Update version

## What type of PR is this? (check all applicable)

- [X ] Feature

## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:

      
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No - will be in release notes

## Description

On CUDA systems, this PR adds a new slider to the install-time configure
script for adjusting the VRAM cache and suggests a good starting value
based on the user's max VRAM (this is subject to verification).

On non-CUDA systems this slider is suppressed.

Please test on both CUDA and non-CUDA systems using:
```
invokeai-configure --root ~/invokeai-main/ --skip-sd --skip-support
```

To see and test the default values, move `invokeai.yaml` out of the way
before running.

**Note added 8 August 2023**

This PR also fixes the configure and model install scripts so that if
the window is too small to fit the user interface, the user will be
prompted to interactively resize the window and/or change font size
(with the option to give up). This will prevent `npyscreen` from
generating its horrible tracebacks.

## Related Tickets & Documents

<!--
For pull requests that relate or close an issue, please include them
below. 

For example having the text: "closes #1234" would connect the current
pull
request to issue 1234.  And when we merge the pull request, Github will
automatically close the issue.
-->

- Related Issue #
- Closes #

## QA Instructions, Screenshots, Recordings

<!-- 
Please provide steps on how to test changes, any hardware or 
software specifications as well as any other pertinent information. 
-->

## Added/updated tests?

- [ ] Yes
- [ ] No : _please replace this line with details on why tests
      have not been included_

## [optional] Are there any post deployment tasks we need to perform?

invokeai/app/api/routers/images.py

@@ -1,22 +1,20 @@
 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 PIL import Image
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
 
 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"])
@@ -152,8 +150,9 @@ async def get_image_metadata(
         raise HTTPException(status_code=404)
 
 
-@images_router.get(
+@images_router.api_route(
     "/i/{image_name}/full",
+    methods=["GET", "HEAD"],
     operation_id="get_image_full",
     response_class=Response,
     responses={

invokeai/app/services/config.py

@@ -24,11 +24,10 @@ InvokeAI:
     sequential_guidance: false
     precision: float16
     max_cache_size: 6
-    max_vram_cache_size: 2.7
+    max_vram_cache_size: 0.5
     always_use_cpu: false
     free_gpu_mem: false
   Features:
-    restore: true
     esrgan: true
     patchmatch: true
     internet_available: true
@@ -165,7 +164,7 @@ import pydoc
 import os
 import sys
 from argparse import ArgumentParser
-from omegaconf import OmegaConf, DictConfig
+from omegaconf import OmegaConf, DictConfig, ListConfig
 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
@@ -173,6 +172,7 @@ 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):
@@ -189,7 +189,12 @@ class InvokeAISettings(BaseSettings):
         opt = parser.parse_args(argv)
         for name in self.__fields__:
             if name not in self._excluded():
-                setattr(self, name, getattr(opt, name))
+                value = getattr(opt, name)
+                if isinstance(value, ListConfig):
+                    value = list(value)
+                elif isinstance(value, DictConfig):
+                    value = dict(value)
+                setattr(self, name, value)
 
     def to_yaml(self) -> str:
         """
@@ -282,14 +287,10 @@ class InvokeAISettings(BaseSettings):
         return [
             "type",
             "initconf",
-            "gpu_mem_reserved",
-            "max_loaded_models",
             "version",
             "from_file",
             "model",
-            "restore",
             "root",
-            "nsfw_checker",
         ]
 
     class Config:
@@ -388,15 +389,11 @@ 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')
@@ -414,9 +411,7 @@ 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')
-
-    model               : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models')
+    ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
 
     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
@@ -426,6 +421,9 @@ 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

@@ -10,12 +10,15 @@ 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
@@ -44,6 +47,8 @@ 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,
@@ -53,6 +58,7 @@ 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 (
@@ -61,6 +67,7 @@ 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()
@@ -76,6 +83,13 @@ 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.
@@ -86,6 +100,12 @@ 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):
@@ -378,13 +398,35 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
         )
         self.max_cache_size = self.add_widget_intelligent(
             IntTitleSlider,
-            name="Size of the RAM cache used for fast model switching (GB)",
+            name="RAM cache size (GB). Make this at least large enough to hold a single full model.",
             value=old_opts.max_cache_size,
-            out_of=20,
+            out_of=MAX_RAM,
             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,
@@ -401,7 +443,7 @@ 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=f"Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
+            name="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,
@@ -476,6 +518,7 @@ 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",
         ]:
@@ -592,13 +635,13 @@ 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
 
-    # 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)
+    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 install-models application spawns a subprocess to install
     # models, and will crash unless this is set before running.
@@ -654,10 +697,13 @@ def migrate_init_file(legacy_format: Path):
     old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
     new = InvokeAIAppConfig.get_config()
 
-    fields = list(get_type_hints(InvokeAIAppConfig).keys())
+    fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"]
     for attr in fields:
         if hasattr(old, attr):
-            setattr(new, attr, getattr(old, attr))
+            try:
+                setattr(new, attr, getattr(old, attr))
+            except ValidationError as e:
+                print(f"* Ignoring incompatible value for field {attr}:\n  {str(e)}")
 
     # a few places where the field names have changed and we have to
     # manually add in the new names/values
@@ -777,6 +823,7 @@ 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")
@@ -802,6 +849,8 @@ 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

@@ -595,9 +595,10 @@ 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]
-        return None
+        else:
+            return None
 
     def list_models(
         self,
@@ -990,7 +991,9 @@ 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_config: ModelConfigBase = model_class.probe_config(
+                                    str(model_path), model_base=cur_base_model
+                                )
                                 self.models[model_key] = model_config
                                 new_models_found = True
                             except DuplicateModelException as e:

invokeai/backend/model_management/models/sdxl.py

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

invokeai/frontend/install/model_install.py

@@ -28,7 +28,6 @@ from npyscreen import widget
 from invokeai.backend.util.logging import InvokeAILogger
 
 from invokeai.backend.install.model_install_backend import (
-    ModelInstallList,
     InstallSelections,
     ModelInstall,
     SchedulerPredictionType,
@@ -41,12 +40,12 @@ 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
 
@@ -156,7 +155,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             BufferBox,
             name="Log Messages",
             editable=False,
-            max_height=15,
+            max_height=6,
         )
 
         self.nextrely += 1
@@ -693,7 +692,11 @@ def select_and_download_models(opt: Namespace):
         # needed to support the probe() method running under a subprocess
         torch.multiprocessing.set_start_method("spawn")
 
-        set_min_terminal_size(MIN_COLS, MIN_LINES)
+        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."
+            )
+
         installApp = AddModelApplication(opt)
         try:
             installApp.run()
@@ -787,6 +790,8 @@ 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

@@ -21,31 +21,40 @@ MIN_COLS = 130
 MIN_LINES = 38
 
 
+class WindowTooSmallException(Exception):
+    pass
+
+
 # -------------------------------------
-def set_terminal_size(columns: int, lines: int):
-    ts = get_terminal_size()
-    width = max(columns, ts.columns)
-    height = max(lines, ts.lines)
-
+def set_terminal_size(columns: int, lines: int) -> bool:
     OS = platform.uname().system
-    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)
+    screen_ok = False
+    while not screen_ok:
+        ts = get_terminal_size()
+        width = max(columns, ts.columns)
+        height = max(lines, ts.lines)
 
-    # 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..")
+        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
 
 
 def _set_terminal_size_powershell(width: int, height: int):
@@ -80,14 +89,14 @@ def _set_terminal_size_unix(width: int, height: int):
     sys.stdout.flush()
 
 
-def set_min_terminal_size(min_cols: int, min_lines: int):
+def set_min_terminal_size(min_cols: int, min_lines: int) -> bool:
     # 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
+        return True
     cols = max(term_cols, min_cols)
     lines = max(term_lines, min_lines)
-    set_terminal_size(cols, lines)
+    return set_terminal_size(cols, lines)
 
 
 class IntSlider(npyscreen.Slider):
@@ -164,7 +173,7 @@ class FloatSlider(npyscreen.Slider):
 
 
 class FloatTitleSlider(npyscreen.TitleText):
-    _entry_type = FloatSlider
+    _entry_type = npyscreen.Slider
 
 
 class SelectColumnBase:

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-6fe89879.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-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`
+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`
   :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-de589048.js"></script>
+    <script type="module" crossorigin src="./assets/index-dd054634.js"></script>
   </head>
 
   <body dir="ltr">

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

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

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

@@ -9,6 +9,7 @@ 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],
@@ -33,11 +34,18 @@ 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;
@@ -71,7 +79,7 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
         dispatch(selectionChanged([imageDTO]));
       }
     },
-    [dispatch, imageDTO, imageDTOs, selection]
+    [dispatch, imageDTO, imageDTOs, selection, isMultiSelectEnabled]
   );
 
   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

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

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

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

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

@@ -0,0 +1,212 @@
+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

@@ -22,6 +22,7 @@ import {
   SDXL_LATENTS_TO_LATENTS,
   SDXL_MODEL_LOADER,
 } from './constants';
+import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 
 /**
  * Builds the Image to Image tab graph.
@@ -364,6 +365,8 @@ 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

@@ -4,6 +4,7 @@ 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 {
@@ -246,6 +247,8 @@ 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

@@ -4,6 +4,7 @@ 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 (
@@ -12,6 +13,7 @@ const SDXLImageToImageTabParameters = () => {
       <ProcessButtons />
       <SDXLImageToImageTabCoreParameters />
       <ParamSDXLRefinerCollapse />
+      <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamNoiseCollapse />
     </>

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

@@ -4,6 +4,7 @@ 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 (
@@ -12,6 +13,7 @@ const SDXLTextToImageTabParameters = () => {
       <ProcessButtons />
       <TextToImageTabCoreParameters />
       <ParamSDXLRefinerCollapse />
+      <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamNoiseCollapse />
     </>

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

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

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"]["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;
+        [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;
       };
       /**
        * 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"]["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"]["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;
       };
       /**
        * Errors 
@@ -1904,6 +1904,40 @@ 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
@@ -1984,6 +2018,40 @@ 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
@@ -2239,6 +2307,40 @@ 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
@@ -4912,6 +5014,82 @@ 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.
@@ -5961,6 +6139,24 @@ 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. 
@@ -5973,24 +6169,6 @@ 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;
@@ -6101,7 +6279,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "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"];
+        "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"];
       };
     };
     responses: {
@@ -6138,7 +6316,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "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"];
+        "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"];
       };
     };
     responses: {

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

@@ -166,6 +166,9 @@ 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__ = "3.0.2a1"
+__version__ = "3.0.2"