Merge branch 'main' into feat/compel_node

invokeai/app/api/dependencies.py

@@ -1,14 +1,12 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import os
-from argparse import Namespace
 
-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 +17,7 @@ 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 +43,16 @@ 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)
 
@@ -70,8 +70,9 @@ 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,
@@ -83,7 +84,7 @@ 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/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_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
     )
 
 

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/generate.py

@@ -46,8 +46,8 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
     prompt: Optional[str] = Field(description="The prompt to generate an image from")
     seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
     steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
-    width:       int = Field(default=512, multiple_of=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", )
+    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
+    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
     cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
     scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
     seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
@@ -150,6 +150,9 @@ class ImageToImageInvocation(TextToImageInvocation):
         )
         mask = None
 
+        if self.fit:
+            image = image.resize((self.width, self.height))
+
         # Handle invalid model parameter
         model = choose_model(context.services.model_manager, self.model)
 

invokeai/app/invocations/latent.py

@@ -113,8 +113,8 @@ class NoiseInvocation(BaseInvocation):
 
     # Inputs
     seed:        int = Field(ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", default_factory=random_seed)
-    width:       int = Field(default=512, multiple_of=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", )
+    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
@@ -149,8 +149,6 @@ class TextToLatentsInvocation(BaseInvocation):
     seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
     noise: Optional[LatentsField] = Field(description="The noise to use")
     steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
-    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
-    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image", )
     cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
     scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
     seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
@@ -365,9 +363,74 @@ class LatentsToImageInvocation(BaseInvocation):
                 session_id=context.graph_execution_state_id, node=self
             )
 
+            torch.cuda.empty_cache()
+
             context.services.images.save(image_type, image_name, image, metadata)
             return build_image_output(
-                image_type=image_type,
-                image_name=image_name,
-                image=image
+                image_type=image_type, image_name=image_name, image=image
             )
+
+
+LATENTS_INTERPOLATION_MODE = Literal[
+    "nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"
+]
+
+
+class ResizeLatentsInvocation(BaseInvocation):
+    """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
+
+    type: Literal["lresize"] = "lresize"
+
+    # Inputs
+    latents: Optional[LatentsField]             = Field(description="The latents to resize")
+    width: int                                  = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
+    height: int                                 = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
+    mode: Optional[LATENTS_INTERPOLATION_MODE]  = Field(default="bilinear", description="The interpolation mode")
+    antialias: Optional[bool]                   = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.services.latents.get(self.latents.latents_name)
+
+        resized_latents = torch.nn.functional.interpolate(
+            latents,
+            size=(self.height // 8, self.width // 8),
+            mode=self.mode,
+            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
+        )
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        torch.cuda.empty_cache()
+
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.set(name, resized_latents)
+        return LatentsOutput(latents=LatentsField(latents_name=name))
+
+
+class ScaleLatentsInvocation(BaseInvocation):
+    """Scales latents by a given factor."""
+
+    type: Literal["lscale"] = "lscale"
+
+    # Inputs
+    latents: Optional[LatentsField]             = Field(description="The latents to scale")
+    scale_factor: float                         = Field(gt=0, description="The factor by which to scale the latents")
+    mode: Optional[LATENTS_INTERPOLATION_MODE]  = Field(default="bilinear", description="The interpolation mode")
+    antialias: Optional[bool]                   = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.services.latents.get(self.latents.latents_name)
+
+        # resizing
+        resized_latents = torch.nn.functional.interpolate(
+            latents,
+            scale_factor=self.scale_factor,
+            mode=self.mode,
+            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
+        )
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        torch.cuda.empty_cache()
+
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.set(name, resized_latents)
+        return LatentsOutput(latents=LatentsField(latents_name=name))

invokeai/app/invocations/util/choose_model.py

@@ -3,12 +3,11 @@ from invokeai.backend.model_management.model_manager import ModelManager
 
 def choose_model(model_manager: ModelManager, model_name: str):
     """Returns the default model if the `model_name` not a valid model, else returns the selected model."""
+    logger = model_manager.logger
     if model_manager.valid_model(model_name):
         model = model_manager.get_model(model_name)
     else:
         model = model_manager.get_model()
-        print(
-            f"* Warning: '{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead."
-        )
+        logger.warning(f"{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead.")
 
     return model

invokeai/app/services/default_graphs.py

@@ -27,10 +27,6 @@ def create_text_to_image() -> LibraryGraph:
                 Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
                 Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
                 Edge(source=EdgeConnection(node_id='seed', field='a'), destination=EdgeConnection(node_id='3', field='seed')),
-                # TODO: remove, when updated TextToLatents merged
-                Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='5', field='width')),
-                Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='5', field='height')),
-                Edge(source=EdgeConnection(node_id='seed', field='a'), destination=EdgeConnection(node_id='5', field='seed')),
                 Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='5', field='noise')),
                 Edge(source=EdgeConnection(node_id='5', field='latents'), destination=EdgeConnection(node_id='6', field='latents')),
                 Edge(source=EdgeConnection(node_id='4', field='positive'), destination=EdgeConnection(node_id='5', field='positive')),

invokeai/app/services/invocation_services.py

@@ -1,4 +1,6 @@
-# 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.metadata import MetadataServiceBase
 from invokeai.backend import ModelManager
 
@@ -29,6 +31,7 @@ class InvocationServices:
             self,
             model_manager: ModelManager,
             events: EventServiceBase,
+            logger: types.ModuleType,
             latents: LatentsStorageBase,
             images: ImageStorageBase,
             metadata: MetadataServiceBase,
@@ -40,6 +43,7 @@ class InvocationServices:
     ):
         self.model_manager = model_manager
         self.events = events
+        self.logger = logger
         self.latents = latents
         self.images = images
         self.metadata = metadata

invokeai/app/services/invoker.py

@@ -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/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/processor.py

@@ -1,5 +1,5 @@
 import traceback
-from threading import Event, Thread
+from threading import Event, Thread, BoundedSemaphore
 
 from ..invocations.baseinvocation import InvocationContext
 from .invocation_queue import InvocationQueueItem
@@ -10,8 +10,11 @@ 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 +23,7 @@ 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()
 
@@ -29,6 +32,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
 
     def __process(self, stop_event: Event):
         try:
+            self.__threadLimit.acquire()
             while not stop_event.is_set():
                 queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
                 if not queue_item:  # Probably stopping
@@ -110,7 +114,7 @@ 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
@@ -127,4 +131,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     )
 
         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/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
@@ -189,7 +190,7 @@ 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 +198,13 @@ 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 +214,7 @@ 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):
@@ -1154,7 +1154,7 @@ 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 +1326,7 @@ 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
@@ -195,12 +196,12 @@ 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 +209,13 @@ 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 +230,8 @@ 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 +247,10 @@ 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 +568,7 @@ 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 +576,11 @@ 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 +610,16 @@ 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 +648,7 @@ 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 +697,8 @@ 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 +721,8 @@ 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 +749,12 @@ 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 +798,8 @@ 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 +810,8 @@ 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 +892,11 @@ 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 +963,15 @@ 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 +985,7 @@ 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 +998,10 @@ 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:
@@ -1066,17 +1067,17 @@ class Generate:
         if self.sampler_name in scheduler_map:
             sampler_class = scheduler_map[self.sampler_name]
             msg = (
-                f">> Setting Sampler to {self.sampler_name} ({sampler_class.__name__})"
+                f"Setting Sampler to {self.sampler_name} ({sampler_class.__name__})"
             )
             self.sampler = sampler_class.from_config(self.model.scheduler.config)
         else:
             msg = (
-                f">> Unsupported Sampler: {self.sampler_name} "
+                f" Unsupported Sampler: {self.sampler_name} "+
                 f"Defaulting to {default}"
             )
             self.sampler = default
 
-        print(msg)
+        logger.info(msg)
 
         if not hasattr(self.sampler, "uses_inpainting_model"):
             # FIXME: terrible kludge!
@@ -1085,17 +1086,17 @@ 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 +1184,14 @@ 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 +1202,11 @@ 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 +1217,8 @@ 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

@@ -25,6 +25,7 @@ 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
@@ -372,7 +373,7 @@ 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 +608,7 @@ 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/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
@@ -372,9 +373,9 @@ 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 +393,8 @@ 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 +1116,7 @@ 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
@@ -1229,15 +1230,15 @@ 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

@@ -18,12 +18,13 @@ 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,
@@ -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"]
@@ -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", "%4.2fs)" % (toc - tic))
         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

@@ -18,6 +18,7 @@ from compel.prompt_parser import (
     PromptParser,
 )
 
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 from ..stable_diffusion import InvokeAIDiffuserComponent
@@ -162,8 +163,8 @@ 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(
@@ -237,12 +238,12 @@ 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]:
@@ -295,8 +296,8 @@ 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/diffusion/cross_attention_control.py

@@ -13,9 +13,9 @@ from compel.cross_attention_control import Arguments
 from diffusers.models.attention_processor import AttentionProcessor
 from torch import nn
 
+import invokeai.backend.util.logging as logger
 from ...util import torch_dtype
 
-
 class CrossAttentionType(enum.Enum):
     SELF = 1
     TOKENS = 2
@@ -421,7 +421,7 @@ def get_cross_attention_modules(
     expected_count = 16
     if cross_attention_modules_in_model_count != expected_count:
         # non-fatal error but .swap() won't work.
-        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

@@ -8,6 +8,7 @@ import torch
 from diffusers.models.attention_processor import AttentionProcessor
 from typing_extensions import TypeAlias
 
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 from .cross_attention_control import (
@@ -466,10 +467,14 @@ 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 +501,11 @@ 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/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 +1,109 @@
+# Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team
+
+"""invokeai.util.logging
+
+Logging class for InvokeAI that produces console messages that follow
+the conventions established in InvokeAI 1.X through 2.X.
+
+
+One way to use it:
+
+from invokeai.backend.util.logging import InvokeAILogger
+
+logger = InvokeAILogger.getLogger(__name__)
+logger.critical('this is critical')
+logger.error('this is an error')
+logger.warning('this is a warning')
+logger.info('this is info')
+logger.debug('this is debugging')
+
+Console messages:
+     ### this is critical
+     *** this is an error ***
+     ** this is a warning
+     >> this is info
+        | this is debugging
+
+Another way:
+import invokeai.backend.util.logging as ialog
+ialogger.debug('this is a debugging message')
+"""
+import logging
+
+# module level functions
+def debug(msg, *args, **kwargs):
+    InvokeAILogger.getLogger().debug(msg, *args, **kwargs)
+
+def info(msg, *args, **kwargs):
+    InvokeAILogger.getLogger().info(msg, *args, **kwargs)
+
+def warning(msg, *args, **kwargs):
+    InvokeAILogger.getLogger().warning(msg, *args, **kwargs)
+
+def error(msg, *args, **kwargs):
+    InvokeAILogger.getLogger().error(msg, *args, **kwargs)
+    
+def critical(msg, *args, **kwargs):
+    InvokeAILogger.getLogger().critical(msg, *args, **kwargs)
+
+def log(level, msg, *args, **kwargs):
+    InvokeAILogger.getLogger().log(level, msg, *args, **kwargs)
+
+def disable(level=logging.CRITICAL):
+    InvokeAILogger.getLogger().disable(level)
+
+def basicConfig(**kwargs):
+    InvokeAILogger.getLogger().basicConfig(**kwargs)
+
+def getLogger(name: str=None)->logging.Logger:
+    return InvokeAILogger.getLogger(name)
+
+class InvokeAILogFormatter(logging.Formatter):
+    '''
+    Repurposed from:
+    https://stackoverflow.com/questions/14844970/modifying-logging-message-format-based-on-message-logging-level-in-python3
+    '''
+    crit_fmt = "### %(msg)s"
+    err_fmt = "*** %(msg)s"
+    warn_fmt = "** %(msg)s"
+    info_fmt = ">> %(msg)s"
+    dbg_fmt = "   | %(msg)s"
+
+    def __init__(self):
+        super().__init__(fmt="%(levelno)d: %(msg)s", datefmt=None, style='%')
+
+    def format(self, record):
+        # Remember the format used when the logging module
+        # was installed (in the event that this formatter is
+        # used with the vanilla logging module.
+        format_orig = self._style._fmt
+        if record.levelno == logging.DEBUG:
+            self._style._fmt = InvokeAILogFormatter.dbg_fmt
+        if record.levelno == logging.INFO:
+            self._style._fmt = InvokeAILogFormatter.info_fmt
+        if record.levelno == logging.WARNING:
+            self._style._fmt = InvokeAILogFormatter.warn_fmt
+        if record.levelno == logging.ERROR:
+            self._style._fmt = InvokeAILogFormatter.err_fmt
+        if record.levelno == logging.CRITICAL:
+            self._style._fmt = InvokeAILogFormatter.crit_fmt
+
+        # parent class does the work
+        result = super().format(record)
+        self._style._fmt = format_orig
+        return result
+
+class InvokeAILogger(object):
+    loggers = dict()
+    
+    @classmethod
+    def getLogger(self, name:str='invokeai')->logging.Logger:
+        if name not in self.loggers:
+            logger = logging.getLogger(name)
+            logger.setLevel(logging.DEBUG)
+            ch = logging.StreamHandler()
+            fmt = InvokeAILogFormatter()
+            ch.setFormatter(fmt)
+            logger.addHandler(ch)
+            self.loggers[name] = logger
+        return self.loggers[name]

invokeai/backend/util/util.py

@@ -18,6 +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
@@ -213,7 +214,7 @@ 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 +232,17 @@ 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 +274,7 @@ 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 +291,7 @@ 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 +331,7 @@ 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 +349,14 @@ 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 +367,14 @@ 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 +455,7 @@ 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 +491,7 @@ 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 +508,8 @@ 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 +699,7 @@ 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 +736,9 @@ 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 +751,8 @@ 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 +862,14 @@ 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 +1264,7 @@ 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 +1330,7 @@ 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/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/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 +1,47 @@
+import react from '@vitejs/plugin-react-swc';
+import path from 'path';
+import { visualizer } from 'rollup-plugin-visualizer';
+import { PluginOption, UserConfig } from 'vite';
+import dts from 'vite-plugin-dts';
+import eslint from 'vite-plugin-eslint';
+import tsconfigPaths from 'vite-tsconfig-paths';
+
+export const packageConfig: UserConfig = {
+  base: './',
+  plugins: [
+    react(),
+    eslint(),
+    tsconfigPaths(),
+    visualizer() as unknown as PluginOption,
+    dts({
+      insertTypesEntry: true,
+    }),
+  ],
+  build: {
+    chunkSizeWarningLimit: 1500,
+    lib: {
+      entry: path.resolve(__dirname, '../src/index.ts'),
+      name: 'InvokeAIUI',
+      fileName: (format) => `invoke-ai-ui.${format}.js`,
+    },
+    rollupOptions: {
+      external: ['react', 'react-dom', '@emotion/react'],
+      output: {
+        globals: {
+          react: 'React',
+          'react-dom': 'ReactDOM',
+        },
+      },
+    },
+  },
+  resolve: {
+    alias: {
+      app: path.resolve(__dirname, '../src/app'),
+      assets: path.resolve(__dirname, '../src/assets'),
+      common: path.resolve(__dirname, '../src/common'),
+      features: path.resolve(__dirname, '../src/features'),
+      services: path.resolve(__dirname, '../src/services'),
+      theme: path.resolve(__dirname, '../src/theme'),
+    },
+  },
+};

invokeai/frontend/web/index.d.ts

@@ -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

@@ -1,7 +1,23 @@
 {
-  "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\"",
@@ -40,81 +56,96 @@
   },
   "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",
     "@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",
     "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-konva-utils": "^1.0.4",
     "react-redux": "^8.0.5",
+    "react-rnd": "^10.4.1",
     "react-transition-group": "^4.4.5",
-    "react-zoom-pan-pinch": "^2.6.1",
+    "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",
+    "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-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",
+    "terser": "^5.17.1",
     "ts-toolbelt": "^9.6.0",
-    "typescript": "4.9.5",
-    "vite": "^4.1.2",
+    "vite": "^4.3.3",
+    "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/public/locales/en.json

@@ -527,10 +527,15 @@
         "useCanvasBeta": "Use Canvas Beta Layout",
         "enableImageDebugging": "Enable Image Debugging",
         "useSlidersForAll": "Use Sliders For All Options",
+        "autoShowProgress": "Auto Show Progress Images",
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",
-        "resetComplete": "Web UI has been reset. Refresh the page to reload."
+        "resetComplete": "Web UI has been reset. Refresh the page to reload.",
+        "consoleLogLevel": "Log Level",
+        "shouldLogToConsole": "Console Logging",
+        "developer": "Developer",
+        "general": "General"
     },
     "toast": {
         "serverError": "Server Error",
@@ -641,5 +646,9 @@
         "betaDarkenOutside": "Darken Outside",
         "betaLimitToBox": "Limit To Box",
         "betaPreserveMasked": "Preserve Masked"
+    },
+    "ui": {
+        "showProgressImages": "Show Progress Images",
+        "hideProgressImages": "Hide Progress Images"
     }
 }

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

@@ -1,9 +1,7 @@
 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';
 
@@ -13,25 +11,34 @@ 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, useCallback, useEffect, useState } from 'react';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import {
+  memo,
+  PropsWithChildren,
+  useCallback,
+  useEffect,
+  useState,
+} from 'react';
 import { motion, AnimatePresence } from 'framer-motion';
 import Loading from 'common/components/Loading/Loading';
 import { useIsApplicationReady } from 'features/system/hooks/useIsApplicationReady';
-import { PartialAppConfig } from './invokeai';
+import { PartialAppConfig } from 'app/types/invokeai';
 import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
 import { configChanged } from 'features/system/store/configSlice';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
+import { useLogger } from 'app/logging/useLogger';
+import ProgressImagePreview from 'features/parameters/components/ProgressImagePreview';
 
-keepGUIAlive();
+const DEFAULT_CONFIG = {};
 
 interface Props extends PropsWithChildren {
   config?: PartialAppConfig;
 }
 
-const App = ({ config = {}, children }: Props) => {
+const App = ({ config = DEFAULT_CONFIG, children }: Props) => {
   useToastWatcher();
   useGlobalHotkeys();
+  const log = useLogger();
 
   const currentTheme = useAppSelector((state) => state.ui.currentTheme);
 
@@ -45,9 +52,9 @@ const App = ({ config = {}, children }: Props) => {
   const dispatch = useAppDispatch();
 
   useEffect(() => {
-    console.log('Received config: ', config);
+    log.info({ namespace: 'App', data: config }, 'Received config');
     dispatch(configChanged(config));
-  }, [dispatch, config]);
+  }, [dispatch, config, log]);
 
   useEffect(() => {
     setColorMode(['light'].includes(currentTheme) ? 'light' : 'dark');
@@ -58,7 +65,7 @@ const App = ({ config = {}, children }: Props) => {
   }, []);
 
   return (
-    <Grid w="100vw" h="100vh" position="relative">
+    <Grid w="100vw" h="100vh" position="relative" overflow="hidden">
       {isLightboxEnabled && <Lightbox />}
       <ImageUploader>
         <ProgressBar />
@@ -114,11 +121,9 @@ const App = ({ config = {}, children }: Props) => {
       <Portal>
         <FloatingGalleryButton />
       </Portal>
-      <Portal>
-        <Console />
-      </Portal>
+      <ProgressImagePreview />
     </Grid>
   );
 };
 
-export default App;
+export default memo(App);

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

@@ -1,8 +1,8 @@
-import React, { lazy, PropsWithChildren, useEffect } from 'react';
+import React, { lazy, memo, PropsWithChildren, useEffect } from 'react';
 import { Provider } from 'react-redux';
 import { PersistGate } from 'redux-persist/integration/react';
-import { buildMiddleware, store } from './app/store';
-import { persistor } from './persistor';
+import { store } from 'app/store/store';
+import { persistor } from '../store/persistor';
 import { OpenAPI } from 'services/api';
 import '@fontsource/inter/100.css';
 import '@fontsource/inter/200.css';
@@ -14,14 +14,15 @@ import '@fontsource/inter/700.css';
 import '@fontsource/inter/800.css';
 import '@fontsource/inter/900.css';
 
-import Loading from './common/components/Loading/Loading';
+import Loading from '../../common/components/Loading/Loading';
 import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
-import { PartialAppConfig } from 'app/invokeai';
+import { PartialAppConfig } from 'app/types/invokeai';
 
-import './i18n';
+import '../../i18n';
+import { socketMiddleware } from 'services/events/middleware';
 
-const App = lazy(() => import('./app/App'));
-const ThemeLocaleProvider = lazy(() => import('./app/ThemeLocaleProvider'));
+const App = lazy(() => import('./App'));
+const ThemeLocaleProvider = lazy(() => import('./ThemeLocaleProvider'));
 
 interface Props extends PropsWithChildren {
   apiUrl?: string;
@@ -29,7 +30,7 @@ interface Props extends PropsWithChildren {
   config?: PartialAppConfig;
 }
 
-export default function Component({ apiUrl, token, config, children }: Props) {
+const InvokeAIUI = ({ apiUrl, token, config, children }: Props) => {
   useEffect(() => {
     // configure API client token
     if (token) {
@@ -50,7 +51,7 @@ export default function Component({ apiUrl, token, config, children }: Props) {
     // the `apiUrl`/`token` dynamically.
 
     // rebuild socket middleware with token and apiUrl
-    addMiddleware(buildMiddleware());
+    addMiddleware(socketMiddleware());
   }, [apiUrl, token]);
 
   return (
@@ -66,4 +67,6 @@ export default function Component({ apiUrl, token, config, children }: Props) {
       </Provider>
     </React.StrictMode>
   );
-}
+};
+
+export default memo(InvokeAIUI);

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

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

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

@@ -1,23 +1,6 @@
 // TODO: use Enums?
 
-import { InProgressImageType } from 'features/system/store/systemSlice';
-
-// Valid samplers
-export const SAMPLERS: Array<string> = [
-  'ddim',
-  'plms',
-  'k_lms',
-  'k_dpm_2',
-  'k_dpm_2_a',
-  'k_dpmpp_2',
-  'k_dpmpp_2_a',
-  'k_euler',
-  'k_euler_a',
-  'k_heun',
-];
-
-// Valid Diffusers Samplers
-export const DIFFUSERS_SAMPLERS: Array<string> = [
+export const DIFFUSERS_SCHEDULERS: Array<string> = [
   'ddim',
   'plms',
   'k_lms',
@@ -48,17 +31,8 @@ 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/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

@@ -4,7 +4,7 @@ import { initialCanvasImageSelector } from 'features/canvas/store/canvasSelector
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { systemSelector } from 'features/system/store/systemSelectors';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 export const readinessSelector = createSelector(
   [

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

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

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

@@ -1,4 +1,4 @@
-import { store } from 'app/store';
+import { store } from 'app/store/store';
 import { persistStore } from 'redux-persist';
 
 export const persistor = persistStore(store);

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

@@ -19,8 +19,6 @@ import hotkeysReducer from 'features/ui/store/hotkeysSlice';
 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 { canvasDenylist } from 'features/canvas/store/canvasPersistDenylist';
 import { galleryDenylist } from 'features/gallery/store/galleryPersistDenylist';
 import { generationDenylist } from 'features/parameters/store/generationPersistDenylist';
@@ -28,8 +26,10 @@ import { lightboxDenylist } from 'features/lightbox/store/lightboxPersistDenylis
 import { modelsDenylist } from 'features/system/store/modelsPersistDenylist';
 import { nodesDenylist } from 'features/nodes/store/nodesPersistDenylist';
 import { postprocessingDenylist } from 'features/parameters/store/postprocessingPersistDenylist';
-import { systemDenylist } from 'features/system/store/systemPersistsDenylist';
+import { systemDenylist } from 'features/system/store/systemPersistDenylist';
 import { uiDenylist } from 'features/ui/store/uiPersistDenylist';
+import { resultsDenylist } from 'features/gallery/store/resultsPersistDenylist';
+import { uploadsDenylist } from 'features/gallery/store/uploadsPersistDenylist';
 
 /**
  * redux-persist provides an easy and reliable way to persist state across reloads.
@@ -82,19 +82,18 @@ const rootPersistConfig = getPersistConfig({
     'hotkeys',
     'config',
   ],
-  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 function buildMiddleware() {
+//   if (import.meta.env.MODE === 'nodes' || import.meta.env.MODE === 'package') {
+//     return socketMiddleware();
+//   } else {
+//     return socketioMiddleware();
+//   }
+// }
 
 export const store = configureStore({
   reducer: persistedReducer,
@@ -114,6 +113,7 @@ export const store = configureStore({
       'canvas/setBoundingBoxDimensions',
       'canvas/setIsDrawing',
       'canvas/addPointToCurrentLine',
+      'socket/generatorProgress',
     ],
   },
 });

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

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

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

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

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

@@ -12,10 +12,11 @@
  * 'gfpgan'.
  */
 
+import { GalleryCategory } from 'features/gallery/store/gallerySlice';
 import { FacetoolType } from 'features/parameters/store/postprocessingSlice';
 import { InvokeTabName } from 'features/ui/store/tabMap';
 import { IRect } from 'konva/lib/types';
-import { ImageMetadata, ImageType } from 'services/api';
+import { ImageResponseMetadata, ImageType } from 'services/api';
 import { AnyInvocation } from 'services/events/types';
 import { O } from 'ts-toolbelt';
 
@@ -28,24 +29,24 @@ import { O } from 'ts-toolbelt';
  * TODO: Better documentation of types.
  */
 
-export declare type PromptItem = {
+export 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 type Prompt = Array<PromptItem> | string;
 
-export declare type SeedWeightPair = {
+export type SeedWeightPair = {
   seed: number;
   weight: number;
 };
 
-export declare type SeedWeights = Array<SeedWeightPair>;
+export type SeedWeights = Array<SeedWeightPair>;
 
 // All generated images contain these metadata.
-export declare type CommonGeneratedImageMetadata = {
-  postprocessing: null | Array<ESRGANMetadata | GFPGANMetadata>;
+export type CommonGeneratedImageMetadata = {
+  postprocessing: null | Array<ESRGANMetadata | FacetoolMetadata>;
   sampler:
     | 'ddim'
     | 'k_dpm_2_a'
@@ -70,11 +71,11 @@ export declare type CommonGeneratedImageMetadata = {
 };
 
 // txt2img and img2img images have some unique attributes.
-export declare type Txt2ImgMetadata = GeneratedImageMetadata & {
+export type Txt2ImgMetadata = CommonGeneratedImageMetadata & {
   type: 'txt2img';
 };
 
-export declare type Img2ImgMetadata = GeneratedImageMetadata & {
+export type Img2ImgMetadata = CommonGeneratedImageMetadata & {
   type: 'img2img';
   orig_hash: string;
   strength: number;
@@ -84,102 +85,80 @@ export declare type Img2ImgMetadata = GeneratedImageMetadata & {
 };
 
 // Superset of  generated image metadata types.
-export declare type GeneratedImageMetadata = Txt2ImgMetadata | Img2ImgMetadata;
+export type GeneratedImageMetadata = Txt2ImgMetadata | Img2ImgMetadata;
 
 // All post processed images contain these metadata.
-export declare type CommonPostProcessedImageMetadata = {
+export type CommonPostProcessedImageMetadata = {
   orig_path: string;
   orig_hash: string;
 };
 
 // esrgan and gfpgan images have some unique attributes.
-export declare type ESRGANMetadata = CommonPostProcessedImageMetadata & {
+export type ESRGANMetadata = CommonPostProcessedImageMetadata & {
   type: 'esrgan';
   scale: 2 | 4;
   strength: number;
   denoise_str: number;
 };
 
-export declare type FacetoolMetadata = CommonPostProcessedImageMetadata & {
+export type FacetoolMetadata = CommonPostProcessedImageMetadata & {
   type: 'gfpgan' | 'codeformer';
   strength: number;
   fidelity?: number;
 };
 
 // Superset of all postprocessed image metadata types..
-export declare type PostProcessedImageMetadata =
-  | ESRGANMetadata
-  | FacetoolMetadata;
+export 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;
-};
+// export type Metadata = SystemGenerationMetadata & {
+//   image: GeneratedImageMetadata | PostProcessedImageMetadata;
+// };
 
 /**
  * ResultImage
  */
-export declare type Image = {
+export 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>;
+  metadata: ImageResponseMetadata;
 };
 
 /**
  * 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;
-};
+// // This represents the processing status of the backend.
+// export type SystemStatus = {
+//   isProcessing: boolean;
+//   currentStep: number;
+//   totalSteps: number;
+//   currentIteration: number;
+//   totalIterations: number;
+//   currentStatus: string;
+//   currentStatusHasSteps: boolean;
+//   hasError: boolean;
+// };
 
-export declare type SystemGenerationMetadata = {
-  model: string;
-  model_weights?: string;
-  model_id?: string;
-  model_hash: string;
-  app_id: string;
-  app_version: string;
-};
+// export 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 type SystemConfig = SystemGenerationMetadata & {
+//   model_list: ModelList;
+//   infill_methods: string[];
+// };
 
-export declare type ModelStatus = 'active' | 'cached' | 'not loaded';
+export type ModelStatus = 'active' | 'cached' | 'not loaded';
 
-export declare type Model = {
+export type Model = {
   status: ModelStatus;
   description: string;
   weights: string;
@@ -191,7 +170,7 @@ export declare type Model = {
   format?: string;
 };
 
-export declare type DiffusersModel = {
+export type DiffusersModel = {
   status: ModelStatus;
   description: string;
   repo_id?: string;
@@ -204,14 +183,14 @@ export declare type DiffusersModel = {
   default?: boolean;
 };
 
-export declare type ModelList = Record<string, Model & DiffusersModel>;
+export type ModelList = Record<string, Model & DiffusersModel>;
 
-export declare type FoundModel = {
+export type FoundModel = {
   name: string;
   location: string;
 };
 
-export declare type InvokeModelConfigProps = {
+export type InvokeModelConfigProps = {
   name: string | undefined;
   description: string | undefined;
   config: string | undefined;
@@ -223,7 +202,7 @@ export declare type InvokeModelConfigProps = {
   format: string | undefined;
 };
 
-export declare type InvokeDiffusersModelConfigProps = {
+export type InvokeDiffusersModelConfigProps = {
   name: string | undefined;
   description: string | undefined;
   repo_id: string | undefined;
@@ -236,13 +215,13 @@ export declare type InvokeDiffusersModelConfigProps = {
   };
 };
 
-export declare type InvokeModelConversionProps = {
+export type InvokeModelConversionProps = {
   model_name: string;
   save_location: string;
   custom_location: string | null;
 };
 
-export declare type InvokeModelMergingProps = {
+export type InvokeModelMergingProps = {
   models_to_merge: string[];
   alpha: number;
   interp: 'weighted_sum' | 'sigmoid' | 'inv_sigmoid' | 'add_difference';
@@ -255,48 +234,48 @@ export declare type InvokeModelMergingProps = {
  * These types type data received from the server via socketio.
  */
 
-export declare type ModelChangeResponse = {
+export type ModelChangeResponse = {
   model_name: string;
   model_list: ModelList;
 };
 
-export declare type ModelConvertedResponse = {
+export type ModelConvertedResponse = {
   converted_model_name: string;
   model_list: ModelList;
 };
 
-export declare type ModelsMergedResponse = {
+export type ModelsMergedResponse = {
   merged_models: string[];
   merged_model_name: string;
   model_list: ModelList;
 };
 
-export declare type ModelAddedResponse = {
+export type ModelAddedResponse = {
   new_model_name: string;
   model_list: ModelList;
   update: boolean;
 };
 
-export declare type ModelDeletedResponse = {
+export type ModelDeletedResponse = {
   deleted_model_name: string;
   model_list: ModelList;
 };
 
-export declare type FoundModelResponse = {
+export type FoundModelResponse = {
   search_folder: string;
   found_models: FoundModel[];
 };
 
-export declare type SystemStatusResponse = SystemStatus;
+// export type SystemStatusResponse = SystemStatus;
 
-export declare type SystemConfigResponse = SystemConfig;
+// export type SystemConfigResponse = SystemConfig;
 
-export declare type ImageResultResponse = Omit<_Image, 'uuid'> & {
+export type ImageResultResponse = Omit<_Image, 'uuid'> & {
   boundingBox?: IRect;
   generationMode: InvokeTabName;
 };
 
-export declare type ImageUploadResponse = {
+export type ImageUploadResponse = {
   // image: Omit<Image, 'uuid' | 'metadata' | 'category'>;
   url: string;
   mtime: number;
@@ -306,33 +285,16 @@ export declare type ImageUploadResponse = {
   // bbox: [number, number, number, number];
 };
 
-export declare type ErrorResponse = {
+export 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 = {
+export type ImageUrlResponse = {
   url: string;
 };
 
-export declare type UploadImagePayload = {
-  file: File;
-  destination?: ImageUploadDestination;
-};
-
-export declare type UploadOutpaintingMergeImagePayload = {
+export type UploadOutpaintingMergeImagePayload = {
   dataURL: string;
   name: string;
 };
@@ -340,7 +302,7 @@ export declare type UploadOutpaintingMergeImagePayload = {
 /**
  * A disable-able application feature
  */
-export declare type AppFeature =
+export type AppFeature =
   | 'faceRestore'
   | 'upscaling'
   | 'lightbox'
@@ -353,7 +315,7 @@ export declare type AppFeature =
 /**
  * A disable-able Stable Diffusion feature
  */
-export declare type StableDiffusionFeature =
+export type StableDiffusionFeature =
   | 'noiseConfig'
   | 'variations'
   | 'symmetry'
@@ -364,7 +326,7 @@ export declare type StableDiffusionFeature =
  * Configuration options for the InvokeAI UI.
  * Distinct from system settings which may be changed inside the app.
  */
-export declare type AppConfig = {
+export type AppConfig = {
   /**
    * Whether or not URLs should be transformed to use a different host
    */
@@ -428,4 +390,4 @@ export declare type AppConfig = {
   };
 };
 
-export declare type PartialAppConfig = O.Partial<AppConfig, 'deep'>;
+export type PartialAppConfig = O.Partial<AppConfig, 'deep'>;

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

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

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

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

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

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

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

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

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

@@ -23,7 +23,7 @@ import {
   Tooltip,
   TooltipProps,
 } from '@chakra-ui/react';
-import { clamp } from 'lodash';
+import { clamp } from 'lodash-es';
 
 import { useTranslation } from 'react-i18next';
 import {
@@ -233,7 +233,7 @@ const IAISlider = (props: IAIFullSliderProps) => {
             hidden={hideTooltip}
             {...sliderTooltipProps}
           >
-            <SliderThumb {...sliderThumbProps} />
+            <SliderThumb {...sliderThumbProps} zIndex={0} />
           </Tooltip>
         </Slider>
 

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

@@ -1,32 +1,11 @@
-import { Badge, Box, ButtonGroup, Flex } from '@chakra-ui/react';
-import { RootState } from 'app/store';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
-import { clearInitialImage } from 'features/parameters/store/generationSlice';
-import { useCallback } from 'react';
-import IAIIconButton from 'common/components/IAIIconButton';
-import { FaUndo, FaUpload } from 'react-icons/fa';
-import { useTranslation } from 'react-i18next';
-import { Image } from 'app/invokeai';
+import { Badge, Box, Flex } from '@chakra-ui/react';
+import { Image } from 'app/types/invokeai';
 
 type ImageToImageOverlayProps = {
-  setIsLoaded: (isLoaded: boolean) => void;
   image: Image;
 };
 
-const ImageToImageOverlay = ({
-  setIsLoaded,
-  image,
-}: ImageToImageOverlayProps) => {
-  const isImageToImageEnabled = useAppSelector(
-    (state: RootState) => state.generation.isImageToImageEnabled
-  );
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-  const handleResetInitialImage = useCallback(() => {
-    dispatch(clearInitialImage());
-    setIsLoaded(false);
-  }, [dispatch, setIsLoaded]);
-
+const ImageToImageOverlay = ({ image }: ImageToImageOverlayProps) => {
   return (
     <Box
       sx={{

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

@@ -1,34 +1,13 @@
-import {
-  Box,
-  ButtonGroup,
-  Collapse,
-  Flex,
-  Heading,
-  HStack,
-  Image,
-  Spacer,
-  Text,
-  useDisclosure,
-  VStack,
-} from '@chakra-ui/react';
-import { motion } from 'framer-motion';
-
-import IAIButton from 'common/components/IAIButton';
-import ImageFit from 'features/parameters/components/AdvancedParameters/ImageToImage/ImageFit';
-import ImageToImageStrength from 'features/parameters/components/AdvancedParameters/ImageToImage/ImageToImageStrength';
+import { ButtonGroup, Flex, Spacer, Text } from '@chakra-ui/react';
 import IAIIconButton from 'common/components/IAIIconButton';
 
 import { useTranslation } from 'react-i18next';
 import { FaUndo, FaUpload } from 'react-icons/fa';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
-import { RootState } from 'app/store';
+import { useAppDispatch } from 'app/store/storeHooks';
 import { useCallback } from 'react';
 import { clearInitialImage } from 'features/parameters/store/generationSlice';
 
 const ImageToImageSettingsHeader = () => {
-  const isImageToImageEnabled = useAppSelector(
-    (state: RootState) => state.generation.isImageToImageEnabled
-  );
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 

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

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

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

@@ -1,5 +1,6 @@
 import { Flex, Image, Spinner } from '@chakra-ui/react';
 import InvokeAILogoImage from 'assets/images/logo.png';
+import { memo } from 'react';
 
 // This component loads before the theme so we cannot use theme tokens here
 
@@ -29,4 +30,4 @@ const Loading = () => {
   );
 };
 
-export default Loading;
+export default memo(Loading);

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

@@ -1,8 +1,8 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { RootState } from 'app/store';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { shiftKeyPressed } from 'features/ui/store/hotkeysSlice';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 import { isHotkeyPressed, useHotkeys } from 'react-hotkeys-hook';
 
 const globalHotkeysSelector = createSelector(

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

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

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

@@ -1,5 +1,6 @@
-import { RootState } from 'app/store';
-import { useAppSelector } from 'app/storeHooks';
+import { RootState } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useCallback } from 'react';
 import { OpenAPI } from 'services/api';
 
 export const getUrlAlt = (url: string, shouldTransformUrls: boolean) => {
@@ -15,14 +16,19 @@ export const useGetUrl = () => {
     (state: RootState) => state.config.shouldTransformUrls
   );
 
-  return {
-    shouldTransformUrls,
-    getUrl: (url?: string) => {
+  const getUrl = useCallback(
+    (url?: string) => {
       if (OpenAPI.BASE && shouldTransformUrls) {
         return [OpenAPI.BASE, url].join('/');
       }
 
       return url;
     },
+    [shouldTransformUrls]
+  );
+
+  return {
+    shouldTransformUrls,
+    getUrl,
   };
 };

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

@@ -1,4 +1,4 @@
-import { forEach, size } from 'lodash';
+import { forEach, size } from 'lodash-es';
 import { ImageField, LatentsField, ConditioningField } from 'services/api';
 
 const OBJECT_TYPESTRING = '[object Object]';

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

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

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

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

invokeai/frontend/web/src/exports.tsx

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1,9 +1,9 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/storeHooks';
+import { useAppSelector } from 'app/store/storeHooks';
 import { useGetUrl } from 'common/util/getUrl';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import { rgbaColorToString } from 'features/canvas/util/colorToString';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 import { Group, Line, Rect } from 'react-konva';
 import {

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

@@ -1,6 +1,6 @@
 import { Flex, Spinner } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import {
   canvasSelector,
   initialCanvasImageSelector,

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

@@ -1,9 +1,9 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/storeHooks';
+import { useAppSelector } from 'app/store/storeHooks';
 import { useGetUrl } from 'common/util/getUrl';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import { GroupConfig } from 'konva/lib/Group';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 import { Group, Rect } from 'react-konva';
 import IAICanvasImage from './IAICanvasImage';

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

@@ -1,7 +1,7 @@
 import { ButtonGroup, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { saveStagingAreaImageToGallery } from 'app/socketio/actions';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+// import { saveStagingAreaImageToGallery } from 'app/socketio/actions';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIIconButton from 'common/components/IAIIconButton';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import {
@@ -12,7 +12,7 @@ import {
   setShouldShowStagingImage,
   setShouldShowStagingOutline,
 } from 'features/canvas/store/canvasSlice';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 import { useCallback } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';

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

@@ -1,8 +1,8 @@
 import { Box, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/storeHooks';
+import { useAppSelector } from 'app/store/storeHooks';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 import { useTranslation } from 'react-i18next';
 import roundToHundreth from '../util/roundToHundreth';

invokeai/frontend/web/src/features/canvas/components/IAICanvasStatusText/IAICanvasStatusTextCursorPos.tsx

@@ -1,9 +1,9 @@
 import { Box } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/storeHooks';
+import { useAppSelector } from 'app/store/storeHooks';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import roundToHundreth from 'features/canvas/util/roundToHundreth';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 import { useTranslation } from 'react-i18next';
 

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

@@ -1,9 +1,9 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/storeHooks';
+import { useAppSelector } from 'app/store/storeHooks';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import { rgbaColorToString } from 'features/canvas/util/colorToString';
 import { GroupConfig } from 'konva/lib/Group';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 import { Circle, Group } from 'react-konva';
 import {

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasBoundingBox.tsx

@@ -1,5 +1,5 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import {
   roundDownToMultiple,
   roundToMultiple,
@@ -16,7 +16,7 @@ import Konva from 'konva';
 import { GroupConfig } from 'konva/lib/Group';
 import { KonvaEventObject } from 'konva/lib/Node';
 import { Vector2d } from 'konva/lib/types';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 import { useCallback, useEffect, useRef, useState } from 'react';
 import { Group, Rect, Transformer } from 'react-konva';

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasMaskOptions.tsx

@@ -1,6 +1,6 @@
 import { ButtonGroup, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIButton from 'common/components/IAIButton';
 import IAICheckbox from 'common/components/IAICheckbox';
 import IAIColorPicker from 'common/components/IAIColorPicker';
@@ -18,7 +18,7 @@ import {
   setShouldPreserveMaskedArea,
 } from 'features/canvas/store/canvasSlice';
 import { rgbaColorToString } from 'features/canvas/util/colorToString';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasRedoButton.tsx

@@ -1,5 +1,5 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIIconButton from 'common/components/IAIIconButton';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
@@ -9,7 +9,7 @@ import { FaRedo } from 'react-icons/fa';
 import { redo } from 'features/canvas/store/canvasSlice';
 import { systemSelector } from 'features/system/store/systemSelectors';
 
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 import { useTranslation } from 'react-i18next';
 
 const canvasRedoSelector = createSelector(

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasSettingsButtonPopover.tsx

@@ -1,6 +1,6 @@
 import { Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAICheckbox from 'common/components/IAICheckbox';
 import IAIIconButton from 'common/components/IAIIconButton';
 import IAIPopover from 'common/components/IAIPopover';
@@ -16,7 +16,7 @@ import {
   setShouldSnapToGrid,
 } from 'features/canvas/store/canvasSlice';
 import EmptyTempFolderButtonModal from 'features/system/components/ClearTempFolderButtonModal';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 import { ChangeEvent } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasToolChooserOptions.tsx

@@ -1,6 +1,6 @@
 import { ButtonGroup, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIColorPicker from 'common/components/IAIColorPicker';
 import IAIIconButton from 'common/components/IAIIconButton';
 import IAIPopover from 'common/components/IAIPopover';
@@ -17,7 +17,7 @@ import {
   setTool,
 } from 'features/canvas/store/canvasSlice';
 import { systemSelector } from 'features/system/store/systemSelectors';
-import { clamp, isEqual } from 'lodash';
+import { clamp, isEqual } from 'lodash-es';
 
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';

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

@@ -1,6 +1,6 @@
 import { ButtonGroup, Flex } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIIconButton from 'common/components/IAIIconButton';
 import IAISelect from 'common/components/IAISelect';
 import useImageUploader from 'common/hooks/useImageUploader';
@@ -24,7 +24,7 @@ import {
 import { mergeAndUploadCanvas } from 'features/canvas/store/thunks/mergeAndUploadCanvas';
 import { getCanvasBaseLayer } from 'features/canvas/util/konvaInstanceProvider';
 import { systemSelector } from 'features/system/store/systemSelectors';
-import { isEqual } from 'lodash';
+import { isEqual } from 'lodash-es';
 
 import { ChangeEvent } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';