Merge branch 'main' into refactor/remove_unused_pipeline_methods

README.md

@@ -306,13 +306,30 @@ InvokeAI. The second will prepare the 2.3 directory for use with 3.0.
 You may now launch the WebUI in the usual way, by selecting option [1]
 from the launcher script
 
-#### Migration Caveats
+#### Migrating Images
 
 The migration script will migrate your invokeai settings and models,
 including textual inversion models, LoRAs and merges that you may have
 installed previously. However it does **not** migrate the generated
-images stored in your 2.3-format outputs directory. You will need to
+images stored in your 2.3-format outputs directory. To do this, you 
-manually import selected images into the 3.0 gallery via drag-and-drop.
+need to run an additional step:
+
+1. From a working InvokeAI 3.0 root directory, start the launcher and
+enter menu option [8] to open the "developer's console".
+
+2. At the developer's console command line, type the command:
+
+```bash
+invokeai-import-images
+```
+
+3. This will lead you through the process of confirming the desired
+   source and destination for the imported images. The images will
+   appear in the gallery board of your choice, and contain the
+   original prompt, model name, and other parameters used to generate
+   the image.
+   
+(Many kudos to **techjedi** for contributing this script.)
 
 ## Hardware Requirements
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -264,7 +264,7 @@ experimental versions later.
 	  you can create several levels of subfolders and drop your models into
       whichever ones you want.
 	
-    - ***Autoimport FolderLICENSE***     
+    - ***LICENSE***     
 
     At the bottom of the screen you will see a checkbox for accepting
     the CreativeML Responsible AI Licenses. You need to accept the license

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
-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"])
@@ -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')
+    ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
-
-    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
@@ -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
+    if not set_min_terminal_size(MIN_COLS, MIN_LINES):
-    # launch a console window running this program.
+        raise WindowTooSmallException(
-    set_min_terminal_size(MIN_COLS, MIN_LINES)
+            "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):
+            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

@@ -595,8 +595,9 @@ 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
 
     def list_models(
@@ -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
+            # avoid circular import
-            pass
+            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/import_images.py

@@ -0,0 +1,795 @@
+# Copyright (c) 2023 - The InvokeAI Team
+# Primary Author: David Lovell (github @f412design, discord @techjedi)
+# co-author, minor tweaks - Lincoln Stein
+
+# pylint: disable=line-too-long
+# pylint: disable=broad-exception-caught
+"""Script to import images into the new database system for 3.0.0"""
+
+import os
+import datetime
+import shutil
+import locale
+import sqlite3
+import json
+import glob
+import re
+import uuid
+import yaml
+import PIL
+import PIL.ImageOps
+import PIL.PngImagePlugin
+
+from pathlib import Path
+from prompt_toolkit import prompt
+from prompt_toolkit.shortcuts import message_dialog
+from prompt_toolkit.completion import PathCompleter
+from prompt_toolkit.key_binding import KeyBindings
+
+from invokeai.app.services.config import InvokeAIAppConfig
+
+app_config = InvokeAIAppConfig.get_config()
+
+bindings = KeyBindings()
+
+
+@bindings.add("c-c")
+def _(event):
+    raise KeyboardInterrupt
+
+
+# release notes
+# "Use All" with size dimensions not selectable in the UI will not load dimensions
+
+
+class Config:
+    """Configuration loader."""
+
+    def __init__(self):
+        pass
+
+    TIMESTAMP_STRING = datetime.datetime.utcnow().strftime("%Y%m%dT%H%M%SZ")
+
+    INVOKE_DIRNAME = "invokeai"
+    YAML_FILENAME = "invokeai.yaml"
+    DATABASE_FILENAME = "invokeai.db"
+
+    database_path = None
+    database_backup_dir = None
+    outputs_path = None
+    thumbnail_path = None
+
+    def find_and_load(self):
+        """find the yaml config file and load"""
+        root = app_config.root_path
+        if not self.confirm_and_load(os.path.abspath(root)):
+            print("\r\nSpecify custom database and outputs paths:")
+            self.confirm_and_load_from_user()
+
+        self.database_backup_dir = os.path.join(os.path.dirname(self.database_path), "backup")
+        self.thumbnail_path = os.path.join(self.outputs_path, "thumbnails")
+
+    def confirm_and_load(self, invoke_root):
+        """Validates a yaml path exists, confirms the user wants to use it and loads config."""
+        yaml_path = os.path.join(invoke_root, self.YAML_FILENAME)
+        if os.path.exists(yaml_path):
+            db_dir, outdir = self.load_paths_from_yaml(yaml_path)
+            if os.path.isabs(db_dir):
+                database_path = os.path.join(db_dir, self.DATABASE_FILENAME)
+            else:
+                database_path = os.path.join(invoke_root, db_dir, self.DATABASE_FILENAME)
+
+            if os.path.isabs(outdir):
+                outputs_path = os.path.join(outdir, "images")
+            else:
+                outputs_path = os.path.join(invoke_root, outdir, "images")
+
+            db_exists = os.path.exists(database_path)
+            outdir_exists = os.path.exists(outputs_path)
+
+            text = f"Found {self.YAML_FILENAME} file at {yaml_path}:"
+            text += f"\n  Database : {database_path}"
+            text += f"\n  Outputs  : {outputs_path}"
+            text += "\n\nUse these paths for import (yes) or choose different ones (no) [Yn]: "
+
+            if db_exists and outdir_exists:
+                if (prompt(text).strip() or "Y").upper().startswith("Y"):
+                    self.database_path = database_path
+                    self.outputs_path = outputs_path
+                    return True
+                else:
+                    return False
+            else:
+                print("  Invalid: One or more paths in this config did not exist and cannot be used.")
+
+        else:
+            message_dialog(
+                title="Path not found",
+                text=f"Auto-discovery of configuration failed! Could not find ({yaml_path}), Custom paths can be specified.",
+            ).run()
+            return False
+
+    def confirm_and_load_from_user(self):
+        default = ""
+        while True:
+            database_path = os.path.expanduser(
+                prompt(
+                    "Database: Specify absolute path to the database to import into: ",
+                    completer=PathCompleter(
+                        expanduser=True, file_filter=lambda x: Path(x).is_dir() or x.endswith((".db"))
+                    ),
+                    default=default,
+                )
+            )
+            if database_path.endswith(".db") and os.path.isabs(database_path) and os.path.exists(database_path):
+                break
+            default = database_path + "/" if Path(database_path).is_dir() else database_path
+
+        default = ""
+        while True:
+            outputs_path = os.path.expanduser(
+                prompt(
+                    "Outputs: Specify absolute path to outputs/images directory to import into: ",
+                    completer=PathCompleter(expanduser=True, only_directories=True),
+                    default=default,
+                )
+            )
+
+            if outputs_path.endswith("images") and os.path.isabs(outputs_path) and os.path.exists(outputs_path):
+                break
+            default = outputs_path + "/" if Path(outputs_path).is_dir() else outputs_path
+
+        self.database_path = database_path
+        self.outputs_path = outputs_path
+
+        return
+
+    def load_paths_from_yaml(self, yaml_path):
+        """Load an Invoke AI yaml file and get the database and outputs paths."""
+        try:
+            with open(yaml_path, "rt", encoding=locale.getpreferredencoding()) as file:
+                yamlinfo = yaml.safe_load(file)
+                db_dir = yamlinfo.get("InvokeAI", {}).get("Paths", {}).get("db_dir", None)
+                outdir = yamlinfo.get("InvokeAI", {}).get("Paths", {}).get("outdir", None)
+                return db_dir, outdir
+        except Exception:
+            print(f"Failed to load paths from yaml file! {yaml_path}!")
+            return None, None
+
+
+class ImportStats:
+    """DTO for tracking work progress."""
+
+    def __init__(self):
+        pass
+
+    time_start = datetime.datetime.utcnow()
+    count_source_files = 0
+    count_skipped_file_exists = 0
+    count_skipped_db_exists = 0
+    count_imported = 0
+    count_imported_by_version = {}
+    count_file_errors = 0
+
+    @staticmethod
+    def get_elapsed_time_string():
+        """Get a friendly time string for the  time elapsed since processing start."""
+        time_now = datetime.datetime.utcnow()
+        total_seconds = (time_now - ImportStats.time_start).total_seconds()
+        hours = int((total_seconds) / 3600)
+        minutes = int(((total_seconds) % 3600) / 60)
+        seconds = total_seconds % 60
+        out_str = f"{hours} hour(s) -" if hours > 0 else ""
+        out_str += f"{minutes} minute(s) -" if minutes > 0 else ""
+        out_str += f"{seconds:.2f} second(s)"
+        return out_str
+
+
+class InvokeAIMetadata:
+    """DTO for core Invoke AI generation properties parsed from metadata."""
+
+    def __init__(self):
+        pass
+
+    def __str__(self):
+        formatted_str = f"{self.generation_mode}~{self.steps}~{self.cfg_scale}~{self.model_name}~{self.scheduler}~{self.seed}~{self.width}~{self.height}~{self.rand_device}~{self.strength}~{self.init_image}"
+        formatted_str += f"\r\npositive_prompt: {self.positive_prompt}"
+        formatted_str += f"\r\nnegative_prompt: {self.negative_prompt}"
+        return formatted_str
+
+    generation_mode = None
+    steps = None
+    cfg_scale = None
+    model_name = None
+    scheduler = None
+    seed = None
+    width = None
+    height = None
+    rand_device = None
+    strength = None
+    init_image = None
+    positive_prompt = None
+    negative_prompt = None
+    imported_app_version = None
+
+    def to_json(self):
+        """Convert the active instance to json format."""
+        prop_dict = {}
+        prop_dict["generation_mode"] = self.generation_mode
+        # dont render prompt nodes if neither are set to avoid the ui thinking it can set them
+        # if at least one exists, render them both, but use empty string instead of None if one of them is empty
+        # this allows the field that is empty to actually be cleared byt he UI instead of leaving the previous value
+        if self.positive_prompt or self.negative_prompt:
+            prop_dict["positive_prompt"] = "" if self.positive_prompt is None else self.positive_prompt
+            prop_dict["negative_prompt"] = "" if self.negative_prompt is None else self.negative_prompt
+        prop_dict["width"] = self.width
+        prop_dict["height"] = self.height
+        # only render seed if it has a value to avoid ui thinking it can set this and then error
+        if self.seed:
+            prop_dict["seed"] = self.seed
+        prop_dict["rand_device"] = self.rand_device
+        prop_dict["cfg_scale"] = self.cfg_scale
+        prop_dict["steps"] = self.steps
+        prop_dict["scheduler"] = self.scheduler
+        prop_dict["clip_skip"] = 0
+        prop_dict["model"] = {}
+        prop_dict["model"]["model_name"] = self.model_name
+        prop_dict["model"]["base_model"] = None
+        prop_dict["controlnets"] = []
+        prop_dict["loras"] = []
+        prop_dict["vae"] = None
+        prop_dict["strength"] = self.strength
+        prop_dict["init_image"] = self.init_image
+        prop_dict["positive_style_prompt"] = None
+        prop_dict["negative_style_prompt"] = None
+        prop_dict["refiner_model"] = None
+        prop_dict["refiner_cfg_scale"] = None
+        prop_dict["refiner_steps"] = None
+        prop_dict["refiner_scheduler"] = None
+        prop_dict["refiner_aesthetic_store"] = None
+        prop_dict["refiner_start"] = None
+        prop_dict["imported_app_version"] = self.imported_app_version
+
+        return json.dumps(prop_dict)
+
+
+class InvokeAIMetadataParser:
+    """Parses strings with json data  to find Invoke AI core metadata properties."""
+
+    def __init__(self):
+        pass
+
+    def parse_meta_tag_dream(self, dream_string):
+        """Take as input an png metadata json node for the 'dream' field variant from prior to 1.15"""
+        props = InvokeAIMetadata()
+
+        props.imported_app_version = "pre1.15"
+        seed_match = re.search("-S\\s*(\\d+)", dream_string)
+        if seed_match is not None:
+            try:
+                props.seed = int(seed_match[1])
+            except ValueError:
+                props.seed = None
+            raw_prompt = re.sub("(-S\\s*\\d+)", "", dream_string)
+        else:
+            raw_prompt = dream_string
+
+        pos_prompt, neg_prompt = self.split_prompt(raw_prompt)
+
+        props.positive_prompt = pos_prompt
+        props.negative_prompt = neg_prompt
+
+        return props
+
+    def parse_meta_tag_sd_metadata(self, tag_value):
+        """Take as input an png metadata json node for the 'sd-metadata' field variant from 1.15 through 2.3.5 post 2"""
+        props = InvokeAIMetadata()
+
+        props.imported_app_version = tag_value.get("app_version")
+        props.model_name = tag_value.get("model_weights")
+        img_node = tag_value.get("image")
+        if img_node is not None:
+            props.generation_mode = img_node.get("type")
+            props.width = img_node.get("width")
+            props.height = img_node.get("height")
+            props.seed = img_node.get("seed")
+            props.rand_device = "cuda"  # hardcoded since all generations pre 3.0 used cuda random noise instead of cpu
+            props.cfg_scale = img_node.get("cfg_scale")
+            props.steps = img_node.get("steps")
+            props.scheduler = self.map_scheduler(img_node.get("sampler"))
+            props.strength = img_node.get("strength")
+            if props.strength is None:
+                props.strength = img_node.get("strength_steps")  # try second name for this property
+            props.init_image = img_node.get("init_image_path")
+            if props.init_image is None:  # try second name for this property
+                props.init_image = img_node.get("init_img")
+            # remove the path info from init_image so if we move the init image, it will be correctly relative in the new location
+            if props.init_image is not None:
+                props.init_image = os.path.basename(props.init_image)
+            raw_prompt = img_node.get("prompt")
+            if isinstance(raw_prompt, list):
+                raw_prompt = raw_prompt[0].get("prompt")
+
+            props.positive_prompt, props.negative_prompt = self.split_prompt(raw_prompt)
+
+        return props
+
+    def parse_meta_tag_invokeai(self, tag_value):
+        """Take as input an png metadata json node for the 'invokeai' field variant from 3.0.0 beta 1 through 5"""
+        props = InvokeAIMetadata()
+
+        props.imported_app_version = "3.0.0 or later"
+        props.generation_mode = tag_value.get("type")
+        if props.generation_mode is not None:
+            props.generation_mode = props.generation_mode.replace("t2l", "txt2img").replace("l2l", "img2img")
+
+        props.width = tag_value.get("width")
+        props.height = tag_value.get("height")
+        props.seed = tag_value.get("seed")
+        props.cfg_scale = tag_value.get("cfg_scale")
+        props.steps = tag_value.get("steps")
+        props.scheduler = tag_value.get("scheduler")
+        props.strength = tag_value.get("strength")
+        props.positive_prompt = tag_value.get("positive_conditioning")
+        props.negative_prompt = tag_value.get("negative_conditioning")
+
+        return props
+
+    def map_scheduler(self, old_scheduler):
+        """Convert the legacy sampler names to matching 3.0 schedulers"""
+        if old_scheduler is None:
+            return None
+
+        match (old_scheduler):
+            case "ddim":
+                return "ddim"
+            case "plms":
+                return "pnmd"
+            case "k_lms":
+                return "lms"
+            case "k_dpm_2":
+                return "kdpm_2"
+            case "k_dpm_2_a":
+                return "kdpm_2_a"
+            case "dpmpp_2":
+                return "dpmpp_2s"
+            case "k_dpmpp_2":
+                return "dpmpp_2m"
+            case "k_dpmpp_2_a":
+                return None  # invalid, in 2.3.x, selecting this sample would just fallback to last run or plms if new session
+            case "k_euler":
+                return "euler"
+            case "k_euler_a":
+                return "euler_a"
+            case "k_heun":
+                return "heun"
+        return None
+
+    def split_prompt(self, raw_prompt: str):
+        """Split the unified prompt strings by extracting all negative prompt blocks out into the negative prompt."""
+        if raw_prompt is None:
+            return "", ""
+        raw_prompt_search = raw_prompt.replace("\r", "").replace("\n", "")
+        matches = re.findall(r"\[(.+?)\]", raw_prompt_search)
+        if len(matches) > 0:
+            negative_prompt = ""
+            if len(matches) == 1:
+                negative_prompt = matches[0].strip().strip(",")
+            else:
+                for match in matches:
+                    negative_prompt += f"({match.strip().strip(',')})"
+            positive_prompt = re.sub(r"(\[.+?\])", "", raw_prompt_search).strip()
+        else:
+            positive_prompt = raw_prompt_search.strip()
+            negative_prompt = ""
+
+        return positive_prompt, negative_prompt
+
+
+class DatabaseMapper:
+    """Class to abstract database functionality."""
+
+    def __init__(self, database_path, database_backup_dir):
+        self.database_path = database_path
+        self.database_backup_dir = database_backup_dir
+        self.connection = None
+        self.cursor = None
+
+    def connect(self):
+        """Open connection to the database."""
+        self.connection = sqlite3.connect(self.database_path)
+        self.cursor = self.connection.cursor()
+
+    def get_board_names(self):
+        """Get a list of the current board names from the database."""
+        sql_get_board_name = "SELECT board_name FROM boards"
+        self.cursor.execute(sql_get_board_name)
+        rows = self.cursor.fetchall()
+        return [row[0] for row in rows]
+
+    def does_image_exist(self, image_name):
+        """Check database if a image name already exists and return a boolean."""
+        sql_get_image_by_name = f"SELECT image_name FROM images WHERE image_name='{image_name}'"
+        self.cursor.execute(sql_get_image_by_name)
+        rows = self.cursor.fetchall()
+        return True if len(rows) > 0 else False
+
+    def add_new_image_to_database(self, filename, width, height, metadata, modified_date_string):
+        """Add an image to the database."""
+        sql_add_image = f"""INSERT INTO images (image_name, image_origin, image_category, width, height, session_id, node_id, metadata, is_intermediate, created_at, updated_at)
+VALUES ('{filename}', 'internal', 'general', {width}, {height}, null, null, '{metadata}', 0, '{modified_date_string}', '{modified_date_string}')"""
+        self.cursor.execute(sql_add_image)
+        self.connection.commit()
+
+    def get_board_id_with_create(self, board_name):
+        """Get the board id for supplied name, and create the board if one does not exist."""
+        sql_find_board = f"SELECT board_id FROM boards WHERE board_name='{board_name}' COLLATE NOCASE"
+        self.cursor.execute(sql_find_board)
+        rows = self.cursor.fetchall()
+        if len(rows) > 0:
+            return rows[0][0]
+        else:
+            board_date_string = datetime.datetime.utcnow().date().isoformat()
+            new_board_id = str(uuid.uuid4())
+            sql_insert_board = f"INSERT INTO boards (board_id, board_name, created_at, updated_at) VALUES ('{new_board_id}', '{board_name}', '{board_date_string}', '{board_date_string}')"
+            self.cursor.execute(sql_insert_board)
+            self.connection.commit()
+            return new_board_id
+
+    def add_image_to_board(self, filename, board_id):
+        """Add an image mapping to a board."""
+        add_datetime_str = datetime.datetime.utcnow().isoformat()
+        sql_add_image_to_board = f"""INSERT INTO board_images (board_id, image_name, created_at, updated_at)
+            VALUES ('{board_id}', '{filename}', '{add_datetime_str}', '{add_datetime_str}')"""
+        self.cursor.execute(sql_add_image_to_board)
+        self.connection.commit()
+
+    def disconnect(self):
+        """Disconnect from the db, cleaning up connections and cursors."""
+        if self.cursor is not None:
+            self.cursor.close()
+        if self.connection is not None:
+            self.connection.close()
+
+    def backup(self, timestamp_string):
+        """Take a backup of the database."""
+        if not os.path.exists(self.database_backup_dir):
+            print(f"Database backup directory {self.database_backup_dir} does not exist -> creating...", end="")
+            os.makedirs(self.database_backup_dir)
+            print("Done!")
+        database_backup_path = os.path.join(self.database_backup_dir, f"backup-{timestamp_string}-invokeai.db")
+        print(f"Making DB Backup at {database_backup_path}...", end="")
+        shutil.copy2(self.database_path, database_backup_path)
+        print("Done!")
+
+
+class MediaImportProcessor:
+    """Containing class for script functionality."""
+
+    def __init__(self):
+        pass
+
+    board_name_id_map = {}
+
+    def get_import_file_list(self):
+        """Ask the user for the import folder and scan for the list of files to return."""
+        while True:
+            default = ""
+            while True:
+                import_dir = os.path.expanduser(
+                    prompt(
+                        "Inputs: Specify absolute path containing InvokeAI .png images to import: ",
+                        completer=PathCompleter(expanduser=True, only_directories=True),
+                        default=default,
+                    )
+                )
+                if len(import_dir) > 0 and Path(import_dir).is_dir():
+                    break
+                default = import_dir
+
+            recurse_directories = (
+                (prompt("Include files from subfolders recursively [yN]? ").strip() or "N").upper().startswith("N")
+            )
+            if recurse_directories:
+                is_recurse = False
+                matching_file_list = glob.glob(import_dir + "/*.png", recursive=False)
+            else:
+                is_recurse = True
+                matching_file_list = glob.glob(import_dir + "/**/*.png", recursive=True)
+
+            if len(matching_file_list) > 0:
+                return import_dir, is_recurse, matching_file_list
+            else:
+                print(f"The specific path {import_dir} exists, but does not contain .png files!")
+
+    def get_file_details(self, filepath):
+        """Retrieve the embedded metedata fields and dimensions from an image file."""
+        with PIL.Image.open(filepath) as img:
+            img.load()
+            png_width, png_height = img.size
+            img_info = img.info
+        return img_info, png_width, png_height
+
+    def select_board_option(self, board_names, timestamp_string):
+        """Allow the user to choose how a board is selected for imported files."""
+        while True:
+            print("\r\nOptions for board selection for imported images:")
+            print(f"1) Select an existing board name. (found {len(board_names)})")
+            print("2) Specify a board name to create/add to.")
+            print("3) Create/add to board named 'IMPORT'.")
+            print(
+                f"4) Create/add to board named 'IMPORT' with the current datetime string appended (.e.g IMPORT_{timestamp_string})."
+            )
+            print(
+                "5) Create/add to board named 'IMPORT' with a the original file app_version appended (.e.g IMPORT_2.2.5)."
+            )
+            input_option = input("Specify desired board option: ")
+            match (input_option):
+                case "1":
+                    if len(board_names) < 1:
+                        print("\r\nThere are no existing board names to choose from. Select another option!")
+                        continue
+                    board_name = self.select_item_from_list(
+                        board_names, "board name", True, "Cancel, go back and choose a different board option."
+                    )
+                    if board_name is not None:
+                        return board_name
+                case "2":
+                    while True:
+                        board_name = input("Specify new/existing board name: ")
+                        if board_name:
+                            return board_name
+                case "3":
+                    return "IMPORT"
+                case "4":
+                    return f"IMPORT_{timestamp_string}"
+                case "5":
+                    return "IMPORT_APPVERSION"
+
+    def select_item_from_list(self, items, entity_name, allow_cancel, cancel_string):
+        """A general function to render a list of items to select in the console, prompt the user for a selection and ensure a valid entry is selected."""
+        print(f"Select a {entity_name.lower()} from the following list:")
+        index = 1
+        for item in items:
+            print(f"{index}) {item}")
+            index += 1
+        if allow_cancel:
+            print(f"{index}) {cancel_string}")
+        while True:
+            try:
+                option_number = int(input("Specify number of selection: "))
+            except ValueError:
+                continue
+            if allow_cancel and option_number == index:
+                return None
+            if option_number >= 1 and option_number <= len(items):
+                return items[option_number - 1]
+
+    def import_image(self, filepath: str, board_name_option: str, db_mapper: DatabaseMapper, config: Config):
+        """Import a single file by its path"""
+        parser = InvokeAIMetadataParser()
+        file_name = os.path.basename(filepath)
+        file_destination_path = os.path.join(config.outputs_path, file_name)
+
+        print("===============================================================================")
+        print(f"Importing {filepath}")
+
+        # check destination to see if the file was previously imported
+        if os.path.exists(file_destination_path):
+            print("File already exists in the destination, skipping!")
+            ImportStats.count_skipped_file_exists += 1
+            return
+
+        # check if file name is already referenced in the database
+        if db_mapper.does_image_exist(file_name):
+            print("A reference to a file with this name already exists in the database, skipping!")
+            ImportStats.count_skipped_db_exists += 1
+            return
+
+        # load image info and dimensions
+        img_info, png_width, png_height = self.get_file_details(filepath)
+
+        # parse metadata
+        destination_needs_meta_update = True
+        log_version_note = "(Unknown)"
+        if "invokeai_metadata" in img_info:
+            # for the latest, we will just re-emit the same json, no need to parse/modify
+            converted_field = None
+            latest_json_string = img_info.get("invokeai_metadata")
+            log_version_note = "3.0.0+"
+            destination_needs_meta_update = False
+        else:
+            if "sd-metadata" in img_info:
+                converted_field = parser.parse_meta_tag_sd_metadata(json.loads(img_info.get("sd-metadata")))
+            elif "invokeai" in img_info:
+                converted_field = parser.parse_meta_tag_invokeai(json.loads(img_info.get("invokeai")))
+            elif "dream" in img_info:
+                converted_field = parser.parse_meta_tag_dream(img_info.get("dream"))
+            elif "Dream" in img_info:
+                converted_field = parser.parse_meta_tag_dream(img_info.get("Dream"))
+            else:
+                converted_field = InvokeAIMetadata()
+                destination_needs_meta_update = False
+                print("File does not have metadata from known Invoke AI versions, add only, no update!")
+
+            # use the loaded img dimensions if the metadata didnt have them
+            if converted_field.width is None:
+                converted_field.width = png_width
+            if converted_field.height is None:
+                converted_field.height = png_height
+
+            log_version_note = converted_field.imported_app_version if converted_field else "NoVersion"
+            log_version_note = log_version_note or "NoVersion"
+
+            latest_json_string = converted_field.to_json()
+
+        print(f"From Invoke AI Version {log_version_note} with dimensions {png_width} x {png_height}.")
+
+        # if metadata needs update, then update metdata and copy in one shot
+        if destination_needs_meta_update:
+            print("Updating metadata while copying...", end="")
+            self.update_file_metadata_while_copying(
+                filepath, file_destination_path, "invokeai_metadata", latest_json_string
+            )
+            print("Done!")
+        else:
+            print("No metadata update necessary, copying only...", end="")
+            shutil.copy2(filepath, file_destination_path)
+            print("Done!")
+
+        # create thumbnail
+        print("Creating thumbnail...", end="")
+        thumbnail_path = os.path.join(config.thumbnail_path, os.path.splitext(file_name)[0]) + ".webp"
+        thumbnail_size = 256, 256
+        with PIL.Image.open(filepath) as source_image:
+            source_image.thumbnail(thumbnail_size)
+            source_image.save(thumbnail_path, "webp")
+        print("Done!")
+
+        # finalize the dynamic board name if there is an APPVERSION token in it.
+        if converted_field is not None:
+            board_name = board_name_option.replace("APPVERSION", converted_field.imported_app_version or "NoVersion")
+        else:
+            board_name = board_name_option.replace("APPVERSION", "Latest")
+
+        # maintain a map of alrady created/looked up ids to avoid DB queries
+        print("Finding/Creating board...", end="")
+        if board_name in self.board_name_id_map:
+            board_id = self.board_name_id_map[board_name]
+        else:
+            board_id = db_mapper.get_board_id_with_create(board_name)
+            self.board_name_id_map[board_name] = board_id
+        print("Done!")
+
+        # add image to db
+        print("Adding image to database......", end="")
+        modified_time = datetime.datetime.utcfromtimestamp(os.path.getmtime(filepath))
+        db_mapper.add_new_image_to_database(file_name, png_width, png_height, latest_json_string, modified_time)
+        print("Done!")
+
+        # add image to board
+        print("Adding image to board......", end="")
+        db_mapper.add_image_to_board(file_name, board_id)
+        print("Done!")
+
+        ImportStats.count_imported += 1
+        if log_version_note in ImportStats.count_imported_by_version:
+            ImportStats.count_imported_by_version[log_version_note] += 1
+        else:
+            ImportStats.count_imported_by_version[log_version_note] = 1
+
+    def update_file_metadata_while_copying(self, filepath, file_destination_path, tag_name, tag_value):
+        """Perform a metadata update with save to a new destination which accomplishes a copy while updating metadata."""
+        with PIL.Image.open(filepath) as target_image:
+            existing_img_info = target_image.info
+            metadata = PIL.PngImagePlugin.PngInfo()
+            # re-add any existing invoke ai tags unless they are the one we are trying to add
+            for key in existing_img_info:
+                if key != tag_name and key in ("dream", "Dream", "sd-metadata", "invokeai", "invokeai_metadata"):
+                    metadata.add_text(key, existing_img_info[key])
+            metadata.add_text(tag_name, tag_value)
+            target_image.save(file_destination_path, pnginfo=metadata)
+
+    def process(self):
+        """Begin main processing."""
+
+        print("===============================================================================")
+        print("This script will import images generated by earlier versions of")
+        print("InvokeAI into the currently installed root directory:")
+        print(f"   {app_config.root_path}")
+        print("If this is not what you want to do, type ctrl-C now to cancel.")
+
+        # load config
+        print("===============================================================================")
+        print("= Configuration & Settings")
+
+        config = Config()
+        config.find_and_load()
+        db_mapper = DatabaseMapper(config.database_path, config.database_backup_dir)
+        db_mapper.connect()
+
+        import_dir, is_recurse, import_file_list = self.get_import_file_list()
+        ImportStats.count_source_files = len(import_file_list)
+
+        board_names = db_mapper.get_board_names()
+        board_name_option = self.select_board_option(board_names, config.TIMESTAMP_STRING)
+
+        print("\r\n===============================================================================")
+        print("= Import Settings Confirmation")
+
+        print()
+        print(f"Database File Path               : {config.database_path}")
+        print(f"Outputs/Images Directory         : {config.outputs_path}")
+        print(f"Import Image Source Directory    : {import_dir}")
+        print(f"  Recurse Source SubDirectories  : {'Yes' if is_recurse else 'No'}")
+        print(f"Count of .png file(s) found      : {len(import_file_list)}")
+        print(f"Board name option specified      : {board_name_option}")
+        print(f"Database backup will be taken at : {config.database_backup_dir}")
+
+        print("\r\nNotes about the import process:")
+        print("- Source image files will not be modified, only copied to the outputs directory.")
+        print("- If the same file name already exists in the destination, the file will be skipped.")
+        print("- If the same file name already has a record in the database, the file will be skipped.")
+        print("- Invoke AI metadata tags will be updated/written into the imported copy only.")
+        print(
+            "- On the imported copy, only Invoke AI known tags (latest and legacy) will be retained (dream, sd-metadata, invokeai, invokeai_metadata)"
+        )
+        print(
+            "- A property 'imported_app_version' will be added to metadata that can be viewed in the UI's metadata viewer."
+        )
+        print(
+            "- The new 3.x InvokeAI outputs folder structure is flat so recursively found source imges will all be placed into the single outputs/images folder."
+        )
+
+        while True:
+            should_continue = prompt("\nDo you wish to continue with the import [Yn] ? ").lower() or "y"
+            if should_continue == "n":
+                print("\r\nCancelling Import")
+                return
+            elif should_continue == "y":
+                print()
+                break
+
+        db_mapper.backup(config.TIMESTAMP_STRING)
+
+        print()
+        ImportStats.time_start = datetime.datetime.utcnow()
+
+        for filepath in import_file_list:
+            try:
+                self.import_image(filepath, board_name_option, db_mapper, config)
+            except sqlite3.Error as sql_ex:
+                print(f"A database related exception was found processing {filepath}, will continue to next file. ")
+                print("Exception detail:")
+                print(sql_ex)
+                ImportStats.count_file_errors += 1
+            except Exception as ex:
+                print(f"Exception processing {filepath}, will continue to next file. ")
+                print("Exception detail:")
+                print(ex)
+                ImportStats.count_file_errors += 1
+
+        print("\r\n===============================================================================")
+        print(f"= Import Complete - Elpased Time: {ImportStats.get_elapsed_time_string()}")
+        print()
+        print(f"Source File(s)                          : {ImportStats.count_source_files}")
+        print(f"Total Imported                          : {ImportStats.count_imported}")
+        print(f"Skipped b/c file already exists on disk : {ImportStats.count_skipped_file_exists}")
+        print(f"Skipped b/c file already exists in db   : {ImportStats.count_skipped_db_exists}")
+        print(f"Errors during import                    : {ImportStats.count_file_errors}")
+        if ImportStats.count_imported > 0:
+            print("\r\nBreakdown of imported files by version:")
+            for key, version in ImportStats.count_imported_by_version.items():
+                print(f"  {key:20} : {version}")
+
+
+def main():
+    try:
+        processor = MediaImportProcessor()
+        processor.process()
+    except KeyboardInterrupt:
+        print("\r\n\r\nUser cancelled execution.")
+
+
+if __name__ == "__main__":
+    main()

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

@@ -21,13 +21,19 @@ MIN_COLS = 130
 MIN_LINES = 38
 
 
+class WindowTooSmallException(Exception):
+    pass
+
+
 # -------------------------------------
-def set_terminal_size(columns: int, lines: int):
+def set_terminal_size(columns: int, lines: int) -> bool:
+    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)
 
-    OS = platform.uname().system
         if OS == "Windows":
             pass
             # not working reliably - ask user to adjust the window
@@ -37,15 +43,18 @@ def set_terminal_size(columns: int, lines: int):
 
         # check whether it worked....
         ts = get_terminal_size()
-    pause = False
+        if ts.columns < columns or ts.lines < lines:
-    if ts.columns < columns:
+            print(
-        print("\033[1mThis window is too narrow for the user interface.\033[0m")
+                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"
-        pause = True
+            )
-    if ts.lines < lines:
+            resp = input(
-        print("\033[1mThis window is too short for the user interface.\033[0m")
+                "Maximize the window and/or decrease the font size then press any key to continue. Type [Q] to give up.."
-        pause = True
+            )
-    if pause:
+            if resp.upper().startswith("Q"):
-        input("Maximize the window then press any key to continue..")
+                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 { lorasArray: map(lora.loras) };
-
-    return { loras };
   },
   defaultSelectorOptions
 );
 
 const ParamLoraList = () => {
-  const { loras } = useAppSelector(selector);
+  const { lorasArray } = useAppSelector(selector);
 
   return (
     <>
-      {map(loras, (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 &&
+          e.source.node_id === modelLoaderNodeId &&
-          ['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

@@ -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 }) => [
+      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 old board totals
+          // 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(
+            const isInDateRange =
-              currentCache.data,
+              (previousTotal || 0) >= IMAGE_LIMIT
-              imageDTO
+                ? 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(
+            const isInDateRange =
-              currentCache.data,
+              (total || 0) >= IMAGE_LIMIT
-              imageDTO
+                ? 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__ = "3.0.2rc1"

pyproject.toml

@@ -77,7 +77,7 @@ dependencies = [
   "realesrgan",
   "requests~=2.28.2",
   "rich~=13.3",
-  "safetensors~=0.3.0",
+  "safetensors==0.3.1",
   "scikit-image~=0.21.0",
   "send2trash",
   "test-tube~=0.7.5",
@@ -139,6 +139,7 @@ dependencies = [
 "invokeai-metadata" = "invokeai.frontend.CLI.sd_metadata:print_metadata"
 "invokeai-node-cli" = "invokeai.app.cli_app:invoke_cli"
 "invokeai-node-web" = "invokeai.app.api_app:invoke_api"
+"invokeai-import-images" = "invokeai.frontend.install.import_images:main"
 
 [project.urls]
 "Homepage" = "https://invoke-ai.github.io/InvokeAI/"