Merge branch 'main' into bugfix/invokeai-configure

- Attempts to run the configure script were giving "ModuleNotFoundError"
- Addresses one of the issues raised by #3006

.github/workflows/mkdocs-material.yml

@@ -2,7 +2,8 @@ name: mkdocs-material
 on:
   push:
     branches:
-      - 'refs/heads/v2.3'
+      - 'main'
+      - 'development'
 
 permissions:
     contents: write
@@ -11,10 +12,6 @@ jobs:
   mkdocs-material:
     if: github.event.pull_request.draft == false
     runs-on: ubuntu-latest
-    env:
-      REPO_URL: '${{ github.server_url }}/${{ github.repository }}'
-      REPO_NAME: '${{ github.repository }}'
-      SITE_URL: 'https://${{ github.repository_owner }}.github.io/InvokeAI'
     steps:
       - name: checkout sources
         uses: actions/checkout@v3
@@ -25,15 +22,11 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: '3.10'
-          cache: pip
-          cache-dependency-path: pyproject.toml
 
       - name: install requirements
-        env:
-          PIP_USE_PEP517: 1
         run: |
           python -m \
-            pip install ".[docs]"
+            pip install -r docs/requirements-mkdocs.txt
 
       - name: confirm buildability
         run: |
@@ -43,7 +36,7 @@ jobs:
             --verbose
 
       - name: deploy to gh-pages
-        if: ${{ github.ref == 'refs/heads/v2.3' }}
+        if: ${{ github.ref == 'refs/heads/main' }}
         run: |
           python -m \
             mkdocs gh-deploy \

README.md

@@ -33,8 +33,6 @@
 
 </div>
 
-_**Note: The UI is not fully functional on `main`. If you need a stable UI based on `main`, use the `pre-nodes` tag while we [migrate to a new backend](https://github.com/invoke-ai/InvokeAI/discussions/3246).**_
-
 InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products.
 
 **Quick links**: [[How to Install](https://invoke-ai.github.io/InvokeAI/#installation)] [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]

docs/installation/010_INSTALL_AUTOMATED.md

@@ -89,7 +89,7 @@ experimental versions later.
          sudo apt update
          sudo apt install -y software-properties-common
          sudo add-apt-repository -y ppa:deadsnakes/ppa
-         sudo apt install -y python3.10 python3-pip python3.10-venv
+         sudo apt install python3.10 python3-pip python3.10-venv
          sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
          ```
 

invokeai/app/api/dependencies.py

@@ -1,12 +1,14 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import os
+from argparse import Namespace
 
-import invokeai.backend.util.logging as logger
+from invokeai.app.services.metadata import PngMetadataService, MetadataServiceBase
-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
@@ -17,7 +19,6 @@ from ..services.invocation_services import InvocationServices
 from ..services.invoker import Invoker
 from ..services.processor import DefaultInvocationProcessor
 from ..services.sqlite import SqliteItemStorage
-from ..services.metadata import PngMetadataService
 from .events import FastAPIEventService
 
 
@@ -43,16 +44,15 @@ class ApiDependencies:
     invoker: Invoker = None
 
     @staticmethod
-    def initialize(config, event_handler_id: int, logger: types.ModuleType=logger):
+    def initialize(config, event_handler_id: int):
         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
+        # TODO: Use a logger
-        # invokeai logging module
+        print(f">> Internet connectivity is {Globals.internet_available}")
-        logger.info(f"Internet connectivity is {Globals.internet_available}")
 
         events = FastAPIEventService(event_handler_id)
 
@@ -70,9 +70,8 @@ class ApiDependencies:
         db_location = os.path.join(output_folder, "invokeai.db")
 
         services = InvocationServices(
-            model_manager=get_model_manager(config,logger),
+            model_manager=get_model_manager(config),
             events=events,
-            logger=logger,
             latents=latents,
             images=images,
             metadata=metadata,
@@ -84,7 +83,7 @@ class ApiDependencies:
                 filename=db_location, table_name="graph_executions"
             ),
             processor=DefaultInvocationProcessor(),
-            restoration=RestorationServices(config,logger),
+            restoration=RestorationServices(config),
         )
 
         create_system_graphs(services.graph_library)

invokeai/app/api/models/images.py

@@ -32,9 +32,3 @@ class ProgressImage(BaseModel):
     width: int = Field(description="The effective width of the image in pixels")
     height: int = Field(description="The effective height of the image in pixels")
     dataURL: str = Field(description="The image data as a b64 data URL")
-
-
-class SavedImage(BaseModel):
-    image_name: str = Field(description="The name of the saved image")
-    thumbnail_name: str = Field(description="The name of the saved thumbnail")
-    created: int = Field(description="The created timestamp of the saved image")

invokeai/app/api/routers/images.py

@@ -6,14 +6,12 @@ import os
 from typing import Any
 import uuid
 
-from fastapi import Body, HTTPException, Path, Query, Request, UploadFile
+from fastapi import HTTPException, Path, Query, Request, UploadFile
 from fastapi.responses import FileResponse, Response
 from fastapi.routing import APIRouter
 from PIL import Image
-from invokeai.app.api.models.images import (
+from invokeai.app.api.models.images import ImageResponse, ImageResponseMetadata
-    ImageResponse,
+from invokeai.app.services.metadata import InvokeAIMetadata
-    ImageResponseMetadata,
-)
 from invokeai.app.services.item_storage import PaginatedResults
 
 from ...services.image_storage import ImageType
@@ -26,8 +24,8 @@ images_router = APIRouter(prefix="/v1/images", tags=["images"])
 async def get_image(
     image_type: ImageType = Path(description="The type of image to get"),
     image_name: str = Path(description="The name of the image to get"),
-) -> FileResponse:
+) -> FileResponse | Response:
-    """Gets an image"""
+    """Gets a result"""
 
     path = ApiDependencies.invoker.services.images.get_path(
         image_type=image_type, image_name=image_name
@@ -39,29 +37,17 @@ async def get_image(
         raise HTTPException(status_code=404)
 
 
-@images_router.delete("/{image_type}/{image_name}", operation_id="delete_image")
-async def delete_image(
-    image_type: ImageType = Path(description="The type of image to delete"),
-    image_name: str = Path(description="The name of the image to delete"),
-) -> None:
-    """Deletes an image and its thumbnail"""
-
-    ApiDependencies.invoker.services.images.delete(
-        image_type=image_type, image_name=image_name
-    )
-
-
 @images_router.get(
-    "/{thumbnail_type}/thumbnails/{thumbnail_name}", operation_id="get_thumbnail"
+    "/{image_type}/thumbnails/{image_name}", operation_id="get_thumbnail"
 )
 async def get_thumbnail(
-    thumbnail_type: ImageType = Path(description="The type of thumbnail to get"),
+    image_type: ImageType = Path(description="The type of image to get"),
-    thumbnail_name: str = Path(description="The name of the thumbnail to get"),
+    image_name: str = Path(description="The name of the image to get"),
 ) -> FileResponse | Response:
     """Gets a thumbnail"""
 
     path = ApiDependencies.invoker.services.images.get_path(
-        image_type=thumbnail_type, image_name=thumbnail_name, is_thumbnail=True
+        image_type=image_type, image_name=image_name, is_thumbnail=True
     )
 
     if ApiDependencies.invoker.services.images.validate_path(path):
@@ -98,27 +84,19 @@ async def upload_image(
 
     filename = f"{uuid.uuid4()}_{str(int(datetime.now(timezone.utc).timestamp()))}.png"
 
-    saved_image = ApiDependencies.invoker.services.images.save(
+    (image_path, thumbnail_path, ctime) = ApiDependencies.invoker.services.images.save(
         ImageType.UPLOAD, 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
-    )
-
-    thumbnail_url = ApiDependencies.invoker.services.images.get_uri(
-        ImageType.UPLOAD, saved_image.image_name, True
-    )
-
     res = ImageResponse(
         image_type=ImageType.UPLOAD,
-        image_name=saved_image.image_name,
+        image_name=filename,
-        image_url=image_url,
+        image_url=f"api/v1/images/{ImageType.UPLOAD.value}/{filename}",
-        thumbnail_url=thumbnail_url,
+        thumbnail_url=f"api/v1/images/{ImageType.UPLOAD.value}/thumbnails/{os.path.splitext(filename)[0]}.webp",
         metadata=ImageResponseMetadata(
-            created=saved_image.created,
+            created=ctime,
             width=img.width,
             height=img.height,
             invokeai=invokeai_metadata,
@@ -126,7 +104,9 @@ async def upload_image(
     )
 
     response.status_code = 201
-    response.headers["Location"] = image_url
+    response.headers["Location"] = request.url_for(
+        "get_image", image_type=ImageType.UPLOAD.value, image_name=filename
+    )
 
     return res
 

invokeai/app/api/routers/models.py

@@ -8,6 +8,10 @@ from fastapi.routing import APIRouter, HTTPException
 from pydantic import BaseModel, Field, parse_obj_as
 from pathlib import Path
 from ..dependencies import ApiDependencies
+from invokeai.backend.globals import Globals, global_converted_ckpts_dir
+from invokeai.backend.args import Args
+
+
 
 models_router = APIRouter(prefix="/v1/models", tags=["models"])
 
@@ -108,20 +112,19 @@ async def update_model(
 async def delete_model(model_name: str) -> None:
     """Delete Model"""
     model_names = ApiDependencies.invoker.services.model_manager.model_names()
-    logger = ApiDependencies.invoker.services.logger
     model_exists = model_name in model_names
 
     # check if model exists
-    logger.info(f"Checking for model {model_name}...")
+    print(f">> Checking for model {model_name}...")
            
     if model_exists:
-        logger.info(f"Deleting Model: {model_name}")
+        print(f">> Deleting Model: {model_name}")
         ApiDependencies.invoker.services.model_manager.del_model(model_name, delete_files=True)
-        logger.info(f"Model Deleted: {model_name}")
+        print(f">> Model Deleted: {model_name}")
         raise HTTPException(status_code=204, detail=f"Model '{model_name}' deleted successfully")
     
     else:
-        logger.error(f"Model not found")
+        print(f">> Model not found")
         raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
     
 

invokeai/app/api/routers/sessions.py

@@ -2,7 +2,8 @@
 
 from typing import Annotated, List, Optional, Union
 
-from fastapi import Body, HTTPException, Path, Query, Response
+from fastapi import Body, Path, Query
+from fastapi.responses import Response
 from fastapi.routing import APIRouter
 from pydantic.fields import Field
 
@@ -75,7 +76,7 @@ async def get_session(
     """Gets a session"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
     else:
         return session
 
@@ -98,7 +99,7 @@ async def add_node(
     """Adds a node to the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.add_node(node)
@@ -107,9 +108,9 @@ async def add_node(
         )  # TODO: can this be done automatically, or add node through an API?
         return session.id
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.put(
@@ -131,7 +132,7 @@ async def update_node(
     """Updates a node in the graph and removes all linked edges"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.update_node(node_path, node)
@@ -140,9 +141,9 @@ async def update_node(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.delete(
@@ -161,7 +162,7 @@ async def delete_node(
     """Deletes a node in the graph and removes all linked edges"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.delete_node(node_path)
@@ -170,9 +171,9 @@ async def delete_node(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.post(
@@ -191,7 +192,7 @@ async def add_edge(
     """Adds an edge to the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.add_edge(edge)
@@ -200,9 +201,9 @@ async def add_edge(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 # TODO: the edge being in the path here is really ugly, find a better solution
@@ -225,7 +226,7 @@ async def delete_edge(
     """Deletes an edge from the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         edge = Edge(
@@ -238,9 +239,9 @@ async def delete_edge(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.put(
@@ -258,14 +259,14 @@ async def invoke_session(
     all: bool = Query(
         default=False, description="Whether or not to invoke all remaining invocations"
     ),
-) -> Response:
+) -> None:
     """Invokes a session"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     if session.is_complete():
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
     ApiDependencies.invoker.invoke(session, invoke_all=all)
     return Response(status_code=202)
@@ -280,7 +281,7 @@ async def invoke_session(
 )
 async def cancel_session_invoke(
     session_id: str = Path(description="The id of the session to cancel"),
-) -> Response:
+) -> None:
     """Invokes a session"""
     ApiDependencies.invoker.cancel(session_id)
     return Response(status_code=202)

invokeai/app/api_app.py

@@ -3,7 +3,6 @@ import asyncio
 from inspect import signature
 
 import uvicorn
-import invokeai.backend.util.logging as logger
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
@@ -17,6 +16,7 @@ from ..backend import Args
 from .api.dependencies import ApiDependencies
 from .api.routers import images, sessions, models
 from .api.sockets import SocketIO
+from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
 
 # Create the app
@@ -56,7 +56,7 @@ async def startup_event():
     config.parse_args()
 
     ApiDependencies.initialize(
-        config=config, event_handler_id=event_handler_id, logger=logger
+        config=config, event_handler_id=event_handler_id
     )
 
 

invokeai/app/cli/commands.py

@@ -2,15 +2,14 @@
 
 from abc import ABC, abstractmethod
 import argparse
-from typing import Any, Callable, Iterable, Literal, Union, get_args, get_origin, get_type_hints
+from typing import Any, Callable, Iterable, Literal, get_args, get_origin, get_type_hints
 from pydantic import BaseModel, Field
 import networkx as nx
 import matplotlib.pyplot as plt
 
-import invokeai.backend.util.logging as logger
 from ..invocations.baseinvocation import BaseInvocation
 from ..invocations.image import ImageField
-from ..services.graph import GraphExecutionState, LibraryGraph, Edge
+from ..services.graph import GraphExecutionState, LibraryGraph, GraphInvocation, Edge
 from ..services.invoker import Invoker
 
 
@@ -230,7 +229,7 @@ class HistoryCommand(BaseCommand):
         for i in range(min(self.count, len(history))):
             entry_id = history[-1 - i]
             entry = context.get_session().graph.get_node(entry_id)
-            logger.info(f"{entry_id}: {get_invocation_command(entry)}")
+            print(f"{entry_id}: {get_invocation_command(entry)}")
 
 
 class SetDefaultCommand(BaseCommand):

invokeai/app/cli/completer.py

@@ -10,7 +10,6 @@ import shlex
 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 ..invocations.baseinvocation import BaseInvocation
 from .commands import BaseCommand
@@ -161,8 +160,8 @@ def set_autocompleter(model_manager: ModelManager) -> Completer:
         pass
     except OSError:  # file likely corrupted
         newname = f"{histfile}.old"
-        logger.error(
+        print(
-            f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
+            f"## Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
         )
         histfile.replace(Path(newname))
     atexit.register(readline.write_history_file, histfile)

invokeai/app/cli_app.py

@@ -13,20 +13,21 @@ from typing import (
 from pydantic import BaseModel
 from pydantic.fields import Field
 
-
-import invokeai.backend.util.logging as logger
 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, get_graph_execution_history
 from .cli.completer import set_autocompleter
+from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
 from .services.model_manager_initializer import get_model_manager
 from .services.restoration_services import RestorationServices
-from .services.graph import Edge, EdgeConnection, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible
+from .services.graph import Edge, EdgeConnection, ExposedNodeInput, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible
 from .services.default_graphs import default_text_to_image_graph_id
 from .services.image_storage import DiskImageStorage
 from .services.invocation_queue import MemoryInvocationQueue
@@ -181,7 +182,7 @@ def invoke_all(context: CliContext):
     # Print any errors
     if context.session.has_error():
         for n in context.session.errors:
-            context.invoker.services.logger.error(
+            print(
                 f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
             )
         
@@ -191,13 +192,13 @@ def invoke_all(context: CliContext):
 def invoke_cli():
     config = Args()
     config.parse_args()
-    model_manager = get_model_manager(config,logger=logger)
+    model_manager = get_model_manager(config)
 
     # 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)
+    completer = set_autocompleter(model_manager)
 
     events = EventServiceBase()
 
@@ -224,8 +225,7 @@ def invoke_cli():
             filename=db_location, table_name="graph_executions"
         ),
         processor=DefaultInvocationProcessor(),
-        restoration=RestorationServices(config,logger=logger),
+        restoration=RestorationServices(config),
-        logger=logger,
     )
 
     system_graphs = create_system_graphs(services.graph_library)
@@ -365,12 +365,12 @@ def invoke_cli():
             invoke_all(context)
 
         except InvalidArgs:
-            invoker.services.logger.warning('Invalid command, use "help" to list commands')
+            print('Invalid command, use "help" to list commands')
             continue
 
         except SessionError:
             # Start a new session
-            invoker.services.logger.warning("Session error: creating a new session")
+            print("Session error: creating a new session")
             context.reset()
 
         except ExitCli:

invokeai/app/invocations/compel.py

@@ -1,245 +0,0 @@
-from typing import Literal, Optional, Union
-from pydantic import BaseModel, Field
-
-from invokeai.app.invocations.util.choose_model import choose_model
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
-
-from ...backend.util.devices import choose_torch_device, torch_dtype
-from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
-from ...backend.stable_diffusion.textual_inversion_manager import TextualInversionManager
-
-from compel import Compel
-from compel.prompt_parser import (
-    Blend,
-    CrossAttentionControlSubstitute,
-    FlattenedPrompt,
-    Fragment,
-)
-
-from invokeai.backend.globals import Globals
-
-
-class ConditioningField(BaseModel):
-    conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
-    class Config:
-        schema_extra = {"required": ["conditioning_name"]}
-
-
-class CompelOutput(BaseInvocationOutput):
-    """Compel parser output"""
-
-    #fmt: off
-    type: Literal["compel_output"] = "compel_output"
-
-    conditioning: ConditioningField = Field(default=None, description="Conditioning")
-    #fmt: on
-
-
-class CompelInvocation(BaseInvocation):
-    """Parse prompt using compel package to conditioning."""
-
-    type: Literal["compel"] = "compel"
-
-    prompt: str = Field(default="", description="Prompt")
-    model: str = Field(default="", description="Model to use")
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "title": "Prompt (Compel)",
-                "tags": ["prompt", "compel"],
-                "type_hints": {
-                  "model": "model"
-                }
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> CompelOutput:
-
-        # TODO: load without model
-        model = choose_model(context.services.model_manager, self.model)
-        pipeline = model["model"]
-        tokenizer = pipeline.tokenizer
-        text_encoder = pipeline.text_encoder
-
-        # TODO: global? input?
-        #use_full_precision = precision == "float32" or precision == "autocast"
-        #use_full_precision = False
-
-        # TODO: redo TI when separate model loding implemented
-        #textual_inversion_manager = TextualInversionManager(
-        #    tokenizer=tokenizer,
-        #    text_encoder=text_encoder,
-        #    full_precision=use_full_precision,
-        #)
-
-        def load_huggingface_concepts(concepts: list[str]):
-            pipeline.textual_inversion_manager.load_huggingface_concepts(concepts)
-
-        # apply the concepts library to the prompt
-        prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers(
-            self.prompt,
-            lambda concepts: load_huggingface_concepts(concepts),
-            pipeline.textual_inversion_manager.get_all_trigger_strings(),
-        )
-
-        # lazy-load any deferred textual inversions.
-        # this might take a couple of seconds the first time a textual inversion is used.
-        pipeline.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
-            prompt_str
-        )
-
-        compel = Compel(
-            tokenizer=tokenizer,
-            text_encoder=text_encoder,
-            textual_inversion_manager=pipeline.textual_inversion_manager,
-            dtype_for_device_getter=torch_dtype,
-            truncate_long_prompts=True, # TODO:
-        )
-
-        # TODO: support legacy blend?
-
-        prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str)
-
-        if getattr(Globals, "log_tokenization", False):
-            log_tokenization_for_prompt_object(prompt, tokenizer)
-
-        c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
-
-        # TODO: long prompt support
-        #if not self.truncate_long_prompts:
-        #    [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
-
-        ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
-            tokens_count_including_eos_bos=get_max_token_count(tokenizer, prompt),
-            cross_attention_control_args=options.get("cross_attention_control", None),
-        )
-
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-
-        # TODO: hacky but works ;D maybe rename latents somehow?
-        context.services.latents.set(conditioning_name, (c, ec))
-
-        return CompelOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
-
-
-def get_max_token_count(
-    tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
-) -> int:
-    if type(prompt) is Blend:
-        blend: Blend = prompt
-        return max(
-            [
-                get_max_token_count(tokenizer, c, truncate_if_too_long)
-                for c in blend.prompts
-            ]
-        )
-    else:
-        return len(
-            get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long)
-        )
-
-
-def get_tokens_for_prompt_object(
-    tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
-) -> [str]:
-    if type(parsed_prompt) is Blend:
-        raise ValueError(
-            "Blend is not supported here - you need to get tokens for each of its .children"
-        )
-
-    text_fragments = [
-        x.text
-        if type(x) is Fragment
-        else (
-            " ".join([f.text for f in x.original])
-            if type(x) is CrossAttentionControlSubstitute
-            else str(x)
-        )
-        for x in parsed_prompt.children
-    ]
-    text = " ".join(text_fragments)
-    tokens = tokenizer.tokenize(text)
-    if truncate_if_too_long:
-        max_tokens_length = tokenizer.model_max_length - 2  # typically 75
-        tokens = tokens[0:max_tokens_length]
-    return tokens
-
-
-def log_tokenization_for_prompt_object(
-    p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
-):
-    display_label_prefix = display_label_prefix or ""
-    if type(p) is Blend:
-        blend: Blend = p
-        for i, c in enumerate(blend.prompts):
-            log_tokenization_for_prompt_object(
-                c,
-                tokenizer,
-                display_label_prefix=f"{display_label_prefix}(blend part {i + 1}, weight={blend.weights[i]})",
-            )
-    elif type(p) is FlattenedPrompt:
-        flattened_prompt: FlattenedPrompt = p
-        if flattened_prompt.wants_cross_attention_control:
-            original_fragments = []
-            edited_fragments = []
-            for f in flattened_prompt.children:
-                if type(f) is CrossAttentionControlSubstitute:
-                    original_fragments += f.original
-                    edited_fragments += f.edited
-                else:
-                    original_fragments.append(f)
-                    edited_fragments.append(f)
-
-            original_text = " ".join([x.text for x in original_fragments])
-            log_tokenization_for_text(
-                original_text,
-                tokenizer,
-                display_label=f"{display_label_prefix}(.swap originals)",
-            )
-            edited_text = " ".join([x.text for x in edited_fragments])
-            log_tokenization_for_text(
-                edited_text,
-                tokenizer,
-                display_label=f"{display_label_prefix}(.swap replacements)",
-            )
-        else:
-            text = " ".join([x.text for x in flattened_prompt.children])
-            log_tokenization_for_text(
-                text, tokenizer, display_label=display_label_prefix
-            )
-
-
-def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
-    """shows how the prompt is tokenized
-    # usually tokens have '</w>' to indicate end-of-word,
-    # but for readability it has been replaced with ' '
-    """
-    tokens = tokenizer.tokenize(text)
-    tokenized = ""
-    discarded = ""
-    usedTokens = 0
-    totalTokens = len(tokens)
-
-    for i in range(0, totalTokens):
-        token = tokens[i].replace("</w>", " ")
-        # alternate color
-        s = (usedTokens % 6) + 1
-        if truncate_if_too_long and i >= tokenizer.model_max_length:
-            discarded = discarded + f"\x1b[0;3{s};40m{token}"
-        else:
-            tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
-            usedTokens += 1
-
-    if usedTokens > 0:
-        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
-        print(f"{tokenized}\x1b[0m")
-
-    if discarded != "":
-        print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
-        print(f"{discarded}\x1b[0m")

invokeai/app/invocations/generate.py

@@ -46,8 +46,8 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
     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")
-    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
+    width:       int = Field(default=512, multiple_of=64, 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", )
+    height:      int = Field(default=512, multiple_of=64, 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", )
@@ -150,9 +150,6 @@ class ImageToImageInvocation(TextToImageInvocation):
         )
         mask = None
 
-        if self.fit:
-            image = image.resize((self.width, self.height))
-
         # Handle invalid model parameter
         model = choose_model(context.services.model_manager, self.model)
 
@@ -250,8 +247,8 @@ class InpaintInvocation(ImageToImageInvocation):
 
         outputs = Inpaint(model).generate(
             prompt=self.prompt,
-            init_image=image,
+            init_img=image,
-            mask_image=mask,
+            init_mask=mask,
             step_callback=partial(self.dispatch_progress, context, source_node_id),
             **self.dict(
                 exclude={"prompt", "image", "mask"}

invokeai/app/invocations/latent.py

@@ -13,13 +13,13 @@ 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.prompting.conditioning import get_uc_and_c_and_ec
 from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 import numpy as np
 from ..services.image_storage import ImageType
 from .baseinvocation import BaseInvocation, InvocationContext
 from .image import ImageField, ImageOutput, build_image_output
-from .compel import ConditioningField
 from ...backend.stable_diffusion import PipelineIntermediateState
 from diffusers.schedulers import SchedulerMixin as Scheduler
 import diffusers
@@ -113,8 +113,8 @@ class NoiseInvocation(BaseInvocation):
 
     # Inputs
     seed:        int = Field(ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", default_factory=random_seed)
-    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting noise", )
+    width:       int = Field(default=512, multiple_of=64, 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", )
+    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting noise", )
 
 
     # Schema customisation
@@ -138,16 +138,19 @@ class NoiseInvocation(BaseInvocation):
 
 # Text to image
 class TextToLatentsInvocation(BaseInvocation):
-    """Generates latents from conditionings."""
+    """Generates latents from a prompt."""
 
     type: Literal["t2l"] = "t2l"
 
     # Inputs
+    # TODO: consider making prompt optional to enable providing prompt through a link
     # fmt: off
-    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
+    prompt: Optional[str] = Field(description="The prompt to generate an image from")
-    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
+    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
     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")
+    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
+    height:      int = Field(default=512, multiple_of=64, 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", )
@@ -203,10 +206,8 @@ class TextToLatentsInvocation(BaseInvocation):
         return model
 
 
-    def get_conditioning_data(self, context: InvocationContext, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
+    def get_conditioning_data(self, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
-        c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(self.prompt, model=model)
-        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
-
         conditioning_data = ConditioningData(
             uc,
             c,
@@ -233,7 +234,7 @@ class TextToLatentsInvocation(BaseInvocation):
             self.dispatch_progress(context, source_node_id, state)
 
         model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(context, model)
+        conditioning_data = self.get_conditioning_data(model)
 
         # TODO: Verify the noise is the right size
 
@@ -362,74 +363,9 @@ class LatentsToImageInvocation(BaseInvocation):
                 session_id=context.graph_execution_state_id, node=self
             )
 
-            torch.cuda.empty_cache()
-
             context.services.images.save(image_type, image_name, image, metadata)
             return build_image_output(
-                image_type=image_type, image_name=image_name, image=image
+                image_type=image_type,
+                image_name=image_name,
+                image=image
             )
-
-
-LATENTS_INTERPOLATION_MODE = Literal[
-    "nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"
-]
-
-
-class ResizeLatentsInvocation(BaseInvocation):
-    """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
-
-    type: Literal["lresize"] = "lresize"
-
-    # Inputs
-    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)")
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        resized_latents = torch.nn.functional.interpolate(
-            latents,
-            size=(self.height // 8, self.width // 8),
-            mode=self.mode,
-            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.set(name, resized_latents)
-        return LatentsOutput(latents=LatentsField(latents_name=name))
-
-
-class ScaleLatentsInvocation(BaseInvocation):
-    """Scales latents by a given factor."""
-
-    type: Literal["lscale"] = "lscale"
-
-    # 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)")
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        # resizing
-        resized_latents = torch.nn.functional.interpolate(
-            latents,
-            scale_factor=self.scale_factor,
-            mode=self.mode,
-            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.set(name, resized_latents)
-        return LatentsOutput(latents=LatentsField(latents_name=name))

invokeai/app/invocations/util/choose_model.py

@@ -3,11 +3,12 @@ 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:
         model = model_manager.get_model()
-        logger.warning(f"{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead.")
+        print(
+            f"* Warning: '{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead."
+        )
 
     return model

invokeai/app/services/default_graphs.py

@@ -1,5 +1,4 @@
 from ..invocations.latent import LatentsToImageInvocation, NoiseInvocation, TextToLatentsInvocation
-from ..invocations.compel import CompelInvocation
 from ..invocations.params import ParamIntInvocation
 from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
 from .item_storage import ItemStorageABC
@@ -17,32 +16,26 @@ def create_text_to_image() -> LibraryGraph:
             nodes={
                 'width': ParamIntInvocation(id='width', a=512),
                 'height': ParamIntInvocation(id='height', a=512),
-                'seed': ParamIntInvocation(id='seed', a=-1),
                 '3': NoiseInvocation(id='3'),
-                '4': CompelInvocation(id='4'),
+                '4': TextToLatentsInvocation(id='4'),
-                '5': CompelInvocation(id='5'),
+                '5': LatentsToImageInvocation(id='5')
-                '6': TextToLatentsInvocation(id='6'),
-                '7': LatentsToImageInvocation(id='7'),
             },
             edges=[
                 Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
                 Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
-                Edge(source=EdgeConnection(node_id='seed', field='a'), destination=EdgeConnection(node_id='3', field='seed')),
+                Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='4', field='width')),
-                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='6', field='noise')),
+                Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='4', field='height')),
-                Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')),
+                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='4', field='noise')),
-                Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')),
+                Edge(source=EdgeConnection(node_id='4', field='latents'), destination=EdgeConnection(node_id='5', field='latents')),
-                Edge(source=EdgeConnection(node_id='5', field='conditioning'), destination=EdgeConnection(node_id='6', field='negative_conditioning')),
             ]
         ),
         exposed_inputs=[
-            ExposedNodeInput(node_path='4', field='prompt', alias='positive_prompt'),
+            ExposedNodeInput(node_path='4', field='prompt', alias='prompt'),
-            ExposedNodeInput(node_path='5', field='prompt', alias='negative_prompt'),
             ExposedNodeInput(node_path='width', field='a', alias='width'),
-            ExposedNodeInput(node_path='height', field='a', alias='height'),
+            ExposedNodeInput(node_path='height', field='a', alias='height')
-            ExposedNodeInput(node_path='seed', field='a', alias='seed'),
         ],
         exposed_outputs=[
-            ExposedNodeOutput(node_path='7', field='image', alias='image')
+            ExposedNodeOutput(node_path='5', field='image', alias='image')
         ])
 
 

invokeai/app/services/image_storage.py

@@ -5,16 +5,11 @@ from glob import glob
 from abc import ABC, abstractmethod
 from pathlib import Path
 from queue import Queue
-from typing import Dict, List
+from typing import Dict, List, Tuple
 
 from PIL.Image import Image
 import PIL.Image as PILImage
-from send2trash import send2trash
+from invokeai.app.api.models.images import ImageResponse, ImageResponseMetadata
-from invokeai.app.api.models.images import (
-    ImageResponse,
-    ImageResponseMetadata,
-    SavedImage,
-)
 from invokeai.app.models.image import ImageType
 from invokeai.app.services.metadata import (
     InvokeAIMetadata,
@@ -46,15 +41,7 @@ class ImageStorageBase(ABC):
     def get_path(
         self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
     ) -> str:
-        """Gets the internal path to an image or its thumbnail."""
+        """Gets the path to an image or its thumbnail."""
-        pass
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    @abstractmethod
-    def get_uri(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        """Gets the external URI to an image or its thumbnail."""
         pass
 
     # TODO: make this a bit more flexible for e.g. cloud storage
@@ -70,8 +57,8 @@ class ImageStorageBase(ABC):
         image_name: str,
         image: Image,
         metadata: InvokeAIMetadata | None = None,
-    ) -> SavedImage:
+    ) -> Tuple[str, str, int]:
-        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
+        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image path, thumbnail path, and created timestamp."""
         pass
 
     @abstractmethod
@@ -139,8 +126,8 @@ class DiskImageStorage(ImageStorageBase):
                     image_type=image_type.value,
                     image_name=filename,
                     # TODO: DiskImageStorage should not be building URLs...?
-                    image_url=self.get_uri(image_type, filename),
+                    image_url=f"api/v1/images/{image_type.value}/{filename}",
-                    thumbnail_url=self.get_uri(image_type, filename, True),
+                    thumbnail_url=f"api/v1/images/{image_type.value}/thumbnails/{os.path.splitext(filename)[0]}.webp",
                     # TODO: Creation of this object should happen elsewhere (?), just making it fit here so it works
                     metadata=ImageResponseMetadata(
                         created=int(os.path.getctime(path)),
@@ -187,23 +174,7 @@ class DiskImageStorage(ImageStorageBase):
         else:
             path = os.path.join(self.__output_folder, image_type, basename)
 
-        abspath = os.path.abspath(path)
+        return path
-
-        return abspath
-
-    def get_uri(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        # strip out any relative path shenanigans
-        basename = os.path.basename(image_name)
-
-        if is_thumbnail:
-            thumbnail_basename = get_thumbnail_name(basename)
-            uri = f"api/v1/images/{image_type.value}/thumbnails/{thumbnail_basename}"
-        else:
-            uri = f"api/v1/images/{image_type.value}/{basename}"
-
-        return uri
 
     def validate_path(self, path: str) -> bool:
         try:
@@ -218,7 +189,7 @@ class DiskImageStorage(ImageStorageBase):
         image_name: str,
         image: Image,
         metadata: InvokeAIMetadata | None = None,
-    ) -> SavedImage:
+    ) -> Tuple[str, str, int]:
         image_path = self.get_path(image_type, image_name)
 
         # TODO: Reading the image and then saving it strips the metadata...
@@ -226,7 +197,7 @@ class DiskImageStorage(ImageStorageBase):
             pnginfo = build_invokeai_metadata_pnginfo(metadata=metadata)
             image.save(image_path, "PNG", pnginfo=pnginfo)
         else:
-            image.save(image_path)  # this saved image has an empty info
+            image.save(image_path) # this saved image has an empty info
 
         thumbnail_name = get_thumbnail_name(image_name)
         thumbnail_path = self.get_path(image_type, thumbnail_name, is_thumbnail=True)
@@ -236,30 +207,24 @@ class DiskImageStorage(ImageStorageBase):
         self.__set_cache(image_path, image)
         self.__set_cache(thumbnail_path, thumbnail_image)
 
-        return SavedImage(
+        return (image_path, thumbnail_path, int(os.path.getctime(image_path)))
-            image_name=image_name,
-            thumbnail_name=thumbnail_name,
-            created=int(os.path.getctime(image_path)),
-        )
 
     def delete(self, image_type: ImageType, image_name: str) -> None:
-        basename = os.path.basename(image_name)
+        image_path = self.get_path(image_type, image_name)
-        image_path = self.get_path(image_type, basename)
+        thumbnail_path = self.get_path(image_type, image_name, True)
-
         if os.path.exists(image_path):
-            send2trash(image_path)
+            os.remove(image_path)
+
         if image_path in self.__cache:
             del self.__cache[image_path]
 
-        thumbnail_name = get_thumbnail_name(image_name)
-        thumbnail_path = self.get_path(image_type, thumbnail_name, True)
-
         if os.path.exists(thumbnail_path):
-            send2trash(thumbnail_path)
+            os.remove(thumbnail_path)
+
         if thumbnail_path in self.__cache:
             del self.__cache[thumbnail_path]
 
-    def __get_cache(self, image_name: str) -> Image | None:
+    def __get_cache(self, image_name: str) -> Image:
         return None if image_name not in self.__cache else self.__cache[image_name]
 
     def __set_cache(self, image_name: str, image: Image):

invokeai/app/services/invocation_services.py

@@ -1,6 +1,4 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import types
 from invokeai.app.services.metadata import MetadataServiceBase
 from invokeai.backend import ModelManager
 
@@ -31,7 +29,6 @@ class InvocationServices:
             self,
             model_manager: ModelManager,
             events: EventServiceBase,
-            logger: types.ModuleType,
             latents: LatentsStorageBase,
             images: ImageStorageBase,
             metadata: MetadataServiceBase,
@@ -43,7 +40,6 @@ class InvocationServices:
     ):
         self.model_manager = model_manager
         self.events = events
-        self.logger = logger
         self.latents = latents
         self.images = images
         self.metadata = metadata

invokeai/app/services/invoker.py

@@ -71,12 +71,18 @@ class Invoker:
         for service in vars(self.services):
             self.__start_service(getattr(self.services, service))
 
+        for service in vars(self.services):
+            self.__start_service(getattr(self.services, service))
+
     def stop(self) -> None:
         """Stops the invoker. A new invoker will have to be created to execute further."""
         # First stop all services
         for service in vars(self.services):
             self.__stop_service(getattr(self.services, service))
 
+        for service in vars(self.services):
+            self.__stop_service(getattr(self.services, service))
+
         self.services.queue.put(None)
 
 

invokeai/app/services/model_manager_initializer.py

@@ -5,7 +5,6 @@ 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 ...backend import ModelManager
@@ -13,16 +12,16 @@ 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:
+def get_model_manager(config: Args) -> ModelManager:
     if not config.conf:
         config_file = os.path.join(Globals.root, "configs", "models.yaml")
         if not os.path.exists(config_file):
             report_model_error(
-                config, FileNotFoundError(f"The file {config_file} could not be found."), logger
+                config, FileNotFoundError(f"The file {config_file} could not be found.")
             )
 
-    logger.info(f"{invokeai.version.__app_name__}, version {invokeai.version.__version__}")
+    print(f">> {invokeai.version.__app_name__}, version {invokeai.version.__version__}")
-    logger.info(f'InvokeAI runtime directory is "{Globals.root}"')
+    print(f'>> InvokeAI runtime directory is "{Globals.root}"')
 
     # these two lines prevent a horrible warning message from appearing
     # when the frozen CLIP tokenizer is imported
@@ -63,12 +62,11 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager:
             device_type=device,
             max_loaded_models=config.max_loaded_models,
             embedding_path = Path(embedding_path),
-            logger = logger,
         )
     except (FileNotFoundError, TypeError, AssertionError) as e:
-        report_model_error(config, e, logger)
+        report_model_error(config, e)
     except (IOError, KeyError) as e:
-        logger.error(f"{e}. Aborting.")
+        print(f"{e}. Aborting.")
         sys.exit(-1)
 
     # try to autoconvert new models
@@ -78,18 +76,18 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager:
             conf_path=config.conf,
             weights_directory=path,
         )
-    logger.info('Model manager initialized')
+
     return model_manager
 
-def report_model_error(opt: Namespace, e: Exception, logger: types.ModuleType):
+def report_model_error(opt: Namespace, e: Exception):
-    logger.error(f'An error occurred while attempting to initialize the model: "{str(e)}"')
+    print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
-    logger.error(
+    print(
-        "This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
+        "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
     )
     yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE")
     if yes_to_all:
-        logger.warning(
+        print(
-            "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
+            "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
         )
     else:
         response = input(
@@ -98,12 +96,13 @@ def report_model_error(opt: Namespace, e: Exception, logger: types.ModuleType):
         if response.startswith(("n", "N")):
             return
 
-    logger.info("invokeai-configure is launching....\n")
+    print("invokeai-configure is launching....\n")
 
     # Match arguments that were set on the CLI
     # only the arguments accepted by the configuration script are parsed
     root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else []
     config = ["--config", opt.conf] if opt.conf is not None else []
+    previous_config = sys.argv
     sys.argv = ["invokeai-configure"]
     sys.argv.extend(root_dir)
     sys.argv.extend(config.to_dict())

invokeai/app/services/processor.py

@@ -1,5 +1,5 @@
 import traceback
-from threading import Event, Thread, BoundedSemaphore
+from threading import Event, Thread
 
 from ..invocations.baseinvocation import InvocationContext
 from .invocation_queue import InvocationQueueItem
@@ -10,11 +10,8 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker_thread: Thread
     __stop_event: Event
     __invoker: Invoker
-    __threadLimit: BoundedSemaphore
 
     def start(self, invoker) -> None:
-        # if we do want multithreading at some point, we could make this configurable
-        self.__threadLimit = BoundedSemaphore(1)
         self.__invoker = invoker
         self.__stop_event = Event()
         self.__invoker_thread = Thread(
@@ -23,7 +20,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
             kwargs=dict(stop_event=self.__stop_event),
         )
         self.__invoker_thread.daemon = (
-            True  # TODO: make async and do not use threads
+            True  # TODO: probably better to just not use threads?
         )
         self.__invoker_thread.start()
 
@@ -32,7 +29,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
 
     def __process(self, stop_event: Event):
         try:
-            self.__threadLimit.acquire()
             while not stop_event.is_set():
                 queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
                 if not queue_item:  # Probably stopping
@@ -131,6 +127,4 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     )
 
         except KeyboardInterrupt:
-            pass  # Log something? KeyboardInterrupt is probably not going to be seen by the processor
+            ...  # Log something?
-        finally:
-            self.__threadLimit.release()

invokeai/app/services/restoration_services.py

@@ -1,7 +1,6 @@
 import sys
 import traceback
 import torch
-from typing import types
 from ...backend.restoration import Restoration
 from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
 
@@ -11,7 +10,7 @@ from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
 class RestorationServices:
     '''Face restoration and upscaling'''
     
-    def __init__(self,args,logger:types.ModuleType):
+    def __init__(self,args):
         try:
             gfpgan, codeformer, esrgan = None, None, None
             if args.restore or args.esrgan:
@@ -21,22 +20,20 @@ class RestorationServices:
                         args.gfpgan_model_path
                     )
                 else:
-                    logger.info("Face restoration disabled")
+                    print(">> Face restoration disabled")
                     if args.esrgan:
                         esrgan = restoration.load_esrgan(args.esrgan_bg_tile)
                     else:
-                        logger.info("Upscaling disabled")
+                        print(">> Upscaling disabled")
             else:
-                logger.info("Face restoration and upscaling disabled")
+                print(">> Face restoration and upscaling disabled")
         except (ModuleNotFoundError, ImportError):
             print(traceback.format_exc(), file=sys.stderr)
-            logger.info("You may need to install the ESRGAN and/or GFPGAN modules")
+            print(">> You may need to install the ESRGAN and/or GFPGAN modules")
         self.device = torch.device(choose_torch_device())
         self.gfpgan = gfpgan
         self.codeformer = codeformer
         self.esrgan = esrgan
-        self.logger = logger
-        self.logger.info('Face restoration initialized')
 
     # note that this one method does gfpgan and codepath reconstruction, as well as
     # esrgan upscaling
@@ -61,15 +58,15 @@ class RestorationServices:
                     if self.gfpgan is not None or self.codeformer is not None:
                         if facetool == "gfpgan":
                             if self.gfpgan is None:
-                                self.logger.info(
+                                print(
-                                    "GFPGAN not found. Face restoration is disabled."
+                                    ">> GFPGAN not found. Face restoration is disabled."
                                 )
                             else:
                                 image = self.gfpgan.process(image, strength, seed)
                         if facetool == "codeformer":
                             if self.codeformer is None:
-                                self.logger.info(
+                                print(
-                                    "CodeFormer not found. Face restoration is disabled."
+                                    ">> CodeFormer not found. Face restoration is disabled."
                                 )
                             else:
                                 cf_device = (
@@ -83,7 +80,7 @@ class RestorationServices:
                                     fidelity=codeformer_fidelity,
                                 )
                     else:
-                        self.logger.info("Face Restoration is disabled.")
+                        print(">> Face Restoration is disabled.")
                 if upscale is not None:
                     if self.esrgan is not None:
                         if len(upscale) < 2:
@@ -96,10 +93,10 @@ class RestorationServices:
                             denoise_str=upscale_denoise_str,
                         )
                     else:
-                        self.logger.info("ESRGAN is disabled. Image not upscaled.")
+                        print(">> ESRGAN is disabled. Image not upscaled.")
             except Exception as e:
-                self.logger.info(
+                print(
-                    f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
+                    f">> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
                 )
 
             if image_callback is not None:

invokeai/backend/args.py

@@ -96,7 +96,6 @@ from pathlib import Path
 from typing import List
 
 import invokeai.version
-import invokeai.backend.util.logging as logger
 from invokeai.backend.image_util import retrieve_metadata
 
 from .globals import Globals
@@ -190,7 +189,7 @@ class Args(object):
                 print(f"{APP_NAME} {APP_VERSION}")
                 sys.exit(0)
 
-            logger.info("Initializing, be patient...")
+            print("* Initializing, be patient...")
             Globals.root = Path(os.path.abspath(switches.root_dir or Globals.root))
             Globals.try_patchmatch = switches.patchmatch
 
@@ -198,13 +197,14 @@ class Args(object):
             initfile = os.path.expanduser(os.path.join(Globals.root, Globals.initfile))
             legacyinit = os.path.expanduser("~/.invokeai")
             if os.path.exists(initfile):
-                logger.info(
+                print(
-                    f"Initialization file {initfile} found. Loading...",
+                    f">> Initialization file {initfile} found. Loading...",
+                    file=sys.stderr,
                 )
                 sysargs.insert(0, f"@{initfile}")
             elif os.path.exists(legacyinit):
-                logger.warning(
+                print(
-                    f"Old initialization file found at {legacyinit}. This location is deprecated. Please move it to {Globals.root}/invokeai.init."
+                    f">> WARNING: Old initialization file found at {legacyinit}. This location is deprecated. Please move it to {Globals.root}/invokeai.init."
                 )
                 sysargs.insert(0, f"@{legacyinit}")
             Globals.log_tokenization = self._arg_parser.parse_args(
@@ -214,7 +214,7 @@ class Args(object):
             self._arg_switches = self._arg_parser.parse_args(sysargs)
             return self._arg_switches
         except Exception as e:
-            logger.error(f"An exception has occurred: {e}")
+            print(f"An exception has occurred: {e}")
             return None
 
     def parse_cmd(self, cmd_string):
@@ -1154,7 +1154,7 @@ class Args(object):
 
 
 def format_metadata(**kwargs):
-    logger.warning("format_metadata() is deprecated. Please use metadata_dumps()")
+    print("format_metadata() is deprecated. Please use metadata_dumps()")
     return metadata_dumps(kwargs)
 
 
@@ -1326,7 +1326,7 @@ def metadata_loads(metadata) -> list:
         import sys
         import traceback
 
-        logger.error("Could not read metadata")
+        print(">> could not read metadata", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
     return results
 

invokeai/backend/generate.py

@@ -27,7 +27,6 @@ from diffusers.utils.import_utils import is_xformers_available
 from omegaconf import OmegaConf
 from pathlib import Path
 
-import invokeai.backend.util.logging as logger
 from .args import metadata_from_png
 from .generator import infill_methods
 from .globals import Globals, global_cache_dir
@@ -196,12 +195,12 @@ class Generate:
         # device to Generate(). However the device was then ignored, so
         # it wasn't actually doing anything. This logic could be reinstated.
         self.device = torch.device(choose_torch_device())
-        logger.info(f"Using device_type {self.device.type}")
+        print(f">> Using device_type {self.device.type}")
         if full_precision:
             if self.precision != "auto":
                 raise ValueError("Remove --full_precision / -F if using --precision")
-            logger.warning("Please remove deprecated --full_precision / -F")
+            print("Please remove deprecated --full_precision / -F")
-            logger.warning("If auto config does not work you can use --precision=float32")
+            print("If auto config does not work you can use --precision=float32")
             self.precision = "float32"
         if self.precision == "auto":
             self.precision = choose_precision(self.device)
@@ -209,13 +208,13 @@ class Generate:
 
         if is_xformers_available():
             if torch.cuda.is_available() and not Globals.disable_xformers:
-                logger.info("xformers memory-efficient attention is available and enabled")
+                print(">> xformers memory-efficient attention is available and enabled")
             else:
-                logger.info(
+                print(
-                    "xformers memory-efficient attention is available but disabled"
+                    ">> xformers memory-efficient attention is available but disabled"
                 )
         else:
-            logger.info("xformers not installed")
+            print(">> xformers not installed")
 
         # model caching system for fast switching
         self.model_manager = ModelManager(
@@ -230,8 +229,8 @@ class Generate:
         fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME
         model = model or fallback
         if not self.model_manager.valid_model(model):
-            logger.warning(
+            print(
-                f'"{model}" is not a known model name; falling back to {fallback}.'
+                f'** "{model}" is not a known model name; falling back to {fallback}.'
             )
             model = None
         self.model_name = model or fallback
@@ -247,10 +246,10 @@ class Generate:
 
         # load safety checker if requested
         if safety_checker:
-            logger.info("Initializing NSFW checker")
+            print(">> Initializing NSFW checker")
             self.safety_checker = SafetyChecker(self.device)
         else:
-            logger.info("NSFW checker is disabled")
+            print(">> NSFW checker is disabled")
 
     def prompt2png(self, prompt, outdir, **kwargs):
         """
@@ -568,7 +567,7 @@ class Generate:
             self.clear_cuda_cache()
 
             if catch_interrupts:
-                logger.warning("Interrupted** Partial results will be returned.")
+                print("**Interrupted** Partial results will be returned.")
             else:
                 raise KeyboardInterrupt
         except RuntimeError:
@@ -576,11 +575,11 @@ class Generate:
             self.clear_cuda_cache()
 
             print(traceback.format_exc(), file=sys.stderr)
-            logger.info("Could not generate image.")
+            print(">> Could not generate image.")
 
         toc = time.time()
-        logger.info("Usage stats:")
+        print("\n>> Usage stats:")
-        logger.info(f"{len(results)} image(s) generated in "+"%4.2fs" % (toc - tic))
+        print(f">>   {len(results)} image(s) generated in", "%4.2fs" % (toc - tic))
         self.print_cuda_stats()
         return results
 
@@ -610,16 +609,16 @@ class Generate:
     def print_cuda_stats(self):
         if self._has_cuda():
             self.gather_cuda_stats()
-            logger.info(
+            print(
-                "Max VRAM used for this generation: "+
+                ">>   Max VRAM used for this generation:",
-                "%4.2fG. " % (self.max_memory_allocated / 1e9)+
+                "%4.2fG." % (self.max_memory_allocated / 1e9),
-                "Current VRAM utilization: "+
+                "Current VRAM utilization:",
-                "%4.2fG" % (self.memory_allocated / 1e9)
+                "%4.2fG" % (self.memory_allocated / 1e9),
             )
 
-            logger.info(
+            print(
-                "Max VRAM used since script start: " +
+                ">>   Max VRAM used since script start: ",
-                "%4.2fG" % (self.session_peakmem / 1e9)
+                "%4.2fG" % (self.session_peakmem / 1e9),
             )
 
     # this needs to be generalized to all sorts of postprocessors, which should be wrapped
@@ -648,7 +647,7 @@ class Generate:
             seed = random.randrange(0, np.iinfo(np.uint32).max)
 
         prompt = opt.prompt or args.prompt or ""
-        logger.info(f'using seed {seed} and prompt "{prompt}" for {image_path}')
+        print(f'>> using seed {seed} and prompt "{prompt}" for {image_path}')
 
         # try to reuse the same filename prefix as the original file.
         # we take everything up to the first period
@@ -697,8 +696,8 @@ class Generate:
                 try:
                     extend_instructions[direction] = int(pixels)
                 except ValueError:
-                    logger.warning(
+                    print(
-                        'invalid extension instruction. Use <directions> <pixels>..., as in "top 64 left 128 right 64 bottom 64"'
+                        '** invalid extension instruction. Use <directions> <pixels>..., as in "top 64 left 128 right 64 bottom 64"'
                     )
 
             opt.seed = seed
@@ -721,8 +720,8 @@ class Generate:
             # fetch the metadata from the image
             generator = self.select_generator(embiggen=True)
             opt.strength = opt.embiggen_strength or 0.40
-            logger.info(
+            print(
-                f"Setting img2img strength to {opt.strength} for happy embiggening"
+                f">> Setting img2img strength to {opt.strength} for happy embiggening"
             )
             generator.generate(
                 prompt,
@@ -749,12 +748,12 @@ class Generate:
             return restorer.process(opt, args, image_callback=callback, prefix=prefix)
 
         elif tool is None:
-            logger.warning(
+            print(
-                "please provide at least one postprocessing option, such as -G or -U"
+                "* please provide at least one postprocessing option, such as -G or -U"
             )
             return None
         else:
-            logger.warning(f"postprocessing tool {tool} is not yet supported")
+            print(f"* postprocessing tool {tool} is not yet supported")
             return None
 
     def select_generator(
@@ -798,8 +797,8 @@ class Generate:
         image = self._load_img(img)
 
         if image.width < self.width and image.height < self.height:
-            logger.warning(
+            print(
-                f"img2img and inpainting may produce unexpected results with initial images smaller than {self.width}x{self.height} in both dimensions"
+                f">> WARNING: img2img and inpainting may produce unexpected results with initial images smaller than {self.width}x{self.height} in both dimensions"
             )
 
         # if image has a transparent area and no mask was provided, then try to generate mask
@@ -810,8 +809,8 @@ class Generate:
         if (image.width * image.height) > (
             self.width * self.height
         ) and self.size_matters:
-            logger.info(
+            print(
-                "This input is larger than your defaults. If you run out of memory, please use a smaller image."
+                ">> This input is larger than your defaults. If you run out of memory, please use a smaller image."
             )
             self.size_matters = False
 
@@ -892,11 +891,11 @@ class Generate:
         try:
             model_data = cache.get_model(model_name)
         except Exception as e:
-            logger.warning(f"model {model_name} could not be loaded: {str(e)}")
+            print(f"** model {model_name} could not be loaded: {str(e)}")
             print(traceback.format_exc(), file=sys.stderr)
             if previous_model_name is None:
                 raise e
-            logger.warning("trying to reload previous model")
+            print("** trying to reload previous model")
             model_data = cache.get_model(previous_model_name)  # load previous
             if model_data is None:
                 raise e
@@ -963,15 +962,15 @@ class Generate:
                     if self.gfpgan is not None or self.codeformer is not None:
                         if facetool == "gfpgan":
                             if self.gfpgan is None:
-                                logger.info(
+                                print(
-                                    "GFPGAN not found. Face restoration is disabled."
+                                    ">> GFPGAN not found. Face restoration is disabled."
                                 )
                             else:
                                 image = self.gfpgan.process(image, strength, seed)
                         if facetool == "codeformer":
                             if self.codeformer is None:
-                                logger.info(
+                                print(
-                                    "CodeFormer not found. Face restoration is disabled."
+                                    ">> CodeFormer not found. Face restoration is disabled."
                                 )
                             else:
                                 cf_device = (
@@ -985,7 +984,7 @@ class Generate:
                                     fidelity=codeformer_fidelity,
                                 )
                     else:
-                        logger.info("Face Restoration is disabled.")
+                        print(">> Face Restoration is disabled.")
                 if upscale is not None:
                     if self.esrgan is not None:
                         if len(upscale) < 2:
@@ -998,10 +997,10 @@ class Generate:
                             denoise_str=upscale_denoise_str,
                         )
                     else:
-                        logger.info("ESRGAN is disabled. Image not upscaled.")
+                        print(">> ESRGAN is disabled. Image not upscaled.")
             except Exception as e:
-                logger.info(
+                print(
-                    f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
+                    f">> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
                 )
 
             if image_callback is not None:
@@ -1067,17 +1066,17 @@ class Generate:
         if self.sampler_name in scheduler_map:
             sampler_class = scheduler_map[self.sampler_name]
             msg = (
-                f"Setting Sampler to {self.sampler_name} ({sampler_class.__name__})"
+                f">> Setting Sampler to {self.sampler_name} ({sampler_class.__name__})"
             )
             self.sampler = sampler_class.from_config(self.model.scheduler.config)
         else:
             msg = (
-                f" Unsupported Sampler: {self.sampler_name} "+
+                f">> Unsupported Sampler: {self.sampler_name} "
                 f"Defaulting to {default}"
             )
             self.sampler = default
 
-        logger.info(msg)
+        print(msg)
 
         if not hasattr(self.sampler, "uses_inpainting_model"):
             # FIXME: terrible kludge!
@@ -1086,17 +1085,17 @@ class Generate:
     def _load_img(self, img) -> Image:
         if isinstance(img, Image.Image):
             image = img
-            logger.info(f"using provided input image of size {image.width}x{image.height}")
+            print(f">> using provided input image of size {image.width}x{image.height}")
         elif isinstance(img, str):
-            assert os.path.exists(img), f"{img}: File not found"
+            assert os.path.exists(img), f">> {img}: File not found"
 
             image = Image.open(img)
-            logger.info(
+            print(
-                f"loaded input image of size {image.width}x{image.height} from {img}"
+                f">> loaded input image of size {image.width}x{image.height} from {img}"
             )
         else:
             image = Image.open(img)
-            logger.info(f"loaded input image of size {image.width}x{image.height}")
+            print(f">> loaded input image of size {image.width}x{image.height}")
         image = ImageOps.exif_transpose(image)
         return image
 
@@ -1184,14 +1183,14 @@ class Generate:
 
     def _transparency_check_and_warning(self, image, mask, force_outpaint=False):
         if not mask:
-           logger.info(
+            print(
-                "Initial image has transparent areas. Will inpaint in these regions."
+                ">> Initial image has transparent areas. Will inpaint in these regions."
             )
-           if (not force_outpaint) and self._check_for_erasure(image):
+            if (not force_outpaint) and self._check_for_erasure(image):
-                logger.info(
+                print(
-                    "Colors underneath the transparent region seem to have been erased.\n" +
+                    ">> WARNING: Colors underneath the transparent region seem to have been erased.\n",
-                    "Inpainting will be suboptimal. Please preserve the colors when making\n" +
+                    ">>          Inpainting will be suboptimal. Please preserve the colors when making\n",
-                    "a transparency mask, or provide mask explicitly using --init_mask (-M)."
+                    ">>          a transparency mask, or provide mask explicitly using --init_mask (-M).",
                 )
 
     def _squeeze_image(self, image):
@@ -1202,11 +1201,11 @@ class Generate:
 
     def _fit_image(self, image, max_dimensions):
         w, h = max_dimensions
-        logger.info(f"image will be resized to fit inside a box {w}x{h} in size.")
+        print(f">> image will be resized to fit inside a box {w}x{h} in size.")
         # note that InitImageResizer does the multiple of 64 truncation internally
         image = InitImageResizer(image).resize(width=w, height=h)
-        logger.info(
+        print(
-            f"after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}"
+            f">> after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}"
         )
         return image
 
@@ -1217,8 +1216,8 @@ class Generate:
         )  # resize to integer multiple of 64
         if h != height or w != width:
             if log:
-                logger.info(
+                print(
-                    f"Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}"
+                    f">> Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}"
                 )
             height = h
             width = w

invokeai/backend/generator/base.py

@@ -25,7 +25,6 @@ from typing import Callable, List, Iterator, Optional, Type
 from dataclasses import dataclass, field
 from diffusers.schedulers import SchedulerMixin as Scheduler
 
-import invokeai.backend.util.logging as logger
 from ..image_util import configure_model_padding
 from ..util.util import rand_perlin_2d
 from ..safety_checker import SafetyChecker
@@ -373,7 +372,7 @@ class Generator:
                     try:
                         x_T = self.get_noise(width, height)
                     except:
-                        logger.error("An error occurred while getting initial noise")
+                        print("** An error occurred while getting initial noise **")
                         print(traceback.format_exc())
 
                 # Pass on the seed in case a layer beneath us needs to generate noise on its own.
@@ -608,7 +607,7 @@ class Generator:
         image = self.sample_to_image(sample)
         dirname = os.path.dirname(filepath) or "."
         if not os.path.exists(dirname):
-            logger.info(f"creating directory {dirname}")
+            print(f"** creating directory {dirname}")
             os.makedirs(dirname, exist_ok=True)
         image.save(filepath, "PNG")
 

invokeai/backend/generator/embiggen.py

@@ -8,11 +8,10 @@ import torch
 from PIL import Image
 from tqdm import trange
 
-import invokeai.backend.util.logging as logger
-
 from .base import Generator
 from .img2img import Img2Img
 
+
 class Embiggen(Generator):
     def __init__(self, model, precision):
         super().__init__(model, precision)
@@ -73,22 +72,22 @@ class Embiggen(Generator):
             embiggen = [1.0]  # If not specified, assume no scaling
         elif embiggen[0] < 0:
             embiggen[0] = 1.0
-            logger.warning(
+            print(
-                "Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !"
+                ">> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !"
             )
         if len(embiggen) < 2:
             embiggen.append(0.75)
         elif embiggen[1] > 1.0 or embiggen[1] < 0:
             embiggen[1] = 0.75
-            logger.warning(
+            print(
-                "Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !"
+                ">> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !"
             )
         if len(embiggen) < 3:
             embiggen.append(0.25)
         elif embiggen[2] < 0:
             embiggen[2] = 0.25
-            logger.warning(
+            print(
-                "Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !"
+                ">> Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !"
             )
 
         # Convert tiles from their user-freindly count-from-one to count-from-zero, because we need to do modulo math
@@ -98,8 +97,8 @@ class Embiggen(Generator):
             embiggen_tiles.sort()
 
         if strength >= 0.5:
-            logger.warning(
+            print(
-                f"Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
+                f"* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
             )
 
         # Prep img2img generator, since we wrap over it
@@ -122,8 +121,8 @@ class Embiggen(Generator):
                 from ..restoration.realesrgan import ESRGAN
 
                 esrgan = ESRGAN()
-                logger.info(
+                print(
-                    f"ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
+                    f">> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
                 )
                 if embiggen[0] > 2:
                     initsuperimage = esrgan.process(
@@ -313,10 +312,10 @@ class Embiggen(Generator):
         def make_image():
             # Make main tiles -------------------------------------------------
             if embiggen_tiles:
-                logger.info(f"Making {len(embiggen_tiles)} Embiggen tiles...")
+                print(f">> Making {len(embiggen_tiles)} Embiggen tiles...")
             else:
-                logger.info(
+                print(
-                    f"Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
+                    f">> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
                 )
 
             emb_tile_store = []
@@ -362,11 +361,11 @@ class Embiggen(Generator):
                 # newinitimage.save(newinitimagepath)
 
                 if embiggen_tiles:
-                    logger.debug(
+                    print(
                         f"Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)"
                     )
                 else:
-                    logger.debug(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles")
+                    print(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles")
 
                 # create a torch tensor from an Image
                 newinitimage = np.array(newinitimage).astype(np.float32) / 255.0
@@ -548,8 +547,8 @@ class Embiggen(Generator):
                     # Layer tile onto final image
                     outputsuperimage.alpha_composite(intileimage, (left, top))
             else:
-                logger.error(
+                print(
-                    "Could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
+                    "Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
                 )
 
             # after internal loops and patching up return Embiggen image

invokeai/backend/generator/txt2img2img.py

@@ -14,8 +14,6 @@ from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeli
 from ..stable_diffusion.diffusers_pipeline import ConditioningData
 from ..stable_diffusion.diffusers_pipeline import trim_to_multiple_of
 
-import invokeai.backend.util.logging as logger
-
 class Txt2Img2Img(Generator):
     def __init__(self, model, precision):
         super().__init__(model, precision)
@@ -79,8 +77,8 @@ class Txt2Img2Img(Generator):
             # the message below is accurate.
             init_width = first_pass_latent_output.size()[3] * self.downsampling_factor
             init_height = first_pass_latent_output.size()[2] * self.downsampling_factor
-            logger.info(
+            print(
-                f"Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
+                f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
             )
 
             # resizing

invokeai/backend/image_util/patchmatch.py

@@ -5,9 +5,10 @@ wraps the actual patchmatch object. It respects the global
 be suppressed or deferred
 """
 import numpy as np
-import invokeai.backend.util.logging as logger
+
 from invokeai.backend.globals import Globals
 
+
 class PatchMatch:
     """
     Thin class wrapper around the patchmatch function.
@@ -27,12 +28,12 @@ class PatchMatch:
             from patchmatch import patch_match as pm
 
             if pm.patchmatch_available:
-                logger.info("Patchmatch initialized")
+                print(">> Patchmatch initialized")
             else:
-                logger.info("Patchmatch not loaded (nonfatal)")
+                print(">> Patchmatch not loaded (nonfatal)")
             self.patch_match = pm
         else:
-            logger.info("Patchmatch loading disabled")
+            print(">> Patchmatch loading disabled")
         self.tried_load = True
 
     @classmethod

invokeai/backend/image_util/txt2mask.py

@@ -30,9 +30,9 @@ work fine.
 import numpy as np
 import torch
 from PIL import Image, ImageOps
+from torchvision import transforms
 from transformers import AutoProcessor, CLIPSegForImageSegmentation
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import global_cache_dir
 
 CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
@@ -83,7 +83,7 @@ class Txt2Mask(object):
     """
 
     def __init__(self, device="cpu", refined=False):
-        logger.info("Initializing clipseg model for text to mask inference")
+        print(">> Initializing clipseg model for text to mask inference")
 
         # BUG: we are not doing anything with the device option at this time
         self.device = device
@@ -101,6 +101,18 @@ class Txt2Mask(object):
         provided image and returns a SegmentedGrayscale object in which the brighter
         pixels indicate where the object is inferred to be.
         """
+        transform = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize(
+                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+                ),
+                transforms.Resize(
+                    (CLIPSEG_SIZE, CLIPSEG_SIZE)
+                ),  # must be multiple of 64...
+            ]
+        )
+
         if type(image) is str:
             image = Image.open(image).convert("RGB")
 

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -25,7 +25,6 @@ from typing import Union
 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 .model_manager import ModelManager, SDLegacyType
@@ -373,9 +372,9 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
     unet_key = "model.diffusion_model."
     # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
     if sum(k.startswith("model_ema") for k in keys) > 100:
-        logger.debug(f"Checkpoint {path} has both EMA and non-EMA weights.")
+        print(f"   | Checkpoint {path} has both EMA and non-EMA weights.")
         if extract_ema:
-            logger.debug("Extracting EMA weights (usually better for inference)")
+            print("   | Extracting EMA weights (usually better for inference)")
             for key in keys:
                 if key.startswith("model.diffusion_model"):
                     flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
@@ -393,8 +392,8 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
                             key
                         )
         else:
-            logger.debug(
+            print(
-                "Extracting only the non-EMA weights (usually better for fine-tuning)"
+                "   | Extracting only the non-EMA weights (usually better for fine-tuning)"
             )
 
     for key in keys:
@@ -1116,7 +1115,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         if "global_step" in checkpoint:
             global_step = checkpoint["global_step"]
         else:
-            logger.debug("global_step key not found in model")
+            print("   | global_step key not found in model")
             global_step = None
 
         # sometimes there is a state_dict key and sometimes not
@@ -1230,15 +1229,15 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         # If a replacement VAE path was specified, we'll incorporate that into
         # the checkpoint model and then convert it
         if vae_path:
-            logger.debug(f"Converting VAE {vae_path}")
+            print(f"   | Converting VAE {vae_path}")
             replace_checkpoint_vae(checkpoint,vae_path)
         # otherwise we use the original VAE, provided that
         # an externally loaded diffusers VAE was not passed
         elif not vae:
-            logger.debug("Using checkpoint model's original VAE")
+            print("   | Using checkpoint model's original VAE")
 
         if vae:
-            logger.debug("Using replacement diffusers VAE")
+            print("   | Using replacement diffusers VAE")
         else:  # convert the original or replacement VAE
             vae_config = create_vae_diffusers_config(
                 original_config, image_size=image_size

invokeai/backend/model_management/model_manager.py

@@ -18,13 +18,12 @@ import warnings
 from enum import Enum, auto
 from pathlib import Path
 from shutil import move, rmtree
-from typing import Any, Optional, Union, Callable, types
+from typing import Any, Optional, Union, Callable
 
 import safetensors
 import safetensors.torch
 import torch
 import transformers
-import invokeai.backend.util.logging as logger
 from diffusers import (
     AutoencoderKL,
     UNet2DConditionModel,
@@ -76,8 +75,6 @@ class ModelManager(object):
     Model manager handles loading, caching, importing, deleting, converting, and editing models.
     """
 
-    logger: types.ModuleType = logger
-
     def __init__(
         self,
         config: OmegaConf | Path,
@@ -86,7 +83,6 @@ class ModelManager(object):
         max_loaded_models=DEFAULT_MAX_MODELS,
         sequential_offload=False,
         embedding_path: Path = None,
-        logger: types.ModuleType = logger,
     ):
         """
         Initialize with the path to the models.yaml config file or
@@ -108,7 +104,6 @@ class ModelManager(object):
         self.current_model = None
         self.sequential_offload = sequential_offload
         self.embedding_path = embedding_path
-        self.logger = logger
 
     def valid_model(self, model_name: str) -> bool:
         """
@@ -137,8 +132,8 @@ class ModelManager(object):
             )
 
         if not self.valid_model(model_name):
-            self.logger.error(
+            print(
-                f'"{model_name}" is not a known model name. Please check your models.yaml file'
+                f'** "{model_name}" is not a known model name. Please check your models.yaml file'
             )
             return self.current_model
 
@@ -149,7 +144,7 @@ class ModelManager(object):
 
         if model_name in self.models:
             requested_model = self.models[model_name]["model"]
-            self.logger.info(f"Retrieving model {model_name} from system RAM cache")
+            print(f">> Retrieving model {model_name} from system RAM cache")
             requested_model.ready()
             width = self.models[model_name]["width"]
             height = self.models[model_name]["height"]
@@ -384,7 +379,7 @@ class ModelManager(object):
         """
         omega = self.config
         if model_name not in omega:
-            self.logger.error(f"Unknown model {model_name}")
+            print(f"** Unknown model {model_name}")
             return
         # save these for use in deletion later
         conf = omega[model_name]
@@ -397,13 +392,13 @@ class ModelManager(object):
             self.stack.remove(model_name)
         if delete_files:
             if weights:
-                self.logger.info(f"Deleting file {weights}")
+                print(f"** Deleting file {weights}")
                 Path(weights).unlink(missing_ok=True)
             elif path:
-                self.logger.info(f"Deleting directory {path}")
+                print(f"** Deleting directory {path}")
                 rmtree(path, ignore_errors=True)
             elif repo_id:
-                self.logger.info(f"Deleting the cached model directory for {repo_id}")
+                print(f"** Deleting the cached model directory for {repo_id}")
                 self._delete_model_from_cache(repo_id)
 
     def add_model(
@@ -444,7 +439,7 @@ class ModelManager(object):
     def _load_model(self, model_name: str):
         """Load and initialize the model from configuration variables passed at object creation time"""
         if model_name not in self.config:
-            self.logger.error(
+            print(
                 f'"{model_name}" is not a known model name. Please check your models.yaml file'
             )
             return
@@ -462,7 +457,7 @@ class ModelManager(object):
         model_format = mconfig.get("format", "ckpt")
         if model_format == "ckpt":
             weights = mconfig.weights
-            self.logger.info(f"Loading {model_name} from {weights}")
+            print(f">> Loading {model_name} from {weights}")
             model, width, height, model_hash = self._load_ckpt_model(
                 model_name, mconfig
             )
@@ -478,15 +473,13 @@ class ModelManager(object):
 
         # usage statistics
         toc = time.time()
-        self.logger.info("Model loaded in " + "%4.2fs" % (toc - tic))
+        print(">> Model loaded in", "%4.2fs" % (toc - tic))
         if self._has_cuda():
-            self.logger.info(
+            print(
-                "Max VRAM used to load the model: "+
+                ">> Max VRAM used to load the model:",
-                "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9)
+                "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9),
-            )
+                "\n>> Current VRAM usage:"
-            self.logger.info(
+                "%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
-                "Current VRAM usage: "+
-                "%4.2fG" % (torch.cuda.memory_allocated() / 1e9)
             )
         return model, width, height, model_hash
 
@@ -494,11 +487,11 @@ class ModelManager(object):
         name_or_path = self.model_name_or_path(mconfig)
         using_fp16 = self.precision == "float16"
 
-        self.logger.info(f"Loading diffusers model from {name_or_path}")
+        print(f">> Loading diffusers model from {name_or_path}")
         if using_fp16:
-            self.logger.debug("Using faster float16 precision")
+            print("   | Using faster float16 precision")
         else:
-            self.logger.debug("Using more accurate float32 precision")
+            print("   | Using more accurate float32 precision")
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
@@ -530,8 +523,8 @@ class ModelManager(object):
                 if str(e).startswith("fp16 is not a valid"):
                     pass
                 else:
-                    self.logger.error(
+                    print(
-                        f"An unexpected error occurred while downloading the model: {e})"
+                        f"** An unexpected error occurred while downloading the model: {e})"
                     )
             if pipeline:
                 break
@@ -549,7 +542,7 @@ class ModelManager(object):
         # square images???
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
-        self.logger.debug(f"Default image dimensions = {width} x {height}")
+        print(f"   | Default image dimensions = {width} x {height}")
 
         return pipeline, width, height, model_hash
 
@@ -566,14 +559,14 @@ class ModelManager(object):
             weights = os.path.normpath(os.path.join(Globals.root, weights))
 
         # Convert to diffusers and return a diffusers pipeline
-        self.logger.info(f"Converting legacy checkpoint {model_name} into a diffusers model...")
+        print(f">> Converting legacy checkpoint {model_name} into a diffusers model...")
 
         from . import load_pipeline_from_original_stable_diffusion_ckpt
 
         try:
             if self.list_models()[self.current_model]["status"] == "active":
                 self.offload_model(self.current_model)
-        except Exception:
+        except Exception as e:
             pass
 
         vae_path = None
@@ -631,7 +624,7 @@ class ModelManager(object):
         if model_name not in self.models:
             return
 
-        self.logger.info(f"Offloading {model_name} to CPU")
+        print(f">> Offloading {model_name} to CPU")
         model = self.models[model_name]["model"]
         model.offload_all()
         self.current_model = None
@@ -647,26 +640,30 @@ class ModelManager(object):
         and option to exit if an infected file is identified.
         """
         # scan model
-        self.logger.debug(f"Scanning Model: {model_name}")
+        print(f"   | Scanning Model: {model_name}")
         scan_result = scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
             if scan_result.infected_files == 1:
-                self.logger.critical(f"Issues Found In Model: {scan_result.issues_count}")
+                print(f"\n### Issues Found In Model: {scan_result.issues_count}")
-                self.logger.critical("The model you are trying to load seems to be infected.")
+                print(
-                self.logger.critical("For your safety, InvokeAI will not load this model.")
+                    "### WARNING: The model you are trying to load seems to be infected."
-                self.logger.critical("Please use checkpoints from trusted sources.")
+                )
-                self.logger.critical("Exiting InvokeAI")
+                print("### For your safety, InvokeAI will not load this model.")
+                print("### Please use checkpoints from trusted sources.")
+                print("### Exiting InvokeAI")
                 sys.exit()
             else:
-                self.logger.warning("InvokeAI was unable to scan the model you are using.")
+                print(
+                    "\n### WARNING: InvokeAI was unable to scan the model you are using."
+                )
                 model_safe_check_fail = ask_user(
                     "Do you want to to continue loading the model?", ["y", "n"]
                 )
                 if model_safe_check_fail.lower() != "y":
-                    self.logger.critical("Exiting InvokeAI")
+                    print("### Exiting InvokeAI")
                     sys.exit()
         else:
-            self.logger.debug("Model scanned ok")
+            print("   | Model scanned ok")
 
     def import_diffuser_model(
         self,
@@ -783,24 +780,26 @@ class ModelManager(object):
         model_path: Path = None
         thing = path_url_or_repo  # to save typing
 
-        self.logger.info(f"Probing {thing} for import")
+        print(f">> Probing {thing} for import")
 
         if thing.startswith(("http:", "https:", "ftp:")):
-            self.logger.info(f"{thing} appears to be a URL")
+            print(f"   | {thing} appears to be a URL")
             model_path = self._resolve_path(
                 thing, "models/ldm/stable-diffusion-v1"
             )  # _resolve_path does a download if needed
 
         elif Path(thing).is_file() and thing.endswith((".ckpt", ".safetensors")):
             if Path(thing).stem in ["model", "diffusion_pytorch_model"]:
-                self.logger.debug(f"{Path(thing).name} appears to be part of a diffusers model. Skipping import")
+                print(
+                    f"   | {Path(thing).name} appears to be part of a diffusers model. Skipping import"
+                )
                 return
             else:
-                self.logger.debug(f"{thing} appears to be a checkpoint file on disk")
+                print(f"   | {thing} appears to be a checkpoint file on disk")
                 model_path = self._resolve_path(thing, "models/ldm/stable-diffusion-v1")
 
         elif Path(thing).is_dir() and Path(thing, "model_index.json").exists():
-            self.logger.debug(f"{thing} appears to be a diffusers file on disk")
+            print(f"   | {thing} appears to be a diffusers file on disk")
             model_name = self.import_diffuser_model(
                 thing,
                 vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
@@ -811,30 +810,34 @@ class ModelManager(object):
 
         elif Path(thing).is_dir():
             if (Path(thing) / "model_index.json").exists():
-                self.logger.debug(f"{thing} appears to be a diffusers model.")
+                print(f"   | {thing} appears to be a diffusers model.")
                 model_name = self.import_diffuser_model(
                     thing, commit_to_conf=commit_to_conf
                 )
             else:
-                self.logger.debug(f"{thing} appears to be a directory. Will scan for models to import")
+                print(
+                    f"   |{thing} appears to be a directory. Will scan for models to import"
+                )
                 for m in list(Path(thing).rglob("*.ckpt")) + list(
                     Path(thing).rglob("*.safetensors")
                 ):
                     if model_name := self.heuristic_import(
                         str(m), commit_to_conf=commit_to_conf
                     ):
-                        self.logger.info(f"{model_name} successfully imported")
+                        print(f" >> {model_name} successfully imported")
                 return model_name
 
         elif re.match(r"^[\w.+-]+/[\w.+-]+$", thing):
-            self.logger.debug(f"{thing} appears to be a HuggingFace diffusers repo_id")
+            print(f"   | {thing} appears to be a HuggingFace diffusers repo_id")
             model_name = self.import_diffuser_model(
                 thing, commit_to_conf=commit_to_conf
             )
             pipeline, _, _, _ = self._load_diffusers_model(self.config[model_name])
             return model_name
         else:
-            self.logger.warning(f"{thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id")
+            print(
+                f"** {thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id"
+            )
 
         # Model_path is set in the event of a legacy checkpoint file.
         # If not set, we're all done
@@ -842,7 +845,7 @@ class ModelManager(object):
             return
 
         if model_path.stem in self.config:  # already imported
-            self.logger.debug("Already imported. Skipping")
+            print("   | Already imported. Skipping")
             return model_path.stem
 
         # another round of heuristics to guess the correct config file.
@@ -858,39 +861,39 @@ class ModelManager(object):
             # look for a like-named .yaml file in same directory
             if model_path.with_suffix(".yaml").exists():
                 model_config_file = model_path.with_suffix(".yaml")
-                self.logger.debug(f"Using config file {model_config_file.name}")
+                print(f"   | Using config file {model_config_file.name}")
 
             else:
                 model_type = self.probe_model_type(checkpoint)
                 if model_type == SDLegacyType.V1:
-                    self.logger.debug("SD-v1 model detected")
+                    print("   | SD-v1 model detected")
                     model_config_file = Path(
                         Globals.root, "configs/stable-diffusion/v1-inference.yaml"
                     )
                 elif model_type == SDLegacyType.V1_INPAINT:
-                    self.logger.debug("SD-v1 inpainting model detected")
+                    print("   | SD-v1 inpainting model detected")
                     model_config_file = Path(
                         Globals.root,
                         "configs/stable-diffusion/v1-inpainting-inference.yaml",
                     )
                 elif model_type == SDLegacyType.V2_v:
-                    self.logger.debug("SD-v2-v model detected")
+                    print("   | SD-v2-v model detected")
                     model_config_file = Path(
                         Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
                     )
                 elif model_type == SDLegacyType.V2_e:
-                    self.logger.debug("SD-v2-e model detected")
+                    print("   | SD-v2-e model detected")
                     model_config_file = Path(
                         Globals.root, "configs/stable-diffusion/v2-inference.yaml"
                     )
                 elif model_type == SDLegacyType.V2:
-                    self.logger.warning(
+                    print(
-                        f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
+                        f"** {thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
                     )
                     return
                 else:
-                    self.logger.warning(
+                    print(
-                        f"{thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
+                        f"** {thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
                     )
                     return
 
@@ -906,7 +909,7 @@ class ModelManager(object):
         for suffix in ["pt", "ckpt", "safetensors"]:
             if (model_path.with_suffix(f".vae.{suffix}")).exists():
                 vae_path = model_path.with_suffix(f".vae.{suffix}")
-                self.logger.debug(f"Using VAE file {vae_path.name}")
+                print(f"   | Using VAE file {vae_path.name}")
         vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
 
         diffuser_path = Path(
@@ -952,14 +955,14 @@ class ModelManager(object):
         from . import convert_ckpt_to_diffusers
 
         if diffusers_path.exists():
-            self.logger.error(
+            print(
-                f"The path {str(diffusers_path)} already exists. Please move or remove it and try again."
+                f"ERROR: The path {str(diffusers_path)} already exists. Please move or remove it and try again."
             )
             return
 
         model_name = model_name or diffusers_path.name
         model_description = model_description or f"Converted version of {model_name}"
-        self.logger.debug(f"Converting {model_name} to diffusers (30-60s)")
+        print(f"   | Converting {model_name} to diffusers (30-60s)")
         try:
             # By passing the specified VAE to the conversion function, the autoencoder
             # will be built into the model rather than tacked on afterward via the config file
@@ -976,10 +979,10 @@ class ModelManager(object):
                 vae_path=vae_path,
                 scan_needed=scan_needed,
             )
-            self.logger.debug(
+            print(
-                f"Success. Converted model is now located at {str(diffusers_path)}"
+                f"   | Success. Converted model is now located at {str(diffusers_path)}"
             )
-            self.logger.debug(f"Writing new config file entry for {model_name}")
+            print(f"   | Writing new config file entry for {model_name}")
             new_config = dict(
                 path=str(diffusers_path),
                 description=model_description,
@@ -990,17 +993,17 @@ class ModelManager(object):
             self.add_model(model_name, new_config, True)
             if commit_to_conf:
                 self.commit(commit_to_conf)
-            self.logger.debug("Conversion succeeded")
+            print("   | Conversion succeeded")
         except Exception as e:
-            self.logger.warning(f"Conversion failed: {str(e)}")
+            print(f"** Conversion failed: {str(e)}")
-            self.logger.warning(
+            print(
-                "If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)"
+                "** If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)"
             )
 
         return model_name
 
     def search_models(self, search_folder):
-        self.logger.info(f"Finding Models In: {search_folder}")
+        print(f">> Finding Models In: {search_folder}")
         models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
         models_folder_safetensors = Path(search_folder).glob("**/*.safetensors")
 
@@ -1024,8 +1027,8 @@ class ModelManager(object):
         num_loaded_models = len(self.models)
         if num_loaded_models >= self.max_loaded_models:
             least_recent_model = self._pop_oldest_model()
-            self.logger.info(
+            print(
-                f"Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}"
+                f">> Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}"
             )
             if least_recent_model is not None:
                 del self.models[least_recent_model]
@@ -1033,8 +1036,8 @@ class ModelManager(object):
 
     def print_vram_usage(self) -> None:
         if self._has_cuda:
-            self.logger.info(
+            print(
-                "Current VRAM usage:"+
+                ">> Current VRAM usage: ",
                 "%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
             )
 
@@ -1123,10 +1126,10 @@ class ModelManager(object):
             dest = hub / model.stem
             if dest.exists() and not source.exists():
                 continue
-            cls.logger.info(f"{source} => {dest}")
+            print(f"** {source} => {dest}")
             if source.exists():
                 if dest.is_symlink():
-                    logger.warning(f"Found symlink at {dest.name}. Not migrating.")
+                    print(f"** Found symlink at {dest.name}. Not migrating.")
                 elif dest.exists():
                     if source.is_dir():
                         rmtree(source)
@@ -1143,7 +1146,7 @@ class ModelManager(object):
         ]
         for d in empty:
             os.rmdir(d)
-        cls.logger.info("Migration is done. Continuing...")
+        print("** Migration is done. Continuing...")
 
     def _resolve_path(
         self, source: Union[str, Path], dest_directory: str
@@ -1186,15 +1189,15 @@ class ModelManager(object):
 
     def _add_embeddings_to_model(self, model: StableDiffusionGeneratorPipeline):
         if self.embedding_path is not None:
-            self.logger.info(f"Loading embeddings from {self.embedding_path}")
+            print(f">> Loading embeddings from {self.embedding_path}")
             for root, _, files in os.walk(self.embedding_path):
                 for name in files:
                     ti_path = os.path.join(root, name)
                     model.textual_inversion_manager.load_textual_inversion(
                         ti_path, defer_injecting_tokens=True
                     )
-            self.logger.info(
+            print(
-                f'Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
+                f'>> Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
             )
 
     def _has_cuda(self) -> bool:
@@ -1216,7 +1219,7 @@ class ModelManager(object):
             with open(hashpath) as f:
                 hash = f.read()
             return hash
-        self.logger.debug("Calculating sha256 hash of model files")
+        print("   | Calculating sha256 hash of model files")
         tic = time.time()
         sha = hashlib.sha256()
         count = 0
@@ -1228,7 +1231,7 @@ 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))
+        print(f"   | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
         with open(hashpath, "w") as f:
             f.write(hash)
         return hash
@@ -1246,13 +1249,13 @@ class ModelManager(object):
                 hash = f.read()
             return hash
 
-        self.logger.debug("Calculating sha256 hash of weights file")
+        print("   | Calculating sha256 hash of weights file")
         tic = time.time()
         sha = hashlib.sha256()
         sha.update(data)
         hash = sha.hexdigest()
         toc = time.time()
-        self.logger.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic))
+        print(f">> sha256 = {hash}", "(%4.2fs)" % (toc - tic))
 
         with open(hashpath, "w") as f:
             f.write(hash)
@@ -1273,12 +1276,12 @@ class ModelManager(object):
             local_files_only=not Globals.internet_available,
         )
 
-        self.logger.debug(f"Loading diffusers VAE from {name_or_path}")
+        print(f"   | Loading diffusers VAE from {name_or_path}")
         if using_fp16:
             vae_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
         else:
-            self.logger.debug("Using more accurate float32 precision")
+            print("   | Using more accurate float32 precision")
             fp_args_list = [{}]
 
         vae = None
@@ -1302,12 +1305,12 @@ class ModelManager(object):
                 break
 
         if not vae and deferred_error:
-            self.logger.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}")
+            print(f"** Could not load VAE {name_or_path}: {str(deferred_error)}")
 
         return vae
 
-    @classmethod
+    @staticmethod
-    def _delete_model_from_cache(cls,repo_id):
+    def _delete_model_from_cache(repo_id):
         cache_info = scan_cache_dir(global_cache_dir("hub"))
 
         # I'm sure there is a way to do this with comprehensions
@@ -1318,8 +1321,8 @@ class ModelManager(object):
                 for revision in repo.revisions:
                     hashes_to_delete.add(revision.commit_hash)
         strategy = cache_info.delete_revisions(*hashes_to_delete)
-        cls.logger.warning(
+        print(
-            f"Deletion of this model is expected to free {strategy.expected_freed_size_str}"
+            f"** Deletion of this model is expected to free {strategy.expected_freed_size_str}"
         )
         strategy.execute()
 

invokeai/backend/prompting/conditioning.py

@@ -18,7 +18,6 @@ from compel.prompt_parser import (
     PromptParser,
 )
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 from ..stable_diffusion import InvokeAIDiffuserComponent
@@ -163,8 +162,8 @@ def log_tokenization(
     negative_prompt: Union[Blend, FlattenedPrompt],
     tokenizer,
 ):
-    logger.info(f"[TOKENLOG] Parsed Prompt: {positive_prompt}")
+    print(f"\n>> [TOKENLOG] Parsed Prompt: {positive_prompt}")
-    logger.info(f"[TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
+    print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
 
     log_tokenization_for_prompt_object(positive_prompt, tokenizer)
     log_tokenization_for_prompt_object(
@@ -238,12 +237,12 @@ def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_t
             usedTokens += 1
 
     if usedTokens > 0:
-        logger.info(f'[TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
+        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
-        logger.debug(f"{tokenized}\x1b[0m")
+        print(f"{tokenized}\x1b[0m")
 
     if discarded != "":
-        logger.info(f"[TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
+        print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
-        logger.debug(f"{discarded}\x1b[0m")
+        print(f"{discarded}\x1b[0m")
 
 
 def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Blend]:
@@ -296,8 +295,8 @@ def split_weighted_subprompts(text, skip_normalize=False) -> list:
         return parsed_prompts
     weight_sum = sum(map(lambda x: x[1], parsed_prompts))
     if weight_sum == 0:
-        logger.warning(
+        print(
-            "Subprompt weights add up to zero. Discarding and using even weights instead."
+            "* Warning: Subprompt weights add up to zero. Discarding and using even weights instead."
         )
         equal_weight = 1 / max(len(parsed_prompts), 1)
         return [(x[0], equal_weight) for x in parsed_prompts]

invokeai/backend/restoration/base.py

@@ -1,5 +1,3 @@
-import invokeai.backend.util.logging as logger
-
 class Restoration:
     def __init__(self) -> None:
         pass
@@ -10,17 +8,17 @@ class Restoration:
         # Load GFPGAN
         gfpgan = self.load_gfpgan(gfpgan_model_path)
         if gfpgan.gfpgan_model_exists:
-            logger.info("GFPGAN Initialized")
+            print(">> GFPGAN Initialized")
         else:
-            logger.info("GFPGAN Disabled")
+            print(">> GFPGAN Disabled")
             gfpgan = None
 
         # Load CodeFormer
         codeformer = self.load_codeformer()
         if codeformer.codeformer_model_exists:
-            logger.info("CodeFormer Initialized")
+            print(">> CodeFormer Initialized")
         else:
-            logger.info("CodeFormer Disabled")
+            print(">> CodeFormer Disabled")
             codeformer = None
 
         return gfpgan, codeformer
@@ -41,5 +39,5 @@ class Restoration:
         from .realesrgan import ESRGAN
 
         esrgan = ESRGAN(esrgan_bg_tile)
-        logger.info("ESRGAN Initialized")
+        print(">> ESRGAN Initialized")
         return esrgan

invokeai/backend/restoration/codeformer.py

@@ -5,7 +5,6 @@ import warnings
 import numpy as np
 import torch
 
-import invokeai.backend.util.logging as logger
 from ..globals import Globals
 
 pretrained_model_url = (
@@ -24,12 +23,12 @@ class CodeFormerRestoration:
         self.codeformer_model_exists = os.path.isfile(self.model_path)
 
         if not self.codeformer_model_exists:
-            logger.error("NOT FOUND: CodeFormer model not found at " + self.model_path)
+            print("## NOT FOUND: CodeFormer model not found at " + self.model_path)
         sys.path.append(os.path.abspath(codeformer_dir))
 
     def process(self, image, strength, device, seed=None, fidelity=0.75):
         if seed is not None:
-            logger.info(f"CodeFormer - Restoring Faces for image seed:{seed}")
+            print(f">> CodeFormer - Restoring Faces for image seed:{seed}")
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=UserWarning)
@@ -98,7 +97,7 @@ class CodeFormerRestoration:
                     del output
                     torch.cuda.empty_cache()
                 except RuntimeError as error:
-                    logger.error(f"Failed inference for CodeFormer: {error}.")
+                    print(f"\tFailed inference for CodeFormer: {error}.")
                     restored_face = cropped_face
 
                 restored_face = restored_face.astype("uint8")

invokeai/backend/restoration/gfpgan.py

@@ -6,9 +6,9 @@ import numpy as np
 import torch
 from PIL import Image
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
+
 class GFPGAN:
     def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
         if not os.path.isabs(gfpgan_model_path):
@@ -19,7 +19,7 @@ class GFPGAN:
         self.gfpgan_model_exists = os.path.isfile(self.model_path)
 
         if not self.gfpgan_model_exists:
-            logger.error("NOT FOUND: GFPGAN model not found at " + self.model_path)
+            print("## NOT FOUND: GFPGAN model not found at " + self.model_path)
             return None
 
     def model_exists(self):
@@ -27,7 +27,7 @@ class GFPGAN:
 
     def process(self, image, strength: float, seed: str = None):
         if seed is not None:
-            logger.info(f"GFPGAN - Restoring Faces for image seed:{seed}")
+            print(f">> GFPGAN - Restoring Faces for image seed:{seed}")
 
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=DeprecationWarning)
@@ -47,14 +47,14 @@ class GFPGAN:
             except Exception:
                 import traceback
 
-                logger.error("Error loading GFPGAN:", file=sys.stderr)
+                print(">> Error loading GFPGAN:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
             os.chdir(cwd)
 
         if self.gfpgan is None:
-            logger.warning("WARNING: GFPGAN not initialized.")
+            print(f">> WARNING: GFPGAN not initialized.")
-            logger.warning(
+            print(
-                f"Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
+                f">> Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
             )
 
         image = image.convert("RGB")

invokeai/backend/restoration/outcrop.py

@@ -1,7 +1,7 @@
 import math
 
 from PIL import Image
-import invokeai.backend.util.logging as logger
+
 
 class Outcrop(object):
     def __init__(
@@ -82,7 +82,7 @@ class Outcrop(object):
             pixels = extents[direction]
             # round pixels up to the nearest 64
             pixels = math.ceil(pixels / 64) * 64
-            logger.info(f"extending image {direction}ward by {pixels} pixels")
+            print(f">> extending image {direction}ward by {pixels} pixels")
             image = self._rotate(image, direction)
             image = self._extend(image, pixels)
             image = self._rotate(image, direction, reverse=True)

invokeai/backend/restoration/realesrgan.py

@@ -6,13 +6,18 @@ import torch
 from PIL import Image
 from PIL.Image import Image as ImageType
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
+
 class ESRGAN:
     def __init__(self, bg_tile_size=400) -> None:
         self.bg_tile_size = bg_tile_size
 
+        if not torch.cuda.is_available():  # CPU or MPS on M1
+            use_half_precision = False
+        else:
+            use_half_precision = True
+
     def load_esrgan_bg_upsampler(self, denoise_str):
         if not torch.cuda.is_available():  # CPU or MPS on M1
             use_half_precision = False
@@ -69,16 +74,16 @@ class ESRGAN:
                 import sys
                 import traceback
 
-                logger.error("Error loading Real-ESRGAN:")
+                print(">> Error loading Real-ESRGAN:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
         if upsampler_scale == 0:
-            logger.warning("Real-ESRGAN: Invalid scaling option. Image not upscaled.")
+            print(">> Real-ESRGAN: Invalid scaling option. Image not upscaled.")
             return image
 
         if seed is not None:
-            logger.info(
+            print(
-                f"Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
+                f">> Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
             )
         # ESRGAN outputs images with partial transparency if given RGBA images; convert to RGB
         image = image.convert("RGB")

invokeai/backend/safety_checker.py

@@ -14,7 +14,6 @@ from PIL import Image, ImageFilter
 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 .util import CPU_DEVICE
 
@@ -41,8 +40,8 @@ class SafetyChecker(object):
                 cache_dir=safety_model_path,
             )
         except Exception:
-            logger.error(
+            print(
-                "An error was encountered while installing the safety checker:"
+                "** An error was encountered while installing the safety checker:"
             )
             print(traceback.format_exc())
 
@@ -66,8 +65,8 @@ class SafetyChecker(object):
         )
         self.safety_checker.to(CPU_DEVICE) # offload
         if has_nsfw_concept[0]:
-            logger.warning(
+            print(
-                "An image with potential non-safe content has been detected. A blurred image will be returned."
+                "** An image with potential non-safe content has been detected. A blurred image will be returned. **"
             )
             return self.blur(image)
         else:

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -17,7 +17,6 @@ from huggingface_hub import (
     hf_hub_url,
 )
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 
@@ -67,11 +66,11 @@ class HuggingFaceConceptsLibrary(object):
                 # when init, add all in dir. when not init, add only concepts added between init and now
                 self.concept_list.extend(list(local_concepts_to_add))
             except Exception as e:
-                logger.warning(
+                print(
-                    f"Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}."
+                    f" ** WARNING: Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}."
                 )
-                logger.warning(
+                print(
-                    "You may load .bin and .pt file(s) manually using the --embedding_directory argument."
+                    " ** You may load .bin and .pt file(s) manually using the --embedding_directory argument."
                 )
             return self.concept_list
         else: 
@@ -84,7 +83,7 @@ class HuggingFaceConceptsLibrary(object):
         be downloaded.
         """
         if not concept_name in self.list_concepts():
-            logger.warning(
+            print(
                 f"{concept_name} is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept."
             )
             return None
@@ -222,7 +221,7 @@ class HuggingFaceConceptsLibrary(object):
             if chunk == 0:
                 bytes += total
 
-        logger.info(f"Downloading {repo_id}...", end="")
+        print(f">> Downloading {repo_id}...", end="")
         try:
             for file in (
                 "README.md",
@@ -236,22 +235,22 @@ class HuggingFaceConceptsLibrary(object):
                 )
         except ul_error.HTTPError as e:
             if e.code == 404:
-                logger.warning(
+                print(
                     f"Concept {concept_name} is not known to the Hugging Face library. Generation will continue without the concept."
                 )
             else:
-                logger.warning(
+                print(
                     f"Failed to download {concept_name}/{file} ({str(e)}. Generation will continue without the concept.)"
                 )
             os.rmdir(dest)
             return False
         except ul_error.URLError as e:
-            logger.error(
+            print(
-                f"an error occurred while downloading {concept_name}: {str(e)}. This may reflect a network issue. Generation will continue without the concept."
+                f"ERROR while downloading {concept_name}: {str(e)}. This may reflect a network issue. Generation will continue without the concept."
             )
             os.rmdir(dest)
             return False
-        logger.info("...{:.2f}Kb".format(bytes / 1024))
+        print("...{:.2f}Kb".format(bytes / 1024))
         return succeeded
 
     def _concept_id(self, concept_name: str) -> str:

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -13,9 +13,9 @@ from compel.cross_attention_control import Arguments
 from diffusers.models.attention_processor import AttentionProcessor
 from torch import nn
 
-import invokeai.backend.util.logging as logger
 from ...util import torch_dtype
 
+
 class CrossAttentionType(enum.Enum):
     SELF = 1
     TOKENS = 2
@@ -421,7 +421,7 @@ def get_cross_attention_modules(
     expected_count = 16
     if cross_attention_modules_in_model_count != expected_count:
         # non-fatal error but .swap() won't work.
-        logger.error(
+        print(
             f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model "
             + f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed "
             + "or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, "

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -8,7 +8,6 @@ import torch
 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 .cross_attention_control import (
@@ -467,14 +466,10 @@ class InvokeAIDiffuserComponent:
             outside = torch.count_nonzero(
                 (latents < -current_threshold) | (latents > current_threshold)
             )
-            logger.info(
+            print(
-                f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})"
+                f"\nThreshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
-                )
+                f"  | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
-            logger.debug(
+                f"  | {outside / latents.numel() * 100:.2f}% values outside threshold"
-                f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}"
-            )
-            logger.debug(   
-                f"{outside / latents.numel() * 100:.2f}% values outside threshold"
             )
 
         if maxval < current_threshold and minval > -current_threshold:
@@ -501,11 +496,9 @@ class InvokeAIDiffuserComponent:
             )
 
         if self.debug_thresholding:
-            logger.debug(
+            print(
-                f"min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})"
+                f"  | min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})\n"
-            )
+                f"  | {num_altered / latents.numel() * 100:.2f}% values altered"
-            logger.debug(
-                f"{num_altered / latents.numel() * 100:.2f}% values altered"
             )
 
         return latents

invokeai/backend/stable_diffusion/image_degradation/utils_image.py

@@ -10,7 +10,7 @@ from torchvision.utils import make_grid
 
 # import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
 
-import invokeai.backend.util.logging as logger
+
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 
@@ -191,7 +191,7 @@ def mkdirs(paths):
 def mkdir_and_rename(path):
     if os.path.exists(path):
         new_name = path + "_archived_" + get_timestamp()
-        logger.error("Path already exists. Rename it to [{:s}]".format(new_name))
+        print("Path already exists. Rename it to [{:s}]".format(new_name))
         os.replace(path, new_name)
     os.makedirs(path)
 

invokeai/backend/stable_diffusion/textual_inversion_manager.py

@@ -10,7 +10,6 @@ from compel.embeddings_provider import BaseTextualInversionManager
 from picklescan.scanner import scan_file_path
 from transformers import CLIPTextModel, CLIPTokenizer
 
-import invokeai.backend.util.logging as logger
 from .concepts_lib import HuggingFaceConceptsLibrary
 
 @dataclass
@@ -60,12 +59,12 @@ class TextualInversionManager(BaseTextualInversionManager):
                 or self.has_textual_inversion_for_trigger_string(concept_name)
                 or self.has_textual_inversion_for_trigger_string(f"<{concept_name}>")
             ):  # in case a token with literal angle brackets encountered
-                logger.info(f"Loaded local embedding for trigger {concept_name}")
+                print(f">> Loaded local embedding for trigger {concept_name}")
                 continue
             bin_file = self.hf_concepts_library.get_concept_model_path(concept_name)
             if not bin_file:
                 continue
-            logger.info(f"Loaded remote embedding for trigger {concept_name}")
+            print(f">> Loaded remote embedding for trigger {concept_name}")
             self.load_textual_inversion(bin_file)
             self.hf_concepts_library.concepts_loaded[concept_name] = True
 
@@ -86,8 +85,8 @@ class TextualInversionManager(BaseTextualInversionManager):
         embedding_list = self._parse_embedding(str(ckpt_path))
         for embedding_info in embedding_list:
             if (self.text_encoder.get_input_embeddings().weight.data[0].shape[0] != embedding_info.token_dim):
-                logger.warning(
+                print(
-                    f"Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info.token_dim}."
+                    f"   ** Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info.token_dim}."
                 )
                 continue
 
@@ -106,8 +105,8 @@ class TextualInversionManager(BaseTextualInversionManager):
                     if ckpt_path.name == "learned_embeds.bin"
                     else f"<{ckpt_path.stem}>"
                 )
-                logger.info(
+                print(
-                    f"{sourcefile}: Trigger token '{trigger_str}' is already claimed by '{self.trigger_to_sourcefile[trigger_str]}'. Trigger this concept with {replacement_trigger_str}"
+                    f">> {sourcefile}: Trigger token '{trigger_str}' is already claimed by '{self.trigger_to_sourcefile[trigger_str]}'. Trigger this concept with {replacement_trigger_str}"
                 )
                 trigger_str = replacement_trigger_str
 
@@ -121,8 +120,8 @@ class TextualInversionManager(BaseTextualInversionManager):
                 self.trigger_to_sourcefile[trigger_str] = sourcefile
 
             except ValueError as e:
-                logger.debug(f'Ignoring incompatible embedding {embedding_info["name"]}')
+                print(f'   | Ignoring incompatible embedding {embedding_info["name"]}')
-                logger.debug(f"The error was {str(e)}")
+                print(f"   | The error was {str(e)}")
 
     def _add_textual_inversion(
         self, trigger_str, embedding, defer_injecting_tokens=False
@@ -134,8 +133,8 @@ class TextualInversionManager(BaseTextualInversionManager):
         :return: The token id for the added embedding, either existing or newly-added.
         """
         if trigger_str in [ti.trigger_string for ti in self.textual_inversions]:
-            logger.warning(
+            print(
-                f"TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'"
+                f"** TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'"
             )
             return
         if not self.full_precision:
@@ -156,11 +155,11 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         except ValueError as e:
             if str(e).startswith("Warning"):
-                logger.warning(f"{str(e)}")
+                print(f">> {str(e)}")
             else:
                 traceback.print_exc()
-                logger.error(
+                print(
-                    f"TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}."
+                    f"** TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}."
                 )
                 raise
 
@@ -220,16 +219,16 @@ class TextualInversionManager(BaseTextualInversionManager):
         for ti in self.textual_inversions:
             if ti.trigger_token_id is None and ti.trigger_string in prompt_string:
                 if ti.embedding_vector_length > 1:
-                    logger.info(
+                    print(
-                        f"Preparing tokens for textual inversion {ti.trigger_string}..."
+                        f">> Preparing tokens for textual inversion {ti.trigger_string}..."
                     )
                 try:
                     self._inject_tokens_and_assign_embeddings(ti)
                 except ValueError as e:
-                    logger.debug(
+                    print(
-                        f"Ignoring incompatible embedding trigger {ti.trigger_string}"
+                        f"   | Ignoring incompatible embedding trigger {ti.trigger_string}"
                     )
-                    logger.debug(f"The error was {str(e)}")
+                    print(f"   | The error was {str(e)}")
                     continue
                 injected_token_ids.append(ti.trigger_token_id)
                 injected_token_ids.extend(ti.pad_token_ids)
@@ -307,16 +306,16 @@ class TextualInversionManager(BaseTextualInversionManager):
             if suffix in [".pt",".ckpt",".bin"]:
                 scan_result = scan_file_path(embedding_file)
                 if scan_result.infected_files > 0:
-                    logger.critical(
+                    print(
-                        f"Security Issues Found in Model: {scan_result.issues_count}"
+                        f"   ** Security Issues Found in Model: {scan_result.issues_count}"
                     )
-                    logger.critical("For your safety, InvokeAI will not load this embed.")
+                    print("   ** For your safety, InvokeAI will not load this embed.")
                     return list()
                 ckpt = torch.load(embedding_file,map_location="cpu")
             else:
                 ckpt = safetensors.torch.load_file(embedding_file)
         except Exception as e:
-            logger.warning(f"Notice: unrecognized embedding file format: {embedding_file}: {e}")
+            print(f"   ** Notice: unrecognized embedding file format: {embedding_file}: {e}")
             return list()
         
         # try to figure out what kind of embedding file it is and parse accordingly
@@ -335,7 +334,7 @@ class TextualInversionManager(BaseTextualInversionManager):
 
     def _parse_embedding_v1(self, embedding_ckpt: dict, file_path: str)->List[EmbeddingInfo]:
         basename = Path(file_path).stem
-        logger.debug(f'Loading v1 embedding file: {basename}')
+        print(f'   | Loading v1 embedding file: {basename}')
 
         embeddings = list()
         token_counter = -1
@@ -343,7 +342,7 @@ class TextualInversionManager(BaseTextualInversionManager):
             if token_counter < 0:
                 trigger = embedding_ckpt["name"]
             elif token_counter == 0:
-                trigger = '<basename>'
+                trigger = f'<basename>'
             else:
                 trigger = f'<{basename}-{int(token_counter:=token_counter)}>'
             token_counter += 1
@@ -366,7 +365,7 @@ class TextualInversionManager(BaseTextualInversionManager):
         This handles embedding .pt file variant #2.
         """
         basename = Path(file_path).stem
-        logger.debug(f'Loading v2 embedding file: {basename}')
+        print(f'   | Loading v2 embedding file: {basename}')
         embeddings = list()
         
         if isinstance(
@@ -385,7 +384,7 @@ class TextualInversionManager(BaseTextualInversionManager):
                 )
                 embeddings.append(embedding_info)
         else:
-            logger.warning(f"{basename}: Unrecognized embedding format")
+            print(f"   ** {basename}: Unrecognized embedding format")
 
         return embeddings
 
@@ -394,7 +393,7 @@ class TextualInversionManager(BaseTextualInversionManager):
         Parse 'version 3' of the .pt textual inversion embedding files.
         """
         basename = Path(file_path).stem
-        logger.debug(f'Loading v3 embedding file: {basename}')
+        print(f'   | Loading v3 embedding file: {basename}')
         embedding = embedding_ckpt['emb_params']
         embedding_info = EmbeddingInfo(
             name = f'<{basename}>',
@@ -412,11 +411,11 @@ class TextualInversionManager(BaseTextualInversionManager):
         basename = Path(filepath).stem
         short_path = Path(filepath).parents[0].name+'/'+Path(filepath).name
         
-        logger.debug(f'Loading v4 embedding file: {short_path}')
+        print(f'   | Loading v4 embedding file: {short_path}')
         
         embeddings = list()
         if list(embedding_ckpt.keys()) == 0:
-            logger.warning(f"Invalid embeddings file: {short_path}")
+            print(f"   ** Invalid embeddings file: {short_path}")
         else:
             for token,embedding in embedding_ckpt.items():
                 embedding_info = EmbeddingInfo(

invokeai/backend/util/logging.py

@@ -1,109 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team
-
-"""invokeai.util.logging
-
-Logging class for InvokeAI that produces console messages that follow
-the conventions established in InvokeAI 1.X through 2.X.
-
-
-One way to use it:
-
-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')
-
-Console messages:
-     ### this is critical
-     *** this is an error ***
-     ** this is a warning
-     >> this is info
-        | this is debugging
-
-Another way:
-import invokeai.backend.util.logging as ialog
-ialogger.debug('this is a debugging message')
-"""
-import logging
-
-# module level functions
-def debug(msg, *args, **kwargs):
-    InvokeAILogger.getLogger().debug(msg, *args, **kwargs)
-
-def info(msg, *args, **kwargs):
-    InvokeAILogger.getLogger().info(msg, *args, **kwargs)
-
-def warning(msg, *args, **kwargs):
-    InvokeAILogger.getLogger().warning(msg, *args, **kwargs)
-
-def error(msg, *args, **kwargs):
-    InvokeAILogger.getLogger().error(msg, *args, **kwargs)
-    
-def critical(msg, *args, **kwargs):
-    InvokeAILogger.getLogger().critical(msg, *args, **kwargs)
-
-def log(level, msg, *args, **kwargs):
-    InvokeAILogger.getLogger().log(level, msg, *args, **kwargs)
-
-def disable(level=logging.CRITICAL):
-    InvokeAILogger.getLogger().disable(level)
-
-def basicConfig(**kwargs):
-    InvokeAILogger.getLogger().basicConfig(**kwargs)
-
-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
-    '''
-    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='%')
-
-    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
-
-        # 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:
-        if name not in self.loggers:
-            logger = logging.getLogger(name)
-            logger.setLevel(logging.DEBUG)
-            ch = logging.StreamHandler()
-            fmt = InvokeAILogFormatter()
-            ch.setFormatter(fmt)
-            logger.addHandler(ch)
-            self.loggers[name] = logger
-        return self.loggers[name]

invokeai/backend/util/util.py

@@ -18,7 +18,6 @@ import torch
 from PIL import Image, ImageDraw, ImageFont
 from tqdm import tqdm
 
-import invokeai.backend.util.logging as logger
 from .devices import torch_dtype
 
 
@@ -39,7 +38,7 @@ def log_txt_as_img(wh, xc, size=10):
         try:
             draw.text((0, 0), lines, fill="black", font=font)
         except UnicodeEncodeError:
-            logger.warning("Cant encode string for logging. Skipping.")
+            print("Cant encode string for logging. Skipping.")
 
         txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
         txts.append(txt)
@@ -81,8 +80,8 @@ def mean_flat(tensor):
 def count_params(model, verbose=False):
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
-        logger.debug(
+        print(
-            f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params."
+            f"   | {model.__class__.__name__} has {total_params * 1.e-6:.2f} M params."
         )
     return total_params
 
@@ -133,8 +132,8 @@ def parallel_data_prefetch(
         raise ValueError("list expected but function got ndarray.")
     elif isinstance(data, abc.Iterable):
         if isinstance(data, dict):
-            logger.warning(
+            print(
-                '"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
+                'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
             )
             data = list(data.values())
         if target_data_type == "ndarray":
@@ -176,7 +175,7 @@ def parallel_data_prefetch(
         processes += [p]
 
     # start processes
-    logger.info("Start prefetching...")
+    print("Start prefetching...")
     import time
 
     start = time.time()
@@ -195,7 +194,7 @@ def parallel_data_prefetch(
                 gather_res[res[0]] = res[1]
 
     except Exception as e:
-        logger.error("Exception: ", e)
+        print("Exception: ", e)
         for p in processes:
             p.terminate()
 
@@ -203,7 +202,7 @@ def parallel_data_prefetch(
     finally:
         for p in processes:
             p.join()
-        logger.info(f"Prefetching complete. [{time.time() - start} sec.]")
+        print(f"Prefetching complete. [{time.time() - start} sec.]")
 
     if target_data_type == "ndarray":
         if not isinstance(gather_res[0], np.ndarray):
@@ -319,23 +318,23 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
         resp = requests.get(url, headers=header, stream=True)  # new request with range
 
     if exist_size > content_length:
-        logger.warning("corrupt existing file found. re-downloading")
+        print("* corrupt existing file found. re-downloading")
         os.remove(dest)
         exist_size = 0
 
     if resp.status_code == 416 or exist_size == content_length:
-        logger.warning(f"{dest}: complete file found. Skipping.")
+        print(f"* {dest}: complete file found. Skipping.")
         return dest
     elif resp.status_code == 206 or exist_size > 0:
-        logger.warning(f"{dest}: partial file found. Resuming...")
+        print(f"* {dest}: partial file found. Resuming...")
     elif resp.status_code != 200:
-        logger.error(f"An error occurred during downloading {dest}: {resp.reason}")
+        print(f"** An error occurred during downloading {dest}: {resp.reason}")
     else:
-        logger.error(f"{dest}: Downloading...")
+        print(f"* {dest}: Downloading...")
 
     try:
         if content_length < 2000:
-            logger.error(f"ERROR DOWNLOADING {url}: {resp.text}")
+            print(f"*** ERROR DOWNLOADING {url}: {resp.text}")
             return None
 
         with open(dest, open_mode) as file, tqdm(
@@ -350,7 +349,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
                 size = file.write(data)
                 bar.update(size)
     except Exception as e:
-        logger.error(f"An error occurred while downloading {dest}: {str(e)}")
+        print(f"An error occurred while downloading {dest}: {str(e)}")
         return None
 
     return dest

invokeai/backend/web/invoke_ai_web_server.py

@@ -19,7 +19,6 @@ from PIL import Image
 from PIL.Image import Image as ImageType
 from werkzeug.utils import secure_filename
 
-import invokeai.backend.util.logging as logger
 import invokeai.frontend.web.dist as frontend
 
 from .. import Generate
@@ -78,6 +77,7 @@ class InvokeAIWebServer:
         mimetypes.add_type("application/javascript", ".js")
         mimetypes.add_type("text/css", ".css")
         # Socket IO
+        logger = True if args.web_verbose else False
         engineio_logger = True if args.web_verbose else False
         max_http_buffer_size = 10000000
 
@@ -213,7 +213,7 @@ class InvokeAIWebServer:
         self.load_socketio_listeners(self.socketio)
 
         if args.gui:
-            logger.info("Launching Invoke AI GUI")
+            print(">> Launching Invoke AI GUI")
             try:
                 from flaskwebgui import FlaskUI
 
@@ -231,17 +231,17 @@ class InvokeAIWebServer:
                 sys.exit(0)
         else:
             useSSL = args.certfile or args.keyfile
-            logger.info("Started Invoke AI Web Server")
+            print(">> Started Invoke AI Web Server")
             if self.host == "0.0.0.0":
-                logger.info(
+                print(
                     f"Point your browser at http{'s' if useSSL else ''}://localhost:{self.port} or use the host's DNS name or IP address."
                 )
             else:
-                logger.info(
+                print(
-                    "Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address."
+                    ">> Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address."
                 )
-                logger.info(
+                print(
-                    f"Point your browser at http{'s' if useSSL else ''}://{self.host}:{self.port}"
+                    f">> Point your browser at http{'s' if useSSL else ''}://{self.host}:{self.port}"
                 )
             if not useSSL:
                 self.socketio.run(app=self.app, host=self.host, port=self.port)
@@ -273,7 +273,7 @@ class InvokeAIWebServer:
         # path for thumbnail images
         self.thumbnail_image_path = os.path.join(self.result_path, "thumbnails/")
         # txt log
-        self.log_path = os.path.join(self.result_path, "invoke_logger.txt")
+        self.log_path = os.path.join(self.result_path, "invoke_log.txt")
         # make all output paths
         [
             os.makedirs(path, exist_ok=True)
@@ -290,7 +290,7 @@ class InvokeAIWebServer:
     def load_socketio_listeners(self, socketio):
         @socketio.on("requestSystemConfig")
         def handle_request_capabilities():
-            logger.info("System config requested")
+            print(">> System config requested")
             config = self.get_system_config()
             config["model_list"] = self.generate.model_manager.list_models()
             config["infill_methods"] = infill_methods()
@@ -330,7 +330,7 @@ class InvokeAIWebServer:
                 if model_name in current_model_list:
                     update = True
 
-                logger.info(f"Adding New Model: {model_name}")
+                print(f">> Adding New Model: {model_name}")
 
                 self.generate.model_manager.add_model(
                     model_name=model_name,
@@ -348,14 +348,14 @@ class InvokeAIWebServer:
                         "update": update,
                     },
                 )
-                logger.info(f"New Model Added: {model_name}")
+                print(f">> New Model Added: {model_name}")
             except Exception as e:
                 self.handle_exceptions(e)
 
         @socketio.on("deleteModel")
         def handle_delete_model(model_name: str):
             try:
-                logger.info(f"Deleting Model: {model_name}")
+                print(f">> Deleting Model: {model_name}")
                 self.generate.model_manager.del_model(model_name)
                 self.generate.model_manager.commit(opt.conf)
                 updated_model_list = self.generate.model_manager.list_models()
@@ -366,14 +366,14 @@ class InvokeAIWebServer:
                         "model_list": updated_model_list,
                     },
                 )
-                logger.info(f"Model Deleted: {model_name}")
+                print(f">> Model Deleted: {model_name}")
             except Exception as e:
                 self.handle_exceptions(e)
 
         @socketio.on("requestModelChange")
         def handle_set_model(model_name: str):
             try:
-                logger.info(f"Model change requested: {model_name}")
+                print(f">> Model change requested: {model_name}")
                 model = self.generate.set_model(model_name)
                 model_list = self.generate.model_manager.list_models()
                 if model is None:
@@ -454,7 +454,7 @@ class InvokeAIWebServer:
                         "update": True,
                     },
                 )
-                logger.info(f"Model Converted: {model_name}")
+                print(f">> Model Converted: {model_name}")
             except Exception as e:
                 self.handle_exceptions(e)
 
@@ -490,7 +490,7 @@ class InvokeAIWebServer:
                 if vae := self.generate.model_manager.config[models_to_merge[0]].get(
                     "vae", None
                 ):
-                    logger.info(f"Using configured VAE assigned to {models_to_merge[0]}")
+                    print(f">> Using configured VAE assigned to {models_to_merge[0]}")
                     merged_model_config.update(vae=vae)
 
                 self.generate.model_manager.import_diffuser_model(
@@ -507,8 +507,8 @@ class InvokeAIWebServer:
                         "update": True,
                     },
                 )
-                logger.info(f"Models Merged: {models_to_merge}")
+                print(f">> Models Merged: {models_to_merge}")
-                logger.info(f"New Model Added: {model_merge_info['merged_model_name']}")
+                print(f">> New Model Added: {model_merge_info['merged_model_name']}")
             except Exception as e:
                 self.handle_exceptions(e)
 
@@ -698,7 +698,7 @@ class InvokeAIWebServer:
                             }
                         )
                     except Exception as e:
-                        logger.info(f"Unable to load {path}")
+                        print(f">> Unable to load {path}")
                         socketio.emit(
                             "error", {"message": f"Unable to load {path}: {str(e)}"}
                         )
@@ -735,9 +735,9 @@ class InvokeAIWebServer:
                         printable_parameters["init_mask"][:64] + "..."
                     )
 
-                logger.info(f"Image Generation Parameters:\n\n{printable_parameters}\n")
+                print(f"\n>> Image Generation Parameters:\n\n{printable_parameters}\n")
-                logger.info(f"ESRGAN Parameters: {esrgan_parameters}")
+                print(f">> ESRGAN Parameters: {esrgan_parameters}")
-                logger.info(f"Facetool Parameters: {facetool_parameters}")
+                print(f">> Facetool Parameters: {facetool_parameters}")
 
                 self.generate_images(
                     generation_parameters,
@@ -750,8 +750,8 @@ class InvokeAIWebServer:
         @socketio.on("runPostprocessing")
         def handle_run_postprocessing(original_image, postprocessing_parameters):
             try:
-                logger.info(
+                print(
-                    f'Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}'
+                    f'>> Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}'
                 )
 
                 progress = Progress()
@@ -861,14 +861,14 @@ class InvokeAIWebServer:
 
         @socketio.on("cancel")
         def handle_cancel():
-            logger.info("Cancel processing requested")
+            print(">> Cancel processing requested")
             self.canceled.set()
 
         # TODO: I think this needs a safety mechanism.
         @socketio.on("deleteImage")
         def handle_delete_image(url, thumbnail, uuid, category):
             try:
-                logger.info(f'Delete requested "{url}"')
+                print(f'>> Delete requested "{url}"')
                 from send2trash import send2trash
 
                 path = self.get_image_path_from_url(url)
@@ -1263,7 +1263,7 @@ class InvokeAIWebServer:
                     image, os.path.basename(path), self.thumbnail_image_path
                 )
 
-                logger.info(f'Image generated: "{path}"\n')
+                print(f'\n\n>> Image generated: "{path}"\n')
                 self.write_log_message(f'[Generated] "{path}": {command}')
 
                 if progress.total_iterations > progress.current_iteration:
@@ -1329,7 +1329,7 @@ class InvokeAIWebServer:
         except Exception as e:
             # Clear the CUDA cache on an exception
             self.empty_cuda_cache()
-            logger.error(e)
+            print(e)
             self.handle_exceptions(e)
 
     def empty_cuda_cache(self):

invokeai/frontend/CLI/CLI.py

@@ -16,7 +16,6 @@ if sys.platform == "darwin":
 import pyparsing  # type: ignore
 
 import invokeai.version as invokeai
-import invokeai.backend.util.logging as logger
 
 from ...backend import Generate, ModelManager
 from ...backend.args import Args, dream_cmd_from_png, metadata_dumps, metadata_from_png
@@ -70,7 +69,7 @@ def main():
     # run any post-install patches needed
     run_patches()
 
-    logger.info(f"Internet connectivity is {Globals.internet_available}")
+    print(f">> Internet connectivity is {Globals.internet_available}")
 
     if not args.conf:
         config_file = os.path.join(Globals.root, "configs", "models.yaml")
@@ -79,8 +78,8 @@ def main():
                 opt, FileNotFoundError(f"The file {config_file} could not be found.")
             )
 
-    logger.info(f"{invokeai.__app_name__}, version {invokeai.__version__}")
+    print(f">> {invokeai.__app_name__}, version {invokeai.__version__}")
-    logger.info(f'InvokeAI runtime directory is "{Globals.root}"')
+    print(f'>> InvokeAI runtime directory is "{Globals.root}"')
 
     # loading here to avoid long delays on startup
     # these two lines prevent a horrible warning message from appearing
@@ -122,7 +121,7 @@ def main():
             else:
                 raise FileNotFoundError(f"{opt.infile} not found.")
         except (FileNotFoundError, IOError) as e:
-            logger.critical('Aborted',exc_info=True)
+            print(f"{e}. Aborting.")
             sys.exit(-1)
 
     # creating a Generate object:
@@ -143,12 +142,12 @@ def main():
         )
     except (FileNotFoundError, TypeError, AssertionError) as e:
         report_model_error(opt, e)
-    except (IOError, KeyError):
+    except (IOError, KeyError) as e:
-        logger.critical("Aborted",exc_info=True)
+        print(f"{e}. Aborting.")
         sys.exit(-1)
 
     if opt.seamless:
-        logger.info("Changed to seamless tiling mode")
+        print(">> changed to seamless tiling mode")
 
     # preload the model
     try:
@@ -181,7 +180,9 @@ def main():
             f'\nGoodbye!\nYou can start InvokeAI again by running the "invoke.bat" (or "invoke.sh") script from {Globals.root}'
         )
     except Exception:
-        logger.error("An error occurred",exc_info=True)
+        print(">> An error occurred:")
+        traceback.print_exc()
+
 
 # TODO: main_loop() has gotten busy. Needs to be refactored.
 def main_loop(gen, opt):
@@ -247,7 +248,7 @@ def main_loop(gen, opt):
                 if not opt.prompt:
                     oldargs = metadata_from_png(opt.init_img)
                     opt.prompt = oldargs.prompt
-                    logger.info(f'Retrieved old prompt "{opt.prompt}" from {opt.init_img}')
+                    print(f'>> Retrieved old prompt "{opt.prompt}" from {opt.init_img}')
             except (OSError, AttributeError, KeyError):
                 pass
 
@@ -264,9 +265,9 @@ def main_loop(gen, opt):
         if opt.init_img is not None and re.match("^-\\d+$", opt.init_img):
             try:
                 opt.init_img = last_results[int(opt.init_img)][0]
-                logger.info(f"Reusing previous image {opt.init_img}")
+                print(f">> Reusing previous image {opt.init_img}")
             except IndexError:
-                logger.info(f"No previous initial image at position {opt.init_img} found")
+                print(f">> No previous initial image at position {opt.init_img} found")
                 opt.init_img = None
                 continue
 
@@ -287,9 +288,9 @@ def main_loop(gen, opt):
         if opt.seed is not None and opt.seed < 0 and operation != "postprocess":
             try:
                 opt.seed = last_results[opt.seed][1]
-                logger.info(f"Reusing previous seed {opt.seed}")
+                print(f">> Reusing previous seed {opt.seed}")
             except IndexError:
-                logger.info(f"No previous seed at position {opt.seed} found")
+                print(f">> No previous seed at position {opt.seed} found")
                 opt.seed = None
                 continue
 
@@ -308,7 +309,7 @@ def main_loop(gen, opt):
             subdir = subdir[: (path_max - 39 - len(os.path.abspath(opt.outdir)))]
             current_outdir = os.path.join(opt.outdir, subdir)
 
-            logger.info('Writing files to directory: "' + current_outdir + '"')
+            print('Writing files to directory: "' + current_outdir + '"')
 
             # make sure the output directory exists
             if not os.path.exists(current_outdir):
@@ -437,14 +438,15 @@ def main_loop(gen, opt):
                         catch_interrupts=catch_ctrl_c,
                         **vars(opt),
                     )
-                except (PromptParser.ParsingException, pyparsing.ParseException):
+                except (PromptParser.ParsingException, pyparsing.ParseException) as e:
-                    logger.error("An error occurred while processing your prompt",exc_info=True)
+                    print("** An error occurred while processing your prompt **")
+                    print(f"** {str(e)} **")
             elif operation == "postprocess":
-                logger.info(f"fixing {opt.prompt}")
+                print(f">> fixing {opt.prompt}")
                 opt.last_operation = do_postprocess(gen, opt, image_writer)
 
             elif operation == "mask":
-                logger.info(f"generating masks from {opt.prompt}")
+                print(f">> generating masks from {opt.prompt}")
                 do_textmask(gen, opt, image_writer)
 
             if opt.grid and len(grid_images) > 0:
@@ -467,12 +469,12 @@ def main_loop(gen, opt):
                 )
                 results = [[path, formatted_dream_prompt]]
 
-        except AssertionError:
+        except AssertionError as e:
-            logger.error(e)
+            print(e)
             continue
 
         except OSError as e:
-            logger.error(e)
+            print(e)
             continue
 
         print("Outputs:")
@@ -511,7 +513,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
             gen.set_model(model_name)
             add_embedding_terms(gen, completer)
         except KeyError as e:
-            logger.error(e)
+            print(str(e))
         except Exception as e:
             report_model_error(opt, e)
         completer.add_history(command)
@@ -525,8 +527,8 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith("!import"):
         path = shlex.split(command)
         if len(path) < 2:
-            logger.warning(
+            print(
-                "please provide (1) a URL to a .ckpt file to import; (2) a local path to a .ckpt file; or (3) a diffusers repository id in the form stabilityai/stable-diffusion-2-1"
+                "** please provide (1) a URL to a .ckpt file to import; (2) a local path to a .ckpt file; or (3) a diffusers repository id in the form stabilityai/stable-diffusion-2-1"
             )
         else:
             try:
@@ -539,7 +541,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith(("!convert", "!optimize")):
         path = shlex.split(command)
         if len(path) < 2:
-            logger.warning("please provide the path to a .ckpt or .safetensors model")
+            print("** please provide the path to a .ckpt or .safetensors model")
         else:
             try:
                 convert_model(path[1], gen, opt, completer)
@@ -551,7 +553,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith("!edit"):
         path = shlex.split(command)
         if len(path) < 2:
-            logger.warning("please provide the name of a model")
+            print("** please provide the name of a model")
         else:
             edit_model(path[1], gen, opt, completer)
         completer.add_history(command)
@@ -560,7 +562,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith("!del"):
         path = shlex.split(command)
         if len(path) < 2:
-            logger.warning("please provide the name of a model")
+            print("** please provide the name of a model")
         else:
             del_config(path[1], gen, opt, completer)
         completer.add_history(command)
@@ -640,8 +642,8 @@ def import_model(model_path: str, gen, opt, completer):
             try:
                 default_name = url_attachment_name(model_path)
                 default_name = Path(default_name).stem
-            except Exception:
+            except Exception as e:
-                logger.warning(f"A problem occurred while assigning the name of the downloaded model",exc_info=True)
+                print(f"** URL: {str(e)}")
             model_name, model_desc = _get_model_name_and_desc(
                 gen.model_manager,
                 completer,
@@ -662,11 +664,11 @@ def import_model(model_path: str, gen, opt, completer):
                 model_config_file=config_file,
             )
     if not imported_name:
-        logger.error("Aborting import.")
+        print("** Aborting import.")
         return
 
     if not _verify_load(imported_name, gen):
-        logger.error("model failed to load. Discarding configuration entry")
+        print("** model failed to load. Discarding configuration entry")
         gen.model_manager.del_model(imported_name)
         return
     if click.confirm("Make this the default model?", default=False):
@@ -674,7 +676,7 @@ def import_model(model_path: str, gen, opt, completer):
 
     gen.model_manager.commit(opt.conf)
     completer.update_models(gen.model_manager.list_models())
-    logger.info(f"{imported_name} successfully installed")
+    print(f">> {imported_name} successfully installed")
 
 def _pick_configuration_file(completer)->Path:
     print(
@@ -718,21 +720,21 @@ Please select the type of this model:
     return choice
 
 def _verify_load(model_name: str, gen) -> bool:
-    logger.info("Verifying that new model loads...")
+    print(">> Verifying that new model loads...")
     current_model = gen.model_name
     try:
         if not gen.set_model(model_name):
             return
     except Exception as e:
-        logger.warning(f"model failed to load: {str(e)}")
+        print(f"** model failed to load: {str(e)}")
-        logger.warning(
+        print(
             "** note that importing 2.X checkpoints is not supported. Please use !convert_model instead."
         )
         return False
     if click.confirm("Keep model loaded?", default=True):
         gen.set_model(model_name)
     else:
-        logger.info("Restoring previous model")
+        print(">> Restoring previous model")
         gen.set_model(current_model)
     return True
 
@@ -755,7 +757,7 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
     ckpt_path = None
     original_config_file = None
     if model_name_or_path == gen.model_name:
-        logger.warning("Can't convert the active model. !switch to another model first. **")
+        print("** Can't convert the active model. !switch to another model first. **")
         return
     elif model_info := manager.model_info(model_name_or_path):
         if "weights" in model_info:
@@ -765,7 +767,7 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             model_description = model_info["description"]
             vae_path = model_info.get("vae")
         else:
-            logger.warning(f"{model_name_or_path} is not a legacy .ckpt weights file")
+            print(f"** {model_name_or_path} is not a legacy .ckpt weights file")
             return
         model_name = manager.convert_and_import(
             ckpt_path,
@@ -786,16 +788,16 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
     manager.commit(opt.conf)
     if click.confirm(f"Delete the original .ckpt file at {ckpt_path}?", default=False):
         ckpt_path.unlink(missing_ok=True)
-        logger.warning(f"{ckpt_path} deleted")
+        print(f"{ckpt_path} deleted")
 
 
 def del_config(model_name: str, gen, opt, completer):
     current_model = gen.model_name
     if model_name == current_model:
-        logger.warning("Can't delete active model. !switch to another model first. **")
+        print("** Can't delete active model. !switch to another model first. **")
         return
     if model_name not in gen.model_manager.config:
-        logger.warning(f"Unknown model {model_name}")
+        print(f"** Unknown model {model_name}")
         return
 
     if not click.confirm(
@@ -808,17 +810,17 @@ def del_config(model_name: str, gen, opt, completer):
     )
     gen.model_manager.del_model(model_name, delete_files=delete_completely)
     gen.model_manager.commit(opt.conf)
-    logger.warning(f"{model_name} deleted")
+    print(f"** {model_name} deleted")
     completer.update_models(gen.model_manager.list_models())
 
 
 def edit_model(model_name: str, gen, opt, completer):
     manager = gen.model_manager
     if not (info := manager.model_info(model_name)):
-        logger.warning(f"** Unknown model {model_name}")
+        print(f"** Unknown model {model_name}")
         return
-    print()
+
-    logger.info(f"Editing model {model_name} from configuration file {opt.conf}")
+    print(f"\n>> Editing model {model_name} from configuration file {opt.conf}")
     new_name = _get_model_name(manager.list_models(), completer, model_name)
 
     for attribute in info.keys():
@@ -856,7 +858,7 @@ def edit_model(model_name: str, gen, opt, completer):
         manager.set_default_model(new_name)
     manager.commit(opt.conf)
     completer.update_models(manager.list_models())
-    logger.info("Model successfully updated")
+    print(">> Model successfully updated")
 
 
 def _get_model_name(existing_names, completer, default_name: str = "") -> str:
@@ -867,11 +869,11 @@ def _get_model_name(existing_names, completer, default_name: str = "") -> str:
         if len(model_name) == 0:
             model_name = default_name
         if not re.match("^[\w._+:/-]+$", model_name):
-            logger.warning(
+            print(
-                'model name must contain only words, digits and the characters "._+:/-" **'
+                '** model name must contain only words, digits and the characters "._+:/-" **'
             )
         elif model_name != default_name and model_name in existing_names:
-            logger.warning(f"the name {model_name} is already in use. Pick another.")
+            print(f"** the name {model_name} is already in use. Pick another.")
         else:
             done = True
     return model_name
@@ -938,10 +940,11 @@ def do_postprocess(gen, opt, callback):
             opt=opt,
         )
     except OSError:
-        logger.error(f"{file_path}: file could not be read",exc_info=True)
+        print(traceback.format_exc(), file=sys.stderr)
+        print(f"** {file_path}: file could not be read")
         return
     except (KeyError, AttributeError):
-        logger.error(f"an error occurred while applying the {tool} postprocessor",exc_info=True)
+        print(traceback.format_exc(), file=sys.stderr)
         return
     return opt.last_operation
 
@@ -996,13 +999,13 @@ def prepare_image_metadata(
         try:
             filename = opt.fnformat.format(**wildcards)
         except KeyError as e:
-            logger.error(
+            print(
-                f"The filename format contains an unknown key '{e.args[0]}'. Will use {{prefix}}.{{seed}}.png' instead"
+                f"** The filename format contains an unknown key '{e.args[0]}'. Will use {{prefix}}.{{seed}}.png' instead"
             )
             filename = f"{prefix}.{seed}.png"
         except IndexError:
-            logger.error(
+            print(
-                "The filename format is broken or complete. Will use '{prefix}.{seed}.png' instead"
+                "** The filename format is broken or complete. Will use '{prefix}.{seed}.png' instead"
             )
             filename = f"{prefix}.{seed}.png"
 
@@ -1091,14 +1094,14 @@ def split_variations(variations_string) -> list:
     for part in variations_string.split(","):
         seed_and_weight = part.split(":")
         if len(seed_and_weight) != 2:
-            logger.warning(f'Could not parse with_variation part "{part}"')
+            print(f'** Could not parse with_variation part "{part}"')
             broken = True
             break
         try:
             seed = int(seed_and_weight[0])
             weight = float(seed_and_weight[1])
         except ValueError:
-            logger.warning(f'Could not parse with_variation part "{part}"')
+            print(f'** Could not parse with_variation part "{part}"')
             broken = True
             break
         parts.append([seed, weight])
@@ -1122,23 +1125,23 @@ def load_face_restoration(opt):
                     opt.gfpgan_model_path
                 )
             else:
-                logger.info("Face restoration disabled")
+                print(">> Face restoration disabled")
             if opt.esrgan:
                 esrgan = restoration.load_esrgan(opt.esrgan_bg_tile)
             else:
-                logger.info("Upscaling disabled")
+                print(">> Upscaling disabled")
         else:
-            logger.info("Face restoration and upscaling disabled")
+            print(">> Face restoration and upscaling disabled")
     except (ModuleNotFoundError, ImportError):
         print(traceback.format_exc(), file=sys.stderr)
-        logger.info("You may need to install the ESRGAN and/or GFPGAN modules")
+        print(">> You may need to install the ESRGAN and/or GFPGAN modules")
     return gfpgan, codeformer, esrgan
 
 
 def make_step_callback(gen, opt, prefix):
     destination = os.path.join(opt.outdir, "intermediates", prefix)
     os.makedirs(destination, exist_ok=True)
-    logger.info(f"Intermediate images will be written into {destination}")
+    print(f">> Intermediate images will be written into {destination}")
 
     def callback(state: PipelineIntermediateState):
         latents = state.latents
@@ -1180,20 +1183,21 @@ def retrieve_dream_command(opt, command, completer):
     try:
         cmd = dream_cmd_from_png(path)
     except OSError:
-        logger.error(f"{tokens[0]}: file could not be read")
+        print(f"## {tokens[0]}: file could not be read")
     except (KeyError, AttributeError, IndexError):
-        logger.error(f"{tokens[0]}: file has no metadata")
+        print(f"## {tokens[0]}: file has no metadata")
     except:
-        logger.error(f"{tokens[0]}: file could not be processed")
+        print(f"## {tokens[0]}: file could not be processed")
     if len(cmd) > 0:
         completer.set_line(cmd)
 
+
 def write_commands(opt, file_path: str, outfilepath: str):
     dir, basename = os.path.split(file_path)
     try:
         paths = sorted(list(Path(dir).glob(basename)))
     except ValueError:
-        logger.error(f'"{basename}": unacceptable pattern')
+        print(f'## "{basename}": unacceptable pattern')
         return
 
     commands = []
@@ -1202,9 +1206,9 @@ def write_commands(opt, file_path: str, outfilepath: str):
         try:
             cmd = dream_cmd_from_png(path)
         except (KeyError, AttributeError, IndexError):
-            logger.error(f"{path}: file has no metadata")
+            print(f"## {path}: file has no metadata")
         except:
-            logger.error(f"{path}: file could not be processed")
+            print(f"## {path}: file could not be processed")
         if cmd:
             commands.append(f"# {path}")
             commands.append(cmd)
@@ -1214,18 +1218,18 @@ def write_commands(opt, file_path: str, outfilepath: str):
             outfilepath = os.path.join(opt.outdir, basename)
         with open(outfilepath, "w", encoding="utf-8") as f:
             f.write("\n".join(commands))
-        logger.info(f"File {outfilepath} with commands created")
+        print(f">> File {outfilepath} with commands created")
 
 
 def report_model_error(opt: Namespace, e: Exception):
-    logger.warning(f'An error occurred while attempting to initialize the model: "{str(e)}"')
+    print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
-    logger.warning(
+    print(
-        "This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
+        "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
     )
     yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE")
     if yes_to_all:
-        logger.warning(
+        print(
-            "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
+            "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
         )
     else:
         if not click.confirm(
@@ -1234,7 +1238,7 @@ def report_model_error(opt: Namespace, e: Exception):
         ):
             return
 
-    logger.info("invokeai-configure is launching....\n")
+    print("invokeai-configure is launching....\n")
 
     # Match arguments that were set on the CLI
     # only the arguments accepted by the configuration script are parsed
@@ -1251,7 +1255,7 @@ def report_model_error(opt: Namespace, e: Exception):
     from ..install import invokeai_configure
 
     invokeai_configure()
-    logger.warning("InvokeAI will now restart")
+    print("** InvokeAI will now restart")
     sys.argv = previous_args
     main()  # would rather do a os.exec(), but doesn't exist?
     sys.exit(0)

invokeai/frontend/install/model_install.py

@@ -22,7 +22,6 @@ import torch
 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 (
@@ -456,8 +455,8 @@ def main():
     Globals.root = os.path.expanduser(get_root(opt.root) or "")
 
     if not global_config_dir().exists():
-        logger.info(
+        print(
-            "Your InvokeAI root directory is not set up. Calling invokeai-configure."
+            ">> Your InvokeAI root directory is not set up. Calling invokeai-configure."
         )
         from invokeai.frontend.install import invokeai_configure
 
@@ -467,18 +466,18 @@ def main():
     try:
         select_and_download_models(opt)
     except AssertionError as e:
-        logger.error(e)
+        print(str(e))
         sys.exit(-1)
     except KeyboardInterrupt:
-        logger.info("Goodbye! Come back soon.")
+        print("\nGoodbye! Come back soon.")
     except widget.NotEnoughSpaceForWidget as e:
         if str(e).startswith("Height of 1 allocated"):
-            logger.error(
+            print(
-                "Insufficient vertical space for the interface. Please make your window taller and try again"
+                "** Insufficient vertical space for the interface. Please make your window taller and try again"
             )
         elif str(e).startswith("addwstr"):
-            logger.error(
+            print(
-                "Insufficient horizontal space for the interface. Please make your window wider and try again."
+                "** Insufficient horizontal space for the interface. Please make your window wider and try again."
             )
 
 

invokeai/frontend/merge/merge_diffusers.py

@@ -27,8 +27,6 @@ from ...backend.globals import (
     global_models_dir,
     global_set_root,
 )
-
-import invokeai.backend.util.logging as logger
 from ...backend.model_management import ModelManager
 from ...frontend.install.widgets import FloatTitleSlider
 
@@ -115,7 +113,7 @@ def merge_diffusion_models_and_commit(
         model_name=merged_model_name, description=f'Merge of models {", ".join(models)}'
     )
     if vae := model_manager.config[models[0]].get("vae", None):
-        logger.info(f"Using configured VAE assigned to {models[0]}")
+        print(f">> Using configured VAE assigned to {models[0]}")
         import_args.update(vae=vae)
     model_manager.import_diffuser_model(dump_path, **import_args)
     model_manager.commit(config_file)
@@ -393,8 +391,10 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
             for name in self.model_manager.model_names()
             if self.model_manager.model_info(name).get("format") == "diffusers"
         ]
+        print(model_names)
         return sorted(model_names)
 
+
 class Mergeapp(npyscreen.NPSAppManaged):
     def __init__(self):
         super().__init__()
@@ -414,7 +414,7 @@ def run_gui(args: Namespace):
 
     args = mergeapp.merge_arguments
     merge_diffusion_models_and_commit(**args)
-    logger.info(f'Models merged into new model: "{args["merged_model_name"]}".')
+    print(f'>> Models merged into new model: "{args["merged_model_name"]}".')
 
 
 def run_cli(args: Namespace):
@@ -425,8 +425,8 @@ def run_cli(args: Namespace):
 
     if not args.merged_model_name:
         args.merged_model_name = "+".join(args.models)
-        logger.info(
+        print(
-            f'No --merged_model_name provided. Defaulting to "{args.merged_model_name}"'
+            f'>> No --merged_model_name provided. Defaulting to "{args.merged_model_name}"'
         )
 
         model_manager = ModelManager(OmegaConf.load(global_config_file()))
@@ -435,7 +435,7 @@ def run_cli(args: Namespace):
         ), f'A model named "{args.merged_model_name}" already exists. Use --clobber to overwrite.'
 
         merge_diffusion_models_and_commit(**vars(args))
-        logger.info(f'Models merged into new model: "{args.merged_model_name}".')
+        print(f'>> Models merged into new model: "{args.merged_model_name}".')
 
 
 def main():
@@ -455,16 +455,17 @@ def main():
             run_cli(args)
     except widget.NotEnoughSpaceForWidget as e:
         if str(e).startswith("Height of 1 allocated"):
-            logger.error(
+            print(
-                "You need to have at least two diffusers models defined in models.yaml in order to merge"
+                "** You need to have at least two diffusers models defined in models.yaml in order to merge"
             )
         else:
-            logger.error(
+            print(
-                "Not enough room for the user interface. Try making this window larger."
+                "** Not enough room for the user interface. Try making this window larger."
             )
         sys.exit(-1)
-    except Exception as e:
+    except Exception:
-        logger.error(e)
+        print(">> An error occurred:")
+        traceback.print_exc()
         sys.exit(-1)
     except KeyboardInterrupt:
         sys.exit(-1)

invokeai/frontend/training/textual_inversion.py

@@ -20,7 +20,6 @@ import npyscreen
 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
@@ -369,14 +368,14 @@ def copy_to_embeddings_folder(args: dict):
     dest_dir_name = args["placeholder_token"].strip("<>")
     destination = Path(Globals.root, "embeddings", dest_dir_name)
     os.makedirs(destination, exist_ok=True)
-    logger.info(f"Training completed. Copying learned_embeds.bin into {str(destination)}")
+    print(f">> Training completed. Copying learned_embeds.bin into {str(destination)}")
     shutil.copy(source, destination)
     if (
         input("Delete training logs and intermediate checkpoints? [y] ") or "y"
     ).startswith(("y", "Y")):
         shutil.rmtree(Path(args["output_dir"]))
     else:
-        logger.info(f'Keeping {args["output_dir"]}')
+        print(f'>> Keeping {args["output_dir"]}')
 
 
 def save_args(args: dict):
@@ -423,10 +422,10 @@ def do_front_end(args: Namespace):
             do_textual_inversion_training(**args)
             copy_to_embeddings_folder(args)
         except Exception as e:
-            logger.error("An exception occurred during training. The exception was:")
+            print("** An exception occurred during training. The exception was:")
-            logger.error(str(e))
+            print(str(e))
-            logger.error("DETAILS:")
+            print("** DETAILS:")
-            logger.error(traceback.format_exc())
+            print(traceback.format_exc())
 
 
 def main():
@@ -438,21 +437,21 @@ def main():
         else:
             do_textual_inversion_training(**vars(args))
     except AssertionError as e:
-        logger.error(e)
+        print(str(e))
         sys.exit(-1)
     except KeyboardInterrupt:
         pass
     except (widget.NotEnoughSpaceForWidget, Exception) as e:
         if str(e).startswith("Height of 1 allocated"):
-            logger.error(
+            print(
-                "You need to have at least one diffusers models defined in models.yaml in order to train"
+                "** You need to have at least one diffusers models defined in models.yaml in order to train"
             )
         elif str(e).startswith("addwstr"):
-            logger.error(
+            print(
-                "Not enough window space for the interface. Please make your window larger and try again."
+                "** Not enough window space for the interface. Please make your window larger and try again."
             )
         else:
-            logger.error(e)
+            print(f"** An error has occurred: {str(e)}")
         sys.exit(-1)
 
 

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

@@ -1,40 +0,0 @@
-import react from '@vitejs/plugin-react-swc';
-import { visualizer } from 'rollup-plugin-visualizer';
-import { PluginOption, UserConfig } from 'vite';
-import eslint from 'vite-plugin-eslint';
-import tsconfigPaths from 'vite-tsconfig-paths';
-
-export const appConfig: UserConfig = {
-  base: './',
-  plugins: [
-    react(),
-    eslint(),
-    tsconfigPaths(),
-    visualizer() as unknown as PluginOption,
-  ],
-  build: {
-    chunkSizeWarningLimit: 1500,
-  },
-  server: {
-    // Proxy HTTP requests to the flask server
-    proxy: {
-      // Proxy socket.io to the nodes socketio server
-      '/ws/socket.io': {
-        target: 'ws://127.0.0.1:9090',
-        ws: true,
-      },
-      // Proxy openapi schema definiton
-      '/openapi.json': {
-        target: 'http://127.0.0.1:9090/openapi.json',
-        rewrite: (path) => path.replace(/^\/openapi.json/, ''),
-        changeOrigin: true,
-      },
-      // proxy nodes api
-      '/api/v1': {
-        target: 'http://127.0.0.1:9090/api/v1',
-        rewrite: (path) => path.replace(/^\/api\/v1/, ''),
-        changeOrigin: true,
-      },
-    },
-  },
-};

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

@@ -1,47 +0,0 @@
-import react from '@vitejs/plugin-react-swc';
-import path from 'path';
-import { visualizer } from 'rollup-plugin-visualizer';
-import { PluginOption, UserConfig } from 'vite';
-import dts from 'vite-plugin-dts';
-import eslint from 'vite-plugin-eslint';
-import tsconfigPaths from 'vite-tsconfig-paths';
-
-export const packageConfig: UserConfig = {
-  base: './',
-  plugins: [
-    react(),
-    eslint(),
-    tsconfigPaths(),
-    visualizer() as unknown as PluginOption,
-    dts({
-      insertTypesEntry: true,
-    }),
-  ],
-  build: {
-    chunkSizeWarningLimit: 1500,
-    lib: {
-      entry: path.resolve(__dirname, '../src/index.ts'),
-      name: 'InvokeAIUI',
-      fileName: (format) => `invoke-ai-ui.${format}.js`,
-    },
-    rollupOptions: {
-      external: ['react', 'react-dom', '@emotion/react'],
-      output: {
-        globals: {
-          react: 'React',
-          'react-dom': 'ReactDOM',
-        },
-      },
-    },
-  },
-  resolve: {
-    alias: {
-      app: path.resolve(__dirname, '../src/app'),
-      assets: path.resolve(__dirname, '../src/assets'),
-      common: path.resolve(__dirname, '../src/common'),
-      features: path.resolve(__dirname, '../src/features'),
-      services: path.resolve(__dirname, '../src/services'),
-      theme: path.resolve(__dirname, '../src/theme'),
-    },
-  },
-};

invokeai/frontend/web/index.d.ts

@@ -0,0 +1,98 @@
+import React, { PropsWithChildren } from 'react';
+import { IAIPopoverProps } from '../web/src/common/components/IAIPopover';
+import { IAIIconButtonProps } from '../web/src/common/components/IAIIconButton';
+import { InvokeTabName } from 'features/ui/store/tabMap';
+
+export {};
+
+declare module 'redux-socket.io-middleware';
+
+declare global {
+  /* eslint-disable @typescript-eslint/no-explicit-any */
+  interface Array<T> {
+    /**
+     * Returns the value of the last element in the array where predicate is true, and undefined
+     * otherwise.
+     * @param predicate findLast calls predicate once for each element of the array, in descending
+     * order, until it finds one where predicate returns true. If such an element is found, findLast
+     * immediately returns that element value. Otherwise, findLast returns undefined.
+     * @param thisArg If provided, it will be used as the this value for each invocation of
+     * predicate. If it is not provided, undefined is used instead.
+     */
+    findLast<S extends T>(
+      predicate: (value: T, index: number, array: T[]) => value is S,
+      thisArg?: any
+    ): S | undefined;
+    findLast(
+      predicate: (value: T, index: number, array: T[]) => unknown,
+      thisArg?: any
+    ): T | undefined;
+
+    /**
+     * Returns the index of the last element in the array where predicate is true, and -1
+     * otherwise.
+     * @param predicate findLastIndex calls predicate once for each element of the array, in descending
+     * order, until it finds one where predicate returns true. If such an element is found,
+     * findLastIndex immediately returns that element index. Otherwise, findLastIndex returns -1.
+     * @param thisArg If provided, it will be used as the this value for each invocation of
+     * predicate. If it is not provided, undefined is used instead.
+     */
+    findLastIndex(
+      predicate: (value: T, index: number, array: T[]) => unknown,
+      thisArg?: any
+    ): number;
+  }
+  /* eslint-enable @typescript-eslint/no-explicit-any */
+}
+
+declare module '@invoke-ai/invoke-ai-ui' {
+  declare class ThemeChanger extends React.Component<ThemeChangerProps> {
+    public constructor(props: ThemeChangerProps);
+  }
+
+  declare class InvokeAiLogoComponent extends React.Component<InvokeAILogoComponentProps> {
+    public constructor(props: InvokeAILogoComponentProps);
+  }
+
+  declare class IAIPopover extends React.Component<IAIPopoverProps> {
+    public constructor(props: IAIPopoverProps);
+  }
+
+  declare class IAIIconButton extends React.Component<IAIIconButtonProps> {
+    public constructor(props: IAIIconButtonProps);
+  }
+
+  declare class SettingsModal extends React.Component<SettingsModalProps> {
+    public constructor(props: SettingsModalProps);
+  }
+
+  declare class StatusIndicator extends React.Component<StatusIndicatorProps> {
+    public constructor(props: StatusIndicatorProps);
+  }
+
+  declare class ModelSelect extends React.Component<ModelSelectProps> {
+    public constructor(props: ModelSelectProps);
+  }
+}
+
+interface InvokeProps extends PropsWithChildren {
+  apiUrl?: string;
+  disabledPanels?: string[];
+  disabledTabs?: InvokeTabName[];
+  token?: string;
+  shouldTransformUrls?: boolean;
+  shouldFetchImages?: boolean;
+}
+
+declare function Invoke(props: InvokeProps): JSX.Element;
+
+export {
+  ThemeChanger,
+  InvokeAiLogoComponent,
+  IAIPopover,
+  IAIIconButton,
+  SettingsModal,
+  StatusIndicator,
+  ModelSelect,
+};
+export = Invoke;

invokeai/frontend/web/package.json

@@ -1,23 +1,7 @@
 {
-  "name": "@invoke-ai/invoke-ai-ui",
+  "name": "invoke-ai-ui",
   "private": true,
   "version": "0.0.1",
-  "publishConfig": {
-    "access": "restricted",
-    "registry": "https://npm.pkg.github.com"
-  },
-  "main": "./dist/invoke-ai-ui.umd.js",
-  "module": "./dist/invoke-ai-ui.es.js",
-  "exports": {
-    ".": {
-      "import": "./dist/invoke-ai-ui.es.js",
-      "require": "./dist/invoke-ai-ui.umd.js"
-    }
-  },
-  "types": "./dist/index.d.ts",
-  "files": [
-    "dist"
-  ],
   "scripts": {
     "prepare": "cd ../../../ && husky install invokeai/frontend/web/.husky",
     "dev": "concurrently \"vite dev\" \"yarn run theme:watch\"",
@@ -56,96 +40,80 @@
   },
   "dependencies": {
     "@chakra-ui/anatomy": "^2.1.1",
-    "@chakra-ui/icons": "^2.0.19",
+    "@chakra-ui/cli": "^2.3.0",
-    "@chakra-ui/react": "^2.6.0",
+    "@chakra-ui/icons": "^2.0.17",
-    "@chakra-ui/styled-system": "^2.9.0",
+    "@chakra-ui/react": "^2.5.1",
+    "@chakra-ui/styled-system": "^2.6.1",
     "@chakra-ui/theme-tools": "^2.0.16",
     "@dagrejs/graphlib": "^2.1.12",
     "@emotion/react": "^11.10.6",
     "@emotion/styled": "^11.10.6",
     "@fontsource/inter": "^4.5.15",
-    "@reduxjs/toolkit": "^1.9.5",
+    "@reduxjs/toolkit": "^1.9.3",
-    "@roarr/browser-log-writer": "^1.1.5",
     "chakra-ui-contextmenu": "^1.0.5",
     "dateformat": "^5.0.3",
     "formik": "^2.2.9",
-    "framer-motion": "^10.12.4",
+    "framer-motion": "^9.0.4",
     "fuse.js": "^6.6.2",
-    "i18next": "^22.4.15",
+    "i18next": "^22.4.10",
     "i18next-browser-languagedetector": "^7.0.1",
-    "i18next-http-backend": "^2.2.0",
+    "i18next-http-backend": "^2.1.1",
-    "konva": "^9.0.1",
+    "konva": "^8.4.2",
-    "lodash-es": "^4.17.21",
+    "lodash": "^4.17.21",
-    "overlayscrollbars": "^2.1.1",
+    "patch-package": "^6.5.1",
-    "overlayscrollbars-react": "^0.5.0",
-    "patch-package": "^7.0.0",
     "re-resizable": "^6.9.9",
     "react": "^18.2.0",
     "react-colorful": "^5.6.1",
     "react-dom": "^18.2.0",
     "react-dropzone": "^14.2.3",
-    "react-hotkeys-hook": "4.4.0",
+    "react-hotkeys-hook": "4.3.5",
-    "react-i18next": "^12.2.2",
+    "react-i18next": "^12.1.5",
     "react-icons": "^4.7.1",
-    "react-konva": "^18.2.7",
+    "react-konva": "^18.2.4",
-    "react-konva-utils": "^1.0.4",
+    "react-konva-utils": "^0.3.2",
     "react-redux": "^8.0.5",
-    "react-rnd": "^10.4.1",
     "react-transition-group": "^4.4.5",
-    "react-use": "^17.4.0",
+    "react-zoom-pan-pinch": "^2.6.1",
-    "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",
-    "roarr": "^7.15.0",
-    "serialize-error": "^11.0.0",
     "socket.io-client": "^4.6.0",
     "use-image": "^1.1.0",
     "uuid": "^9.0.0"
   },
-  "peerDependencies": {
-    "@chakra-ui/cli": "^2.4.0",
-    "react": "^18.2.0",
-    "react-dom": "^18.2.0",
-    "ts-toolbelt": "^9.6.0"
-  },
   "devDependencies": {
-    "@chakra-ui/cli": "^2.4.0",
     "@types/dateformat": "^5.0.0",
-    "@types/lodash-es": "^4.14.194",
+    "@types/lodash": "^4.14.194",
-    "@types/node": "^18.16.2",
+    "@types/react": "^18.0.28",
-    "@types/react": "^18.2.0",
+    "@types/react-dom": "^18.0.11",
-    "@types/react-dom": "^18.2.1",
     "@types/react-transition-group": "^4.4.5",
     "@types/uuid": "^9.0.0",
-    "@typescript-eslint/eslint-plugin": "^5.59.1",
+    "@typescript-eslint/eslint-plugin": "^5.52.0",
-    "@typescript-eslint/parser": "^5.59.1",
+    "@typescript-eslint/parser": "^5.52.0",
-    "@vitejs/plugin-react-swc": "^3.3.0",
+    "@vitejs/plugin-react-swc": "^3.2.0",
-    "axios": "^1.4.0",
+    "axios": "^1.3.4",
     "babel-plugin-transform-imports": "^2.0.0",
-    "concurrently": "^8.0.1",
+    "concurrently": "^7.6.0",
-    "eslint": "^8.39.0",
+    "eslint": "^8.34.0",
-    "eslint-config-prettier": "^8.8.0",
+    "eslint-config-prettier": "^8.6.0",
     "eslint-plugin-prettier": "^4.2.1",
     "eslint-plugin-react": "^7.32.2",
     "eslint-plugin-react-hooks": "^4.6.0",
     "form-data": "^4.0.0",
     "husky": "^8.0.3",
-    "lint-staged": "^13.2.2",
+    "lint-staged": "^13.1.2",
     "madge": "^6.0.0",
     "openapi-types": "^12.1.0",
-    "openapi-typescript-codegen": "^0.24.0",
+    "openapi-typescript-codegen": "^0.23.0",
     "postinstall-postinstall": "^2.1.0",
-    "prettier": "^2.8.8",
+    "prettier": "^2.8.4",
     "rollup-plugin-visualizer": "^5.9.0",
-    "terser": "^5.17.1",
+    "terser": "^5.16.4",
-    "ts-toolbelt": "^9.6.0",
+    "typescript": "4.9.5",
-    "vite": "^4.3.3",
+    "vite": "^4.1.2",
-    "vite-plugin-dts": "^2.3.0",
     "vite-plugin-eslint": "^1.8.1",
-    "vite-tsconfig-paths": "^4.2.0",
+    "vite-tsconfig-paths": "^4.0.5",
     "yarn": "^1.22.19"
   }
 }

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

@@ -63,7 +63,7 @@
         "postProcessDesc3": "The Invoke AI Command Line Interface offers various other features including Embiggen.",
         "training": "Training",
         "trainingDesc1": "A dedicated workflow for training your own embeddings and checkpoints using Textual Inversion and Dreambooth from the web interface.",
-        "trainingDesc2": "InvokeAI already supports training custom embeddourings using Textual Inversion using the main script.",
+        "trainingDesc2": "InvokeAI already supports training custom embeddings using Textual Inversion using the main script.",
         "upload": "Upload",
         "close": "Close",
         "cancel": "Cancel",
@@ -97,12 +97,7 @@
         "statusMergedModels": "Models Merged",
         "pinOptionsPanel": "Pin Options Panel",
         "loading": "Loading",
-        "loadingInvokeAI": "Loading Invoke AI",
+        "loadingInvokeAI": "Loading Invoke AI"
-        "random": "Random",
-        "generate": "Generate",
-        "openInNewTab": "Open in New Tab",
-        "dontAskMeAgain": "Don't ask me again",
-        "areYouSure": "Are you sure?"
     },
     "gallery": {
         "generations": "Generations",
@@ -118,10 +113,7 @@
         "pinGallery": "Pin Gallery",
         "allImagesLoaded": "All Images Loaded",
         "loadMore": "Load More",
-        "noImagesInGallery": "No Images In Gallery",
+        "noImagesInGallery": "No Images In Gallery"
-        "deleteImage": "Delete Image",
-        "deleteImageBin": "Deleted images will be sent to your operating system's Bin.",
-        "deleteImagePermanent": "Deleted images cannot be restored."
     },
     "hotkeys": {
         "keyboardShortcuts": "Keyboard Shortcuts",
@@ -513,6 +505,7 @@
         "useAll": "Use All",
         "useInitImg": "Use Initial Image",
         "info": "Info",
+        "deleteImage": "Delete Image",
         "initialImage": "Initial Image",
         "showOptionsPanel": "Show Options Panel",
         "hidePreview": "Hide Preview",
@@ -527,15 +520,10 @@
         "useCanvasBeta": "Use Canvas Beta Layout",
         "enableImageDebugging": "Enable Image Debugging",
         "useSlidersForAll": "Use Sliders For All Options",
-        "autoShowProgress": "Auto Show 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.",
-        "resetComplete": "Web UI has been reset. Refresh the page to reload.",
+        "resetComplete": "Web UI has been reset. Refresh the page to reload."
-        "consoleLogLevel": "Log Level",
-        "shouldLogToConsole": "Console Logging",
-        "developer": "Developer",
-        "general": "General"
     },
     "toast": {
         "serverError": "Server Error",
@@ -646,9 +634,5 @@
         "betaDarkenOutside": "Darken Outside",
         "betaLimitToBox": "Limit To Box",
         "betaPreserveMasked": "Preserve Masked"
-    },
-    "ui": {
-        "showProgressImages": "Show Progress Images",
-        "hideProgressImages": "Hide Progress Images"
     }
 }

invokeai/frontend/web/src/Loading.tsx

@@ -0,0 +1,39 @@
+import { Flex, Spinner, Text } from '@chakra-ui/react';
+import { useTranslation } from 'react-i18next';
+
+interface LoaderProps {
+  showText?: boolean;
+  text?: string;
+}
+
+// This component loads before the theme so we cannot use theme tokens here
+
+const Loading = (props: LoaderProps) => {
+  const { t } = useTranslation();
+  const { showText = false, text = t('common.loadingInvokeAI') } = props;
+
+  return (
+    <Flex
+      width="100vw"
+      height="100vh"
+      alignItems="center"
+      justifyContent="center"
+      bg="#121212"
+      flexDirection="column"
+      rowGap={4}
+    >
+      <Spinner color="grey" w="5rem" h="5rem" />
+      {showText && (
+        <Text
+          color="grey"
+          fontWeight="semibold"
+          fontFamily="'Inter', sans-serif"
+        >
+          {text}
+        </Text>
+      )}
+    </Flex>
+  );
+};
+
+export default Loading;

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

@@ -0,0 +1,100 @@
+import ImageUploader from 'common/components/ImageUploader';
+import Console from 'features/system/components/Console';
+import ProgressBar from 'features/system/components/ProgressBar';
+import SiteHeader from 'features/system/components/SiteHeader';
+import InvokeTabs from 'features/ui/components/InvokeTabs';
+import { keepGUIAlive } from './utils';
+
+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 { APP_HEIGHT, APP_WIDTH } from 'theme/util/constants';
+import ImageGalleryPanel from 'features/gallery/components/ImageGalleryPanel';
+import Lightbox from 'features/lightbox/components/Lightbox';
+import { useAppDispatch, useAppSelector } from './storeHooks';
+import { PropsWithChildren, useEffect } from 'react';
+import { setDisabledPanels, setDisabledTabs } from 'features/ui/store/uiSlice';
+import { InvokeTabName } from 'features/ui/store/tabMap';
+import { shouldTransformUrlsChanged } from 'features/system/store/systemSlice';
+import { setShouldFetchImages } from 'features/gallery/store/resultsSlice';
+
+keepGUIAlive();
+
+interface Props extends PropsWithChildren {
+  options: {
+    disabledPanels: string[];
+    disabledTabs: InvokeTabName[];
+    shouldTransformUrls?: boolean;
+    shouldFetchImages: boolean;
+  };
+}
+
+const App = (props: Props) => {
+  useToastWatcher();
+
+  const currentTheme = useAppSelector((state) => state.ui.currentTheme);
+  const { setColorMode } = useColorMode();
+  const dispatch = useAppDispatch();
+
+  useEffect(() => {
+    dispatch(setDisabledPanels(props.options.disabledPanels));
+  }, [dispatch, props.options.disabledPanels]);
+
+  useEffect(() => {
+    dispatch(setDisabledTabs(props.options.disabledTabs));
+  }, [dispatch, props.options.disabledTabs]);
+
+  useEffect(() => {
+    dispatch(
+      shouldTransformUrlsChanged(Boolean(props.options.shouldTransformUrls))
+    );
+  }, [dispatch, props.options.shouldTransformUrls]);
+
+  useEffect(() => {
+    dispatch(setShouldFetchImages(props.options.shouldFetchImages));
+  }, [dispatch, props.options.shouldFetchImages]);
+
+  useEffect(() => {
+    setColorMode(['light'].includes(currentTheme) ? 'light' : 'dark');
+  }, [setColorMode, currentTheme]);
+
+  return (
+    <Grid w="100vw" h="100vh">
+      <Lightbox />
+      <ImageUploader>
+        <ProgressBar />
+        <Grid
+          gap={4}
+          p={4}
+          gridAutoRows="min-content auto"
+          w={APP_WIDTH}
+          h={APP_HEIGHT}
+        >
+          {props.children || <SiteHeader />}
+          <Flex
+            gap={4}
+            w={{ base: '100vw', xl: 'full' }}
+            h="full"
+            flexDir={{ base: 'column', xl: 'row' }}
+          >
+            <InvokeTabs />
+            <ImageGalleryPanel />
+          </Flex>
+        </Grid>
+        <Box>
+          <Console />
+        </Box>
+      </ImageUploader>
+      <Portal>
+        <FloatingParametersPanelButtons />
+      </Portal>
+      <Portal>
+        <FloatingGalleryButton />
+      </Portal>
+    </Grid>
+  );
+};
+
+export default App;

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

@@ -2,8 +2,8 @@ import { ChakraProvider, extendTheme } from '@chakra-ui/react';
 import { ReactNode, useEffect } from 'react';
 import { useTranslation } from 'react-i18next';
 import { theme as invokeAITheme } from 'theme/theme';
-import { RootState } from 'app/store/store';
+import { RootState } from './store';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from './storeHooks';
 
 import { greenTeaThemeColors } from 'theme/colors/greenTea';
 import { invokeAIThemeColors } from 'theme/colors/invokeAI';
@@ -18,8 +18,6 @@ import '@fontsource/inter/600.css';
 import '@fontsource/inter/700.css';
 import '@fontsource/inter/800.css';
 import '@fontsource/inter/900.css';
-import 'overlayscrollbars/overlayscrollbars.css';
-import 'theme/css/overlayscrollbars.css';
 
 type ThemeLocaleProviderProps = {
   children: ReactNode;

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

@@ -1,129 +0,0 @@
-import ImageUploader from 'common/components/ImageUploader';
-import ProgressBar from 'features/system/components/ProgressBar';
-import SiteHeader from 'features/system/components/SiteHeader';
-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 { APP_HEIGHT, APP_WIDTH } from 'theme/util/constants';
-import ImageGalleryPanel from 'features/gallery/components/ImageGalleryPanel';
-import Lightbox from 'features/lightbox/components/Lightbox';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import {
-  memo,
-  PropsWithChildren,
-  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';
-
-const DEFAULT_CONFIG = {};
-
-interface Props extends PropsWithChildren {
-  config?: PartialAppConfig;
-}
-
-const App = ({ config = DEFAULT_CONFIG, children }: Props) => {
-  useToastWatcher();
-  useGlobalHotkeys();
-  const log = useLogger();
-
-  const currentTheme = useAppSelector((state) => state.ui.currentTheme);
-
-  const isLightboxEnabled = useFeatureStatus('lightbox').isFeatureEnabled;
-
-  const isApplicationReady = useIsApplicationReady();
-
-  const [loadingOverridden, setLoadingOverridden] = useState(false);
-
-  const { setColorMode } = useColorMode();
-  const dispatch = useAppDispatch();
-
-  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);
-  }, []);
-
-  return (
-    <Grid w="100vw" h="100vh" position="relative" overflow="hidden">
-      {isLightboxEnabled && <Lightbox />}
-      <ImageUploader>
-        <ProgressBar />
-        <Grid
-          gap={4}
-          p={4}
-          gridAutoRows="min-content auto"
-          w={APP_WIDTH}
-          h={APP_HEIGHT}
-        >
-          {children || <SiteHeader />}
-          <Flex
-            gap={4}
-            w={{ base: '100vw', xl: 'full' }}
-            h="full"
-            flexDir={{ base: 'column', xl: 'row' }}
-          >
-            <InvokeTabs />
-            <ImageGalleryPanel />
-          </Flex>
-        </Grid>
-      </ImageUploader>
-
-      <AnimatePresence>
-        {!isApplicationReady && !loadingOverridden && (
-          <motion.div
-            key="loading"
-            initial={{ opacity: 1 }}
-            animate={{ opacity: 1 }}
-            exit={{ opacity: 0 }}
-            transition={{ duration: 0.3 }}
-            style={{ zIndex: 3 }}
-          >
-            <Box position="absolute" top={0} left={0} w="100vw" h="100vh">
-              <Loading />
-            </Box>
-            <Box
-              onClick={handleOverrideClicked}
-              position="absolute"
-              top={0}
-              right={0}
-              cursor="pointer"
-              w="2rem"
-              h="2rem"
-            />
-          </motion.div>
-        )}
-      </AnimatePresence>
-
-      <Portal>
-        <FloatingParametersPanelButtons />
-      </Portal>
-      <Portal>
-        <FloatingGalleryButton />
-      </Portal>
-      <ProgressImagePreview />
-    </Grid>
-  );
-};
-
-export default memo(App);

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

@@ -1,6 +1,23 @@
 // TODO: use Enums?
 
-export const DIFFUSERS_SCHEDULERS: Array<string> = [
+import { InProgressImageType } from 'features/system/store/systemSlice';
+
+// Valid samplers
+export const SAMPLERS: Array<string> = [
+  'ddim',
+  'plms',
+  'k_lms',
+  'k_dpm_2',
+  'k_dpm_2_a',
+  'k_dpmpp_2',
+  'k_dpmpp_2_a',
+  'k_euler',
+  'k_euler_a',
+  'k_heun',
+];
+
+// Valid Diffusers Samplers
+export const DIFFUSERS_SAMPLERS: Array<string> = [
   'ddim',
   'plms',
   'k_lms',
@@ -31,8 +48,17 @@ export const UPSCALING_LEVELS: Array<{ key: string; value: number }> = [
 
 export const NUMPY_RAND_MIN = 0;
 
-export const NUMPY_RAND_MAX = 2147483647;
+export const NUMPY_RAND_MAX = 4294967295;
 
 export const FACETOOL_TYPES = ['gfpgan', 'codeformer'] as const;
 
+export const IN_PROGRESS_IMAGE_TYPES: Array<{
+  key: string;
+  value: InProgressImageType;
+}> = [
+  { key: 'None', value: 'none' },
+  { key: 'Fast', value: 'latents' },
+  { key: 'Accurate', value: 'full-res' },
+];
+
 export const NODE_MIN_WIDTH = 250;

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

@@ -0,0 +1,336 @@
+/**
+ * Types for images, the things they are made of, and the things
+ * they make up.
+ *
+ * Generated images are txt2img and img2img images. They may have
+ * had additional postprocessing done on them when they were first
+ * generated.
+ *
+ * Postprocessed images are images which were not generated here
+ * but only postprocessed by the app. They only get postprocessing
+ * metadata and have a different image type, e.g. 'esrgan' or
+ * 'gfpgan'.
+ */
+
+import { InvokeTabName } from 'features/ui/store/tabMap';
+import { IRect } from 'konva/lib/types';
+import { ImageMetadata, ImageType } from 'services/api';
+import { AnyInvocation } from 'services/events/types';
+
+/**
+ * TODO:
+ * Once an image has been generated, if it is postprocessed again,
+ * additional postprocessing steps are added to its postprocessing
+ * array.
+ *
+ * TODO: Better documentation of types.
+ */
+
+export declare type PromptItem = {
+  prompt: string;
+  weight: number;
+};
+
+// TECHDEBT: We need to retain compatibility with plain prompt strings and the structure Prompt type
+export declare type Prompt = Array<PromptItem> | string;
+
+export declare type SeedWeightPair = {
+  seed: number;
+  weight: number;
+};
+
+export declare type SeedWeights = Array<SeedWeightPair>;
+
+// All generated images contain these metadata.
+export declare type CommonGeneratedImageMetadata = {
+  postprocessing: null | Array<ESRGANMetadata | GFPGANMetadata>;
+  sampler:
+    | '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';
+  prompt: Prompt;
+  seed: number;
+  variations: SeedWeights;
+  steps: number;
+  cfg_scale: number;
+  width: number;
+  height: number;
+  seamless: boolean;
+  hires_fix: boolean;
+  extra: null | Record<string, never>; // Pending development of RFC #266
+};
+
+// txt2img and img2img images have some unique attributes.
+export declare type Txt2ImgMetadata = GeneratedImageMetadata & {
+  type: 'txt2img';
+};
+
+export declare type Img2ImgMetadata = GeneratedImageMetadata & {
+  type: 'img2img';
+  orig_hash: string;
+  strength: number;
+  fit: boolean;
+  init_image_path: string;
+  mask_image_path?: string;
+};
+
+// Superset of  generated image metadata types.
+export declare type GeneratedImageMetadata = Txt2ImgMetadata | Img2ImgMetadata;
+
+// All post processed images contain these metadata.
+export declare type CommonPostProcessedImageMetadata = {
+  orig_path: string;
+  orig_hash: string;
+};
+
+// esrgan and gfpgan images have some unique attributes.
+export declare type ESRGANMetadata = CommonPostProcessedImageMetadata & {
+  type: 'esrgan';
+  scale: 2 | 4;
+  strength: number;
+  denoise_str: number;
+};
+
+export declare type FacetoolMetadata = CommonPostProcessedImageMetadata & {
+  type: 'gfpgan' | 'codeformer';
+  strength: number;
+  fidelity?: number;
+};
+
+// Superset of all postprocessed image metadata types..
+export declare type PostProcessedImageMetadata =
+  | ESRGANMetadata
+  | FacetoolMetadata;
+
+// Metadata includes the system config and image metadata.
+export declare type Metadata = SystemGenerationMetadata & {
+  image: GeneratedImageMetadata | PostProcessedImageMetadata;
+};
+
+// An Image has a UUID, url, modified timestamp, width, height and maybe metadata
+export declare type _Image = {
+  uuid: string;
+  url: string;
+  thumbnail: string;
+  mtime: number;
+  metadata?: Metadata;
+  width: number;
+  height: number;
+  category: GalleryCategory;
+  isBase64?: boolean;
+  dreamPrompt?: 'string';
+  name?: string;
+};
+
+/**
+ * ResultImage
+ */
+export declare type Image = {
+  name: string;
+  type: ImageType;
+  url: string;
+  thumbnail: string;
+  metadata: ImageMetadata;
+};
+
+// GalleryImages is an array of Image.
+export declare type GalleryImages = {
+  images: Array<_Image>;
+};
+
+/**
+ * Types related to the system status.
+ */
+
+// This represents the processing status of the backend.
+export declare type SystemStatus = {
+  isProcessing: boolean;
+  currentStep: number;
+  totalSteps: number;
+  currentIteration: number;
+  totalIterations: number;
+  currentStatus: string;
+  currentStatusHasSteps: boolean;
+  hasError: boolean;
+};
+
+export declare type SystemGenerationMetadata = {
+  model: string;
+  model_weights?: string;
+  model_id?: string;
+  model_hash: string;
+  app_id: string;
+  app_version: string;
+};
+
+export declare type SystemConfig = SystemGenerationMetadata & {
+  model_list: ModelList;
+  infill_methods: string[];
+};
+
+export declare type ModelStatus = 'active' | 'cached' | 'not loaded';
+
+export declare type Model = {
+  status: ModelStatus;
+  description: string;
+  weights: string;
+  config?: string;
+  vae?: string;
+  width?: number;
+  height?: number;
+  default?: boolean;
+  format?: string;
+};
+
+export declare type DiffusersModel = {
+  status: ModelStatus;
+  description: string;
+  repo_id?: string;
+  path?: string;
+  vae?: {
+    repo_id?: string;
+    path?: string;
+  };
+  format?: string;
+  default?: boolean;
+};
+
+export declare type ModelList = Record<string, Model & DiffusersModel>;
+
+export declare type FoundModel = {
+  name: string;
+  location: string;
+};
+
+export declare type InvokeModelConfigProps = {
+  name: string | undefined;
+  description: string | undefined;
+  config: string | undefined;
+  weights: string | undefined;
+  vae: string | undefined;
+  width: number | undefined;
+  height: number | undefined;
+  default: boolean | undefined;
+  format: string | undefined;
+};
+
+export declare type InvokeDiffusersModelConfigProps = {
+  name: string | undefined;
+  description: string | undefined;
+  repo_id: string | undefined;
+  path: string | undefined;
+  default: boolean | undefined;
+  format: string | undefined;
+  vae: {
+    repo_id: string | undefined;
+    path: string | undefined;
+  };
+};
+
+export declare type InvokeModelConversionProps = {
+  model_name: string;
+  save_location: string;
+  custom_location: string | null;
+};
+
+export declare type InvokeModelMergingProps = {
+  models_to_merge: string[];
+  alpha: number;
+  interp: 'weighted_sum' | 'sigmoid' | 'inv_sigmoid' | 'add_difference';
+  force: boolean;
+  merged_model_name: string;
+  model_merge_save_path: string | null;
+};
+
+/**
+ * These types type data received from the server via socketio.
+ */
+
+export declare type ModelChangeResponse = {
+  model_name: string;
+  model_list: ModelList;
+};
+
+export declare type ModelConvertedResponse = {
+  converted_model_name: string;
+  model_list: ModelList;
+};
+
+export declare type ModelsMergedResponse = {
+  merged_models: string[];
+  merged_model_name: string;
+  model_list: ModelList;
+};
+
+export declare type ModelAddedResponse = {
+  new_model_name: string;
+  model_list: ModelList;
+  update: boolean;
+};
+
+export declare type ModelDeletedResponse = {
+  deleted_model_name: string;
+  model_list: ModelList;
+};
+
+export declare type FoundModelResponse = {
+  search_folder: string;
+  found_models: FoundModel[];
+};
+
+export declare type SystemStatusResponse = SystemStatus;
+
+export declare type SystemConfigResponse = SystemConfig;
+
+export declare type ImageResultResponse = Omit<_Image, 'uuid'> & {
+  boundingBox?: IRect;
+  generationMode: InvokeTabName;
+};
+
+export declare type ImageUploadResponse = {
+  // image: Omit<Image, 'uuid' | 'metadata' | 'category'>;
+  url: string;
+  mtime: number;
+  width: number;
+  height: number;
+  thumbnail: string;
+  // bbox: [number, number, number, number];
+};
+
+export declare type ErrorResponse = {
+  message: string;
+  additionalData?: string;
+};
+
+export declare type GalleryImagesResponse = {
+  images: Array<Omit<_Image, 'uuid'>>;
+  areMoreImagesAvailable: boolean;
+  category: GalleryCategory;
+};
+
+export declare type ImageDeletedResponse = {
+  uuid: string;
+  url: string;
+  category: GalleryCategory;
+};
+
+export declare type ImageUrlResponse = {
+  url: string;
+};
+
+export declare type UploadImagePayload = {
+  file: File;
+  destination?: ImageUploadDestination;
+};
+
+export declare type UploadOutpaintingMergeImagePayload = {
+  dataURL: string;
+  name: string;
+};

invokeai/frontend/web/src/app/logging/useLogger.ts

@@ -1,94 +0,0 @@
-import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/store/storeHooks';
-import { systemSelector } from 'features/system/store/systemSelectors';
-import { isEqual } from 'lodash-es';
-import { useEffect } from 'react';
-import { LogLevelName, ROARR, Roarr } from 'roarr';
-import { createLogWriter } from '@roarr/browser-log-writer';
-
-// Base logging context includes only the package name
-const baseContext = { package: '@invoke-ai/invoke-ai-ui' };
-
-// Create browser log writer
-ROARR.write = createLogWriter();
-
-// Module-scoped logger - can be imported and used anywhere
-export let log = Roarr.child(baseContext);
-
-// Translate human-readable log levels to numbers, used for log filtering
-export const LOG_LEVEL_MAP: Record<LogLevelName, number> = {
-  trace: 10,
-  debug: 20,
-  info: 30,
-  warn: 40,
-  error: 50,
-  fatal: 60,
-};
-
-export const VALID_LOG_LEVELS = [
-  'trace',
-  'debug',
-  'info',
-  'warn',
-  'error',
-  'fatal',
-] as const;
-
-export type InvokeLogLevel = (typeof VALID_LOG_LEVELS)[number];
-
-const selector = createSelector(
-  systemSelector,
-  (system) => {
-    const { app_version, consoleLogLevel, shouldLogToConsole } = system;
-
-    return {
-      version: app_version,
-      consoleLogLevel,
-      shouldLogToConsole,
-    };
-  },
-  {
-    memoizeOptions: {
-      resultEqualityCheck: isEqual,
-    },
-  }
-);
-
-export const useLogger = () => {
-  const { version, consoleLogLevel, shouldLogToConsole } =
-    useAppSelector(selector);
-
-  // The provided Roarr browser log writer uses localStorage to config logging to console
-  useEffect(() => {
-    if (shouldLogToConsole) {
-      // Enable console log output
-      localStorage.setItem('ROARR_LOG', 'true');
-
-      // Use a filter to show only logs of the given level
-      localStorage.setItem(
-        'ROARR_FILTER',
-        `context.logLevel:>=${LOG_LEVEL_MAP[consoleLogLevel]}`
-      );
-    } else {
-      // Disable console log output
-      localStorage.setItem('ROARR_LOG', 'false');
-    }
-    ROARR.write = createLogWriter();
-  }, [consoleLogLevel, shouldLogToConsole]);
-
-  // Update the module-scoped logger context as needed
-  useEffect(() => {
-    const newContext: Record<string, any> = {
-      ...baseContext,
-    };
-
-    if (version) {
-      newContext.version = version;
-    }
-
-    log = Roarr.child(newContext);
-  }, [version]);
-
-  // Use the logger within components - no different than just importing it directly
-  return log;
-};

invokeai/frontend/web/src/app/selectors/readinessSelector.ts

@@ -4,7 +4,7 @@ import { initialCanvasImageSelector } from 'features/canvas/store/canvasSelector
 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';
+import { isEqual } from 'lodash';
 
 export const readinessSelector = createSelector(
   [

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

@@ -1,67 +1,65 @@
-// import { createAction } from '@reduxjs/toolkit';
+import { createAction } from '@reduxjs/toolkit';
-// import * as InvokeAI from 'app/types/invokeai';
+import * as InvokeAI from 'app/invokeai';
-// import { GalleryCategory } from 'features/gallery/store/gallerySlice';
+import { GalleryCategory } from 'features/gallery/store/gallerySlice';
-// import { InvokeTabName } from 'features/ui/store/tabMap';
+import { InvokeTabName } from 'features/ui/store/tabMap';
 
-// /**
+/**
-//  * We can't use redux-toolkit's createSlice() to make these actions,
+ * We can't use redux-toolkit's createSlice() to make these actions,
-//  * because they have no associated reducer. They only exist to dispatch
+ * because they have no associated reducer. They only exist to dispatch
-//  * requests to the server via socketio. These actions will be handled
+ * requests to the server via socketio. These actions will be handled
-//  * by the middleware.
+ * by the middleware.
-//  */
+ */
 
-// export const generateImage = createAction<InvokeTabName>(
+export const generateImage = createAction<InvokeTabName>(
-//   'socketio/generateImage'
+  'socketio/generateImage'
-// );
+);
-// export const runESRGAN = createAction<InvokeAI._Image>('socketio/runESRGAN');
+export const runESRGAN = createAction<InvokeAI._Image>('socketio/runESRGAN');
-// export const runFacetool = createAction<InvokeAI._Image>(
+export const runFacetool = createAction<InvokeAI._Image>(
-//   'socketio/runFacetool'
+  'socketio/runFacetool'
-// );
+);
-// export const deleteImage = createAction<InvokeAI._Image>(
+export const deleteImage = createAction<InvokeAI._Image>(
-//   'socketio/deleteImage'
+  'socketio/deleteImage'
-// );
+);
-// export const requestImages = createAction<GalleryCategory>(
+export const requestImages = createAction<GalleryCategory>(
-//   'socketio/requestImages'
+  'socketio/requestImages'
-// );
+);
-// export const requestNewImages = createAction<GalleryCategory>(
+export const requestNewImages = createAction<GalleryCategory>(
-//   'socketio/requestNewImages'
+  'socketio/requestNewImages'
-// );
+);
-// export const cancelProcessing = createAction<undefined>(
+export const cancelProcessing = createAction<undefined>(
-//   'socketio/cancelProcessing'
+  'socketio/cancelProcessing'
-// );
+);
 
-// export const requestSystemConfig = createAction<undefined>(
+export const requestSystemConfig = createAction<undefined>(
-//   'socketio/requestSystemConfig'
+  'socketio/requestSystemConfig'
-// );
+);
 
-// export const searchForModels = createAction<string>('socketio/searchForModels');
+export const searchForModels = createAction<string>('socketio/searchForModels');
 
-// export const addNewModel = createAction<
+export const addNewModel = createAction<
-//   InvokeAI.InvokeModelConfigProps | InvokeAI.InvokeDiffusersModelConfigProps
+  InvokeAI.InvokeModelConfigProps | InvokeAI.InvokeDiffusersModelConfigProps
-// >('socketio/addNewModel');
+>('socketio/addNewModel');
 
-// export const deleteModel = createAction<string>('socketio/deleteModel');
+export const deleteModel = createAction<string>('socketio/deleteModel');
 
-// export const convertToDiffusers =
+export const convertToDiffusers =
-//   createAction<InvokeAI.InvokeModelConversionProps>(
+  createAction<InvokeAI.InvokeModelConversionProps>(
-//     'socketio/convertToDiffusers'
+    'socketio/convertToDiffusers'
-//   );
+  );
 
-// export const mergeDiffusersModels =
+export const mergeDiffusersModels =
-//   createAction<InvokeAI.InvokeModelMergingProps>(
+  createAction<InvokeAI.InvokeModelMergingProps>(
-//     'socketio/mergeDiffusersModels'
+    'socketio/mergeDiffusersModels'
-//   );
+  );
 
-// export const requestModelChange = createAction<string>(
+export const requestModelChange = createAction<string>(
-//   'socketio/requestModelChange'
+  'socketio/requestModelChange'
-// );
+);
 
-// export const saveStagingAreaImageToGallery = createAction<string>(
+export const saveStagingAreaImageToGallery = createAction<string>(
-//   'socketio/saveStagingAreaImageToGallery'
+  'socketio/saveStagingAreaImageToGallery'
-// );
+);
 
-// export const emptyTempFolder = createAction<undefined>(
+export const emptyTempFolder = createAction<undefined>(
-//   'socketio/requestEmptyTempFolder'
+  'socketio/requestEmptyTempFolder'
-// );
+);
-
-export default {};

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

@@ -1,209 +1,208 @@
-// import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
+import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
-// import * as InvokeAI from 'app/types/invokeai';
+import * as InvokeAI from 'app/invokeai';
-// import type { RootState } from 'app/store/store';
+import type { RootState } from 'app/store';
-// import {
+import {
-//   frontendToBackendParameters,
+  frontendToBackendParameters,
-//   FrontendToBackendParametersConfig,
+  FrontendToBackendParametersConfig,
-// } from 'common/util/parameterTranslation';
+} from 'common/util/parameterTranslation';
-// import dateFormat from 'dateformat';
+import dateFormat from 'dateformat';
-// import {
+import {
-//   GalleryCategory,
+  GalleryCategory,
-//   GalleryState,
+  GalleryState,
-//   removeImage,
+  removeImage,
-// } from 'features/gallery/store/gallerySlice';
+} from 'features/gallery/store/gallerySlice';
-// import {
+import {
-//   generationRequested,
+  addLogEntry,
-//   modelChangeRequested,
+  generationRequested,
-//   modelConvertRequested,
+  modelChangeRequested,
-//   modelMergingRequested,
+  modelConvertRequested,
-//   setIsProcessing,
+  modelMergingRequested,
-// } from 'features/system/store/systemSlice';
+  setIsProcessing,
-// import { InvokeTabName } from 'features/ui/store/tabMap';
+} from 'features/system/store/systemSlice';
-// import { Socket } from 'socket.io-client';
+import { InvokeTabName } from 'features/ui/store/tabMap';
+import { Socket } from 'socket.io-client';
 
-// /**
+/**
-//  * Returns an object containing all functions which use `socketio.emit()`.
+ * Returns an object containing all functions which use `socketio.emit()`.
-//  * i.e. those which make server requests.
+ * i.e. those which make server requests.
-//  */
+ */
-// const makeSocketIOEmitters = (
+const makeSocketIOEmitters = (
-//   store: MiddlewareAPI<Dispatch<AnyAction>, RootState>,
+  store: MiddlewareAPI<Dispatch<AnyAction>, RootState>,
-//   socketio: Socket
+  socketio: Socket
-// ) => {
+) => {
-//   // We need to dispatch actions to redux and get pieces of state from the store.
+  // We need to dispatch actions to redux and get pieces of state from the store.
-//   const { dispatch, getState } = store;
+  const { dispatch, getState } = store;
 
-//   return {
+  return {
-//     emitGenerateImage: (generationMode: InvokeTabName) => {
+    emitGenerateImage: (generationMode: InvokeTabName) => {
-//       dispatch(setIsProcessing(true));
+      dispatch(setIsProcessing(true));
 
-//       const state: RootState = getState();
+      const state: RootState = getState();
 
-//       const {
+      const {
-//         generation: generationState,
+        generation: generationState,
-//         postprocessing: postprocessingState,
+        postprocessing: postprocessingState,
-//         system: systemState,
+        system: systemState,
-//         canvas: canvasState,
+        canvas: canvasState,
-//       } = state;
+      } = state;
 
-//       const frontendToBackendParametersConfig: FrontendToBackendParametersConfig =
+      const frontendToBackendParametersConfig: FrontendToBackendParametersConfig =
-//         {
+        {
-//           generationMode,
+          generationMode,
-//           generationState,
+          generationState,
-//           postprocessingState,
+          postprocessingState,
-//           canvasState,
+          canvasState,
-//           systemState,
+          systemState,
-//         };
+        };
 
-//       dispatch(generationRequested());
+      dispatch(generationRequested());
 
-//       const { generationParameters, esrganParameters, facetoolParameters } =
+      const { generationParameters, esrganParameters, facetoolParameters } =
-//         frontendToBackendParameters(frontendToBackendParametersConfig);
+        frontendToBackendParameters(frontendToBackendParametersConfig);
 
-//       socketio.emit(
+      socketio.emit(
-//         'generateImage',
+        'generateImage',
-//         generationParameters,
+        generationParameters,
-//         esrganParameters,
+        esrganParameters,
-//         facetoolParameters
+        facetoolParameters
-//       );
+      );
 
-//       // we need to truncate the init_mask base64 else it takes up the whole log
+      // we need to truncate the init_mask base64 else it takes up the whole log
-//       // TODO: handle maintaining masks for reproducibility in future
+      // TODO: handle maintaining masks for reproducibility in future
-//       if (generationParameters.init_mask) {
+      if (generationParameters.init_mask) {
-//         generationParameters.init_mask = generationParameters.init_mask
+        generationParameters.init_mask = generationParameters.init_mask
-//           .substr(0, 64)
+          .substr(0, 64)
-//           .concat('...');
+          .concat('...');
-//       }
+      }
-//       if (generationParameters.init_img) {
+      if (generationParameters.init_img) {
-//         generationParameters.init_img = generationParameters.init_img
+        generationParameters.init_img = generationParameters.init_img
-//           .substr(0, 64)
+          .substr(0, 64)
-//           .concat('...');
+          .concat('...');
-//       }
+      }
 
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Image generation requested: ${JSON.stringify({
+          message: `Image generation requested: ${JSON.stringify({
-//             ...generationParameters,
+            ...generationParameters,
-//             ...esrganParameters,
+            ...esrganParameters,
-//             ...facetoolParameters,
+            ...facetoolParameters,
-//           })}`,
+          })}`,
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     emitRunESRGAN: (imageToProcess: InvokeAI._Image) => {
+    emitRunESRGAN: (imageToProcess: InvokeAI._Image) => {
-//       dispatch(setIsProcessing(true));
+      dispatch(setIsProcessing(true));
 
-//       const {
+      const {
-//         postprocessing: {
+        postprocessing: {
-//           upscalingLevel,
+          upscalingLevel,
-//           upscalingDenoising,
+          upscalingDenoising,
-//           upscalingStrength,
+          upscalingStrength,
-//         },
+        },
-//       } = getState();
+      } = getState();
 
-//       const esrganParameters = {
+      const esrganParameters = {
-//         upscale: [upscalingLevel, upscalingDenoising, upscalingStrength],
+        upscale: [upscalingLevel, upscalingDenoising, upscalingStrength],
-//       };
+      };
-//       socketio.emit('runPostprocessing', imageToProcess, {
+      socketio.emit('runPostprocessing', imageToProcess, {
-//         type: 'esrgan',
+        type: 'esrgan',
-//         ...esrganParameters,
+        ...esrganParameters,
-//       });
+      });
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `ESRGAN upscale requested: ${JSON.stringify({
+          message: `ESRGAN upscale requested: ${JSON.stringify({
-//             file: imageToProcess.url,
+            file: imageToProcess.url,
-//             ...esrganParameters,
+            ...esrganParameters,
-//           })}`,
+          })}`,
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     emitRunFacetool: (imageToProcess: InvokeAI._Image) => {
+    emitRunFacetool: (imageToProcess: InvokeAI._Image) => {
-//       dispatch(setIsProcessing(true));
+      dispatch(setIsProcessing(true));
 
-//       const {
+      const {
-//         postprocessing: { facetoolType, facetoolStrength, codeformerFidelity },
+        postprocessing: { facetoolType, facetoolStrength, codeformerFidelity },
-//       } = getState();
+      } = getState();
 
-//       const facetoolParameters: Record<string, unknown> = {
+      const facetoolParameters: Record<string, unknown> = {
-//         facetool_strength: facetoolStrength,
+        facetool_strength: facetoolStrength,
-//       };
+      };
 
-//       if (facetoolType === 'codeformer') {
+      if (facetoolType === 'codeformer') {
-//         facetoolParameters.codeformer_fidelity = codeformerFidelity;
+        facetoolParameters.codeformer_fidelity = codeformerFidelity;
-//       }
+      }
 
-//       socketio.emit('runPostprocessing', imageToProcess, {
+      socketio.emit('runPostprocessing', imageToProcess, {
-//         type: facetoolType,
+        type: facetoolType,
-//         ...facetoolParameters,
+        ...facetoolParameters,
-//       });
+      });
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Face restoration (${facetoolType}) requested: ${JSON.stringify(
+          message: `Face restoration (${facetoolType}) requested: ${JSON.stringify(
-//             {
+            {
-//               file: imageToProcess.url,
+              file: imageToProcess.url,
-//               ...facetoolParameters,
+              ...facetoolParameters,
-//             }
+            }
-//           )}`,
+          )}`,
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     emitDeleteImage: (imageToDelete: InvokeAI._Image) => {
+    emitDeleteImage: (imageToDelete: InvokeAI._Image) => {
-//       const { url, uuid, category, thumbnail } = imageToDelete;
+      const { url, uuid, category, thumbnail } = imageToDelete;
-//       dispatch(removeImage(imageToDelete));
+      dispatch(removeImage(imageToDelete));
-//       socketio.emit('deleteImage', url, thumbnail, uuid, category);
+      socketio.emit('deleteImage', url, thumbnail, uuid, category);
-//     },
+    },
-//     emitRequestImages: (category: GalleryCategory) => {
+    emitRequestImages: (category: GalleryCategory) => {
-//       const gallery: GalleryState = getState().gallery;
+      const gallery: GalleryState = getState().gallery;
-//       const { earliest_mtime } = gallery.categories[category];
+      const { earliest_mtime } = gallery.categories[category];
-//       socketio.emit('requestImages', category, earliest_mtime);
+      socketio.emit('requestImages', category, earliest_mtime);
-//     },
+    },
-//     emitRequestNewImages: (category: GalleryCategory) => {
+    emitRequestNewImages: (category: GalleryCategory) => {
-//       const gallery: GalleryState = getState().gallery;
+      const gallery: GalleryState = getState().gallery;
-//       const { latest_mtime } = gallery.categories[category];
+      const { latest_mtime } = gallery.categories[category];
-//       socketio.emit('requestLatestImages', category, latest_mtime);
+      socketio.emit('requestLatestImages', category, latest_mtime);
-//     },
+    },
-//     emitCancelProcessing: () => {
+    emitCancelProcessing: () => {
-//       socketio.emit('cancel');
+      socketio.emit('cancel');
-//     },
+    },
-//     emitRequestSystemConfig: () => {
+    emitRequestSystemConfig: () => {
-//       socketio.emit('requestSystemConfig');
+      socketio.emit('requestSystemConfig');
-//     },
+    },
-//     emitSearchForModels: (modelFolder: string) => {
+    emitSearchForModels: (modelFolder: string) => {
-//       socketio.emit('searchForModels', modelFolder);
+      socketio.emit('searchForModels', modelFolder);
-//     },
+    },
-//     emitAddNewModel: (modelConfig: InvokeAI.InvokeModelConfigProps) => {
+    emitAddNewModel: (modelConfig: InvokeAI.InvokeModelConfigProps) => {
-//       socketio.emit('addNewModel', modelConfig);
+      socketio.emit('addNewModel', modelConfig);
-//     },
+    },
-//     emitDeleteModel: (modelName: string) => {
+    emitDeleteModel: (modelName: string) => {
-//       socketio.emit('deleteModel', modelName);
+      socketio.emit('deleteModel', modelName);
-//     },
+    },
-//     emitConvertToDiffusers: (
+    emitConvertToDiffusers: (
-//       modelToConvert: InvokeAI.InvokeModelConversionProps
+      modelToConvert: InvokeAI.InvokeModelConversionProps
-//     ) => {
+    ) => {
-//       dispatch(modelConvertRequested());
+      dispatch(modelConvertRequested());
-//       socketio.emit('convertToDiffusers', modelToConvert);
+      socketio.emit('convertToDiffusers', modelToConvert);
-//     },
+    },
-//     emitMergeDiffusersModels: (
+    emitMergeDiffusersModels: (
-//       modelMergeInfo: InvokeAI.InvokeModelMergingProps
+      modelMergeInfo: InvokeAI.InvokeModelMergingProps
-//     ) => {
+    ) => {
-//       dispatch(modelMergingRequested());
+      dispatch(modelMergingRequested());
-//       socketio.emit('mergeDiffusersModels', modelMergeInfo);
+      socketio.emit('mergeDiffusersModels', modelMergeInfo);
-//     },
+    },
-//     emitRequestModelChange: (modelName: string) => {
+    emitRequestModelChange: (modelName: string) => {
-//       dispatch(modelChangeRequested());
+      dispatch(modelChangeRequested());
-//       socketio.emit('requestModelChange', modelName);
+      socketio.emit('requestModelChange', modelName);
-//     },
+    },
-//     emitSaveStagingAreaImageToGallery: (url: string) => {
+    emitSaveStagingAreaImageToGallery: (url: string) => {
-//       socketio.emit('requestSaveStagingAreaImageToGallery', url);
+      socketio.emit('requestSaveStagingAreaImageToGallery', url);
-//     },
+    },
-//     emitRequestEmptyTempFolder: () => {
+    emitRequestEmptyTempFolder: () => {
-//       socketio.emit('requestEmptyTempFolder');
+      socketio.emit('requestEmptyTempFolder');
-//     },
+    },
-//   };
+  };
-// };
+};
 
-// export default makeSocketIOEmitters;
+export default makeSocketIOEmitters;
-
-export default {};

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

@@ -1,502 +1,501 @@
-// import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
+import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
-// import dateFormat from 'dateformat';
+import dateFormat from 'dateformat';
-// import i18n from 'i18n';
+import i18n from 'i18n';
-// import { v4 as uuidv4 } from 'uuid';
+import { v4 as uuidv4 } from 'uuid';
 
-// import * as InvokeAI from 'app/types/invokeai';
+import * as InvokeAI from 'app/invokeai';
 
-// import {
+import {
-//   addToast,
+  addLogEntry,
-//   errorOccurred,
+  addToast,
-//   processingCanceled,
+  errorOccurred,
-//   setCurrentStatus,
+  processingCanceled,
-//   setFoundModels,
+  setCurrentStatus,
-//   setIsCancelable,
+  setFoundModels,
-//   setIsConnected,
+  setIsCancelable,
-//   setIsProcessing,
+  setIsConnected,
-//   setModelList,
+  setIsProcessing,
-//   setSearchFolder,
+  setModelList,
-//   setSystemConfig,
+  setSearchFolder,
-//   setSystemStatus,
+  setSystemConfig,
-// } from 'features/system/store/systemSlice';
+  setSystemStatus,
+} from 'features/system/store/systemSlice';
 
-// import {
+import {
-//   addGalleryImages,
+  addGalleryImages,
-//   addImage,
+  addImage,
-//   clearIntermediateImage,
+  clearIntermediateImage,
-//   GalleryState,
+  GalleryState,
-//   removeImage,
+  removeImage,
-//   setIntermediateImage,
+  setIntermediateImage,
-// } from 'features/gallery/store/gallerySlice';
+} from 'features/gallery/store/gallerySlice';
 
-// import type { RootState } from 'app/store/store';
+import type { RootState } from 'app/store';
-// import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
+import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
-// import {
+import {
-//   clearInitialImage,
+  clearInitialImage,
-//   initialImageSelected,
+  initialImageSelected,
-//   setInfillMethod,
+  setInfillMethod,
-//   // setInitialImage,
+  // setInitialImage,
-//   setMaskPath,
+  setMaskPath,
-// } from 'features/parameters/store/generationSlice';
+} from 'features/parameters/store/generationSlice';
-// import { tabMap } from 'features/ui/store/tabMap';
+import { tabMap } from 'features/ui/store/tabMap';
-// import {
+import {
-//   requestImages,
+  requestImages,
-//   requestNewImages,
+  requestNewImages,
-//   requestSystemConfig,
+  requestSystemConfig,
-// } from './actions';
+} from './actions';
 
-// /**
+/**
-//  * Returns an object containing listener callbacks for socketio events.
+ * Returns an object containing listener callbacks for socketio events.
-//  * TODO: This file is large, but simple. Should it be split up further?
+ * TODO: This file is large, but simple. Should it be split up further?
-//  */
+ */
-// const makeSocketIOListeners = (
+const makeSocketIOListeners = (
-//   store: MiddlewareAPI<Dispatch<AnyAction>, RootState>
+  store: MiddlewareAPI<Dispatch<AnyAction>, RootState>
-// ) => {
+) => {
-//   const { dispatch, getState } = store;
+  const { dispatch, getState } = store;
 
-//   return {
+  return {
-//     /**
+    /**
-//      * Callback to run when we receive a 'connect' event.
+     * Callback to run when we receive a 'connect' event.
-//      */
+     */
-//     onConnect: () => {
+    onConnect: () => {
-//       try {
+      try {
-//         dispatch(setIsConnected(true));
+        dispatch(setIsConnected(true));
-//         dispatch(setCurrentStatus(i18n.t('common.statusConnected')));
+        dispatch(setCurrentStatus(i18n.t('common.statusConnected')));
-//         dispatch(requestSystemConfig());
+        dispatch(requestSystemConfig());
-//         const gallery: GalleryState = getState().gallery;
+        const gallery: GalleryState = getState().gallery;
 
-//         if (gallery.categories.result.latest_mtime) {
+        if (gallery.categories.result.latest_mtime) {
-//           dispatch(requestNewImages('result'));
+          dispatch(requestNewImages('result'));
-//         } else {
+        } else {
-//           dispatch(requestImages('result'));
+          dispatch(requestImages('result'));
-//         }
+        }
 
-//         if (gallery.categories.user.latest_mtime) {
+        if (gallery.categories.user.latest_mtime) {
-//           dispatch(requestNewImages('user'));
+          dispatch(requestNewImages('user'));
-//         } else {
+        } else {
-//           dispatch(requestImages('user'));
+          dispatch(requestImages('user'));
-//         }
+        }
-//       } catch (e) {
+      } catch (e) {
-//         console.error(e);
+        console.error(e);
-//       }
+      }
-//     },
+    },
-//     /**
+    /**
-//      * Callback to run when we receive a 'disconnect' event.
+     * Callback to run when we receive a 'disconnect' event.
-//      */
+     */
-//     onDisconnect: () => {
+    onDisconnect: () => {
-//       try {
+      try {
-//         dispatch(setIsConnected(false));
+        dispatch(setIsConnected(false));
-//         dispatch(setCurrentStatus(i18n.t('common.statusDisconnected')));
+        dispatch(setCurrentStatus(i18n.t('common.statusDisconnected')));
 
-//         dispatch(
+        dispatch(
-//           addLogEntry({
+          addLogEntry({
-//             timestamp: dateFormat(new Date(), 'isoDateTime'),
+            timestamp: dateFormat(new Date(), 'isoDateTime'),
-//             message: `Disconnected from server`,
+            message: `Disconnected from server`,
-//             level: 'warning',
+            level: 'warning',
-//           })
+          })
-//         );
+        );
-//       } catch (e) {
+      } catch (e) {
-//         console.error(e);
+        console.error(e);
-//       }
+      }
-//     },
+    },
-//     /**
+    /**
-//      * Callback to run when we receive a 'generationResult' event.
+     * Callback to run when we receive a 'generationResult' event.
-//      */
+     */
-//     onGenerationResult: (data: InvokeAI.ImageResultResponse) => {
+    onGenerationResult: (data: InvokeAI.ImageResultResponse) => {
-//       try {
+      try {
-//         const state = getState();
+        const state = getState();
-//         const { activeTab } = state.ui;
+        const { activeTab } = state.ui;
-//         const { shouldLoopback } = state.postprocessing;
+        const { shouldLoopback } = state.postprocessing;
-//         const { boundingBox: _, generationMode, ...rest } = data;
+        const { boundingBox: _, generationMode, ...rest } = data;
 
-//         const newImage = {
+        const newImage = {
-//           uuid: uuidv4(),
+          uuid: uuidv4(),
-//           ...rest,
+          ...rest,
-//         };
+        };
 
-//         if (['txt2img', 'img2img'].includes(generationMode)) {
+        if (['txt2img', 'img2img'].includes(generationMode)) {
-//           dispatch(
+          dispatch(
-//             addImage({
+            addImage({
-//               category: 'result',
+              category: 'result',
-//               image: { ...newImage, category: 'result' },
+              image: { ...newImage, category: 'result' },
-//             })
+            })
-//           );
+          );
-//         }
+        }
 
-//         if (generationMode === 'unifiedCanvas' && data.boundingBox) {
+        if (generationMode === 'unifiedCanvas' && data.boundingBox) {
-//           const { boundingBox } = data;
+          const { boundingBox } = data;
-//           dispatch(
+          dispatch(
-//             addImageToStagingArea({
+            addImageToStagingArea({
-//               image: { ...newImage, category: 'temp' },
+              image: { ...newImage, category: 'temp' },
-//               boundingBox,
+              boundingBox,
-//             })
+            })
-//           );
+          );
 
-//           if (state.canvas.shouldAutoSave) {
+          if (state.canvas.shouldAutoSave) {
-//             dispatch(
+            dispatch(
-//               addImage({
+              addImage({
-//                 image: { ...newImage, category: 'result' },
+                image: { ...newImage, category: 'result' },
-//                 category: 'result',
+                category: 'result',
-//               })
+              })
-//             );
+            );
-//           }
+          }
-//         }
+        }
 
-//         // TODO: fix
+        // TODO: fix
-//         // if (shouldLoopback) {
+        // if (shouldLoopback) {
-//         //   const activeTabName = tabMap[activeTab];
+        //   const activeTabName = tabMap[activeTab];
-//         //   switch (activeTabName) {
+        //   switch (activeTabName) {
-//         //     case 'img2img': {
+        //     case 'img2img': {
-//         //       dispatch(initialImageSelected(newImage.uuid));
+        //       dispatch(initialImageSelected(newImage.uuid));
-//         //       // dispatch(setInitialImage(newImage));
+        //       // dispatch(setInitialImage(newImage));
-//         //       break;
+        //       break;
-//         //     }
+        //     }
-//         //   }
+        //   }
-//         // }
+        // }
 
-//         dispatch(clearIntermediateImage());
+        dispatch(clearIntermediateImage());
 
-//         dispatch(
+        dispatch(
-//           addLogEntry({
+          addLogEntry({
-//             timestamp: dateFormat(new Date(), 'isoDateTime'),
+            timestamp: dateFormat(new Date(), 'isoDateTime'),
-//             message: `Image generated: ${data.url}`,
+            message: `Image generated: ${data.url}`,
-//           })
+          })
-//         );
+        );
-//       } catch (e) {
+      } catch (e) {
-//         console.error(e);
+        console.error(e);
-//       }
+      }
-//     },
+    },
-//     /**
+    /**
-//      * Callback to run when we receive a 'intermediateResult' event.
+     * Callback to run when we receive a 'intermediateResult' event.
-//      */
+     */
-//     onIntermediateResult: (data: InvokeAI.ImageResultResponse) => {
+    onIntermediateResult: (data: InvokeAI.ImageResultResponse) => {
-//       try {
+      try {
-//         dispatch(
+        dispatch(
-//           setIntermediateImage({
+          setIntermediateImage({
-//             uuid: uuidv4(),
+            uuid: uuidv4(),
-//             ...data,
+            ...data,
-//             category: 'result',
+            category: 'result',
-//           })
+          })
-//         );
+        );
-//         if (!data.isBase64) {
+        if (!data.isBase64) {
-//           dispatch(
+          dispatch(
-//             addLogEntry({
+            addLogEntry({
-//               timestamp: dateFormat(new Date(), 'isoDateTime'),
+              timestamp: dateFormat(new Date(), 'isoDateTime'),
-//               message: `Intermediate image generated: ${data.url}`,
+              message: `Intermediate image generated: ${data.url}`,
-//             })
+            })
-//           );
+          );
-//         }
+        }
-//       } catch (e) {
+      } catch (e) {
-//         console.error(e);
+        console.error(e);
-//       }
+      }
-//     },
+    },
-//     /**
+    /**
-//      * Callback to run when we receive an 'esrganResult' event.
+     * Callback to run when we receive an 'esrganResult' event.
-//      */
+     */
-//     onPostprocessingResult: (data: InvokeAI.ImageResultResponse) => {
+    onPostprocessingResult: (data: InvokeAI.ImageResultResponse) => {
-//       try {
+      try {
-//         dispatch(
+        dispatch(
-//           addImage({
+          addImage({
-//             category: 'result',
+            category: 'result',
-//             image: {
+            image: {
-//               uuid: uuidv4(),
+              uuid: uuidv4(),
-//               ...data,
+              ...data,
-//               category: 'result',
+              category: 'result',
-//             },
+            },
-//           })
+          })
-//         );
+        );
 
-//         dispatch(
+        dispatch(
-//           addLogEntry({
+          addLogEntry({
-//             timestamp: dateFormat(new Date(), 'isoDateTime'),
+            timestamp: dateFormat(new Date(), 'isoDateTime'),
-//             message: `Postprocessed: ${data.url}`,
+            message: `Postprocessed: ${data.url}`,
-//           })
+          })
-//         );
+        );
-//       } catch (e) {
+      } catch (e) {
-//         console.error(e);
+        console.error(e);
-//       }
+      }
-//     },
+    },
-//     /**
+    /**
-//      * Callback to run when we receive a 'progressUpdate' event.
+     * Callback to run when we receive a 'progressUpdate' event.
-//      * TODO: Add additional progress phases
+     * TODO: Add additional progress phases
-//      */
+     */
-//     onProgressUpdate: (data: InvokeAI.SystemStatus) => {
+    onProgressUpdate: (data: InvokeAI.SystemStatus) => {
-//       try {
+      try {
-//         dispatch(setIsProcessing(true));
+        dispatch(setIsProcessing(true));
-//         dispatch(setSystemStatus(data));
+        dispatch(setSystemStatus(data));
-//       } catch (e) {
+      } catch (e) {
-//         console.error(e);
+        console.error(e);
-//       }
+      }
-//     },
+    },
-//     /**
+    /**
-//      * Callback to run when we receive a 'progressUpdate' event.
+     * Callback to run when we receive a 'progressUpdate' event.
-//      */
+     */
-//     onError: (data: InvokeAI.ErrorResponse) => {
+    onError: (data: InvokeAI.ErrorResponse) => {
-//       const { message, additionalData } = data;
+      const { message, additionalData } = data;
 
-//       if (additionalData) {
+      if (additionalData) {
-//         // TODO: handle more data than short message
+        // TODO: handle more data than short message
-//       }
+      }
 
-//       try {
+      try {
-//         dispatch(
+        dispatch(
-//           addLogEntry({
+          addLogEntry({
-//             timestamp: dateFormat(new Date(), 'isoDateTime'),
+            timestamp: dateFormat(new Date(), 'isoDateTime'),
-//             message: `Server error: ${message}`,
+            message: `Server error: ${message}`,
-//             level: 'error',
+            level: 'error',
-//           })
+          })
-//         );
+        );
-//         dispatch(errorOccurred());
+        dispatch(errorOccurred());
-//         dispatch(clearIntermediateImage());
+        dispatch(clearIntermediateImage());
-//       } catch (e) {
+      } catch (e) {
-//         console.error(e);
+        console.error(e);
-//       }
+      }
-//     },
+    },
-//     /**
+    /**
-//      * Callback to run when we receive a 'galleryImages' event.
+     * Callback to run when we receive a 'galleryImages' event.
-//      */
+     */
-//     onGalleryImages: (data: InvokeAI.GalleryImagesResponse) => {
+    onGalleryImages: (data: InvokeAI.GalleryImagesResponse) => {
-//       const { images, areMoreImagesAvailable, category } = data;
+      const { images, areMoreImagesAvailable, category } = data;
 
-//       /**
+      /**
-//        * the logic here ideally would be in the reducer but we have a side effect:
+       * the logic here ideally would be in the reducer but we have a side effect:
-//        * generating a uuid. so the logic needs to be here, outside redux.
+       * generating a uuid. so the logic needs to be here, outside redux.
-//        */
+       */
 
-//       // Generate a UUID for each image
+      // Generate a UUID for each image
-//       const preparedImages = images.map((image): InvokeAI._Image => {
+      const preparedImages = images.map((image): InvokeAI._Image => {
-//         return {
+        return {
-//           uuid: uuidv4(),
+          uuid: uuidv4(),
-//           ...image,
+          ...image,
-//         };
+        };
-//       });
+      });
 
-//       dispatch(
+      dispatch(
-//         addGalleryImages({
+        addGalleryImages({
-//           images: preparedImages,
+          images: preparedImages,
-//           areMoreImagesAvailable,
+          areMoreImagesAvailable,
-//           category,
+          category,
-//         })
+        })
-//       );
+      );
 
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Loaded ${images.length} images`,
+          message: `Loaded ${images.length} images`,
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     /**
+    /**
-//      * Callback to run when we receive a 'processingCanceled' event.
+     * Callback to run when we receive a 'processingCanceled' event.
-//      */
+     */
-//     onProcessingCanceled: () => {
+    onProcessingCanceled: () => {
-//       dispatch(processingCanceled());
+      dispatch(processingCanceled());
 
-//       const { intermediateImage } = getState().gallery;
+      const { intermediateImage } = getState().gallery;
 
-//       if (intermediateImage) {
+      if (intermediateImage) {
-//         if (!intermediateImage.isBase64) {
+        if (!intermediateImage.isBase64) {
-//           dispatch(
+          dispatch(
-//             addImage({
+            addImage({
-//               category: 'result',
+              category: 'result',
-//               image: intermediateImage,
+              image: intermediateImage,
-//             })
+            })
-//           );
+          );
-//           dispatch(
+          dispatch(
-//             addLogEntry({
+            addLogEntry({
-//               timestamp: dateFormat(new Date(), 'isoDateTime'),
+              timestamp: dateFormat(new Date(), 'isoDateTime'),
-//               message: `Intermediate image saved: ${intermediateImage.url}`,
+              message: `Intermediate image saved: ${intermediateImage.url}`,
-//             })
+            })
-//           );
+          );
-//         }
+        }
-//         dispatch(clearIntermediateImage());
+        dispatch(clearIntermediateImage());
-//       }
+      }
 
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Processing canceled`,
+          message: `Processing canceled`,
-//           level: 'warning',
+          level: 'warning',
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     /**
+    /**
-//      * Callback to run when we receive a 'imageDeleted' event.
+     * Callback to run when we receive a 'imageDeleted' event.
-//      */
+     */
-//     onImageDeleted: (data: InvokeAI.ImageDeletedResponse) => {
+    onImageDeleted: (data: InvokeAI.ImageDeletedResponse) => {
-//       const { url } = data;
+      const { url } = data;
 
-//       // remove image from gallery
+      // remove image from gallery
-//       dispatch(removeImage(data));
+      dispatch(removeImage(data));
 
-//       // remove references to image in options
+      // remove references to image in options
-//       const {
+      const {
-//         generation: { initialImage, maskPath },
+        generation: { initialImage, maskPath },
-//       } = getState();
+      } = getState();
 
-//       if (
+      if (
-//         initialImage === url ||
+        initialImage === url ||
-//         (initialImage as InvokeAI._Image)?.url === url
+        (initialImage as InvokeAI._Image)?.url === url
-//       ) {
+      ) {
-//         dispatch(clearInitialImage());
+        dispatch(clearInitialImage());
-//       }
+      }
 
-//       if (maskPath === url) {
+      if (maskPath === url) {
-//         dispatch(setMaskPath(''));
+        dispatch(setMaskPath(''));
-//       }
+      }
 
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Image deleted: ${url}`,
+          message: `Image deleted: ${url}`,
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     onSystemConfig: (data: InvokeAI.SystemConfig) => {
+    onSystemConfig: (data: InvokeAI.SystemConfig) => {
-//       dispatch(setSystemConfig(data));
+      dispatch(setSystemConfig(data));
-//       if (!data.infill_methods.includes('patchmatch')) {
+      if (!data.infill_methods.includes('patchmatch')) {
-//         dispatch(setInfillMethod(data.infill_methods[0]));
+        dispatch(setInfillMethod(data.infill_methods[0]));
-//       }
+      }
-//     },
+    },
-//     onFoundModels: (data: InvokeAI.FoundModelResponse) => {
+    onFoundModels: (data: InvokeAI.FoundModelResponse) => {
-//       const { search_folder, found_models } = data;
+      const { search_folder, found_models } = data;
-//       dispatch(setSearchFolder(search_folder));
+      dispatch(setSearchFolder(search_folder));
-//       dispatch(setFoundModels(found_models));
+      dispatch(setFoundModels(found_models));
-//     },
+    },
-//     onNewModelAdded: (data: InvokeAI.ModelAddedResponse) => {
+    onNewModelAdded: (data: InvokeAI.ModelAddedResponse) => {
-//       const { new_model_name, model_list, update } = data;
+      const { new_model_name, model_list, update } = data;
-//       dispatch(setModelList(model_list));
+      dispatch(setModelList(model_list));
-//       dispatch(setIsProcessing(false));
+      dispatch(setIsProcessing(false));
-//       dispatch(setCurrentStatus(i18n.t('modelManager.modelAdded')));
+      dispatch(setCurrentStatus(i18n.t('modelManager.modelAdded')));
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Model Added: ${new_model_name}`,
+          message: `Model Added: ${new_model_name}`,
-//           level: 'info',
+          level: 'info',
-//         })
+        })
-//       );
+      );
-//       dispatch(
+      dispatch(
-//         addToast({
+        addToast({
-//           title: !update
+          title: !update
-//             ? `${i18n.t('modelManager.modelAdded')}: ${new_model_name}`
+            ? `${i18n.t('modelManager.modelAdded')}: ${new_model_name}`
-//             : `${i18n.t('modelManager.modelUpdated')}: ${new_model_name}`,
+            : `${i18n.t('modelManager.modelUpdated')}: ${new_model_name}`,
-//           status: 'success',
+          status: 'success',
-//           duration: 2500,
+          duration: 2500,
-//           isClosable: true,
+          isClosable: true,
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     onModelDeleted: (data: InvokeAI.ModelDeletedResponse) => {
+    onModelDeleted: (data: InvokeAI.ModelDeletedResponse) => {
-//       const { deleted_model_name, model_list } = data;
+      const { deleted_model_name, model_list } = data;
-//       dispatch(setModelList(model_list));
+      dispatch(setModelList(model_list));
-//       dispatch(setIsProcessing(false));
+      dispatch(setIsProcessing(false));
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `${i18n.t(
+          message: `${i18n.t(
-//             'modelManager.modelAdded'
+            'modelManager.modelAdded'
-//           )}: ${deleted_model_name}`,
+          )}: ${deleted_model_name}`,
-//           level: 'info',
+          level: 'info',
-//         })
+        })
-//       );
+      );
-//       dispatch(
+      dispatch(
-//         addToast({
+        addToast({
-//           title: `${i18n.t(
+          title: `${i18n.t(
-//             'modelManager.modelEntryDeleted'
+            'modelManager.modelEntryDeleted'
-//           )}: ${deleted_model_name}`,
+          )}: ${deleted_model_name}`,
-//           status: 'success',
+          status: 'success',
-//           duration: 2500,
+          duration: 2500,
-//           isClosable: true,
+          isClosable: true,
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     onModelConverted: (data: InvokeAI.ModelConvertedResponse) => {
+    onModelConverted: (data: InvokeAI.ModelConvertedResponse) => {
-//       const { converted_model_name, model_list } = data;
+      const { converted_model_name, model_list } = data;
-//       dispatch(setModelList(model_list));
+      dispatch(setModelList(model_list));
-//       dispatch(setCurrentStatus(i18n.t('common.statusModelConverted')));
+      dispatch(setCurrentStatus(i18n.t('common.statusModelConverted')));
-//       dispatch(setIsProcessing(false));
+      dispatch(setIsProcessing(false));
-//       dispatch(setIsCancelable(true));
+      dispatch(setIsCancelable(true));
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Model converted: ${converted_model_name}`,
+          message: `Model converted: ${converted_model_name}`,
-//           level: 'info',
+          level: 'info',
-//         })
+        })
-//       );
+      );
-//       dispatch(
+      dispatch(
-//         addToast({
+        addToast({
-//           title: `${i18n.t(
+          title: `${i18n.t(
-//             'modelManager.modelConverted'
+            'modelManager.modelConverted'
-//           )}: ${converted_model_name}`,
+          )}: ${converted_model_name}`,
-//           status: 'success',
+          status: 'success',
-//           duration: 2500,
+          duration: 2500,
-//           isClosable: true,
+          isClosable: true,
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     onModelsMerged: (data: InvokeAI.ModelsMergedResponse) => {
+    onModelsMerged: (data: InvokeAI.ModelsMergedResponse) => {
-//       const { merged_models, merged_model_name, model_list } = data;
+      const { merged_models, merged_model_name, model_list } = data;
-//       dispatch(setModelList(model_list));
+      dispatch(setModelList(model_list));
-//       dispatch(setCurrentStatus(i18n.t('common.statusMergedModels')));
+      dispatch(setCurrentStatus(i18n.t('common.statusMergedModels')));
-//       dispatch(setIsProcessing(false));
+      dispatch(setIsProcessing(false));
-//       dispatch(setIsCancelable(true));
+      dispatch(setIsCancelable(true));
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Models merged: ${merged_models}`,
+          message: `Models merged: ${merged_models}`,
-//           level: 'info',
+          level: 'info',
-//         })
+        })
-//       );
+      );
-//       dispatch(
+      dispatch(
-//         addToast({
+        addToast({
-//           title: `${i18n.t('modelManager.modelsMerged')}: ${merged_model_name}`,
+          title: `${i18n.t('modelManager.modelsMerged')}: ${merged_model_name}`,
-//           status: 'success',
+          status: 'success',
-//           duration: 2500,
+          duration: 2500,
-//           isClosable: true,
+          isClosable: true,
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     onModelChanged: (data: InvokeAI.ModelChangeResponse) => {
+    onModelChanged: (data: InvokeAI.ModelChangeResponse) => {
-//       const { model_name, model_list } = data;
+      const { model_name, model_list } = data;
-//       dispatch(setModelList(model_list));
+      dispatch(setModelList(model_list));
-//       dispatch(setCurrentStatus(i18n.t('common.statusModelChanged')));
+      dispatch(setCurrentStatus(i18n.t('common.statusModelChanged')));
-//       dispatch(setIsProcessing(false));
+      dispatch(setIsProcessing(false));
-//       dispatch(setIsCancelable(true));
+      dispatch(setIsCancelable(true));
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Model changed: ${model_name}`,
+          message: `Model changed: ${model_name}`,
-//           level: 'info',
+          level: 'info',
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     onModelChangeFailed: (data: InvokeAI.ModelChangeResponse) => {
+    onModelChangeFailed: (data: InvokeAI.ModelChangeResponse) => {
-//       const { model_name, model_list } = data;
+      const { model_name, model_list } = data;
-//       dispatch(setModelList(model_list));
+      dispatch(setModelList(model_list));
-//       dispatch(setIsProcessing(false));
+      dispatch(setIsProcessing(false));
-//       dispatch(setIsCancelable(true));
+      dispatch(setIsCancelable(true));
-//       dispatch(errorOccurred());
+      dispatch(errorOccurred());
-//       dispatch(
+      dispatch(
-//         addLogEntry({
+        addLogEntry({
-//           timestamp: dateFormat(new Date(), 'isoDateTime'),
+          timestamp: dateFormat(new Date(), 'isoDateTime'),
-//           message: `Model change failed: ${model_name}`,
+          message: `Model change failed: ${model_name}`,
-//           level: 'error',
+          level: 'error',
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//     onTempFolderEmptied: () => {
+    onTempFolderEmptied: () => {
-//       dispatch(
+      dispatch(
-//         addToast({
+        addToast({
-//           title: i18n.t('toast.tempFoldersEmptied'),
+          title: i18n.t('toast.tempFoldersEmptied'),
-//           status: 'success',
+          status: 'success',
-//           duration: 2500,
+          duration: 2500,
-//           isClosable: true,
+          isClosable: true,
-//         })
+        })
-//       );
+      );
-//     },
+    },
-//   };
+  };
-// };
+};
 
-// export default makeSocketIOListeners;
+export default makeSocketIOListeners;
-
-export default {};

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

@@ -1,248 +1,246 @@
-// import { Middleware } from '@reduxjs/toolkit';
+import { Middleware } from '@reduxjs/toolkit';
-// import { io } from 'socket.io-client';
+import { io } from 'socket.io-client';
 
-// import makeSocketIOEmitters from './emitters';
+import makeSocketIOEmitters from './emitters';
-// import makeSocketIOListeners from './listeners';
+import makeSocketIOListeners from './listeners';
 
-// import * as InvokeAI from 'app/types/invokeai';
+import * as InvokeAI from 'app/invokeai';
 
-// /**
+/**
-//  * Creates a socketio middleware to handle communication with server.
+ * Creates a socketio middleware to handle communication with server.
-//  *
+ *
-//  * Special `socketio/actionName` actions are created in actions.ts and
+ * Special `socketio/actionName` actions are created in actions.ts and
-//  * exported for use by the application, which treats them like any old
+ * exported for use by the application, which treats them like any old
-//  * action, using `dispatch` to dispatch them.
+ * action, using `dispatch` to dispatch them.
-//  *
+ *
-//  * These actions are intercepted here, where `socketio.emit()` calls are
+ * These actions are intercepted here, where `socketio.emit()` calls are
-//  * made on their behalf - see `emitters.ts`. The emitter functions
+ * made on their behalf - see `emitters.ts`. The emitter functions
-//  * are the outbound communication to the server.
+ * are the outbound communication to the server.
-//  *
+ *
-//  * Listeners are also established here - see `listeners.ts`. The listener
+ * Listeners are also established here - see `listeners.ts`. The listener
-//  * functions receive communication from the server and usually dispatch
+ * functions receive communication from the server and usually dispatch
-//  * some new action to handle whatever data was sent from the server.
+ * some new action to handle whatever data was sent from the server.
-//  */
+ */
-// export const socketioMiddleware = () => {
+export const socketioMiddleware = () => {
-//   const { origin } = new URL(window.location.href);
+  const { origin } = new URL(window.location.href);
 
-//   const socketio = io(origin, {
+  const socketio = io(origin, {
-//     timeout: 60000,
+    timeout: 60000,
-//     path: `${window.location.pathname}socket.io`,
+    path: `${window.location.pathname}socket.io`,
-//   });
+  });
 
-//   socketio.disconnect();
+  socketio.disconnect();
 
-//   let areListenersSet = false;
+  let areListenersSet = false;
 
-//   const middleware: Middleware = (store) => (next) => (action) => {
+  const middleware: Middleware = (store) => (next) => (action) => {
-//     const {
+    const {
-//       onConnect,
+      onConnect,
-//       onDisconnect,
+      onDisconnect,
-//       onError,
+      onError,
-//       onPostprocessingResult,
+      onPostprocessingResult,
-//       onGenerationResult,
+      onGenerationResult,
-//       onIntermediateResult,
+      onIntermediateResult,
-//       onProgressUpdate,
+      onProgressUpdate,
-//       onGalleryImages,
+      onGalleryImages,
-//       onProcessingCanceled,
+      onProcessingCanceled,
-//       onImageDeleted,
+      onImageDeleted,
-//       onSystemConfig,
+      onSystemConfig,
-//       onModelChanged,
+      onModelChanged,
-//       onFoundModels,
+      onFoundModels,
-//       onNewModelAdded,
+      onNewModelAdded,
-//       onModelDeleted,
+      onModelDeleted,
-//       onModelConverted,
+      onModelConverted,
-//       onModelsMerged,
+      onModelsMerged,
-//       onModelChangeFailed,
+      onModelChangeFailed,
-//       onTempFolderEmptied,
+      onTempFolderEmptied,
-//     } = makeSocketIOListeners(store);
+    } = makeSocketIOListeners(store);
 
-//     const {
+    const {
-//       emitGenerateImage,
+      emitGenerateImage,
-//       emitRunESRGAN,
+      emitRunESRGAN,
-//       emitRunFacetool,
+      emitRunFacetool,
-//       emitDeleteImage,
+      emitDeleteImage,
-//       emitRequestImages,
+      emitRequestImages,
-//       emitRequestNewImages,
+      emitRequestNewImages,
-//       emitCancelProcessing,
+      emitCancelProcessing,
-//       emitRequestSystemConfig,
+      emitRequestSystemConfig,
-//       emitSearchForModels,
+      emitSearchForModels,
-//       emitAddNewModel,
+      emitAddNewModel,
-//       emitDeleteModel,
+      emitDeleteModel,
-//       emitConvertToDiffusers,
+      emitConvertToDiffusers,
-//       emitMergeDiffusersModels,
+      emitMergeDiffusersModels,
-//       emitRequestModelChange,
+      emitRequestModelChange,
-//       emitSaveStagingAreaImageToGallery,
+      emitSaveStagingAreaImageToGallery,
-//       emitRequestEmptyTempFolder,
+      emitRequestEmptyTempFolder,
-//     } = makeSocketIOEmitters(store, socketio);
+    } = makeSocketIOEmitters(store, socketio);
 
-//     /**
+    /**
-//      * If this is the first time the middleware has been called (e.g. during store setup),
+     * If this is the first time the middleware has been called (e.g. during store setup),
-//      * initialize all our socket.io listeners.
+     * initialize all our socket.io listeners.
-//      */
+     */
-//     if (!areListenersSet) {
+    if (!areListenersSet) {
-//       socketio.on('connect', () => onConnect());
+      socketio.on('connect', () => onConnect());
 
-//       socketio.on('disconnect', () => onDisconnect());
+      socketio.on('disconnect', () => onDisconnect());
 
-//       socketio.on('error', (data: InvokeAI.ErrorResponse) => onError(data));
+      socketio.on('error', (data: InvokeAI.ErrorResponse) => onError(data));
 
-//       socketio.on('generationResult', (data: InvokeAI.ImageResultResponse) =>
+      socketio.on('generationResult', (data: InvokeAI.ImageResultResponse) =>
-//         onGenerationResult(data)
+        onGenerationResult(data)
-//       );
+      );
 
-//       socketio.on(
+      socketio.on(
-//         'postprocessingResult',
+        'postprocessingResult',
-//         (data: InvokeAI.ImageResultResponse) => onPostprocessingResult(data)
+        (data: InvokeAI.ImageResultResponse) => onPostprocessingResult(data)
-//       );
+      );
 
-//       socketio.on('intermediateResult', (data: InvokeAI.ImageResultResponse) =>
+      socketio.on('intermediateResult', (data: InvokeAI.ImageResultResponse) =>
-//         onIntermediateResult(data)
+        onIntermediateResult(data)
-//       );
+      );
 
-//       socketio.on('progressUpdate', (data: InvokeAI.SystemStatus) =>
+      socketio.on('progressUpdate', (data: InvokeAI.SystemStatus) =>
-//         onProgressUpdate(data)
+        onProgressUpdate(data)
-//       );
+      );
 
-//       socketio.on('galleryImages', (data: InvokeAI.GalleryImagesResponse) =>
+      socketio.on('galleryImages', (data: InvokeAI.GalleryImagesResponse) =>
-//         onGalleryImages(data)
+        onGalleryImages(data)
-//       );
+      );
 
-//       socketio.on('processingCanceled', () => {
+      socketio.on('processingCanceled', () => {
-//         onProcessingCanceled();
+        onProcessingCanceled();
-//       });
+      });
 
-//       socketio.on('imageDeleted', (data: InvokeAI.ImageDeletedResponse) => {
+      socketio.on('imageDeleted', (data: InvokeAI.ImageDeletedResponse) => {
-//         onImageDeleted(data);
+        onImageDeleted(data);
-//       });
+      });
 
-//       socketio.on('systemConfig', (data: InvokeAI.SystemConfig) => {
+      socketio.on('systemConfig', (data: InvokeAI.SystemConfig) => {
-//         onSystemConfig(data);
+        onSystemConfig(data);
-//       });
+      });
 
-//       socketio.on('foundModels', (data: InvokeAI.FoundModelResponse) => {
+      socketio.on('foundModels', (data: InvokeAI.FoundModelResponse) => {
-//         onFoundModels(data);
+        onFoundModels(data);
-//       });
+      });
 
-//       socketio.on('newModelAdded', (data: InvokeAI.ModelAddedResponse) => {
+      socketio.on('newModelAdded', (data: InvokeAI.ModelAddedResponse) => {
-//         onNewModelAdded(data);
+        onNewModelAdded(data);
-//       });
+      });
 
-//       socketio.on('modelDeleted', (data: InvokeAI.ModelDeletedResponse) => {
+      socketio.on('modelDeleted', (data: InvokeAI.ModelDeletedResponse) => {
-//         onModelDeleted(data);
+        onModelDeleted(data);
-//       });
+      });
 
-//       socketio.on('modelConverted', (data: InvokeAI.ModelConvertedResponse) => {
+      socketio.on('modelConverted', (data: InvokeAI.ModelConvertedResponse) => {
-//         onModelConverted(data);
+        onModelConverted(data);
-//       });
+      });
 
-//       socketio.on('modelsMerged', (data: InvokeAI.ModelsMergedResponse) => {
+      socketio.on('modelsMerged', (data: InvokeAI.ModelsMergedResponse) => {
-//         onModelsMerged(data);
+        onModelsMerged(data);
-//       });
+      });
 
-//       socketio.on('modelChanged', (data: InvokeAI.ModelChangeResponse) => {
+      socketio.on('modelChanged', (data: InvokeAI.ModelChangeResponse) => {
-//         onModelChanged(data);
+        onModelChanged(data);
-//       });
+      });
 
-//       socketio.on('modelChangeFailed', (data: InvokeAI.ModelChangeResponse) => {
+      socketio.on('modelChangeFailed', (data: InvokeAI.ModelChangeResponse) => {
-//         onModelChangeFailed(data);
+        onModelChangeFailed(data);
-//       });
+      });
 
-//       socketio.on('tempFolderEmptied', () => {
+      socketio.on('tempFolderEmptied', () => {
-//         onTempFolderEmptied();
+        onTempFolderEmptied();
-//       });
+      });
 
-//       areListenersSet = true;
+      areListenersSet = true;
-//     }
+    }
 
-//     /**
+    /**
-//      * Handle redux actions caught by middleware.
+     * Handle redux actions caught by middleware.
-//      */
+     */
-//     switch (action.type) {
+    switch (action.type) {
-//       case 'socketio/generateImage': {
+      case 'socketio/generateImage': {
-//         emitGenerateImage(action.payload);
+        emitGenerateImage(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/runESRGAN': {
+      case 'socketio/runESRGAN': {
-//         emitRunESRGAN(action.payload);
+        emitRunESRGAN(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/runFacetool': {
+      case 'socketio/runFacetool': {
-//         emitRunFacetool(action.payload);
+        emitRunFacetool(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/deleteImage': {
+      case 'socketio/deleteImage': {
-//         emitDeleteImage(action.payload);
+        emitDeleteImage(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/requestImages': {
+      case 'socketio/requestImages': {
-//         emitRequestImages(action.payload);
+        emitRequestImages(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/requestNewImages': {
+      case 'socketio/requestNewImages': {
-//         emitRequestNewImages(action.payload);
+        emitRequestNewImages(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/cancelProcessing': {
+      case 'socketio/cancelProcessing': {
-//         emitCancelProcessing();
+        emitCancelProcessing();
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/requestSystemConfig': {
+      case 'socketio/requestSystemConfig': {
-//         emitRequestSystemConfig();
+        emitRequestSystemConfig();
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/searchForModels': {
+      case 'socketio/searchForModels': {
-//         emitSearchForModels(action.payload);
+        emitSearchForModels(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/addNewModel': {
+      case 'socketio/addNewModel': {
-//         emitAddNewModel(action.payload);
+        emitAddNewModel(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/deleteModel': {
+      case 'socketio/deleteModel': {
-//         emitDeleteModel(action.payload);
+        emitDeleteModel(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/convertToDiffusers': {
+      case 'socketio/convertToDiffusers': {
-//         emitConvertToDiffusers(action.payload);
+        emitConvertToDiffusers(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/mergeDiffusersModels': {
+      case 'socketio/mergeDiffusersModels': {
-//         emitMergeDiffusersModels(action.payload);
+        emitMergeDiffusersModels(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/requestModelChange': {
+      case 'socketio/requestModelChange': {
-//         emitRequestModelChange(action.payload);
+        emitRequestModelChange(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/saveStagingAreaImageToGallery': {
+      case 'socketio/saveStagingAreaImageToGallery': {
-//         emitSaveStagingAreaImageToGallery(action.payload);
+        emitSaveStagingAreaImageToGallery(action.payload);
-//         break;
+        break;
-//       }
+      }
 
-//       case 'socketio/requestEmptyTempFolder': {
+      case 'socketio/requestEmptyTempFolder': {
-//         emitRequestEmptyTempFolder();
+        emitRequestEmptyTempFolder();
-//         break;
+        break;
-//       }
+      }
-//     }
+    }
 
-//     next(action);
+    next(action);
-//   };
+  };
 
-//   return middleware;
+  return middleware;
-// };
+};
-
-export default {};

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

@@ -13,23 +13,21 @@ import lightboxReducer from 'features/lightbox/store/lightboxSlice';
 import generationReducer from 'features/parameters/store/generationSlice';
 import postprocessingReducer from 'features/parameters/store/postprocessingSlice';
 import systemReducer from 'features/system/store/systemSlice';
-import configReducer from 'features/system/store/configSlice';
 import uiReducer from 'features/ui/store/uiSlice';
-import hotkeysReducer from 'features/ui/store/hotkeysSlice';
 import modelsReducer from 'features/system/store/modelSlice';
 import nodesReducer from 'features/nodes/store/nodesSlice';
 
-import { canvasDenylist } from 'features/canvas/store/canvasPersistDenylist';
+import { socketioMiddleware } from './socketio/middleware';
-import { galleryDenylist } from 'features/gallery/store/galleryPersistDenylist';
+import { socketMiddleware } from 'services/events/middleware';
-import { generationDenylist } from 'features/parameters/store/generationPersistDenylist';
+import { canvasBlacklist } from 'features/canvas/store/canvasPersistBlacklist';
-import { lightboxDenylist } from 'features/lightbox/store/lightboxPersistDenylist';
+import { galleryBlacklist } from 'features/gallery/store/galleryPersistBlacklist';
-import { modelsDenylist } from 'features/system/store/modelsPersistDenylist';
+import { generationBlacklist } from 'features/parameters/store/generationPersistBlacklist';
-import { nodesDenylist } from 'features/nodes/store/nodesPersistDenylist';
+import { lightboxBlacklist } from 'features/lightbox/store/lightboxPersistBlacklist';
-import { postprocessingDenylist } from 'features/parameters/store/postprocessingPersistDenylist';
+import { modelsBlacklist } from 'features/system/store/modelsPersistBlacklist';
-import { systemDenylist } from 'features/system/store/systemPersistDenylist';
+import { nodesBlacklist } from 'features/nodes/store/nodesPersistBlacklist';
-import { uiDenylist } from 'features/ui/store/uiPersistDenylist';
+import { postprocessingBlacklist } from 'features/parameters/store/postprocessingPersistBlacklist';
-import { resultsDenylist } from 'features/gallery/store/resultsPersistDenylist';
+import { systemBlacklist } from 'features/system/store/systemPersistsBlacklist';
-import { uploadsDenylist } from 'features/gallery/store/uploadsPersistDenylist';
+import { uiBlacklist } from 'features/ui/store/uiPersistBlacklist';
 
 /**
  * redux-persist provides an easy and reliable way to persist state across reloads.
@@ -40,9 +38,9 @@ import { uploadsDenylist } from 'features/gallery/store/uploadsPersistDenylist';
  *   - Connection/processing status
  *   - Availability of external libraries like ESRGAN/GFPGAN
  *
- * These can be denylisted in redux-persist.
+ * These can be blacklisted in redux-persist.
  *
- * The necesssary nested persistors with denylists are configured below.
+ * The necesssary nested persistors with blacklists are configured below.
  */
 
 const rootReducer = combineReducers({
@@ -55,10 +53,8 @@ const rootReducer = combineReducers({
   postprocessing: postprocessingReducer,
   results: resultsReducer,
   system: systemReducer,
-  config: configReducer,
   ui: uiReducer,
   uploads: uploadsReducer,
-  hotkeys: hotkeysReducer,
 });
 
 const rootPersistConfig = getPersistConfig({
@@ -66,34 +62,33 @@ const rootPersistConfig = getPersistConfig({
   storage,
   rootReducer,
   blacklist: [
-    ...canvasDenylist,
+    ...canvasBlacklist,
-    ...galleryDenylist,
+    ...galleryBlacklist,
-    ...generationDenylist,
+    ...generationBlacklist,
-    ...lightboxDenylist,
+    ...lightboxBlacklist,
-    ...modelsDenylist,
+    ...modelsBlacklist,
-    ...nodesDenylist,
+    ...nodesBlacklist,
-    ...postprocessingDenylist,
+    ...postprocessingBlacklist,
-    // ...resultsDenylist,
+    // ...resultsBlacklist,
     'results',
-    ...systemDenylist,
+    ...systemBlacklist,
-    ...uiDenylist,
+    ...uiBlacklist,
-    // ...uploadsDenylist,
+    // ...uploadsBlacklist,
     'uploads',
-    'hotkeys',
-    'config',
   ],
+  debounce: 300,
 });
 
 const persistedReducer = persistReducer(rootPersistConfig, rootReducer);
 
 // TODO: rip the old middleware out when nodes is complete
-// export function buildMiddleware() {
+export function buildMiddleware() {
-//   if (import.meta.env.MODE === 'nodes' || import.meta.env.MODE === 'package') {
+  if (import.meta.env.MODE === 'nodes' || import.meta.env.MODE === 'package') {
-//     return socketMiddleware();
+    return socketMiddleware();
-//   } else {
+  } else {
-//     return socketioMiddleware();
+    return socketioMiddleware();
-//   }
+  }
-// }
+}
 
 export const store = configureStore({
   reducer: persistedReducer,
@@ -113,7 +108,6 @@ export const store = configureStore({
       'canvas/setBoundingBoxDimensions',
       'canvas/setIsDrawing',
       'canvas/addPointToCurrentLine',
-      'socket/generatorProgress',
     ],
   },
 });

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

@@ -1,5 +1,5 @@
 import { TypedUseSelectorHook, useDispatch, useSelector } from 'react-redux';
-import { AppDispatch, RootState } from 'app/store/store';
+import { AppDispatch, RootState } from './store';
 
 // Use throughout your app instead of plain `useDispatch` and `useSelector`
 export const useAppDispatch: () => AppDispatch = useDispatch;

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

@@ -1,5 +1,5 @@
 import { createAsyncThunk } from '@reduxjs/toolkit';
-import { AppDispatch, RootState } from 'app/store/store';
+import { AppDispatch, RootState } from './store';
 
 // https://redux-toolkit.js.org/usage/usage-with-typescript#defining-a-pre-typed-createasyncthunk
 export const createAppAsyncThunk = createAsyncThunk.withTypes<{

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

@@ -1,393 +0,0 @@
-/**
- * Types for images, the things they are made of, and the things
- * they make up.
- *
- * Generated images are txt2img and img2img images. They may have
- * had additional postprocessing done on them when they were first
- * generated.
- *
- * Postprocessed images are images which were not generated here
- * but only postprocessed by the app. They only get postprocessing
- * metadata and have a different image type, e.g. 'esrgan' or
- * 'gfpgan'.
- */
-
-import { GalleryCategory } from 'features/gallery/store/gallerySlice';
-import { FacetoolType } from 'features/parameters/store/postprocessingSlice';
-import { InvokeTabName } from 'features/ui/store/tabMap';
-import { IRect } from 'konva/lib/types';
-import { ImageResponseMetadata, ImageType } from 'services/api';
-import { AnyInvocation } from 'services/events/types';
-import { O } from 'ts-toolbelt';
-
-/**
- * TODO:
- * Once an image has been generated, if it is postprocessed again,
- * additional postprocessing steps are added to its postprocessing
- * array.
- *
- * TODO: Better documentation of types.
- */
-
-export type PromptItem = {
-  prompt: string;
-  weight: number;
-};
-
-// TECHDEBT: We need to retain compatibility with plain prompt strings and the structure Prompt type
-export type Prompt = Array<PromptItem> | string;
-
-export type SeedWeightPair = {
-  seed: number;
-  weight: number;
-};
-
-export type SeedWeights = Array<SeedWeightPair>;
-
-// All generated images contain these metadata.
-export type CommonGeneratedImageMetadata = {
-  postprocessing: null | Array<ESRGANMetadata | FacetoolMetadata>;
-  sampler:
-    | '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';
-  prompt: Prompt;
-  seed: number;
-  variations: SeedWeights;
-  steps: number;
-  cfg_scale: number;
-  width: number;
-  height: number;
-  seamless: boolean;
-  hires_fix: boolean;
-  extra: null | Record<string, never>; // Pending development of RFC #266
-};
-
-// txt2img and img2img images have some unique attributes.
-export type Txt2ImgMetadata = CommonGeneratedImageMetadata & {
-  type: 'txt2img';
-};
-
-export type Img2ImgMetadata = CommonGeneratedImageMetadata & {
-  type: 'img2img';
-  orig_hash: string;
-  strength: number;
-  fit: boolean;
-  init_image_path: string;
-  mask_image_path?: string;
-};
-
-// Superset of  generated image metadata types.
-export type GeneratedImageMetadata = Txt2ImgMetadata | Img2ImgMetadata;
-
-// All post processed images contain these metadata.
-export type CommonPostProcessedImageMetadata = {
-  orig_path: string;
-  orig_hash: string;
-};
-
-// esrgan and gfpgan images have some unique attributes.
-export type ESRGANMetadata = CommonPostProcessedImageMetadata & {
-  type: 'esrgan';
-  scale: 2 | 4;
-  strength: number;
-  denoise_str: number;
-};
-
-export type FacetoolMetadata = CommonPostProcessedImageMetadata & {
-  type: 'gfpgan' | 'codeformer';
-  strength: number;
-  fidelity?: number;
-};
-
-// Superset of all postprocessed image metadata types..
-export type PostProcessedImageMetadata = ESRGANMetadata | FacetoolMetadata;
-
-// Metadata includes the system config and image metadata.
-// export type Metadata = SystemGenerationMetadata & {
-//   image: GeneratedImageMetadata | PostProcessedImageMetadata;
-// };
-
-/**
- * ResultImage
- */
-export type Image = {
-  name: string;
-  type: ImageType;
-  url: string;
-  thumbnail: string;
-  metadata: ImageResponseMetadata;
-};
-
-/**
- * Types related to the system status.
- */
-
-// // This represents the processing status of the backend.
-// export type SystemStatus = {
-//   isProcessing: boolean;
-//   currentStep: number;
-//   totalSteps: number;
-//   currentIteration: number;
-//   totalIterations: number;
-//   currentStatus: string;
-//   currentStatusHasSteps: boolean;
-//   hasError: boolean;
-// };
-
-// export type SystemGenerationMetadata = {
-//   model: string;
-//   model_weights?: string;
-//   model_id?: string;
-//   model_hash: string;
-//   app_id: string;
-//   app_version: string;
-// };
-
-// export type SystemConfig = SystemGenerationMetadata & {
-//   model_list: ModelList;
-//   infill_methods: string[];
-// };
-
-export type ModelStatus = 'active' | 'cached' | 'not loaded';
-
-export type Model = {
-  status: ModelStatus;
-  description: string;
-  weights: string;
-  config?: string;
-  vae?: string;
-  width?: number;
-  height?: number;
-  default?: boolean;
-  format?: string;
-};
-
-export type DiffusersModel = {
-  status: ModelStatus;
-  description: string;
-  repo_id?: string;
-  path?: string;
-  vae?: {
-    repo_id?: string;
-    path?: string;
-  };
-  format?: string;
-  default?: boolean;
-};
-
-export type ModelList = Record<string, Model & DiffusersModel>;
-
-export type FoundModel = {
-  name: string;
-  location: string;
-};
-
-export type InvokeModelConfigProps = {
-  name: string | undefined;
-  description: string | undefined;
-  config: string | undefined;
-  weights: string | undefined;
-  vae: string | undefined;
-  width: number | undefined;
-  height: number | undefined;
-  default: boolean | undefined;
-  format: string | undefined;
-};
-
-export type InvokeDiffusersModelConfigProps = {
-  name: string | undefined;
-  description: string | undefined;
-  repo_id: string | undefined;
-  path: string | undefined;
-  default: boolean | undefined;
-  format: string | undefined;
-  vae: {
-    repo_id: string | undefined;
-    path: string | undefined;
-  };
-};
-
-export type InvokeModelConversionProps = {
-  model_name: string;
-  save_location: string;
-  custom_location: string | null;
-};
-
-export type InvokeModelMergingProps = {
-  models_to_merge: string[];
-  alpha: number;
-  interp: 'weighted_sum' | 'sigmoid' | 'inv_sigmoid' | 'add_difference';
-  force: boolean;
-  merged_model_name: string;
-  model_merge_save_path: string | null;
-};
-
-/**
- * These types type data received from the server via socketio.
- */
-
-export type ModelChangeResponse = {
-  model_name: string;
-  model_list: ModelList;
-};
-
-export type ModelConvertedResponse = {
-  converted_model_name: string;
-  model_list: ModelList;
-};
-
-export type ModelsMergedResponse = {
-  merged_models: string[];
-  merged_model_name: string;
-  model_list: ModelList;
-};
-
-export type ModelAddedResponse = {
-  new_model_name: string;
-  model_list: ModelList;
-  update: boolean;
-};
-
-export type ModelDeletedResponse = {
-  deleted_model_name: string;
-  model_list: ModelList;
-};
-
-export type FoundModelResponse = {
-  search_folder: string;
-  found_models: FoundModel[];
-};
-
-// export type SystemStatusResponse = SystemStatus;
-
-// export type SystemConfigResponse = SystemConfig;
-
-export type ImageResultResponse = Omit<_Image, 'uuid'> & {
-  boundingBox?: IRect;
-  generationMode: InvokeTabName;
-};
-
-export type ImageUploadResponse = {
-  // image: Omit<Image, 'uuid' | 'metadata' | 'category'>;
-  url: string;
-  mtime: number;
-  width: number;
-  height: number;
-  thumbnail: string;
-  // bbox: [number, number, number, number];
-};
-
-export type ErrorResponse = {
-  message: string;
-  additionalData?: string;
-};
-
-export type ImageUrlResponse = {
-  url: string;
-};
-
-export type UploadOutpaintingMergeImagePayload = {
-  dataURL: string;
-  name: string;
-};
-
-/**
- * A disable-able application feature
- */
-export type AppFeature =
-  | 'faceRestore'
-  | 'upscaling'
-  | 'lightbox'
-  | 'modelManager'
-  | 'githubLink'
-  | 'discordLink'
-  | 'bugLink'
-  | 'localization';
-
-/**
- * A disable-able Stable Diffusion feature
- */
-export type StableDiffusionFeature =
-  | 'noiseConfig'
-  | 'variations'
-  | 'symmetry'
-  | 'tiling'
-  | 'hires';
-
-/**
- * Configuration options for the InvokeAI UI.
- * Distinct from system settings which may be changed inside the app.
- */
-export type AppConfig = {
-  /**
-   * Whether or not URLs should be transformed to use a different host
-   */
-  shouldTransformUrls: boolean;
-  /**
-   * Whether or not we need to re-fetch images
-   */
-  shouldFetchImages: boolean;
-  disabledTabs: InvokeTabName[];
-  disabledFeatures: AppFeature[];
-  canRestoreDeletedImagesFromBin: boolean;
-  sd: {
-    iterations: {
-      initial: number;
-      min: number;
-      sliderMax: number;
-      inputMax: number;
-      fineStep: number;
-      coarseStep: number;
-    };
-    width: {
-      initial: number;
-      min: number;
-      sliderMax: number;
-      inputMax: number;
-      fineStep: number;
-      coarseStep: number;
-    };
-    height: {
-      initial: number;
-      min: number;
-      sliderMax: number;
-      inputMax: number;
-      fineStep: number;
-      coarseStep: number;
-    };
-    steps: {
-      initial: number;
-      min: number;
-      sliderMax: number;
-      inputMax: number;
-      fineStep: number;
-      coarseStep: number;
-    };
-    guidance: {
-      initial: number;
-      min: number;
-      sliderMax: number;
-      inputMax: number;
-      fineStep: number;
-      coarseStep: number;
-    };
-    img2imgStrength: {
-      initial: number;
-      min: number;
-      sliderMax: number;
-      inputMax: number;
-      fineStep: number;
-      coarseStep: number;
-    };
-  };
-};
-
-export type PartialAppConfig = O.Partial<AppConfig, 'deep'>;

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

@@ -0,0 +1,25 @@
+export function keepGUIAlive() {
+  async function getRequest(url = '') {
+    const response = await fetch(url, {
+      method: 'GET',
+      cache: 'no-cache',
+    });
+    return response;
+  }
+
+  const keepAliveServer = () => {
+    const url = document.location;
+    const route = '/flaskwebgui-keep-server-alive';
+    getRequest(url + route).then((data) => {
+      return data;
+    });
+  };
+
+  if (!import.meta.env.NODE_ENV || import.meta.env.NODE_ENV === 'production') {
+    document.addEventListener('DOMContentLoaded', () => {
+      const intervalRequest = 3 * 1000;
+      keepAliveServer();
+      setInterval(keepAliveServer, intervalRequest);
+    });
+  }
+}

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

@@ -8,7 +8,7 @@ import {
 } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { Feature, useFeatureHelpInfo } from 'app/features';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/storeHooks';
 import { systemSelector } from 'features/system/store/systemSelectors';
 import { SystemState } from 'features/system/store/systemSlice';
 import { memo, ReactElement } from 'react';

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

@@ -14,7 +14,7 @@ import {
   Tooltip,
   TooltipProps,
 } from '@chakra-ui/react';
-import { clamp } from 'lodash-es';
+import { clamp } from 'lodash';
 
 import { FocusEvent, memo, useEffect, useState } from 'react';
 

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

@@ -16,23 +16,13 @@ type IAISelectProps = SelectProps & {
   validValues:
     | Array<number | string>
     | Array<{ key: string; value: string | number }>;
-  horizontal?: boolean;
-  spaceEvenly?: boolean;
 };
 /**
  * Customized Chakra FormControl + Select multi-part component.
  */
 const IAISelect = (props: IAISelectProps) => {
-  const {
+  const { label, isDisabled, validValues, tooltip, tooltipProps, ...rest } =
-    label,
+    props;
-    isDisabled,
-    validValues,
-    tooltip,
-    tooltipProps,
-    horizontal,
-    spaceEvenly,
-    ...rest
-  } = props;
   return (
     <FormControl
       isDisabled={isDisabled}
@@ -42,28 +32,10 @@ const IAISelect = (props: IAISelectProps) => {
         e.nativeEvent.stopPropagation();
         e.nativeEvent.cancelBubble = true;
       }}
-      sx={
-        horizontal
-          ? {
-              display: 'flex',
-              flexDirection: 'row',
-              alignItems: 'center',
-              justifyContent: 'space-between',
-              gap: 4,
-            }
-          : {}
-      }
     >
-      {label && (
+      {label && <FormLabel>{label}</FormLabel>}
-        <FormLabel sx={spaceEvenly ? { flexBasis: 0, flexGrow: 1 } : {}}>
-          {label}
-        </FormLabel>
-      )}
       <Tooltip label={tooltip} {...tooltipProps}>
-        <Select
+        <Select {...rest}>
-          {...rest}
-          rootProps={{ sx: spaceEvenly ? { flexBasis: 0, flexGrow: 1 } : {} }}
-        >
           {validValues.map((opt) => {
             return typeof opt === 'string' || typeof opt === 'number' ? (
               <IAIOption key={opt} value={opt}>

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

@@ -23,21 +23,12 @@ import {
   Tooltip,
   TooltipProps,
 } from '@chakra-ui/react';
-import { clamp } from 'lodash-es';
+import { clamp } from 'lodash';
 
 import { useTranslation } from 'react-i18next';
-import {
+import { FocusEvent, memo, useEffect, useMemo, useState } from 'react';
-  FocusEvent,
-  memo,
-  MouseEvent,
-  useCallback,
-  useEffect,
-  useMemo,
-  useState,
-} from 'react';
 import { BiReset } from 'react-icons/bi';
 import IAIIconButton, { IAIIconButtonProps } from './IAIIconButton';
-import { roundDownToMultiple } from 'common/util/roundDownToMultiple';
 
 export type IAIFullSliderProps = {
   label: string;
@@ -117,52 +108,31 @@ const IAISlider = (props: IAIFullSliderProps) => {
     [max, sliderNumberInputProps?.max]
   );
 
-  const handleSliderChange = useCallback(
+  const handleSliderChange = (v: number) => {
-    (v: number) => {
+    onChange(v);
-      onChange(v);
+  };
-    },
-    [onChange]
-  );
 
-  const handleInputBlur = useCallback(
+  const handleInputBlur = (e: FocusEvent<HTMLInputElement>) => {
-    (e: FocusEvent<HTMLInputElement>) => {
+    if (e.target.value === '') e.target.value = String(min);
-      if (e.target.value === '') {
+    const clamped = clamp(
-        e.target.value = String(min);
+      isInteger ? Math.floor(Number(e.target.value)) : Number(localInputValue),
-      }
+      min,
-      const clamped = clamp(
+      numberInputMax
-        isInteger
+    );
-          ? Math.floor(Number(e.target.value))
+    onChange(clamped);
-          : Number(localInputValue),
+  };
-        min,
-        numberInputMax
-      );
-      const quantized = roundDownToMultiple(clamped, step);
-      onChange(quantized);
-      setLocalInputValue(quantized);
-    },
-    [isInteger, localInputValue, min, numberInputMax, onChange, step]
-  );
 
-  const handleInputChange = useCallback((v: number | string) => {
+  const handleInputChange = (v: number | string) => {
     setLocalInputValue(v);
-  }, []);
+  };
 
-  const handleResetDisable = useCallback(() => {
+  const handleResetDisable = () => {
-    if (!handleReset) {
+    if (!handleReset) return;
-      return;
-    }
     handleReset();
-  }, [handleReset]);
+  };
-
-  const forceInputBlur = useCallback((e: MouseEvent) => {
-    if (e.target instanceof HTMLDivElement) {
-      e.target.focus();
-    }
-  }, []);
 
   return (
     <FormControl
-      onClick={forceInputBlur}
       sx={
         isCompact
           ? {
@@ -233,7 +203,7 @@ const IAISlider = (props: IAIFullSliderProps) => {
             hidden={hideTooltip}
             {...sliderTooltipProps}
           >
-            <SliderThumb {...sliderThumbProps} zIndex={0} />
+            <SliderThumb {...sliderThumbProps} />
           </Tooltip>
         </Slider>
 
@@ -245,7 +215,6 @@ const IAISlider = (props: IAIFullSliderProps) => {
             value={localInputValue}
             onChange={handleInputChange}
             onBlur={handleInputBlur}
-            focusInputOnChange={false}
             {...sliderNumberInputProps}
           >
             <NumberInputField
@@ -268,7 +237,7 @@ const IAISlider = (props: IAIFullSliderProps) => {
           <IAIIconButton
             size="sm"
             aria-label={t('accessibility.reset')}
-            tooltip={t('accessibility.reset')}
+            tooltip="Reset"
             icon={<BiReset />}
             isDisabled={isDisabled}
             onClick={handleResetDisable}

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

@@ -34,9 +34,10 @@ const IAISwitch = (props: Props) => {
       display="flex"
       gap={4}
       alignItems="center"
+      justifyContent="space-between"
       {...formControlProps}
     >
-      <FormLabel my={1} flexGrow={1} {...formLabelProps}>
+      <FormLabel my={1} {...formLabelProps}>
         {label}
       </FormLabel>
       <Switch {...rest} />

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

@@ -1,11 +1,32 @@
-import { Badge, Box, Flex } from '@chakra-ui/react';
+import { Badge, Box, ButtonGroup, Flex } from '@chakra-ui/react';
-import { Image } from 'app/types/invokeai';
+import { RootState } from 'app/store';
+import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import { clearInitialImage } from 'features/parameters/store/generationSlice';
+import { useCallback } from 'react';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { FaUndo, FaUpload } from 'react-icons/fa';
+import { useTranslation } from 'react-i18next';
+import { Image } from 'app/invokeai';
 
 type ImageToImageOverlayProps = {
+  setIsLoaded: (isLoaded: boolean) => void;
   image: Image;
 };
 
-const ImageToImageOverlay = ({ image }: ImageToImageOverlayProps) => {
+const ImageToImageOverlay = ({
+  setIsLoaded,
+  image,
+}: ImageToImageOverlayProps) => {
+  const isImageToImageEnabled = useAppSelector(
+    (state: RootState) => state.generation.isImageToImageEnabled
+  );
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const handleResetInitialImage = useCallback(() => {
+    dispatch(clearInitialImage());
+    setIsLoaded(false);
+  }, [dispatch, setIsLoaded]);
+
   return (
     <Box
       sx={{
@@ -16,12 +37,34 @@ const ImageToImageOverlay = ({ image }: ImageToImageOverlayProps) => {
         position: 'absolute',
       }}
     >
-      <Flex
+      <ButtonGroup
         sx={{
           position: 'absolute',
           top: 0,
           right: 0,
           p: 2,
+        }}
+      >
+        <IAIIconButton
+          size="sm"
+          isDisabled={!isImageToImageEnabled}
+          icon={<FaUndo />}
+          aria-label={t('accessibility.reset')}
+          onClick={handleResetInitialImage}
+        />
+        <IAIIconButton
+          size="sm"
+          isDisabled={!isImageToImageEnabled}
+          icon={<FaUpload />}
+          aria-label={t('common.upload')}
+        />
+      </ButtonGroup>
+      <Flex
+        sx={{
+          position: 'absolute',
+          bottom: 0,
+          left: 0,
+          p: 2,
           alignItems: 'flex-start',
         }}
       >

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

@@ -1,41 +0,0 @@
-import { ButtonGroup, Flex, Spacer, Text } from '@chakra-ui/react';
-import IAIIconButton from 'common/components/IAIIconButton';
-
-import { useTranslation } from 'react-i18next';
-import { FaUndo, FaUpload } from 'react-icons/fa';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { useCallback } from 'react';
-import { clearInitialImage } from 'features/parameters/store/generationSlice';
-
-const ImageToImageSettingsHeader = () => {
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  const handleResetInitialImage = useCallback(() => {
-    dispatch(clearInitialImage());
-  }, [dispatch]);
-
-  return (
-    <Flex w="full" alignItems="center">
-      <Text size="sm" fontWeight={500} color="base.300">
-        Image to Image
-      </Text>
-      <Spacer />
-      <ButtonGroup>
-        <IAIIconButton
-          size="sm"
-          icon={<FaUndo />}
-          aria-label={t('accessibility.reset')}
-          onClick={handleResetInitialImage}
-        />
-        <IAIIconButton
-          size="sm"
-          icon={<FaUpload />}
-          aria-label={t('common.upload')}
-        />
-      </ButtonGroup>
-    </Flex>
-  );
-};
-
-export default ImageToImageSettingsHeader;

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

@@ -1,6 +1,6 @@
 import { Box, useToast } from '@chakra-ui/react';
 import { ImageUploaderTriggerContext } from 'app/contexts/ImageUploaderTriggerContext';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/storeHooks';
 import useImageUploader from 'common/hooks/useImageUploader';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { ResourceKey } from 'i18next';

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

@@ -1,33 +0,0 @@
-import { Flex, Image, Spinner } from '@chakra-ui/react';
-import InvokeAILogoImage from 'assets/images/logo.png';
-import { memo } from 'react';
-
-// This component loads before the theme so we cannot use theme tokens here
-
-const Loading = () => {
-  return (
-    <Flex
-      position="relative"
-      width="100vw"
-      height="100vh"
-      alignItems="center"
-      justifyContent="center"
-      bg="#151519"
-    >
-      <Image src={InvokeAILogoImage} w="8rem" h="8rem" />
-      <Spinner
-        label="Loading"
-        color="grey"
-        position="absolute"
-        size="sm"
-        width="24px !important"
-        height="24px !important"
-        right="1.5rem"
-        bottom="1.5rem"
-        speed="1.2s"
-      />
-    </Flex>
-  );
-};
-
-export default memo(Loading);

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

@@ -3,17 +3,7 @@ import { FaImage } from 'react-icons/fa';
 
 const SelectImagePlaceholder = () => {
   return (
-    <Flex
+    <Flex sx={{ h: 36, alignItems: 'center', justifyContent: 'center' }}>
-      sx={{
-        w: 'full',
-        h: 'full',
-        bg: 'base.800',
-        borderRadius: 'base',
-        alignItems: 'center',
-        justifyContent: 'center',
-        aspectRatio: '1/1',
-      }}
-    >
       <Icon color="base.400" boxSize={32} as={FaImage}></Icon>
     </Flex>
   );

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

@@ -0,0 +1,160 @@
+// import WorkInProgress from './WorkInProgress';
+// import ReactFlow, {
+//   applyEdgeChanges,
+//   applyNodeChanges,
+//   Background,
+//   Controls,
+//   Edge,
+//   Handle,
+//   Node,
+//   NodeTypes,
+//   OnEdgesChange,
+//   OnNodesChange,
+//   Position,
+// } from 'reactflow';
+
+// import 'reactflow/dist/style.css';
+// import {
+//   Fragment,
+//   FunctionComponent,
+//   ReactNode,
+//   useCallback,
+//   useMemo,
+//   useState,
+// } from 'react';
+// import { OpenAPIV3 } from 'openapi-types';
+// import { filter, map, reduce } from 'lodash';
+// import {
+//   Box,
+//   Flex,
+//   FormControl,
+//   FormLabel,
+//   Input,
+//   Select,
+//   Switch,
+//   Text,
+//   NumberInput,
+//   NumberInputField,
+//   NumberInputStepper,
+//   NumberIncrementStepper,
+//   NumberDecrementStepper,
+//   Tooltip,
+//   chakra,
+//   Badge,
+//   Heading,
+//   VStack,
+//   HStack,
+//   Menu,
+//   MenuButton,
+//   MenuList,
+//   MenuItem,
+//   MenuItemOption,
+//   MenuGroup,
+//   MenuOptionGroup,
+//   MenuDivider,
+//   IconButton,
+// } from '@chakra-ui/react';
+// import { FaPlus } from 'react-icons/fa';
+// import {
+//   FIELD_NAMES as FIELD_NAMES,
+//   FIELDS,
+//   INVOCATION_NAMES as INVOCATION_NAMES,
+//   INVOCATIONS,
+// } from 'features/nodeEditor/constants';
+
+// console.log('invocations', INVOCATIONS);
+
+// const nodeTypes = reduce(
+//   INVOCATIONS,
+//   (acc, val, key) => {
+//     acc[key] = val.component;
+//     return acc;
+//   },
+//   {} as NodeTypes
+// );
+
+// console.log('nodeTypes', nodeTypes);
+
+// // make initial nodes one of every node for now
+// let n = 0;
+// const initialNodes = map(INVOCATIONS, (i) => ({
+//   id: i.type,
+//   type: i.title,
+//   position: { x: (n += 20), y: (n += 20) },
+//   data: {},
+// }));
+
+// console.log('initialNodes', initialNodes);
+
+// export default function NodesWIP() {
+//   const [nodes, setNodes] = useState<Node[]>([]);
+//   const [edges, setEdges] = useState<Edge[]>([]);
+
+//   const onNodesChange: OnNodesChange = useCallback(
+//     (changes) => setNodes((nds) => applyNodeChanges(changes, nds)),
+//     []
+//   );
+
+//   const onEdgesChange: OnEdgesChange = useCallback(
+//     (changes) => setEdges((eds: Edge[]) => applyEdgeChanges(changes, eds)),
+//     []
+//   );
+
+//   return (
+//     <Box
+//       sx={{
+//         position: 'relative',
+//         width: 'full',
+//         height: 'full',
+//         borderRadius: 'md',
+//       }}
+//     >
+//       <ReactFlow
+//         nodeTypes={nodeTypes}
+//         nodes={nodes}
+//         edges={edges}
+//         onNodesChange={onNodesChange}
+//         onEdgesChange={onEdgesChange}
+//       >
+//         <Background />
+//         <Controls />
+//       </ReactFlow>
+//       <HStack sx={{ position: 'absolute', top: 2, right: 2 }}>
+//         {FIELD_NAMES.map((field) => (
+//           <Badge
+//             key={field}
+//             colorScheme={FIELDS[field].color}
+//             sx={{ userSelect: 'none' }}
+//           >
+//             {field}
+//           </Badge>
+//         ))}
+//       </HStack>
+//       <Menu>
+//         <MenuButton
+//           as={IconButton}
+//           aria-label="Options"
+//           icon={<FaPlus />}
+//           sx={{ position: 'absolute', top: 2, left: 2 }}
+//         />
+//         <MenuList>
+//           {INVOCATION_NAMES.map((name) => {
+//             const invocation = INVOCATIONS[name];
+//             return (
+//               <Tooltip
+//                 key={name}
+//                 label={invocation.description}
+//                 placement="end"
+//                 hasArrow
+//               >
+//                 <MenuItem>{invocation.title}</MenuItem>
+//               </Tooltip>
+//             );
+//           })}
+//         </MenuList>
+//       </Menu>
+//     </Box>
+//   );
+// }
+
+export default {};

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

@@ -1,39 +0,0 @@
-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 { isEqual } from 'lodash-es';
-import { isHotkeyPressed, useHotkeys } from 'react-hotkeys-hook';
-
-const globalHotkeysSelector = createSelector(
-  (state: RootState) => state.hotkeys,
-  (hotkeys) => {
-    const { shift } = hotkeys;
-    return { shift };
-  },
-  {
-    memoizeOptions: {
-      resultEqualityCheck: isEqual,
-    },
-  }
-);
-
-// TODO: Does not catch keypresses while focused in an input. Maybe there is a way?
-
-export const useGlobalHotkeys = () => {
-  const dispatch = useAppDispatch();
-  const { shift } = useAppSelector(globalHotkeysSelector);
-
-  useHotkeys(
-    '*',
-    () => {
-      if (isHotkeyPressed('shift')) {
-        !shift && dispatch(shiftKeyPressed(true));
-      } else {
-        shift && dispatch(shiftKeyPressed(false));
-      }
-    },
-    { keyup: true, keydown: true },
-    [shift]
-  );
-};

invokeai/frontend/web/src/common/util/getPromptAndNegative.ts

@@ -1,4 +1,4 @@
-import * as InvokeAI from 'app/types/invokeai';
+import * as InvokeAI from 'app/invokeai';
 import promptToString from './promptToString';
 
 export function getPromptAndNegative(inputPrompt: InvokeAI.Prompt) {

invokeai/frontend/web/src/common/util/getUrl.ts

@@ -1,6 +1,5 @@
-import { RootState } from 'app/store/store';
+import { RootState } from 'app/store';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/storeHooks';
-import { useCallback } from 'react';
 import { OpenAPI } from 'services/api';
 
 export const getUrlAlt = (url: string, shouldTransformUrls: boolean) => {
@@ -13,22 +12,17 @@ export const getUrlAlt = (url: string, shouldTransformUrls: boolean) => {
 
 export const useGetUrl = () => {
   const shouldTransformUrls = useAppSelector(
-    (state: RootState) => state.config.shouldTransformUrls
+    (state: RootState) => state.system.shouldTransformUrls
   );
 
-  const getUrl = useCallback(
+  return {
-    (url?: string) => {
+    shouldTransformUrls,
+    getUrl: (url?: string) => {
       if (OpenAPI.BASE && shouldTransformUrls) {
         return [OpenAPI.BASE, url].join('/');
       }
 
       return url;
     },
-    [shouldTransformUrls]
-  );
-
-  return {
-    shouldTransformUrls,
-    getUrl,
   };
 };

invokeai/frontend/web/src/common/util/parseMetadata.ts

@@ -1,5 +1,5 @@
-import { forEach, size } from 'lodash-es';
+import { forEach, size } from 'lodash';
-import { ImageField, LatentsField, ConditioningField } from 'services/api';
+import { ImageField, LatentsField } from 'services/api';
 
 const OBJECT_TYPESTRING = '[object Object]';
 const STRING_TYPESTRING = '[object String]';
@@ -74,38 +74,8 @@ const parseLatentsField = (latentsField: unknown): LatentsField | undefined => {
   };
 };
 
-const parseConditioningField = (
-  conditioningField: unknown
-): ConditioningField | undefined => {
-  // Must be an object
-  if (!isObject(conditioningField)) {
-    return;
-  }
-
-  // A ConditioningField must have a `conditioning_name`
-  if (!('conditioning_name' in conditioningField)) {
-    return;
-  }
-
-  // A ConditioningField's `conditioning_name` must be a string
-  if (typeof conditioningField.conditioning_name !== 'string') {
-    return;
-  }
-
-  // Build a valid ConditioningField
-  return {
-    conditioning_name: conditioningField.conditioning_name,
-  };
-};
-
 type NodeMetadata = {
-  [key: string]:
+  [key: string]: string | number | boolean | ImageField | LatentsField;
-    | string
-    | number
-    | boolean
-    | ImageField
-    | LatentsField
-    | ConditioningField;
 };
 
 type InvokeAIMetadata = {
@@ -131,7 +101,7 @@ export const parseNodeMetadata = (
       return;
     }
 
-    // the only valid object types are ImageField, LatentsField and ConditioningField
+    // the only valid object types are ImageField and LatentsField
     if (isObject(nodeItem)) {
       if ('image_name' in nodeItem || 'image_type' in nodeItem) {
         const imageField = parseImageField(nodeItem);
@@ -148,14 +118,6 @@ export const parseNodeMetadata = (
         }
         return;
       }
-
-      if ('conditioning_name' in nodeItem) {
-        const conditioningField = parseConditioningField(nodeItem);
-        if (conditioningField) {
-          parsed[nodeKey] = conditioningField;
-        }
-        return;
-      }
     }
 
     // otherwise we accept any string, number or boolean

invokeai/frontend/web/src/common/util/promptToString.ts

@@ -1,4 +1,4 @@
-import * as InvokeAI from 'app/types/invokeai';
+import * as InvokeAI from 'app/invokeai';
 
 const promptToString = (prompt: InvokeAI.Prompt): string => {
   if (typeof prompt === 'string') {

invokeai/frontend/web/src/common/util/seedWeightPairs.ts

@@ -1,4 +1,4 @@
-import * as InvokeAI from 'app/types/invokeai';
+import * as InvokeAI from 'app/invokeai';
 
 export const stringToSeedWeights = (
   string: string

invokeai/frontend/web/src/component.tsx

@@ -1,9 +1,10 @@
-import React, { lazy, memo, PropsWithChildren, useEffect } from 'react';
+import React, { lazy, PropsWithChildren, useEffect, useState } from 'react';
 import { Provider } from 'react-redux';
 import { PersistGate } from 'redux-persist/integration/react';
-import { store } from 'app/store/store';
+import { buildMiddleware, store } from './app/store';
-import { persistor } from '../store/persistor';
+import { persistor } from './persistor';
 import { OpenAPI } from 'services/api';
+import { InvokeTabName } from 'features/ui/store/tabMap';
 import '@fontsource/inter/100.css';
 import '@fontsource/inter/200.css';
 import '@fontsource/inter/300.css';
@@ -14,23 +15,33 @@ import '@fontsource/inter/700.css';
 import '@fontsource/inter/800.css';
 import '@fontsource/inter/900.css';
 
-import Loading from '../../common/components/Loading/Loading';
+import Loading from './Loading';
+
+// Localization
+import './i18n';
 import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
-import { PartialAppConfig } from 'app/types/invokeai';
 
-import '../../i18n';
+const App = lazy(() => import('./app/App'));
-import { socketMiddleware } from 'services/events/middleware';
+const ThemeLocaleProvider = lazy(() => import('./app/ThemeLocaleProvider'));
-
-const App = lazy(() => import('./App'));
-const ThemeLocaleProvider = lazy(() => import('./ThemeLocaleProvider'));
 
 interface Props extends PropsWithChildren {
   apiUrl?: string;
+  disabledPanels?: string[];
+  disabledTabs?: InvokeTabName[];
   token?: string;
-  config?: PartialAppConfig;
+  shouldTransformUrls?: boolean;
+  shouldFetchImages?: boolean;
 }
 
-const InvokeAIUI = ({ apiUrl, token, config, children }: Props) => {
+export default function Component({
+  apiUrl,
+  disabledPanels = [],
+  disabledTabs = [],
+  token,
+  children,
+  shouldTransformUrls,
+  shouldFetchImages = false,
+}: Props) {
   useEffect(() => {
     // configure API client token
     if (token) {
@@ -51,22 +62,29 @@ const InvokeAIUI = ({ apiUrl, token, config, children }: Props) => {
     // the `apiUrl`/`token` dynamically.
 
     // rebuild socket middleware with token and apiUrl
-    addMiddleware(socketMiddleware());
+    addMiddleware(buildMiddleware());
   }, [apiUrl, token]);
 
   return (
     <React.StrictMode>
       <Provider store={store}>
         <PersistGate loading={<Loading />} persistor={persistor}>
-          <React.Suspense fallback={<Loading />}>
+          <React.Suspense fallback={<Loading showText />}>
             <ThemeLocaleProvider>
-              <App config={config}>{children}</App>
+              <App
+                options={{
+                  disabledPanels,
+                  disabledTabs,
+                  shouldTransformUrls,
+                  shouldFetchImages,
+                }}
+              >
+                {children}
+              </App>
             </ThemeLocaleProvider>
           </React.Suspense>
         </PersistGate>
       </Provider>
     </React.StrictMode>
   );
-};
+}
-
-export default memo(InvokeAIUI);

invokeai/frontend/web/src/exports.tsx

@@ -0,0 +1,20 @@
+import Component from './component';
+
+import InvokeAiLogoComponent from './features/system/components/InvokeAILogoComponent';
+import ThemeChanger from './features/system/components/ThemeChanger';
+import IAIPopover from './common/components/IAIPopover';
+import IAIIconButton from './common/components/IAIIconButton';
+import SettingsModal from './features/system/components/SettingsModal/SettingsModal';
+import StatusIndicator from './features/system/components/StatusIndicator';
+import ModelSelect from 'features/system/components/ModelSelect';
+
+export default Component;
+export {
+  InvokeAiLogoComponent,
+  ThemeChanger,
+  IAIPopover,
+  IAIIconButton,
+  SettingsModal,
+  StatusIndicator,
+  ModelSelect,
+};

invokeai/frontend/web/src/features/canvas/components/ClearCanvasHistoryButtonModal.tsx

@@ -1,4 +1,4 @@
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/storeHooks';
 import IAIAlertDialog from 'common/components/IAIAlertDialog';
 import IAIButton from 'common/components/IAIButton';
 import { clearCanvasHistory } from 'features/canvas/store/canvasSlice';

invokeai/frontend/web/src/features/canvas/components/IAICanvas.tsx

@@ -1,6 +1,6 @@
 import { Box, chakra, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/storeHooks';
 import {
   canvasSelector,
   isStagingSelector,
@@ -8,7 +8,7 @@ import {
 import Konva from 'konva';
 import { KonvaEventObject } from 'konva/lib/Node';
 import { Vector2d } from 'konva/lib/types';
-import { isEqual } from 'lodash-es';
+import { isEqual } from 'lodash';
 
 import { useCallback, useRef } from 'react';
 import { Layer, Stage } from 'react-konva';

invokeai/frontend/web/src/features/canvas/components/IAICanvasBoundingBoxOverlay.tsx

@@ -1,6 +1,6 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/storeHooks';
-import { isEqual } from 'lodash-es';
+import { isEqual } from 'lodash';
 
 import { Group, Rect } from 'react-konva';
 import { canvasSelector } from '../store/canvasSelectors';

invokeai/frontend/web/src/features/canvas/components/IAICanvasGrid.tsx

@@ -2,10 +2,10 @@
 
 import { useToken } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { RootState } from 'app/store/store';
+import { RootState } from 'app/store';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/storeHooks';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
-import { isEqual, range } from 'lodash-es';
+import { isEqual, range } from 'lodash';
 
 import { ReactNode, useCallback, useLayoutEffect, useState } from 'react';
 import { Group, Line as KonvaLine } from 'react-konva';

invokeai/frontend/web/src/features/canvas/components/IAICanvasIntermediateImage.tsx

@@ -1,10 +1,10 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { RootState } from 'app/store/store';
+import { RootState } from 'app/store';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/storeHooks';
 import { useGetUrl } from 'common/util/getUrl';
 import { GalleryState } from 'features/gallery/store/gallerySlice';
 import { ImageConfig } from 'konva/lib/shapes/Image';
-import { isEqual } from 'lodash-es';
+import { isEqual } from 'lodash';
 
 import { useEffect, useState } from 'react';
 import { Image as KonvaImage } from 'react-konva';

invokeai/frontend/web/src/features/canvas/components/IAICanvasMaskCompositer.tsx

@@ -1,12 +1,12 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/storeHooks';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import { RectConfig } from 'konva/lib/shapes/Rect';
 import { Rect } from 'react-konva';
 
 import { rgbaColorToString } from 'features/canvas/util/colorToString';
 import Konva from 'konva';
-import { isNumber } from 'lodash-es';
+import { isNumber } from 'lodash';
 import { useCallback, useEffect, useRef, useState } from 'react';
 
 export const canvasMaskCompositerSelector = createSelector(

invokeai/frontend/web/src/features/canvas/components/IAICanvasMaskLines.tsx

@@ -1,8 +1,8 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/storeHooks';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import { GroupConfig } from 'konva/lib/Group';
-import { isEqual } from 'lodash-es';
+import { isEqual } from 'lodash';
 
 import { Group, Line } from 'react-konva';
 import { isCanvasMaskLine } from '../store/canvasTypes';