feat(nodes): wip outputs_service

- refactor image uploading, fix init image upload button 
- refactor toast and hotkey hooks into logical components
- restore canvas save/download/copy/merge functionality
- clean up unused files and packages
- fix canvas rendering issue resulting from fractional stage coords

.github/workflows/mkdocs-material.yml

@@ -2,8 +2,7 @@ name: mkdocs-material
 on:
   push:
     branches:
-      - 'main'
-      - 'development'
+      - 'refs/heads/v2.3'
 
 permissions:
     contents: write
@@ -12,6 +11,10 @@ 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
@@ -22,11 +25,15 @@ 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 -r docs/requirements-mkdocs.txt
+            pip install ".[docs]"
 
       - name: confirm buildability
         run: |
@@ -36,7 +43,7 @@ jobs:
             --verbose
 
       - name: deploy to gh-pages
-        if: ${{ github.ref == 'refs/heads/main' }}
+        if: ${{ github.ref == 'refs/heads/v2.3' }}
         run: |
           python -m \
             mkdocs gh-deploy \

README.md

@@ -33,6 +33,8 @@
 
 </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 python3.10 python3-pip python3.10-venv
+         sudo apt install -y python3.10 python3-pip python3.10-venv
          sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
          ```
 

installer/lib/installer.py

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

invokeai/app/api/dependencies.py

@@ -1,14 +1,14 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import os
-from argparse import Namespace
+from invokeai.app.models.image import ImageField
+from invokeai.app.services.outputs_sqlite import OutputsSqliteItemStorage
 
-from invokeai.app.services.metadata import PngMetadataService, MetadataServiceBase
+import invokeai.backend.util.logging as logger
+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
@@ -19,6 +19,7 @@ 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
 
 
@@ -44,15 +45,16 @@ class ApiDependencies:
     invoker: Invoker = None
 
     @staticmethod
-    def initialize(config, event_handler_id: int):
+    def initialize(config, event_handler_id: int, logger: types.ModuleType=logger):
         Globals.try_patchmatch = config.patchmatch
         Globals.always_use_cpu = config.always_use_cpu
         Globals.internet_available = config.internet_available and check_internet()
         Globals.disable_xformers = not config.xformers
         Globals.ckpt_convert = config.ckpt_convert
 
-        # TODO: Use a logger
-        print(f">> Internet connectivity is {Globals.internet_available}")
+        # TO DO: Use the config to select the logger rather than use the default
+        # invokeai logging module
+        logger.info(f"Internet connectivity is {Globals.internet_available}")
 
         events = FastAPIEventService(event_handler_id)
 
@@ -70,11 +72,13 @@ class ApiDependencies:
         db_location = os.path.join(output_folder, "invokeai.db")
 
         services = InvocationServices(
-            model_manager=get_model_manager(config),
+            model_manager=get_model_manager(config,logger),
             events=events,
+            logger=logger,
             latents=latents,
             images=images,
             metadata=metadata,
+            outputs=OutputsSqliteItemStorage(filename=db_location),
             queue=MemoryInvocationQueue(),
             graph_library=SqliteItemStorage[LibraryGraph](
                 filename=db_location, table_name="graphs"
@@ -83,7 +87,7 @@ class ApiDependencies:
                 filename=db_location, table_name="graph_executions"
             ),
             processor=DefaultInvocationProcessor(),
-            restoration=RestorationServices(config),
+            restoration=RestorationServices(config,logger),
         )
 
         create_system_graphs(services.graph_library)

invokeai/app/api/models/images.py

@@ -32,3 +32,9 @@ 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

@@ -6,12 +6,14 @@ import os
 from typing import Any
 import uuid
 
-from fastapi import HTTPException, Path, Query, Request, UploadFile
+from fastapi import Body, 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 ImageResponse, ImageResponseMetadata
-from invokeai.app.services.metadata import InvokeAIMetadata
+from invokeai.app.api.models.images import (
+    ImageResponse,
+    ImageResponseMetadata,
+)
 from invokeai.app.services.item_storage import PaginatedResults
 
 from ...services.image_storage import ImageType
@@ -24,8 +26,8 @@ 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 | Response:
-    """Gets a result"""
+) -> FileResponse:
+    """Gets an image"""
 
     path = ApiDependencies.invoker.services.images.get_path(
         image_type=image_type, image_name=image_name
@@ -37,17 +39,29 @@ 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(
-    "/{image_type}/thumbnails/{image_name}", operation_id="get_thumbnail"
+    "/{thumbnail_type}/thumbnails/{thumbnail_name}", operation_id="get_thumbnail"
 )
 async def get_thumbnail(
-    image_type: ImageType = Path(description="The type of image to get"),
-    image_name: str = Path(description="The name of the image to get"),
+    thumbnail_type: ImageType = Path(description="The type of thumbnail to get"),
+    thumbnail_name: str = Path(description="The name of the thumbnail to get"),
 ) -> FileResponse | Response:
     """Gets a thumbnail"""
 
     path = ApiDependencies.invoker.services.images.get_path(
-        image_type=image_type, image_name=image_name, is_thumbnail=True
+        image_type=thumbnail_type, image_name=thumbnail_name, is_thumbnail=True
     )
 
     if ApiDependencies.invoker.services.images.validate_path(path):
@@ -69,7 +83,7 @@ async def get_thumbnail(
     status_code=201,
 )
 async def upload_image(
-    file: UploadFile, request: Request, response: Response
+    file: UploadFile, image_type: ImageType, request: Request, response: Response
 ) -> ImageResponse:
     if not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
@@ -84,19 +98,27 @@ async def upload_image(
 
     filename = f"{uuid.uuid4()}_{str(int(datetime.now(timezone.utc).timestamp()))}.png"
 
-    (image_path, thumbnail_path, ctime) = ApiDependencies.invoker.services.images.save(
-        ImageType.UPLOAD, filename, img
+    saved_image = ApiDependencies.invoker.services.images.save(
+        image_type, filename, img
     )
 
     invokeai_metadata = ApiDependencies.invoker.services.metadata.get_metadata(img)
 
+    image_url = ApiDependencies.invoker.services.images.get_uri(
+        image_type, saved_image.image_name
+    )
+
+    thumbnail_url = ApiDependencies.invoker.services.images.get_uri(
+        image_type, saved_image.image_name, True
+    )
+
     res = ImageResponse(
-        image_type=ImageType.UPLOAD,
-        image_name=filename,
-        image_url=f"api/v1/images/{ImageType.UPLOAD.value}/{filename}",
-        thumbnail_url=f"api/v1/images/{ImageType.UPLOAD.value}/thumbnails/{os.path.splitext(filename)[0]}.webp",
+        image_type=image_type,
+        image_name=saved_image.image_name,
+        image_url=image_url,
+        thumbnail_url=thumbnail_url,
         metadata=ImageResponseMetadata(
-            created=ctime,
+            created=saved_image.created,
             width=img.width,
             height=img.height,
             invokeai=invokeai_metadata,
@@ -104,9 +126,7 @@ async def upload_image(
     )
 
     response.status_code = 201
-    response.headers["Location"] = request.url_for(
-        "get_image", image_type=ImageType.UPLOAD.value, image_name=filename
-    )
+    response.headers["Location"] = image_url
 
     return res
 

invokeai/app/api/routers/models.py

@@ -8,10 +8,6 @@ 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"])
 
@@ -112,19 +108,20 @@ 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
-    print(f">> Checking for model {model_name}...")
+    logger.info(f"Checking for model {model_name}...")
            
     if model_exists:
-        print(f">> Deleting Model: {model_name}")
+        logger.info(f"Deleting Model: {model_name}")
         ApiDependencies.invoker.services.model_manager.del_model(model_name, delete_files=True)
-        print(f">> Model Deleted: {model_name}")
+        logger.info(f"Model Deleted: {model_name}")
         raise HTTPException(status_code=204, detail=f"Model '{model_name}' deleted successfully")
     
     else:
-        print(f">> Model not found")
+        logger.error(f"Model not found")
         raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
     
 
@@ -248,4 +245,4 @@ async def delete_model(model_name: str) -> None:
         #         )
         #         print(f">> Models Merged: {models_to_merge}")
         #         print(f">> New Model Added: {model_merge_info['merged_model_name']}")
-        #     except Exception as e:
+        #     except Exception as e:

invokeai/app/api/routers/sessions.py

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

invokeai/app/api_app.py

@@ -3,6 +3,7 @@ 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
@@ -16,7 +17,6 @@ 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
+        config=config, event_handler_id=event_handler_id, logger=logger
     )
 
 
@@ -126,7 +126,6 @@ app.openapi = custom_openapi
 # Override API doc favicons
 app.mount("/static", StaticFiles(directory="static/dream_web"), name="static")
 
-
 @app.get("/docs", include_in_schema=False)
 def overridden_swagger():
     return get_swagger_ui_html(
@@ -144,6 +143,8 @@ def overridden_redoc():
         redoc_favicon_url="/static/favicon.ico",
     )
 
+# Must mount *after* the other routes else it borks em
+app.mount("/", StaticFiles(directory="invokeai/frontend/web/dist", html=True), name="ui")
 
 def invoke_api():
     # Start our own event loop for eventing usage

invokeai/app/cli/commands.py

@@ -2,14 +2,15 @@
 
 from abc import ABC, abstractmethod
 import argparse
-from typing import Any, Callable, Iterable, Literal, get_args, get_origin, get_type_hints
+from typing import Any, Callable, Iterable, Literal, Union, 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, GraphInvocation, Edge
+from ..services.graph import GraphExecutionState, LibraryGraph, Edge
 from ..services.invoker import Invoker
 
 
@@ -229,7 +230,7 @@ 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)
-            print(f"{entry_id}: {get_invocation_command(entry)}")
+            logger.info(f"{entry_id}: {get_invocation_command(entry)}")
 
 
 class SetDefaultCommand(BaseCommand):

invokeai/app/cli/completer.py

@@ -10,6 +10,7 @@ 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
@@ -160,8 +161,8 @@ def set_autocompleter(model_manager: ModelManager) -> Completer:
         pass
     except OSError:  # file likely corrupted
         newname = f"{histfile}.old"
-        print(
-            f"## Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
+        logger.error(
+            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

@@ -13,21 +13,20 @@ 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, get_graph_execution_history
+from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers
 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, ExposedNodeInput, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible
+from .services.graph import Edge, EdgeConnection, 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
@@ -182,7 +181,7 @@ def invoke_all(context: CliContext):
     # Print any errors
     if context.session.has_error():
         for n in context.session.errors:
-            print(
+            context.invoker.services.logger.error(
                 f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
             )
         
@@ -192,13 +191,13 @@ def invoke_all(context: CliContext):
 def invoke_cli():
     config = Args()
     config.parse_args()
-    model_manager = get_model_manager(config)
+    model_manager = get_model_manager(config,logger=logger)
 
     # This initializes the autocompleter and returns it.
     # Currently nothing is done with the returned Completer
     # object, but the object can be used to change autocompletion
     # behavior on the fly, if desired.
-    completer = set_autocompleter(model_manager)
+    set_autocompleter(model_manager)
 
     events = EventServiceBase()
 
@@ -225,7 +224,8 @@ def invoke_cli():
             filename=db_location, table_name="graph_executions"
         ),
         processor=DefaultInvocationProcessor(),
-        restoration=RestorationServices(config),
+        restoration=RestorationServices(config,logger=logger),
+        logger=logger,
     )
 
     system_graphs = create_system_graphs(services.graph_library)
@@ -365,12 +365,12 @@ def invoke_cli():
             invoke_all(context)
 
         except InvalidArgs:
-            print('Invalid command, use "help" to list commands')
+            invoker.services.logger.warning('Invalid command, use "help" to list commands')
             continue
 
         except SessionError:
             # Start a new session
-            print("Session error: creating a new session")
+            invoker.services.logger.warning("Session error: creating a new session")
             context.reset()
 
         except ExitCli:

invokeai/app/invocations/collections.py

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

invokeai/app/invocations/compel.py

@@ -0,0 +1,246 @@
+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?
+
+        conjunction = Compel.parse_prompt_string(prompt_str)
+        prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0]
+
+        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

@@ -1,15 +1,17 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from functools import partial
-from typing import Literal, Optional, Union
+from typing import Literal, Optional, Union, get_args
 
 import numpy as np
 from torch import Tensor
 
 from pydantic import BaseModel, Field
 
-from invokeai.app.models.image import ImageField, ImageType
+from invokeai.app.models.image import ColorField, ImageField, ImageType
 from invokeai.app.invocations.util.choose_model import choose_model
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
+from invokeai.backend.generator.inpaint import infill_methods
 from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
 from .image import ImageOutput, build_image_output
 from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
@@ -17,7 +19,8 @@ from ...backend.stable_diffusion import PipelineIntermediateState
 from ..util.step_callback import stable_diffusion_step_callback
 
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
-
+INFILL_METHODS = Literal[tuple(infill_methods())]
+DEFAULT_INFILL_METHOD = 'patchmatch' if 'patchmatch' in get_args(INFILL_METHODS) else 'tile'
 
 class SDImageInvocation(BaseModel):
     """Helper class to provide all Stable Diffusion raster image invocations with additional config"""
@@ -44,15 +47,13 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
     # TODO: consider making prompt optional to enable providing prompt through a link
     # fmt: off
     prompt: Optional[str] = Field(description="The prompt to generate an image from")
-    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
-    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
-    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", )
+    seed:        int = Field(ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed)
+    steps:       int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
+    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
+    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
+    cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
     model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
     # fmt: on
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
@@ -105,6 +106,11 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
         context.services.images.save(
             image_type, image_name, generate_output.image, metadata
         )
+
+        context.services.outputs.set(image_name, context.graph_execution_state_id)
+        
+        s = context.services.outputs.get(image_name)
+        print(s)
         return build_image_output(
             image_type=image_type,
             image_name=image_name,
@@ -148,7 +154,9 @@ class ImageToImageInvocation(TextToImageInvocation):
                 self.image.image_type, self.image.image_name
             )
         )
-        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)
@@ -162,7 +170,6 @@ class ImageToImageInvocation(TextToImageInvocation):
         outputs = Img2Img(model).generate(
             prompt=self.prompt,
             init_image=image,
-            init_mask=mask,
             step_callback=partial(self.dispatch_progress, context, source_node_id),
             **self.dict(
                 exclude={"prompt", "image", "mask"}
@@ -194,7 +201,6 @@ class ImageToImageInvocation(TextToImageInvocation):
             image=result_image,
         )
 
-
 class InpaintInvocation(ImageToImageInvocation):
     """Generates an image using inpaint."""
 
@@ -202,6 +208,17 @@ class InpaintInvocation(ImageToImageInvocation):
 
     # Inputs
     mask: Union[ImageField, None] = Field(description="The mask")
+    seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)")
+    seam_blur: int = Field(default=16, ge=0, description="The seam inpaint blur radius (px)")
+    seam_strength: float = Field(
+        default=0.75, gt=0, le=1, description="The seam inpaint strength"
+    )
+    seam_steps: int = Field(default=30, ge=1, description="The number of steps to use for seam inpaint")
+    tile_size: int = Field(default=32, ge=1, description="The tile infill method size (px)")
+    infill_method: INFILL_METHODS = Field(default=DEFAULT_INFILL_METHOD, description="The method used to infill empty regions (px)")
+    inpaint_width:       Optional[int] = Field(default=None, multiple_of=8, gt=0, description="The width of the inpaint region (px)")
+    inpaint_height:      Optional[int] = Field(default=None, multiple_of=8, gt=0, description="The height of the inpaint region (px)")
+    inpaint_fill: Optional[ColorField] = Field(default=ColorField(r=127, g=127, b=127, a=255), description="The solid infill method color")
     inpaint_replace: float = Field(
         default=0.0,
         ge=0.0,
@@ -247,8 +264,8 @@ class InpaintInvocation(ImageToImageInvocation):
 
         outputs = Inpaint(model).generate(
             prompt=self.prompt,
-            init_img=image,
-            init_mask=mask,
+            init_image=image,
+            mask_image=mask,
             step_callback=partial(self.dispatch_progress, context, source_node_id),
             **self.dict(
                 exclude={"prompt", "image", "mask"}

invokeai/app/invocations/image.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+import io
 from typing import Literal, Optional
 
 import numpy
@@ -32,14 +33,12 @@ class ImageOutput(BaseInvocationOutput):
     # fmt: off
     type: Literal["image"] = "image"
     image:      ImageField = Field(default=None, description="The output image")
-    width:   Optional[int] = Field(default=None, description="The width of the image in pixels")
-    height:  Optional[int] = Field(default=None, description="The height of the image in pixels")
+    width:             int = Field(description="The width of the image in pixels")
+    height:            int = Field(description="The height of the image in pixels")
     # fmt: on
 
     class Config:
-        schema_extra = {
-            "required": ["type", "image", "width", "height", "mode"]
-        }
+        schema_extra = {"required": ["type", "image", "width", "height"]}
 
 
 def build_image_output(
@@ -54,7 +53,6 @@ def build_image_output(
         image=image_field,
         width=image.width,
         height=image.height,
-        mode=image.mode,
     )
 
 
@@ -151,7 +149,7 @@ class CropImageInvocation(BaseInvocation, PILInvocationConfig):
         metadata = context.services.metadata.build_metadata(
             session_id=context.graph_execution_state_id, node=self
         )
-        
+
         context.services.images.save(image_type, image_name, image_crop, metadata)
         return build_image_output(
             image_type=image_type,
@@ -209,7 +207,7 @@ class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
         metadata = context.services.metadata.build_metadata(
             session_id=context.graph_execution_state_id, node=self
         )
-        
+
         context.services.images.save(image_type, image_name, new_image, metadata)
         return build_image_output(
             image_type=image_type,

invokeai/app/invocations/infill.py

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

invokeai/app/invocations/latent.py

@@ -1,11 +1,13 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
 import random
-from typing import Literal, Optional
+from typing import Literal, Optional, Union
+import einops
 from pydantic import BaseModel, Field
 import torch
 
 from invokeai.app.invocations.util.choose_model import choose_model
+from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 
@@ -14,12 +16,14 @@ 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 ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline, image_resized_to_grid_as_tensor
+from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 import numpy as np
 from ..services.image_storage import ImageType
 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
@@ -37,41 +41,55 @@ class LatentsField(BaseModel):
 class LatentsOutput(BaseInvocationOutput):
     """Base class for invocations that output latents"""
     #fmt: off
-    type: Literal["latent_output"] = "latent_output"
-    latents: LatentsField            = Field(default=None, description="The output latents")
+    type: Literal["latents_output"] = "latents_output"
+
+    # Inputs
+    latents: LatentsField          = Field(default=None, description="The output latents")
+    width:                     int = Field(description="The width of the latents in pixels")
+    height:                    int = Field(description="The height of the latents in pixels")
     #fmt: on
 
+
+def build_latents_output(latents_name: str, latents: torch.Tensor):
+      return LatentsOutput(
+          latents=LatentsField(latents_name=latents_name),
+          width=latents.size()[3] * 8,
+          height=latents.size()[2] * 8,
+      )
+
 class NoiseOutput(BaseInvocationOutput):
     """Invocation noise output"""
     #fmt: off
-    type: Literal["noise_output"] = "noise_output"
+    type:  Literal["noise_output"] = "noise_output"
+
+    # Inputs
     noise: LatentsField            = Field(default=None, description="The output noise")
+    width:                     int = Field(description="The width of the noise in pixels")
+    height:                    int = Field(description="The height of the noise in pixels")
     #fmt: on
 
-
-# TODO: this seems like a hack
-scheduler_map = dict(
-    ddim=diffusers.DDIMScheduler,
-    dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-    k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-    k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-    k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-    k_euler=diffusers.EulerDiscreteScheduler,
-    k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-    k_heun=diffusers.HeunDiscreteScheduler,
-    k_lms=diffusers.LMSDiscreteScheduler,
-    plms=diffusers.PNDMScheduler,
-)
+def build_noise_output(latents_name: str, latents: torch.Tensor):
+      return NoiseOutput(
+          noise=LatentsField(latents_name=latents_name),
+          width=latents.size()[3] * 8,
+          height=latents.size()[2] * 8,
+      )
 
 
 SAMPLER_NAME_VALUES = Literal[
-    tuple(list(scheduler_map.keys()))
+    tuple(list(SCHEDULER_MAP.keys()))
 ]
 
 
 def get_scheduler(scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-    scheduler_class = scheduler_map.get(scheduler_name,'ddim')
-    scheduler = scheduler_class.from_config(model.scheduler.config)
+    scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
+    
+    scheduler_config = model.scheduler.config
+    if "_backup" in scheduler_config:
+        scheduler_config = scheduler_config["_backup"]
+    scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
+    scheduler = scheduler_class.from_config(scheduler_config)
+    
     # hack copied over from generate.py
     if not hasattr(scheduler, 'uses_inpainting_model'):
         scheduler.uses_inpainting_model = lambda: False
@@ -102,19 +120,15 @@ def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_c
     return x
 
 
-def random_seed():
-    return random.randint(0, np.iinfo(np.uint32).max)
-
-
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""
 
     type: Literal["noise"] = "noise"
 
     # Inputs
-    seed:        int = Field(ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", default_factory=random_seed)
-    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting noise", )
-    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting noise", )
+    seed:       int = Field(ge=0, le=SEED_MAX, description="The seed to use", default_factory=get_random_seed)
+    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting noise", )
+    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting noise", )
 
 
     # Schema customisation
@@ -131,32 +145,26 @@ class NoiseInvocation(BaseInvocation):
 
         name = f'{context.graph_execution_state_id}__{self.id}'
         context.services.latents.set(name, noise)
-        return NoiseOutput(
-            noise=LatentsField(latents_name=name)
-        )
+        return build_noise_output(latents_name=name, latents=noise)
 
 
 # Text to image
 class TextToLatentsInvocation(BaseInvocation):
-    """Generates latents from a prompt."""
+    """Generates latents from conditionings."""
 
     type: Literal["t2l"] = "t2l"
 
     # Inputs
-    # TODO: consider making prompt optional to enable providing prompt through a link
     # fmt: off
-    prompt: Optional[str] = Field(description="The prompt to generate an image from")
-    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
+    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
+    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
     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" )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
+    model:       str = Field(default="", description="The model to use (currently ignored)")
     seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
     seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
-    model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
     # fmt: on
 
     # Schema customisation
@@ -206,8 +214,10 @@ class TextToLatentsInvocation(BaseInvocation):
         return model
 
 
-    def get_conditioning_data(self, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
-        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(self.prompt, model=model)
+    def get_conditioning_data(self, context: InvocationContext, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
+        c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
+
         conditioning_data = ConditioningData(
             uc,
             c,
@@ -219,7 +229,7 @@ class TextToLatentsInvocation(BaseInvocation):
                 h_symmetry_time_pct=None,#h_symmetry_time_pct,
                 v_symmetry_time_pct=None#v_symmetry_time_pct,
             ),
-        ).add_scheduler_args_if_applicable(model.scheduler, eta=None)#ddim_eta)
+        ).add_scheduler_args_if_applicable(model.scheduler, eta=0.0)#ddim_eta)
         return conditioning_data
 
 
@@ -234,7 +244,7 @@ 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(model)
+        conditioning_data = self.get_conditioning_data(context, model)
 
         # TODO: Verify the noise is the right size
 
@@ -251,9 +261,7 @@ class TextToLatentsInvocation(BaseInvocation):
 
         name = f'{context.graph_execution_state_id}__{self.id}'
         context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
+        return build_latents_output(latents_name=name, latents=result_latents)
 
 
 class LatentsToLatentsInvocation(TextToLatentsInvocation):
@@ -261,6 +269,10 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
     type: Literal["l2l"] = "l2l"
 
+    # Inputs
+    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
+    strength: float = Field(default=0.5, description="The strength of the latents to use")
+
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
@@ -272,10 +284,6 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             },
         }
 
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
-    strength: float = Field(default=0.5, description="The strength of the latents to use")
-
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         noise = context.services.latents.get(self.noise.latents_name)
         latent = context.services.latents.get(self.latents.latents_name)
@@ -288,7 +296,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             self.dispatch_progress(context, source_node_id, state)
 
         model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(model)
+        conditioning_data = self.get_conditioning_data(context, model)
 
         # TODO: Verify the noise is the right size
 
@@ -296,11 +304,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             latent, device=model.device, dtype=latent.dtype
         )
 
-        timesteps, _ = model.get_img2img_timesteps(
-            self.steps,
-            self.strength,
-            device=model.device,
-        )
+        timesteps, _ = model.get_img2img_timesteps(self.steps, self.strength)
 
         result_latents, result_attention_map_saver = model.latents_from_embeddings(
             latents=initial_latents,
@@ -316,9 +320,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
         name = f'{context.graph_execution_state_id}__{self.id}'
         context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
+        return build_latents_output(latents_name=name, latents=result_latents)
 
 
 # Latent to image
@@ -363,9 +365,118 @@ 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:   LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
+    antialias:                    bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.services.latents.get(self.latents.latents_name)
+
+        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 build_latents_output(latents_name=name, latents=resized_latents)
+
+
+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:  LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
+    antialias:                   bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.services.latents.get(self.latents.latents_name)
+
+        # 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 build_latents_output(latents_name=name, latents=resized_latents)
+
+
+class ImageToLatentsInvocation(BaseInvocation):
+    """Encodes an image into latents."""
+
+    type: Literal["i2l"] = "i2l"
+
+    # Inputs
+    image: Union[ImageField, None] = Field(description="The image to encode")
+    model: str = Field(default="", description="The model to use")
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents", "image"],
+                "type_hints": {"model": "model"},
+            },
+        }
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
+        )
+
+        # TODO: this only really needs the vae
+        model_info = choose_model(context.services.model_manager, self.model)
+        model: StableDiffusionGeneratorPipeline = model_info["model"]
+
+        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+
+        if image_tensor.dim() == 3:
+            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
+
+        latents = model.non_noised_latents_from_image(
+            image_tensor,
+            device=model._model_group.device_for(model.unet),
+            dtype=model.unet.dtype,
+        )
+
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.set(name, latents)
+        return build_latents_output(latents_name=name, latents=latents)

invokeai/app/invocations/math.py

@@ -3,6 +3,7 @@
 from typing import Literal
 
 from pydantic import BaseModel, Field
+import numpy as np
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 
@@ -73,3 +74,12 @@ class DivideInvocation(BaseInvocation, MathInvocationConfig):
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=int(self.a / self.b))
+
+
+class RandomIntInvocation(BaseInvocation):
+    """Outputs a single random integer."""
+    #fmt: off
+    type: Literal["rand_int"] = "rand_int"
+    #fmt: on
+    def invoke(self, context: InvocationContext) -> IntOutput:
+        return IntOutput(a=np.random.randint(0, np.iinfo(np.int32).max))

invokeai/app/invocations/util/choose_model.py

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

invokeai/app/models/image.py

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

invokeai/app/services/default_graphs.py

@@ -1,4 +1,5 @@
 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
@@ -16,38 +17,45 @@ 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': TextToLatentsInvocation(id='4'),
-                '5': LatentsToImageInvocation(id='5')
+                '4': CompelInvocation(id='4'),
+                '5': CompelInvocation(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='width', field='a'), destination=EdgeConnection(node_id='4', field='width')),
-                Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='4', field='height')),
-                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='4', field='noise')),
-                Edge(source=EdgeConnection(node_id='4', field='latents'), destination=EdgeConnection(node_id='5', field='latents')),
+                Edge(source=EdgeConnection(node_id='seed', field='a'), destination=EdgeConnection(node_id='3', field='seed')),
+                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='6', field='noise')),
+                Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')),
+                Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')),
+                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='prompt'),
+            ExposedNodeInput(node_path='4', field='prompt', alias='positive_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='5', field='image', alias='image')
+            ExposedNodeOutput(node_path='7', field='image', alias='image')
         ])
 
 
 def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[LibraryGraph]:
     """Creates the default system graphs, or adds new versions if the old ones don't match"""
-
+    
+    # TODO: Uncomment this when we are ready to fix this up to prevent breaking changes
     graphs: list[LibraryGraph] = list()
 
-    text_to_image = graph_library.get(default_text_to_image_graph_id)
+    # text_to_image = graph_library.get(default_text_to_image_graph_id)
     
-    # TODO: Check if the graph is the same as the default one, and if not, update it
-    #if text_to_image is None:
+    # # TODO: Check if the graph is the same as the default one, and if not, update it
+    # #if text_to_image is None:
     text_to_image = create_text_to_image()
     graph_library.set(text_to_image)
 

invokeai/app/services/image_storage.py

@@ -5,11 +5,16 @@ from glob import glob
 from abc import ABC, abstractmethod
 from pathlib import Path
 from queue import Queue
-from typing import Dict, List, Tuple
+from typing import Dict, List
 
 from PIL.Image import Image
 import PIL.Image as PILImage
-from invokeai.app.api.models.images import ImageResponse, ImageResponseMetadata
+from send2trash import send2trash
+from invokeai.app.api.models.images import (
+    ImageResponse,
+    ImageResponseMetadata,
+    SavedImage,
+)
 from invokeai.app.models.image import ImageType
 from invokeai.app.services.metadata import (
     InvokeAIMetadata,
@@ -41,7 +46,15 @@ class ImageStorageBase(ABC):
     def get_path(
         self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
     ) -> str:
-        """Gets the path to an image or its thumbnail."""
+        """Gets the internal 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
@@ -57,8 +70,8 @@ class ImageStorageBase(ABC):
         image_name: str,
         image: Image,
         metadata: InvokeAIMetadata | None = None,
-    ) -> Tuple[str, str, int]:
-        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image path, thumbnail path, and created timestamp."""
+    ) -> SavedImage:
+        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
         pass
 
     @abstractmethod
@@ -126,8 +139,8 @@ class DiskImageStorage(ImageStorageBase):
                     image_type=image_type.value,
                     image_name=filename,
                     # TODO: DiskImageStorage should not be building URLs...?
-                    image_url=f"api/v1/images/{image_type.value}/{filename}",
-                    thumbnail_url=f"api/v1/images/{image_type.value}/thumbnails/{os.path.splitext(filename)[0]}.webp",
+                    image_url=self.get_uri(image_type, filename),
+                    thumbnail_url=self.get_uri(image_type, filename, True),
                     # TODO: Creation of this object should happen elsewhere (?), just making it fit here so it works
                     metadata=ImageResponseMetadata(
                         created=int(os.path.getctime(path)),
@@ -174,7 +187,23 @@ class DiskImageStorage(ImageStorageBase):
         else:
             path = os.path.join(self.__output_folder, image_type, basename)
 
-        return path
+        abspath = os.path.abspath(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:
@@ -189,7 +218,7 @@ class DiskImageStorage(ImageStorageBase):
         image_name: str,
         image: Image,
         metadata: InvokeAIMetadata | None = None,
-    ) -> Tuple[str, str, int]:
+    ) -> SavedImage:
         image_path = self.get_path(image_type, image_name)
 
         # TODO: Reading the image and then saving it strips the metadata...
@@ -197,7 +226,7 @@ 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)
@@ -207,24 +236,30 @@ class DiskImageStorage(ImageStorageBase):
         self.__set_cache(image_path, image)
         self.__set_cache(thumbnail_path, thumbnail_image)
 
-        return (image_path, thumbnail_path, int(os.path.getctime(image_path)))
+        return SavedImage(
+            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:
-        image_path = self.get_path(image_type, image_name)
-        thumbnail_path = self.get_path(image_type, image_name, True)
-        if os.path.exists(image_path):
-            os.remove(image_path)
+        basename = os.path.basename(image_name)
+        image_path = self.get_path(image_type, basename)
 
+        if os.path.exists(image_path):
+            send2trash(image_path)
         if image_path in self.__cache:
             del self.__cache[image_path]
 
-        if os.path.exists(thumbnail_path):
-            os.remove(thumbnail_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)
         if thumbnail_path in self.__cache:
             del self.__cache[thumbnail_path]
 
-    def __get_cache(self, image_name: str) -> Image:
+    def __get_cache(self, image_name: str) -> Image | None:
         return None if image_name not in self.__cache else self.__cache[image_name]
 
     def __set_cache(self, image_name: str, image: Image):
@@ -235,4 +270,5 @@ class DiskImageStorage(ImageStorageBase):
             )  # TODO: this should refresh position for LRU cache
             if len(self.__cache) > self.__max_cache_size:
                 cache_id = self.__cache_ids.get()
-                del self.__cache[cache_id]
+                if cache_id in self.__cache:
+                    del self.__cache[cache_id]

invokeai/app/services/invocation_services.py

@@ -1,4 +1,8 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
+
+from typing import types
+
+from invokeai.app.services.outputs_session_storage import OutputsSessionStorageABC
 from invokeai.app.services.metadata import MetadataServiceBase
 from invokeai.backend import ModelManager
 
@@ -15,6 +19,7 @@ class InvocationServices:
     events: EventServiceBase
     latents: LatentsStorageBase
     images: ImageStorageBase
+    outputs: OutputsSessionStorageABC
     metadata: MetadataServiceBase
     queue: InvocationQueueABC
     model_manager: ModelManager
@@ -29,8 +34,10 @@ class InvocationServices:
             self,
             model_manager: ModelManager,
             events: EventServiceBase,
+            logger: types.ModuleType,
             latents: LatentsStorageBase,
             images: ImageStorageBase,
+            outputs: OutputsSessionStorageABC,
             metadata: MetadataServiceBase,
             queue: InvocationQueueABC,
             graph_library: ItemStorageABC["LibraryGraph"],
@@ -40,8 +47,10 @@ class InvocationServices:
     ):
         self.model_manager = model_manager
         self.events = events
+        self.logger = logger
         self.latents = latents
         self.images = images
+        self.outputs = outputs
         self.metadata = metadata
         self.queue = queue
         self.graph_library = graph_library

invokeai/app/services/invoker.py

@@ -49,7 +49,7 @@ class Invoker:
         new_state = GraphExecutionState(graph=Graph() if graph is None else graph)
         self.services.graph_execution_manager.set(new_state)
         return new_state
-    
+
     def cancel(self, graph_execution_state_id: str) -> None:
         """Cancels the given execution state"""
         self.services.queue.cancel(graph_execution_state_id)
@@ -71,18 +71,12 @@ 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/metadata.py

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

invokeai/app/services/model_manager_initializer.py

@@ -5,6 +5,7 @@ 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
@@ -12,16 +13,16 @@ 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) -> ModelManager:
+def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager:
     if not config.conf:
         config_file = os.path.join(Globals.root, "configs", "models.yaml")
         if not os.path.exists(config_file):
             report_model_error(
-                config, FileNotFoundError(f"The file {config_file} could not be found.")
+                config, FileNotFoundError(f"The file {config_file} could not be found."), logger
             )
 
-    print(f">> {invokeai.version.__app_name__}, version {invokeai.version.__version__}")
-    print(f'>> InvokeAI runtime directory is "{Globals.root}"')
+    logger.info(f"{invokeai.version.__app_name__}, version {invokeai.version.__version__}")
+    logger.info(f'InvokeAI runtime directory is "{Globals.root}"')
 
     # these two lines prevent a horrible warning message from appearing
     # when the frozen CLIP tokenizer is imported
@@ -62,11 +63,12 @@ def get_model_manager(config: Args) -> 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)
+        report_model_error(config, e, logger)
     except (IOError, KeyError) as e:
-        print(f"{e}. Aborting.")
+        logger.error(f"{e}. Aborting.")
         sys.exit(-1)
 
     # try to autoconvert new models
@@ -76,18 +78,18 @@ def get_model_manager(config: Args) -> ModelManager:
             conf_path=config.conf,
             weights_directory=path,
         )
-
+    logger.info('Model manager initialized')
     return model_manager
 
-def report_model_error(opt: Namespace, e: Exception):
-    print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
-    print(
-        "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
+def report_model_error(opt: Namespace, e: Exception, logger: types.ModuleType):
+    logger.error(f'An error occurred while attempting to initialize the model: "{str(e)}"')
+    logger.error(
+        "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:
-        print(
-            "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
+        logger.warning(
+            "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
         )
     else:
         response = input(
@@ -96,13 +98,12 @@ def report_model_error(opt: Namespace, e: Exception):
         if response.startswith(("n", "N")):
             return
 
-    print("invokeai-configure is launching....\n")
+    logger.info("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/outputs_session_storage.py

@@ -0,0 +1,59 @@
+from abc import ABC, abstractmethod
+from typing import Callable, Generic, TypeVar
+
+from pydantic import BaseModel, Field
+from pydantic.generics import GenericModel
+
+class PaginatedStringResults(GenericModel):
+    """Paginated results"""
+    #fmt: off
+    items: list[str] = Field(description="Session IDs")
+    page: int = Field(description="Current Page")
+    pages: int = Field(description="Total number of pages")
+    per_page: int = Field(description="Number of items per page")
+    total: int = Field(description="Total number of items in result")
+    #fmt: on
+
+class OutputsSessionStorageABC(ABC):
+    _on_changed_callbacks: list[Callable[[str], None]]
+    _on_deleted_callbacks: list[Callable[[str], None]]
+
+    def __init__(self) -> None:
+        self._on_changed_callbacks = list()
+        self._on_deleted_callbacks = list()
+
+    """Base item storage class"""
+
+    @abstractmethod
+    def get(self, output_id: str) -> str:
+        pass
+
+    @abstractmethod
+    def set(self, output_id: str, session_id: str) -> None:
+        pass
+
+    @abstractmethod
+    def list(self, page: int = 0, per_page: int = 10) -> PaginatedStringResults:
+        pass
+
+    @abstractmethod
+    def search(
+        self, query: str, page: int = 0, per_page: int = 10
+    ) -> PaginatedStringResults:
+        pass
+
+    def on_changed(self, on_changed: Callable[[str], None]) -> None:
+        """Register a callback for when an item is changed"""
+        self._on_changed_callbacks.append(on_changed)
+
+    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
+        """Register a callback for when an item is deleted"""
+        self._on_deleted_callbacks.append(on_deleted)
+
+    def _on_changed(self, foreign_key_value: str) -> None:
+        for callback in self._on_changed_callbacks:
+            callback(foreign_key_value)
+
+    def _on_deleted(self, item_id: str) -> None:
+        for callback in self._on_deleted_callbacks:
+            callback(item_id)

invokeai/app/services/outputs_sqlite.py

@@ -0,0 +1,162 @@
+import json
+import sqlite3
+from threading import Lock
+from typing import Union
+
+from invokeai.app.services.outputs_session_storage import (
+    OutputsSessionStorageABC,
+    PaginatedStringResults,
+)
+
+sqlite_memory = ":memory:"
+
+
+class OutputsSqliteItemStorage(OutputsSessionStorageABC):
+    _filename: str
+    _conn: sqlite3.Connection
+    _cursor: sqlite3.Cursor
+    _lock: Lock
+
+    def __init__(
+        self,
+        filename: str,
+    ):
+        super().__init__()
+
+        self._filename = filename
+        self._lock = Lock()
+
+        self._conn = sqlite3.connect(
+            self._filename, check_same_thread=False
+        )  # TODO: figure out a better threading solution
+        self._cursor = self._conn.cursor()
+
+        self._create_table()
+
+    def _create_table(self):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""CREATE TABLE IF NOT EXISTS outputs (
+                id TEXT NOT NULL PRIMARY KEY,
+                session_id TEXT NOT NULL
+                );"""
+            )
+            self._cursor.execute(
+                f"""CREATE UNIQUE INDEX IF NOT EXISTS outputs_id ON outputs(id);"""
+            )
+        finally:
+            self._lock.release()
+
+    def set(self, output_id: str, session_id: str):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""INSERT OR REPLACE INTO outputs (id, session_id) VALUES (?, ?);""",
+                (output_id, session_id),
+            )
+            self._conn.commit()
+        finally:
+            self._lock.release()
+        self._on_changed(output_id)
+
+    def get(self, output_id: str) -> Union[str, None]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""
+                    SELECT graph_executions.item session
+                    FROM graph_executions
+                    INNER JOIN outputs ON outputs.session_id = graph_executions.id
+                    WHERE outputs.id = ?;
+                """,
+                (output_id,),
+            )
+            result = self._cursor.fetchone()
+        finally:
+            self._lock.release()
+
+        if not result:
+            return None
+
+        return result[0]
+
+    def delete(self, output_id: str):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""DELETE FROM outputs WHERE id = ?;""", (str(id),)
+            )
+            self._conn.commit()
+        finally:
+            self._lock.release()
+        self._on_deleted(output_id)
+
+    def list(self, page: int = 0, per_page: int = 10) -> PaginatedStringResults:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""
+                    SELECT graph_executions.item session
+                    FROM graph_executions
+                    INNER JOIN outputs ON outputs.session_id = graph_executions.id
+                    LIMIT ? OFFSET ?;
+                """,
+                (per_page, page * per_page),
+            )
+            result = self._cursor.fetchall()
+
+            items = list(map(lambda r: r[0], result))
+
+            self._cursor.execute(
+                f"""
+                    SELECT count(*)
+                    FROM graph_executions
+                    INNER JOIN outputs ON outputs.session_id = graph_executions.id;
+                """)
+            count = self._cursor.fetchone()[0]
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedStringResults(
+            items=items, page=page, pages=pageCount, per_page=per_page, total=count
+        )
+
+    def search(
+        self, query: str, page: int = 0, per_page: int = 10
+    ) -> PaginatedStringResults:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""
+                    SELECT graph_executions.item session
+                    FROM graph_executions
+                    INNER JOIN outputs ON outputs.session_id = graph_executions.id
+                    WHERE outputs.id LIKE ? LIMIT ? OFFSET ?;
+                """,
+                (f"%{query}%", per_page, page * per_page),
+            )
+            result = self._cursor.fetchall()
+
+            items = list(map(lambda r: r[0], result))
+
+            self._cursor.execute(
+                f"""
+                    SELECT count(*)
+                    FROM graph_executions
+                    INNER JOIN outputs ON outputs.session_id = graph_executions.id
+                    WHERE outputs.id LIKE ?;
+                """,                
+                (f"%{query}%",),
+            )
+            count = self._cursor.fetchone()[0]
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedStringResults(
+            items=items, page=page, pages=pageCount, per_page=per_page, total=count
+        )

invokeai/app/services/processor.py

@@ -1,17 +1,22 @@
+import time
 import traceback
-from threading import Event, Thread
+from threading import Event, Thread, BoundedSemaphore
 
 from ..invocations.baseinvocation import InvocationContext
 from .invocation_queue import InvocationQueueItem
 from .invoker import InvocationProcessorABC, Invoker
 from ..models.exceptions import CanceledException
 
+import invokeai.backend.util.logging as logger
 class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker_thread: Thread
     __stop_event: Event
     __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(
@@ -20,7 +25,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
             kwargs=dict(stop_event=self.__stop_event),
         )
         self.__invoker_thread.daemon = (
-            True  # TODO: probably better to just not use threads?
+            True  # TODO: make async and do not use threads
         )
         self.__invoker_thread.start()
 
@@ -29,9 +34,16 @@ 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()
+                try:
+                    queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
+                except Exception as e:
+                    logger.debug("Exception while getting from queue: %s" % e)
+
                 if not queue_item:  # Probably stopping
+                    # do not hammer the queue
+                    time.sleep(0.5)
                     continue
 
                 graph_execution_state = (
@@ -110,7 +122,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     )
 
                     pass
-                
+
                 # Check queue to see if this is canceled, and skip if so
                 if self.__invoker.services.queue.is_canceled(
                     graph_execution_state.id
@@ -120,11 +132,22 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                 # Queue any further commands if invoking all
                 is_complete = graph_execution_state.is_complete()
                 if queue_item.invoke_all and not is_complete:
-                    self.__invoker.invoke(graph_execution_state, invoke_all=True)
+                    try:
+                        self.__invoker.invoke(graph_execution_state, invoke_all=True)
+                    except Exception as e:
+                        logger.error("Error while invoking: %s" % e)
+                        self.__invoker.services.events.emit_invocation_error(
+                            graph_execution_state_id=graph_execution_state.id,
+                            node=invocation.dict(),
+                            source_node_id=source_node_id,
+                            error=traceback.format_exc()
+                        )
                 elif is_complete:
                     self.__invoker.services.events.emit_graph_execution_complete(
                         graph_execution_state.id
                     )
 
         except KeyboardInterrupt:
-            ...  # Log something?
+            pass  # Log something? KeyboardInterrupt is probably not going to be seen by the processor
+        finally:
+            self.__threadLimit.release()

invokeai/app/services/restoration_services.py

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

invokeai/app/util/misc.py

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

invokeai/backend/args.py

@@ -96,6 +96,7 @@ 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
@@ -107,17 +108,21 @@ APP_VERSION = invokeai.version.__version__
 
 SAMPLER_CHOICES = [
     "ddim",
-    "k_dpm_2_a",
-    "k_dpm_2",
-    "k_dpmpp_2_a",
-    "k_dpmpp_2",
-    "k_euler_a",
-    "k_euler",
-    "k_heun",
-    "k_lms",
-    "plms",
-    # diffusers:
+    "ddpm",
+    "deis",
+    "lms",
     "pndm",
+    "heun",
+    "heun_k",
+    "euler",
+    "euler_k",
+    "euler_a",
+    "kdpm_2",
+    "kdpm_2_a",
+    "dpmpp_2s",
+    "dpmpp_2m",
+    "dpmpp_2m_k",
+    "unipc",
 ]
 
 PRECISION_CHOICES = [
@@ -189,7 +194,7 @@ class Args(object):
                 print(f"{APP_NAME} {APP_VERSION}")
                 sys.exit(0)
 
-            print("* Initializing, be patient...")
+            logger.info("Initializing, be patient...")
             Globals.root = Path(os.path.abspath(switches.root_dir or Globals.root))
             Globals.try_patchmatch = switches.patchmatch
 
@@ -197,14 +202,13 @@ class Args(object):
             initfile = os.path.expanduser(os.path.join(Globals.root, Globals.initfile))
             legacyinit = os.path.expanduser("~/.invokeai")
             if os.path.exists(initfile):
-                print(
-                    f">> Initialization file {initfile} found. Loading...",
-                    file=sys.stderr,
+                logger.info(
+                    f"Initialization file {initfile} found. Loading...",
                 )
                 sysargs.insert(0, f"@{initfile}")
             elif os.path.exists(legacyinit):
-                print(
-                    f">> WARNING: Old initialization file found at {legacyinit}. This location is deprecated. Please move it to {Globals.root}/invokeai.init."
+                logger.warning(
+                    f"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 +218,7 @@ class Args(object):
             self._arg_switches = self._arg_parser.parse_args(sysargs)
             return self._arg_switches
         except Exception as e:
-            print(f"An exception has occurred: {e}")
+            logger.error(f"An exception has occurred: {e}")
             return None
 
     def parse_cmd(self, cmd_string):
@@ -631,7 +635,7 @@ class Args(object):
             choices=SAMPLER_CHOICES,
             metavar="SAMPLER_NAME",
             help=f'Set the default sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
-            default="k_lms",
+            default="lms",
         )
         render_group.add_argument(
             "--log_tokenization",
@@ -1154,7 +1158,7 @@ class Args(object):
 
 
 def format_metadata(**kwargs):
-    print("format_metadata() is deprecated. Please use metadata_dumps()")
+    logger.warning("format_metadata() is deprecated. Please use metadata_dumps()")
     return metadata_dumps(kwargs)
 
 
@@ -1326,7 +1330,7 @@ def metadata_loads(metadata) -> list:
         import sys
         import traceback
 
-        print(">> could not read metadata", file=sys.stderr)
+        logger.error("Could not read metadata")
         print(traceback.format_exc(), file=sys.stderr)
     return results
 

invokeai/backend/generate.py

@@ -27,6 +27,7 @@ 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
@@ -36,6 +37,7 @@ from .safety_checker import SafetyChecker
 from .prompting import get_uc_and_c_and_ec
 from .prompting.conditioning import log_tokenization
 from .stable_diffusion import HuggingFaceConceptsLibrary
+from .stable_diffusion.schedulers import SCHEDULER_MAP
 from .util import choose_precision, choose_torch_device
 
 def fix_func(orig):
@@ -140,7 +142,7 @@ class Generate:
         model=None,
         conf="configs/models.yaml",
         embedding_path=None,
-        sampler_name="k_lms",
+        sampler_name="lms",
         ddim_eta=0.0,  # deterministic
         full_precision=False,
         precision="auto",
@@ -195,12 +197,12 @@ 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())
-        print(f">> Using device_type {self.device.type}")
+        logger.info(f"Using device_type {self.device.type}")
         if full_precision:
             if self.precision != "auto":
                 raise ValueError("Remove --full_precision / -F if using --precision")
-            print("Please remove deprecated --full_precision / -F")
-            print("If auto config does not work you can use --precision=float32")
+            logger.warning("Please remove deprecated --full_precision / -F")
+            logger.warning("If auto config does not work you can use --precision=float32")
             self.precision = "float32"
         if self.precision == "auto":
             self.precision = choose_precision(self.device)
@@ -208,13 +210,13 @@ class Generate:
 
         if is_xformers_available():
             if torch.cuda.is_available() and not Globals.disable_xformers:
-                print(">> xformers memory-efficient attention is available and enabled")
+                logger.info("xformers memory-efficient attention is available and enabled")
             else:
-                print(
-                    ">> xformers memory-efficient attention is available but disabled"
+                logger.info(
+                    "xformers memory-efficient attention is available but disabled"
                 )
         else:
-            print(">> xformers not installed")
+            logger.info("xformers not installed")
 
         # model caching system for fast switching
         self.model_manager = ModelManager(
@@ -229,8 +231,8 @@ class Generate:
         fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME
         model = model or fallback
         if not self.model_manager.valid_model(model):
-            print(
-                f'** "{model}" is not a known model name; falling back to {fallback}.'
+            logger.warning(
+                f'"{model}" is not a known model name; falling back to {fallback}.'
             )
             model = None
         self.model_name = model or fallback
@@ -246,10 +248,10 @@ class Generate:
 
         # load safety checker if requested
         if safety_checker:
-            print(">> Initializing NSFW checker")
+            logger.info("Initializing NSFW checker")
             self.safety_checker = SafetyChecker(self.device)
         else:
-            print(">> NSFW checker is disabled")
+            logger.info("NSFW checker is disabled")
 
     def prompt2png(self, prompt, outdir, **kwargs):
         """
@@ -567,7 +569,7 @@ class Generate:
             self.clear_cuda_cache()
 
             if catch_interrupts:
-                print("**Interrupted** Partial results will be returned.")
+                logger.warning("Interrupted** Partial results will be returned.")
             else:
                 raise KeyboardInterrupt
         except RuntimeError:
@@ -575,11 +577,11 @@ class Generate:
             self.clear_cuda_cache()
 
             print(traceback.format_exc(), file=sys.stderr)
-            print(">> Could not generate image.")
+            logger.info("Could not generate image.")
 
         toc = time.time()
-        print("\n>> Usage stats:")
-        print(f">>   {len(results)} image(s) generated in", "%4.2fs" % (toc - tic))
+        logger.info("Usage stats:")
+        logger.info(f"{len(results)} image(s) generated in "+"%4.2fs" % (toc - tic))
         self.print_cuda_stats()
         return results
 
@@ -609,16 +611,16 @@ class Generate:
     def print_cuda_stats(self):
         if self._has_cuda():
             self.gather_cuda_stats()
-            print(
-                ">>   Max VRAM used for this generation:",
-                "%4.2fG." % (self.max_memory_allocated / 1e9),
-                "Current VRAM utilization:",
-                "%4.2fG" % (self.memory_allocated / 1e9),
+            logger.info(
+                "Max VRAM used for this generation: "+
+                "%4.2fG. " % (self.max_memory_allocated / 1e9)+
+                "Current VRAM utilization: "+
+                "%4.2fG" % (self.memory_allocated / 1e9)
             )
 
-            print(
-                ">>   Max VRAM used since script start: ",
-                "%4.2fG" % (self.session_peakmem / 1e9),
+            logger.info(
+                "Max VRAM used since script start: " +
+                "%4.2fG" % (self.session_peakmem / 1e9)
             )
 
     # this needs to be generalized to all sorts of postprocessors, which should be wrapped
@@ -647,7 +649,7 @@ class Generate:
             seed = random.randrange(0, np.iinfo(np.uint32).max)
 
         prompt = opt.prompt or args.prompt or ""
-        print(f'>> using seed {seed} and prompt "{prompt}" for {image_path}')
+        logger.info(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
@@ -696,8 +698,8 @@ class Generate:
                 try:
                     extend_instructions[direction] = int(pixels)
                 except ValueError:
-                    print(
-                        '** invalid extension instruction. Use <directions> <pixels>..., as in "top 64 left 128 right 64 bottom 64"'
+                    logger.warning(
+                        'invalid extension instruction. Use <directions> <pixels>..., as in "top 64 left 128 right 64 bottom 64"'
                     )
 
             opt.seed = seed
@@ -720,8 +722,8 @@ class Generate:
             # fetch the metadata from the image
             generator = self.select_generator(embiggen=True)
             opt.strength = opt.embiggen_strength or 0.40
-            print(
-                f">> Setting img2img strength to {opt.strength} for happy embiggening"
+            logger.info(
+                f"Setting img2img strength to {opt.strength} for happy embiggening"
             )
             generator.generate(
                 prompt,
@@ -748,12 +750,12 @@ class Generate:
             return restorer.process(opt, args, image_callback=callback, prefix=prefix)
 
         elif tool is None:
-            print(
-                "* please provide at least one postprocessing option, such as -G or -U"
+            logger.warning(
+                "please provide at least one postprocessing option, such as -G or -U"
             )
             return None
         else:
-            print(f"* postprocessing tool {tool} is not yet supported")
+            logger.warning(f"postprocessing tool {tool} is not yet supported")
             return None
 
     def select_generator(
@@ -797,8 +799,8 @@ class Generate:
         image = self._load_img(img)
 
         if image.width < self.width and image.height < self.height:
-            print(
-                f">> WARNING: img2img and inpainting may produce unexpected results with initial images smaller than {self.width}x{self.height} in both dimensions"
+            logger.warning(
+                f"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
@@ -809,8 +811,8 @@ class Generate:
         if (image.width * image.height) > (
             self.width * self.height
         ) and self.size_matters:
-            print(
-                ">> This input is larger than your defaults. If you run out of memory, please use a smaller image."
+            logger.info(
+                "This input is larger than your defaults. If you run out of memory, please use a smaller image."
             )
             self.size_matters = False
 
@@ -891,11 +893,11 @@ class Generate:
         try:
             model_data = cache.get_model(model_name)
         except Exception as e:
-            print(f"** model {model_name} could not be loaded: {str(e)}")
+            logger.warning(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
-            print("** trying to reload previous model")
+            logger.warning("trying to reload previous model")
             model_data = cache.get_model(previous_model_name)  # load previous
             if model_data is None:
                 raise e
@@ -962,15 +964,15 @@ class Generate:
                     if self.gfpgan is not None or self.codeformer is not None:
                         if facetool == "gfpgan":
                             if self.gfpgan is None:
-                                print(
-                                    ">> GFPGAN not found. Face restoration is disabled."
+                                logger.info(
+                                    "GFPGAN not found. Face restoration is disabled."
                                 )
                             else:
                                 image = self.gfpgan.process(image, strength, seed)
                         if facetool == "codeformer":
                             if self.codeformer is None:
-                                print(
-                                    ">> CodeFormer not found. Face restoration is disabled."
+                                logger.info(
+                                    "CodeFormer not found. Face restoration is disabled."
                                 )
                             else:
                                 cf_device = (
@@ -984,7 +986,7 @@ class Generate:
                                     fidelity=codeformer_fidelity,
                                 )
                     else:
-                        print(">> Face Restoration is disabled.")
+                        logger.info("Face Restoration is disabled.")
                 if upscale is not None:
                     if self.esrgan is not None:
                         if len(upscale) < 2:
@@ -997,10 +999,10 @@ class Generate:
                             denoise_str=upscale_denoise_str,
                         )
                     else:
-                        print(">> ESRGAN is disabled. Image not upscaled.")
+                        logger.info("ESRGAN is disabled. Image not upscaled.")
             except Exception as e:
-                print(
-                    f">> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
+                logger.info(
+                    f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
                 )
 
             if image_callback is not None:
@@ -1046,37 +1048,20 @@ class Generate:
     def _set_scheduler(self):
         default = self.model.scheduler
 
-        # See https://github.com/huggingface/diffusers/issues/277#issuecomment-1371428672
-        scheduler_map = dict(
-            ddim=diffusers.DDIMScheduler,
-            dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-            k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-            k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-            # DPMSolverMultistepScheduler is technically not `k_` anything, as it is neither
-            # the k-diffusers implementation nor included in EDM (Karras 2022), but we can
-            # provide an alias for compatibility.
-            k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-            k_euler=diffusers.EulerDiscreteScheduler,
-            k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-            k_heun=diffusers.HeunDiscreteScheduler,
-            k_lms=diffusers.LMSDiscreteScheduler,
-            plms=diffusers.PNDMScheduler,
-        )
-
-        if self.sampler_name in scheduler_map:
-            sampler_class = scheduler_map[self.sampler_name]
+        if self.sampler_name in SCHEDULER_MAP:
+            sampler_class, sampler_extra_config = 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)
+            self.sampler = sampler_class.from_config({**self.model.scheduler.config, **sampler_extra_config})
         else:
             msg = (
-                f">> Unsupported Sampler: {self.sampler_name} "
+                f" Unsupported Sampler: {self.sampler_name} "+
                 f"Defaulting to {default}"
             )
             self.sampler = default
 
-        print(msg)
+        logger.info(msg)
 
         if not hasattr(self.sampler, "uses_inpainting_model"):
             # FIXME: terrible kludge!
@@ -1085,17 +1070,17 @@ class Generate:
     def _load_img(self, img) -> Image:
         if isinstance(img, Image.Image):
             image = img
-            print(f">> using provided input image of size {image.width}x{image.height}")
+            logger.info(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)
-            print(
-                f">> loaded input image of size {image.width}x{image.height} from {img}"
+            logger.info(
+                f"loaded input image of size {image.width}x{image.height} from {img}"
             )
         else:
             image = Image.open(img)
-            print(f">> loaded input image of size {image.width}x{image.height}")
+            logger.info(f"loaded input image of size {image.width}x{image.height}")
         image = ImageOps.exif_transpose(image)
         return image
 
@@ -1183,14 +1168,14 @@ class Generate:
 
     def _transparency_check_and_warning(self, image, mask, force_outpaint=False):
         if not mask:
-            print(
-                ">> Initial image has transparent areas. Will inpaint in these regions."
+           logger.info(
+                "Initial image has transparent areas. Will inpaint in these regions."
             )
-            if (not force_outpaint) and self._check_for_erasure(image):
-                print(
-                    ">> WARNING: Colors underneath the transparent region seem to have been erased.\n",
-                    ">>          Inpainting will be suboptimal. Please preserve the colors when making\n",
-                    ">>          a transparency mask, or provide mask explicitly using --init_mask (-M).",
+           if (not force_outpaint) and self._check_for_erasure(image):
+                logger.info(
+                    "Colors underneath the transparent region seem to have been erased.\n" +
+                    "Inpainting will be suboptimal. Please preserve the colors when making\n" +
+                    "a transparency mask, or provide mask explicitly using --init_mask (-M)."
                 )
 
     def _squeeze_image(self, image):
@@ -1201,11 +1186,11 @@ class Generate:
 
     def _fit_image(self, image, max_dimensions):
         w, h = max_dimensions
-        print(f">> image will be resized to fit inside a box {w}x{h} in size.")
+        logger.info(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)
-        print(
-            f">> after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}"
+        logger.info(
+            f"after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}"
         )
         return image
 
@@ -1216,8 +1201,8 @@ class Generate:
         )  # resize to integer multiple of 64
         if h != height or w != width:
             if log:
-                print(
-                    f">> Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}"
+                logger.info(
+                    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

@@ -25,11 +25,13 @@ 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
 from ..prompting.conditioning import get_uc_and_c_and_ec
 from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
+from ..stable_diffusion.schedulers import SCHEDULER_MAP
 
 downsampling = 8
 
@@ -70,19 +72,6 @@ class InvokeAIGeneratorOutput:
 # we are interposing a wrapper around the original Generator classes so that
 # old code that calls Generate will continue to work.
 class InvokeAIGenerator(metaclass=ABCMeta):
-    scheduler_map = dict(
-        ddim=diffusers.DDIMScheduler,
-        dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-        k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-        k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-        k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-        k_euler=diffusers.EulerDiscreteScheduler,
-        k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-        k_heun=diffusers.HeunDiscreteScheduler,
-        k_lms=diffusers.LMSDiscreteScheduler,
-        plms=diffusers.PNDMScheduler,
-    )
-
     def __init__(self,
                  model_info: dict,
                  params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
@@ -174,14 +163,20 @@ class InvokeAIGenerator(metaclass=ABCMeta):
         '''
         Return list of all the schedulers that we currently handle.
         '''
-        return list(self.scheduler_map.keys())
+        return list(SCHEDULER_MAP.keys())
 
     def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
         return generator_class(model, self.params.precision)
 
     def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-        scheduler_class = self.scheduler_map.get(scheduler_name,'ddim')
-        scheduler = scheduler_class.from_config(model.scheduler.config)
+        scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
+        
+        scheduler_config = model.scheduler.config
+        if "_backup" in scheduler_config:
+            scheduler_config = scheduler_config["_backup"]
+        scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
+        scheduler = scheduler_class.from_config(scheduler_config)
+        
         # hack copied over from generate.py
         if not hasattr(scheduler, 'uses_inpainting_model'):
             scheduler.uses_inpainting_model = lambda: False
@@ -225,10 +220,10 @@ class Inpaint(Img2Img):
     def generate(self,
                  mask_image: Image.Image | torch.FloatTensor,
                  # Seam settings - when 0, doesn't fill seam
-                 seam_size: int = 0,
-                 seam_blur: int = 0,
+                 seam_size: int = 96,
+                 seam_blur: int = 16,
                  seam_strength: float = 0.7,
-                 seam_steps: int = 10,
+                 seam_steps: int = 30,
                  tile_size: int = 32,
                  inpaint_replace=False,
                  infill_method=None,
@@ -372,7 +367,7 @@ class Generator:
                     try:
                         x_T = self.get_noise(width, height)
                     except:
-                        print("** An error occurred while getting initial noise **")
+                        logger.error("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.
@@ -607,7 +602,7 @@ class Generator:
         image = self.sample_to_image(sample)
         dirname = os.path.dirname(filepath) or "."
         if not os.path.exists(dirname):
-            print(f"** creating directory {dirname}")
+            logger.info(f"creating directory {dirname}")
             os.makedirs(dirname, exist_ok=True)
         image.save(filepath, "PNG")
 

invokeai/backend/generator/embiggen.py

@@ -8,10 +8,11 @@ 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)
@@ -72,22 +73,22 @@ class Embiggen(Generator):
             embiggen = [1.0]  # If not specified, assume no scaling
         elif embiggen[0] < 0:
             embiggen[0] = 1.0
-            print(
-                ">> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !"
+            logger.warning(
+                "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
-            print(
-                ">> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !"
+            logger.warning(
+                "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
-            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 !"
+            logger.warning(
+                "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
@@ -97,8 +98,8 @@ class Embiggen(Generator):
             embiggen_tiles.sort()
 
         if strength >= 0.5:
-            print(
-                f"* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
+            logger.warning(
+                f"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
@@ -121,8 +122,8 @@ class Embiggen(Generator):
                 from ..restoration.realesrgan import ESRGAN
 
                 esrgan = ESRGAN()
-                print(
-                    f">> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
+                logger.info(
+                    f"ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
                 )
                 if embiggen[0] > 2:
                     initsuperimage = esrgan.process(
@@ -312,10 +313,10 @@ class Embiggen(Generator):
         def make_image():
             # Make main tiles -------------------------------------------------
             if embiggen_tiles:
-                print(f">> Making {len(embiggen_tiles)} Embiggen tiles...")
+                logger.info(f"Making {len(embiggen_tiles)} Embiggen tiles...")
             else:
-                print(
-                    f">> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
+                logger.info(
+                    f"Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
                 )
 
             emb_tile_store = []
@@ -361,11 +362,11 @@ class Embiggen(Generator):
                 # newinitimage.save(newinitimagepath)
 
                 if embiggen_tiles:
-                    print(
+                    logger.debug(
                         f"Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)"
                     )
                 else:
-                    print(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles")
+                    logger.debug(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
@@ -547,8 +548,8 @@ class Embiggen(Generator):
                     # Layer tile onto final image
                     outputsuperimage.alpha_composite(intileimage, (left, top))
             else:
-                print(
-                    "Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
+                logger.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/inpaint.py

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

invokeai/backend/generator/txt2img2img.py

@@ -14,6 +14,8 @@ 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)
@@ -77,8 +79,8 @@ 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
-            print(
-                f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
+            logger.info(
+                f"Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
             )
 
             # resizing

invokeai/backend/image_util/patchmatch.py

@@ -5,10 +5,9 @@ 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.
@@ -28,12 +27,12 @@ class PatchMatch:
             from patchmatch import patch_match as pm
 
             if pm.patchmatch_available:
-                print(">> Patchmatch initialized")
+                logger.info("Patchmatch initialized")
             else:
-                print(">> Patchmatch not loaded (nonfatal)")
+                logger.info("Patchmatch not loaded (nonfatal)")
             self.patch_match = pm
         else:
-            print(">> Patchmatch loading disabled")
+            logger.info("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):
-        print(">> Initializing clipseg model for text to mask inference")
+        logger.info("Initializing clipseg model for text to mask inference")
 
         # BUG: we are not doing anything with the device option at this time
         self.device = device
@@ -101,18 +101,6 @@ 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

@@ -25,6 +25,7 @@ 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
@@ -46,6 +47,7 @@ from diffusers import (
     LDMTextToImagePipeline,
     LMSDiscreteScheduler,
     PNDMScheduler,
+    UniPCMultistepScheduler,
     StableDiffusionPipeline,
     UNet2DConditionModel,
 )
@@ -372,9 +374,9 @@ 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:
-        print(f"   | Checkpoint {path} has both EMA and non-EMA weights.")
+        logger.debug(f"Checkpoint {path} has both EMA and non-EMA weights.")
         if extract_ema:
-            print("   | Extracting EMA weights (usually better for inference)")
+            logger.debug("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:])
@@ -392,8 +394,8 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
                             key
                         )
         else:
-            print(
-                "   | Extracting only the non-EMA weights (usually better for fine-tuning)"
+            logger.debug(
+                "Extracting only the non-EMA weights (usually better for fine-tuning)"
             )
 
     for key in keys:
@@ -1115,7 +1117,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         if "global_step" in checkpoint:
             global_step = checkpoint["global_step"]
         else:
-            print("   | global_step key not found in model")
+            logger.debug("global_step key not found in model")
             global_step = None
 
         # sometimes there is a state_dict key and sometimes not
@@ -1208,6 +1210,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
             scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
         elif scheduler_type == "dpm":
             scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+        elif scheduler_type == 'unipc':
+            scheduler = UniPCMultistepScheduler.from_config(scheduler.config)
         elif scheduler_type == "ddim":
             scheduler = scheduler
         else:
@@ -1229,15 +1233,15 @@ 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:
-            print(f"   | Converting VAE {vae_path}")
+            logger.debug(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:
-            print("   | Using checkpoint model's original VAE")
+            logger.debug("Using checkpoint model's original VAE")
 
         if vae:
-            print("   | Using replacement diffusers VAE")
+            logger.debug("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

@@ -18,18 +18,19 @@ import warnings
 from enum import Enum, auto
 from pathlib import Path
 from shutil import move, rmtree
-from typing import Any, Optional, Union, Callable
+from typing import Any, Optional, Union, Callable, types
 
 import safetensors
 import safetensors.torch
 import torch
 import transformers
+import invokeai.backend.util.logging as logger
 from diffusers import (
     AutoencoderKL,
     UNet2DConditionModel,
     SchedulerMixin,
     logging as dlogging,
-)    
+)
 from huggingface_hub import scan_cache_dir
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
@@ -67,7 +68,7 @@ class SDModelComponent(Enum):
     scheduler="scheduler"
     safety_checker="safety_checker"
     feature_extractor="feature_extractor"
-    
+
 DEFAULT_MAX_MODELS = 2
 
 class ModelManager(object):
@@ -75,6 +76,8 @@ class ModelManager(object):
     Model manager handles loading, caching, importing, deleting, converting, and editing models.
     """
 
+    logger: types.ModuleType = logger
+
     def __init__(
         self,
         config: OmegaConf | Path,
@@ -83,6 +86,7 @@ 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
@@ -104,6 +108,7 @@ 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:
         """
@@ -132,8 +137,8 @@ class ModelManager(object):
             )
 
         if not self.valid_model(model_name):
-            print(
-                f'** "{model_name}" is not a known model name. Please check your models.yaml file'
+            self.logger.error(
+                f'"{model_name}" is not a known model name. Please check your models.yaml file'
             )
             return self.current_model
 
@@ -144,7 +149,7 @@ class ModelManager(object):
 
         if model_name in self.models:
             requested_model = self.models[model_name]["model"]
-            print(f">> Retrieving model {model_name} from system RAM cache")
+            self.logger.info(f"Retrieving model {model_name} from system RAM cache")
             requested_model.ready()
             width = self.models[model_name]["width"]
             height = self.models[model_name]["height"]
@@ -177,7 +182,7 @@ class ModelManager(object):
         vae from the model currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.vae)
-                                   
+
     def get_model_tokenizer(self, model_name: str=None)->CLIPTokenizer:
         """Given a model name identified in models.yaml, load the model into
         GPU if necessary and return its assigned CLIPTokenizer. If no
@@ -185,12 +190,12 @@ class ModelManager(object):
         currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.tokenizer)
-                                   
+
     def get_model_unet(self, model_name: str=None)->UNet2DConditionModel:
         """Given a model name identified in models.yaml, load the model into
         GPU if necessary and return its assigned UNet2DConditionModel. If no model
         name is provided, return the UNet from the model
-        currently in the GPU.  
+        currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.unet)
 
@@ -217,7 +222,7 @@ class ModelManager(object):
         currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.scheduler)
-    
+
     def _get_sub_model(
             self,
             model_name: str=None,
@@ -379,7 +384,7 @@ class ModelManager(object):
         """
         omega = self.config
         if model_name not in omega:
-            print(f"** Unknown model {model_name}")
+            self.logger.error(f"Unknown model {model_name}")
             return
         # save these for use in deletion later
         conf = omega[model_name]
@@ -392,13 +397,13 @@ class ModelManager(object):
             self.stack.remove(model_name)
         if delete_files:
             if weights:
-                print(f"** Deleting file {weights}")
+                self.logger.info(f"Deleting file {weights}")
                 Path(weights).unlink(missing_ok=True)
             elif path:
-                print(f"** Deleting directory {path}")
+                self.logger.info(f"Deleting directory {path}")
                 rmtree(path, ignore_errors=True)
             elif repo_id:
-                print(f"** Deleting the cached model directory for {repo_id}")
+                self.logger.info(f"Deleting the cached model directory for {repo_id}")
                 self._delete_model_from_cache(repo_id)
 
     def add_model(
@@ -439,7 +444,7 @@ 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:
-            print(
+            self.logger.error(
                 f'"{model_name}" is not a known model name. Please check your models.yaml file'
             )
             return
@@ -457,7 +462,7 @@ class ModelManager(object):
         model_format = mconfig.get("format", "ckpt")
         if model_format == "ckpt":
             weights = mconfig.weights
-            print(f">> Loading {model_name} from {weights}")
+            self.logger.info(f"Loading {model_name} from {weights}")
             model, width, height, model_hash = self._load_ckpt_model(
                 model_name, mconfig
             )
@@ -473,13 +478,15 @@ class ModelManager(object):
 
         # usage statistics
         toc = time.time()
-        print(">> Model loaded in", "%4.2fs" % (toc - tic))
+        self.logger.info("Model loaded in " + "%4.2fs" % (toc - tic))
         if self._has_cuda():
-            print(
-                ">> Max VRAM used to load the model:",
-                "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9),
-                "\n>> Current VRAM usage:"
-                "%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
+            self.logger.info(
+                "Max VRAM used to load the model: "+
+                "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9)
+            )
+            self.logger.info(
+                "Current VRAM usage: "+
+                "%4.2fG" % (torch.cuda.memory_allocated() / 1e9)
             )
         return model, width, height, model_hash
 
@@ -487,11 +494,11 @@ class ModelManager(object):
         name_or_path = self.model_name_or_path(mconfig)
         using_fp16 = self.precision == "float16"
 
-        print(f">> Loading diffusers model from {name_or_path}")
+        self.logger.info(f"Loading diffusers model from {name_or_path}")
         if using_fp16:
-            print("   | Using faster float16 precision")
+            self.logger.debug("Using faster float16 precision")
         else:
-            print("   | Using more accurate float32 precision")
+            self.logger.debug("Using more accurate float32 precision")
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
@@ -523,8 +530,8 @@ class ModelManager(object):
                 if str(e).startswith("fp16 is not a valid"):
                     pass
                 else:
-                    print(
-                        f"** An unexpected error occurred while downloading the model: {e})"
+                    self.logger.error(
+                        f"An unexpected error occurred while downloading the model: {e})"
                     )
             if pipeline:
                 break
@@ -542,7 +549,7 @@ class ModelManager(object):
         # square images???
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
-        print(f"   | Default image dimensions = {width} x {height}")
+        self.logger.debug(f"Default image dimensions = {width} x {height}")
 
         return pipeline, width, height, model_hash
 
@@ -559,14 +566,14 @@ class ModelManager(object):
             weights = os.path.normpath(os.path.join(Globals.root, weights))
 
         # Convert to diffusers and return a diffusers pipeline
-        print(f">> Converting legacy checkpoint {model_name} into a diffusers model...")
+        self.logger.info(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 as e:
+        except Exception:
             pass
 
         vae_path = None
@@ -624,7 +631,7 @@ class ModelManager(object):
         if model_name not in self.models:
             return
 
-        print(f">> Offloading {model_name} to CPU")
+        self.logger.info(f"Offloading {model_name} to CPU")
         model = self.models[model_name]["model"]
         model.offload_all()
         self.current_model = None
@@ -640,30 +647,26 @@ class ModelManager(object):
         and option to exit if an infected file is identified.
         """
         # scan model
-        print(f"   | Scanning Model: {model_name}")
+        self.logger.debug(f"Scanning Model: {model_name}")
         scan_result = scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
             if scan_result.infected_files == 1:
-                print(f"\n### Issues Found In Model: {scan_result.issues_count}")
-                print(
-                    "### WARNING: The model you are trying to load seems to be infected."
-                )
-                print("### For your safety, InvokeAI will not load this model.")
-                print("### Please use checkpoints from trusted sources.")
-                print("### Exiting InvokeAI")
+                self.logger.critical(f"Issues Found In Model: {scan_result.issues_count}")
+                self.logger.critical("The model you are trying to load seems to be infected.")
+                self.logger.critical("For your safety, InvokeAI will not load this model.")
+                self.logger.critical("Please use checkpoints from trusted sources.")
+                self.logger.critical("Exiting InvokeAI")
                 sys.exit()
             else:
-                print(
-                    "\n### WARNING: InvokeAI was unable to scan the model you are using."
-                )
+                self.logger.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":
-                    print("### Exiting InvokeAI")
+                    self.logger.critical("Exiting InvokeAI")
                     sys.exit()
         else:
-            print("   | Model scanned ok")
+            self.logger.debug("Model scanned ok")
 
     def import_diffuser_model(
         self,
@@ -780,26 +783,24 @@ class ModelManager(object):
         model_path: Path = None
         thing = path_url_or_repo  # to save typing
 
-        print(f">> Probing {thing} for import")
+        self.logger.info(f"Probing {thing} for import")
 
         if thing.startswith(("http:", "https:", "ftp:")):
-            print(f"   | {thing} appears to be a URL")
+            self.logger.info(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"]:
-                print(
-                    f"   | {Path(thing).name} appears to be part of a diffusers model. Skipping import"
-                )
+                self.logger.debug(f"{Path(thing).name} appears to be part of a diffusers model. Skipping import")
                 return
             else:
-                print(f"   | {thing} appears to be a checkpoint file on disk")
+                self.logger.debug(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():
-            print(f"   | {thing} appears to be a diffusers file on disk")
+            self.logger.debug(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"),
@@ -810,34 +811,30 @@ class ModelManager(object):
 
         elif Path(thing).is_dir():
             if (Path(thing) / "model_index.json").exists():
-                print(f"   | {thing} appears to be a diffusers model.")
+                self.logger.debug(f"{thing} appears to be a diffusers model.")
                 model_name = self.import_diffuser_model(
                     thing, commit_to_conf=commit_to_conf
                 )
             else:
-                print(
-                    f"   |{thing} appears to be a directory. Will scan for models to import"
-                )
+                self.logger.debug(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
                     ):
-                        print(f" >> {model_name} successfully imported")
+                        self.logger.info(f"{model_name} successfully imported")
                 return model_name
 
         elif re.match(r"^[\w.+-]+/[\w.+-]+$", thing):
-            print(f"   | {thing} appears to be a HuggingFace diffusers repo_id")
+            self.logger.debug(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:
-            print(
-                f"** {thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id"
-            )
+            self.logger.warning(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
@@ -845,7 +842,7 @@ class ModelManager(object):
             return
 
         if model_path.stem in self.config:  # already imported
-            print("   | Already imported. Skipping")
+            self.logger.debug("Already imported. Skipping")
             return model_path.stem
 
         # another round of heuristics to guess the correct config file.
@@ -861,39 +858,39 @@ 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")
-                print(f"   | Using config file {model_config_file.name}")
+                self.logger.debug(f"Using config file {model_config_file.name}")
 
             else:
                 model_type = self.probe_model_type(checkpoint)
                 if model_type == SDLegacyType.V1:
-                    print("   | SD-v1 model detected")
+                    self.logger.debug("SD-v1 model detected")
                     model_config_file = Path(
                         Globals.root, "configs/stable-diffusion/v1-inference.yaml"
                     )
                 elif model_type == SDLegacyType.V1_INPAINT:
-                    print("   | SD-v1 inpainting model detected")
+                    self.logger.debug("SD-v1 inpainting model detected")
                     model_config_file = Path(
                         Globals.root,
                         "configs/stable-diffusion/v1-inpainting-inference.yaml",
                     )
                 elif model_type == SDLegacyType.V2_v:
-                    print("   | SD-v2-v model detected")
+                    self.logger.debug("SD-v2-v model detected")
                     model_config_file = Path(
                         Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
                     )
                 elif model_type == SDLegacyType.V2_e:
-                    print("   | SD-v2-e model detected")
+                    self.logger.debug("SD-v2-e model detected")
                     model_config_file = Path(
                         Globals.root, "configs/stable-diffusion/v2-inference.yaml"
                     )
                 elif model_type == SDLegacyType.V2:
-                    print(
-                        f"** {thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
+                    self.logger.warning(
+                        f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
                     )
                     return
                 else:
-                    print(
-                        f"** {thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
+                    self.logger.warning(
+                        f"{thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
                     )
                     return
 
@@ -909,7 +906,7 @@ 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}")
-                print(f"   | Using VAE file {vae_path.name}")
+                self.logger.debug(f"Using VAE file {vae_path.name}")
         vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
 
         diffuser_path = Path(
@@ -955,14 +952,14 @@ class ModelManager(object):
         from . import convert_ckpt_to_diffusers
 
         if diffusers_path.exists():
-            print(
-                f"ERROR: The path {str(diffusers_path)} already exists. Please move or remove it and try again."
+            self.logger.error(
+                f"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}"
-        print(f"   | Converting {model_name} to diffusers (30-60s)")
+        self.logger.debug(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
@@ -979,10 +976,10 @@ class ModelManager(object):
                 vae_path=vae_path,
                 scan_needed=scan_needed,
             )
-            print(
-                f"   | Success. Converted model is now located at {str(diffusers_path)}"
+            self.logger.debug(
+                f"Success. Converted model is now located at {str(diffusers_path)}"
             )
-            print(f"   | Writing new config file entry for {model_name}")
+            self.logger.debug(f"Writing new config file entry for {model_name}")
             new_config = dict(
                 path=str(diffusers_path),
                 description=model_description,
@@ -993,17 +990,17 @@ class ModelManager(object):
             self.add_model(model_name, new_config, True)
             if commit_to_conf:
                 self.commit(commit_to_conf)
-            print("   | Conversion succeeded")
+            self.logger.debug("Conversion succeeded")
         except Exception as e:
-            print(f"** Conversion failed: {str(e)}")
-            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)"
+            self.logger.warning(f"Conversion failed: {str(e)}")
+            self.logger.warning(
+                "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):
-        print(f">> Finding Models In: {search_folder}")
+        self.logger.info(f"Finding Models In: {search_folder}")
         models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
         models_folder_safetensors = Path(search_folder).glob("**/*.safetensors")
 
@@ -1027,8 +1024,8 @@ class ModelManager(object):
         num_loaded_models = len(self.models)
         if num_loaded_models >= self.max_loaded_models:
             least_recent_model = self._pop_oldest_model()
-            print(
-                f">> Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}"
+            self.logger.info(
+                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]
@@ -1036,8 +1033,8 @@ class ModelManager(object):
 
     def print_vram_usage(self) -> None:
         if self._has_cuda:
-            print(
-                ">> Current VRAM usage: ",
+            self.logger.info(
+                "Current VRAM usage:"+
                 "%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
             )
 
@@ -1126,10 +1123,10 @@ class ModelManager(object):
             dest = hub / model.stem
             if dest.exists() and not source.exists():
                 continue
-            print(f"** {source} => {dest}")
+            cls.logger.info(f"{source} => {dest}")
             if source.exists():
                 if dest.is_symlink():
-                    print(f"** Found symlink at {dest.name}. Not migrating.")
+                    logger.warning(f"Found symlink at {dest.name}. Not migrating.")
                 elif dest.exists():
                     if source.is_dir():
                         rmtree(source)
@@ -1146,7 +1143,7 @@ class ModelManager(object):
         ]
         for d in empty:
             os.rmdir(d)
-        print("** Migration is done. Continuing...")
+        cls.logger.info("Migration is done. Continuing...")
 
     def _resolve_path(
         self, source: Union[str, Path], dest_directory: str
@@ -1189,15 +1186,15 @@ class ModelManager(object):
 
     def _add_embeddings_to_model(self, model: StableDiffusionGeneratorPipeline):
         if self.embedding_path is not None:
-            print(f">> Loading embeddings from {self.embedding_path}")
+            self.logger.info(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
                     )
-            print(
-                f'>> Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
+            self.logger.info(
+                f'Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
             )
 
     def _has_cuda(self) -> bool:
@@ -1219,7 +1216,7 @@ class ModelManager(object):
             with open(hashpath) as f:
                 hash = f.read()
             return hash
-        print("   | Calculating sha256 hash of model files")
+        self.logger.debug("Calculating sha256 hash of model files")
         tic = time.time()
         sha = hashlib.sha256()
         count = 0
@@ -1231,7 +1228,7 @@ class ModelManager(object):
                         sha.update(chunk)
         hash = sha.hexdigest()
         toc = time.time()
-        print(f"   | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
+        self.logger.debug(f"sha256 = {hash} ({count} files hashed in {toc - tic:4.2f}s)")
         with open(hashpath, "w") as f:
             f.write(hash)
         return hash
@@ -1249,13 +1246,13 @@ class ModelManager(object):
                 hash = f.read()
             return hash
 
-        print("   | Calculating sha256 hash of weights file")
+        self.logger.debug("Calculating sha256 hash of weights file")
         tic = time.time()
         sha = hashlib.sha256()
         sha.update(data)
         hash = sha.hexdigest()
         toc = time.time()
-        print(f">> sha256 = {hash}", "(%4.2fs)" % (toc - tic))
+        self.logger.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic))
 
         with open(hashpath, "w") as f:
             f.write(hash)
@@ -1276,12 +1273,12 @@ class ModelManager(object):
             local_files_only=not Globals.internet_available,
         )
 
-        print(f"   | Loading diffusers VAE from {name_or_path}")
+        self.logger.debug(f"Loading diffusers VAE from {name_or_path}")
         if using_fp16:
             vae_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
         else:
-            print("   | Using more accurate float32 precision")
+            self.logger.debug("Using more accurate float32 precision")
             fp_args_list = [{}]
 
         vae = None
@@ -1305,12 +1302,12 @@ class ModelManager(object):
                 break
 
         if not vae and deferred_error:
-            print(f"** Could not load VAE {name_or_path}: {str(deferred_error)}")
+            self.logger.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}")
 
         return vae
 
-    @staticmethod
-    def _delete_model_from_cache(repo_id):
+    @classmethod
+    def _delete_model_from_cache(cls,repo_id):
         cache_info = scan_cache_dir(global_cache_dir("hub"))
 
         # I'm sure there is a way to do this with comprehensions
@@ -1321,8 +1318,8 @@ class ModelManager(object):
                 for revision in repo.revisions:
                     hashes_to_delete.add(revision.commit_hash)
         strategy = cache_info.delete_revisions(*hashes_to_delete)
-        print(
-            f"** Deletion of this model is expected to free {strategy.expected_freed_size_str}"
+        cls.logger.warning(
+            f"Deletion of this model is expected to free {strategy.expected_freed_size_str}"
         )
         strategy.execute()
 

invokeai/backend/prompting/conditioning.py

@@ -16,66 +16,58 @@ from compel.prompt_parser import (
     FlattenedPrompt,
     Fragment,
     PromptParser,
+    Conjunction,
 )
 
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 from ..stable_diffusion import InvokeAIDiffuserComponent
 from ..util import torch_dtype
 
 
-def get_uc_and_c_and_ec(
-    prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False
-):
+def get_uc_and_c_and_ec(prompt_string,
+                        model: InvokeAIDiffuserComponent,
+                        log_tokens=False, skip_normalize_legacy_blend=False):
     # lazy-load any deferred textual inversions.
     # this might take a couple of seconds the first time a textual inversion is used.
-    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
-        prompt_string
-    )
+    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(prompt_string)
 
-    tokenizer = model.tokenizer
-    compel = Compel(
-        tokenizer=tokenizer,
-        text_encoder=model.text_encoder,
-        textual_inversion_manager=model.textual_inversion_manager,
-        dtype_for_device_getter=torch_dtype,
-        truncate_long_prompts=False
-    )
+    compel = Compel(tokenizer=model.tokenizer,
+                    text_encoder=model.text_encoder,
+                    textual_inversion_manager=model.textual_inversion_manager,
+                    dtype_for_device_getter=torch_dtype,
+                    truncate_long_prompts=False,
+                    )
 
     # get rid of any newline characters
     prompt_string = prompt_string.replace("\n", " ")
-    (
-        positive_prompt_string,
-        negative_prompt_string,
-    ) = split_prompt_to_positive_and_negative(prompt_string)
-    legacy_blend = try_parse_legacy_blend(
-        positive_prompt_string, skip_normalize_legacy_blend
-    )
-    positive_prompt: Union[FlattenedPrompt, Blend]
-    if legacy_blend is not None:
-        positive_prompt = legacy_blend
-    else:
-        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
-        negative_prompt_string
-    )
+    positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
 
+    legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
+    positive_conjunction: Conjunction
+    if legacy_blend is not None:
+        positive_conjunction = legacy_blend
+    else:
+        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
+    positive_prompt = positive_conjunction.prompts[0]
+
+    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
+    negative_prompt: FlattenedPrompt | Blend = negative_conjunction.prompts[0]
+
+    tokens_count = get_max_token_count(model.tokenizer, positive_prompt)
     if log_tokens or getattr(Globals, "log_tokenization", False):
-        log_tokenization(positive_prompt, negative_prompt, tokenizer=tokenizer)
+        log_tokenization(positive_prompt, negative_prompt, tokenizer=model.tokenizer)
 
     c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
     uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
     [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
 
-    tokens_count = get_max_token_count(tokenizer, positive_prompt)
-
-    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
-        tokens_count_including_eos_bos=tokens_count,
-        cross_attention_control_args=options.get("cross_attention_control", None),
-    )
+    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
+                                                         cross_attention_control_args=options.get(
+                                                             'cross_attention_control', None))
     return uc, c, ec
 
-
 def get_prompt_structure(
     prompt_string, skip_normalize_legacy_blend: bool = False
 ) -> (Union[FlattenedPrompt, Blend], FlattenedPrompt):
@@ -86,18 +78,17 @@ def get_prompt_structure(
     legacy_blend = try_parse_legacy_blend(
         positive_prompt_string, skip_normalize_legacy_blend
     )
-    positive_prompt: Union[FlattenedPrompt, Blend]
+    positive_prompt: Conjunction
     if legacy_blend is not None:
-        positive_prompt = legacy_blend
+        positive_conjunction = legacy_blend
     else:
-        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
-        negative_prompt_string
-    )
+        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
+    positive_prompt = positive_conjunction.prompts[0]
+    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
+    negative_prompt: FlattenedPrompt|Blend = negative_conjunction.prompts[0]
 
     return positive_prompt, negative_prompt
 
-
 def get_max_token_count(
     tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
 ) -> int:
@@ -162,8 +153,8 @@ def log_tokenization(
     negative_prompt: Union[Blend, FlattenedPrompt],
     tokenizer,
 ):
-    print(f"\n>> [TOKENLOG] Parsed Prompt: {positive_prompt}")
-    print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
+    logger.info(f"[TOKENLOG] Parsed Prompt: {positive_prompt}")
+    logger.info(f"[TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
 
     log_tokenization_for_prompt_object(positive_prompt, tokenizer)
     log_tokenization_for_prompt_object(
@@ -237,29 +228,28 @@ def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_t
             usedTokens += 1
 
     if usedTokens > 0:
-        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
-        print(f"{tokenized}\x1b[0m")
+        logger.info(f'[TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
+        logger.debug(f"{tokenized}\x1b[0m")
 
     if discarded != "":
-        print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
-        print(f"{discarded}\x1b[0m")
+        logger.info(f"[TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
+        logger.debug(f"{discarded}\x1b[0m")
 
-
-def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Blend]:
+def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Conjunction]:
     weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
     if len(weighted_subprompts) <= 1:
         return None
     strings = [x[0] for x in weighted_subprompts]
-    weights = [x[1] for x in weighted_subprompts]
 
     pp = PromptParser()
     parsed_conjunctions = [pp.parse_conjunction(x) for x in strings]
-    flattened_prompts = [x.prompts[0] for x in parsed_conjunctions]
-
-    return Blend(
-        prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize
-    )
-
+    flattened_prompts = []
+    weights = []
+    for i, x in enumerate(parsed_conjunctions):
+        if len(x.prompts)>0:
+            flattened_prompts.append(x.prompts[0])
+            weights.append(weighted_subprompts[i][1])
+    return Conjunction([Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)])
 
 def split_weighted_subprompts(text, skip_normalize=False) -> list:
     """
@@ -295,8 +285,8 @@ 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:
-        print(
-            "* Warning: Subprompt weights add up to zero. Discarding and using even weights instead."
+        logger.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

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

invokeai/backend/restoration/codeformer.py

@@ -5,6 +5,7 @@ import warnings
 import numpy as np
 import torch
 
+import invokeai.backend.util.logging as logger
 from ..globals import Globals
 
 pretrained_model_url = (
@@ -23,12 +24,12 @@ class CodeFormerRestoration:
         self.codeformer_model_exists = os.path.isfile(self.model_path)
 
         if not self.codeformer_model_exists:
-            print("## NOT FOUND: CodeFormer model not found at " + self.model_path)
+            logger.error("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:
-            print(f">> CodeFormer - Restoring Faces for image seed:{seed}")
+            logger.info(f"CodeFormer - Restoring Faces for image seed:{seed}")
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=UserWarning)
@@ -97,7 +98,7 @@ class CodeFormerRestoration:
                     del output
                     torch.cuda.empty_cache()
                 except RuntimeError as error:
-                    print(f"\tFailed inference for CodeFormer: {error}.")
+                    logger.error(f"Failed 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:
-            print("## NOT FOUND: GFPGAN model not found at " + self.model_path)
+            logger.error("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:
-            print(f">> GFPGAN - Restoring Faces for image seed:{seed}")
+            logger.info(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
 
-                print(">> Error loading GFPGAN:", file=sys.stderr)
+                logger.error("Error loading GFPGAN:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
             os.chdir(cwd)
 
         if self.gfpgan is None:
-            print(f">> WARNING: GFPGAN not initialized.")
-            print(
-                f">> Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
+            logger.warning("WARNING: GFPGAN not initialized.")
+            logger.warning(
+                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
-            print(f">> extending image {direction}ward by {pixels} pixels")
+            logger.info(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

@@ -6,18 +6,13 @@ 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
@@ -74,16 +69,16 @@ class ESRGAN:
                 import sys
                 import traceback
 
-                print(">> Error loading Real-ESRGAN:", file=sys.stderr)
+                logger.error("Error loading Real-ESRGAN:")
                 print(traceback.format_exc(), file=sys.stderr)
 
         if upsampler_scale == 0:
-            print(">> Real-ESRGAN: Invalid scaling option. Image not upscaled.")
+            logger.warning("Real-ESRGAN: Invalid scaling option. Image not upscaled.")
             return image
 
         if seed is not None:
-            print(
-                f">> Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
+            logger.info(
+                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

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

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

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -509,10 +509,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         run_id=None,
         callback: Callable[[PipelineIntermediateState], None] = None,
     ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
+        if self.scheduler.config.get("cpu_only", False):
+            scheduler_device = torch.device('cpu')
+        else:
+            scheduler_device = self._model_group.device_for(self.unet)
+
         if timesteps is None:
-            self.scheduler.set_timesteps(
-                num_inference_steps, device=self._model_group.device_for(self.unet)
-            )
+            self.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
             timesteps = self.scheduler.timesteps
         infer_latents_from_embeddings = GeneratorToCallbackinator(
             self.generate_latents_from_embeddings, PipelineIntermediateState
@@ -545,8 +548,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             additional_guidance = []
         extra_conditioning_info = conditioning_data.extra
         with self.invokeai_diffuser.custom_attention_context(
-            extra_conditioning_info=extra_conditioning_info,
-            step_count=len(self.scheduler.timesteps),
+                self.invokeai_diffuser.model,
+                extra_conditioning_info=extra_conditioning_info,
+                step_count=len(self.scheduler.timesteps),
         ):
             yield PipelineIntermediateState(
                 run_id=run_id,
@@ -725,12 +729,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         noise: torch.Tensor,
         run_id=None,
         callback=None,
-    ) -> InvokeAIStableDiffusionPipelineOutput:
-        timesteps, _ = self.get_img2img_timesteps(
-            num_inference_steps,
-            strength,
-            device=self._model_group.device_for(self.unet),
-        )
+    ) -> InvokeAIStableDiffusionPipelineOutput:        
+        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
         result_latents, result_attention_maps = self.latents_from_embeddings(
             latents=initial_latents if strength < 1.0 else torch.zeros_like(
                 initial_latents, device=initial_latents.device, dtype=initial_latents.dtype
@@ -756,13 +756,19 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
     def get_img2img_timesteps(
-        self, num_inference_steps: int, strength: float, device
+        self, num_inference_steps: int, strength: float, device=None
     ) -> (torch.Tensor, int):
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         assert img2img_pipeline.scheduler is self.scheduler
-        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
+
+        if self.scheduler.config.get("cpu_only", False):
+            scheduler_device = torch.device('cpu')
+        else:
+            scheduler_device = self._model_group.device_for(self.unet)
+
+        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
         timesteps, adjusted_steps = img2img_pipeline.get_timesteps(
-            num_inference_steps, strength, device=device
+            num_inference_steps, strength, device=scheduler_device
         )
         # Workaround for low strength resulting in zero timesteps.
         # TODO: submit upstream fix for zero-step img2img
@@ -796,9 +802,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if init_image.dim() == 3:
             init_image = init_image.unsqueeze(0)
 
-        timesteps, _ = self.get_img2img_timesteps(
-            num_inference_steps, strength, device=device
-        )
+        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
 
         # 6. Prepare latent variables
         # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -10,12 +10,13 @@ import diffusers
 import psutil
 import torch
 from compel.cross_attention_control import Arguments
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from diffusers.models.attention_processor import AttentionProcessor
 from torch import nn
 
+import invokeai.backend.util.logging as logger
 from ...util import torch_dtype
 
-
 class CrossAttentionType(enum.Enum):
     SELF = 1
     TOKENS = 2
@@ -352,8 +353,7 @@ def restore_default_cross_attention(
     else:
         remove_attention_function(model)
 
-
-def override_cross_attention(model, context: Context, is_running_diffusers=False):
+def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: Context):
     """
     Inject attention parameters and functions into the passed in model to enable cross attention editing.
 
@@ -372,37 +372,22 @@ def override_cross_attention(model, context: Context, is_running_diffusers=False
     indices = torch.arange(max_length, dtype=torch.long)
     for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
         if b0 < max_length:
-            if name == "equal":  # or (name == "replace" and a1 - a0 == b1 - b0):
+            if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
                 # these tokens have not been edited
                 indices[b0:b1] = indices_target[a0:a1]
                 mask[b0:b1] = 1
 
     context.cross_attention_mask = mask.to(device)
     context.cross_attention_index_map = indices.to(device)
-    if is_running_diffusers:
-        unet = model
-        old_attn_processors = unet.attn_processors
-        if torch.backends.mps.is_available():
-            # see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
-            unet.set_attn_processor(SwapCrossAttnProcessor())
-        else:
-            # try to re-use an existing slice size
-            default_slice_size = 4
-            slice_size = next(
-                (
-                    p.slice_size
-                    for p in old_attn_processors.values()
-                    if type(p) is SlicedAttnProcessor
-                ),
-                default_slice_size,
-            )
-            unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
-        return old_attn_processors
+    old_attn_processors = unet.attn_processors
+    if torch.backends.mps.is_available():
+        # see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
+        unet.set_attn_processor(SwapCrossAttnProcessor())
     else:
-        context.register_cross_attention_modules(model)
-        inject_attention_function(model, context)
-        return None
-
+        # try to re-use an existing slice size
+        default_slice_size = 4
+        slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
+        unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
 
 def get_cross_attention_modules(
     model, which: CrossAttentionType
@@ -421,7 +406,7 @@ 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.
-        print(
+        logger.error(
             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

@@ -5,9 +5,11 @@ from typing import Any, Callable, Dict, Optional, Union
 
 import numpy as np
 import torch
+from diffusers import UNet2DConditionModel
 from diffusers.models.attention_processor import AttentionProcessor
 from typing_extensions import TypeAlias
 
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 from .cross_attention_control import (
@@ -16,8 +18,8 @@ from .cross_attention_control import (
     CrossAttentionType,
     SwapCrossAttnContext,
     get_cross_attention_modules,
-    override_cross_attention,
     restore_default_cross_attention,
+    setup_cross_attention_control_attention_processors,
 )
 from .cross_attention_map_saving import AttentionMapSaver
 
@@ -78,24 +80,35 @@ class InvokeAIDiffuserComponent:
         self.cross_attention_control_context = None
         self.sequential_guidance = Globals.sequential_guidance
 
+    @classmethod
     @contextmanager
     def custom_attention_context(
-        self, extra_conditioning_info: Optional[ExtraConditioningInfo], step_count: int
+        cls,
+        unet: UNet2DConditionModel, # note: also may futz with the text encoder depending on requested LoRAs
+        extra_conditioning_info: Optional[ExtraConditioningInfo],
+        step_count: int
     ):
-        do_swap = (
-            extra_conditioning_info is not None
-            and extra_conditioning_info.wants_cross_attention_control
-        )
-        old_attn_processor = None
-        if do_swap:
-            old_attn_processor = self.override_cross_attention(
-                extra_conditioning_info, step_count=step_count
-            )
+        old_attn_processors = None
+        if extra_conditioning_info and (
+            extra_conditioning_info.wants_cross_attention_control
+        ):
+            old_attn_processors = unet.attn_processors
+            # Load lora conditions into the model
+            if extra_conditioning_info.wants_cross_attention_control:
+                cross_attention_control_context = Context(
+                    arguments=extra_conditioning_info.cross_attention_control_args,
+                    step_count=step_count,
+                )
+                setup_cross_attention_control_attention_processors(
+                    unet,
+                    cross_attention_control_context,
+                )
+
         try:
             yield None
         finally:
-            if old_attn_processor is not None:
-                self.restore_default_cross_attention(old_attn_processor)
+            if old_attn_processors is not None:
+                unet.set_attn_processor(old_attn_processors)
             # TODO resuscitate attention map saving
             # self.remove_attention_map_saving()
 
@@ -466,10 +479,14 @@ class InvokeAIDiffuserComponent:
             outside = torch.count_nonzero(
                 (latents < -current_threshold) | (latents > current_threshold)
             )
-            print(
-                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"
-                f"  | {outside / latents.numel() * 100:.2f}% values outside threshold"
+            logger.info(
+                f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})"
+                )
+            logger.debug(
+                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:
@@ -496,9 +513,11 @@ class InvokeAIDiffuserComponent:
             )
 
         if self.debug_thresholding:
-            print(
-                f"  | min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})\n"
-                f"  | {num_altered / latents.numel() * 100:.2f}% values altered"
+            logger.debug(
+                f"min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})"
+            )
+            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()
-        print("Path already exists. Rename it to [{:s}]".format(new_name))
+        logger.error("Path already exists. Rename it to [{:s}]".format(new_name))
         os.replace(path, new_name)
     os.makedirs(path)
 

invokeai/backend/stable_diffusion/schedulers/__init__.py

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

invokeai/backend/stable_diffusion/schedulers/schedulers.py

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

invokeai/backend/stable_diffusion/textual_inversion_manager.py

@@ -10,6 +10,7 @@ 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
@@ -59,12 +60,12 @@ 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
-                print(f">> Loaded local embedding for trigger {concept_name}")
+                logger.info(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
-            print(f">> Loaded remote embedding for trigger {concept_name}")
+            logger.info(f"Loaded remote embedding for trigger {concept_name}")
             self.load_textual_inversion(bin_file)
             self.hf_concepts_library.concepts_loaded[concept_name] = True
 
@@ -85,8 +86,8 @@ 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):
-                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}."
+                logger.warning(
+                    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
 
@@ -105,8 +106,8 @@ class TextualInversionManager(BaseTextualInversionManager):
                     if ckpt_path.name == "learned_embeds.bin"
                     else f"<{ckpt_path.stem}>"
                 )
-                print(
-                    f">> {sourcefile}: Trigger token '{trigger_str}' is already claimed by '{self.trigger_to_sourcefile[trigger_str]}'. Trigger this concept with {replacement_trigger_str}"
+                logger.info(
+                    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
 
@@ -120,8 +121,8 @@ class TextualInversionManager(BaseTextualInversionManager):
                 self.trigger_to_sourcefile[trigger_str] = sourcefile
 
             except ValueError as e:
-                print(f'   | Ignoring incompatible embedding {embedding_info["name"]}')
-                print(f"   | The error was {str(e)}")
+                logger.debug(f'Ignoring incompatible embedding {embedding_info["name"]}')
+                logger.debug(f"The error was {str(e)}")
 
     def _add_textual_inversion(
         self, trigger_str, embedding, defer_injecting_tokens=False
@@ -133,8 +134,8 @@ 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]:
-            print(
-                f"** TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'"
+            logger.warning(
+                f"TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'"
             )
             return
         if not self.full_precision:
@@ -155,11 +156,11 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         except ValueError as e:
             if str(e).startswith("Warning"):
-                print(f">> {str(e)}")
+                logger.warning(f"{str(e)}")
             else:
                 traceback.print_exc()
-                print(
-                    f"** TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}."
+                logger.error(
+                    f"TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}."
                 )
                 raise
 
@@ -219,16 +220,16 @@ 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:
-                    print(
-                        f">> Preparing tokens for textual inversion {ti.trigger_string}..."
+                    logger.info(
+                        f"Preparing tokens for textual inversion {ti.trigger_string}..."
                     )
                 try:
                     self._inject_tokens_and_assign_embeddings(ti)
                 except ValueError as e:
-                    print(
-                        f"   | Ignoring incompatible embedding trigger {ti.trigger_string}"
+                    logger.debug(
+                        f"Ignoring incompatible embedding trigger {ti.trigger_string}"
                     )
-                    print(f"   | The error was {str(e)}")
+                    logger.debug(f"The error was {str(e)}")
                     continue
                 injected_token_ids.append(ti.trigger_token_id)
                 injected_token_ids.extend(ti.pad_token_ids)
@@ -306,16 +307,16 @@ class TextualInversionManager(BaseTextualInversionManager):
             if suffix in [".pt",".ckpt",".bin"]:
                 scan_result = scan_file_path(embedding_file)
                 if scan_result.infected_files > 0:
-                    print(
-                        f"   ** Security Issues Found in Model: {scan_result.issues_count}"
+                    logger.critical(
+                        f"Security Issues Found in Model: {scan_result.issues_count}"
                     )
-                    print("   ** For your safety, InvokeAI will not load this embed.")
+                    logger.critical("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:
-            print(f"   ** Notice: unrecognized embedding file format: {embedding_file}: {e}")
+            logger.warning(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
@@ -334,7 +335,7 @@ class TextualInversionManager(BaseTextualInversionManager):
 
     def _parse_embedding_v1(self, embedding_ckpt: dict, file_path: str)->List[EmbeddingInfo]:
         basename = Path(file_path).stem
-        print(f'   | Loading v1 embedding file: {basename}')
+        logger.debug(f'Loading v1 embedding file: {basename}')
 
         embeddings = list()
         token_counter = -1
@@ -342,7 +343,7 @@ class TextualInversionManager(BaseTextualInversionManager):
             if token_counter < 0:
                 trigger = embedding_ckpt["name"]
             elif token_counter == 0:
-                trigger = f'<basename>'
+                trigger = '<basename>'
             else:
                 trigger = f'<{basename}-{int(token_counter:=token_counter)}>'
             token_counter += 1
@@ -365,7 +366,7 @@ class TextualInversionManager(BaseTextualInversionManager):
         This handles embedding .pt file variant #2.
         """
         basename = Path(file_path).stem
-        print(f'   | Loading v2 embedding file: {basename}')
+        logger.debug(f'Loading v2 embedding file: {basename}')
         embeddings = list()
         
         if isinstance(
@@ -384,7 +385,7 @@ class TextualInversionManager(BaseTextualInversionManager):
                 )
                 embeddings.append(embedding_info)
         else:
-            print(f"   ** {basename}: Unrecognized embedding format")
+            logger.warning(f"{basename}: Unrecognized embedding format")
 
         return embeddings
 
@@ -393,7 +394,7 @@ class TextualInversionManager(BaseTextualInversionManager):
         Parse 'version 3' of the .pt textual inversion embedding files.
         """
         basename = Path(file_path).stem
-        print(f'   | Loading v3 embedding file: {basename}')
+        logger.debug(f'Loading v3 embedding file: {basename}')
         embedding = embedding_ckpt['emb_params']
         embedding_info = EmbeddingInfo(
             name = f'<{basename}>',
@@ -411,11 +412,11 @@ class TextualInversionManager(BaseTextualInversionManager):
         basename = Path(filepath).stem
         short_path = Path(filepath).parents[0].name+'/'+Path(filepath).name
         
-        print(f'   | Loading v4 embedding file: {short_path}')
+        logger.debug(f'Loading v4 embedding file: {short_path}')
         
         embeddings = list()
         if list(embedding_ckpt.keys()) == 0:
-            print(f"   ** Invalid embeddings file: {short_path}")
+            logger.warning(f"Invalid embeddings file: {short_path}")
         else:
             for token,embedding in embedding_ckpt.items():
                 embedding_info = EmbeddingInfo(

invokeai/backend/util/logging.py

@@ -0,0 +1,110 @@
+# Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team
+
+"""invokeai.util.logging
+
+Logging class for InvokeAI that produces console messages
+
+Usage:
+
+from invokeai.backend.util.logging import InvokeAILogger
+
+logger = InvokeAILogger.getLogger(name='InvokeAI') // Initialization
+(or)
+logger = InvokeAILogger.getLogger(__name__) // To use the filename
+
+logger.critical('this is critical') // Critical Message
+logger.error('this is an error') // Error Message
+logger.warning('this is a warning') // Warning Message
+logger.info('this is info') // Info Message
+logger.debug('this is debugging') // Debug Message
+
+Console messages:
+    [12-05-2023 20]::[InvokeAI]::CRITICAL --> This is an info message [In Bold Red]
+    [12-05-2023 20]::[InvokeAI]::ERROR --> This is an info message [In Red]
+    [12-05-2023 20]::[InvokeAI]::WARNING --> This is an info message [In Yellow]
+    [12-05-2023 20]::[InvokeAI]::INFO --> This is an info message [In Grey]
+    [12-05-2023 20]::[InvokeAI]::DEBUG --> This is an info message [In Grey]
+
+Alternate Method (in this case the logger name will be set to InvokeAI):
+import invokeai.backend.util.logging as IAILogger
+IAILogger.debug('this is a debugging message')
+"""
+
+import logging
+
+
+# module level functions
+def debug(msg, *args, **kwargs):
+    InvokeAILogger.getLogger().debug(msg, *args, **kwargs)
+
+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):
+    '''
+    Custom Formatting for the InvokeAI Logger
+    '''
+
+    # Color Codes
+    grey = "\x1b[38;20m"
+    yellow = "\x1b[33;20m"
+    red = "\x1b[31;20m"
+    cyan = "\x1b[36;20m"
+    bold_red = "\x1b[31;1m"
+    reset = "\x1b[0m"
+
+    # Log Format
+    format = "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
+    ## More Formatting Options: %(pathname)s, %(filename)s, %(module)s, %(lineno)d
+
+    # Format Map
+    FORMATS = {
+        logging.DEBUG: cyan + format + reset,
+        logging.INFO: grey + format + reset,
+        logging.WARNING: yellow + format + reset,
+        logging.ERROR: red + format + reset,
+        logging.CRITICAL: bold_red + format + reset
+    }
+
+    def format(self, record):
+        log_fmt = self.FORMATS.get(record.levelno)
+        formatter = logging.Formatter(log_fmt, datefmt="%d-%m-%Y %H:%M:%S")
+        return formatter.format(record)
+
+
+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

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

invokeai/backend/web/invoke_ai_web_server.py

@@ -19,6 +19,7 @@ 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
@@ -77,7 +78,6 @@ 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:
-            print(">> Launching Invoke AI GUI")
+            logger.info("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
-            print(">> Started Invoke AI Web Server")
+            logger.info("Started Invoke AI Web Server")
             if self.host == "0.0.0.0":
-                print(
+                logger.info(
                     f"Point your browser at http{'s' if useSSL else ''}://localhost:{self.port} or use the host's DNS name or IP address."
                 )
             else:
-                print(
-                    ">> Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address."
+                logger.info(
+                    "Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address."
                 )
-                print(
-                    f">> Point your browser at http{'s' if useSSL else ''}://{self.host}:{self.port}"
+                logger.info(
+                    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_log.txt")
+        self.log_path = os.path.join(self.result_path, "invoke_logger.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():
-            print(">> System config requested")
+            logger.info("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
 
-                print(f">> Adding New Model: {model_name}")
+                logger.info(f"Adding New Model: {model_name}")
 
                 self.generate.model_manager.add_model(
                     model_name=model_name,
@@ -348,14 +348,14 @@ class InvokeAIWebServer:
                         "update": update,
                     },
                 )
-                print(f">> New Model Added: {model_name}")
+                logger.info(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:
-                print(f">> Deleting Model: {model_name}")
+                logger.info(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,
                     },
                 )
-                print(f">> Model Deleted: {model_name}")
+                logger.info(f"Model Deleted: {model_name}")
             except Exception as e:
                 self.handle_exceptions(e)
 
         @socketio.on("requestModelChange")
         def handle_set_model(model_name: str):
             try:
-                print(f">> Model change requested: {model_name}")
+                logger.info(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,
                     },
                 )
-                print(f">> Model Converted: {model_name}")
+                logger.info(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
                 ):
-                    print(f">> Using configured VAE assigned to {models_to_merge[0]}")
+                    logger.info(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,
                     },
                 )
-                print(f">> Models Merged: {models_to_merge}")
-                print(f">> New Model Added: {model_merge_info['merged_model_name']}")
+                logger.info(f"Models Merged: {models_to_merge}")
+                logger.info(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:
-                        print(f">> Unable to load {path}")
+                        logger.info(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] + "..."
                     )
 
-                print(f"\n>> Image Generation Parameters:\n\n{printable_parameters}\n")
-                print(f">> ESRGAN Parameters: {esrgan_parameters}")
-                print(f">> Facetool Parameters: {facetool_parameters}")
+                logger.info(f"Image Generation Parameters:\n\n{printable_parameters}\n")
+                logger.info(f"ESRGAN Parameters: {esrgan_parameters}")
+                logger.info(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:
-                print(
-                    f'>> Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}'
+                logger.info(
+                    f'Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}'
                 )
 
                 progress = Progress()
@@ -861,14 +861,14 @@ class InvokeAIWebServer:
 
         @socketio.on("cancel")
         def handle_cancel():
-            print(">> Cancel processing requested")
+            logger.info("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:
-                print(f'>> Delete requested "{url}"')
+                logger.info(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
                 )
 
-                print(f'\n\n>> Image generated: "{path}"\n')
+                logger.info(f'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()
-            print(e)
+            logger.error(e)
             self.handle_exceptions(e)
 
     def empty_cuda_cache(self):

invokeai/backend/web/modules/parameters.py

@@ -4,17 +4,21 @@ from .parse_seed_weights import parse_seed_weights
 
 SAMPLER_CHOICES = [
     "ddim",
-    "k_dpm_2_a",
-    "k_dpm_2",
-    "k_dpmpp_2_a",
-    "k_dpmpp_2",
-    "k_euler_a",
-    "k_euler",
-    "k_heun",
-    "k_lms",
-    "plms",
-    # diffusers:
+    "ddpm",
+    "deis",
+    "lms",
     "pndm",
+    "heun",
+    'heun_k',
+    "euler",
+    "euler_k",
+    "euler_a",
+    "kdpm_2",
+    "kdpm_2_a",
+    "dpmpp_2s",
+    "dpmpp_2m",
+    "dpmpp_2m_k",
+    "unipc",
 ]
 
 

invokeai/frontend/CLI/CLI.py

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

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

invokeai/frontend/merge/merge_diffusers.py

@@ -27,6 +27,8 @@ 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
 
@@ -113,7 +115,7 @@ 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):
-        print(f">> Using configured VAE assigned to {models[0]}")
+        logger.info(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)
@@ -391,10 +393,8 @@ 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)
-    print(f'>> Models merged into new model: "{args["merged_model_name"]}".')
+    logger.info(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)
-        print(
-            f'>> No --merged_model_name provided. Defaulting to "{args.merged_model_name}"'
+        logger.info(
+            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))
-        print(f'>> Models merged into new model: "{args.merged_model_name}".')
+        logger.info(f'Models merged into new model: "{args.merged_model_name}".')
 
 
 def main():
@@ -455,17 +455,16 @@ def main():
             run_cli(args)
     except widget.NotEnoughSpaceForWidget as e:
         if str(e).startswith("Height of 1 allocated"):
-            print(
-                "** You need to have at least two diffusers models defined in models.yaml in order to merge"
+            logger.error(
+                "You need to have at least two diffusers models defined in models.yaml in order to merge"
             )
         else:
-            print(
-                "** Not enough room for the user interface. Try making this window larger."
+            logger.error(
+                "Not enough room for the user interface. Try making this window larger."
             )
         sys.exit(-1)
-    except Exception:
-        print(">> An error occurred:")
-        traceback.print_exc()
+    except Exception as e:
+        logger.error(e)
         sys.exit(-1)
     except KeyboardInterrupt:
         sys.exit(-1)

invokeai/frontend/training/textual_inversion.py

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

invokeai/frontend/web/.babelrc

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

invokeai/frontend/web/.gitignore

@@ -34,4 +34,8 @@ stats.html
 !.yarn/plugins
 !.yarn/releases
 !.yarn/sdks
-!.yarn/versions
+!.yarn/versions
+
+# Yalc
+.yalc
+yalc.lock

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

@@ -0,0 +1,40 @@
+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

@@ -0,0 +1,50 @@
+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';
+import cssInjectedByJsPlugin from 'vite-plugin-css-injected-by-js';
+
+export const packageConfig: UserConfig = {
+  base: './',
+  plugins: [
+    react(),
+    eslint(),
+    tsconfigPaths(),
+    visualizer() as unknown as PluginOption,
+    dts({
+      insertTypesEntry: true,
+    }),
+    cssInjectedByJsPlugin(),
+  ],
+  build: {
+    cssCodeSplit: true,
+    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',
+          '@emotion/react': 'EmotionReact',
+        },
+      },
+    },
+  },
+  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/docs/PACKAGE_SCRIPTS.md

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

invokeai/frontend/web/docs/README.md

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

invokeai/frontend/web/index.d.ts

@@ -1,98 +0,0 @@
-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

@@ -1,11 +1,26 @@
 {
-  "name": "invoke-ai-ui",
+  "name": "@invoke-ai/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\"",
-    "dev:nodes": "concurrently \"vite dev --mode nodes\" \"yarn run theme:watch\"",
     "dev:host": "concurrently \"vite dev --host\" \"yarn run theme:watch\"",
     "build": "yarn run lint && vite build",
     "api:web": "openapi -i http://localhost:9090/openapi.json -o src/services/api --client axios --useOptions --useUnionTypes --exportSchemas true --indent 2 --request src/services/fixtures/request.ts",
@@ -40,80 +55,97 @@
   },
   "dependencies": {
     "@chakra-ui/anatomy": "^2.1.1",
-    "@chakra-ui/cli": "^2.3.0",
-    "@chakra-ui/icons": "^2.0.17",
-    "@chakra-ui/react": "^2.5.1",
-    "@chakra-ui/styled-system": "^2.6.1",
+    "@chakra-ui/icons": "^2.0.19",
+    "@chakra-ui/react": "^2.6.0",
+    "@chakra-ui/styled-system": "^2.9.0",
     "@chakra-ui/theme-tools": "^2.0.16",
     "@dagrejs/graphlib": "^2.1.12",
     "@emotion/react": "^11.10.6",
     "@emotion/styled": "^11.10.6",
+    "@floating-ui/react-dom": "^2.0.0",
     "@fontsource/inter": "^4.5.15",
-    "@reduxjs/toolkit": "^1.9.3",
+    "@reduxjs/toolkit": "^1.9.5",
+    "@roarr/browser-log-writer": "^1.1.5",
     "chakra-ui-contextmenu": "^1.0.5",
     "dateformat": "^5.0.3",
+    "downshift": "^7.6.0",
     "formik": "^2.2.9",
-    "framer-motion": "^9.0.4",
+    "framer-motion": "^10.12.4",
     "fuse.js": "^6.6.2",
-    "i18next": "^22.4.10",
+    "i18next": "^22.4.15",
     "i18next-browser-languagedetector": "^7.0.1",
-    "i18next-http-backend": "^2.1.1",
-    "konva": "^8.4.2",
-    "lodash": "^4.17.21",
-    "patch-package": "^6.5.1",
+    "i18next-http-backend": "^2.2.0",
+    "konva": "^9.0.1",
+    "lodash-es": "^4.17.21",
+    "overlayscrollbars": "^2.1.1",
+    "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": "4.3.5",
-    "react-i18next": "^12.1.5",
+    "react-hotkeys-hook": "4.4.0",
+    "react-i18next": "^12.2.2",
     "react-icons": "^4.7.1",
-    "react-konva": "^18.2.4",
-    "react-konva-utils": "^0.3.2",
+    "react-konva": "^18.2.7",
     "react-redux": "^8.0.5",
-    "react-transition-group": "^4.4.5",
-    "react-zoom-pan-pinch": "^2.6.1",
+    "react-resizable-panels": "^0.0.42",
+    "react-use": "^17.4.0",
+    "react-virtuoso": "^4.3.5",
+    "react-zoom-pan-pinch": "^3.0.7",
     "reactflow": "^11.7.0",
-    "redux-deep-persist": "^1.0.7",
     "redux-dynamic-middlewares": "^2.2.0",
-    "redux-persist": "^6.0.0",
+    "redux-remember": "^3.3.1",
+    "roarr": "^7.15.0",
+    "serialize-error": "^11.0.0",
     "socket.io-client": "^4.6.0",
     "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": "^4.14.194",
-    "@types/react": "^18.0.28",
-    "@types/react-dom": "^18.0.11",
+    "@types/lodash-es": "^4.14.194",
+    "@types/node": "^18.16.2",
+    "@types/react": "^18.2.0",
+    "@types/react-dom": "^18.2.1",
+    "@types/react-redux": "^7.1.25",
     "@types/react-transition-group": "^4.4.5",
     "@types/uuid": "^9.0.0",
-    "@typescript-eslint/eslint-plugin": "^5.52.0",
-    "@typescript-eslint/parser": "^5.52.0",
-    "@vitejs/plugin-react-swc": "^3.2.0",
-    "axios": "^1.3.4",
+    "@typescript-eslint/eslint-plugin": "^5.59.1",
+    "@typescript-eslint/parser": "^5.59.1",
+    "@vitejs/plugin-react-swc": "^3.3.0",
+    "axios": "^1.4.0",
     "babel-plugin-transform-imports": "^2.0.0",
-    "concurrently": "^7.6.0",
-    "eslint": "^8.34.0",
-    "eslint-config-prettier": "^8.6.0",
+    "concurrently": "^8.0.1",
+    "eslint": "^8.39.0",
+    "eslint-config-prettier": "^8.8.0",
     "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": "^13.1.2",
+    "lint-staged": "^13.2.2",
     "madge": "^6.0.0",
     "openapi-types": "^12.1.0",
-    "openapi-typescript-codegen": "^0.23.0",
+    "openapi-typescript-codegen": "^0.24.0",
     "postinstall-postinstall": "^2.1.0",
-    "prettier": "^2.8.4",
+    "prettier": "^2.8.8",
     "rollup-plugin-visualizer": "^5.9.0",
-    "terser": "^5.16.4",
-    "typescript": "4.9.5",
-    "vite": "^4.1.2",
+    "terser": "^5.17.1",
+    "ts-toolbelt": "^9.6.0",
+    "vite": "^4.3.3",
+    "vite-plugin-css-injected-by-js": "^3.1.1",
+    "vite-plugin-dts": "^2.3.0",
     "vite-plugin-eslint": "^1.8.1",
-    "vite-tsconfig-paths": "^4.0.5",
+    "vite-tsconfig-paths": "^4.2.0",
     "yarn": "^1.22.19"
   }
 }

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

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

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

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

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

@@ -25,7 +25,7 @@
     "common": {
         "hotkeysLabel": "Hotkeys",
         "themeLabel": "Theme",
-        "languagePickerLabel": "Language Picker",
+        "languagePickerLabel": "Language",
         "reportBugLabel": "Report Bug",
         "githubLabel": "Github",
         "discordLabel": "Discord",
@@ -54,7 +54,7 @@
         "img2img": "Image To Image",
         "unifiedCanvas": "Unified Canvas",
         "linear": "Linear",
-        "nodes": "Nodes",
+        "nodes": "Node Editor",
         "postprocessing": "Post Processing",
         "nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
         "postProcessing": "Post Processing",
@@ -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 embeddings using Textual Inversion using the main script.",
+        "trainingDesc2": "InvokeAI already supports training custom embeddourings using Textual Inversion using the main script.",
         "upload": "Upload",
         "close": "Close",
         "cancel": "Cancel",
@@ -97,7 +97,13 @@
         "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?",
+        "imagePrompt": "Image Prompt"
     },
     "gallery": {
         "generations": "Generations",
@@ -113,7 +119,10 @@
         "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",
@@ -441,13 +450,14 @@
         "cfgScale": "CFG Scale",
         "width": "Width",
         "height": "Height",
-        "sampler": "Sampler",
+        "scheduler": "Scheduler",
         "seed": "Seed",
         "imageToImage": "Image to Image",
         "randomizeSeed": "Randomize Seed",
-        "shuffle": "Shuffle",
+        "shuffle": "Shuffle Seed",
         "noiseThreshold": "Noise Threshold",
         "perlinNoise": "Perlin Noise",
+        "noiseSettings": "Noise",
         "variations": "Variations",
         "variationAmount": "Variation Amount",
         "seedWeights": "Seed Weights",
@@ -462,6 +472,8 @@
         "scale": "Scale",
         "otherOptions": "Other Options",
         "seamlessTiling": "Seamless Tiling",
+        "seamlessXAxis": "X Axis",
+        "seamlessYAxis": "Y Axis",
         "hiresOptim": "High Res Optimization",
         "hiresStrength": "High Res Strength",
         "imageFit": "Fit Initial Image To Output Size",
@@ -505,7 +517,6 @@
         "useAll": "Use All",
         "useInitImg": "Use Initial Image",
         "info": "Info",
-        "deleteImage": "Delete Image",
         "initialImage": "Initial Image",
         "showOptionsPanel": "Show Options Panel",
         "hidePreview": "Hide Preview",
@@ -520,10 +531,19 @@
         "useCanvasBeta": "Use Canvas Beta Layout",
         "enableImageDebugging": "Enable Image Debugging",
         "useSlidersForAll": "Use Sliders For All Options",
+        "showProgressInViewer": "Show Progress Images in Viewer",
+        "antialiasProgressImages": "Antialias Progress Images",
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",
-        "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",
+        "generation": "Generation",
+        "ui": "User Interface",
+        "availableSchedulers": "Available Schedulers"
     },
     "toast": {
         "serverError": "Server Error",
@@ -532,13 +552,14 @@
         "canceled": "Processing Canceled",
         "tempFoldersEmptied": "Temp Folder Emptied",
         "uploadFailed": "Upload failed",
-        "uploadFailedMultipleImagesDesc": "Multiple images pasted, may only upload one image at a time",
         "uploadFailedUnableToLoadDesc": "Unable to load file",
+        "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "downloadImageStarted": "Image Download Started",
         "imageCopied": "Image Copied",
         "imageLinkCopied": "Image Link Copied",
+        "problemCopyingImageLink": "Unable to Copy Image Link",
         "imageNotLoaded": "No Image Loaded",
-        "imageNotLoadedDesc": "No image found to send to image to image module",
+        "imageNotLoadedDesc": "Could not find image",
         "imageSavedToGallery": "Image Saved to Gallery",
         "canvasMerged": "Canvas Merged",
         "sentToImageToImage": "Sent To Image To Image",
@@ -633,6 +654,11 @@
         "betaClear": "Clear",
         "betaDarkenOutside": "Darken Outside",
         "betaLimitToBox": "Limit To Box",
-        "betaPreserveMasked": "Preserve Masked"
+        "betaPreserveMasked": "Preserve Masked",
+        "antialiasing": "Antialiasing"
+    },
+    "ui": {
+        "showProgressImages": "Show Progress Images",
+        "hideProgressImages": "Hide Progress Images"
     }
 }

invokeai/frontend/web/src/Loading.tsx

@@ -1,39 +0,0 @@
-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

@@ -1,100 +0,0 @@
-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/components/App.tsx

@@ -0,0 +1,141 @@
+import ImageUploader from 'common/components/ImageUploader';
+import SiteHeader from 'features/system/components/SiteHeader';
+import ProgressBar from 'features/system/components/ProgressBar';
+import InvokeTabs from 'features/ui/components/InvokeTabs';
+import FloatingGalleryButton from 'features/ui/components/FloatingGalleryButton';
+import FloatingParametersPanelButtons from 'features/ui/components/FloatingParametersPanelButtons';
+import { Box, Flex, Grid, Portal } from '@chakra-ui/react';
+import { APP_HEIGHT, APP_WIDTH } from 'theme/util/constants';
+import GalleryDrawer from 'features/gallery/components/GalleryPanel';
+import Lightbox from 'features/lightbox/components/Lightbox';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { memo, ReactNode, useCallback, useEffect, useState } from 'react';
+import { motion, AnimatePresence } from 'framer-motion';
+import Loading from 'common/components/Loading/Loading';
+import { useIsApplicationReady } from 'features/system/hooks/useIsApplicationReady';
+import { PartialAppConfig } from 'app/types/invokeai';
+import { configChanged } from 'features/system/store/configSlice';
+import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { useLogger } from 'app/logging/useLogger';
+import ParametersDrawer from 'features/ui/components/ParametersDrawer';
+import { languageSelector } from 'features/system/store/systemSelectors';
+import i18n from 'i18n';
+import Toaster from './Toaster';
+import GlobalHotkeys from './GlobalHotkeys';
+
+const DEFAULT_CONFIG = {};
+
+interface Props {
+  config?: PartialAppConfig;
+  headerComponent?: ReactNode;
+  setIsReady?: (isReady: boolean) => void;
+}
+
+const App = ({
+  config = DEFAULT_CONFIG,
+  headerComponent,
+  setIsReady,
+}: Props) => {
+  const language = useAppSelector(languageSelector);
+
+  const log = useLogger();
+
+  const isLightboxEnabled = useFeatureStatus('lightbox').isFeatureEnabled;
+
+  const isApplicationReady = useIsApplicationReady();
+
+  const [loadingOverridden, setLoadingOverridden] = useState(false);
+
+  const dispatch = useAppDispatch();
+
+  useEffect(() => {
+    i18n.changeLanguage(language);
+  }, [language]);
+
+  useEffect(() => {
+    log.info({ namespace: 'App', data: config }, 'Received config');
+    dispatch(configChanged(config));
+  }, [dispatch, config, log]);
+
+  const handleOverrideClicked = useCallback(() => {
+    setLoadingOverridden(true);
+  }, []);
+
+  useEffect(() => {
+    if (isApplicationReady && setIsReady) {
+      setIsReady(true);
+    }
+
+    return () => {
+      setIsReady && setIsReady(false);
+    };
+  }, [isApplicationReady, setIsReady]);
+
+  return (
+    <>
+      <Grid w="100vw" h="100vh" position="relative" overflow="hidden">
+        {isLightboxEnabled && <Lightbox />}
+        <ImageUploader>
+          <ProgressBar />
+          <Grid
+            gap={4}
+            p={4}
+            gridAutoRows="min-content auto"
+            w={APP_WIDTH}
+            h={APP_HEIGHT}
+          >
+            {headerComponent || <SiteHeader />}
+            <Flex
+              gap={4}
+              w={{ base: '100vw', xl: 'full' }}
+              h="full"
+              flexDir={{ base: 'column', xl: 'row' }}
+            >
+              <InvokeTabs />
+            </Flex>
+          </Grid>
+        </ImageUploader>
+
+        <GalleryDrawer />
+        <ParametersDrawer />
+
+        <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>
+      </Grid>
+      <Toaster />
+      <GlobalHotkeys />
+    </>
+  );
+};
+
+export default memo(App);

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

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

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

@@ -0,0 +1,87 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { shiftKeyPressed } from 'features/ui/store/hotkeysSlice';
+import {
+  setActiveTab,
+  toggleGalleryPanel,
+  toggleParametersPanel,
+  togglePinGalleryPanel,
+  togglePinParametersPanel,
+} from 'features/ui/store/uiSlice';
+import { isEqual } from 'lodash-es';
+import React, { memo } from 'react';
+import { isHotkeyPressed, useHotkeys } from 'react-hotkeys-hook';
+
+const globalHotkeysSelector = createSelector(
+  (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?
+
+/**
+ * Logical component. Handles app-level global hotkeys.
+ * @returns null
+ */
+const GlobalHotkeys: React.FC = () => {
+  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]
+  );
+
+  useHotkeys('o', () => {
+    dispatch(toggleParametersPanel());
+  });
+
+  useHotkeys(['shift+o'], () => {
+    dispatch(togglePinParametersPanel());
+  });
+
+  useHotkeys('g', () => {
+    dispatch(toggleGalleryPanel());
+  });
+
+  useHotkeys(['shift+g'], () => {
+    dispatch(togglePinGalleryPanel());
+  });
+
+  useHotkeys('1', () => {
+    dispatch(setActiveTab('txt2img'));
+  });
+
+  useHotkeys('2', () => {
+    dispatch(setActiveTab('img2img'));
+  });
+
+  useHotkeys('3', () => {
+    dispatch(setActiveTab('unifiedCanvas'));
+  });
+
+  useHotkeys('4', () => {
+    dispatch(setActiveTab('nodes'));
+  });
+
+  return null;
+};
+
+export default memo(GlobalHotkeys);

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

@@ -0,0 +1,77 @@
+import React, {
+  lazy,
+  memo,
+  PropsWithChildren,
+  ReactNode,
+  useEffect,
+} from 'react';
+import { Provider } from 'react-redux';
+import { store } from 'app/store/store';
+import { OpenAPI } from 'services/api';
+
+import Loading from '../../common/components/Loading/Loading';
+import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
+import { PartialAppConfig } from 'app/types/invokeai';
+
+import '../../i18n';
+import { socketMiddleware } from 'services/events/middleware';
+
+const App = lazy(() => import('./App'));
+const ThemeLocaleProvider = lazy(() => import('./ThemeLocaleProvider'));
+
+interface Props extends PropsWithChildren {
+  apiUrl?: string;
+  token?: string;
+  config?: PartialAppConfig;
+  headerComponent?: ReactNode;
+  setIsReady?: (isReady: boolean) => void;
+}
+
+const InvokeAIUI = ({
+  apiUrl,
+  token,
+  config,
+  headerComponent,
+  setIsReady,
+}: Props) => {
+  useEffect(() => {
+    // configure API client token
+    if (token) {
+      OpenAPI.TOKEN = token;
+    }
+
+    // configure API client base url
+    if (apiUrl) {
+      OpenAPI.BASE = apiUrl;
+    }
+
+    // reset dynamically added middlewares
+    resetMiddlewares();
+
+    // TODO: at this point, after resetting the middleware, we really ought to clean up the socket
+    // stuff by calling `dispatch(socketReset())`. but we cannot dispatch from here as we are
+    // outside the provider. it's not needed until there is the possibility that we will change
+    // the `apiUrl`/`token` dynamically.
+
+    // rebuild socket middleware with token and apiUrl
+    addMiddleware(socketMiddleware());
+  }, [apiUrl, token]);
+
+  return (
+    <React.StrictMode>
+      <Provider store={store}>
+        <React.Suspense fallback={<Loading />}>
+          <ThemeLocaleProvider>
+            <App
+              config={config}
+              headerComponent={headerComponent}
+              setIsReady={setIsReady}
+            />
+          </ThemeLocaleProvider>
+        </React.Suspense>
+      </Provider>
+    </React.StrictMode>
+  );
+};
+
+export default memo(InvokeAIUI);

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

@@ -1,23 +1,22 @@
-import { ChakraProvider, extendTheme } from '@chakra-ui/react';
+import {
+  ChakraProvider,
+  createLocalStorageManager,
+  extendTheme,
+} from '@chakra-ui/react';
 import { ReactNode, useEffect } from 'react';
 import { useTranslation } from 'react-i18next';
 import { theme as invokeAITheme } from 'theme/theme';
-import { RootState } from './store';
-import { useAppSelector } from './storeHooks';
+import { RootState } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
 
 import { greenTeaThemeColors } from 'theme/colors/greenTea';
 import { invokeAIThemeColors } from 'theme/colors/invokeAI';
 import { lightThemeColors } from 'theme/colors/lightTheme';
 import { oceanBlueColors } from 'theme/colors/oceanBlue';
-import '@fontsource/inter/100.css';
-import '@fontsource/inter/200.css';
-import '@fontsource/inter/300.css';
-import '@fontsource/inter/400.css';
-import '@fontsource/inter/500.css';
-import '@fontsource/inter/600.css';
-import '@fontsource/inter/700.css';
-import '@fontsource/inter/800.css';
-import '@fontsource/inter/900.css';
+
+import '@fontsource/inter/variable.css';
+import 'overlayscrollbars/overlayscrollbars.css';
+import 'theme/css/overlayscrollbars.css';
 
 type ThemeLocaleProviderProps = {
   children: ReactNode;
@@ -30,6 +29,8 @@ const THEMES = {
   ocean: oceanBlueColors,
 };
 
+const manager = createLocalStorageManager('@@invokeai-color-mode');
+
 function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
   const { i18n } = useTranslation();
 
@@ -49,7 +50,11 @@ function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
     document.body.dir = direction;
   }, [direction]);
 
-  return <ChakraProvider theme={theme}>{children}</ChakraProvider>;
+  return (
+    <ChakraProvider theme={theme} colorModeManager={manager}>
+      {children}
+    </ChakraProvider>
+  );
 }
 
 export default ThemeLocaleProvider;

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

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

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

@@ -1,34 +1,28 @@
 // TODO: use Enums?
 
-import { InProgressImageType } from 'features/system/store/systemSlice';
-
-// Valid samplers
-export const SAMPLERS: Array<string> = [
+export const SCHEDULERS = [
   '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',
-];
+  'lms',
+  'euler',
+  'euler_k',
+  'euler_a',
+  'dpmpp_2s',
+  'dpmpp_2m',
+  'dpmpp_2m_k',
+  'kdpm_2',
+  'kdpm_2_a',
+  'deis',
+  'ddpm',
+  'pndm',
+  'heun',
+  'heun_k',
+  'unipc',
+] as const;
 
-// Valid Diffusers Samplers
-export const DIFFUSERS_SAMPLERS: Array<string> = [
-  'ddim',
-  'plms',
-  'k_lms',
-  'dpmpp_2',
-  'k_dpm_2',
-  'k_dpm_2_a',
-  'k_dpmpp_2',
-  'k_euler',
-  'k_euler_a',
-  'k_heun',
-];
+export type Scheduler = (typeof SCHEDULERS)[number];
+
+export const isScheduler = (x: string): x is Scheduler =>
+  SCHEDULERS.includes(x as Scheduler);
 
 // Valid image widths
 export const WIDTHS: Array<number> = Array.from(Array(64)).map(
@@ -48,17 +42,8 @@ export const UPSCALING_LEVELS: Array<{ key: string; value: number }> = [
 
 export const NUMPY_RAND_MIN = 0;
 
-export const NUMPY_RAND_MAX = 4294967295;
+export const NUMPY_RAND_MAX = 2147483647;
 
 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

@@ -1,336 +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 { 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

@@ -0,0 +1,94 @@
+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

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

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

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

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

@@ -1,6 +1,6 @@
 import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
-import * as InvokeAI from 'app/invokeai';
-import type { RootState } from 'app/store';
+import * as InvokeAI from 'app/types/invokeai';
+import type { RootState } from 'app/store/store';
 import {
   frontendToBackendParameters,
   FrontendToBackendParametersConfig,
@@ -12,7 +12,6 @@ import {
   removeImage,
 } from 'features/gallery/store/gallerySlice';
 import {
-  addLogEntry,
   generationRequested,
   modelChangeRequested,
   modelConvertRequested,
@@ -206,3 +205,5 @@ const makeSocketIOEmitters = (
 };
 
 export default makeSocketIOEmitters;
+
+export default {};

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

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

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

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

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

@@ -1,117 +0,0 @@
-import { combineReducers, configureStore } from '@reduxjs/toolkit';
-
-import { persistReducer } from 'redux-persist';
-import storage from 'redux-persist/lib/storage'; // defaults to localStorage for web
-import dynamicMiddlewares from 'redux-dynamic-middlewares';
-import { getPersistConfig } from 'redux-deep-persist';
-
-import canvasReducer from 'features/canvas/store/canvasSlice';
-import galleryReducer from 'features/gallery/store/gallerySlice';
-import resultsReducer from 'features/gallery/store/resultsSlice';
-import uploadsReducer from 'features/gallery/store/uploadsSlice';
-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 uiReducer from 'features/ui/store/uiSlice';
-import modelsReducer from 'features/system/store/modelSlice';
-import nodesReducer from 'features/nodes/store/nodesSlice';
-
-import { socketioMiddleware } from './socketio/middleware';
-import { socketMiddleware } from 'services/events/middleware';
-import { canvasBlacklist } from 'features/canvas/store/canvasPersistBlacklist';
-import { galleryBlacklist } from 'features/gallery/store/galleryPersistBlacklist';
-import { generationBlacklist } from 'features/parameters/store/generationPersistBlacklist';
-import { lightboxBlacklist } from 'features/lightbox/store/lightboxPersistBlacklist';
-import { modelsBlacklist } from 'features/system/store/modelsPersistBlacklist';
-import { nodesBlacklist } from 'features/nodes/store/nodesPersistBlacklist';
-import { postprocessingBlacklist } from 'features/parameters/store/postprocessingPersistBlacklist';
-import { systemBlacklist } from 'features/system/store/systemPersistsBlacklist';
-import { uiBlacklist } from 'features/ui/store/uiPersistBlacklist';
-
-/**
- * redux-persist provides an easy and reliable way to persist state across reloads.
- *
- * While we definitely want generation parameters to be persisted, there are a number
- * of things we do *not* want to be persisted across reloads:
- *   - Gallery/selected image (user may add/delete images from disk between page loads)
- *   - Connection/processing status
- *   - Availability of external libraries like ESRGAN/GFPGAN
- *
- * These can be blacklisted in redux-persist.
- *
- * The necesssary nested persistors with blacklists are configured below.
- */
-
-const rootReducer = combineReducers({
-  canvas: canvasReducer,
-  gallery: galleryReducer,
-  generation: generationReducer,
-  lightbox: lightboxReducer,
-  models: modelsReducer,
-  nodes: nodesReducer,
-  postprocessing: postprocessingReducer,
-  results: resultsReducer,
-  system: systemReducer,
-  ui: uiReducer,
-  uploads: uploadsReducer,
-});
-
-const rootPersistConfig = getPersistConfig({
-  key: 'root',
-  storage,
-  rootReducer,
-  blacklist: [
-    ...canvasBlacklist,
-    ...galleryBlacklist,
-    ...generationBlacklist,
-    ...lightboxBlacklist,
-    ...modelsBlacklist,
-    ...nodesBlacklist,
-    ...postprocessingBlacklist,
-    // ...resultsBlacklist,
-    'results',
-    ...systemBlacklist,
-    ...uiBlacklist,
-    // ...uploadsBlacklist,
-    'uploads',
-  ],
-  debounce: 300,
-});
-
-const persistedReducer = persistReducer(rootPersistConfig, rootReducer);
-
-// TODO: rip the old middleware out when nodes is complete
-export function buildMiddleware() {
-  if (import.meta.env.MODE === 'nodes' || import.meta.env.MODE === 'package') {
-    return socketMiddleware();
-  } else {
-    return socketioMiddleware();
-  }
-}
-
-export const store = configureStore({
-  reducer: persistedReducer,
-  middleware: (getDefaultMiddleware) =>
-    getDefaultMiddleware({
-      immutableCheck: false,
-      serializableCheck: false,
-    }).concat(dynamicMiddlewares),
-  devTools: {
-    // Uncommenting these very rapidly called actions makes the redux dev tools output much more readable
-    actionsDenylist: [
-      'canvas/setCursorPosition',
-      'canvas/setStageCoordinates',
-      'canvas/setStageScale',
-      'canvas/setIsDrawing',
-      'canvas/setBoundingBoxCoordinates',
-      'canvas/setBoundingBoxDimensions',
-      'canvas/setIsDrawing',
-      'canvas/addPointToCurrentLine',
-    ],
-  },
-});
-
-export type AppGetState = typeof store.getState;
-export type RootState = ReturnType<typeof store.getState>;
-export type AppDispatch = typeof store.dispatch;

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

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

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

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

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

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

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

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

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

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