderProps) => {
   const { children } = props;
   const dispatch = useAppDispatch();
-  const activeTabName = useAppSelector(activeTabNameSelector);
+  const { isUploaderDisabled, activeTabName } = useAppSelector(selector);
-  const toast = useToast({});
+  const toaster = useAppToaster();
   const { t } = useTranslation();
   const [isHandlingUpload, setIsHandlingUpload] = useState<boolean>(false);
-  const { setOpenUploader } = useImageUploader();
+  const { setOpenUploaderFunction } = useImageUploader();
 
   const fileRejectionCallback = useCallback(
     (rejection: FileRejection) => {
       setIsHandlingUpload(true);
-      const msg = rejection.errors.reduce(
+
-        (acc: string, cur: { message: string }) => `${acc}\n${cur.message}`,
+      toaster({
-        ''
-      );
-      toast({
         title: t('toast.uploadFailed'),
-        description: msg,
+        description: rejection.errors.map((error) => error.message).join('\n'),
         status: 'error',
-        isClosable: true,
       });
     },
-    [t, toast]
+    [t, toaster]
   );
 
   const fileAcceptedCallback = useCallback(
     async (file: File) => {
-      dispatch(imageUploaded({ imageType: 'uploads', formData: { file } }));
+      dispatch(
+        imageUploaded({
+          imageType: 'uploads',
+          formData: { file },
+          activeTabName,
+        })
+      );
     },
-    [dispatch]
+    [dispatch, activeTabName]
   );
 
   const onDrop = useCallback(
     (acceptedFiles: Array<File>, fileRejections: Array<FileRejection>) => {
+      if (fileRejections.length > 1) {
+        toaster({
+          title: t('toast.uploadFailed'),
+          description: t('toast.uploadFailedInvalidUploadDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
       fileRejections.forEach((rejection: FileRejection) => {
         fileRejectionCallback(rejection);
       });
@@ -64,7 +96,7 @@ const ImageUploader = (props: ImageUploaderProps) => {
         fileAcceptedCallback(file);
       });
     },
-    [fileAcceptedCallback, fileRejectionCallback]
+    [t, toaster, fileAcceptedCallback, fileRejectionCallback]
   );
 
   const {
@@ -73,73 +105,55 @@ const ImageUploader = (props: ImageUploaderProps) => {
     isDragAccept,
     isDragReject,
     isDragActive,
+    inputRef,
     open,
   } = useDropzone({
     accept: { 'image/png': ['.png'], 'image/jpeg': ['.jpg', '.jpeg', '.png'] },
     noClick: true,
     onDrop,
     onDragOver: () => setIsHandlingUpload(true),
-    maxFiles: 1,
+    disabled: isUploaderDisabled,
+    multiple: false,
   });
 
-  setOpenUploader(open);
-
   useEffect(() => {
-    const pasteImageListener = (e: ClipboardEvent) => {
+    // This is a hack to allow pasting images into the uploader
-      const dataTransferItemList = e.clipboardData?.items;
+    const handlePaste = async (e: ClipboardEvent) => {
-      if (!dataTransferItemList) return;
+      if (!inputRef.current) {
-
-      const imageItems: Array<DataTransferItem> = [];
-
-      for (const item of dataTransferItemList) {
-        if (
-          item.kind === 'file' &&
-          ['image/png', 'image/jpg'].includes(item.type)
-        ) {
-          imageItems.push(item);
-        }
-      }
-
-      if (!imageItems.length) return;
-
-      e.stopImmediatePropagation();
-
-      if (imageItems.length > 1) {
-        toast({
-          description: t('toast.uploadFailedMultipleImagesDesc'),
-          status: 'error',
-          isClosable: true,
-        });
         return;
       }
 
-      const file = imageItems[0].getAsFile();
+      if (e.clipboardData?.files) {
-
+        // Set the files on the inputRef
-      if (!file) {
+        inputRef.current.files = e.clipboardData.files;
-        toast({
+        // Dispatch the change event, dropzone catches this and we get to use its own validation
-          description: t('toast.uploadFailedUnableToLoadDesc'),
+        inputRef.current?.dispatchEvent(new Event('change', { bubbles: true }));
-          status: 'error',
-          isClosable: true,
-        });
-        return;
       }
-
-      dispatch(imageUploaded({ imageType: 'uploads', formData: { file } }));
     };
-    document.addEventListener('paste', pasteImageListener);
+
+    // Set the open function so we can open the uploader from anywhere
+    setOpenUploaderFunction(open);
+
+    // Add the paste event listener
+    document.addEventListener('paste', handlePaste);
+
     return () => {
-      document.removeEventListener('paste', pasteImageListener);
+      document.removeEventListener('paste', handlePaste);
+      setOpenUploaderFunction(() => {
+        return;
+      });
     };
-  }, [t, dispatch, toast, activeTabName]);
+  }, [inputRef, open, setOpenUploaderFunction]);
 
-  const overlaySecondaryText = ['img2img', 'unifiedCanvas'].includes(
+  const overlaySecondaryText = useMemo(() => {
-    activeTabName
+    if (['img2img', 'unifiedCanvas'].includes(activeTabName)) {
-  )
+      return ` to ${String(t(`common.${activeTabName}` as ResourceKey))}`;
-    ? ` to ${String(t(`common.${activeTabName}` as ResourceKey))}`
+    }
-    : ``;
+
+    return '';
+  }, [t, activeTabName]);
 
   return (
-    <ImageUploaderTriggerContext.Provider value={open}>
     <Box
       {...getRootProps({ style: {} })}
       onKeyDown={(e: KeyboardEvent) => {
@@ -158,7 +172,6 @@ const ImageUploader = (props: ImageUploaderProps) => {
         />
       )}
     </Box>
-    </ImageUploaderTriggerContext.Provider>
   );
 };
 

invokeai/frontend/web/src/common/components/ImageUploaderButton.tsx

@@ -1,6 +1,5 @@
 import { Flex, Heading, Icon } from '@chakra-ui/react';
-import { ImageUploaderTriggerContext } from 'app/contexts/ImageUploaderTriggerContext';
+import useImageUploader from 'common/hooks/useImageUploader';
-import { useContext } from 'react';
 import { FaUpload } from 'react-icons/fa';
 
 type ImageUploaderButtonProps = {
@@ -9,11 +8,7 @@ type ImageUploaderButtonProps = {
 
 const ImageUploaderButton = (props: ImageUploaderButtonProps) => {
   const { styleClass } = props;
-  const open = useContext(ImageUploaderTriggerContext);
+  const { openUploader } = useImageUploader();
-
-  const handleClickUpload = () => {
-    open && open();
-  };
 
   return (
     <Flex
@@ -26,7 +21,7 @@ const ImageUploaderButton = (props: ImageUploaderButtonProps) => {
       className={styleClass}
     >
       <Flex
-        onClick={handleClickUpload}
+        onClick={openUploader}
         sx={{
           display: 'flex',
           flexDirection: 'column',

invokeai/frontend/web/src/common/components/ImageUploaderIconButton.tsx

@@ -1,19 +1,18 @@
-import { ImageUploaderTriggerContext } from 'app/contexts/ImageUploaderTriggerContext';
-import { useContext } from 'react';
 import { useTranslation } from 'react-i18next';
 import { FaUpload } from 'react-icons/fa';
 import IAIIconButton from './IAIIconButton';
+import useImageUploader from 'common/hooks/useImageUploader';
 
 const ImageUploaderIconButton = () => {
   const { t } = useTranslation();
-  const openImageUploader = useContext(ImageUploaderTriggerContext);
+  const { openUploader } = useImageUploader();
 
   return (
     <IAIIconButton
       aria-label={t('accessibility.uploadImage')}
       tooltip="Upload Image"
       icon={<FaUpload />}
-      onClick={openImageUploader || undefined}
+      onClick={openUploader}
     />
   );
 };

invokeai/frontend/web/src/common/components/InitialImageButtons.tsx

@@ -6,10 +6,12 @@ import { FaUndo, FaUpload } from 'react-icons/fa';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { useCallback } from 'react';
 import { clearInitialImage } from 'features/parameters/store/generationSlice';
+import useImageUploader from 'common/hooks/useImageUploader';
 
 const InitialImageButtons = () => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
+  const { openUploader } = useImageUploader();
 
   const handleResetInitialImage = useCallback(() => {
     dispatch(clearInitialImage());
@@ -27,7 +29,11 @@ const InitialImageButtons = () => {
           aria-label={t('accessibility.reset')}
           onClick={handleResetInitialImage}
         />
-        <IAIIconButton icon={<FaUpload />} aria-label={t('common.upload')} />
+        <IAIIconButton
+          icon={<FaUpload />}
+          onClick={openUploader}
+          aria-label={t('common.upload')}
+        />
       </ButtonGroup>
     </Flex>
   );

invokeai/frontend/web/src/common/components/Loading/Loading.tsx

@@ -24,7 +24,6 @@ const Loading = () => {
         height="24px !important"
         right="1.5rem"
         bottom="1.5rem"
-        speed="1.2s"
       />
     </Flex>
   );

invokeai/frontend/web/src/common/components/WorkInProgress/PostProcessingWIP.tsx

@@ -1,29 +0,0 @@
-import { Flex, Heading, Text, VStack } from '@chakra-ui/react';
-import { useTranslation } from 'react-i18next';
-import WorkInProgress from './WorkInProgress';
-
-export const PostProcessingWIP = () => {
-  const { t } = useTranslation();
-  return (
-    <WorkInProgress>
-      <Flex
-        sx={{
-          flexDirection: 'column',
-          alignItems: 'center',
-          justifyContent: 'center',
-          w: '100%',
-          h: '100%',
-          gap: 4,
-          textAlign: 'center',
-        }}
-      >
-        <Heading>{t('common.postProcessing')}</Heading>
-        <VStack maxW="50rem" gap={4}>
-          <Text>{t('common.postProcessDesc1')}</Text>
-          <Text>{t('common.postProcessDesc2')}</Text>
-          <Text>{t('common.postProcessDesc3')}</Text>
-        </VStack>
-      </Flex>
-    </WorkInProgress>
-  );
-};

invokeai/frontend/web/src/common/components/WorkInProgress/Training.tsx

@@ -1,28 +0,0 @@
-import { Flex, Heading, Text, VStack } from '@chakra-ui/react';
-import { useTranslation } from 'react-i18next';
-import WorkInProgress from './WorkInProgress';
-
-export default function TrainingWIP() {
-  const { t } = useTranslation();
-  return (
-    <WorkInProgress>
-      <Flex
-        sx={{
-          flexDirection: 'column',
-          alignItems: 'center',
-          justifyContent: 'center',
-          w: '100%',
-          h: '100%',
-          gap: 4,
-          textAlign: 'center',
-        }}
-      >
-        <Heading>{t('common.training')}</Heading>
-        <VStack maxW="50rem" gap={4}>
-          <Text>{t('common.trainingDesc1')}</Text>
-          <Text>{t('common.trainingDesc2')}</Text>
-        </VStack>
-      </Flex>
-    </WorkInProgress>
-  );
-}

invokeai/frontend/web/src/common/components/WorkInProgress/WorkInProgress.tsx

@@ -1,26 +0,0 @@
-import { Flex } from '@chakra-ui/react';
-import { ReactNode } from 'react';
-
-type WorkInProgressProps = {
-  children: ReactNode;
-};
-
-const WorkInProgress = (props: WorkInProgressProps) => {
-  const { children } = props;
-
-  return (
-    <Flex
-      sx={{
-        width: '100%',
-        height: '100%',
-        bg: 'base.850',
-        borderRadius: 'base',
-        position: 'relative',
-      }}
-    >
-      {children}
-    </Flex>
-  );
-};
-
-export default WorkInProgress;

invokeai/frontend/web/src/common/hooks/useClickOutsideWatcher.ts

@@ -1,35 +0,0 @@
-import { RefObject, useEffect } from 'react';
-
-const watchers: {
-  ref: RefObject<HTMLElement>;
-  enable: boolean;
-  callback: () => void;
-}[] = [];
-
-const useClickOutsideWatcher = () => {
-  useEffect(() => {
-    function handleClickOutside(e: MouseEvent) {
-      watchers.forEach(({ ref, enable, callback }) => {
-        if (enable && ref.current && !ref.current.contains(e.target as Node)) {
-          callback();
-        }
-      });
-    }
-    document.addEventListener('mousedown', handleClickOutside);
-    return () => {
-      document.removeEventListener('mousedown', handleClickOutside);
-    };
-  }, []);
-
-  return {
-    addWatcher: (watcher: {
-      ref: RefObject<HTMLElement>;
-      callback: () => void;
-      enable: boolean;
-    }) => {
-      watchers.push(watcher);
-    },
-  };
-};
-
-export default useClickOutsideWatcher;

invokeai/frontend/web/src/common/hooks/useImageUploader.ts

@@ -1,13 +1,22 @@
-let openFunction: () => void;
+import { useCallback } from 'react';
+
+let openUploader = () => {
+  return;
+};
 
 const useImageUploader = () => {
-  return {
+  const setOpenUploaderFunction = useCallback(
-    setOpenUploader: (open?: () => void) => {
+    (openUploaderFunction?: () => void) => {
-      if (open) {
+      if (openUploaderFunction) {
-        openFunction = open;
+        openUploader = openUploaderFunction;
       }
     },
-    openUploader: openFunction,
+    []
+  );
+
+  return {
+    setOpenUploaderFunction,
+    openUploader,
   };
 };
 

invokeai/frontend/web/src/common/hooks/useUpdateTranslations.ts

@@ -1,17 +0,0 @@
-import React from 'react';
-import { useTranslation } from 'react-i18next';
-
-export default function useUpdateTranslations(fn: () => void) {
-  const { i18n } = useTranslation();
-  const currentLang = localStorage.getItem('i18nextLng');
-
-  React.useEffect(() => {
-    fn();
-  }, [fn]);
-
-  React.useEffect(() => {
-    i18n.on('languageChanged', () => {
-      fn();
-    });
-  }, [fn, i18n, currentLang]);
-}

invokeai/frontend/web/src/common/icons/ImageToImageIcon.tsx

@@ -1,20 +0,0 @@
-import { createIcon } from '@chakra-ui/react';
-
-const ImageToImageIcon = createIcon({
-  displayName: 'ImageToImageIcon',
-  viewBox: '0 0 3543 3543',
-  path: (
-    <g transform="matrix(1.10943,0,0,1.10943,-206.981,-213.533)">
-      <path
-        fill="currentColor"
-        fillRule="evenodd"
-        clipRule="evenodd"
-        d="M688.533,2405.95L542.987,2405.95C349.532,2405.95 192.47,2248.89 192.47,2055.44L192.47,542.987C192.47,349.532 349.532,192.47 542.987,192.47L2527.88,192.47C2721.33,192.47 2878.4,349.532 2878.4,542.987L2878.4,1172.79L3023.94,1172.79C3217.4,1172.79 3374.46,1329.85 3374.46,1523.3C3374.46,1523.3 3374.46,3035.75 3374.46,3035.75C3374.46,3229.21 3217.4,3386.27 3023.94,3386.27L1039.05,3386.27C845.595,3386.27 688.533,3229.21 688.533,3035.75L688.533,2405.95ZM3286.96,2634.37L3286.96,1523.3C3286.96,1378.14 3169.11,1260.29 3023.94,1260.29C3023.94,1260.29 1039.05,1260.29 1039.05,1260.29C893.887,1260.29 776.033,1378.14 776.033,1523.3L776.033,2489.79L1440.94,1736.22L2385.83,2775.59L2880.71,2200.41L3286.96,2634.37ZM2622.05,1405.51C2778.5,1405.51 2905.51,1532.53 2905.51,1688.98C2905.51,1845.42 2778.5,1972.44 2622.05,1972.44C2465.6,1972.44 2338.58,1845.42 2338.58,1688.98C2338.58,1532.53 2465.6,1405.51 2622.05,1405.51ZM2790.9,1172.79L1323.86,1172.79L944.882,755.906L279.97,1509.47L279.97,542.987C279.97,397.824 397.824,279.97 542.987,279.97C542.987,279.97 2527.88,279.97 2527.88,279.97C2673.04,279.97 2790.9,397.824 2790.9,542.987L2790.9,1172.79ZM2125.98,425.197C2282.43,425.197 2409.45,552.213 2409.45,708.661C2409.45,865.11 2282.43,992.126 2125.98,992.126C1969.54,992.126 1842.52,865.11 1842.52,708.661C1842.52,552.213 1969.54,425.197 2125.98,425.197Z"
-      />
-    </g>
-  ),
-  defaultProps: {
-    boxSize: '24px',
-  },
-});
-export default ImageToImageIcon;

invokeai/frontend/web/src/common/icons/NodesIcon.tsx

@@ -1,19 +0,0 @@
-import { createIcon } from '@chakra-ui/react';
-
-const NodesIcon = createIcon({
-  displayName: 'NodesIcon',
-  viewBox: '0 0 3543 3543',
-  path: (
-    <path
-      fill="currentColor"
-      fillRule="evenodd"
-      clipRule="evenodd"
-      d="M3543.31,770.787C3543.31,515.578 3336.11,308.38 3080.9,308.38L462.407,308.38C207.197,308.38 0,515.578 0,770.787L0,2766.03C0,3021.24 207.197,3228.44 462.407,3228.44L3080.9,3228.44C3336.11,3228.44 3543.31,3021.24 3543.31,2766.03C3543.31,2766.03 3543.31,770.787 3543.31,770.787ZM3427.88,770.787L3427.88,2766.03C3427.88,2957.53 3272.4,3113.01 3080.9,3113.01C3080.9,3113.01 462.407,3113.01 462.407,3113.01C270.906,3113.01 115.431,2957.53 115.431,2766.03L115.431,770.787C115.431,579.286 270.906,423.812 462.407,423.812L3080.9,423.812C3272.4,423.812 3427.88,579.286 3427.88,770.787ZM1214.23,1130.69L1321.47,1130.69C1324.01,1130.69 1326.54,1130.53 1329.05,1130.2C1329.05,1130.2 1367.3,1125.33 1397.94,1149.8C1421.63,1168.72 1437.33,1204.3 1437.33,1265.48L1437.33,2078.74L1220.99,2078.74C1146.83,2078.74 1086.61,2138.95 1086.61,2213.12L1086.61,2762.46C1086.61,2836.63 1146.83,2896.84 1220.99,2896.84L1770.34,2896.84C1844.5,2896.84 1904.71,2836.63 1904.71,2762.46L1904.71,2213.12C1904.71,2138.95 1844.5,2078.74 1770.34,2078.74L1554,2078.74L1554,1604.84C1625.84,1658.19 1703.39,1658.1 1703.39,1658.1C1703.54,1658.1 1703.69,1658.11 1703.84,1658.11L2362.2,1658.11L2362.2,1874.44C2362.2,1948.61 2422.42,2008.82 2496.58,2008.82L3045.93,2008.82C3120.09,2008.82 3180.3,1948.61 3180.3,1874.44L3180.3,1325.1C3180.3,1250.93 3120.09,1190.72 3045.93,1190.72L2496.58,1190.72C2422.42,1190.72 2362.2,1250.93 2362.2,1325.1L2362.2,1558.97L2362.2,1541.44L1704.23,1541.44C1702.2,1541.37 1650.96,1539.37 1609.51,1499.26C1577.72,1468.49 1554,1416.47 1554,1331.69L1554,1265.48C1554,1153.86 1513.98,1093.17 1470.76,1058.64C1411.24,1011.1 1338.98,1012.58 1319.15,1014.03L1214.23,1014.03L1214.23,796.992C1214.23,722.828 1154.02,662.617 1079.85,662.617L530.507,662.617C456.343,662.617 396.131,722.828 396.131,796.992L396.131,1346.34C396.131,1420.5 456.343,1480.71 530.507,1480.71L1079.85,1480.71C1154.02,1480.71 1214.23,1420.5 1214.23,1346.34L1214.23,1130.69Z"
-    />
-  ),
-  defaultProps: {
-    boxSize: '24px',
-  },
-});
-
-export default NodesIcon;

invokeai/frontend/web/src/common/icons/PostprocessingIcon.tsx

@@ -1,19 +0,0 @@
-import { createIcon } from '@chakra-ui/react';
-
-const PostprocessingIcon = createIcon({
-  displayName: 'PostprocessingIcon',
-  viewBox: '0 0 3543 3543',
-  path: (
-    <path
-      fill="currentColor"
-      fillRule="evenodd"
-      clipRule="evenodd"
-      d="M709.477,1596.53L992.591,1275.66L2239.09,2646.81L2891.95,1888.03L3427.88,2460.51L3427.88,994.78C3427.88,954.66 3421.05,916.122 3408.5,880.254L3521.9,855.419C3535.8,899.386 3543.31,946.214 3543.31,994.78L3543.31,2990.02C3543.31,3245.23 3336.11,3452.43 3080.9,3452.43C3080.9,3452.43 462.407,3452.43 462.407,3452.43C207.197,3452.43 -0,3245.23 -0,2990.02L-0,994.78C-0,739.571 207.197,532.373 462.407,532.373L505.419,532.373L504.644,532.546L807.104,600.085C820.223,601.729 832.422,607.722 841.77,617.116C850.131,625.517 855.784,636.21 858.055,647.804L462.407,647.804C270.906,647.804 115.431,803.279 115.431,994.78L115.431,2075.73L-0,2101.5L115.431,2127.28L115.431,2269.78L220.47,2150.73L482.345,2209.21C503.267,2211.83 522.722,2221.39 537.63,2236.37C552.538,2251.35 562.049,2270.9 564.657,2291.93L671.84,2776.17L779.022,2291.93C781.631,2270.9 791.141,2251.35 806.05,2236.37C820.958,2221.39 840.413,2211.83 861.334,2209.21L1353.15,2101.5L861.334,1993.8C840.413,1991.18 820.958,1981.62 806.05,1966.64C791.141,1951.66 781.631,1932.11 779.022,1911.08L709.477,1596.53ZM671.84,1573.09L725.556,2006.07C726.863,2016.61 731.63,2026.4 739.101,2033.91C746.573,2041.42 756.323,2046.21 766.808,2047.53L1197.68,2101.5L766.808,2155.48C756.323,2156.8 746.573,2161.59 739.101,2169.09C731.63,2176.6 726.863,2186.4 725.556,2196.94L671.84,2629.92L618.124,2196.94C616.817,2186.4 612.05,2176.6 604.579,2169.09C597.107,2161.59 587.357,2156.8 576.872,2155.48L146.001,2101.5L576.872,2047.53C587.357,2046.21 597.107,2041.42 604.579,2033.91C612.05,2026.4 616.817,2016.61 618.124,2006.07L671.84,1573.09ZM609.035,1710.36L564.657,1911.08C562.049,1932.11 552.538,1951.66 537.63,1966.64C522.722,1981.62 503.267,1991.18 482.345,1993.8L328.665,2028.11L609.035,1710.36ZM2297.12,938.615L2451.12,973.003C2480.59,976.695 2507.99,990.158 2528.99,1011.26C2549.99,1032.37 2563.39,1059.9 2567.07,1089.52L2672.73,1566.9C2634.5,1580.11 2593.44,1587.29 2550.72,1587.29C2344.33,1587.29 2176.77,1419.73 2176.77,1213.34C2176.77,1104.78 2223.13,1006.96 2297.12,938.615ZM2718.05,76.925L2793.72,686.847C2795.56,701.69 2802.27,715.491 2812.8,726.068C2823.32,736.644 2837.06,743.391 2851.83,745.242L3458.78,821.28L2851.83,897.318C2837.06,899.168 2823.32,905.916 2812.8,916.492C2802.27,927.068 2795.56,940.87 2793.72,955.712L2718.05,1565.63L2642.38,955.712C2640.54,940.87 2633.83,927.068 2623.3,916.492C2612.78,905.916 2599.04,899.168 2584.27,897.318L1977.32,821.28L2584.27,745.242C2599.04,743.391 2612.78,736.644 2623.3,726.068C2633.83,715.491 2640.54,701.69 2642.38,686.847L2718.05,76.925ZM2883.68,1043.06C2909.88,1094.13 2924.67,1152.02 2924.67,1213.34C2924.67,1335.4 2866.06,1443.88 2775.49,1512.14L2869.03,1089.52C2871.07,1073.15 2876.07,1057.42 2883.68,1043.06ZM925.928,201.2L959.611,472.704C960.431,479.311 963.42,485.455 968.105,490.163C972.79,494.871 978.904,497.875 985.479,498.698L1255.66,532.546L985.479,566.395C978.904,567.218 972.79,570.222 968.105,574.93C963.42,579.638 960.431,585.781 959.611,592.388L925.928,863.893L892.245,592.388C891.425,585.781 888.436,579.638 883.751,574.93C879.066,570.222 872.952,567.218 866.378,566.395L596.195,532.546L866.378,498.698C872.952,497.875 879.066,494.871 883.751,490.163C888.436,485.455 891.425,479.311 892.245,472.704L925.928,201.2ZM2864.47,532.373L3080.9,532.373C3258.7,532.373 3413.2,632.945 3490.58,780.281L3319.31,742.773C3257.14,683.925 3173.2,647.804 3080.9,647.804L2927.07,647.804C2919.95,642.994 2913.25,637.473 2907.11,631.298C2886.11,610.194 2872.71,582.655 2869.03,553.04L2864.47,532.373ZM1352.36,532.373L2571.64,532.373L2567.07,553.04C2563.39,582.655 2549.99,610.194 2528.99,631.298C2522.85,637.473 2516.16,642.994 2509.03,647.804L993.801,647.804C996.072,636.21 1001.73,625.517 1010.09,617.116C1019.43,607.722 1031.63,601.729 1044.75,600.085L1353.15,532.546L1352.36,532.373Z"
-    />
-  ),
-  defaultProps: {
-    boxSize: '24px',
-  },
-});
-
-export default PostprocessingIcon;

invokeai/frontend/web/src/common/icons/TrainingIcon.tsx

@@ -1,19 +0,0 @@
-import { createIcon } from '@chakra-ui/react';
-
-const TrainingIcon = createIcon({
-  displayName: 'TrainingIcon',
-  viewBox: '0 0 3544 3544',
-  path: (
-    <path
-      fill="currentColor"
-      fillRule="evenodd"
-      clipRule="evenodd"
-      d="M0,768.593L0,2774.71C0,2930.6 78.519,3068.3 198.135,3150.37C273.059,3202.68 364.177,3233.38 462.407,3233.38C462.407,3233.38 3080.9,3233.38 3080.9,3233.38C3179.13,3233.38 3270.25,3202.68 3345.17,3150.37C3464.79,3068.3 3543.31,2930.6 3543.31,2774.71L3543.31,768.593C3543.31,517.323 3339.31,313.324 3088.04,313.324L455.269,313.324C203.999,313.324 0,517.323 0,768.593ZM3427.88,775.73L3427.88,2770.97C3427.88,2962.47 3272.4,3117.95 3080.9,3117.95L462.407,3117.95C270.906,3117.95 115.431,2962.47 115.431,2770.97C115.431,2770.97 115.431,775.73 115.431,775.73C115.431,584.229 270.906,428.755 462.407,428.755C462.407,428.755 3080.9,428.755 3080.9,428.755C3272.4,428.755 3427.88,584.229 3427.88,775.73ZM796.24,1322.76L796.24,1250.45C796.24,1199.03 836.16,1157.27 885.331,1157.27C885.331,1157.27 946.847,1157.27 946.847,1157.27C996.017,1157.27 1035.94,1199.03 1035.94,1250.45L1035.94,1644.81L2507.37,1644.81L2507.37,1250.45C2507.37,1199.03 2547.29,1157.27 2596.46,1157.27C2596.46,1157.27 2657.98,1157.27 2657.98,1157.27C2707.15,1157.27 2747.07,1199.03 2747.07,1250.45L2747.07,1322.76C2756.66,1319.22 2767.02,1317.29 2777.83,1317.29C2777.83,1317.29 2839.34,1317.29 2839.34,1317.29C2888.51,1317.29 2928.43,1357.21 2928.43,1406.38L2928.43,1527.32C2933.51,1526.26 2938.77,1525.71 2944.16,1525.71L2995.3,1525.71C3036.18,1525.71 3069.37,1557.59 3069.37,1596.86C3069.37,1596.86 3069.37,1946.44 3069.37,1946.44C3069.37,1985.72 3036.18,2017.6 2995.3,2017.6C2995.3,2017.6 2944.16,2017.6 2944.16,2017.6C2938.77,2017.6 2933.51,2017.04 2928.43,2015.99L2928.43,2136.92C2928.43,2186.09 2888.51,2226.01 2839.34,2226.01L2777.83,2226.01C2767.02,2226.01 2756.66,2224.08 2747.07,2220.55L2747.07,2292.85C2747.07,2344.28 2707.15,2386.03 2657.98,2386.03C2657.98,2386.03 2596.46,2386.03 2596.46,2386.03C2547.29,2386.03 2507.37,2344.28 2507.37,2292.85L2507.37,1898.5L1035.94,1898.5L1035.94,2292.85C1035.94,2344.28 996.017,2386.03 946.847,2386.03C946.847,2386.03 885.331,2386.03 885.331,2386.03C836.16,2386.03 796.24,2344.28 796.24,2292.85L796.24,2220.55C786.651,2224.08 776.29,2226.01 765.482,2226.01L703.967,2226.01C654.796,2226.01 614.876,2186.09 614.876,2136.92L614.876,2015.99C609.801,2017.04 604.539,2017.6 599.144,2017.6C599.144,2017.6 548.003,2017.6 548.003,2017.6C507.125,2017.6 473.937,1985.72 473.937,1946.44C473.937,1946.44 473.937,1596.86 473.937,1596.86C473.937,1557.59 507.125,1525.71 548.003,1525.71L599.144,1525.71C604.539,1525.71 609.801,1526.26 614.876,1527.32L614.876,1406.38C614.876,1357.21 654.796,1317.29 703.967,1317.29C703.967,1317.29 765.482,1317.29 765.482,1317.29C776.29,1317.29 786.651,1319.22 796.24,1322.76ZM977.604,1250.45C977.604,1232.7 963.822,1218.29 946.847,1218.29L885.331,1218.29C868.355,1218.29 854.573,1232.7 854.573,1250.45L854.573,2292.85C854.573,2310.61 868.355,2325.02 885.331,2325.02L946.847,2325.02C963.822,2325.02 977.604,2310.61 977.604,2292.85L977.604,1250.45ZM2565.7,1250.45C2565.7,1232.7 2579.49,1218.29 2596.46,1218.29L2657.98,1218.29C2674.95,1218.29 2688.73,1232.7 2688.73,1250.45L2688.73,2292.85C2688.73,2310.61 2674.95,2325.02 2657.98,2325.02L2596.46,2325.02C2579.49,2325.02 2565.7,2310.61 2565.7,2292.85L2565.7,1250.45ZM673.209,1406.38L673.209,2136.92C673.209,2153.9 686.991,2167.68 703.967,2167.68L765.482,2167.68C782.458,2167.68 796.24,2153.9 796.24,2136.92L796.24,1406.38C796.24,1389.41 782.458,1375.63 765.482,1375.63L703.967,1375.63C686.991,1375.63 673.209,1389.41 673.209,1406.38ZM2870.1,1406.38L2870.1,2136.92C2870.1,2153.9 2856.32,2167.68 2839.34,2167.68L2777.83,2167.68C2760.85,2167.68 2747.07,2153.9 2747.07,2136.92L2747.07,1406.38C2747.07,1389.41 2760.85,1375.63 2777.83,1375.63L2839.34,1375.63C2856.32,1375.63 2870.1,1389.41 2870.1,1406.38ZM614.876,1577.5C610.535,1574.24 605.074,1572.3 599.144,1572.3L548.003,1572.3C533.89,1572.3 522.433,1583.3 522.433,1596.86L522.433,1946.44C522.433,1960 533.89,1971.01 548.003,1971.01L599.144,1971.01C605.074,1971.01 610.535,1969.07 614.876,1965.81L614.876,1577.5ZM2928.43,1965.81L2928.43,1577.5C2932.77,1574.24 2938.23,1572.3 2944.16,1572.3L2995.3,1572.3C3009.42,1572.3 3020.87,1583.3 3020.87,1596.86L3020.87,1946.44C3020.87,1960 3009.42,1971.01 2995.3,1971.01L2944.16,1971.01C2938.23,1971.01 2932.77,1969.07 2928.43,1965.81ZM2507.37,1703.14L1035.94,1703.14L1035.94,1840.16L2507.37,1840.16L2507.37,1898.38L2507.37,1659.46L2507.37,1703.14Z"
-    />
-  ),
-  defaultProps: {
-    boxSize: '24px',
-  },
-});
-
-export default TrainingIcon;

invokeai/frontend/web/src/common/icons/design_files/ImageToImage.svg

@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
-<svg width="100%" height="100%" viewBox="0 0 3543 3543" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linejoin:round;stroke-miterlimit:2;">
-    <g transform="matrix(1.10943,0,0,1.10943,-206.981,-213.533)">
-        <path d="M688.533,2405.95L542.987,2405.95C349.532,2405.95 192.47,2248.89 192.47,2055.44L192.47,542.987C192.47,349.532 349.532,192.47 542.987,192.47L2527.88,192.47C2721.33,192.47 2878.4,349.532 2878.4,542.987L2878.4,1172.79L3023.94,1172.79C3217.4,1172.79 3374.46,1329.85 3374.46,1523.3C3374.46,1523.3 3374.46,3035.75 3374.46,3035.75C3374.46,3229.21 3217.4,3386.27 3023.94,3386.27L1039.05,3386.27C845.595,3386.27 688.533,3229.21 688.533,3035.75L688.533,2405.95ZM3286.96,2634.37L3286.96,1523.3C3286.96,1378.14 3169.11,1260.29 3023.94,1260.29C3023.94,1260.29 1039.05,1260.29 1039.05,1260.29C893.887,1260.29 776.033,1378.14 776.033,1523.3L776.033,2489.79L1440.94,1736.22L2385.83,2775.59L2880.71,2200.41L3286.96,2634.37ZM2622.05,1405.51C2778.5,1405.51 2905.51,1532.53 2905.51,1688.98C2905.51,1845.42 2778.5,1972.44 2622.05,1972.44C2465.6,1972.44 2338.58,1845.42 2338.58,1688.98C2338.58,1532.53 2465.6,1405.51 2622.05,1405.51ZM2790.9,1172.79L1323.86,1172.79L944.882,755.906L279.97,1509.47L279.97,542.987C279.97,397.824 397.824,279.97 542.987,279.97C542.987,279.97 2527.88,279.97 2527.88,279.97C2673.04,279.97 2790.9,397.824 2790.9,542.987L2790.9,1172.79ZM2125.98,425.197C2282.43,425.197 2409.45,552.213 2409.45,708.661C2409.45,865.11 2282.43,992.126 2125.98,992.126C1969.54,992.126 1842.52,865.11 1842.52,708.661C1842.52,552.213 1969.54,425.197 2125.98,425.197Z"/>
-    </g>
-</svg>

invokeai/frontend/web/src/common/icons/design_files/Nodes.svg

@@ -1,5 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
-<svg width="100%" height="100%" viewBox="0 0 3543 3543" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linejoin:round;stroke-miterlimit:2;">
-    <path d="M3543.31,770.787C3543.31,515.578 3336.11,308.38 3080.9,308.38L462.407,308.38C207.197,308.38 0,515.578 0,770.787L0,2766.03C0,3021.24 207.197,3228.44 462.407,3228.44L3080.9,3228.44C3336.11,3228.44 3543.31,3021.24 3543.31,2766.03C3543.31,2766.03 3543.31,770.787 3543.31,770.787ZM3427.88,770.787L3427.88,2766.03C3427.88,2957.53 3272.4,3113.01 3080.9,3113.01C3080.9,3113.01 462.407,3113.01 462.407,3113.01C270.906,3113.01 115.431,2957.53 115.431,2766.03L115.431,770.787C115.431,579.286 270.906,423.812 462.407,423.812L3080.9,423.812C3272.4,423.812 3427.88,579.286 3427.88,770.787ZM1214.23,1130.69L1321.47,1130.69C1324.01,1130.69 1326.54,1130.53 1329.05,1130.2C1329.05,1130.2 1367.3,1125.33 1397.94,1149.8C1421.63,1168.72 1437.33,1204.3 1437.33,1265.48L1437.33,2078.74L1220.99,2078.74C1146.83,2078.74 1086.61,2138.95 1086.61,2213.12L1086.61,2762.46C1086.61,2836.63 1146.83,2896.84 1220.99,2896.84L1770.34,2896.84C1844.5,2896.84 1904.71,2836.63 1904.71,2762.46L1904.71,2213.12C1904.71,2138.95 1844.5,2078.74 1770.34,2078.74L1554,2078.74L1554,1604.84C1625.84,1658.19 1703.39,1658.1 1703.39,1658.1C1703.54,1658.1 1703.69,1658.11 1703.84,1658.11L2362.2,1658.11L2362.2,1874.44C2362.2,1948.61 2422.42,2008.82 2496.58,2008.82L3045.93,2008.82C3120.09,2008.82 3180.3,1948.61 3180.3,1874.44L3180.3,1325.1C3180.3,1250.93 3120.09,1190.72 3045.93,1190.72L2496.58,1190.72C2422.42,1190.72 2362.2,1250.93 2362.2,1325.1L2362.2,1558.97L2362.2,1541.44L1704.23,1541.44C1702.2,1541.37 1650.96,1539.37 1609.51,1499.26C1577.72,1468.49 1554,1416.47 1554,1331.69L1554,1265.48C1554,1153.86 1513.98,1093.17 1470.76,1058.64C1411.24,1011.1 1338.98,1012.58 1319.15,1014.03L1214.23,1014.03L1214.23,796.992C1214.23,722.828 1154.02,662.617 1079.85,662.617L530.507,662.617C456.343,662.617 396.131,722.828 396.131,796.992L396.131,1346.34C396.131,1420.5 456.343,1480.71 530.507,1480.71L1079.85,1480.71C1154.02,1480.71 1214.23,1420.5 1214.23,1346.34L1214.23,1130.69Z"/>
-</svg>

invokeai/frontend/web/src/common/icons/design_files/Postprocessing.svg

@@ -1,5 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
-<svg width="100%" height="100%" viewBox="0 0 3543 3543" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:1.5;">
-    <path d="M709.477,1596.53L992.591,1275.66L2239.09,2646.81L2891.95,1888.03L3427.88,2460.51L3427.88,994.78C3427.88,954.66 3421.05,916.122 3408.5,880.254L3521.9,855.419C3535.8,899.386 3543.31,946.214 3543.31,994.78L3543.31,2990.02C3543.31,3245.23 3336.11,3452.43 3080.9,3452.43C3080.9,3452.43 462.407,3452.43 462.407,3452.43C207.197,3452.43 -0,3245.23 -0,2990.02L-0,994.78C-0,739.571 207.197,532.373 462.407,532.373L505.419,532.373L504.644,532.546L807.104,600.085C820.223,601.729 832.422,607.722 841.77,617.116C850.131,625.517 855.784,636.21 858.055,647.804L462.407,647.804C270.906,647.804 115.431,803.279 115.431,994.78L115.431,2075.73L-0,2101.5L115.431,2127.28L115.431,2269.78L220.47,2150.73L482.345,2209.21C503.267,2211.83 522.722,2221.39 537.63,2236.37C552.538,2251.35 562.049,2270.9 564.657,2291.93L671.84,2776.17L779.022,2291.93C781.631,2270.9 791.141,2251.35 806.05,2236.37C820.958,2221.39 840.413,2211.83 861.334,2209.21L1353.15,2101.5L861.334,1993.8C840.413,1991.18 820.958,1981.62 806.05,1966.64C791.141,1951.66 781.631,1932.11 779.022,1911.08L709.477,1596.53ZM671.84,1573.09L725.556,2006.07C726.863,2016.61 731.63,2026.4 739.101,2033.91C746.573,2041.42 756.323,2046.21 766.808,2047.53L1197.68,2101.5L766.808,2155.48C756.323,2156.8 746.573,2161.59 739.101,2169.09C731.63,2176.6 726.863,2186.4 725.556,2196.94L671.84,2629.92L618.124,2196.94C616.817,2186.4 612.05,2176.6 604.579,2169.09C597.107,2161.59 587.357,2156.8 576.872,2155.48L146.001,2101.5L576.872,2047.53C587.357,2046.21 597.107,2041.42 604.579,2033.91C612.05,2026.4 616.817,2016.61 618.124,2006.07L671.84,1573.09ZM609.035,1710.36L564.657,1911.08C562.049,1932.11 552.538,1951.66 537.63,1966.64C522.722,1981.62 503.267,1991.18 482.345,1993.8L328.665,2028.11L609.035,1710.36ZM2297.12,938.615L2451.12,973.003C2480.59,976.695 2507.99,990.158 2528.99,1011.26C2549.99,1032.37 2563.39,1059.9 2567.07,1089.52L2672.73,1566.9C2634.5,1580.11 2593.44,1587.29 2550.72,1587.29C2344.33,1587.29 2176.77,1419.73 2176.77,1213.34C2176.77,1104.78 2223.13,1006.96 2297.12,938.615ZM2718.05,76.925L2793.72,686.847C2795.56,701.69 2802.27,715.491 2812.8,726.068C2823.32,736.644 2837.06,743.391 2851.83,745.242L3458.78,821.28L2851.83,897.318C2837.06,899.168 2823.32,905.916 2812.8,916.492C2802.27,927.068 2795.56,940.87 2793.72,955.712L2718.05,1565.63L2642.38,955.712C2640.54,940.87 2633.83,927.068 2623.3,916.492C2612.78,905.916 2599.04,899.168 2584.27,897.318L1977.32,821.28L2584.27,745.242C2599.04,743.391 2612.78,736.644 2623.3,726.068C2633.83,715.491 2640.54,701.69 2642.38,686.847L2718.05,76.925ZM2883.68,1043.06C2909.88,1094.13 2924.67,1152.02 2924.67,1213.34C2924.67,1335.4 2866.06,1443.88 2775.49,1512.14L2869.03,1089.52C2871.07,1073.15 2876.07,1057.42 2883.68,1043.06ZM925.928,201.2L959.611,472.704C960.431,479.311 963.42,485.455 968.105,490.163C972.79,494.871 978.904,497.875 985.479,498.698L1255.66,532.546L985.479,566.395C978.904,567.218 972.79,570.222 968.105,574.93C963.42,579.638 960.431,585.781 959.611,592.388L925.928,863.893L892.245,592.388C891.425,585.781 888.436,579.638 883.751,574.93C879.066,570.222 872.952,567.218 866.378,566.395L596.195,532.546L866.378,498.698C872.952,497.875 879.066,494.871 883.751,490.163C888.436,485.455 891.425,479.311 892.245,472.704L925.928,201.2ZM2864.47,532.373L3080.9,532.373C3258.7,532.373 3413.2,632.945 3490.58,780.281L3319.31,742.773C3257.14,683.925 3173.2,647.804 3080.9,647.804L2927.07,647.804C2919.95,642.994 2913.25,637.473 2907.11,631.298C2886.11,610.194 2872.71,582.655 2869.03,553.04L2864.47,532.373ZM1352.36,532.373L2571.64,532.373L2567.07,553.04C2563.39,582.655 2549.99,610.194 2528.99,631.298C2522.85,637.473 2516.16,642.994 2509.03,647.804L993.801,647.804C996.072,636.21 1001.73,625.517 1010.09,617.116C1019.43,607.722 1031.63,601.729 1044.75,600.085L1353.15,532.546L1352.36,532.373Z" style="stroke:white;stroke-opacity:0;stroke-width:1px;"/>
-</svg>