Merge branch 'main' into Convert-Model-Endpoint

# Application-wide configuration service

This PR creates a new `InvokeAIAppConfig` object that reads
application-wide settings from an init file, the environment, and the
command line.

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
  Cross-Origin Resource Sharing:
    allow_origins: []
    allow_credentials: true
    allow_methods:
    - '*'
    allow_headers:
    - '*'
  Web Server:
    host: 127.0.0.1
    port: 8081

```

The default name of the configuration file is `invokeai.yaml`, located
in INVOKEAI_ROOT. You can use any OmegaConf dictionary by passing it to
the config object at initialization time:

```
 omegaconf = OmegaConf.load('/tmp/init.yaml')
 conf = InvokeAIAppConfig(conf=omegaconf)
```
The default name of the configuration file is `invokeai.yaml`, located
in INVOKEAI_ROOT. You can replace supersede this by providing
anyOmegaConf 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

# get global configuration and print its nsfw_checker value
conf = InvokeAIAppConfig()
print(conf.nsfw_checker)
```
Finally, the configuration object is able to recreate its (modified)
yaml file, by calling its `to_yaml()` method:

```
conf = InvokeAIAppConfig(outdir='/tmp', port=8080)
print(conf.to_yaml())
```

# Legacy code removal and porting

This PR replaces Globals with the InvokeAIAppConfig system throughout,
and therefore removes the `globals.py` and `args.py` modules. It also
removes `generate` and the legacy CLI. ***The old CLI and web servers
are now gone.***

I have ported the functionality of the configuration script, the model
installer, and the merge and textual inversion scripts. The `invokeai`
command will now launch `invokeai-node-cli`, and `invokeai-web` will
launch the web server.

I have changed the continuous invocation tests to accommodate the new
command syntax in `invokeai-node-cli`. As a convenience function, you
can also pass invocations to `invokeai-node-cli` (or its alias
`invokeai`) on the command line as as standard input:

```
invokeai-node-cli "t2i --positive_prompt 'banana sushi' --seed 42"
invokeai < invocation_commands.txt
```

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

@@ -80,12 +80,7 @@ jobs:
         uses: actions/checkout@v3
 
       - name: set test prompt to main branch validation
-        if: ${{ github.ref == 'refs/heads/main' }}
-        run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> ${{ matrix.github-env }}
-
-      - name: set test prompt to Pull Request validation
-        if: ${{ github.ref != 'refs/heads/main' }}
-        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
+        run:echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
 
       - name: setup python
         uses: actions/setup-python@v4
@@ -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/*
 

installer/lib/installer.py

@@ -247,8 +247,8 @@ class InvokeAiInstance:
             pip[
                 "install",
                 "--require-virtualenv",
-                "torch",
-                "torchvision",
+                "torch~=2.0.0",
+                "torchvision>=0.14.1",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,

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.model_manager_initializer import get_model_manager
 from ..services.restoration_services import RestorationServices
 from ..services.graph import GraphExecutionState, LibraryGraph
@@ -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
-        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}")
+        logger.info(f"Internet connectivity is {config.internet_available}")
 
         events = FastAPIEventService(event_handler_id)
 
@@ -72,7 +62,6 @@ class ApiDependencies:
         services = InvocationServices(
             model_manager=get_model_manager(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/routers/images.py

@@ -83,7 +83,7 @@ async def get_thumbnail(
     status_code=201,
 )
 async def upload_image(
-    file: UploadFile, request: Request, response: Response
+    file: UploadFile, image_type: ImageType, request: Request, response: Response
 ) -> ImageResponse:
     if not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
@@ -99,21 +99,21 @@ async def upload_image(
     filename = f"{uuid.uuid4()}_{str(int(datetime.now(timezone.utc).timestamp()))}.png"
 
     saved_image = ApiDependencies.invoker.services.images.save(
-        ImageType.UPLOAD, filename, img
+        image_type, filename, img
     )
 
     invokeai_metadata = ApiDependencies.invoker.services.metadata.get_metadata(img)
 
     image_url = ApiDependencies.invoker.services.images.get_uri(
-        ImageType.UPLOAD, saved_image.image_name
+        image_type, saved_image.image_name
     )
 
     thumbnail_url = ApiDependencies.invoker.services.images.get_uri(
-        ImageType.UPLOAD, saved_image.image_name, True
+        image_type, saved_image.image_name, True
     )
 
     res = ImageResponse(
-        image_type=ImageType.UPLOAD,
+        image_type=image_type,
         image_name=saved_image.image_name,
         image_url=image_url,
         thumbnail_url=thumbnail_url,

invokeai/app/api/routers/models.py

@@ -1,7 +1,7 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and 2023 Kent Keirsey (https://github.com/hipsterusername)
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and Kent Keirsey (https://github.com/hipsterusername)
 
 import shutil
-import asyncio
+import os
 from typing import Annotated, Any, List, Literal, Optional, Union
 
 from fastapi.routing import APIRouter, HTTPException
@@ -47,10 +47,8 @@ class CreateModelResponse(BaseModel):
 
 class ConversionRequest(BaseModel):
     name: str = Field(description="The name of the new model")
-    info: CkptModelInfo = Field(description="The converted model info")
     save_location: str = Field(description="The path to save the converted model weights")
 
-
 class ConvertedModelResponse(BaseModel):
     name: str = Field(description="The name of the new model")
     info: DiffusersModelInfo = Field(description="The converted model info")
@@ -124,6 +122,95 @@ async def delete_model(model_name: str) -> None:
         logger.error(f"Model not found")
         raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
 
+# TODO: Refactor these support functions below to live somewhere more appropriate
+
+def get_model_info(model_name: str):
+    model_info = ApiDependencies.invoker.services.model_manager.model_info(
+        model_name=model_name
+    )
+    if not model_info:
+        raise HTTPException(status_code=404, detail=f"Unable to retrieve model info for '{model_name}'")
+    return model_info
+
+
+def ckpt_validate(model_info: dict, model_name: str):
+    if "weights" not in model_info:
+        raise HTTPException(status_code=404, detail=f"Model '{model_name}' is not a valid checkpoint model")
+    
+
+def get_paths(model: ConversionRequest, root: Path) -> tuple:
+    model_info = get_model_info(model.name)
+    ckpt_path = Path(model_info.weights)
+    config_path = Path(model_info.config)
+
+    if not ckpt_path.is_absolute():
+        ckpt_path = Path(root, ckpt_path)
+
+    if config_path and not config_path.is_absolute():
+        config_path = Path(root, config_path)
+
+    return ckpt_path, config_path
+
+
+def get_diffusers_path(convert_request: ConversionRequest, model_name: str) -> Path:
+    if convert_request.save_location == "root":
+        diffusers_path = Path(global_converted_ckpts_dir(), f"{model_name}_diffusers")
+    elif convert_request.save_location == "custom" and convert_request.save_location is not None:
+        diffusers_path = Path(convert_request.save_location, f"{model_name}_diffusers")
+    else:
+        raise ValueError("Invalid save_location value")
+
+    if diffusers_path.exists():
+        shutil.rmtree(diffusers_path)
+
+    return diffusers_path
+
+
+@models_router.post(
+    "/{model_to_convert}",
+    operation_id="convert_model",
+    responses={
+        200: {
+            "model_response": "Model converted successfully.",
+        }
+    },
+)
+async def convert_model(convert_request: ConversionRequest) -> ConvertedModelResponse:
+    """Convert Model"""
+
+    opt=Args()
+    args = opt.parse_args()
+
+    # Set the root directory for static files and relative paths
+    args.root_dir = os.path.expanduser(args.root_dir or "..")
+    if not os.path.isabs(args.outdir):
+        args.outdir = os.path.join(args.root_dir, args.outdir)
+
+    # normalize the config directory relative to root
+    if not os.path.isabs(opt.conf):
+        opt.conf = os.path.normpath(os.path.join(Globals.root, opt.conf))
+    model_info = get_model_info(convert_request.name)
+    ckpt_validate(model_info, convert_request.name)
+    ckpt_path, original_config_file = get_paths(convert_request, Globals.root)
+    diffusers_path = get_diffusers_path(convert_request, convert_request.name)
+
+    ApiDependencies.invoker.services.model_manager.convert_and_import(
+        ckpt_path,
+        diffusers_path,
+        model_name=convert_request.name,
+        model_description=model_info.description,
+        vae=None,
+        original_config_file=original_config_file,
+        commit_to_conf=opt.conf,
+    )
+
+    model_info = get_model_info(convert_request.name)
+    convert_response = ConvertedModelResponse(name=f"{convert_request.name}_diffusers", info=model_info)
+    
+    print(f">> Model Converted: {convert_request.name}")
+
+    return convert_response
+
     
             # @socketio.on("convertToDiffusers")
         # def convert_to_diffusers(model_to_convert: dict):

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()
-    config.parse_args()
+    app.add_middleware(
+        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
     )
 
 
@@ -126,7 +121,6 @@ app.openapi = custom_openapi
 # Override API doc favicons
 app.mount("/static", StaticFiles(directory="static/dream_web"), name="static")
 
-
 @app.get("/docs", include_in_schema=False)
 def overridden_swagger():
     return get_swagger_ui_html(
@@ -144,17 +138,16 @@ def overridden_redoc():
         redoc_favicon_url="/static/favicon.ico",
     )
 
+# Must mount *after* the other routes else it borks em
+app.mount("/", StaticFiles(directory="invokeai/frontend/web/dist", html=True), name="ui")
 
 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
 
 
@@ -19,8 +20,7 @@ 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
+from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers, SortedHelpFormatter
 from .cli.completer import set_autocompleter
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
@@ -34,7 +34,7 @@ from .services.invocation_services import InvocationServices
 from .services.invoker import Invoker
 from .services.processor import DefaultInvocationProcessor
 from .services.sqlite import SqliteItemStorage
-
+from .services.config import get_invokeai_config
 
 class CliCommand(BaseModel):
     command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type")  # type: ignore
@@ -64,7 +64,7 @@ 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
@@ -189,24 +189,25 @@ def invoke_all(context: CliContext):
 
 
 def invoke_cli():
-    config = Args()
-    config.parse_args()
+    # this gets the basic configuration
+    config = get_invokeai_config()
+
+    # get the optional list of invocations to execute on the command line
+    parser = config.get_parser()
+    parser.add_argument('commands',nargs='*')
+    invocation_commands = parser.parse_args().commands
+
+    # get the optional file to read commands from.
+    # Simplest is to use it for STDIN
+    if infile := config.from_file:
+        sys.stdin = open(infile,"r")
+    
     model_manager = get_model_manager(config,logger=logger)
     
-    # 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")
 
@@ -226,6 +227,7 @@ def invoke_cli():
         processor=DefaultInvocationProcessor(),
         restoration=RestorationServices(config,logger=logger),
         logger=logger,
+        configuration=config,
     )
 
     system_graphs = create_system_graphs(services.graph_library)
@@ -241,9 +243,17 @@ 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
@@ -368,6 +378,9 @@ 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/collections.py

@@ -3,12 +3,12 @@
 from typing import Literal, Optional
 
 import numpy as np
-import numpy.random
 from pydantic import Field
 
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
+
 from .baseinvocation import (
     BaseInvocation,
-    InvocationConfig,
     InvocationContext,
     BaseInvocationOutput,
 )
@@ -50,11 +50,11 @@ class RandomRangeInvocation(BaseInvocation):
         default=np.iinfo(np.int32).max, description="The exclusive high value"
     )
     size: int = Field(default=1, description="The number of values to generate")
-    seed: Optional[int] = Field(
+    seed: int = Field(
         ge=0,
-        le=np.iinfo(np.int32).max,
-        description="The seed for the RNG",
-        default_factory=lambda: numpy.random.randint(0, np.iinfo(np.int32).max),
+        le=SEED_MAX,
+        description="The seed for the RNG (omit for random)",
+        default_factory=get_random_seed,
     )
 
     def invoke(self, context: InvocationContext) -> IntCollectionOutput:

invokeai/app/invocations/compel.py

@@ -16,8 +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")
@@ -100,9 +98,10 @@ class CompelInvocation(BaseInvocation):
 
         # TODO: support legacy blend?
 
-        prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str)
+        conjunction = Compel.parse_prompt_string(prompt_str)
+        prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0]
 
-        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/generate.py

@@ -1,15 +1,17 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from functools import partial
-from typing import Literal, Optional, Union
+from typing import Literal, Optional, Union, get_args
 
 import numpy as np
 from torch import Tensor
 
 from pydantic import BaseModel, Field
 
-from invokeai.app.models.image import ImageField, ImageType
+from invokeai.app.models.image import ColorField, ImageField, ImageType
 from invokeai.app.invocations.util.choose_model import choose_model
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
+from invokeai.backend.generator.inpaint import infill_methods
 from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
 from .image import ImageOutput, build_image_output
 from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
@@ -17,7 +19,8 @@ from ...backend.stable_diffusion import PipelineIntermediateState
 from ..util.step_callback import stable_diffusion_step_callback
 
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
-
+INFILL_METHODS = Literal[tuple(infill_methods())]
+DEFAULT_INFILL_METHOD = 'patchmatch' if 'patchmatch' in get_args(INFILL_METHODS) else 'tile'
 
 class SDImageInvocation(BaseModel):
     """Helper class to provide all Stable Diffusion raster image invocations with additional config"""
@@ -44,15 +47,13 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
     # TODO: consider making prompt optional to enable providing prompt through a link
     # fmt: off
     prompt: Optional[str] = Field(description="The prompt to generate an image from")
-    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
-    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
+    seed:        int = Field(ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed)
+    steps:       int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
     width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
     height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
-    cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
-    seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
+    cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
     model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
     # fmt: on
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
@@ -148,7 +149,6 @@ class ImageToImageInvocation(TextToImageInvocation):
                 self.image.image_type, self.image.image_name
             )
         )
-        mask = None
 
         if self.fit:
             image = image.resize((self.width, self.height))
@@ -165,7 +165,6 @@ class ImageToImageInvocation(TextToImageInvocation):
         outputs = Img2Img(model).generate(
             prompt=self.prompt,
             init_image=image,
-            init_mask=mask,
             step_callback=partial(self.dispatch_progress, context, source_node_id),
             **self.dict(
                 exclude={"prompt", "image", "mask"}
@@ -197,7 +196,6 @@ class ImageToImageInvocation(TextToImageInvocation):
             image=result_image,
         )
 
-
 class InpaintInvocation(ImageToImageInvocation):
     """Generates an image using inpaint."""
 
@@ -205,6 +203,17 @@ class InpaintInvocation(ImageToImageInvocation):
 
     # Inputs
     mask: Union[ImageField, None] = Field(description="The mask")
+    seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)")
+    seam_blur: int = Field(default=16, ge=0, description="The seam inpaint blur radius (px)")
+    seam_strength: float = Field(
+        default=0.75, gt=0, le=1, description="The seam inpaint strength"
+    )
+    seam_steps: int = Field(default=30, ge=1, description="The number of steps to use for seam inpaint")
+    tile_size: int = Field(default=32, ge=1, description="The tile infill method size (px)")
+    infill_method: INFILL_METHODS = Field(default=DEFAULT_INFILL_METHOD, description="The method used to infill empty regions (px)")
+    inpaint_width:       Optional[int] = Field(default=None, multiple_of=8, gt=0, description="The width of the inpaint region (px)")
+    inpaint_height:      Optional[int] = Field(default=None, multiple_of=8, gt=0, description="The height of the inpaint region (px)")
+    inpaint_fill: Optional[ColorField] = Field(default=ColorField(r=127, g=127, b=127, a=255), description="The solid infill method color")
     inpaint_replace: float = Field(
         default=0.0,
         ge=0.0,

invokeai/app/invocations/image.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+import io
 from typing import Literal, Optional
 
 import numpy
@@ -32,14 +33,12 @@ class ImageOutput(BaseInvocationOutput):
     # fmt: off
     type: Literal["image"] = "image"
     image:      ImageField = Field(default=None, description="The output image")
-    width:   Optional[int] = Field(default=None, description="The width of the image in pixels")
-    height:  Optional[int] = Field(default=None, description="The height of the image in pixels")
+    width:             int = Field(description="The width of the image in pixels")
+    height:            int = Field(description="The height of the image in pixels")
     # fmt: on
 
     class Config:
-        schema_extra = {
-            "required": ["type", "image", "width", "height", "mode"]
-        }
+        schema_extra = {"required": ["type", "image", "width", "height"]}
 
 
 def build_image_output(
@@ -54,7 +53,6 @@ def build_image_output(
         image=image_field,
         width=image.width,
         height=image.height,
-        mode=image.mode,
     )
 
 

invokeai/app/invocations/infill.py

@@ -0,0 +1,233 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from typing import Literal, Optional, Union, get_args
+
+import numpy as np
+import math
+from PIL import Image, ImageOps
+from pydantic import Field
+
+from invokeai.app.invocations.image import ImageOutput, build_image_output
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
+from invokeai.backend.image_util.patchmatch import PatchMatch
+
+from ..models.image import ColorField, ImageField, ImageType
+from .baseinvocation import (
+    BaseInvocation,
+    InvocationContext,
+)
+
+
+def infill_methods() -> list[str]:
+    methods = [
+        "tile",
+        "solid",
+    ]
+    if PatchMatch.patchmatch_available():
+        methods.insert(0, "patchmatch")
+    return methods
+
+
+INFILL_METHODS = Literal[tuple(infill_methods())]
+DEFAULT_INFILL_METHOD = (
+    "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
+)
+
+
+def infill_patchmatch(im: Image.Image) -> Image.Image:
+    if im.mode != "RGBA":
+        return im
+
+    # Skip patchmatch if patchmatch isn't available
+    if not PatchMatch.patchmatch_available():
+        return im
+
+    # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
+    im_patched_np = PatchMatch.inpaint(
+        im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3
+    )
+    im_patched = Image.fromarray(im_patched_np, mode="RGB")
+    return im_patched
+
+
+def get_tile_images(image: np.ndarray, width=8, height=8):
+    _nrows, _ncols, depth = image.shape
+    _strides = image.strides
+
+    nrows, _m = divmod(_nrows, height)
+    ncols, _n = divmod(_ncols, width)
+    if _m != 0 or _n != 0:
+        return None
+
+    return np.lib.stride_tricks.as_strided(
+        np.ravel(image),
+        shape=(nrows, ncols, height, width, depth),
+        strides=(height * _strides[0], width * _strides[1], *_strides),
+        writeable=False,
+    )
+
+
+def tile_fill_missing(
+    im: Image.Image, tile_size: int = 16, seed: Union[int, None] = None
+) -> Image.Image:
+    # Only fill if there's an alpha layer
+    if im.mode != "RGBA":
+        return im
+
+    a = np.asarray(im, dtype=np.uint8)
+
+    tile_size_tuple = (tile_size, tile_size)
+
+    # Get the image as tiles of a specified size
+    tiles = get_tile_images(a, *tile_size_tuple).copy()
+
+    # Get the mask as tiles
+    tiles_mask = tiles[:, :, :, :, 3]
+
+    # Find any mask tiles with any fully transparent pixels (we will be replacing these later)
+    tmask_shape = tiles_mask.shape
+    tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
+    n, ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
+    tiles_mask = tiles_mask > 0
+    tiles_mask = tiles_mask.reshape((n, ny)).all(axis=1)
+
+    # Get RGB tiles in single array and filter by the mask
+    tshape = tiles.shape
+    tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), *tiles.shape[2:]))
+    filtered_tiles = tiles_all[tiles_mask]
+
+    if len(filtered_tiles) == 0:
+        return im
+
+    # Find all invalid tiles and replace with a random valid tile
+    replace_count = (tiles_mask == False).sum()
+    rng = np.random.default_rng(seed=seed)
+    tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[
+        rng.choice(filtered_tiles.shape[0], replace_count), :, :, :
+    ]
+
+    # Convert back to an image
+    tiles_all = tiles_all.reshape(tshape)
+    tiles_all = tiles_all.swapaxes(1, 2)
+    st = tiles_all.reshape(
+        (
+            math.prod(tiles_all.shape[0:2]),
+            math.prod(tiles_all.shape[2:4]),
+            tiles_all.shape[4],
+        )
+    )
+    si = Image.fromarray(st, mode="RGBA")
+
+    return si
+
+
+class InfillColorInvocation(BaseInvocation):
+    """Infills transparent areas of an image with a solid color"""
+
+    type: Literal["infill_rgba"] = "infill_rgba"
+    image: Optional[ImageField] = Field(default=None, description="The image to infill")
+    color: Optional[ColorField] = Field(
+        default=ColorField(r=127, g=127, b=127, a=255),
+        description="The color to use to infill",
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+
+        solid_bg = Image.new("RGBA", image.size, self.color.tuple())
+        infilled = Image.alpha_composite(solid_bg, image)
+
+        infilled.paste(image, (0, 0), image.split()[-1])
+
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
+
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, infilled, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=image,
+        )
+
+
+class InfillTileInvocation(BaseInvocation):
+    """Infills transparent areas of an image with tiles of the image"""
+
+    type: Literal["infill_tile"] = "infill_tile"
+
+    image: Optional[ImageField] = Field(default=None, description="The image to infill")
+    tile_size: int = Field(default=32, ge=1, description="The tile size (px)")
+    seed: int = Field(
+        ge=0,
+        le=SEED_MAX,
+        description="The seed to use for tile generation (omit for random)",
+        default_factory=get_random_seed,
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+
+        infilled = tile_fill_missing(
+            image.copy(), seed=self.seed, tile_size=self.tile_size
+        )
+        infilled.paste(image, (0, 0), image.split()[-1])
+
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
+
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, infilled, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=image,
+        )
+
+
+class InfillPatchMatchInvocation(BaseInvocation):
+    """Infills transparent areas of an image using the PatchMatch algorithm"""
+
+    type: Literal["infill_patchmatch"] = "infill_patchmatch"
+
+    image: Optional[ImageField] = Field(default=None, description="The image to infill")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+
+        if PatchMatch.patchmatch_available():
+            infilled = infill_patchmatch(image.copy())
+        else:
+            raise ValueError("PatchMatch is not available on this system")
+
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
+
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, infilled, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=image,
+        )

invokeai/app/invocations/latent.py

@@ -1,11 +1,13 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
 import random
-from typing import Literal, Optional
+from typing import Literal, Optional, Union
+import einops
 from pydantic import BaseModel, Field
 import torch
 
 from invokeai.app.invocations.util.choose_model import choose_model
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 
@@ -13,7 +15,9 @@ from ...backend.model_management.model_manager import ModelManager
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.image_util.seamless import configure_model_padding
-from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline
+from ...backend.prompting.conditioning import get_uc_and_c_and_ec
+from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline, image_resized_to_grid_as_tensor
+from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 import numpy as np
 from ..services.image_storage import ImageType
@@ -37,41 +41,55 @@ class LatentsField(BaseModel):
 class LatentsOutput(BaseInvocationOutput):
     """Base class for invocations that output latents"""
     #fmt: off
-    type: Literal["latent_output"] = "latent_output"
+    type: Literal["latents_output"] = "latents_output"
+
+    # Inputs
     latents: LatentsField          = Field(default=None, description="The output latents")
+    width:                     int = Field(description="The width of the latents in pixels")
+    height:                    int = Field(description="The height of the latents in pixels")
     #fmt: on
 
+
+def build_latents_output(latents_name: str, latents: torch.Tensor):
+      return LatentsOutput(
+          latents=LatentsField(latents_name=latents_name),
+          width=latents.size()[3] * 8,
+          height=latents.size()[2] * 8,
+      )
+
 class NoiseOutput(BaseInvocationOutput):
     """Invocation noise output"""
     #fmt: off
     type:  Literal["noise_output"] = "noise_output"
+
+    # Inputs
     noise: LatentsField            = Field(default=None, description="The output noise")
+    width:                     int = Field(description="The width of the noise in pixels")
+    height:                    int = Field(description="The height of the noise in pixels")
     #fmt: on
 
-
-# TODO: this seems like a hack
-scheduler_map = dict(
-    ddim=diffusers.DDIMScheduler,
-    dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-    k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-    k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-    k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-    k_euler=diffusers.EulerDiscreteScheduler,
-    k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-    k_heun=diffusers.HeunDiscreteScheduler,
-    k_lms=diffusers.LMSDiscreteScheduler,
-    plms=diffusers.PNDMScheduler,
-)
+def build_noise_output(latents_name: str, latents: torch.Tensor):
+      return NoiseOutput(
+          noise=LatentsField(latents_name=latents_name),
+          width=latents.size()[3] * 8,
+          height=latents.size()[2] * 8,
+      )
 
 
 SAMPLER_NAME_VALUES = Literal[
-    tuple(list(scheduler_map.keys()))
+    tuple(list(SCHEDULER_MAP.keys()))
 ]
 
 
 def get_scheduler(scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-    scheduler_class = scheduler_map.get(scheduler_name,'ddim')
-    scheduler = scheduler_class.from_config(model.scheduler.config)
+    scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
+    
+    scheduler_config = model.scheduler.config
+    if "_backup" in scheduler_config:
+        scheduler_config = scheduler_config["_backup"]
+    scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
+    scheduler = scheduler_class.from_config(scheduler_config)
+    
     # hack copied over from generate.py
     if not hasattr(scheduler, 'uses_inpainting_model'):
         scheduler.uses_inpainting_model = lambda: False
@@ -102,17 +120,13 @@ def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_c
     return x
 
 
-def random_seed():
-    return random.randint(0, np.iinfo(np.uint32).max)
-
-
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""
 
     type: Literal["noise"] = "noise"
 
     # Inputs
-    seed:        int = Field(ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", default_factory=random_seed)
+    seed:       int = Field(ge=0, le=SEED_MAX, description="The seed to use", default_factory=get_random_seed)
     width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting noise", )
     height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting noise", )
 
@@ -131,9 +145,7 @@ class NoiseInvocation(BaseInvocation):
 
         name = f'{context.graph_execution_state_id}__{self.id}'
         context.services.latents.set(name, noise)
-        return NoiseOutput(
-            noise=LatentsField(latents_name=name)
-        )
+        return build_noise_output(latents_name=name, latents=noise)
 
 
 # Text to image
@@ -149,11 +161,10 @@ class TextToLatentsInvocation(BaseInvocation):
     noise: Optional[LatentsField] = Field(description="The noise to use")
     steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
     cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
+    model:       str = Field(default="", description="The model to use (currently ignored)")
     seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
     seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
-    model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
     # fmt: on
 
     # Schema customisation
@@ -218,7 +229,7 @@ class TextToLatentsInvocation(BaseInvocation):
                 h_symmetry_time_pct=None,#h_symmetry_time_pct,
                 v_symmetry_time_pct=None#v_symmetry_time_pct,
             ),
-        ).add_scheduler_args_if_applicable(model.scheduler, eta=None)#ddim_eta)
+        ).add_scheduler_args_if_applicable(model.scheduler, eta=0.0)#ddim_eta)
         return conditioning_data
 
 
@@ -250,9 +261,7 @@ class TextToLatentsInvocation(BaseInvocation):
 
         name = f'{context.graph_execution_state_id}__{self.id}'
         context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
+        return build_latents_output(latents_name=name, latents=result_latents)
 
 
 class LatentsToLatentsInvocation(TextToLatentsInvocation):
@@ -260,6 +269,10 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
     type: Literal["l2l"] = "l2l"
 
+    # Inputs
+    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
+    strength: float = Field(default=0.5, description="The strength of the latents to use")
+
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
@@ -271,10 +284,6 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             },
         }
 
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
-    strength: float = Field(default=0.5, description="The strength of the latents to use")
-
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         noise = context.services.latents.get(self.noise.latents_name)
         latent = context.services.latents.get(self.latents.latents_name)
@@ -287,7 +296,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             self.dispatch_progress(context, source_node_id, state)
 
         model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(model)
+        conditioning_data = self.get_conditioning_data(context, model)
 
         # TODO: Verify the noise is the right size
 
@@ -295,11 +304,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             latent, device=model.device, dtype=latent.dtype
         )
 
-        timesteps, _ = model.get_img2img_timesteps(
-            self.steps,
-            self.strength,
-            device=model.device,
-        )
+        timesteps, _ = model.get_img2img_timesteps(self.steps, self.strength)
 
         result_latents, result_attention_map_saver = model.latents_from_embeddings(
             latents=initial_latents,
@@ -315,9 +320,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
         name = f'{context.graph_execution_state_id}__{self.id}'
         context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
+        return build_latents_output(latents_name=name, latents=result_latents)
 
 
 # Latent to image
@@ -384,8 +387,8 @@ class ResizeLatentsInvocation(BaseInvocation):
     latents:    Optional[LatentsField] = Field(description="The latents to resize")
     width:                         int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
     height:                        int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
-    mode: Optional[LATENTS_INTERPOLATION_MODE]  = Field(default="bilinear", description="The interpolation mode")
-    antialias: Optional[bool]                   = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+    mode:   LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
+    antialias:                    bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
@@ -402,7 +405,7 @@ class ResizeLatentsInvocation(BaseInvocation):
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         context.services.latents.set(name, resized_latents)
-        return LatentsOutput(latents=LatentsField(latents_name=name))
+        return build_latents_output(latents_name=name, latents=resized_latents)
 
 
 class ScaleLatentsInvocation(BaseInvocation):
@@ -413,8 +416,8 @@ class ScaleLatentsInvocation(BaseInvocation):
     # Inputs
     latents:   Optional[LatentsField] = Field(description="The latents to scale")
     scale_factor:               float = Field(gt=0, description="The factor by which to scale the latents")
-    mode: Optional[LATENTS_INTERPOLATION_MODE]  = Field(default="bilinear", description="The interpolation mode")
-    antialias: Optional[bool]                   = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+    mode:  LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
+    antialias:                   bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.services.latents.get(self.latents.latents_name)
@@ -432,4 +435,48 @@ class ScaleLatentsInvocation(BaseInvocation):
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         context.services.latents.set(name, resized_latents)
-        return LatentsOutput(latents=LatentsField(latents_name=name))
+        return build_latents_output(latents_name=name, latents=resized_latents)
+
+
+class ImageToLatentsInvocation(BaseInvocation):
+    """Encodes an image into latents."""
+
+    type: Literal["i2l"] = "i2l"
+
+    # Inputs
+    image: Union[ImageField, None] = Field(description="The image to encode")
+    model: str = Field(default="", description="The model to use")
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents", "image"],
+                "type_hints": {"model": "model"},
+            },
+        }
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+
+        # TODO: this only really needs the vae
+        model_info = choose_model(context.services.model_manager, self.model)
+        model: StableDiffusionGeneratorPipeline = model_info["model"]
+
+        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+
+        if image_tensor.dim() == 3:
+            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
+
+        latents = model.non_noised_latents_from_image(
+            image_tensor,
+            device=model._model_group.device_for(model.unet),
+            dtype=model.unet.dtype,
+        )
+
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.set(name, latents)
+        return build_latents_output(latents_name=name, latents=latents)

invokeai/app/invocations/math.py

@@ -3,8 +3,14 @@
 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):
@@ -21,19 +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)
@@ -41,11 +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)
@@ -53,11 +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)
@@ -65,11 +75,26 @@ 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))
+
+
+class RandomIntInvocation(BaseInvocation):
+    """Outputs a single random integer."""
+
+    # fmt: off
+    type: Literal["rand_int"] = "rand_int"
+    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(self.low, self.high))

invokeai/app/invocations/util/choose_model.py

@@ -4,10 +4,11 @@ from invokeai.backend.model_management.model_manager import ModelManager
 def choose_model(model_manager: ModelManager, model_name: str):
     """Returns the default model if the `model_name` not a valid model, else returns the selected model."""
     logger = model_manager.logger
-    if model_manager.valid_model(model_name):
-        model = model_manager.get_model(model_name)
-    else:
+    if model_name and not model_manager.valid_model(model_name):
+        default_model_name = model_manager.default_model()
+        logger.warning(f"\'{model_name}\' is not a valid model name. Using default model \'{default_model_name}\' instead.")
         model = model_manager.get_model()
-        logger.warning(f"{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead.")
+    else:
+        model = model_manager.get_model(model_name)
 
     return model

invokeai/app/models/image.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Optional
+from typing import Optional, Tuple
 from pydantic import BaseModel, Field
 
 
@@ -27,3 +27,13 @@ class ImageField(BaseModel):
 
     class Config:
         schema_extra = {"required": ["image_type", "image_name"]}
+
+
+class ColorField(BaseModel):
+    r: int = Field(ge=0, le=255, description="The red component")
+    g: int = Field(ge=0, le=255, description="The green component")
+    b: int = Field(ge=0, le=255, description="The blue component")
+    a: int = Field(ge=0, le=255, description="The alpha component")
+
+    def tuple(self) -> Tuple[int, int, int, int]:
+        return (self.r, self.g, self.b, self.a)

invokeai/app/services/config.py

@@ -0,0 +1,521 @@
+# 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 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/default_graphs.py

@@ -49,12 +49,13 @@ def create_text_to_image() -> LibraryGraph:
 def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[LibraryGraph]:
     """Creates the default system graphs, or adds new versions if the old ones don't match"""
     
+    # TODO: Uncomment this when we are ready to fix this up to prevent breaking changes
     graphs: list[LibraryGraph] = list()
 
-    text_to_image = graph_library.get(default_text_to_image_graph_id)
+    # text_to_image = graph_library.get(default_text_to_image_graph_id)
     
-    # TODO: Check if the graph is the same as the default one, and if not, update it
-    #if text_to_image is None:
+    # # TODO: Check if the graph is the same as the default one, and if not, update it
+    # #if text_to_image is None:
     text_to_image = create_text_to_image()
     graph_library.set(text_to_image)
 

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/image_storage.py

@@ -270,4 +270,5 @@ class DiskImageStorage(ImageStorageBase):
             )  # TODO: this should refresh position for LRU cache
             if len(self.__cache) > self.__max_cache_size:
                 cache_id = self.__cache_ids.get()
+                if cache_id in self.__cache:
                     del self.__cache[cache_id]

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/app/services/metadata.py

@@ -20,9 +20,18 @@ class MetadataLatentsField(TypedDict):
     latents_name: str
 
 
+class MetadataColorField(TypedDict):
+    """Pydantic-less ColorField, used for metadata parsing"""
+    r: int
+    g: int
+    b: int
+    a: int
+
+
+
 # TODO: This is a placeholder for `InvocationsUnion` pending resolution of circular imports
 NodeMetadata = Dict[
-    str, str | int | float | bool | MetadataImageField | MetadataLatentsField
+    str, None | str | int | float | bool | MetadataImageField | MetadataLatentsField | MetadataColorField
 ]
 
 

invokeai/app/services/model_manager_initializer.py

@@ -2,27 +2,25 @@ import os
 import sys
 import torch
 from argparse import Namespace
-from invokeai.backend import Args
 from omegaconf import OmegaConf
 from pathlib import Path
 from typing import types
 
 import invokeai.version
+from .config import InvokeAISettings
 from ...backend import ModelManager
 from ...backend.util import choose_precision, choose_torch_device
-from ...backend import Globals
 
 # TODO: Replace with an abstract class base ModelManagerBase
-def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager:
-    if not config.conf:
-        config_file = os.path.join(Globals.root, "configs", "models.yaml")
-        if not os.path.exists(config_file):
+def get_model_manager(config: InvokeAISettings, logger: types.ModuleType) -> ModelManager:
+    model_config = config.model_conf_path
+    if not model_config.exists():
         report_model_error(
-                config, FileNotFoundError(f"The file {config_file} could not be found."), logger
+            config, FileNotFoundError(f"The file {model_config} could not be found."), logger
         )
 
     logger.info(f"{invokeai.version.__app_name__}, version {invokeai.version.__version__}")
-    logger.info(f'InvokeAI runtime directory is "{Globals.root}"')
+    logger.info(f'InvokeAI runtime directory is "{config.root}"')
 
     # these two lines prevent a horrible warning message from appearing
     # when the frozen CLIP tokenizer is imported
@@ -32,20 +30,7 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager:
     import diffusers
 
     diffusers.logging.set_verbosity_error()
-
-    # normalize the config directory relative to root
-    if not os.path.isabs(config.conf):
-        config.conf = os.path.normpath(os.path.join(Globals.root, config.conf))
-
-    if config.embeddings:
-        if not os.path.isabs(config.embedding_path):
-            embedding_path = os.path.normpath(
-                os.path.join(Globals.root, config.embedding_path)
-            )
-        else:
     embedding_path = config.embedding_path
-    else:
-        embedding_path = None
 
     # migrate legacy models
     ModelManager.migrate_models()
@@ -58,11 +43,11 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager:
         else choose_precision(device)
         
         model_manager = ModelManager(
-            OmegaConf.load(config.conf),
+            OmegaConf.load(config.model_conf_path),
             precision=precision,
             device_type=device,
             max_loaded_models=config.max_loaded_models,
-            embedding_path = Path(embedding_path),
+            embedding_path = embedding_path,
             logger = logger,
         )
     except (FileNotFoundError, TypeError, AssertionError) as e:
@@ -73,12 +58,10 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager:
 
     # try to autoconvert new models
     # autoimport new .ckpt files
-    if path := config.autoconvert:
-        model_manager.autoconvert_weights(
-            conf_path=config.conf,
-            weights_directory=path,
+    if config.autoconvert_path:
+        model_manager.heuristic_import(
+            config.autoconvert_path,
         )
-    logger.info('Model manager initialized')
     return model_manager
 
 def report_model_error(opt: Namespace, e: Exception, logger: types.ModuleType):

invokeai/app/services/processor.py

@@ -1,3 +1,4 @@
+import time
 import traceback
 from threading import Event, Thread, BoundedSemaphore
 
@@ -6,6 +7,7 @@ from .invocation_queue import InvocationQueueItem
 from .invoker import InvocationProcessorABC, Invoker
 from ..models.exceptions import CanceledException
 
+import invokeai.backend.util.logging as logger
 class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker_thread: Thread
     __stop_event: Event
@@ -34,8 +36,14 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
         try:
             self.__threadLimit.acquire()
             while not stop_event.is_set():
+                try:
                     queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
+                except Exception as e:
+                    logger.debug("Exception while getting from queue: %s" % e)
+
                 if not queue_item:  # Probably stopping
+                    # do not hammer the queue
+                    time.sleep(0.5)
                     continue
 
                 graph_execution_state = (
@@ -124,7 +132,16 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                 # Queue any further commands if invoking all
                 is_complete = graph_execution_state.is_complete()
                 if queue_item.invoke_all and not is_complete:
+                    try:
                         self.__invoker.invoke(graph_execution_state, invoke_all=True)
+                    except Exception as e:
+                        logger.error("Error while invoking: %s" % e)
+                        self.__invoker.services.events.emit_invocation_error(
+                            graph_execution_state_id=graph_execution_state.id,
+                            node=invocation.dict(),
+                            source_node_id=source_node_id,
+                            error=traceback.format_exc()
+                        )
                 elif is_complete:
                     self.__invoker.services.events.emit_graph_execution_complete(
                         graph_execution_state.id

invokeai/app/util/misc.py

@@ -1,5 +1,13 @@
 import datetime
+import numpy as np
 
 
 def get_timestamp():
     return int(datetime.datetime.now(datetime.timezone.utc).timestamp())
+
+
+SEED_MAX = np.iinfo(np.int32).max
+
+
+def get_random_seed():
+    return np.random.randint(0, SEED_MAX)

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, SDModelComponent
 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 ...frontend.install.widgets import (
+from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
+from invokeai.frontend.install.widgets import (
     CenteredButtonPress,
     IntTitleSlider,
     set_min_terminal_size,
 )
-from ..args import PRECISION_CHOICES, Args
-from ..globals import Globals, global_cache_dir, global_config_dir, global_config_file
-from .model_install_backend import (
+from invokeai.backend.config.legacy_arg_parsing import legacy_parser
+from invokeai.backend.config.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"
-SD_Configs = Path(global_config_dir()) / "stable-diffusion"
+Default_config_file = config.model_conf_path
+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 --host 0.0.0.0 # (connect to http://your-lan-ip:9090 from another computer on the local network)
+   invokeai-web
 
-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",
-            'to allow InvokeAI to download styles & subjects from the "Concept Library".',
+            "If you have an account at HuggingFace you may optionally paste your access token here",
+            'to allow InvokeAI to download restricted styles & subjects from the "Concept Library".',
             "See https://huggingface.co/settings/tokens",
         ]:
             self.add_widget_intelligent(
@@ -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,
-            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,
+            value=old_opts.xformers_enabled,
             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",
-            "ckpt_convert",
+                "embedding_dir",
+                "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
-    # any other initialization values the user has entered. There is
-    # no good way to do this because of the one-way nature of
-    # argparse: i.e. --outdir could be --outdir, --out, or -o
-    # initfile needs to be replaced with a fully structured format
-    # 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)
+    # this will load current settings
+    config = InvokeAIAppConfig()
+    for key,value in opts.__dict__.items():
+        if hasattr(config,key):
+            setattr(config,key,value)
 
+    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)
-        if not init_file.exists() or not global_config_file().exists():
-            initialize_rootdir(Globals.root, opt.yes_to_all)
+        old_init_file = Path(config.root, 'invokeai.init')
+        new_init_file = Path(config.root, 'invokeai.yaml')
+        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)
-        replacement = Path(Globals.root, f"{Globals.initfile}.new")
+        print('** The global initfile is no longer supported; rewrite to support new yaml format **')
+        initfile = Path(config.root, 'invokeai.init')
+        replacement = Path(config.root, f"invokeai.init.new")
         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/generator/base.py

@@ -31,6 +31,7 @@ from ..util.util import rand_perlin_2d
 from ..safety_checker import SafetyChecker
 from ..prompting.conditioning import get_uc_and_c_and_ec
 from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
+from ..stable_diffusion.schedulers import SCHEDULER_MAP
 
 downsampling = 8
 
@@ -71,19 +72,6 @@ class InvokeAIGeneratorOutput:
 # we are interposing a wrapper around the original Generator classes so that
 # old code that calls Generate will continue to work.
 class InvokeAIGenerator(metaclass=ABCMeta):
-    scheduler_map = dict(
-        ddim=diffusers.DDIMScheduler,
-        dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-        k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-        k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-        k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-        k_euler=diffusers.EulerDiscreteScheduler,
-        k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-        k_heun=diffusers.HeunDiscreteScheduler,
-        k_lms=diffusers.LMSDiscreteScheduler,
-        plms=diffusers.PNDMScheduler,
-    )
-
     def __init__(self,
                  model_info: dict,
                  params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
@@ -175,14 +163,20 @@ class InvokeAIGenerator(metaclass=ABCMeta):
         '''
         Return list of all the schedulers that we currently handle.
         '''
-        return list(self.scheduler_map.keys())
+        return list(SCHEDULER_MAP.keys())
 
     def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
         return generator_class(model, self.params.precision)
 
     def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-        scheduler_class = self.scheduler_map.get(scheduler_name,'ddim')
-        scheduler = scheduler_class.from_config(model.scheduler.config)
+        scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
+        
+        scheduler_config = model.scheduler.config
+        if "_backup" in scheduler_config:
+            scheduler_config = scheduler_config["_backup"]
+        scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
+        scheduler = scheduler_class.from_config(scheduler_config)
+        
         # hack copied over from generate.py
         if not hasattr(scheduler, 'uses_inpainting_model'):
             scheduler.uses_inpainting_model = lambda: False
@@ -226,10 +220,10 @@ class Inpaint(Img2Img):
     def generate(self,
                  mask_image: Image.Image | torch.FloatTensor,
                  # Seam settings - when 0, doesn't fill seam
-                 seam_size: int = 0,
-                 seam_blur: int = 0,
+                 seam_size: int = 96,
+                 seam_blur: int = 16,
                  seam_strength: float = 0.7,
-                 seam_steps: int = 10,
+                 seam_steps: int = 30,
                  tile_size: int = 32,
                  inpaint_replace=False,
                  infill_method=None,

invokeai/backend/generator/inpaint.py

@@ -4,6 +4,7 @@ invokeai.backend.generator.inpaint descends from .generator
 from __future__ import annotations
 
 import math
+from typing import Tuple, Union
 
 import cv2
 import numpy as np
@@ -59,7 +60,7 @@ class Inpaint(Img2Img):
             writeable=False,
         )
 
-    def infill_patchmatch(self, im: Image.Image) -> Image:
+    def infill_patchmatch(self, im: Image.Image) -> Image.Image:
         if im.mode != "RGBA":
             return im
 
@@ -75,18 +76,18 @@ class Inpaint(Img2Img):
         return im_patched
 
     def tile_fill_missing(
-        self, im: Image.Image, tile_size: int = 16, seed: int = None
-    ) -> Image:
+        self, im: Image.Image, tile_size: int = 16, seed: Union[int, None] = None
+    ) -> Image.Image:
         # Only fill if there's an alpha layer
         if im.mode != "RGBA":
             return im
 
         a = np.asarray(im, dtype=np.uint8)
 
-        tile_size = (tile_size, tile_size)
+        tile_size_tuple = (tile_size, tile_size)
 
         # Get the image as tiles of a specified size
-        tiles = self.get_tile_images(a, *tile_size).copy()
+        tiles = self.get_tile_images(a, *tile_size_tuple).copy()
 
         # Get the mask as tiles
         tiles_mask = tiles[:, :, :, :, 3]
@@ -127,7 +128,9 @@ class Inpaint(Img2Img):
 
         return si
 
-    def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
+    def mask_edge(
+        self, mask: Image.Image, edge_size: int, edge_blur: int
+    ) -> Image.Image:
         npimg = np.asarray(mask, dtype=np.uint8)
 
         # Detect any partially transparent regions
@@ -206,15 +209,15 @@ class Inpaint(Img2Img):
         cfg_scale,
         ddim_eta,
         conditioning,
-        init_image: PIL.Image.Image | torch.FloatTensor,
-        mask_image: PIL.Image.Image | torch.FloatTensor,
+        init_image: Image.Image | torch.FloatTensor,
+        mask_image: Image.Image | torch.FloatTensor,
         strength: float,
         mask_blur_radius: int = 8,
         # Seam settings - when 0, doesn't fill seam
-        seam_size: int = 0,
-        seam_blur: int = 0,
+        seam_size: int = 96,
+        seam_blur: int = 16,
         seam_strength: float = 0.7,
-        seam_steps: int = 10,
+        seam_steps: int = 30,
         tile_size: int = 32,
         step_callback=None,
         inpaint_replace=False,
@@ -222,7 +225,7 @@ class Inpaint(Img2Img):
         infill_method=None,
         inpaint_width=None,
         inpaint_height=None,
-        inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
+        inpaint_fill: Tuple[int, int, int, int] = (0x7F, 0x7F, 0x7F, 0xFF),
         attention_maps_callback=None,
         **kwargs,
     ):
@@ -239,7 +242,7 @@ class Inpaint(Img2Img):
         self.inpaint_width = inpaint_width
         self.inpaint_height = inpaint_height
 
-        if isinstance(init_image, PIL.Image.Image):
+        if isinstance(init_image, Image.Image):
             self.pil_image = init_image.copy()
 
             # Do infill
@@ -250,8 +253,8 @@ class Inpaint(Img2Img):
                     self.pil_image.copy(), seed=self.seed, tile_size=tile_size
                 )
             elif infill_method == "solid":
-                solid_bg = PIL.Image.new("RGBA", init_image.size, inpaint_fill)
-                init_filled = PIL.Image.alpha_composite(solid_bg, init_image)
+                solid_bg = Image.new("RGBA", init_image.size, inpaint_fill)
+                init_filled = Image.alpha_composite(solid_bg, init_image)
             else:
                 raise ValueError(
                     f"Non-supported infill type {infill_method}", infill_method
@@ -269,7 +272,7 @@ class Inpaint(Img2Img):
             # Create init tensor
             init_image = image_resized_to_grid_as_tensor(init_filled.convert("RGB"))
 
-        if isinstance(mask_image, PIL.Image.Image):
+        if isinstance(mask_image, Image.Image):
             self.pil_mask = mask_image.copy()
             debug_image(
                 mask_image,

invokeai/backend/globals.py

@@ -1,122 +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_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
 
@@ -47,6 +47,7 @@ from diffusers import (
     LDMTextToImagePipeline,
     LMSDiscreteScheduler,
     PNDMScheduler,
+    UniPCMultistepScheduler,
     StableDiffusionPipeline,
     UNet2DConditionModel,
 )
@@ -73,7 +74,6 @@ 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.
@@ -842,7 +842,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
 
 def convert_ldm_clip_checkpoint(checkpoint):
     text_model = CLIPTextModel.from_pretrained(
-        "openai/clip-vit-large-patch14", cache_dir=global_cache_dir("hub")
+        "openai/clip-vit-large-patch14", cache_dir=get_invokeai_config().cache_dir
     )
 
     keys = list(checkpoint.keys())
@@ -897,7 +897,7 @@ 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
     )
@@ -969,7 +969,7 @@ 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
     )
@@ -1092,7 +1092,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     :param vae: A diffusers VAE to load into the pipeline.
     :param vae_path: Path to a checkpoint VAE that will be converted into diffusers and loaded into the pipeline.
     """
-
+    config = get_invokeai_config()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
         verbosity = dlogging.get_verbosity()
@@ -1105,7 +1105,7 @@ 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
@@ -1129,25 +1129,23 @@ 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()
-                    / "stable-diffusion"
-                    / "v1-inpainting-inference.yaml"
+                    config.legacy_conf_path / "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:
@@ -1209,6 +1207,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
             scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
         elif scheduler_type == "dpm":
             scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+        elif scheduler_type == 'unipc':
+            scheduler = UniPCMultistepScheduler.from_config(scheduler.config)
         elif scheduler_type == "ddim":
             scheduler = scheduler
         else:
@@ -1297,7 +1297,7 @@ 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_manager.py

@@ -36,8 +36,6 @@ from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
 from picklescan.scanner import scan_file_path
 
-from invokeai.backend.globals import Globals, global_cache_dir
-
 from transformers import (
     CLIPTextModel,
     CLIPTokenizer,
@@ -49,9 +47,9 @@ from diffusers.pipelines.stable_diffusion.safety_checker import (
 from ..stable_diffusion import (
     StableDiffusionGeneratorPipeline,
 )
+from invokeai.app.services.config import get_invokeai_config
 from ..util import CUDA_DEVICE, ask_user, download_with_resume
 
-
 class SDLegacyType(Enum):
     V1 = auto()
     V1_INPAINT = auto()
@@ -100,6 +98,7 @@ class ModelManager(object):
         if not isinstance(config, DictConfig):
             config = OmegaConf.load(config)
         self.config = config
+        self.globals = get_invokeai_config()
         self.precision = precision
         self.device = torch.device(device_type)
         self.max_loaded_models = max_loaded_models
@@ -292,7 +291,7 @@ 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)
         if "weights" in info and info["weights"].endswith((".ckpt", ".safetensors")):
@@ -502,13 +501,13 @@ class ModelManager(object):
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
-            safety_checker=None, local_files_only=not Globals.internet_available
+            safety_checker=None, local_files_only=not self.globals.internet_available
         )
         if "vae" in mconfig and mconfig["vae"] is not None:
             if vae := self._load_vae(mconfig["vae"]):
                 pipeline_args.update(vae=vae)
         if not isinstance(name_or_path, Path):
-            pipeline_args.update(cache_dir=global_cache_dir("hub"))
+            pipeline_args.update(cache_dir=self.globals.cache_dir)
         if using_fp16:
             pipeline_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
@@ -560,10 +559,9 @@ class ModelManager(object):
         width = mconfig.width
         height = mconfig.height
 
-        if not os.path.isabs(config):
-            config = os.path.join(Globals.root, config)
-        if not os.path.isabs(weights):
-            weights = os.path.normpath(os.path.join(Globals.root, weights))
+        root_dir = self.globals.root_dir
+        config = str(root_dir / config)
+        weights = str(root_dir / weights)
 
         # Convert to diffusers and return a diffusers pipeline
         self.logger.info(f"Converting legacy checkpoint {model_name} into a diffusers model...")
@@ -578,11 +576,7 @@ class ModelManager(object):
 
         vae_path = None
         if vae:
-            vae_path = (
-                vae
-                if os.path.isabs(vae)
-                else os.path.normpath(os.path.join(Globals.root, vae))
-            )
+            vae_path = str(root_dir / vae)
         if self._has_cuda():
             torch.cuda.empty_cache()
         pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
@@ -614,9 +608,7 @@ class ModelManager(object):
             )
 
         if "path" in mconfig and mconfig["path"] is not None:
-            path = Path(mconfig["path"])
-            if not path.is_absolute():
-                path = Path(Globals.root, path).resolve()
+            path = self.globals.root_dir / Path(mconfig["path"])
             return path
         elif "repo_id" in mconfig:
             return mconfig["repo_id"]
@@ -864,25 +856,16 @@ class ModelManager(object):
                 model_type = self.probe_model_type(checkpoint)
                 if model_type == SDLegacyType.V1:
                     self.logger.debug("SD-v1 model detected")
-                    model_config_file = Path(
-                        Globals.root, "configs/stable-diffusion/v1-inference.yaml"
-                    )
+                    model_config_file = self.globals.legacy_conf_path / "v1-inference.yaml"
                 elif model_type == SDLegacyType.V1_INPAINT:
                     self.logger.debug("SD-v1 inpainting model detected")
-                    model_config_file = Path(
-                        Globals.root,
-                        "configs/stable-diffusion/v1-inpainting-inference.yaml",
-                    )
+                    model_config_file = self.globals.legacy_conf_path / "v1-inpainting-inference.yaml",
                 elif model_type == SDLegacyType.V2_v:
                     self.logger.debug("SD-v2-v model detected")
-                    model_config_file = Path(
-                        Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
-                    )
+                    model_config_file = self.globals.legacy_conf_path / "v2-inference-v.yaml"
                 elif model_type == SDLegacyType.V2_e:
                     self.logger.debug("SD-v2-e model detected")
-                    model_config_file = Path(
-                        Globals.root, "configs/stable-diffusion/v2-inference.yaml"
-                    )
+                    model_config_file = self.globals.legacy_conf_path / "v2-inference.yaml"
                 elif model_type == SDLegacyType.V2:
                     self.logger.warning(
                         f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
@@ -909,9 +892,7 @@ class ModelManager(object):
                 self.logger.debug(f"Using VAE file {vae_path.name}")
         vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
 
-        diffuser_path = Path(
-            Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
-        )
+        diffuser_path = self.globals.root_dir / "models/converted_ckpts" / model_path.stem
         model_name = self.convert_and_import(
             model_path,
             diffusers_path=diffuser_path,
@@ -1044,9 +1025,7 @@ class ModelManager(object):
         """
         yaml_str = OmegaConf.to_yaml(self.config)
         if not os.path.isabs(config_file_path):
-            config_file_path = os.path.normpath(
-                os.path.join(Globals.root, config_file_path)
-            )
+            config_file_path = self.globals.model_conf_path
         tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp")
         with open(tmpfile, "w", encoding="utf-8") as outfile:
             outfile.write(self.preamble())
@@ -1078,7 +1057,8 @@ class ModelManager(object):
         """
         # Three transformer models to check: bert, clip and safety checker, and
         # the diffusers as well
-        models_dir = Path(Globals.root, "models")
+        config = get_invokeai_config()
+        models_dir = config.root_dir / "models"
         legacy_locations = [
             Path(
                 models_dir,
@@ -1090,8 +1070,8 @@ class ModelManager(object):
                 "openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14",
             ),
         ]
-        legacy_locations.extend(list(global_cache_dir("diffusers").glob("*")))
-
+        legacy_cache_dir = config.cache_dir / "../diffusers"
+        legacy_locations.extend(list(legacy_cache_dir.glob("*")))
         legacy_layout = False
         for model in legacy_locations:
             legacy_layout = legacy_layout or model.exists()
@@ -1113,7 +1093,7 @@ class ModelManager(object):
 
         # transformer files get moved into the hub directory
         if cls._is_huggingface_hub_directory_present():
-            hub = global_cache_dir("hub")
+            hub = config.cache_dir
         else:
             hub = models_dir / "hub"
 
@@ -1152,13 +1132,12 @@ class ModelManager(object):
         if str(source).startswith(("http:", "https:", "ftp:")):
             dest_directory = Path(dest_directory)
             if not dest_directory.is_absolute():
-                dest_directory = Globals.root / dest_directory
+                dest_directory = self.globals.root_dir / dest_directory
             dest_directory.mkdir(parents=True, exist_ok=True)
             resolved_path = download_with_resume(str(source), dest_directory)
         else:
-            if not os.path.isabs(source):
-                source = os.path.join(Globals.root, source)
-            resolved_path = Path(source)
+            source = self.globals.root_dir / source
+            resolved_path = source
         return resolved_path
 
     def _invalidate_cached_model(self, model_name: str) -> None:
@@ -1208,7 +1187,7 @@ class ModelManager(object):
             path = name_or_path
         else:
             owner, repo = name_or_path.split("/")
-            path = Path(global_cache_dir("hub") / f"models--{owner}--{repo}")
+            path = self.globals.cache_dir / f"models--{owner}--{repo}"
         if not path.exists():
             return None
         hashpath = path / "checksum.sha256"
@@ -1228,7 +1207,7 @@ class ModelManager(object):
                         sha.update(chunk)
         hash = sha.hexdigest()
         toc = time.time()
-        self.logger.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
+        self.logger.debug(f"sha256 = {hash} ({count} files hashed in {toc - tic:4.2f}s)")
         with open(hashpath, "w") as f:
             f.write(hash)
         return hash
@@ -1269,8 +1248,8 @@ class ModelManager(object):
         using_fp16 = self.precision == "float16"
 
         vae_args.update(
-            cache_dir=global_cache_dir("hub"),
-            local_files_only=not Globals.internet_available,
+            cache_dir=self.globals.cache_dir,
+            local_files_only=not self.globals.internet_available,
         )
 
         self.logger.debug(f"Loading diffusers VAE from {name_or_path}")
@@ -1308,7 +1287,7 @@ 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!
@@ -1325,9 +1304,10 @@ class ModelManager(object):
 
     @staticmethod
     def _abs_path(path: str | Path) -> Path:
+        globals = get_invokeai_config()
         if path is None or Path(path).is_absolute():
             return path
-        return Path(Globals.root, path).resolve()
+        return Path(globals.root_dir, path).resolve()
 
     @staticmethod
     def _is_huggingface_hub_directory_present() -> bool:

invokeai/backend/prompting/conditioning.py

@@ -16,67 +16,59 @@ 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, model, log_tokens=False, skip_normalize_legacy_blend=False
-):
+def get_uc_and_c_and_ec(prompt_string,
+                        model: InvokeAIDiffuserComponent,
+                        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(
-        prompt_string
-    )
+    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(prompt_string)
 
-    tokenizer = model.tokenizer
-    compel = Compel(
-        tokenizer=tokenizer,
+    compel = Compel(tokenizer=model.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)
-    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
-    )
+    positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
 
-    if log_tokens or getattr(Globals, "log_tokenization", False):
-        log_tokenization(positive_prompt, negative_prompt, tokenizer=tokenizer)
+    legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
+    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("cross_attention_control", None),
-    )
+    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(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):
@@ -87,18 +79,17 @@ 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)
-    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
-        negative_prompt_string
-    )
+        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]
 
     return positive_prompt, negative_prompt
 
-
 def get_max_token_count(
     tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
 ) -> int:
@@ -245,22 +236,21 @@ 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[Blend]:
+def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Conjunction]:
     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]
-
-    return Blend(
-        prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize
-    )
-
+    flattened_prompts = []
+    weights = []
+    for i, x in enumerate(parsed_conjunctions):
+        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.codeformer_model_exists = os.path.isfile(self.model_path)
+        self.globals = get_invokeai_config()
+        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(
-                    Globals.root, "models", "gfpgan", "weights"
-                ),
+                model_rootpath = self.globals.root_dir / "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(
-                os.path.join(Globals.root, gfpgan_model_path)
-            )
+            gfpgan_model_path = self.globals.root_dir / 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(
-            Globals.root, "models/realesrgan/realesr-general-x4v3.pth"
-        )
-        wdn_model_path = os.path.join(
-            Globals.root, "models/realesrgan/realesr-general-wdn-x4v3.pth"
-        )
+        model_path = config.root_dir / "models/realesrgan/realesr-general-x4v3.pth"
+        wdn_model_path = config.root_dir / "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:
@@ -509,10 +508,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         run_id=None,
         callback: Callable[[PipelineIntermediateState], None] = None,
     ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
+        if self.scheduler.config.get("cpu_only", False):
+            scheduler_device = torch.device('cpu')
+        else:
+            scheduler_device = self._model_group.device_for(self.unet)
+
         if timesteps is None:
-            self.scheduler.set_timesteps(
-                num_inference_steps, device=self._model_group.device_for(self.unet)
-            )
+            self.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
             timesteps = self.scheduler.timesteps
         infer_latents_from_embeddings = GeneratorToCallbackinator(
             self.generate_latents_from_embeddings, PipelineIntermediateState
@@ -545,6 +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),
         ):
@@ -726,11 +729,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         run_id=None,
         callback=None,
     ) -> InvokeAIStableDiffusionPipelineOutput:        
-        timesteps, _ = self.get_img2img_timesteps(
-            num_inference_steps,
-            strength,
-            device=self._model_group.device_for(self.unet),
-        )
+        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
         result_latents, result_attention_maps = self.latents_from_embeddings(
             latents=initial_latents if strength < 1.0 else torch.zeros_like(
                 initial_latents, device=initial_latents.device, dtype=initial_latents.dtype
@@ -756,13 +755,19 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
     def get_img2img_timesteps(
-        self, num_inference_steps: int, strength: float, device
+        self, num_inference_steps: int, strength: float, device=None
     ) -> (torch.Tensor, int):
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         assert img2img_pipeline.scheduler is self.scheduler
-        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
+
+        if self.scheduler.config.get("cpu_only", False):
+            scheduler_device = torch.device('cpu')
+        else:
+            scheduler_device = self._model_group.device_for(self.unet)
+
+        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
         timesteps, adjusted_steps = img2img_pipeline.get_timesteps(
-            num_inference_steps, strength, device=device
+            num_inference_steps, strength, device=scheduler_device
         )
         # Workaround for low strength resulting in zero timesteps.
         # TODO: submit upstream fix for zero-step img2img
@@ -796,9 +801,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if init_image.dim() == 3:
             init_image = init_image.unsqueeze(0)
 
-        timesteps, _ = self.get_img2img_timesteps(
-            num_inference_steps, strength, device=device
-        )
+        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
 
         # 6. Prepare latent variables
         # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents

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 override_cross_attention(model, context: Context, is_running_diffusers=False):
+def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: Context):
     """
     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(
-                (
-                    p.slice_size
-                    for p in old_attn_processors.values()
-                    if type(p) is SlicedAttnProcessor
-                ),
-                default_slice_size,
-            )
+        slice_size = next((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 = (
-            extra_conditioning_info is not None
-            and extra_conditioning_info.wants_cross_attention_control
+        old_attn_processors = None
+        if extra_conditioning_info and (
+            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
-        if do_swap:
-            old_attn_processor = self.override_cross_attention(
-                extra_conditioning_info, step_count=step_count
+                setup_cross_attention_control_attention_processors(
+                    unet,
+                    cross_attention_control_context,
                 )
+
         try:
             yield None
         finally:
-            if old_attn_processor is not None:
-                self.restore_default_cross_attention(old_attn_processor)
+            if old_attn_processors is not None:
+                unet.set_attn_processor(old_attn_processors)
             # TODO resuscitate attention map saving
             # self.remove_attention_map_saving()
 

invokeai/backend/stable_diffusion/schedulers/__init__.py

@@ -0,0 +1 @@
+from .schedulers import SCHEDULER_MAP

invokeai/backend/stable_diffusion/schedulers/schedulers.py

@@ -0,0 +1,23 @@
+from diffusers import DDIMScheduler, DPMSolverMultistepScheduler, KDPM2DiscreteScheduler, \
+    KDPM2AncestralDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, \
+    HeunDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, UniPCMultistepScheduler, \
+    DPMSolverSinglestepScheduler, DEISMultistepScheduler, DDPMScheduler
+
+SCHEDULER_MAP = dict(
+    ddim=(DDIMScheduler, dict()),
+    ddpm=(DDPMScheduler, dict()),
+    deis=(DEISMultistepScheduler, dict()),
+    lms=(LMSDiscreteScheduler, dict()),
+    pndm=(PNDMScheduler, 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()),
+    kdpm_2=(KDPM2DiscreteScheduler, dict()),
+    kdpm_2_a=(KDPM2AncestralDiscreteScheduler, dict()),
+    dpmpp_2s=(DPMSolverSinglestepScheduler, dict()),
+    dpmpp_2m=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=False)),
+    dpmpp_2m_k=(DPMSolverMultistepScheduler, dict(use_karras_sigmas=True)),
+    unipc=(UniPCMultistepScheduler, dict(cpu_only=True))
+)

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 ..globals import Globals, global_cache_dir
+from invokeai.app.services.config import InvokeAIAppConfig,PagingArgumentParser
 
 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.backend.globals import Globals
+from invokeai.app.services.config import get_invokeai_config
 
 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
-the conventions established in InvokeAI 1.X through 2.X.
+Logging class for InvokeAI that produces console messages
 
-
-One way to use it:
+Usage:
 
 from invokeai.backend.util.logging import InvokeAILogger
 
-logger = InvokeAILogger.getLogger(__name__)
-logger.critical('this is critical')
-logger.error('this is an error')
-logger.warning('this is a warning')
-logger.info('this is info')
-logger.debug('this is debugging')
+logger = InvokeAILogger.getLogger(name='InvokeAI') // Initialization
+(or)
+logger = InvokeAILogger.getLogger(__name__) // To use the filename
+
+logger.critical('this is critical') // Critical Message
+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
-     *** this is an error ***
-     ** this is a warning
-     >> this is info
-        | this is debugging
+    [12-05-2023 20]::[InvokeAI]::CRITICAL --> This is an info message [In Bold Red]
+    [12-05-2023 20]::[InvokeAI]::ERROR --> This is an info message [In Red]
+    [12-05-2023 20]::[InvokeAI]::WARNING --> This is an info message [In Yellow]
+    [12-05-2023 20]::[InvokeAI]::INFO --> This is an info message [In Grey]
+    [12-05-2023 20]::[InvokeAI]::DEBUG --> This is an info message [In Grey]
 
-Another way:
-import invokeai.backend.util.logging as ialog
-ialogger.debug('this is a debugging message')
+Alternate Method (in this case the logger name will be set to InvokeAI):
+import invokeai.backend.util.logging as IAILogger
+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:
-    https://stackoverflow.com/questions/14844970/modifying-logging-message-format-based-on-message-logging-level-in-python3
+    Custom Formatting for the InvokeAI Logger
     '''
-    crit_fmt = "### %(msg)s"
-    err_fmt = "*** %(msg)s"
-    warn_fmt = "** %(msg)s"
-    info_fmt = ">> %(msg)s"
-    dbg_fmt = "   | %(msg)s"
 
-    def __init__(self):
-        super().__init__(fmt="%(levelno)d: %(msg)s", datefmt=None, style='%')
+    # Color Codes
+    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
-        # was installed (in the event that this formatter is
-        # used with the vanilla logging module.
-        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
+        log_fmt = self.FORMATS.get(record.levelno)
+        formatter = logging.Formatter(log_fmt, datefmt="%d-%m-%Y %H:%M:%S")
+        return formatter.format(record)
 
-        # 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

@@ -4,17 +4,21 @@ from .parse_seed_weights import parse_seed_weights
 
 SAMPLER_CHOICES = [
     "ddim",
-    "k_dpm_2_a",
-    "k_dpm_2",
-    "k_dpmpp_2_a",
-    "k_dpmpp_2",
-    "k_euler_a",
-    "k_euler",
-    "k_heun",
-    "k_lms",
-    "plms",
-    # diffusers:
+    "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",
 ]
 
 

invokeai/frontend/CLI/readline.py

@@ -1,497 +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:
-            format = self.models[s]["format"]
-            if format == "vae":
-                continue
-            if ckpt_only and format != "ckpt":
-                continue
-            if s.startswith(partial):
-                matches.append(switch + s)
-        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/.babelrc

@@ -1,13 +0,0 @@
-{
-  "plugins": [
-    [
-      "transform-imports",
-      {
-        "lodash": {
-          "transform": "lodash/${member}",
-          "preventFullImport": true
-        }
-      }
-    ]
-  ]
-}

invokeai/frontend/web/.gitignore

@@ -35,3 +35,7 @@ stats.html
 !.yarn/releases
 !.yarn/sdks
 !.yarn/versions
+
+# Yalc
+.yalc
+yalc.lock

invokeai/frontend/web/config/vite.package.config.ts

@@ -5,6 +5,7 @@ import { PluginOption, UserConfig } from 'vite';
 import dts from 'vite-plugin-dts';
 import eslint from 'vite-plugin-eslint';
 import tsconfigPaths from 'vite-tsconfig-paths';
+import cssInjectedByJsPlugin from 'vite-plugin-css-injected-by-js';
 
 export const packageConfig: UserConfig = {
   base: './',
@@ -16,9 +17,10 @@ export const packageConfig: UserConfig = {
     dts({
       insertTypesEntry: true,
     }),
+    cssInjectedByJsPlugin(),
   ],
   build: {
-    chunkSizeWarningLimit: 1500,
+    cssCodeSplit: true,
     lib: {
       entry: path.resolve(__dirname, '../src/index.ts'),
       name: 'InvokeAIUI',
@@ -30,6 +32,7 @@ export const packageConfig: UserConfig = {
         globals: {
           react: 'React',
           'react-dom': 'ReactDOM',
+          '@emotion/react': 'EmotionReact',
         },
       },
     },

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/docs/README.md

@@ -37,7 +37,7 @@ From `invokeai/frontend/web/` run `yarn install` to get everything set up.
 Start everything in dev mode:
 
 1. Start the dev server: `yarn dev`
-2. Start the InvokeAI UI per usual: `invokeai --web`
+2. Start the InvokeAI Nodes backend: `python scripts/invokeai-new.py --web # run from the repo root`
 3. Point your browser to the dev server address e.g. <http://localhost:5173/>
 
 ### Production builds

invokeai/frontend/web/package.json

@@ -21,7 +21,6 @@
   "scripts": {
     "prepare": "cd ../../../ && husky install invokeai/frontend/web/.husky",
     "dev": "concurrently \"vite dev\" \"yarn run theme:watch\"",
-    "dev:nodes": "concurrently \"vite dev --mode nodes\" \"yarn run theme:watch\"",
     "dev:host": "concurrently \"vite dev --host\" \"yarn run theme:watch\"",
     "build": "yarn run lint && vite build",
     "api:web": "openapi -i http://localhost:9090/openapi.json -o src/services/api --client axios --useOptions --useUnionTypes --exportSchemas true --indent 2 --request src/services/fixtures/request.ts",
@@ -63,11 +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",
@@ -88,17 +89,14 @@
     "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-rnd": "^10.4.1",
-    "react-transition-group": "^4.4.5",
+    "react-resizable-panels": "^0.0.42",
     "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",
     "socket.io-client": "^4.6.0",
@@ -118,6 +116,7 @@
     "@types/node": "^18.16.2",
     "@types/react": "^18.2.0",
     "@types/react-dom": "^18.2.1",
+    "@types/react-redux": "^7.1.25",
     "@types/react-transition-group": "^4.4.5",
     "@types/uuid": "^9.0.0",
     "@typescript-eslint/eslint-plugin": "^5.59.1",
@@ -143,6 +142,7 @@
     "terser": "^5.17.1",
     "ts-toolbelt": "^9.6.0",
     "vite": "^4.3.3",
+    "vite-plugin-css-injected-by-js": "^3.1.1",
     "vite-plugin-dts": "^2.3.0",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^4.2.0",

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

@@ -25,7 +25,7 @@
     "common": {
         "hotkeysLabel": "Hotkeys",
         "themeLabel": "Theme",
-        "languagePickerLabel": "Language Picker",
+        "languagePickerLabel": "Language",
         "reportBugLabel": "Report Bug",
         "githubLabel": "Github",
         "discordLabel": "Discord",
@@ -54,7 +54,7 @@
         "img2img": "Image To Image",
         "unifiedCanvas": "Unified Canvas",
         "linear": "Linear",
-        "nodes": "Nodes",
+        "nodes": "Node Editor",
         "postprocessing": "Post Processing",
         "nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
         "postProcessing": "Post Processing",
@@ -102,7 +102,8 @@
         "generate": "Generate",
         "openInNewTab": "Open in New Tab",
         "dontAskMeAgain": "Don't ask me again",
-        "areYouSure": "Are you sure?"
+        "areYouSure": "Are you sure?",
+        "imagePrompt": "Image Prompt"
     },
     "gallery": {
         "generations": "Generations",
@@ -449,13 +450,14 @@
         "cfgScale": "CFG Scale",
         "width": "Width",
         "height": "Height",
-        "sampler": "Sampler",
+        "scheduler": "Scheduler",
         "seed": "Seed",
         "imageToImage": "Image to Image",
         "randomizeSeed": "Randomize Seed",
-        "shuffle": "Shuffle",
+        "shuffle": "Shuffle Seed",
         "noiseThreshold": "Noise Threshold",
         "perlinNoise": "Perlin Noise",
+        "noiseSettings": "Noise",
         "variations": "Variations",
         "variationAmount": "Variation Amount",
         "seedWeights": "Seed Weights",
@@ -470,6 +472,8 @@
         "scale": "Scale",
         "otherOptions": "Other Options",
         "seamlessTiling": "Seamless Tiling",
+        "seamlessXAxis": "X Axis",
+        "seamlessYAxis": "Y Axis",
         "hiresOptim": "High Res Optimization",
         "hiresStrength": "High Res Strength",
         "imageFit": "Fit Initial Image To Output Size",
@@ -527,7 +531,8 @@
         "useCanvasBeta": "Use Canvas Beta Layout",
         "enableImageDebugging": "Enable Image Debugging",
         "useSlidersForAll": "Use Sliders For All Options",
-        "autoShowProgress": "Auto Show Progress Images",
+        "showProgressInViewer": "Show Progress Images in Viewer",
+        "antialiasProgressImages": "Antialias Progress Images",
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",
@@ -535,7 +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",
@@ -544,13 +552,14 @@
         "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",
+        "problemCopyingImageLink": "Unable to Copy Image Link",
         "imageNotLoaded": "No Image Loaded",
-        "imageNotLoadedDesc": "No image found to send to image to image module",
+        "imageNotLoadedDesc": "Could not find image",
         "imageSavedToGallery": "Image Saved to Gallery",
         "canvasMerged": "Canvas Merged",
         "sentToImageToImage": "Sent To Image To Image",
@@ -645,7 +654,8 @@
         "betaClear": "Clear",
         "betaDarkenOutside": "Darken Outside",
         "betaLimitToBox": "Limit To Box",
-        "betaPreserveMasked": "Preserve Masked"
+        "betaPreserveMasked": "Preserve Masked",
+        "antialiasing": "Antialiasing"
     },
     "ui": {
         "showProgressImages": "Show Progress Images",

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

@@ -1,46 +1,44 @@
 import ImageUploader from 'common/components/ImageUploader';
-import ProgressBar from 'features/system/components/ProgressBar';
 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, useColorMode } from '@chakra-ui/react';
+import { Box, Flex, Grid, Portal } from '@chakra-ui/react';
 import { APP_HEIGHT, APP_WIDTH } from 'theme/util/constants';
-import ImageGalleryPanel 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,
-  PropsWithChildren,
-  useCallback,
-  useEffect,
-  useState,
-} from 'react';
+import { memo, ReactNode, useCallback, useEffect, useState } from 'react';
 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 ProgressImagePreview from 'features/parameters/components/ProgressImagePreview';
+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 extends PropsWithChildren {
+interface Props {
   config?: PartialAppConfig;
+  headerComponent?: ReactNode;
+  setIsReady?: (isReady: boolean) => void;
 }
 
-const App = ({ config = DEFAULT_CONFIG, children }: Props) => {
-  useToastWatcher();
-  useGlobalHotkeys();
-  const log = useLogger();
+const App = ({
+  config = DEFAULT_CONFIG,
+  headerComponent,
+  setIsReady,
+}: Props) => {
+  const language = useAppSelector(languageSelector);
 
-  const currentTheme = useAppSelector((state) => state.ui.currentTheme);
+  const log = useLogger();
 
   const isLightboxEnabled = useFeatureStatus('lightbox').isFeatureEnabled;
 
@@ -48,23 +46,33 @@ const App = ({ config = DEFAULT_CONFIG, children }: Props) => {
 
   const [loadingOverridden, setLoadingOverridden] = useState(false);
 
-  const { setColorMode } = useColorMode();
   const dispatch = useAppDispatch();
 
+  useEffect(() => {
+    i18n.changeLanguage(language);
+  }, [language]);
+
   useEffect(() => {
     log.info({ namespace: 'App', data: config }, 'Received config');
     dispatch(configChanged(config));
   }, [dispatch, config, log]);
 
-  useEffect(() => {
-    setColorMode(['light'].includes(currentTheme) ? 'light' : 'dark');
-  }, [setColorMode, currentTheme]);
-
   const handleOverrideClicked = useCallback(() => {
     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>
@@ -76,7 +84,7 @@ const App = ({ config = DEFAULT_CONFIG, children }: Props) => {
             w={APP_WIDTH}
             h={APP_HEIGHT}
           >
-          {children || <SiteHeader />}
+            {headerComponent || <SiteHeader />}
             <Flex
               gap={4}
               w={{ base: '100vw', xl: 'full' }}
@@ -84,11 +92,13 @@ const App = ({ config = DEFAULT_CONFIG, children }: Props) => {
               flexDir={{ base: 'column', xl: 'row' }}
             >
               <InvokeTabs />
-            <ImageGalleryPanel />
             </Flex>
           </Grid>
         </ImageUploader>
 
+        <GalleryDrawer />
+        <ParametersDrawer />
+
         <AnimatePresence>
           {!isApplicationReady && !loadingOverridden && (
             <motion.div
@@ -121,8 +131,10 @@ const App = ({ config = DEFAULT_CONFIG, children }: Props) => {
         <Portal>
           <FloatingGalleryButton />
         </Portal>
-      <ProgressImagePreview />
       </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

@@ -2,7 +2,15 @@ import { createSelector } from '@reduxjs/toolkit';
 import { RootState } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { shiftKeyPressed } from 'features/ui/store/hotkeysSlice';
+import {
+  setActiveTab,
+  toggleGalleryPanel,
+  toggleParametersPanel,
+  togglePinGalleryPanel,
+  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(
@@ -20,7 +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);
 
@@ -36,4 +48,40 @@ export const useGlobalHotkeys = () => {
     { keyup: true, keydown: true },
     [shift]
   );
+
+  useHotkeys('o', () => {
+    dispatch(toggleParametersPanel());
+  });
+
+  useHotkeys(['shift+o'], () => {
+    dispatch(togglePinParametersPanel());
+  });
+
+  useHotkeys('g', () => {
+    dispatch(toggleGalleryPanel());
+  });
+
+  useHotkeys(['shift+g'], () => {
+    dispatch(togglePinGalleryPanel());
+  });
+
+  useHotkeys('1', () => {
+    dispatch(setActiveTab('txt2img'));
+  });
+
+  useHotkeys('2', () => {
+    dispatch(setActiveTab('img2img'));
+  });
+
+  useHotkeys('3', () => {
+    dispatch(setActiveTab('unifiedCanvas'));
+  });
+
+  useHotkeys('4', () => {
+    dispatch(setActiveTab('nodes'));
+  });
+
+  return null;
 };
+
+export default memo(GlobalHotkeys);

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

@@ -1,18 +1,13 @@
-import React, { lazy, memo, PropsWithChildren, useEffect } from 'react';
+import React, {
+  lazy,
+  memo,
+  PropsWithChildren,
+  ReactNode,
+  useEffect,
+} from 'react';
 import { Provider } from 'react-redux';
-import { PersistGate } from 'redux-persist/integration/react';
 import { store } from 'app/store/store';
-import { persistor } from '../store/persistor';
 import { OpenAPI } from 'services/api';
-import '@fontsource/inter/100.css';
-import '@fontsource/inter/200.css';
-import '@fontsource/inter/300.css';
-import '@fontsource/inter/400.css';
-import '@fontsource/inter/500.css';
-import '@fontsource/inter/600.css';
-import '@fontsource/inter/700.css';
-import '@fontsource/inter/800.css';
-import '@fontsource/inter/900.css';
 
 import Loading from '../../common/components/Loading/Loading';
 import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
@@ -28,9 +23,17 @@ interface Props extends PropsWithChildren {
   apiUrl?: string;
   token?: string;
   config?: PartialAppConfig;
+  headerComponent?: ReactNode;
+  setIsReady?: (isReady: boolean) => void;
 }
 
-const InvokeAIUI = ({ apiUrl, token, config, children }: Props) => {
+const InvokeAIUI = ({
+  apiUrl,
+  token,
+  config,
+  headerComponent,
+  setIsReady,
+}: Props) => {
   useEffect(() => {
     // configure API client token
     if (token) {
@@ -57,13 +60,15 @@ const InvokeAIUI = ({ apiUrl, token, config, children }: Props) => {
   return (
     <React.StrictMode>
       <Provider store={store}>
-        <PersistGate loading={<Loading />} persistor={persistor}>
         <React.Suspense fallback={<Loading />}>
           <ThemeLocaleProvider>
-              <App config={config}>{children}</App>
+            <App
+              config={config}
+              headerComponent={headerComponent}
+              setIsReady={setIsReady}
+            />
           </ThemeLocaleProvider>
         </React.Suspense>
-        </PersistGate>
       </Provider>
     </React.StrictMode>
   );

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

@@ -1,4 +1,8 @@
-import { ChakraProvider, extendTheme } from '@chakra-ui/react';
+import {
+  ChakraProvider,
+  createLocalStorageManager,
+  extendTheme,
+} from '@chakra-ui/react';
 import { ReactNode, useEffect } from 'react';
 import { useTranslation } from 'react-i18next';
 import { theme as invokeAITheme } from 'theme/theme';
@@ -9,15 +13,8 @@ import { greenTeaThemeColors } from 'theme/colors/greenTea';
 import { invokeAIThemeColors } from 'theme/colors/invokeAI';
 import { lightThemeColors } from 'theme/colors/lightTheme';
 import { oceanBlueColors } from 'theme/colors/oceanBlue';
-import '@fontsource/inter/100.css';
-import '@fontsource/inter/200.css';
-import '@fontsource/inter/300.css';
-import '@fontsource/inter/400.css';
-import '@fontsource/inter/500.css';
-import '@fontsource/inter/600.css';
-import '@fontsource/inter/700.css';
-import '@fontsource/inter/800.css';
-import '@fontsource/inter/900.css';
+
+import '@fontsource/inter/variable.css';
 import 'overlayscrollbars/overlayscrollbars.css';
 import 'theme/css/overlayscrollbars.css';
 
@@ -32,6 +29,8 @@ const THEMES = {
   ocean: oceanBlueColors,
 };
 
+const manager = createLocalStorageManager('@@invokeai-color-mode');
+
 function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
   const { i18n } = useTranslation();
 
@@ -51,7 +50,11 @@ function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
     document.body.dir = direction;
   }, [direction]);
 
-  return <ChakraProvider theme={theme}>{children}</ChakraProvider>;
+  return (
+    <ChakraProvider theme={theme} colorModeManager={manager}>
+      {children}
+    </ChakraProvider>
+  );
 }
 
 export default ThemeLocaleProvider;

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

@@ -1,17 +1,28 @@
 // TODO: use Enums?
 
-export const DIFFUSERS_SCHEDULERS: Array<string> = [
+export const SCHEDULERS = [
   'ddim',
-  'plms',
-  'k_lms',
-  'dpmpp_2',
-  'k_dpm_2',
-  'k_dpm_2_a',
-  'k_dpmpp_2',
-  'k_euler',
-  'k_euler_a',
-  'k_heun',
-];
+  'lms',
+  'euler',
+  'euler_k',
+  'euler_a',
+  'dpmpp_2s',
+  'dpmpp_2m',
+  'dpmpp_2m_k',
+  'kdpm_2',
+  'kdpm_2_a',
+  'deis',
+  'ddpm',
+  'pndm',
+  'heun',
+  'heun_k',
+  'unipc',
+] as const;
+
+export type Scheduler = (typeof SCHEDULERS)[number];
+
+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/selectors/readinessSelector.ts

@@ -1,26 +1,20 @@
 import { createSelector } from '@reduxjs/toolkit';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import { validateSeedWeights } from 'common/util/seedWeightPairs';
-import { initialCanvasImageSelector } from 'features/canvas/store/canvasSelectors';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { systemSelector } from 'features/system/store/systemSelectors';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { isEqual } from 'lodash-es';
 
 export const readinessSelector = createSelector(
-  [
-    generationSelector,
-    systemSelector,
-    initialCanvasImageSelector,
-    activeTabNameSelector,
-  ],
-  (generation, system, initialCanvasImage, activeTabName) => {
+  [generationSelector, systemSelector, activeTabNameSelector],
+  (generation, system, activeTabName) => {
     const {
       prompt,
       shouldGenerateVariations,
       seedWeights,
       initialImage,
       seed,
-      isImageToImageEnabled,
     } = generation;
 
     const { isProcessing, isConnected } = system;
@@ -34,7 +28,7 @@ export const readinessSelector = createSelector(
       reasonsWhyNotReady.push('Missing prompt');
     }
 
-    if (isImageToImageEnabled && !initialImage) {
+    if (activeTabName === 'img2img' && !initialImage) {
       isReady = false;
       reasonsWhyNotReady.push('No initial image selected');
     }
@@ -64,10 +58,5 @@ export const readinessSelector = createSelector(
     // All good
     return { isReady, reasonsWhyNotReady };
   },
-  {
-    memoizeOptions: {
-      equalityCheck: isEqual,
-      resultEqualityCheck: isEqual,
-    },
-  }
+  defaultSelectorOptions
 );

invokeai/frontend/web/src/app/socketio/emitters.ts

@@ -1,209 +1,209 @@
-// import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
-// import * as InvokeAI from 'app/types/invokeai';
-// import type { RootState } from 'app/store/store';
-// import {
-//   frontendToBackendParameters,
-//   FrontendToBackendParametersConfig,
-// } from 'common/util/parameterTranslation';
-// import dateFormat from 'dateformat';
-// import {
-//   GalleryCategory,
-//   GalleryState,
-//   removeImage,
-// } from 'features/gallery/store/gallerySlice';
-// import {
-//   generationRequested,
-//   modelChangeRequested,
-//   modelConvertRequested,
-//   modelMergingRequested,
-//   setIsProcessing,
-// } from 'features/system/store/systemSlice';
-// import { InvokeTabName } from 'features/ui/store/tabMap';
-// import { Socket } from 'socket.io-client';
+import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
+import * as InvokeAI from 'app/types/invokeai';
+import type { RootState } from 'app/store/store';
+import {
+  frontendToBackendParameters,
+  FrontendToBackendParametersConfig,
+} from 'common/util/parameterTranslation';
+import dateFormat from 'dateformat';
+import {
+  GalleryCategory,
+  GalleryState,
+  removeImage,
+} from 'features/gallery/store/gallerySlice';
+import {
+  generationRequested,
+  modelChangeRequested,
+  modelConvertRequested,
+  modelMergingRequested,
+  setIsProcessing,
+} from 'features/system/store/systemSlice';
+import { InvokeTabName } from 'features/ui/store/tabMap';
+import { Socket } from 'socket.io-client';
 
-// /**
-//  * Returns an object containing all functions which use `socketio.emit()`.
-//  * i.e. those which make server requests.
-//  */
-// const makeSocketIOEmitters = (
-//   store: MiddlewareAPI<Dispatch<AnyAction>, RootState>,
-//   socketio: Socket
-// ) => {
-//   // We need to dispatch actions to redux and get pieces of state from the store.
-//   const { dispatch, getState } = store;
+/**
+ * Returns an object containing all functions which use `socketio.emit()`.
+ * i.e. those which make server requests.
+ */
+const makeSocketIOEmitters = (
+  store: MiddlewareAPI<Dispatch<AnyAction>, RootState>,
+  socketio: Socket
+) => {
+  // We need to dispatch actions to redux and get pieces of state from the store.
+  const { dispatch, getState } = store;
 
-//   return {
-//     emitGenerateImage: (generationMode: InvokeTabName) => {
-//       dispatch(setIsProcessing(true));
+  return {
+    emitGenerateImage: (generationMode: InvokeTabName) => {
+      dispatch(setIsProcessing(true));
 
-//       const state: RootState = getState();
+      const state: RootState = getState();
 
-//       const {
-//         generation: generationState,
-//         postprocessing: postprocessingState,
-//         system: systemState,
-//         canvas: canvasState,
-//       } = state;
+      const {
+        generation: generationState,
+        postprocessing: postprocessingState,
+        system: systemState,
+        canvas: canvasState,
+      } = state;
 
-//       const frontendToBackendParametersConfig: FrontendToBackendParametersConfig =
-//         {
-//           generationMode,
-//           generationState,
-//           postprocessingState,
-//           canvasState,
-//           systemState,
-//         };
+      const frontendToBackendParametersConfig: FrontendToBackendParametersConfig =
+        {
+          generationMode,
+          generationState,
+          postprocessingState,
+          canvasState,
+          systemState,
+        };
 
-//       dispatch(generationRequested());
+      dispatch(generationRequested());
 
-//       const { generationParameters, esrganParameters, facetoolParameters } =
-//         frontendToBackendParameters(frontendToBackendParametersConfig);
+      const { generationParameters, esrganParameters, facetoolParameters } =
+        frontendToBackendParameters(frontendToBackendParametersConfig);
 
-//       socketio.emit(
-//         'generateImage',
-//         generationParameters,
-//         esrganParameters,
-//         facetoolParameters
-//       );
+      socketio.emit(
+        'generateImage',
+        generationParameters,
+        esrganParameters,
+        facetoolParameters
+      );
 
-//       // we need to truncate the init_mask base64 else it takes up the whole log
-//       // TODO: handle maintaining masks for reproducibility in future
-//       if (generationParameters.init_mask) {
-//         generationParameters.init_mask = generationParameters.init_mask
-//           .substr(0, 64)
-//           .concat('...');
-//       }
-//       if (generationParameters.init_img) {
-//         generationParameters.init_img = generationParameters.init_img
-//           .substr(0, 64)
-//           .concat('...');
-//       }
+      // we need to truncate the init_mask base64 else it takes up the whole log
+      // TODO: handle maintaining masks for reproducibility in future
+      if (generationParameters.init_mask) {
+        generationParameters.init_mask = generationParameters.init_mask
+          .substr(0, 64)
+          .concat('...');
+      }
+      if (generationParameters.init_img) {
+        generationParameters.init_img = generationParameters.init_img
+          .substr(0, 64)
+          .concat('...');
+      }
 
-//       dispatch(
-//         addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Image generation requested: ${JSON.stringify({
-//             ...generationParameters,
-//             ...esrganParameters,
-//             ...facetoolParameters,
-//           })}`,
-//         })
-//       );
-//     },
-//     emitRunESRGAN: (imageToProcess: InvokeAI._Image) => {
-//       dispatch(setIsProcessing(true));
+      dispatch(
+        addLogEntry({
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
+          message: `Image generation requested: ${JSON.stringify({
+            ...generationParameters,
+            ...esrganParameters,
+            ...facetoolParameters,
+          })}`,
+        })
+      );
+    },
+    emitRunESRGAN: (imageToProcess: InvokeAI._Image) => {
+      dispatch(setIsProcessing(true));
 
-//       const {
-//         postprocessing: {
-//           upscalingLevel,
-//           upscalingDenoising,
-//           upscalingStrength,
-//         },
-//       } = getState();
+      const {
+        postprocessing: {
+          upscalingLevel,
+          upscalingDenoising,
+          upscalingStrength,
+        },
+      } = getState();
 
-//       const esrganParameters = {
-//         upscale: [upscalingLevel, upscalingDenoising, upscalingStrength],
-//       };
-//       socketio.emit('runPostprocessing', imageToProcess, {
-//         type: 'esrgan',
-//         ...esrganParameters,
-//       });
-//       dispatch(
-//         addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `ESRGAN upscale requested: ${JSON.stringify({
-//             file: imageToProcess.url,
-//             ...esrganParameters,
-//           })}`,
-//         })
-//       );
-//     },
-//     emitRunFacetool: (imageToProcess: InvokeAI._Image) => {
-//       dispatch(setIsProcessing(true));
+      const esrganParameters = {
+        upscale: [upscalingLevel, upscalingDenoising, upscalingStrength],
+      };
+      socketio.emit('runPostprocessing', imageToProcess, {
+        type: 'esrgan',
+        ...esrganParameters,
+      });
+      dispatch(
+        addLogEntry({
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
+          message: `ESRGAN upscale requested: ${JSON.stringify({
+            file: imageToProcess.url,
+            ...esrganParameters,
+          })}`,
+        })
+      );
+    },
+    emitRunFacetool: (imageToProcess: InvokeAI._Image) => {
+      dispatch(setIsProcessing(true));
 
-//       const {
-//         postprocessing: { facetoolType, facetoolStrength, codeformerFidelity },
-//       } = getState();
+      const {
+        postprocessing: { facetoolType, facetoolStrength, codeformerFidelity },
+      } = getState();
 
-//       const facetoolParameters: Record<string, unknown> = {
-//         facetool_strength: facetoolStrength,
-//       };
+      const facetoolParameters: Record<string, unknown> = {
+        facetool_strength: facetoolStrength,
+      };
 
-//       if (facetoolType === 'codeformer') {
-//         facetoolParameters.codeformer_fidelity = codeformerFidelity;
-//       }
+      if (facetoolType === 'codeformer') {
+        facetoolParameters.codeformer_fidelity = codeformerFidelity;
+      }
 
-//       socketio.emit('runPostprocessing', imageToProcess, {
-//         type: facetoolType,
-//         ...facetoolParameters,
-//       });
-//       dispatch(
-//         addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Face restoration (${facetoolType}) requested: ${JSON.stringify(
-//             {
-//               file: imageToProcess.url,
-//               ...facetoolParameters,
-//             }
-//           )}`,
-//         })
-//       );
-//     },
-//     emitDeleteImage: (imageToDelete: InvokeAI._Image) => {
-//       const { url, uuid, category, thumbnail } = imageToDelete;
-//       dispatch(removeImage(imageToDelete));
-//       socketio.emit('deleteImage', url, thumbnail, uuid, category);
-//     },
-//     emitRequestImages: (category: GalleryCategory) => {
-//       const gallery: GalleryState = getState().gallery;
-//       const { earliest_mtime } = gallery.categories[category];
-//       socketio.emit('requestImages', category, earliest_mtime);
-//     },
-//     emitRequestNewImages: (category: GalleryCategory) => {
-//       const gallery: GalleryState = getState().gallery;
-//       const { latest_mtime } = gallery.categories[category];
-//       socketio.emit('requestLatestImages', category, latest_mtime);
-//     },
-//     emitCancelProcessing: () => {
-//       socketio.emit('cancel');
-//     },
-//     emitRequestSystemConfig: () => {
-//       socketio.emit('requestSystemConfig');
-//     },
-//     emitSearchForModels: (modelFolder: string) => {
-//       socketio.emit('searchForModels', modelFolder);
-//     },
-//     emitAddNewModel: (modelConfig: InvokeAI.InvokeModelConfigProps) => {
-//       socketio.emit('addNewModel', modelConfig);
-//     },
-//     emitDeleteModel: (modelName: string) => {
-//       socketio.emit('deleteModel', modelName);
-//     },
-//     emitConvertToDiffusers: (
-//       modelToConvert: InvokeAI.InvokeModelConversionProps
-//     ) => {
-//       dispatch(modelConvertRequested());
-//       socketio.emit('convertToDiffusers', modelToConvert);
-//     },
-//     emitMergeDiffusersModels: (
-//       modelMergeInfo: InvokeAI.InvokeModelMergingProps
-//     ) => {
-//       dispatch(modelMergingRequested());
-//       socketio.emit('mergeDiffusersModels', modelMergeInfo);
-//     },
-//     emitRequestModelChange: (modelName: string) => {
-//       dispatch(modelChangeRequested());
-//       socketio.emit('requestModelChange', modelName);
-//     },
-//     emitSaveStagingAreaImageToGallery: (url: string) => {
-//       socketio.emit('requestSaveStagingAreaImageToGallery', url);
-//     },
-//     emitRequestEmptyTempFolder: () => {
-//       socketio.emit('requestEmptyTempFolder');
-//     },
-//   };
-// };
+      socketio.emit('runPostprocessing', imageToProcess, {
+        type: facetoolType,
+        ...facetoolParameters,
+      });
+      dispatch(
+        addLogEntry({
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
+          message: `Face restoration (${facetoolType}) requested: ${JSON.stringify(
+            {
+              file: imageToProcess.url,
+              ...facetoolParameters,
+            }
+          )}`,
+        })
+      );
+    },
+    emitDeleteImage: (imageToDelete: InvokeAI._Image) => {
+      const { url, uuid, category, thumbnail } = imageToDelete;
+      dispatch(removeImage(imageToDelete));
+      socketio.emit('deleteImage', url, thumbnail, uuid, category);
+    },
+    emitRequestImages: (category: GalleryCategory) => {
+      const gallery: GalleryState = getState().gallery;
+      const { earliest_mtime } = gallery.categories[category];
+      socketio.emit('requestImages', category, earliest_mtime);
+    },
+    emitRequestNewImages: (category: GalleryCategory) => {
+      const gallery: GalleryState = getState().gallery;
+      const { latest_mtime } = gallery.categories[category];
+      socketio.emit('requestLatestImages', category, latest_mtime);
+    },
+    emitCancelProcessing: () => {
+      socketio.emit('cancel');
+    },
+    emitRequestSystemConfig: () => {
+      socketio.emit('requestSystemConfig');
+    },
+    emitSearchForModels: (modelFolder: string) => {
+      socketio.emit('searchForModels', modelFolder);
+    },
+    emitAddNewModel: (modelConfig: InvokeAI.InvokeModelConfigProps) => {
+      socketio.emit('addNewModel', modelConfig);
+    },
+    emitDeleteModel: (modelName: string) => {
+      socketio.emit('deleteModel', modelName);
+    },
+    emitConvertToDiffusers: (
+      modelToConvert: InvokeAI.InvokeModelConversionProps
+    ) => {
+      dispatch(modelConvertRequested());
+      socketio.emit('convertToDiffusers', modelToConvert);
+    },
+    emitMergeDiffusersModels: (
+      modelMergeInfo: InvokeAI.InvokeModelMergingProps
+    ) => {
+      dispatch(modelMergingRequested());
+      socketio.emit('mergeDiffusersModels', modelMergeInfo);
+    },
+    emitRequestModelChange: (modelName: string) => {
+      dispatch(modelChangeRequested());
+      socketio.emit('requestModelChange', modelName);
+    },
+    emitSaveStagingAreaImageToGallery: (url: string) => {
+      socketio.emit('requestSaveStagingAreaImageToGallery', url);
+    },
+    emitRequestEmptyTempFolder: () => {
+      socketio.emit('requestEmptyTempFolder');
+    },
+  };
+};
 
-// export default makeSocketIOEmitters;
+export default makeSocketIOEmitters;
 
 export default {};

invokeai/frontend/web/src/app/store/actions.ts

@@ -0,0 +1,4 @@
+import { createAction } from '@reduxjs/toolkit';
+import { InvokeTabName } from 'features/ui/store/tabMap';
+
+export const userInvoked = createAction<InvokeTabName>('app/userInvoked');

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

@@ -0,0 +1,8 @@
+export const LOCALSTORAGE_KEYS = [
+  'chakra-ui-color-mode',
+  'i18nextLng',
+  'ROARR_FILTER',
+  'ROARR_LOG',
+];
+
+export const LOCALSTORAGE_PREFIX = '@@invokeai-';

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts

@@ -0,0 +1,36 @@
+import { canvasPersistDenylist } from 'features/canvas/store/canvasPersistDenylist';
+import { galleryPersistDenylist } from 'features/gallery/store/galleryPersistDenylist';
+import { resultsPersistDenylist } from 'features/gallery/store/resultsPersistDenylist';
+import { uploadsPersistDenylist } from 'features/gallery/store/uploadsPersistDenylist';
+import { lightboxPersistDenylist } from 'features/lightbox/store/lightboxPersistDenylist';
+import { nodesPersistDenylist } from 'features/nodes/store/nodesPersistDenylist';
+import { generationPersistDenylist } from 'features/parameters/store/generationPersistDenylist';
+import { postprocessingPersistDenylist } from 'features/parameters/store/postprocessingPersistDenylist';
+import { modelsPersistDenylist } from 'features/system/store/modelsPersistDenylist';
+import { systemPersistDenylist } from 'features/system/store/systemPersistDenylist';
+import { uiPersistDenylist } from 'features/ui/store/uiPersistDenylist';
+import { omit } from 'lodash-es';
+import { SerializeFunction } from 'redux-remember';
+
+const serializationDenylist: {
+  [key: string]: string[];
+} = {
+  canvas: canvasPersistDenylist,
+  gallery: galleryPersistDenylist,
+  generation: generationPersistDenylist,
+  lightbox: lightboxPersistDenylist,
+  models: modelsPersistDenylist,
+  nodes: nodesPersistDenylist,
+  postprocessing: postprocessingPersistDenylist,
+  results: resultsPersistDenylist,
+  system: systemPersistDenylist,
+  // config: configPersistDenyList,
+  ui: uiPersistDenylist,
+  uploads: uploadsPersistDenylist,
+  // hotkeys: hotkeysPersistDenylist,
+};
+
+export const serialize: SerializeFunction = (data, key) => {
+  const result = omit(data, serializationDenylist[key]);
+  return JSON.stringify(result);
+};

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/unserialize.ts

@@ -0,0 +1,38 @@
+import { initialCanvasState } from 'features/canvas/store/canvasSlice';
+import { initialGalleryState } from 'features/gallery/store/gallerySlice';
+import { initialResultsState } from 'features/gallery/store/resultsSlice';
+import { initialUploadsState } from 'features/gallery/store/uploadsSlice';
+import { initialLightboxState } from 'features/lightbox/store/lightboxSlice';
+import { initialNodesState } from 'features/nodes/store/nodesSlice';
+import { initialGenerationState } from 'features/parameters/store/generationSlice';
+import { initialPostprocessingState } from 'features/parameters/store/postprocessingSlice';
+import { initialConfigState } from 'features/system/store/configSlice';
+import { initialModelsState } from 'features/system/store/modelSlice';
+import { initialSystemState } from 'features/system/store/systemSlice';
+import { initialHotkeysState } from 'features/ui/store/hotkeysSlice';
+import { initialUIState } from 'features/ui/store/uiSlice';
+import { defaultsDeep } from 'lodash-es';
+import { UnserializeFunction } from 'redux-remember';
+
+const initialStates: {
+  [key: string]: any;
+} = {
+  canvas: initialCanvasState,
+  gallery: initialGalleryState,
+  generation: initialGenerationState,
+  lightbox: initialLightboxState,
+  models: initialModelsState,
+  nodes: initialNodesState,
+  postprocessing: initialPostprocessingState,
+  results: initialResultsState,
+  system: initialSystemState,
+  config: initialConfigState,
+  ui: initialUIState,
+  uploads: initialUploadsState,
+  hotkeys: initialHotkeysState,
+};
+
+export const unserialize: UnserializeFunction = (data, key) => {
+  const result = defaultsDeep(JSON.parse(data), initialStates[key]);
+  return result;
+};

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -0,0 +1,30 @@
+import { AnyAction } from '@reduxjs/toolkit';
+import { isAnyGraphBuilt } from 'features/nodes/store/actions';
+import { forEach } from 'lodash-es';
+import { Graph } from 'services/api';
+
+export const actionSanitizer = <A extends AnyAction>(action: A): A => {
+  if (isAnyGraphBuilt(action)) {
+    if (action.payload.nodes) {
+      const sanitizedNodes: Graph['nodes'] = {};
+
+      // Sanitize nodes as needed
+      forEach(action.payload.nodes, (node, key) => {
+        // Don't log the whole freaking dataURL
+        if (node.type === 'dataURL_image') {
+          const { dataURL, ...rest } = node;
+          sanitizedNodes[key] = { ...rest, dataURL: '<dataURL>' };
+        } else {
+          sanitizedNodes[key] = { ...node };
+        }
+      });
+
+      return {
+        ...action,
+        payload: { ...action.payload, nodes: sanitizedNodes },
+      };
+    }
+  }
+
+  return action;
+};

invokeai/frontend/web/src/app/store/middleware/devtools/actionsDenylist.ts

@@ -0,0 +1,11 @@
+export const actionsDenylist = [
+  'canvas/setCursorPosition',
+  'canvas/setStageCoordinates',
+  'canvas/setStageScale',
+  'canvas/setIsDrawing',
+  'canvas/setBoundingBoxCoordinates',
+  'canvas/setBoundingBoxDimensions',
+  'canvas/setIsDrawing',
+  'canvas/addPointToCurrentLine',
+  'socket/generatorProgress',
+];

invokeai/frontend/web/src/app/store/middleware/devtools/stateSanitizer.ts

@@ -0,0 +1,3 @@
+export const stateSanitizer = <S>(state: S): S => {
+  return state;
+};

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

@@ -0,0 +1,54 @@
+import {
+  createListenerMiddleware,
+  addListener,
+  ListenerEffect,
+  AnyAction,
+} from '@reduxjs/toolkit';
+import type { TypedStartListening, TypedAddListener } from '@reduxjs/toolkit';
+
+import type { RootState, AppDispatch } from '../../store';
+import { addInitialImageSelectedListener } from './listeners/initialImageSelected';
+import { addImageResultReceivedListener } from './listeners/invocationComplete';
+import { addImageUploadedListener } from './listeners/imageUploaded';
+import { addRequestedImageDeletionListener } from './listeners/imageDeleted';
+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();
+
+export type AppStartListening = TypedStartListening<RootState, AppDispatch>;
+
+export const startAppListening =
+  listenerMiddleware.startListening as AppStartListening;
+
+export const addAppListener = addListener as TypedAddListener<
+  RootState,
+  AppDispatch
+>;
+
+export type AppListenerEffect = ListenerEffect<
+  AnyAction,
+  RootState,
+  AppDispatch
+>;
+
+addImageUploadedListener();
+addInitialImageSelectedListener();
+addImageResultReceivedListener();
+addRequestedImageDeletionListener();
+
+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/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/imageDeleted.ts

@@ -0,0 +1,59 @@
+import { requestedImageDeletion } from 'features/gallery/store/actions';
+import { startAppListening } from '..';
+import { imageDeleted } from 'services/thunks/image';
+import { log } from 'app/logging/useLogger';
+import { clamp } from 'lodash-es';
+import { imageSelected } from 'features/gallery/store/gallerySlice';
+
+const moduleLog = log.child({ namespace: 'addRequestedImageDeletionListener' });
+
+export const addRequestedImageDeletionListener = () => {
+  startAppListening({
+    actionCreator: requestedImageDeletion,
+    effect: (action, { dispatch, getState }) => {
+      const image = action.payload;
+      if (!image) {
+        moduleLog.warn('No image provided');
+        return;
+      }
+
+      const { name, type } = image;
+
+      if (type !== 'uploads' && type !== 'results') {
+        moduleLog.warn({ data: image }, `Invalid image type ${type}`);
+        return;
+      }
+
+      const selectedImageName = getState().gallery.selectedImage?.name;
+
+      if (selectedImageName === name) {
+        const allIds = getState()[type].ids;
+        const allEntities = getState()[type].entities;
+
+        const deletedImageIndex = allIds.findIndex(
+          (result) => result.toString() === name
+        );
+
+        const filteredIds = allIds.filter((id) => id.toString() !== name);
+
+        const newSelectedImageIndex = clamp(
+          deletedImageIndex,
+          0,
+          filteredIds.length - 1
+        );
+
+        const newSelectedImageId = filteredIds[newSelectedImageIndex];
+
+        const newSelectedImage = allEntities[newSelectedImageId];
+
+        if (newSelectedImageId) {
+          dispatch(imageSelected(newSelectedImage));
+        } else {
+          dispatch(imageSelected());
+        }
+      }
+
+      dispatch(imageDeleted({ imageName: name, imageType: type }));
+    },
+  });
+};

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

@@ -0,0 +1,46 @@
+import { deserializeImageResponse } from 'services/util/deserializeImageResponse';
+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({
+    predicate: (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
+      imageUploaded.fulfilled.match(action) &&
+      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

@@ -0,0 +1,54 @@
+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 { 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({
+    actionCreator: initialImageSelected,
+    effect: (action, { getState, dispatch }) => {
+      if (!action.payload) {
+        dispatch(
+          addToast(
+            makeToast({ title: t('toast.imageNotLoadedDesc'), status: 'error' })
+          )
+        );
+        return;
+      }
+
+      if (isInvokeAIImage(action.payload)) {
+        dispatch(initialImageChanged(action.payload));
+        dispatch(addToast(makeToast(t('toast.sentToImageToImage'))));
+        return;
+      }
+
+      const { name, type } = action.payload;
+
+      let image: Image | undefined;
+      const state = getState();
+
+      if (type === 'results') {
+        image = selectResultsById(state, name);
+      } else if (type === 'uploads') {
+        image = selectUploadsById(state, name);
+      }
+
+      if (!image) {
+        dispatch(
+          addToast(
+            makeToast({ title: t('toast.imageNotLoadedDesc'), status: 'error' })
+          )
+        );
+        return;
+      }
+
+      dispatch(initialImageChanged(image));
+      dispatch(addToast(makeToast(t('toast.sentToImageToImage'))));
+    },
+  });
+};

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

@@ -0,0 +1,88 @@
+import { invocationComplete } from 'services/events/actions';
+import { isImageOutput } from 'services/types/guards';
+import {
+  buildImageUrls,
+  extractTimestampFromImageName,
+} from 'services/util/deserializeImageField';
+import { Image } from 'app/types/invokeai';
+import { resultAdded } from 'features/gallery/store/resultsSlice';
+import { imageReceived, thumbnailReceived } from 'services/thunks/image';
+import { startAppListening } from '..';
+import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
+
+const nodeDenylist = ['dataURL_image'];
+
+export const addImageResultReceivedListener = () => {
+  startAppListening({
+    predicate: (action) => {
+      if (
+        invocationComplete.match(action) &&
+        isImageOutput(action.payload.data.result)
+      ) {
+        return true;
+      }
+      return false;
+    },
+    effect: (action, { getState, dispatch }) => {
+      if (!invocationComplete.match(action)) {
+        return;
+      }
+
+      const { data, shouldFetchImages } = action.payload;
+      const { result, node, graph_execution_state_id } = data;
+
+      if (isImageOutput(result) && !nodeDenylist.includes(node.type)) {
+        const name = result.image.image_name;
+        const type = result.image.image_type;
+        const state = getState();
+
+        // if we need to refetch, set URLs to placeholder for now
+        const { url, thumbnail } = shouldFetchImages
+          ? { url: '', thumbnail: '' }
+          : buildImageUrls(type, name);
+
+        const timestamp = extractTimestampFromImageName(name);
+
+        const image: Image = {
+          name,
+          type,
+          url,
+          thumbnail,
+          metadata: {
+            created: timestamp,
+            width: result.width,
+            height: result.height,
+            invokeai: {
+              session_id: graph_execution_state_id,
+              ...(node ? { node } : {}),
+            },
+          },
+        };
+
+        dispatch(resultAdded(image));
+
+        if (state.gallery.shouldAutoSwitchToNewImages) {
+          dispatch(imageSelected(image));
+        }
+
+        if (state.config.shouldFetchImages) {
+          dispatch(imageReceived({ imageName: name, imageType: type }));
+          dispatch(
+            thumbnailReceived({
+              thumbnailName: name,
+              thumbnailType: type,
+            })
+          );
+        }
+
+        if (
+          graph_execution_state_id ===
+          state.canvas.layerState.stagingArea.sessionId
+        ) {
+          dispatch(addImageToStagingArea(image));
+        }
+      }
+    },
+  });
+};

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

@@ -0,0 +1,164 @@
+import { startAppListening } from '..';
+import { sessionCreated, sessionInvoked } from 'services/thunks/session';
+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';
+import { v4 as uuidv4 } from 'uuid';
+import { Graph } from 'services/api';
+import {
+  canvasSessionIdChanged,
+  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> =>
+      userInvoked.match(action) && action.payload === 'unifiedCanvas',
+    effect: async (action, { getState, dispatch, take }) => {
+      const state = getState();
+
+      // Build canvas blobs
+      const canvasBlobsAndImageData = await getCanvasData(state);
+
+      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: ${generationMode}`);
+
+      // Build the canvas graph
+      const graphComponents = await buildCanvasGraphComponents(
+        state,
+        generationMode
+      );
+
+      if (!graphComponents) {
+        moduleLog.error('Problem building graph');
+        return;
+      }
+
+      const { rangeNode, iterateNode, baseNode, edges } = graphComponents;
+
+      // Upload the base layer, to be used as init image
+      const baseFilename = `${uuidv4()}.png`;
+
+      dispatch(
+        imageUploaded({
+          imageType: 'intermediates',
+          formData: {
+            file: new File([baseBlob], baseFilename, { type: 'image/png' }),
+          },
+        })
+      );
+
+      if (baseNode.type === 'img2img' || baseNode.type === 'inpaint') {
+        const [{ payload: basePayload }] = await take(
+          (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
+            imageUploaded.fulfilled.match(action) &&
+            action.meta.arg.formData.file.name === baseFilename
+        );
+
+        const { image_name: baseName, image_type: baseType } =
+          basePayload.response;
+
+        baseNode.image = {
+          image_name: baseName,
+          image_type: baseType,
+        };
+      }
+
+      // Upload the mask layer image
+      const maskFilename = `${uuidv4()}.png`;
+
+      if (baseNode.type === 'inpaint') {
+        dispatch(
+          imageUploaded({
+            imageType: 'intermediates',
+            formData: {
+              file: new File([maskBlob], maskFilename, { type: 'image/png' }),
+            },
+          })
+        );
+
+        const [{ payload: maskPayload }] = await take(
+          (action): action is ReturnType<typeof imageUploaded.fulfilled> =>
+            imageUploaded.fulfilled.match(action) &&
+            action.meta.arg.formData.file.name === maskFilename
+        );
+
+        const { image_name: maskName, image_type: maskType } =
+          maskPayload.response;
+
+        baseNode.mask = {
+          image_name: maskName,
+          image_type: maskType,
+        };
+      }
+
+      // Assemble!
+      const nodes: Graph['nodes'] = {
+        [rangeNode.id]: rangeNode,
+        [iterateNode.id]: iterateNode,
+        [baseNode.id]: baseNode,
+      };
+
+      const graph = { nodes, edges };
+
+      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) {
+        dispatch(
+          stagingAreaInitialized({
+            sessionId,
+            boundingBox: {
+              ...state.canvas.boundingBoxCoordinates,
+              ...state.canvas.boundingBoxDimensions,
+            },
+          })
+        );
+      }
+
+      dispatch(canvasSessionIdChanged(sessionId));
+    },
+  });
+};

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

@@ -0,0 +1,24 @@
+import { startAppListening } from '..';
+import { buildImageToImageGraph } from 'features/nodes/util/graphBuilders/buildImageToImageGraph';
+import { sessionCreated } from 'services/thunks/session';
+import { log } from 'app/logging/useLogger';
+import { imageToImageGraphBuilt } from 'features/nodes/store/actions';
+import { userInvoked } from 'app/store/actions';
+
+const moduleLog = log.child({ namespace: 'invoke' });
+
+export const addUserInvokedImageToImageListener = () => {
+  startAppListening({
+    predicate: (action): action is ReturnType<typeof userInvoked> =>
+      userInvoked.match(action) && action.payload === 'img2img',
+    effect: (action, { getState, dispatch }) => {
+      const state = getState();
+
+      const graph = buildImageToImageGraph(state);
+      dispatch(imageToImageGraphBuilt(graph));
+      moduleLog({ data: graph }, 'Image to Image graph built');
+
+      dispatch(sessionCreated({ graph }));
+    },
+  });
+};

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

@@ -0,0 +1,24 @@
+import { startAppListening } from '..';
+import { sessionCreated } from 'services/thunks/session';
+import { buildNodesGraph } from 'features/nodes/util/graphBuilders/buildNodesGraph';
+import { log } from 'app/logging/useLogger';
+import { nodesGraphBuilt } from 'features/nodes/store/actions';
+import { userInvoked } from 'app/store/actions';
+
+const moduleLog = log.child({ namespace: 'invoke' });
+
+export const addUserInvokedNodesListener = () => {
+  startAppListening({
+    predicate: (action): action is ReturnType<typeof userInvoked> =>
+      userInvoked.match(action) && action.payload === 'nodes',
+    effect: (action, { getState, dispatch }) => {
+      const state = getState();
+
+      const graph = buildNodesGraph(state);
+      dispatch(nodesGraphBuilt(graph));
+      moduleLog({ data: graph }, 'Nodes graph built');
+
+      dispatch(sessionCreated({ graph }));
+    },
+  });
+};

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

@@ -0,0 +1,24 @@
+import { startAppListening } from '..';
+import { buildTextToImageGraph } from 'features/nodes/util/graphBuilders/buildTextToImageGraph';
+import { sessionCreated } from 'services/thunks/session';
+import { log } from 'app/logging/useLogger';
+import { textToImageGraphBuilt } from 'features/nodes/store/actions';
+import { userInvoked } from 'app/store/actions';
+
+const moduleLog = log.child({ namespace: 'invoke' });
+
+export const addUserInvokedTextToImageListener = () => {
+  startAppListening({
+    predicate: (action): action is ReturnType<typeof userInvoked> =>
+      userInvoked.match(action) && action.payload === 'txt2img',
+    effect: (action, { getState, dispatch }) => {
+      const state = getState();
+
+      const graph = buildTextToImageGraph(state);
+      dispatch(textToImageGraphBuilt(graph));
+      moduleLog({ data: graph }, 'Text to Image graph built');
+
+      dispatch(sessionCreated({ graph }));
+    },
+  });
+};