Update probe to always use cpu for loading models

docs/installation/INSTALL_DEVELOPMENT.md

@@ -32,5 +32,5 @@ As described in the [frontend dev toolchain] docs, you can run the UI using a de
 [Fork and clone]: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo
 [InvokeAI repo]: https://github.com/invoke-ai/InvokeAI
 [frontend dev toolchain]: ../contributing/frontend/OVERVIEW.md
-[manual installation]: ./020_INSTALL_MANUAL.md
+[manual installation]: installation/020_INSTALL_MANUAL.md
 [editable install]: https://pip.pypa.io/en/latest/cli/pip_install/#cmdoption-e

invokeai/app/invocations/custom_nodes/init.py

@@ -3,7 +3,6 @@ Invoke-managed custom node loader. See README.md for more information.
 """
 
 import sys
-import traceback
 from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
 
@@ -42,15 +41,11 @@ for d in Path(__file__).parent.iterdir():
 
     logger.info(f"Loading node pack {module_name}")
 
-    try:
-        module = module_from_spec(spec)
-        sys.modules[spec.name] = module
-        spec.loader.exec_module(module)
+    module = module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
 
-        loaded_count += 1
-    except Exception:
-        full_error = traceback.format_exc()
-        logger.error(f"Failed to load node pack {module_name}:\n{full_error}")
+    loaded_count += 1
 
     del init, module_name
 

invokeai/app/services/config/config_default.py

@@ -373,16 +373,13 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
             if k == "conf_path":
                 parsed_config_dict["legacy_models_yaml_path"] = v
             if k == "legacy_conf_dir":
-                # The old default for this was "configs/stable-diffusion" ("configs\stable-diffusion" on Windows).
-                if v == "configs/stable-diffusion" or v == "configs\\stable-diffusion":
-                    # If if the incoming config has the default value, skip
-                    continue
-                elif Path(v).name == "stable-diffusion":
-                    # Else if the path ends in "stable-diffusion", we assume the parent is the new correct path.
-                    parsed_config_dict["legacy_conf_dir"] = str(Path(v).parent)
-                else:
-                    # Else we do not attempt to migrate this setting
+                # The old default for this was "configs/stable-diffusion". If if the incoming config has that as the value, we won't set it.
+                # Else if the path ends in "stable-diffusion", we assume the parent is the new correct path.
+                # Else we do not attempt to migrate this setting
+                if v != "configs/stable-diffusion":
                     parsed_config_dict["legacy_conf_dir"] = v
+                elif Path(v).name == "stable-diffusion":
+                    parsed_config_dict["legacy_conf_dir"] = str(Path(v).parent)
             elif k in InvokeAIAppConfig.model_fields:
                 # skip unknown fields
                 parsed_config_dict[k] = v

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -11,7 +11,6 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_5 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_6 import build_migration_6
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_7 import build_migration_7
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_8 import build_migration_8
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_9 import build_migration_9
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -40,7 +39,6 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_6())
     migrator.register_migration(build_migration_7())
     migrator.register_migration(build_migration_8(app_config=config))
-    migrator.register_migration(build_migration_9())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_9.py

@@ -1,29 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration9Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._empty_session_queue(cursor)
-
-    def _empty_session_queue(self, cursor: sqlite3.Cursor) -> None:
-        """Empties the session queue. This is done to prevent any lingering session queue items from causing pydantic errors due to changed schemas."""
-
-        cursor.execute("DELETE FROM session_queue;")
-
-
-def build_migration_9() -> Migration:
-    """
-    Build the migration from database version 8 to 9.
-
-    This migration does the following:
-    - Empties the session queue. This is done to prevent any lingering session queue items from causing pydantic errors due to changed schemas.
-    """
-    migration_9 = Migration(
-        from_version=8,
-        to_version=9,
-        callback=Migration9Callback(),
-    )
-
-    return migration_9

invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_impl.py

@@ -1,6 +1,4 @@
 import sqlite3
-from contextlib import closing
-from datetime import datetime
 from pathlib import Path
 from typing import Optional
 
@@ -34,7 +32,6 @@ class SqliteMigrator:
         self._db = db
         self._logger = db.logger
         self._migration_set = MigrationSet()
-        self._backup_path: Optional[Path] = None
 
     def register_migration(self, migration: Migration) -> None:
         """Registers a migration."""
@@ -58,18 +55,6 @@ class SqliteMigrator:
                 return False
 
             self._logger.info("Database update needed")
-
-            # Make a backup of the db if it needs to be updated and is a file db
-            if self._db.db_path is not None:
-                timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
-                self._backup_path = self._db.db_path.parent / f"{self._db.db_path.stem}_backup_{timestamp}.db"
-                self._logger.info(f"Backing up database to {str(self._backup_path)}")
-                # Use SQLite to do the backup
-                with closing(sqlite3.connect(self._backup_path)) as backup_conn:
-                    self._db.conn.backup(backup_conn)
-            else:
-                self._logger.info("Using in-memory database, no backup needed")
-
             next_migration = self._migration_set.get(from_version=self._get_current_version(cursor))
             while next_migration is not None:
                 self._run_migration(next_migration)

invokeai/backend/lora.py

@@ -3,7 +3,7 @@
 
 import bisect
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple, Type, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 from safetensors.torch import load_file
@@ -457,55 +457,6 @@ class LoRAModelRaw(RawModel):  # (torch.nn.Module):
 
         return new_state_dict
 
-    @classmethod
-    def _keys_match(cls, keys: set[str], required_keys: set[str], optional_keys: set[str]) -> bool:
-        """Check if the set of keys matches the required and optional keys."""
-        if len(required_keys - keys) > 0:
-            # missing required keys.
-            return False
-
-        non_required_keys = keys - required_keys
-        for k in non_required_keys:
-            if k not in optional_keys:
-                # unexpected key
-                return False
-
-        return True
-
-    @classmethod
-    def get_layer_type_from_state_dict_keys(cls, peft_layer_keys: set[str]) -> Type[AnyLoRALayer]:
-        """Infer the parameter-efficient finetuning model type from the state dict keys."""
-        common_optional_keys = {"alpha", "bias_indices", "bias_values", "bias_size"}
-        if cls._keys_match(
-            peft_layer_keys,
-            required_keys={"lora_down.weight", "lora_up.weight"},
-            optional_keys=common_optional_keys | {"lora_mid.weight"},
-        ):
-            return LoRALayer
-
-        if cls._keys_match(
-            peft_layer_keys,
-            required_keys={"hada_w1_b", "hada_w1_a", "hada_w2_b", "hada_w2_a"},
-            optional_keys=common_optional_keys | {"hada_t1", "hada_t2"},
-        ):
-            return LoHALayer
-
-        if cls._keys_match(
-            peft_layer_keys,
-            required_keys=set(),
-            optional_keys=common_optional_keys
-            | {"lokr_w1", "lokr_w1_a", "lokr_w1_b", "lokr_w2", "lokr_w2_a", "lokr_w2_b", "lokr_t2"},
-        ):
-            return LoKRLayer
-
-        if cls._keys_match(peft_layer_keys, required_keys={"diff"}, optional_keys=common_optional_keys):
-            return FullLayer
-
-        if cls._keys_match(peft_layer_keys, required_keys={"weight", "on_input"}, optional_keys=common_optional_keys):
-            return IA3Layer
-
-        raise ValueError(f"Unsupported PEFT model type with keys: {peft_layer_keys}")
-
     @classmethod
     def from_checkpoint(
         cls,
@@ -535,21 +486,37 @@ class LoRAModelRaw(RawModel):  # (torch.nn.Module):
         if base_model == BaseModelType.StableDiffusionXL:
             state_dict = cls._convert_sdxl_keys_to_diffusers_format(state_dict)
 
-        # We assume that all layers have the same PEFT layer type. This saves time by not having to infer the type for
-        # each layer.
-        first_module_key = next(iter(state_dict))
-        peft_layer_keys = set(state_dict[first_module_key].keys())
-        layer_cls = cls.get_layer_type_from_state_dict_keys(peft_layer_keys)
-
         for layer_key, values in state_dict.items():
-            layer = layer_cls(layer_key, values)
+            # lora and locon
+            if "lora_down.weight" in values:
+                layer: AnyLoRALayer = LoRALayer(layer_key, values)
+
+            # loha
+            elif "hada_w1_b" in values:
+                layer = LoHALayer(layer_key, values)
+
+            # lokr
+            elif "lokr_w1_b" in values or "lokr_w1" in values:
+                layer = LoKRLayer(layer_key, values)
+
+            # diff
+            elif "diff" in values:
+                layer = FullLayer(layer_key, values)
+
+            # ia3
+            elif "weight" in values and "on_input" in values:
+                layer = IA3Layer(layer_key, values)
+
+            else:
+                print(f">> Encountered unknown lora layer module in {model.name}: {layer_key} - {list(values.keys())}")
+                raise Exception("Unknown lora format!")
+
+            # lower memory consumption by removing already parsed layer values
+            state_dict[layer_key].clear()
 
-            # TODO(ryand): This .to() call causes an implicit CUDA sync point in a tight loop. This is very slow (even
-            # slower than loading the weights from disk). We should ideally only be copying the weights once - right
-            # before they are used. Or, if we want to do this here, then setting non_blocking = True would probably
-            # help.
             layer.to(device=device, dtype=dtype)
             model.layers[layer_key] = layer
+
         return model
 
     @staticmethod

invokeai/backend/model_manager/probe.py

@@ -323,7 +323,7 @@ class ModelProbe(object):
         with SilenceWarnings():
             if model_path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin")):
                 cls._scan_model(model_path.name, model_path)
-                model = torch.load(model_path)
+                model = torch.load(model_path, map_location="cpu")
                 assert isinstance(model, dict)
                 return model
             else:

invokeai/frontend/web/package.json

@@ -94,7 +94,6 @@
     "reactflow": "^11.10.4",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
-    "rfdc": "^1.3.1",
     "roarr": "^7.21.1",
     "serialize-error": "^11.0.3",
     "socket.io-client": "^4.7.5",

invokeai/frontend/web/pnpm-lock.yaml

@@ -137,9 +137,6 @@ dependencies:
   redux-remember:
     specifier: ^5.1.0
     version: 5.1.0(redux@5.0.1)
-  rfdc:
-    specifier: ^1.3.1
-    version: 1.3.1
   roarr:
     specifier: ^7.21.1
     version: 7.21.1
@@ -12131,10 +12128,6 @@ packages:
     resolution: {integrity: sha512-/x8uIPdTafBqakK0TmPNJzgkLP+3H+yxpUJhCQHsLBg1rYEVNR2D8BRYNWQhVBjyOd7oo1dZRVzIkwMY2oqfYQ==}
     dev: true
 
-  /rfdc@1.3.1:
-    resolution: {integrity: sha512-r5a3l5HzYlIC68TpmYKlxWjmOP6wiPJ1vWv2HeLhNsRZMrCkxeqxiHlQ21oXmQ4F3SiryXBHhAD7JZqvOJjFmg==}
-    dev: false
-
   /rimraf@2.6.3:
     resolution: {integrity: sha512-mwqeW5XsA2qAejG46gYdENaxXjx9onRNCfn7L0duuP4hCuTIi/QO7PDK07KJfp1d+izWPrzEJDcSqBa0OZQriA==}
     hasBin: true

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

@@ -4,7 +4,7 @@
         "reportBugLabel": "Fehler melden",
         "settingsLabel": "Einstellungen",
         "img2img": "Bild zu Bild",
-        "nodes": "Arbeitsabläufe",
+        "nodes": "Knoten Editor",
         "upload": "Hochladen",
         "load": "Laden",
         "statusDisconnected": "Getrennt",
@@ -74,8 +74,7 @@
         "updated": "Aktualisiert",
         "copy": "Kopieren",
         "aboutHeading": "Nutzen Sie Ihre kreative Energie",
-        "toResolve": "Lösen",
-        "add": "Hinzufügen"
+        "toResolve": "Lösen"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -105,16 +104,11 @@
         "dropToUpload": "$t(gallery.drop) zum hochladen",
         "dropOrUpload": "$t(gallery.drop) oder hochladen",
         "drop": "Ablegen",
-        "problemDeletingImages": "Problem beim Löschen der Bilder",
-        "bulkDownloadRequested": "Download vorbereiten",
-        "bulkDownloadRequestedDesc": "Dein Download wird vorbereitet. Dies kann ein paar Momente dauern.",
-        "bulkDownloadRequestFailed": "Problem beim Download vorbereiten",
-        "bulkDownloadFailed": "Download fehlgeschlagen",
-        "alwaysShowImageSizeBadge": "Zeige immer Bilder Größe Abzeichen"
+        "problemDeletingImages": "Problem beim Löschen der Bilder"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tastenkürzel",
-        "appHotkeys": "App",
+        "appHotkeys": "App-Tastenkombinationen",
         "generalHotkeys": "Allgemein",
         "galleryHotkeys": "Galerie",
         "unifiedCanvasHotkeys": "Leinwand",
@@ -763,9 +757,7 @@
         "scheduler": "Planer",
         "noRecallParameters": "Es wurden keine Parameter zum Abrufen gefunden",
         "recallParameters": "Parameter wiederherstellen",
-        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
-        "allPrompts": "Alle Prompts",
-        "imageDimensions": "Bilder Auslösungen"
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -1076,10 +1068,5 @@
     },
     "dynamicPrompts": {
         "showDynamicPrompts": "Dynamische Prompts anzeigen"
-    },
-    "prompt": {
-        "noMatchingTriggers": "Keine passenden Auslöser",
-        "addPromptTrigger": "Auslöse Text hinzufügen",
-        "compatibleEmbeddings": "Kompatible Einbettungen"
     }
 }

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

@@ -73,8 +73,7 @@
         "ai": "ia",
         "file": "File",
         "toResolve": "Da risolvere",
-        "add": "Aggiungi",
-        "loglevel": "Livello di log"
+        "add": "Aggiungi"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -935,9 +934,7 @@
         "base": "Base",
         "lineart": "Linea",
         "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
-        "mediapipeFace": "Mediapipe Volto",
-        "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
-        "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))"
+        "mediapipeFace": "Mediapipe Volto"
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
@@ -1493,8 +1490,7 @@
             "title": "Generazione"
         },
         "advanced": {
-            "title": "Avanzate",
-            "options": "Opzioni $t(accordions.advanced.title)"
+            "title": "Avanzate"
         },
         "image": {
             "title": "Immagine"

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

@@ -75,8 +75,7 @@
         "copy": "Копировать",
         "localSystem": "Локальная система",
         "aboutDesc": "Используя Invoke для работы? Проверьте это:",
-        "add": "Добавить",
-        "loglevel": "Уровень логов"
+        "add": "Добавить"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -1506,8 +1505,7 @@
             "title": "Генерация"
         },
         "advanced": {
-            "title": "Расширенные",
-            "options": "Опции $t(accordions.advanced.title)"
+            "title": "Расширенные"
         },
         "image": {
             "title": "Изображение"

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

@@ -1,7 +1,7 @@
 import type { UnknownAction } from '@reduxjs/toolkit';
-import { deepClone } from 'common/util/deepClone';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
 import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
+import { cloneDeep } from 'lodash-es';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';
 import { socketGeneratorProgress } from 'services/events/actions';
@@ -33,7 +33,7 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
   }
 
   if (socketGeneratorProgress.match(action)) {
-    const sanitized = deepClone(action);
+    const sanitized = cloneDeep(action);
     if (sanitized.payload.data.progress_image) {
       sanitized.payload.data.progress_image.dataURL = '<Progress image omitted>';
     }

invokeai/frontend/web/src/common/components/OverlayScrollbars/constants.ts

@@ -1,5 +1,4 @@
-import { deepClone } from 'common/util/deepClone';
-import { merge } from 'lodash-es';
+import { cloneDeep, merge } from 'lodash-es';
 import { ClickScrollPlugin, OverlayScrollbars } from 'overlayscrollbars';
 import type { UseOverlayScrollbarsParams } from 'overlayscrollbars-react';
 
@@ -23,7 +22,7 @@ export const getOverlayScrollbarsParams = (
   overflowX: 'hidden' | 'scroll' = 'hidden',
   overflowY: 'hidden' | 'scroll' = 'scroll'
 ) => {
-  const params = deepClone(overlayScrollbarsParams);
+  const params = cloneDeep(overlayScrollbarsParams);
   merge(params, { options: { overflow: { y: overflowY, x: overflowX } } });
   return params;
 };

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

@@ -1,15 +0,0 @@
-import rfdc from 'rfdc';
-const _rfdc = rfdc();
-
-/**
- * Deep-clones an object using Really Fast Deep Clone.
- * This is the fastest deep clone library on Chrome, but not the fastest on FF. Still, it's much faster than lodash
- * and structuredClone, so it's the best all-around choice.
- *
- * Simple Benchmark: https://www.measurethat.net/Benchmarks/Show/30358/0/lodash-clonedeep-vs-jsonparsejsonstringify-vs-recursive
- * Repo: https://github.com/davidmarkclements/rfdc
- *
- * @param obj The object to deep-clone
- * @returns The cloned object
- */
-export const deepClone = <T>(obj: T): T => _rfdc(obj);

invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts

@@ -1,7 +1,6 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
-import { deepClone } from 'common/util/deepClone';
 import { roundDownToMultiple, roundToMultiple } from 'common/util/roundDownToMultiple';
 import calculateCoordinates from 'features/canvas/util/calculateCoordinates';
 import calculateScale from 'features/canvas/util/calculateScale';
@@ -14,7 +13,7 @@ import { modelChanged } from 'features/parameters/store/generationSlice';
 import type { PayloadActionWithOptimalDimension } from 'features/parameters/store/types';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import type { IRect, Vector2d } from 'konva/lib/types';
-import { clamp } from 'lodash-es';
+import { clamp, cloneDeep } from 'lodash-es';
 import type { RgbaColor } from 'react-colorful';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { ImageDTO } from 'services/api/types';
@@ -37,7 +36,7 @@ import { CANVAS_GRID_SIZE_FINE } from './constants';
 /**
  * The maximum history length to keep in the past/future layer states.
  */
-const MAX_HISTORY = 100;
+const MAX_HISTORY = 128;
 
 const initialLayerState: CanvasLayerState = {
   objects: [],
@@ -122,7 +121,7 @@ export const canvasSlice = createSlice({
       state.brushSize = action.payload;
     },
     clearMask: (state) => {
-      pushToPrevLayerStates(state);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
       state.layerState.objects = state.layerState.objects.filter((obj) => !isCanvasMaskLine(obj));
       state.futureLayerStates = [];
       state.shouldPreserveMaskedArea = false;
@@ -164,10 +163,10 @@ export const canvasSlice = createSlice({
         state.boundingBoxDimensions = newBoundingBoxDimensions;
         state.boundingBoxCoordinates = newBoundingBoxCoordinates;
 
-        pushToPrevLayerStates(state);
+        state.pastLayerStates.push(cloneDeep(state.layerState));
 
         state.layerState = {
-          ...deepClone(initialLayerState),
+          ...cloneDeep(initialLayerState),
           objects: [
             {
               kind: 'image',
@@ -262,7 +261,11 @@ export const canvasSlice = createSlice({
         return;
       }
 
-      pushToPrevLayerStates(state);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
+
+      if (state.pastLayerStates.length > MAX_HISTORY) {
+        state.pastLayerStates.shift();
+      }
 
       state.layerState.stagingArea.images.push({
         kind: 'image',
@@ -276,9 +279,13 @@ export const canvasSlice = createSlice({
       state.futureLayerStates = [];
     },
     discardStagedImages: (state) => {
-      pushToPrevLayerStates(state);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
 
-      state.layerState.stagingArea = deepClone(initialLayerState.stagingArea);
+      if (state.pastLayerStates.length > MAX_HISTORY) {
+        state.pastLayerStates.shift();
+      }
+
+      state.layerState.stagingArea = cloneDeep(cloneDeep(initialLayerState)).stagingArea;
 
       state.futureLayerStates = [];
       state.shouldShowStagingOutline = true;
@@ -287,7 +294,11 @@ export const canvasSlice = createSlice({
     },
     discardStagedImage: (state) => {
       const { images, selectedImageIndex } = state.layerState.stagingArea;
-      pushToPrevLayerStates(state);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
+
+      if (state.pastLayerStates.length > MAX_HISTORY) {
+        state.pastLayerStates.shift();
+      }
 
       if (!images.length) {
         return;
@@ -309,7 +320,11 @@ export const canvasSlice = createSlice({
     addFillRect: (state) => {
       const { boundingBoxCoordinates, boundingBoxDimensions, brushColor } = state;
 
-      pushToPrevLayerStates(state);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
+
+      if (state.pastLayerStates.length > MAX_HISTORY) {
+        state.pastLayerStates.shift();
+      }
 
       state.layerState.objects.push({
         kind: 'fillRect',
@@ -324,7 +339,11 @@ export const canvasSlice = createSlice({
     addEraseRect: (state) => {
       const { boundingBoxCoordinates, boundingBoxDimensions } = state;
 
-      pushToPrevLayerStates(state);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
+
+      if (state.pastLayerStates.length > MAX_HISTORY) {
+        state.pastLayerStates.shift();
+      }
 
       state.layerState.objects.push({
         kind: 'eraseRect',
@@ -348,7 +367,11 @@ export const canvasSlice = createSlice({
       // set & then spread this to only conditionally add the "color" key
       const newColor = layer === 'base' && tool === 'brush' ? { color: brushColor } : {};
 
-      pushToPrevLayerStates(state);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
+
+      if (state.pastLayerStates.length > MAX_HISTORY) {
+        state.pastLayerStates.shift();
+      }
 
       const newLine: CanvasMaskLine | CanvasBaseLine = {
         kind: 'line',
@@ -386,7 +409,11 @@ export const canvasSlice = createSlice({
         return;
       }
 
-      pushToFutureLayerStates(state);
+      state.futureLayerStates.unshift(cloneDeep(state.layerState));
+
+      if (state.futureLayerStates.length > MAX_HISTORY) {
+        state.futureLayerStates.pop();
+      }
 
       state.layerState = targetState;
     },
@@ -397,7 +424,11 @@ export const canvasSlice = createSlice({
         return;
       }
 
-      pushToPrevLayerStates(state);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
+
+      if (state.pastLayerStates.length > MAX_HISTORY) {
+        state.pastLayerStates.shift();
+      }
 
       state.layerState = targetState;
     },
@@ -414,8 +445,8 @@ export const canvasSlice = createSlice({
       state.shouldShowIntermediates = action.payload;
     },
     resetCanvas: (state) => {
-      pushToPrevLayerStates(state);
-      state.layerState = deepClone(initialLayerState);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
+      state.layerState = cloneDeep(initialLayerState);
       state.futureLayerStates = [];
       state.batchIds = [];
       state.boundingBoxCoordinates = {
@@ -509,7 +540,11 @@ export const canvasSlice = createSlice({
 
       const { images, selectedImageIndex } = state.layerState.stagingArea;
 
-      pushToPrevLayerStates(state);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
+
+      if (state.pastLayerStates.length > MAX_HISTORY) {
+        state.pastLayerStates.shift();
+      }
 
       const imageToCommit = images[selectedImageIndex];
 
@@ -518,7 +553,7 @@ export const canvasSlice = createSlice({
           ...imageToCommit,
         });
       }
-      state.layerState.stagingArea = deepClone(initialLayerState.stagingArea);
+      state.layerState.stagingArea = cloneDeep(initialLayerState).stagingArea;
 
       state.futureLayerStates = [];
       state.shouldShowStagingOutline = true;
@@ -588,7 +623,7 @@ export const canvasSlice = createSlice({
       };
     },
     setMergedCanvas: (state, action: PayloadAction<CanvasImage>) => {
-      pushToPrevLayerStates(state);
+      state.pastLayerStates.push(cloneDeep(state.layerState));
 
       state.futureLayerStates = [];
 
@@ -708,17 +743,3 @@ export const canvasPersistConfig: PersistConfig<CanvasState> = {
   migrate: migrateCanvasState,
   persistDenylist: [],
 };
-
-const pushToPrevLayerStates = (state: CanvasState) => {
-  state.pastLayerStates.push(deepClone(state.layerState));
-  if (state.pastLayerStates.length > MAX_HISTORY) {
-    state.pastLayerStates = state.pastLayerStates.slice(-MAX_HISTORY);
-  }
-};
-
-const pushToFutureLayerStates = (state: CanvasState) => {
-  state.futureLayerStates.unshift(deepClone(state.layerState));
-  if (state.futureLayerStates.length > MAX_HISTORY) {
-    state.futureLayerStates = state.futureLayerStates.slice(0, MAX_HISTORY);
-  }
-};

invokeai/frontend/web/src/features/controlAdapters/store/controlAdaptersSlice.ts

@@ -2,11 +2,10 @@ import type { PayloadAction, Update } from '@reduxjs/toolkit';
 import { createEntityAdapter, createSlice, isAnyOf } from '@reduxjs/toolkit';
 import { getSelectorsOptions } from 'app/store/createMemoizedSelector';
 import type { PersistConfig, RootState } from 'app/store/store';
-import { deepClone } from 'common/util/deepClone';
 import { buildControlAdapter } from 'features/controlAdapters/util/buildControlAdapter';
 import { buildControlAdapterProcessor } from 'features/controlAdapters/util/buildControlAdapterProcessor';
 import { zModelIdentifierField } from 'features/nodes/types/common';
-import { merge, uniq } from 'lodash-es';
+import { cloneDeep, merge, uniq } from 'lodash-es';
 import type { ControlNetModelConfig, IPAdapterModelConfig, T2IAdapterModelConfig } from 'services/api/types';
 import { socketInvocationError } from 'services/events/actions';
 import { v4 as uuidv4 } from 'uuid';
@@ -115,7 +114,7 @@ export const controlAdaptersSlice = createSlice({
         if (!controlAdapter) {
           return;
         }
-        const newControlAdapter = merge(deepClone(controlAdapter), {
+        const newControlAdapter = merge(cloneDeep(controlAdapter), {
           id: newId,
           isEnabled: true,
         });
@@ -271,7 +270,7 @@ export const controlAdaptersSlice = createSlice({
         return;
       }
 
-      const processorNode = merge(deepClone(cn.processorNode), params);
+      const processorNode = merge(cloneDeep(cn.processorNode), params);
 
       caAdapter.updateOne(state, {
         id,
@@ -294,7 +293,7 @@ export const controlAdaptersSlice = createSlice({
         return;
       }
 
-      const processorNode = deepClone(
+      const processorNode = cloneDeep(
         CONTROLNET_PROCESSORS[processorType].buildDefaults(cn.model?.base)
       ) as RequiredControlAdapterProcessorNode;
 
@@ -334,7 +333,7 @@ export const controlAdaptersSlice = createSlice({
       caAdapter.updateOne(state, update);
     },
     controlAdaptersReset: () => {
-      return deepClone(initialControlAdaptersState);
+      return cloneDeep(initialControlAdaptersState);
     },
     pendingControlImagesCleared: (state) => {
       state.pendingControlImages = [];
@@ -407,7 +406,7 @@ const migrateControlAdaptersState = (state: any): any => {
     state._version = 1;
   }
   if (state._version === 1) {
-    state = deepClone(initialControlAdaptersState);
+    state = cloneDeep(initialControlAdaptersState);
   }
   return state;
 };

invokeai/frontend/web/src/features/controlAdapters/util/buildControlAdapter.ts

@@ -1,4 +1,3 @@
-import { deepClone } from 'common/util/deepClone';
 import { CONTROLNET_PROCESSORS } from 'features/controlAdapters/store/constants';
 import type {
   ControlAdapterConfig,
@@ -8,7 +7,7 @@ import type {
   RequiredCannyImageProcessorInvocation,
   T2IAdapterConfig,
 } from 'features/controlAdapters/store/types';
-import { merge } from 'lodash-es';
+import { cloneDeep, merge } from 'lodash-es';
 
 export const initialControlNet: Omit<ControlNetConfig, 'id'> = {
   type: 'controlnet',
@@ -58,11 +57,11 @@ export const buildControlAdapter = (
 ): ControlAdapterConfig => {
   switch (type) {
     case 'controlnet':
-      return merge(deepClone(initialControlNet), { id, ...overrides });
+      return merge(cloneDeep(initialControlNet), { id, ...overrides });
     case 't2i_adapter':
-      return merge(deepClone(initialT2IAdapter), { id, ...overrides });
+      return merge(cloneDeep(initialT2IAdapter), { id, ...overrides });
     case 'ip_adapter':
-      return merge(deepClone(initialIPAdapter), { id, ...overrides });
+      return merge(cloneDeep(initialIPAdapter), { id, ...overrides });
     default:
       throw new Error(`Unknown control adapter type: ${type}`);
   }

invokeai/frontend/web/src/features/lora/store/loraSlice.ts

@@ -1,9 +1,9 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
-import { deepClone } from 'common/util/deepClone';
 import { zModelIdentifierField } from 'features/nodes/types/common';
 import type { ParameterLoRAModel } from 'features/parameters/types/parameterSchemas';
+import { cloneDeep } from 'lodash-es';
 import type { LoRAModelConfig } from 'services/api/types';
 
 export type LoRA = {
@@ -58,7 +58,7 @@ export const loraSlice = createSlice({
       }
       lora.isEnabled = isEnabled;
     },
-    lorasReset: () => deepClone(initialLoraState),
+    lorasReset: () => cloneDeep(initialLoraState),
   },
 });
 
@@ -74,7 +74,7 @@ const migrateLoRAState = (state: any): any => {
   }
   if (state._version === 1) {
     // Model type has changed, so we need to reset the state - too risky to migrate
-    state = deepClone(initialLoraState);
+    state = cloneDeep(initialLoraState);
   }
   return state;
 };

invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts

@@ -1,7 +1,6 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice, isAnyOf } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
-import { deepClone } from 'common/util/deepClone';
 import { workflowLoaded } from 'features/nodes/store/actions';
 import { SHARED_NODE_PROPERTIES } from 'features/nodes/types/constants';
 import type {
@@ -45,7 +44,7 @@ import {
 } from 'features/nodes/types/field';
 import type { AnyNode, InvocationTemplate, NodeExecutionState } from 'features/nodes/types/invocation';
 import { isInvocationNode, isNotesNode, zNodeStatus } from 'features/nodes/types/invocation';
-import { forEach } from 'lodash-es';
+import { cloneDeep, forEach } from 'lodash-es';
 import type {
   Connection,
   Edge,
@@ -572,23 +571,8 @@ export const nodesSlice = createSlice({
       );
     },
     selectionCopied: (state) => {
-      const nodesToCopy: AnyNode[] = [];
-      const edgesToCopy: Edge[] = [];
-
-      for (const node of state.nodes) {
-        if (node.selected) {
-          nodesToCopy.push(deepClone(node));
-        }
-      }
-
-      for (const edge of state.edges) {
-        if (edge.selected) {
-          edgesToCopy.push(deepClone(edge));
-        }
-      }
-
-      state.nodesToCopy = nodesToCopy;
-      state.edgesToCopy = edgesToCopy;
+      state.nodesToCopy = state.nodes.filter((n) => n.selected).map(cloneDeep);
+      state.edgesToCopy = state.edges.filter((e) => e.selected).map(cloneDeep);
 
       if (state.nodesToCopy.length > 0) {
         const averagePosition = { x: 0, y: 0 };
@@ -610,21 +594,11 @@ export const nodesSlice = createSlice({
     },
     selectionPasted: (state, action: PayloadAction<{ cursorPosition?: XYPosition }>) => {
       const { cursorPosition } = action.payload;
-      const newNodes: AnyNode[] = [];
-
-      for (const node of state.nodesToCopy) {
-        newNodes.push(deepClone(node));
-      }
-
+      const newNodes = state.nodesToCopy.map(cloneDeep);
       const oldNodeIds = newNodes.map((n) => n.data.id);
-
-      const newEdges: Edge[] = [];
-
-      for (const edge of state.edgesToCopy) {
-        if (oldNodeIds.includes(edge.source) && oldNodeIds.includes(edge.target)) {
-          newEdges.push(deepClone(edge));
-        }
-      }
+      const newEdges = state.edgesToCopy
+        .filter((e) => oldNodeIds.includes(e.source) && oldNodeIds.includes(e.target))
+        .map(cloneDeep);
 
       newEdges.forEach((e) => (e.selected = true));
 

invokeai/frontend/web/src/features/nodes/store/workflowSlice.ts

@@ -1,7 +1,6 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
-import { deepClone } from 'common/util/deepClone';
 import { workflowLoaded } from 'features/nodes/store/actions';
 import { isAnyNodeOrEdgeMutation, nodeEditorReset, nodesChanged, nodesDeleted } from 'features/nodes/store/nodesSlice';
 import type {
@@ -12,7 +11,7 @@ import type {
 import type { FieldIdentifier } from 'features/nodes/types/field';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import type { WorkflowCategory, WorkflowV3 } from 'features/nodes/types/workflow';
-import { isEqual, omit, uniqBy } from 'lodash-es';
+import { cloneDeep, isEqual, omit, uniqBy } from 'lodash-es';
 
 const blankWorkflow: Omit<WorkflowV3, 'nodes' | 'edges'> = {
   name: '',
@@ -132,8 +131,8 @@ export const workflowSlice = createSlice({
       });
 
       return {
-        ...deepClone(initialWorkflowState),
-        ...deepClone(workflowExtra),
+        ...cloneDeep(initialWorkflowState),
+        ...cloneDeep(workflowExtra),
         originalExposedFieldValues,
         mode: state.mode,
       };
@@ -145,7 +144,7 @@ export const workflowSlice = createSlice({
       });
     });
 
-    builder.addCase(nodeEditorReset, () => deepClone(initialWorkflowState));
+    builder.addCase(nodeEditorReset, () => cloneDeep(initialWorkflowState));
 
     builder.addCase(nodesChanged, (state, action) => {
       // Not all changes to nodes should result in the workflow being marked touched

invokeai/frontend/web/src/features/nodes/util/node/nodeUpdate.ts

@@ -1,9 +1,8 @@
-import { deepClone } from 'common/util/deepClone';
 import { satisfies } from 'compare-versions';
 import { NodeUpdateError } from 'features/nodes/types/error';
 import type { InvocationNode, InvocationTemplate } from 'features/nodes/types/invocation';
 import { zParsedSemver } from 'features/nodes/types/semver';
-import { defaultsDeep, keys, pick } from 'lodash-es';
+import { cloneDeep, defaultsDeep, keys, pick } from 'lodash-es';
 
 import { buildInvocationNode } from './buildInvocationNode';
 
@@ -51,7 +50,7 @@ export const updateNode = (node: InvocationNode, template: InvocationTemplate):
   // The updateability of a node, via semver comparison, relies on the this kind of recursive merge
   // being valid. We rely on the template's major version to be majorly incremented if this kind of
   // merge would result in an invalid node.
-  const clone = deepClone(node);
+  const clone = cloneDeep(node);
   clone.data.version = template.version;
   defaultsDeep(clone, defaults); // mutates!
 

invokeai/frontend/web/src/features/nodes/util/workflow/buildWorkflow.ts

@@ -1,12 +1,11 @@
 import { logger } from 'app/logging/logger';
-import { deepClone } from 'common/util/deepClone';
 import { parseify } from 'common/util/serialize';
 import type { NodesState, WorkflowsState } from 'features/nodes/store/types';
 import { isInvocationNode, isNotesNode } from 'features/nodes/types/invocation';
 import type { WorkflowV3 } from 'features/nodes/types/workflow';
 import { zWorkflowV3 } from 'features/nodes/types/workflow';
 import i18n from 'i18n';
-import { pick } from 'lodash-es';
+import { cloneDeep, pick } from 'lodash-es';
 import { fromZodError } from 'zod-validation-error';
 
 export type BuildWorkflowArg = {
@@ -31,7 +30,7 @@ const workflowKeys = [
 type BuildWorkflowFunction = (arg: BuildWorkflowArg) => WorkflowV3;
 
 export const buildWorkflowFast: BuildWorkflowFunction = ({ nodes, edges, workflow }: BuildWorkflowArg): WorkflowV3 => {
-  const clonedWorkflow = pick(deepClone(workflow), workflowKeys);
+  const clonedWorkflow = pick(cloneDeep(workflow), workflowKeys);
 
   const newWorkflow: WorkflowV3 = {
     ...clonedWorkflow,
@@ -44,14 +43,14 @@ export const buildWorkflowFast: BuildWorkflowFunction = ({ nodes, edges, workflo
       newWorkflow.nodes.push({
         id: node.id,
         type: node.type,
-        data: deepClone(node.data),
+        data: cloneDeep(node.data),
         position: { ...node.position },
       });
     } else if (isNotesNode(node) && node.type) {
       newWorkflow.nodes.push({
         id: node.id,
         type: node.type,
-        data: deepClone(node.data),
+        data: cloneDeep(node.data),
         position: { ...node.position },
       });
     }

invokeai/frontend/web/src/features/nodes/util/workflow/migrations.ts

@@ -1,5 +1,4 @@
 import { $store } from 'app/store/nanostores/store';
-import { deepClone } from 'common/util/deepClone';
 import { WorkflowMigrationError, WorkflowVersionError } from 'features/nodes/types/error';
 import type { FieldType } from 'features/nodes/types/field';
 import type { InvocationNodeData } from 'features/nodes/types/invocation';
@@ -12,7 +11,7 @@ import { zWorkflowV2 } from 'features/nodes/types/v2/workflow';
 import type { WorkflowV3 } from 'features/nodes/types/workflow';
 import { zWorkflowV3 } from 'features/nodes/types/workflow';
 import { t } from 'i18next';
-import { forEach } from 'lodash-es';
+import { cloneDeep, forEach } from 'lodash-es';
 import { z } from 'zod';
 
 /**
@@ -90,7 +89,7 @@ export const parseAndMigrateWorkflow = (data: unknown): WorkflowV3 => {
     throw new WorkflowVersionError(t('nodes.unableToGetWorkflowVersion'));
   }
 
-  let workflow = deepClone(data) as WorkflowV1 | WorkflowV2 | WorkflowV3;
+  let workflow = cloneDeep(data) as WorkflowV1 | WorkflowV2 | WorkflowV3;
 
   if (workflow.meta.version === '1.0.0') {
     const v1 = zWorkflowV1.parse(workflow);

invokeai/frontend/web/src/features/parameters/store/generationSlice.ts

@@ -280,7 +280,6 @@ const migrateGenerationState = (state: any): GenerationState => {
     // The signature of the model has changed, so we need to reset it
     state._version = 2;
     state.model = null;
-    state.canvasCoherenceMode = initialGenerationState.canvasCoherenceMode;
   }
   return state;
 };

invokeai/frontend/web/src/services/api/endpoints/queue.ts

@@ -192,7 +192,7 @@ export const queueApi = api.injectEndpoints({
       { batch_id: string }
     >({
       query: ({ batch_id }) => ({
-        url: buildQueueUrl(`b/${batch_id}/status`),
+        url: buildQueueUrl(`/b/${batch_id}/status`),
         method: 'GET',
       }),
       providesTags: (result) => {

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "4.0.1"
+__version__ = "4.0.0rc6"

tests/test_config.py

@@ -98,32 +98,6 @@ def test_migrate_v3_config_from_file(tmp_path: Path, patch_rootdir: None):
     assert not hasattr(config, "esrgan")
 
 
-@pytest.mark.parametrize(
-    "legacy_conf_dir,expected_value,expected_is_set",
-    [
-        # not set, expected value is the default value
-        ("configs/stable-diffusion", Path("configs"), False),
-        # not set, expected value is the default value
-        ("configs\\stable-diffusion", Path("configs"), False),
-        # set, best-effort resolution of the path
-        ("partial_custom_path/stable-diffusion", Path("partial_custom_path"), True),
-        # set, exact path
-        ("full/custom/path", Path("full/custom/path"), True),
-    ],
-)
-def test_migrate_v3_legacy_conf_dir_defaults(
-    tmp_path: Path, patch_rootdir: None, legacy_conf_dir: str, expected_value: Path, expected_is_set: bool
-):
-    """Test reading configuration from a file."""
-    config_content = f"InvokeAI:\n    Paths:\n        legacy_conf_dir: {legacy_conf_dir}"
-    temp_config_file = tmp_path / "temp_invokeai.yaml"
-    temp_config_file.write_text(config_content)
-
-    config = load_and_migrate_config(temp_config_file)
-    assert config.legacy_conf_dir == expected_value
-    assert ("legacy_conf_dir" in config.model_fields_set) is expected_is_set
-
-
 def test_migrate_v3_backup(tmp_path: Path, patch_rootdir: None):
     """Test the backup of the config file."""
     temp_config_file = tmp_path / "temp_invokeai.yaml"

tests/test_sqlite_migrator.py

@@ -250,32 +250,6 @@ def test_migrator_runs_all_migrations_file(logger: Logger) -> None:
         db.conn.close()
 
 
-def test_migrator_backs_up_db(logger: Logger) -> None:
-    with TemporaryDirectory() as tempdir:
-        original_db_path = Path(tempdir) / "invokeai.db"
-        db = SqliteDatabase(db_path=original_db_path, logger=logger, verbose=False)
-        # Write some data to the db to test for successful backup
-        temp_cursor = db.conn.cursor()
-        temp_cursor.execute("CREATE TABLE test (id INTEGER PRIMARY KEY);")
-        db.conn.commit()
-        # Set up the migrator
-        migrator = SqliteMigrator(db=db)
-        migrations = [Migration(from_version=i, to_version=i + 1, callback=create_migrate(i)) for i in range(0, 3)]
-        for migration in migrations:
-            migrator.register_migration(migration)
-        migrator.run_migrations()
-        # Must manually close else we get an error on Windows
-        db.conn.close()
-        assert original_db_path.exists()
-        # We should have a backup file when we migrated a file db
-        assert migrator._backup_path
-        # Check that the test table exists as a proxy for successful backup
-        with closing(sqlite3.connect(migrator._backup_path)) as backup_db_conn:
-            backup_db_cursor = backup_db_conn.cursor()
-            backup_db_cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='test';")
-            assert backup_db_cursor.fetchone() is not None
-
-
 def test_migrator_makes_no_changes_on_failed_migration(
     migrator: SqliteMigrator, migration_no_op: Migration, failing_migrate_callback: MigrateCallback
 ) -> None: