fix(backend): use random seed for SDE & Ancestral schedulers

SDE and Ancestral schedulers use some randomness at each step when applying conditioning. We were using a static seed of `0` for this, regardless of the initial noise used. This could cause results to be a bit same-y.

Unfortunately, we do not have easy access to the seed used to create the initial noise at this time.

Changing this to use a random seed value instead of always 0.

invokeai/app/api/routers/images.py

@@ -1,20 +1,22 @@
 import io
 from typing import Optional
 
-from PIL import Image
 from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
-from pydantic import BaseModel
+from PIL import Image
+from pydantic import BaseModel, Field
 
 from invokeai.app.invocations.metadata import ImageMetadata
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
 from invokeai.app.services.image_record_storage import OffsetPaginatedResults
+from invokeai.app.services.item_storage import PaginatedResults
 from invokeai.app.services.models.image_record import (
     ImageDTO,
     ImageRecordChanges,
     ImageUrlsDTO,
 )
+
 from ..dependencies import ApiDependencies
 
 images_router = APIRouter(prefix="/v1/images", tags=["images"])
@@ -150,9 +152,8 @@ async def get_image_metadata(
         raise HTTPException(status_code=404)
 
 
-@images_router.api_route(
+@images_router.get(
     "/i/{image_name}/full",
-    methods=["GET", "HEAD"],
     operation_id="get_image_full",
     response_class=Response,
     responses={

invokeai/app/invocations/image.py

@@ -661,23 +661,27 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to adjust")
-    hue: int = Field(default=0, description="The degrees by which to rotate the hue, 0-360")
+    hue: int = Field(default=0, description="The degrees by which to rotate the hue")
     # fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         pil_image = context.services.images.get_pil_image(self.image.image_name)
 
+        # Convert PIL image to OpenCV format (numpy array), note color channel
+        # ordering is changed from RGB to BGR
+        image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
+
         # Convert image to HSV color space
-        hsv_image = numpy.array(pil_image.convert("HSV"))
+        hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
 
-        # Convert hue from 0..360 to 0..256
-        hue = int(256 * ((self.hue % 360) / 360))
+        # Adjust the hue
+        hsv_image[:, :, 0] = (hsv_image[:, :, 0] + self.hue) % 180
 
-        # Increment each hue and wrap around at 255
-        hsv_image[:, :, 0] = (hsv_image[:, :, 0] + hue) % 256
+        # Convert image back to BGR color space
+        image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
 
         # Convert back to PIL format and to original color mode
-        pil_image = Image.fromarray(hsv_image, mode="HSV").convert("RGBA")
+        pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
 
         image_dto = context.services.images.create(
             image=pil_image,

invokeai/app/invocations/latent.py

@@ -180,6 +180,10 @@ class TextToLatentsInvocation(BaseInvocation):
         negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
         uc = negative_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
 
+        # for ancestral and sde schedulers
+        generator = torch.Generator(device=unet.device)
+        generator.seed()
+
         conditioning_data = ConditioningData(
             unconditioned_embeddings=uc,
             text_embeddings=c,
@@ -198,7 +202,7 @@ class TextToLatentsInvocation(BaseInvocation):
             # for ddim scheduler
             eta=0.0,  # ddim_eta
             # for ancestral and sde schedulers
-            generator=torch.Generator(device=unet.device).manual_seed(0),
+            generator=generator,
         )
         return conditioning_data
 

invokeai/app/services/config.py

@@ -24,10 +24,11 @@ InvokeAI:
     sequential_guidance: false
     precision: float16
     max_cache_size: 6
-    max_vram_cache_size: 0.5
+    max_vram_cache_size: 2.7
     always_use_cpu: false
     free_gpu_mem: false
   Features:
+    restore: true
     esrgan: true
     patchmatch: true
     internet_available: true
@@ -164,7 +165,7 @@ import pydoc
 import os
 import sys
 from argparse import ArgumentParser
-from omegaconf import OmegaConf, DictConfig, ListConfig
+from omegaconf import OmegaConf, DictConfig
 from pathlib import Path
 from pydantic import BaseSettings, Field, parse_obj_as
 from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
@@ -172,7 +173,6 @@ from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_ty
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
 LEGACY_INIT_FILE = Path("invokeai.init")
-DEFAULT_MAX_VRAM = 0.5
 
 
 class InvokeAISettings(BaseSettings):
@@ -189,12 +189,7 @@ class InvokeAISettings(BaseSettings):
         opt = parser.parse_args(argv)
         for name in self.__fields__:
             if name not in self._excluded():
-                value = getattr(opt, name)
-                if isinstance(value, ListConfig):
-                    value = list(value)
-                elif isinstance(value, DictConfig):
-                    value = dict(value)
-                setattr(self, name, value)
+                setattr(self, name, getattr(opt, name))
 
     def to_yaml(self) -> str:
         """
@@ -287,10 +282,14 @@ class InvokeAISettings(BaseSettings):
         return [
             "type",
             "initconf",
+            "gpu_mem_reserved",
+            "max_loaded_models",
             "version",
             "from_file",
             "model",
+            "restore",
             "root",
+            "nsfw_checker",
         ]
 
     class Config:
@@ -389,11 +388,15 @@ class InvokeAIAppConfig(InvokeAISettings):
     internet_available  : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
     log_tokenization    : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
     patchmatch          : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
+    restore             : bool = Field(default=True, description="Enable/disable face restoration code (DEPRECATED)", category='DEPRECATED')
 
     always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
     free_gpu_mem        : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
+    max_loaded_models   : int = Field(default=3, gt=0, description="(DEPRECATED: use max_cache_size) Maximum number of models to keep in memory for rapid switching", category='DEPRECATED')
     max_cache_size      : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
     max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
+    gpu_mem_reserved    : float = Field(default=2.75, ge=0, description="DEPRECATED: use max_vram_cache_size. Amount of VRAM reserved for model storage", category='DEPRECATED')
+    nsfw_checker        : bool = Field(default=True, description="DEPRECATED: use Web settings to enable/disable", category='DEPRECATED')
     precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Memory/Performance')
     sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
     xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
@@ -411,7 +414,9 @@ class InvokeAIAppConfig(InvokeAISettings):
     outdir              : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
     from_file           : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
     use_memory_db       : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
-    ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
+    ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert')
+
+    model               : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models')
 
     log_handlers        : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
     # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
@@ -421,9 +426,6 @@ class InvokeAIAppConfig(InvokeAISettings):
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
     # fmt: on
 
-    class Config:
-        validate_assignment = True
-
     def parse_args(self, argv: List[str] = None, conf: DictConfig = None, clobber=False):
         """
         Update settings with contents of init file, environment, and

invokeai/backend/install/invokeai_configure.py

@@ -10,15 +10,12 @@ import sys
 import argparse
 import io
 import os
-import psutil
 import shutil
 import textwrap
-import torch
 import traceback
 import yaml
 import warnings
 from argparse import Namespace
-from enum import Enum
 from pathlib import Path
 from shutil import get_terminal_size
 from typing import get_type_hints
@@ -47,8 +44,6 @@ from invokeai.app.services.config import (
 )
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
-
-# TO DO - Move all the frontend code into invokeai.frontend.install
 from invokeai.frontend.install.widgets import (
     SingleSelectColumns,
     CenteredButtonPress,
@@ -58,7 +53,6 @@ from invokeai.frontend.install.widgets import (
     CyclingForm,
     MIN_COLS,
     MIN_LINES,
-    WindowTooSmallException,
 )
 from invokeai.backend.install.legacy_arg_parsing import legacy_parser
 from invokeai.backend.install.model_install_backend import (
@@ -67,7 +61,6 @@ from invokeai.backend.install.model_install_backend import (
     ModelInstall,
 )
 from invokeai.backend.model_management.model_probe import ModelType, BaseModelType
-from pydantic.error_wrappers import ValidationError
 
 warnings.filterwarnings("ignore")
 transformers.logging.set_verbosity_error()
@@ -83,13 +76,6 @@ Default_config_file = config.model_conf_path
 SD_Configs = config.legacy_conf_path
 
 PRECISION_CHOICES = ["auto", "float16", "float32"]
-GB = 1073741824  # GB in bytes
-HAS_CUDA = torch.cuda.is_available()
-_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0, 0)
-
-
-MAX_VRAM /= GB
-MAX_RAM = psutil.virtual_memory().total / GB
 
 INIT_FILE_PREAMBLE = """# InvokeAI initialization file
 # This is the InvokeAI initialization file, which contains command-line default values.
@@ -100,12 +86,6 @@ INIT_FILE_PREAMBLE = """# InvokeAI initialization file
 logger = InvokeAILogger.getLogger()
 
 
-class DummyWidgetValue(Enum):
-    zero = 0
-    true = True
-    false = False
-
-
 # --------------------------------------------
 def postscript(errors: None):
     if not any(errors):
@@ -398,35 +378,13 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
         )
         self.max_cache_size = self.add_widget_intelligent(
             IntTitleSlider,
-            name="RAM cache size (GB). Make this at least large enough to hold a single full model.",
+            name="Size of the RAM cache used for fast model switching (GB)",
             value=old_opts.max_cache_size,
-            out_of=MAX_RAM,
+            out_of=20,
             lowest=3,
             begin_entry_at=6,
             scroll_exit=True,
         )
-        if HAS_CUDA:
-            self.nextrely += 1
-            self.add_widget_intelligent(
-                npyscreen.TitleFixedText,
-                name="VRAM cache size (GB). Reserving a small amount of VRAM will modestly speed up the start of image generation.",
-                begin_entry_at=0,
-                editable=False,
-                color="CONTROL",
-                scroll_exit=True,
-            )
-            self.nextrely -= 1
-            self.max_vram_cache_size = self.add_widget_intelligent(
-                npyscreen.Slider,
-                value=old_opts.max_vram_cache_size,
-                out_of=round(MAX_VRAM * 2) / 2,
-                lowest=0.0,
-                relx=8,
-                step=0.25,
-                scroll_exit=True,
-            )
-        else:
-            self.max_vram_cache_size = DummyWidgetValue.zero
         self.nextrely += 1
         self.outdir = self.add_widget_intelligent(
             FileBox,
@@ -443,7 +401,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
         self.autoimport_dirs = {}
         self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent(
             FileBox,
-            name="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
+            name=f"Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
             value=str(config.root_path / config.autoimport_dir),
             select_dir=True,
             must_exist=False,
@@ -518,7 +476,6 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
             "outdir",
             "free_gpu_mem",
             "max_cache_size",
-            "max_vram_cache_size",
             "xformers_enabled",
             "always_use_cpu",
         ]:
@@ -635,13 +592,13 @@ def maybe_create_models_yaml(root: Path):
 
 # -------------------------------------
 def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace, Namespace):
+    # parse_args() will read from init file if present
     invokeai_opts = default_startup_options(initfile)
     invokeai_opts.root = program_opts.root
 
-    if not set_min_terminal_size(MIN_COLS, MIN_LINES):
-        raise WindowTooSmallException(
-            "Could not increase terminal size. Try running again with a larger window or smaller font size."
-        )
+    # The third argument is needed in the Windows 11 environment to
+    # launch a console window running this program.
+    set_min_terminal_size(MIN_COLS, MIN_LINES)
 
     # the install-models application spawns a subprocess to install
     # models, and will crash unless this is set before running.
@@ -697,13 +654,10 @@ def migrate_init_file(legacy_format: Path):
     old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
     new = InvokeAIAppConfig.get_config()
 
-    fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"]
+    fields = list(get_type_hints(InvokeAIAppConfig).keys())
     for attr in fields:
         if hasattr(old, attr):
-            try:
-                setattr(new, attr, getattr(old, attr))
-            except ValidationError as e:
-                print(f"* Ignoring incompatible value for field {attr}:\n  {str(e)}")
+            setattr(new, attr, getattr(old, attr))
 
     # a few places where the field names have changed and we have to
     # manually add in the new names/values
@@ -823,7 +777,6 @@ def main():
 
         models_to_download = default_user_selections(opt)
         new_init_file = config.root_path / "invokeai.yaml"
-
         if opt.yes_to_all:
             write_default_options(opt, new_init_file)
             init_options = Namespace(precision="float32" if opt.full_precision else "float16")
@@ -849,8 +802,6 @@ def main():
         postscript(errors=errors)
         if not opt.yes_to_all:
             input("Press any key to continue...")
-    except WindowTooSmallException as e:
-        logger.error(str(e))
     except KeyboardInterrupt:
         print("\nGoodbye! Come back soon.")
 

invokeai/backend/model_management/model_manager.py

@@ -228,19 +228,19 @@ the root is the InvokeAI ROOTDIR.
 """
 from __future__ import annotations
 
-import hashlib
 import os
+import hashlib
 import textwrap
-import types
+import yaml
 from dataclasses import dataclass
 from pathlib import Path
+from typing import Literal, Optional, List, Tuple, Union, Dict, Set, Callable, types
 from shutil import rmtree, move
-from typing import Optional, List, Literal, Tuple, Union, Dict, Set, Callable
 
 import torch
-import yaml
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
+
 from pydantic import BaseModel, Field
 
 import invokeai.backend.util.logging as logger
@@ -259,7 +259,6 @@ from .models import (
     ModelNotFoundException,
     InvalidModelException,
     DuplicateModelException,
-    ModelBase,
 )
 
 # We are only starting to number the config file with release 3.
@@ -362,7 +361,7 @@ class ModelManager(object):
             if model_key.startswith("_"):
                 continue
             model_name, base_model, model_type = self.parse_key(model_key)
-            model_class = self._get_implementation(base_model, model_type)
+            model_class = MODEL_CLASSES[base_model][model_type]
             # alias for config file
             model_config["model_format"] = model_config.pop("format")
             self.models[model_key] = model_class.create_config(**model_config)
@@ -382,24 +381,18 @@ class ModelManager(object):
         # causing otherwise unreferenced models to be removed from memory
         self._read_models()
 
-    def model_exists(self, model_name: str, base_model: BaseModelType, model_type: ModelType, *, rescan=False) -> bool:
+    def model_exists(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+    ) -> bool:
         """
-        Given a model name, returns True if it is a valid identifier.
-
-        :param model_name: symbolic name of the model in models.yaml
-        :param model_type: ModelType enum indicating the type of model to return
-        :param base_model: BaseModelType enum indicating the base model used by this model
-        :param rescan: if True, scan_models_directory
+        Given a model name, returns True if it is a valid
+        identifier.
         """
         model_key = self.create_key(model_name, base_model, model_type)
-        exists = model_key in self.models
-
-        # if model not found try to find it (maybe file just pasted)
-        if rescan and not exists:
-            self.scan_models_directory(base_model=base_model, model_type=model_type)
-            exists = self.model_exists(model_name, base_model, model_type, rescan=False)
-
-        return exists
+        return model_key in self.models
 
     @classmethod
     def create_key(
@@ -450,32 +443,39 @@ class ModelManager(object):
         :param model_name: symbolic name of the model in models.yaml
         :param model_type: ModelType enum indicating the type of model to return
         :param base_model: BaseModelType enum indicating the base model used by this model
-        :param submodel_type: an ModelType enum indicating the portion of
+        :param submode_typel: an ModelType enum indicating the portion of
                the model to retrieve (e.g. ModelType.Vae)
         """
+        model_class = MODEL_CLASSES[base_model][model_type]
         model_key = self.create_key(model_name, base_model, model_type)
 
-        if not self.model_exists(model_name, base_model, model_type, rescan=True):
-            raise ModelNotFoundException(f"Model not found - {model_key}")
+        # if model not found try to find it (maybe file just pasted)
+        if model_key not in self.models:
+            self.scan_models_directory(base_model=base_model, model_type=model_type)
+            if model_key not in self.models:
+                raise ModelNotFoundException(f"Model not found - {model_key}")
 
-        model_config = self._get_model_config(base_model, model_name, model_type)
-
-        model_path, is_submodel_override = self._get_model_path(model_config, submodel_type)
-
-        if is_submodel_override:
-            model_type = submodel_type
-            submodel_type = None
-
-        model_class = self._get_implementation(base_model, model_type)
+        model_config = self.models[model_key]
+        model_path = self.resolve_model_path(model_config.path)
 
         if not model_path.exists():
             if model_class.save_to_config:
                 self.models[model_key].error = ModelError.NotFound
-                raise Exception(f'Files for model "{model_key}" not found at {model_path}')
+                raise Exception(f'Files for model "{model_key}" not found')
 
             else:
                 self.models.pop(model_key, None)
-                raise ModelNotFoundException(f'Files for model "{model_key}" not found at {model_path}')
+                raise ModelNotFoundException(f"Model not found - {model_key}")
+
+        # vae/movq override
+        # TODO:
+        if submodel_type is not None and hasattr(model_config, submodel_type):
+            override_path = getattr(model_config, submodel_type)
+            if override_path:
+                model_path = self.resolve_path(override_path)
+                model_type = submodel_type
+                submodel_type = None
+                model_class = MODEL_CLASSES[base_model][model_type]
 
         # TODO: path
         # TODO: is it accurate to use path as id
@@ -513,55 +513,6 @@ class ModelManager(object):
             _cache=self.cache,
         )
 
-    def _get_model_path(
-        self, model_config: ModelConfigBase, submodel_type: Optional[SubModelType] = None
-    ) -> (Path, bool):
-        """Extract a model's filesystem path from its config.
-
-        :return: The fully qualified Path of the module (or submodule).
-        """
-        model_path = model_config.path
-        is_submodel_override = False
-
-        # Does the config explicitly override the submodel?
-        if submodel_type is not None and hasattr(model_config, submodel_type):
-            submodel_path = getattr(model_config, submodel_type)
-            if submodel_path is not None:
-                model_path = getattr(model_config, submodel_type)
-                is_submodel_override = True
-
-        model_path = self.resolve_model_path(model_path)
-        return model_path, is_submodel_override
-
-    def _get_model_config(self, base_model: BaseModelType, model_name: str, model_type: ModelType) -> ModelConfigBase:
-        """Get a model's config object."""
-        model_key = self.create_key(model_name, base_model, model_type)
-        try:
-            model_config = self.models[model_key]
-        except KeyError:
-            raise ModelNotFoundException(f"Model not found - {model_key}")
-        return model_config
-
-    def _get_implementation(self, base_model: BaseModelType, model_type: ModelType) -> type[ModelBase]:
-        """Get the concrete implementation class for a specific model type."""
-        model_class = MODEL_CLASSES[base_model][model_type]
-        return model_class
-
-    def _instantiate(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> ModelBase:
-        """Make a new instance of this model, without loading it."""
-        model_config = self._get_model_config(base_model, model_name, model_type)
-        model_path, is_submodel_override = self._get_model_path(model_config, submodel_type)
-        # FIXME: do non-overriden submodels get the right class?
-        constructor = self._get_implementation(base_model, model_type)
-        instance = constructor(model_path, base_model, model_type)
-        return instance
-
     def model_info(
         self,
         model_name: str,
@@ -595,10 +546,9 @@ class ModelManager(object):
         the combined format of the list_models() method.
         """
         models = self.list_models(base_model, model_type, model_name)
-        if len(models) >= 1:
+        if len(models) > 1:
             return models[0]
-        else:
-            return None
+        return None
 
     def list_models(
         self,
@@ -710,7 +660,7 @@ class ModelManager(object):
         if path := model_attributes.get("path"):
             model_attributes["path"] = str(self.relative_model_path(Path(path)))
 
-        model_class = self._get_implementation(base_model, model_type)
+        model_class = MODEL_CLASSES[base_model][model_type]
         model_config = model_class.create_config(**model_attributes)
         model_key = self.create_key(model_name, base_model, model_type)
 
@@ -901,7 +851,7 @@ class ModelManager(object):
 
         for model_key, model_config in self.models.items():
             model_name, base_model, model_type = self.parse_key(model_key)
-            model_class = self._get_implementation(base_model, model_type)
+            model_class = MODEL_CLASSES[base_model][model_type]
             if model_class.save_to_config:
                 # TODO: or exclude_unset better fits here?
                 data_to_save[model_key] = model_config.dict(exclude_defaults=True, exclude={"error"})
@@ -959,7 +909,7 @@ class ModelManager(object):
 
                 model_path = self.resolve_model_path(model_config.path).absolute()
                 if not model_path.exists():
-                    model_class = self._get_implementation(cur_base_model, cur_model_type)
+                    model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
                     if model_class.save_to_config:
                         model_config.error = ModelError.NotFound
                         self.models.pop(model_key, None)
@@ -975,7 +925,7 @@ class ModelManager(object):
                 for cur_model_type in ModelType:
                     if model_type is not None and cur_model_type != model_type:
                         continue
-                    model_class = self._get_implementation(cur_base_model, cur_model_type)
+                    model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
                     models_dir = self.resolve_model_path(Path(cur_base_model.value, cur_model_type.value))
 
                     if not models_dir.exists():
@@ -991,9 +941,7 @@ class ModelManager(object):
                                     raise DuplicateModelException(f"Model with key {model_key} added twice")
 
                                 model_path = self.relative_model_path(model_path)
-                                model_config: ModelConfigBase = model_class.probe_config(
-                                    str(model_path), model_base=cur_base_model
-                                )
+                                model_config: ModelConfigBase = model_class.probe_config(str(model_path))
                                 self.models[model_key] = model_config
                                 new_models_found = True
                             except DuplicateModelException as e:

invokeai/backend/model_management/models/sdxl.py

@@ -80,10 +80,8 @@ class StableDiffusionXLModel(DiffusersModel):
             raise Exception("Unkown stable diffusion 2.* model format")
 
         if ckpt_config_path is None:
-            # avoid circular import
-            from .stable_diffusion import _select_ckpt_config
-
-            ckpt_config_path = _select_ckpt_config(kwargs.get("model_base", BaseModelType.StableDiffusionXL), variant)
+            # TO DO: implement picking
+            pass
 
         return cls.create_config(
             path=path,

invokeai/backend/model_management/models/vae.py

@@ -1,14 +1,9 @@
 import os
+import torch
+import safetensors
 from enum import Enum
 from pathlib import Path
-from typing import Optional
-
-import safetensors
-import torch
-from diffusers.utils import is_safetensors_available
-from omegaconf import OmegaConf
-
-from invokeai.app.services.config import InvokeAIAppConfig
+from typing import Optional, Union, Literal
 from .base import (
     ModelBase,
     ModelConfigBase,
@@ -23,6 +18,9 @@ from .base import (
     InvalidModelException,
     ModelNotFoundException,
 )
+from invokeai.app.services.config import InvokeAIAppConfig
+from diffusers.utils import is_safetensors_available
+from omegaconf import OmegaConf
 
 
 class VaeModelFormat(str, Enum):
@@ -82,7 +80,7 @@ class VaeModel(ModelBase):
     @classmethod
     def detect_format(cls, path: str):
         if not os.path.exists(path):
-            raise ModelNotFoundException(f"Does not exist as local file: {path}")
+            raise ModelNotFoundException()
 
         if os.path.isdir(path):
             if os.path.exists(os.path.join(path, "config.json")):

invokeai/frontend/install/model_install.py

@@ -28,6 +28,7 @@ from npyscreen import widget
 from invokeai.backend.util.logging import InvokeAILogger
 
 from invokeai.backend.install.model_install_backend import (
+    ModelInstallList,
     InstallSelections,
     ModelInstall,
     SchedulerPredictionType,
@@ -40,12 +41,12 @@ from invokeai.frontend.install.widgets import (
     SingleSelectColumns,
     TextBox,
     BufferBox,
+    FileBox,
     set_min_terminal_size,
     select_stable_diffusion_config_file,
     CyclingForm,
     MIN_COLS,
     MIN_LINES,
-    WindowTooSmallException,
 )
 from invokeai.app.services.config import InvokeAIAppConfig
 
@@ -155,7 +156,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             BufferBox,
             name="Log Messages",
             editable=False,
-            max_height=6,
+            max_height=15,
         )
 
         self.nextrely += 1
@@ -692,11 +693,7 @@ def select_and_download_models(opt: Namespace):
         # needed to support the probe() method running under a subprocess
         torch.multiprocessing.set_start_method("spawn")
 
-        if not set_min_terminal_size(MIN_COLS, MIN_LINES):
-            raise WindowTooSmallException(
-                "Could not increase terminal size. Try running again with a larger window or smaller font size."
-            )
-
+        set_min_terminal_size(MIN_COLS, MIN_LINES)
         installApp = AddModelApplication(opt)
         try:
             installApp.run()
@@ -790,8 +787,6 @@ def main():
         curses.echo()
         curses.endwin()
         logger.info("Goodbye! Come back soon.")
-    except WindowTooSmallException as e:
-        logger.error(str(e))
     except widget.NotEnoughSpaceForWidget as e:
         if str(e).startswith("Height of 1 allocated"):
             logger.error("Insufficient vertical space for the interface. Please make your window taller and try again")

invokeai/frontend/install/widgets.py

@@ -21,40 +21,31 @@ MIN_COLS = 130
 MIN_LINES = 38
 
 
-class WindowTooSmallException(Exception):
-    pass
-
-
 # -------------------------------------
-def set_terminal_size(columns: int, lines: int) -> bool:
+def set_terminal_size(columns: int, lines: int):
+    ts = get_terminal_size()
+    width = max(columns, ts.columns)
+    height = max(lines, ts.lines)
+
     OS = platform.uname().system
-    screen_ok = False
-    while not screen_ok:
-        ts = get_terminal_size()
-        width = max(columns, ts.columns)
-        height = max(lines, ts.lines)
+    if OS == "Windows":
+        pass
+        # not working reliably - ask user to adjust the window
+        # _set_terminal_size_powershell(width,height)
+    elif OS in ["Darwin", "Linux"]:
+        _set_terminal_size_unix(width, height)
 
-        if OS == "Windows":
-            pass
-            # not working reliably - ask user to adjust the window
-            # _set_terminal_size_powershell(width,height)
-        elif OS in ["Darwin", "Linux"]:
-            _set_terminal_size_unix(width, height)
-
-        # check whether it worked....
-        ts = get_terminal_size()
-        if ts.columns < columns or ts.lines < lines:
-            print(
-                f"\033[1mThis window is too small for the interface. InvokeAI requires {columns}x{lines} (w x h) characters, but window is {ts.columns}x{ts.lines}\033[0m"
-            )
-            resp = input(
-                "Maximize the window and/or decrease the font size then press any key to continue. Type [Q] to give up.."
-            )
-            if resp.upper().startswith("Q"):
-                break
-        else:
-            screen_ok = True
-    return screen_ok
+    # check whether it worked....
+    ts = get_terminal_size()
+    pause = False
+    if ts.columns < columns:
+        print("\033[1mThis window is too narrow for the user interface.\033[0m")
+        pause = True
+    if ts.lines < lines:
+        print("\033[1mThis window is too short for the user interface.\033[0m")
+        pause = True
+    if pause:
+        input("Maximize the window then press any key to continue..")
 
 
 def _set_terminal_size_powershell(width: int, height: int):
@@ -89,14 +80,14 @@ def _set_terminal_size_unix(width: int, height: int):
     sys.stdout.flush()
 
 
-def set_min_terminal_size(min_cols: int, min_lines: int) -> bool:
+def set_min_terminal_size(min_cols: int, min_lines: int):
     # make sure there's enough room for the ui
     term_cols, term_lines = get_terminal_size()
     if term_cols >= min_cols and term_lines >= min_lines:
-        return True
+        return
     cols = max(term_cols, min_cols)
     lines = max(term_lines, min_lines)
-    return set_terminal_size(cols, lines)
+    set_terminal_size(cols, lines)
 
 
 class IntSlider(npyscreen.Slider):
@@ -173,7 +164,7 @@ class FloatSlider(npyscreen.Slider):
 
 
 class FloatTitleSlider(npyscreen.TitleText):
-    _entry_type = npyscreen.Slider
+    _entry_type = FloatSlider
 
 
 class SelectColumnBase:

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-31ab5c15.js

@@ -1,4 +1,4 @@
-import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-dd054634.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-b42141e3.js";var za=String.raw,Ca=za`
+import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-de589048.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-11348abc.js";var za=String.raw,Ca=za`
   :root,
   :host {
     --chakra-vh: 100vh;

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-dd054634.js"></script>
+    <script type="module" crossorigin src="./assets/index-de589048.js"></script>
   </head>
 
   <body dir="ltr">

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

@@ -96,8 +96,7 @@ export type AppFeature =
   | 'consoleLogging'
   | 'dynamicPrompting'
   | 'batches'
-  | 'syncModels'
-  | 'multiselect';
+  | 'syncModels';
 
 /**
  * A disable-able Stable Diffusion feature

invokeai/frontend/web/src/features/gallery/hooks/useMultiselect.ts.ts

@@ -9,7 +9,6 @@ import { useListImagesQuery } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
 import { selectionChanged } from '../store/gallerySlice';
 import { imagesSelectors } from 'services/api/util';
-import { useFeatureStatus } from '../../system/hooks/useFeatureStatus';
 
 const selector = createSelector(
   [stateSelector, selectListImagesBaseQueryArgs],
@@ -34,18 +33,11 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
     }),
   });
 
-  const isMultiSelectEnabled = useFeatureStatus('multiselect').isFeatureEnabled;
-
   const handleClick = useCallback(
     (e: MouseEvent<HTMLDivElement>) => {
       if (!imageDTO) {
         return;
       }
-      if (!isMultiSelectEnabled) {
-        dispatch(selectionChanged([imageDTO]));
-        return;
-      }
-
       if (e.shiftKey) {
         const rangeEndImageName = imageDTO.image_name;
         const lastSelectedImage = selection[selection.length - 1]?.image_name;
@@ -79,7 +71,7 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
         dispatch(selectionChanged([imageDTO]));
       }
     },
-    [dispatch, imageDTO, imageDTOs, selection, isMultiSelectEnabled]
+    [dispatch, imageDTO, imageDTOs, selection]
   );
 
   const isSelected = useMemo(

invokeai/frontend/web/src/features/lora/components/ParamLoraCollapse.tsx

@@ -31,7 +31,7 @@ const ParamLoraCollapse = () => {
   }
 
   return (
-    <IAICollapse label="LoRA" activeLabel={activeLabel}>
+    <IAICollapse label={'LoRA'} activeLabel={activeLabel}>
       <Flex sx={{ flexDir: 'column', gap: 2 }}>
         <ParamLoRASelect />
         <ParamLoraList />

invokeai/frontend/web/src/features/lora/components/ParamLoraList.tsx

@@ -1,4 +1,3 @@
-import { Divider } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
@@ -9,21 +8,20 @@ import ParamLora from './ParamLora';
 const selector = createSelector(
   stateSelector,
   ({ lora }) => {
-    return { lorasArray: map(lora.loras) };
+    const { loras } = lora;
+
+    return { loras };
   },
   defaultSelectorOptions
 );
 
 const ParamLoraList = () => {
-  const { lorasArray } = useAppSelector(selector);
+  const { loras } = useAppSelector(selector);
 
   return (
     <>
-      {lorasArray.map((lora, i) => (
-        <>
-          {i > 0 && <Divider key={`${lora.model_name}-divider`} pt={1} />}
-          <ParamLora key={lora.model_name} lora={lora} />
-        </>
+      {map(loras, (lora) => (
+        <ParamLora key={lora.model_name} lora={lora} />
       ))}
     </>
   );

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts

@@ -9,6 +9,7 @@ import {
   CLIP_SKIP,
   LORA_LOADER,
   MAIN_MODEL_LOADER,
+  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
@@ -35,11 +36,15 @@ export const addLoRAsToGraph = (
     | undefined;
 
   if (loraCount > 0) {
-    // Remove modelLoaderNodeId unet connection to feed it to LoRAs
+    // Remove MAIN_MODEL_LOADER unet connection to feed it to LoRAs
     graph.edges = graph.edges.filter(
       (e) =>
         !(
-          e.source.node_id === modelLoaderNodeId &&
+          e.source.node_id === MAIN_MODEL_LOADER &&
+          ['unet'].includes(e.source.field)
+        ) &&
+        !(
+          e.source.node_id === ONNX_MODEL_LOADER &&
           ['unet'].includes(e.source.field)
         )
     );

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts

@@ -1,212 +0,0 @@
-import { RootState } from 'app/store/store';
-import { NonNullableGraph } from 'features/nodes/types/types';
-import { forEach, size } from 'lodash-es';
-import {
-  MetadataAccumulatorInvocation,
-  SDXLLoraLoaderInvocation,
-} from 'services/api/types';
-import {
-  LORA_LOADER,
-  METADATA_ACCUMULATOR,
-  NEGATIVE_CONDITIONING,
-  POSITIVE_CONDITIONING,
-  SDXL_MODEL_LOADER,
-} from './constants';
-
-export const addSDXLLoRAsToGraph = (
-  state: RootState,
-  graph: NonNullableGraph,
-  baseNodeId: string,
-  modelLoaderNodeId: string = SDXL_MODEL_LOADER
-): void => {
-  /**
-   * LoRA nodes get the UNet and CLIP models from the main model loader and apply the LoRA to them.
-   * They then output the UNet and CLIP models references on to either the next LoRA in the chain,
-   * or to the inference/conditioning nodes.
-   *
-   * So we need to inject a LoRA chain into the graph.
-   */
-
-  const { loras } = state.lora;
-  const loraCount = size(loras);
-  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    | MetadataAccumulatorInvocation
-    | undefined;
-
-  if (loraCount > 0) {
-    // Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs
-    graph.edges = graph.edges.filter(
-      (e) =>
-        !(
-          e.source.node_id === modelLoaderNodeId &&
-          ['unet'].includes(e.source.field)
-        ) &&
-        !(
-          e.source.node_id === modelLoaderNodeId &&
-          ['clip'].includes(e.source.field)
-        ) &&
-        !(
-          e.source.node_id === modelLoaderNodeId &&
-          ['clip2'].includes(e.source.field)
-        )
-    );
-  }
-
-  // we need to remember the last lora so we can chain from it
-  let lastLoraNodeId = '';
-  let currentLoraIndex = 0;
-
-  forEach(loras, (lora) => {
-    const { model_name, base_model, weight } = lora;
-    const currentLoraNodeId = `${LORA_LOADER}_${model_name.replace('.', '_')}`;
-
-    const loraLoaderNode: SDXLLoraLoaderInvocation = {
-      type: 'sdxl_lora_loader',
-      id: currentLoraNodeId,
-      is_intermediate: true,
-      lora: { model_name, base_model },
-      weight,
-    };
-
-    // add the lora to the metadata accumulator
-    if (metadataAccumulator) {
-      metadataAccumulator.loras.push({
-        lora: { model_name, base_model },
-        weight,
-      });
-    }
-
-    // add to graph
-    graph.nodes[currentLoraNodeId] = loraLoaderNode;
-    if (currentLoraIndex === 0) {
-      // first lora = start the lora chain, attach directly to model loader
-      graph.edges.push({
-        source: {
-          node_id: modelLoaderNodeId,
-          field: 'unet',
-        },
-        destination: {
-          node_id: currentLoraNodeId,
-          field: 'unet',
-        },
-      });
-
-      graph.edges.push({
-        source: {
-          node_id: modelLoaderNodeId,
-          field: 'clip',
-        },
-        destination: {
-          node_id: currentLoraNodeId,
-          field: 'clip',
-        },
-      });
-
-      graph.edges.push({
-        source: {
-          node_id: modelLoaderNodeId,
-          field: 'clip2',
-        },
-        destination: {
-          node_id: currentLoraNodeId,
-          field: 'clip2',
-        },
-      });
-    } else {
-      // we are in the middle of the lora chain, instead connect to the previous lora
-      graph.edges.push({
-        source: {
-          node_id: lastLoraNodeId,
-          field: 'unet',
-        },
-        destination: {
-          node_id: currentLoraNodeId,
-          field: 'unet',
-        },
-      });
-      graph.edges.push({
-        source: {
-          node_id: lastLoraNodeId,
-          field: 'clip',
-        },
-        destination: {
-          node_id: currentLoraNodeId,
-          field: 'clip',
-        },
-      });
-
-      graph.edges.push({
-        source: {
-          node_id: lastLoraNodeId,
-          field: 'clip2',
-        },
-        destination: {
-          node_id: currentLoraNodeId,
-          field: 'clip2',
-        },
-      });
-    }
-
-    if (currentLoraIndex === loraCount - 1) {
-      // final lora, end the lora chain - we need to connect up to inference and conditioning nodes
-      graph.edges.push({
-        source: {
-          node_id: currentLoraNodeId,
-          field: 'unet',
-        },
-        destination: {
-          node_id: baseNodeId,
-          field: 'unet',
-        },
-      });
-
-      graph.edges.push({
-        source: {
-          node_id: currentLoraNodeId,
-          field: 'clip',
-        },
-        destination: {
-          node_id: POSITIVE_CONDITIONING,
-          field: 'clip',
-        },
-      });
-
-      graph.edges.push({
-        source: {
-          node_id: currentLoraNodeId,
-          field: 'clip',
-        },
-        destination: {
-          node_id: NEGATIVE_CONDITIONING,
-          field: 'clip',
-        },
-      });
-
-      graph.edges.push({
-        source: {
-          node_id: currentLoraNodeId,
-          field: 'clip2',
-        },
-        destination: {
-          node_id: POSITIVE_CONDITIONING,
-          field: 'clip2',
-        },
-      });
-
-      graph.edges.push({
-        source: {
-          node_id: currentLoraNodeId,
-          field: 'clip2',
-        },
-        destination: {
-          node_id: NEGATIVE_CONDITIONING,
-          field: 'clip2',
-        },
-      });
-    }
-
-    // increment the lora for the next one in the chain
-    lastLoraNodeId = currentLoraNodeId;
-    currentLoraIndex += 1;
-  });
-};

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts

@@ -22,7 +22,6 @@ import {
   SDXL_LATENTS_TO_LATENTS,
   SDXL_MODEL_LOADER,
 } from './constants';
-import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 
 /**
  * Builds the Image to Image tab graph.
@@ -365,8 +364,6 @@ export const buildLinearSDXLImageToImageGraph = (
     },
   });
 
-  addSDXLLoRAsToGraph(state, graph, SDXL_LATENTS_TO_LATENTS, SDXL_MODEL_LOADER);
-
   // Add Refiner if enabled
   if (shouldUseSDXLRefiner) {
     addSDXLRefinerToGraph(state, graph, SDXL_LATENTS_TO_LATENTS);

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts

@@ -4,7 +4,6 @@ import { NonNullableGraph } from 'features/nodes/types/types';
 import { initialGenerationState } from 'features/parameters/store/generationSlice';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
-import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
@@ -247,8 +246,6 @@ export const buildLinearSDXLTextToImageGraph = (
     },
   });
 
-  addSDXLLoRAsToGraph(state, graph, SDXL_TEXT_TO_LATENTS, SDXL_MODEL_LOADER);
-
   // Add Refiner if enabled
   if (shouldUseSDXLRefiner) {
     addSDXLRefinerToGraph(state, graph, SDXL_TEXT_TO_LATENTS);

invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx

@@ -4,7 +4,6 @@ import ProcessButtons from 'features/parameters/components/ProcessButtons/Proces
 import ParamSDXLPromptArea from './ParamSDXLPromptArea';
 import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
 import SDXLImageToImageTabCoreParameters from './SDXLImageToImageTabCoreParameters';
-import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
 
 const SDXLImageToImageTabParameters = () => {
   return (
@@ -13,7 +12,6 @@ const SDXLImageToImageTabParameters = () => {
       <ProcessButtons />
       <SDXLImageToImageTabCoreParameters />
       <ParamSDXLRefinerCollapse />
-      <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamNoiseCollapse />
     </>

invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx

@@ -4,7 +4,6 @@ import ProcessButtons from 'features/parameters/components/ProcessButtons/Proces
 import TextToImageTabCoreParameters from 'features/ui/components/tabs/TextToImage/TextToImageTabCoreParameters';
 import ParamSDXLPromptArea from './ParamSDXLPromptArea';
 import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
-import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
 
 const SDXLTextToImageTabParameters = () => {
   return (
@@ -13,7 +12,6 @@ const SDXLTextToImageTabParameters = () => {
       <ProcessButtons />
       <TextToImageTabCoreParameters />
       <ParamSDXLRefinerCollapse />
-      <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamNoiseCollapse />
     </>

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

@@ -4,7 +4,6 @@ import {
   ASSETS_CATEGORIES,
   BoardId,
   IMAGE_CATEGORIES,
-  IMAGE_LIMIT,
 } from 'features/gallery/store/types';
 import { keyBy } from 'lodash';
 import { ApiFullTagDescription, LIST_TAG, api } from '..';
@@ -168,14 +167,7 @@ export const imagesApi = api.injectEndpoints({
           },
         };
       },
-      invalidatesTags: (result, error, { imageDTOs }) => {
-        // for now, assume bulk delete is all on one board
-        const boardId = imageDTOs[0]?.board_id;
-        return [
-          { type: 'BoardImagesTotal', id: boardId ?? 'none' },
-          { type: 'BoardAssetsTotal', id: boardId ?? 'none' },
-        ];
-      },
+      invalidatesTags: (result, error, imageDTOs) => [],
       async onQueryStarted({ imageDTOs }, { dispatch, queryFulfilled }) {
         /**
          * Cache changes for `deleteImages`:
@@ -897,25 +889,18 @@ export const imagesApi = api.injectEndpoints({
           board_id,
         },
       }),
-      invalidatesTags: (result, error, { imageDTOs, board_id }) => {
-        //assume all images are being moved from one board for now
-        const oldBoardId = imageDTOs[0]?.board_id;
-        return [
-          // update the destination board
-          { type: 'Board', id: board_id ?? 'none' },
-          // update new board totals
-          { type: 'BoardImagesTotal', id: board_id ?? 'none' },
-          { type: 'BoardAssetsTotal', id: board_id ?? 'none' },
-          // update old board totals
-          { type: 'BoardImagesTotal', id: oldBoardId ?? 'none' },
-          { type: 'BoardAssetsTotal', id: oldBoardId ?? 'none' },
-          // update the no_board totals
-          { type: 'BoardImagesTotal', id: 'none' },
-          { type: 'BoardAssetsTotal', id: 'none' },
-        ];
-      },
+      invalidatesTags: (result, error, { board_id }) => [
+        // update the destination board
+        { type: 'Board', id: board_id ?? 'none' },
+        // update old board totals
+        { type: 'BoardImagesTotal', id: board_id ?? 'none' },
+        { type: 'BoardAssetsTotal', id: board_id ?? 'none' },
+        // update the no_board totals
+        { type: 'BoardImagesTotal', id: 'none' },
+        { type: 'BoardAssetsTotal', id: 'none' },
+      ],
       async onQueryStarted(
-        { board_id: new_board_id, imageDTOs },
+        { board_id, imageDTOs },
         { dispatch, queryFulfilled, getState }
       ) {
         try {
@@ -935,7 +920,7 @@ export const imagesApi = api.injectEndpoints({
                 'getImageDTO',
                 image_name,
                 (draft) => {
-                  draft.board_id = new_board_id;
+                  draft.board_id = board_id;
                 }
               )
             );
@@ -961,7 +946,7 @@ export const imagesApi = api.injectEndpoints({
             );
 
             const queryArgs = {
-              board_id: new_board_id,
+              board_id,
               categories,
             };
 
@@ -969,24 +954,23 @@ export const imagesApi = api.injectEndpoints({
               queryArgs
             )(getState());
 
-            const { data: previousTotal } = IMAGE_CATEGORIES.includes(
+            const { data: total } = IMAGE_CATEGORIES.includes(
               imageDTO.image_category
             )
               ? boardsApi.endpoints.getBoardImagesTotal.select(
-                  new_board_id ?? 'none'
+                  imageDTO.board_id ?? 'none'
                 )(getState())
               : boardsApi.endpoints.getBoardAssetsTotal.select(
-                  new_board_id ?? 'none'
+                  imageDTO.board_id ?? 'none'
                 )(getState());
 
             const isCacheFullyPopulated =
-              currentCache.data &&
-              currentCache.data.ids.length >= (previousTotal ?? 0);
+              currentCache.data && currentCache.data.ids.length >= (total ?? 0);
 
-            const isInDateRange =
-              (previousTotal || 0) >= IMAGE_LIMIT
-                ? getIsImageInDateRange(currentCache.data, imageDTO)
-                : true;
+            const isInDateRange = getIsImageInDateRange(
+              currentCache.data,
+              imageDTO
+            );
 
             if (isCacheFullyPopulated || isInDateRange) {
               // *upsert* to $cache
@@ -997,7 +981,7 @@ export const imagesApi = api.injectEndpoints({
                   (draft) => {
                     imagesAdapter.upsertOne(draft, {
                       ...imageDTO,
-                      board_id: new_board_id,
+                      board_id,
                     });
                   }
                 )
@@ -1113,10 +1097,10 @@ export const imagesApi = api.injectEndpoints({
             const isCacheFullyPopulated =
               currentCache.data && currentCache.data.ids.length >= (total ?? 0);
 
-            const isInDateRange =
-              (total || 0) >= IMAGE_LIMIT
-                ? getIsImageInDateRange(currentCache.data, imageDTO)
-                : true;
+            const isInDateRange = getIsImageInDateRange(
+              currentCache.data,
+              imageDTO
+            );
 
             if (isCacheFullyPopulated || isInDateRange) {
               // *upsert* to $cache
@@ -1127,7 +1111,7 @@ export const imagesApi = api.injectEndpoints({
                   (draft) => {
                     imagesAdapter.upsertOne(draft, {
                       ...imageDTO,
-                      board_id: 'none',
+                      board_id: undefined,
                     });
                   }
                 )

invokeai/frontend/web/src/services/api/schema.d.ts

@@ -1443,7 +1443,7 @@ export type components = {
        * @description The nodes in this graph
        */
       nodes?: {
-        [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
+        [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
       };
       /**
        * Edges 
@@ -1486,7 +1486,7 @@ export type components = {
        * @description The results of node executions
        */
       results: {
-        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["SDXLLoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
+        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
       };
       /**
        * Errors 
@@ -1904,40 +1904,6 @@ export type components = {
        */
       image_name: string;
     };
-    /**
-     * ImageHueAdjustmentInvocation 
-     * @description Adjusts the Hue of an image.
-     */
-    ImageHueAdjustmentInvocation: {
-      /**
-       * Id 
-       * @description The id of this node. Must be unique among all nodes.
-       */
-      id: string;
-      /**
-       * Is Intermediate 
-       * @description Whether or not this node is an intermediate node. 
-       * @default false
-       */
-      is_intermediate?: boolean;
-      /**
-       * Type 
-       * @default img_hue_adjust 
-       * @enum {string}
-       */
-      type?: "img_hue_adjust";
-      /**
-       * Image 
-       * @description The image to adjust
-       */
-      image?: components["schemas"]["ImageField"];
-      /**
-       * Hue 
-       * @description The degrees by which to rotate the hue, 0-360 
-       * @default 0
-       */
-      hue?: number;
-    };
     /**
      * ImageInverseLerpInvocation 
      * @description Inverse linear interpolation of all pixels of an image
@@ -2018,40 +1984,6 @@ export type components = {
        */
       max?: number;
     };
-    /**
-     * ImageLuminosityAdjustmentInvocation 
-     * @description Adjusts the Luminosity (Value) of an image.
-     */
-    ImageLuminosityAdjustmentInvocation: {
-      /**
-       * Id 
-       * @description The id of this node. Must be unique among all nodes.
-       */
-      id: string;
-      /**
-       * Is Intermediate 
-       * @description Whether or not this node is an intermediate node. 
-       * @default false
-       */
-      is_intermediate?: boolean;
-      /**
-       * Type 
-       * @default img_luminosity_adjust 
-       * @enum {string}
-       */
-      type?: "img_luminosity_adjust";
-      /**
-       * Image 
-       * @description The image to adjust
-       */
-      image?: components["schemas"]["ImageField"];
-      /**
-       * Luminosity 
-       * @description The factor by which to adjust the luminosity (value) 
-       * @default 1
-       */
-      luminosity?: number;
-    };
     /**
      * ImageMetadata 
      * @description An image's generation metadata
@@ -2307,40 +2239,6 @@ export type components = {
        */
       resample_mode?: "nearest" | "box" | "bilinear" | "hamming" | "bicubic" | "lanczos";
     };
-    /**
-     * ImageSaturationAdjustmentInvocation 
-     * @description Adjusts the Saturation of an image.
-     */
-    ImageSaturationAdjustmentInvocation: {
-      /**
-       * Id 
-       * @description The id of this node. Must be unique among all nodes.
-       */
-      id: string;
-      /**
-       * Is Intermediate 
-       * @description Whether or not this node is an intermediate node. 
-       * @default false
-       */
-      is_intermediate?: boolean;
-      /**
-       * Type 
-       * @default img_saturation_adjust 
-       * @enum {string}
-       */
-      type?: "img_saturation_adjust";
-      /**
-       * Image 
-       * @description The image to adjust
-       */
-      image?: components["schemas"]["ImageField"];
-      /**
-       * Saturation 
-       * @description The factor by which to adjust the saturation 
-       * @default 1
-       */
-      saturation?: number;
-    };
     /**
      * ImageScaleInvocation 
      * @description Scales an image by a factor
@@ -5014,82 +4912,6 @@ export type components = {
        */
       denoising_end?: number;
     };
-    /**
-     * SDXLLoraLoaderInvocation 
-     * @description Apply selected lora to unet and text_encoder.
-     */
-    SDXLLoraLoaderInvocation: {
-      /**
-       * Id 
-       * @description The id of this node. Must be unique among all nodes.
-       */
-      id: string;
-      /**
-       * Is Intermediate 
-       * @description Whether or not this node is an intermediate node. 
-       * @default false
-       */
-      is_intermediate?: boolean;
-      /**
-       * Type 
-       * @default sdxl_lora_loader 
-       * @enum {string}
-       */
-      type?: "sdxl_lora_loader";
-      /**
-       * Lora 
-       * @description Lora model name
-       */
-      lora?: components["schemas"]["LoRAModelField"];
-      /**
-       * Weight 
-       * @description With what weight to apply lora 
-       * @default 0.75
-       */
-      weight?: number;
-      /**
-       * Unet 
-       * @description UNet model for applying lora
-       */
-      unet?: components["schemas"]["UNetField"];
-      /**
-       * Clip 
-       * @description Clip model for applying lora
-       */
-      clip?: components["schemas"]["ClipField"];
-      /**
-       * Clip2 
-       * @description Clip2 model for applying lora
-       */
-      clip2?: components["schemas"]["ClipField"];
-    };
-    /**
-     * SDXLLoraLoaderOutput 
-     * @description Model loader output
-     */
-    SDXLLoraLoaderOutput: {
-      /**
-       * Type 
-       * @default sdxl_lora_loader_output 
-       * @enum {string}
-       */
-      type?: "sdxl_lora_loader_output";
-      /**
-       * Unet 
-       * @description UNet submodel
-       */
-      unet?: components["schemas"]["UNetField"];
-      /**
-       * Clip 
-       * @description Tokenizer and text_encoder submodels
-       */
-      clip?: components["schemas"]["ClipField"];
-      /**
-       * Clip2 
-       * @description Tokenizer2 and text_encoder2 submodels
-       */
-      clip2?: components["schemas"]["ClipField"];
-    };
     /**
      * SDXLModelLoaderInvocation 
      * @description Loads an sdxl base model, outputting its submodels.
@@ -6139,24 +5961,6 @@ export type components = {
        */
       image?: components["schemas"]["ImageField"];
     };
-    /**
-     * ControlNetModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    ControlNetModelFormat: "checkpoint" | "diffusers";
-    /**
-     * StableDiffusionXLModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
-    /**
-     * StableDiffusion1ModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusionOnnxModelFormat 
      * @description An enumeration. 
@@ -6169,6 +5973,24 @@ export type components = {
      * @enum {string}
      */
     StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusion1ModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusionXLModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
+    /**
+     * ControlNetModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    ControlNetModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;
@@ -6279,7 +6101,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -6316,7 +6138,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {

invokeai/frontend/web/src/services/api/types.ts

@@ -166,9 +166,6 @@ export type OnnxModelLoaderInvocation = TypeReq<
 export type LoraLoaderInvocation = TypeReq<
   components['schemas']['LoraLoaderInvocation']
 >;
-export type SDXLLoraLoaderInvocation = TypeReq<
-  components['schemas']['SDXLLoraLoaderInvocation']
->;
 export type MetadataAccumulatorInvocation = TypeReq<
   components['schemas']['MetadataAccumulatorInvocation']
 >;

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "3.0.2"
+__version__ = "3.0.2a1"

pyproject.toml

@@ -100,7 +100,7 @@ dependencies = [
 "dev" = [
   "pudb",
 ]
-"test" = ["pytest>6.0.0", "pytest-cov", "pytest-datadir", "black"]
+"test" = ["pytest>6.0.0", "pytest-cov", "black"]
 "xformers" = [
 	   "xformers~=0.0.19; sys_platform!='darwin'",
 	   "triton; sys_platform=='linux'",

tests/test_model_manager.py

@@ -1,38 +0,0 @@
-from pathlib import Path
-
-import pytest
-
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend import ModelManager, BaseModelType, ModelType, SubModelType
-
-BASIC_MODEL_NAME = ("SDXL base", BaseModelType.StableDiffusionXL, ModelType.Main)
-VAE_OVERRIDE_MODEL_NAME = ("SDXL with VAE", BaseModelType.StableDiffusionXL, ModelType.Main)
-
-
-@pytest.fixture
-def model_manager(datadir) -> ModelManager:
-    InvokeAIAppConfig.get_config(root=datadir)
-    return ModelManager(datadir / "configs" / "relative_sub.models.yaml")
-
-
-def test_get_model_names(model_manager: ModelManager):
-    names = model_manager.model_names()
-    assert names[:2] == [BASIC_MODEL_NAME, VAE_OVERRIDE_MODEL_NAME]
-
-
-def test_get_model_path_for_diffusers(model_manager: ModelManager, datadir: Path):
-    model_config = model_manager._get_model_config(BASIC_MODEL_NAME[1], BASIC_MODEL_NAME[0], BASIC_MODEL_NAME[2])
-    top_model_path, is_override = model_manager._get_model_path(model_config)
-    expected_model_path = datadir / "models" / "sdxl" / "main" / "SDXL base 1_0"
-    assert top_model_path == expected_model_path
-    assert not is_override
-
-
-def test_get_model_path_for_overridden_vae(model_manager: ModelManager, datadir: Path):
-    model_config = model_manager._get_model_config(
-        VAE_OVERRIDE_MODEL_NAME[1], VAE_OVERRIDE_MODEL_NAME[0], VAE_OVERRIDE_MODEL_NAME[2]
-    )
-    vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae)
-    expected_vae_path = datadir / "models" / "sdxl" / "vae" / "sdxl-vae-fp16-fix"
-    assert vae_model_path == expected_vae_path
-    assert is_override

tests/test_model_manager/configs/relative_sub.models.yaml

@@ -1,15 +0,0 @@
-__metadata__:
-  version: 3.0.0
-
-sdxl/main/SDXL base:
-  path: sdxl/main/SDXL base 1_0
-  description: SDXL base v1.0
-  variant: normal
-  format: diffusers
-
-sdxl/main/SDXL with VAE:
-  path: sdxl/main/SDXL base 1_0
-  description: SDXL with customized VAE
-  vae: sdxl/vae/sdxl-vae-fp16-fix/
-  variant: normal
-  format: diffusers