#!/usr/bin/env python # Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein) # Before running stable-diffusion on an internet-isolated machine, # run this script from one with internet connectivity. The # two machines must share a common .cache directory. """ This is the npyscreen frontend to the model installation application. The work is actually done in backend code in model_install_backend.py. """ import argparse import curses import os import sys from argparse import Namespace from pathlib import Path from shutil import get_terminal_size from typing import List import npyscreen import torch from npyscreen import widget from omegaconf import OmegaConf import invokeai.backend.util.logging as logger from dataclasses import dataclass,field from ...backend.install.model_install_backend import ( Dataset_path, default_config_file, default_dataset, install_requested_models, recommended_datasets, ModelInstallList, ) from ...backend import ModelManager from ...backend.util import choose_precision, choose_torch_device from .widgets import ( CenteredTitleText, MultiSelectColumns, SingleSelectColumns, OffsetButtonPress, TextBox, set_min_terminal_size, ) from invokeai.app.services.config import get_invokeai_config # minimum size for the UI MIN_COLS = 120 MIN_LINES = 50 config = get_invokeai_config() class addModelsForm(npyscreen.FormMultiPage): # for responsive resizing - disabled # FIX_MINIMUM_SIZE_WHEN_CREATED = False # for persistence current_tab = 0 def __init__(self, parentApp, name, multipage=False, *args, **keywords): self.multipage = multipage model_manager = ModelManager(config.model_conf_path) self.initial_models = OmegaConf.load(Dataset_path)['diffusers'] self.installed_cn_models = model_manager.list_controlnet_models() self.installed_lora_models = model_manager.list_lora_models() self.installed_ti_models = model_manager.list_ti_models() try: self.existing_models = OmegaConf.load(default_config_file()) except: self.existing_models = dict() self.starter_model_list = list(self.initial_models.keys()) self.installed_models = dict() super().__init__(parentApp=parentApp, name=name, *args, **keywords) def create(self): window_width, window_height = get_terminal_size() self.nextrely -= 1 self.add_widget_intelligent( npyscreen.FixedText, value="Use ctrl-N and ctrl-P to move to the ext and

revious fields,", editable=False, color="CAUTION", ) self.add_widget_intelligent( npyscreen.FixedText, value="Use cursor arrows to make a selection, and space to toggle checkboxes.", editable=False, color="CAUTION", ) self.nextrely += 1 self.tabs = self.add_widget_intelligent( SingleSelectColumns, values=[ 'DIFFUSERS MODELS', 'CONTROLNET MODELS', 'LORA/LYCORIS MODELS', 'TEXTUAL INVERSION MODELS' ], value=[self.current_tab], columns = 4, max_height = 2, relx=8, scroll_exit = True, ) self.tabs.on_changed = self._toggle_tables top_of_table = self.nextrely self.diffusers_models = self.add_diffusers() bottom_of_table = self.nextrely self.nextrely = top_of_table self.controlnet_models = self.add_controlnets() self.nextrely = top_of_table self.lora_models = self.add_loras() self.nextrely = top_of_table self.ti_models = self.add_tis() self.nextrely = bottom_of_table self.nextrely += 1 done_label = "INSTALL/REMOVE" back_label = "BACK" button_length = len(done_label) button_offset = 0 if self.multipage: button_length += len(back_label) + 1 button_offset += len(back_label) + 1 self.back_button = self.add_widget_intelligent( OffsetButtonPress, name=back_label, relx=(window_width - button_length) // 2, offset=-3, rely=-3, when_pressed_function=self.on_back, ) self.ok_button = self.add_widget_intelligent( OffsetButtonPress, name=done_label, offset=+3, relx=button_offset + 1 + (window_width - button_length) // 2, rely=-3, when_pressed_function=self.on_ok, ) self.cancel = self.add_widget_intelligent( npyscreen.ButtonPress, name="QUIT", rely=-3, relx=window_width-20, when_pressed_function=self.on_cancel, ) # This restores the selected page on return from an installation for i in range(1,self.current_tab+1): self.tabs.h_cursor_line_down(1) self._toggle_tables([self.current_tab]) def add_diffusers(self)->dict[str, npyscreen.widget]: '''Add widgets responsible for selecting diffusers models''' widgets = dict() starter_model_labels = self._get_starter_model_labels() recommended_models = [ x for x in self.starter_model_list if self.initial_models[x].get("recommended", False) ] self.installed_models = sorted( [x for x in list(self.initial_models.keys()) if x in self.existing_models] ) widgets.update( label1 = self.add_widget_intelligent( CenteredTitleText, name="Select from a starter set of Stable Diffusion models from HuggingFace.", editable=False, labelColor="CAUTION", ) ) self.nextrely -= 1 # if user has already installed some initial models, then don't patronize them # by showing more recommendations show_recommended = not self.existing_models widgets.update( models_selected = self.add_widget_intelligent( npyscreen.MultiSelect, name="Install Starter Models", values=starter_model_labels, value=[ self.starter_model_list.index(x) for x in self.starter_model_list if (show_recommended and x in recommended_models)\ or (x in self.existing_models) ], max_height=len(starter_model_labels) + 1, relx=4, scroll_exit=True, ) ) widgets.update( purge_deleted = self.add_widget_intelligent( npyscreen.Checkbox, name="Purge unchecked diffusers models from disk", value=False, scroll_exit=True, relx=4, ) ) self.nextrely += 1 widgets.update( label3 = self.add_widget_intelligent( CenteredTitleText, name="== IMPORT MORE DIFFUSERS MODELS FROM YOUR LOCAL DISK OR THE INTERNET ==", editable=False, color="CONTROL", ) ) self.nextrely -= 1 widgets.update( label4 = self.add_widget_intelligent( CenteredTitleText, name="Enter URLs, file paths, or HuggingFace repository IDs, separated by spaces. Use shift-control-V to paste:", editable=False, labelColor="CONTROL", relx=4, ) ) self.nextrely -= 1 widgets.update( download_ids = self.add_widget_intelligent( TextBox, max_height=4, scroll_exit=True, editable=True, relx=4 ) ) self.nextrely += 1 widgets.update( autoload_directory = self.add_widget_intelligent( npyscreen.TitleFilename, name="Directory to scan for models to import ( autocompletes):", select_dir=True, must_exist=True, use_two_lines=False, labelColor="DANGER", begin_entry_at=65, scroll_exit=True, ) ) widgets.update( autoscan_on_startup = self.add_widget_intelligent( npyscreen.Checkbox, name="Scan and import from this directory each time InvokeAI starts", value=False, relx=4, scroll_exit=True, ) ) return widgets def add_controlnets(self)->dict[str, npyscreen.widget]: widgets = dict() cn_model_list = sorted(self.installed_cn_models.keys()) widgets.update( label1 = self.add_widget_intelligent( CenteredTitleText, name="Select the desired ControlNet models to install. Unchecked models will be purged from disk.", editable=False, labelColor="CAUTION", ) ) columns=6 widgets.update( models_selected = self.add_widget_intelligent( MultiSelectColumns, columns=columns, name="Install ControlNet Models", values=cn_model_list, value=[ cn_model_list.index(x) for x in cn_model_list if self.installed_cn_models[x] ], max_height=len(cn_model_list)//columns + 1, relx=4, scroll_exit=True, ) ) self.nextrely += 1 widgets.update( label2 = self.add_widget_intelligent( npyscreen.TitleFixedText, name='Additional ControlNet HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):', relx=4, color='CONTROL', editable=False, scroll_exit=True ) ) self.nextrely -= 1 widgets.update( download_ids = self.add_widget_intelligent( TextBox, max_height=4, scroll_exit=True, editable=True, relx=4 ) ) return widgets # TO DO - create generic function for loras and textual inversions def add_loras(self)->dict[str,npyscreen.widget]: widgets = dict() model_list = sorted(self.installed_lora_models.keys()) widgets.update( label1 = self.add_widget_intelligent( CenteredTitleText, name="Select the desired LoRA/LyCORIS models to install. Unchecked models will be purged from disk.", editable=False, labelColor="CAUTION", ) ) columns=min(len(model_list),3) or 1 widgets.update( models_selected = self.add_widget_intelligent( MultiSelectColumns, columns=columns, name="Install ControlNet Models", values=model_list, value=[ model_list.index(x) for x in model_list if self.installed_lora_models[x] ], max_height=len(model_list)//columns + 1, relx=4, scroll_exit=True, ) ) self.nextrely += 1 widgets.update( label2 = self.add_widget_intelligent( npyscreen.TitleFixedText, name='URLs for new LoRA/LYCORIS models to download and install (Space separated. Use shift-control-V to paste):', relx=4, color='CONTROL', editable=False, hidden=True, scroll_exit=True ) ) self.nextrely -= 1 widgets.update( download_ids = self.add_widget_intelligent( TextBox, max_height=4, scroll_exit=True, editable=True, relx=4, hidden=True, ) ) return widgets def add_tis(self)->dict[str, npyscreen.widget]: widgets = dict() model_list = sorted(self.installed_ti_models.keys()) widgets.update( label1 = self.add_widget_intelligent( CenteredTitleText, name="Select the desired models to install. Unchecked models will be purged from disk.", editable=False, labelColor="CAUTION", ) ) columns=min(len(model_list),6) or 1 widgets.update( models_selected = self.add_widget_intelligent( MultiSelectColumns, columns=columns, name="Install Textual Inversion Embeddings", values=model_list, value=[ model_list.index(x) for x in model_list if self.installed_ti_models[x] ], max_height=len(model_list)//columns + 1, relx=4, scroll_exit=True, ) ) widgets.update( label2 = self.add_widget_intelligent( npyscreen.TitleFixedText, name='Textual Inversion models to download, use URLs or HugggingFace repo_ids (Space separated. Use shift-control-V to paste):', relx=4, color='CONTROL', editable=False, hidden=True, scroll_exit=True ) ) self.nextrely -= 1 widgets.update( download_ids = self.add_widget_intelligent( TextBox, max_height=4, scroll_exit=True, editable=True, relx=4, hidden=True, ) ) return widgets def resize(self): super().resize() if (s := self.diffusers_models.get("models_selected")): s.values = self._get_starter_model_labels() def _toggle_tables(self, value=None): selected_tab = value[0] widgets = [ self.diffusers_models, self.controlnet_models, self.lora_models, self.ti_models, ] for group in widgets: for k,v in group.items(): v.hidden = True for k,v in widgets[selected_tab].items(): v.hidden = False self.__class__.current_tab = selected_tab # for persistence self.display() def _get_starter_model_labels(self) -> List[str]: window_width, window_height = get_terminal_size() label_width = 25 checkbox_width = 4 spacing_width = 2 description_width = window_width - label_width - checkbox_width - spacing_width im = self.initial_models names = self.starter_model_list descriptions = [ im[x].description[0 : description_width - 3] + "..." if len(im[x].description) > description_width else im[x].description for x in names ] return [ f"%-{label_width}s %s" % (names[x], descriptions[x]) for x in range(0, len(names)) ] def _get_columns(self) -> int: window_width, window_height = get_terminal_size() cols = ( 4 if window_width > 240 else 3 if window_width > 160 else 2 if window_width > 80 else 1 ) return min(cols, len(self.installed_models)) def on_ok(self): self.parentApp.setNextForm(None) self.editing = False self.parentApp.user_cancelled = False self.marshall_arguments() def on_back(self): self.parentApp.switchFormPrevious() self.editing = False def on_cancel(self): self.parentApp.setNextForm(None) self.parentApp.user_cancelled = True self.editing = False def marshall_arguments(self): """ Assemble arguments and store as attributes of the application: .starter_models: dict of model names to install from INITIAL_CONFIGURE.yaml True => Install False => Remove .scan_directory: Path to a directory of models to scan and import .autoscan_on_startup: True if invokeai should scan and import at startup time .import_model_paths: list of URLs, repo_ids and file paths to import """ # we're using a global here rather than storing the result in the parentapp # due to some bug in npyscreen that is causing attributes to be lost selections = self.parentApp.user_selections # starter models to install/remove starter_models = dict( map( lambda x: (self.starter_model_list[x], True), self.diffusers_models['models_selected'].value, ) ) selections.purge_deleted_models = self.diffusers_models['purge_deleted'].value selections.install_models = [x for x in starter_models if x not in self.existing_models] selections.remove_models = [x for x in self.starter_model_list if x in self.existing_models and x not in starter_models] # TODO: REFACTOR THIS REPETITIVE CODE cn_models_selected = self.controlnet_models['models_selected'] selections.install_cn_models = [cn_models_selected.values[x] for x in cn_models_selected.value if not self.installed_cn_models[cn_models_selected.values[x]] ] selections.remove_cn_models = [x for x in cn_models_selected.values if self.installed_cn_models[x] and cn_models_selected.values.index(x) not in cn_models_selected.value ] if (additional_cns := self.controlnet_models['download_ids'].value.split()): valid_cns = [x for x in additional_cns if '/' in x] selections.install_cn_models.extend(valid_cns) # same thing, for LoRAs loras_selected = self.lora_models['models_selected'] selections.install_lora_models = [loras_selected.values[x] for x in loras_selected.value if not self.installed_lora_models[loras_selected.values[x]] ] selections.remove_lora_models = [x for x in loras_selected.values if self.installed_lora_models[x] and loras_selected.values.index(x) not in loras_selected.value ] if (additional_loras := self.lora_models['download_ids'].value.split()): selections.install_lora_models.extend(additional_loras) # same thing, for TIs # TODO: refactor tis_selected = self.ti_models['models_selected'] selections.install_ti_models = [tis_selected.values[x] for x in tis_selected.value if not self.installed_ti_models[tis_selected.values[x]] ] selections.remove_ti_models = [x for x in tis_selected.values if self.installed_ti_models[x] and tis_selected.values.index(x) not in tis_selected.value ] if (additional_tis := self.ti_models['download_ids'].value.split()): selections.install_ti_models.extend(additional_tis) # load directory and whether to scan on startup selections.scan_directory = self.diffusers_models['autoload_directory'].value selections.autoscan_on_startup = self.diffusers_models['autoscan_on_startup'].value # URLs and the like selections.import_model_paths = self.diffusers_models['download_ids'].value.split() @dataclass class UserSelections(): install_models: List[str]= field(default_factory=list) remove_models: List[str]=field(default_factory=list) purge_deleted_models: bool=field(default_factory=list) install_cn_models: List[str] = field(default_factory=list) remove_cn_models: List[str] = field(default_factory=list) install_lora_models: List[str] = field(default_factory=list) remove_lora_models: List[str] = field(default_factory=list) install_ti_models: List[str] = field(default_factory=list) remove_ti_models: List[str] = field(default_factory=list) scan_directory: Path = None autoscan_on_startup: bool=False import_model_paths: str=None class AddModelApplication(npyscreen.NPSAppManaged): def __init__(self): super().__init__() self.user_cancelled = False self.user_selections = UserSelections() def onStart(self): npyscreen.setTheme(npyscreen.Themes.DefaultTheme) self.main_form = self.addForm( "MAIN", addModelsForm, name="Install Stable Diffusion Models" ) # -------------------------------------------------------- def process_and_execute(opt: Namespace, selections: Namespace): models_to_install = selections.install_models models_to_remove = selections.remove_models directory_to_scan = selections.scan_directory scan_at_startup = selections.autoscan_on_startup potential_models_to_install = selections.import_model_paths install_requested_models( diffusers = ModelInstallList(models_to_install, models_to_remove), controlnet = ModelInstallList(selections.install_cn_models, selections.remove_cn_models), lora = ModelInstallList(selections.install_lora_models, selections.remove_lora_models), ti = ModelInstallList(selections.install_ti_models, selections.remove_ti_models), scan_directory=Path(directory_to_scan) if directory_to_scan else None, external_models=potential_models_to_install, scan_at_startup=scan_at_startup, precision="float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device())), purge_deleted=selections.purge_deleted_models, config_file_path=Path(opt.config_file) if opt.config_file else None, ) # -------------------------------------------------------- def select_and_download_models(opt: Namespace): precision = ( "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device())) ) if opt.default_only: install_requested_models( install_initial_models=default_dataset(), precision=precision, ) elif opt.yes_to_all: install_requested_models( install_initial_models=recommended_datasets(), precision=precision, ) else: set_min_terminal_size(MIN_COLS, MIN_LINES) installApp = AddModelApplication() installApp.run() process_and_execute(opt, installApp.user_selections) # ------------------------------------- def main(): parser = argparse.ArgumentParser(description="InvokeAI model downloader") parser.add_argument( "--full-precision", dest="full_precision", action=argparse.BooleanOptionalAction, type=bool, default=False, help="use 32-bit weights instead of faster 16-bit weights", ) parser.add_argument( "--yes", "-y", dest="yes_to_all", action="store_true", help='answer "yes" to all prompts', ) parser.add_argument( "--default_only", action="store_true", help="only install the default model", ) parser.add_argument( "--config_file", "-c", dest="config_file", type=str, default=None, help="path to configuration file to create", ) parser.add_argument( "--root_dir", dest="root", type=str, default=None, help="path to root of install directory", ) opt = parser.parse_args() # setting a global here if opt.root and Path(opt.root).exists(): config.root = Path(opt.root) if not (config.root_dir / config.conf_path.parent).exists(): logger.info( "Your InvokeAI root directory is not set up. Calling invokeai-configure." ) from invokeai.frontend.install import invokeai_configure invokeai_configure() sys.exit(0) try: select_and_download_models(opt) except AssertionError as e: logger.error(e) sys.exit(-1) except KeyboardInterrupt: curses.nocbreak() curses.echo() curses.endwin() logger.info("Goodbye! Come back soon.") 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" ) elif str(e).startswith("addwstr"): logger.error( "Insufficient horizontal space for the interface. Please make your window wider and try again." ) # ------------------------------------- if __name__ == "__main__": main()