mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
821 lines
28 KiB
Python
821 lines
28 KiB
Python
#!/usr/bin/env python
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# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
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# Before running stable-diffusion on an internet-isolated machine,
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# run this script from one with internet connectivity. The
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# two machines must share a common .cache directory.
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"""
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This is the npyscreen frontend to the model installation application.
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The work is actually done in backend code in model_install_backend.py.
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"""
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import argparse
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import curses
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import os
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import sys
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from argparse import Namespace
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from multiprocessing import Process
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from multiprocessing.connection import Connection, Pipe
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from pathlib import Path
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from shutil import get_terminal_size
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from typing import List
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import logging
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import npyscreen
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import torch
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from npyscreen import widget
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from omegaconf import OmegaConf
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import invokeai.backend.util.logging as logger
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from ...backend.install.model_install_backend import (
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Dataset_path,
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default_config_file,
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default_dataset,
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install_requested_models,
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recommended_datasets,
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ModelInstallList,
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UserSelections,
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)
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from ...backend import ModelManager
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from ...backend.util import choose_precision, choose_torch_device
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from ...backend.util.logging import InvokeAILogger
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from .widgets import (
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CenteredTitleText,
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MultiSelectColumns,
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SingleSelectColumns,
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OffsetButtonPress,
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TextBox,
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BufferBox,
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set_min_terminal_size,
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)
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from invokeai.app.services.config import get_invokeai_config
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# minimum size for the UI
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MIN_COLS = 120
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MIN_LINES = 52
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config = get_invokeai_config()
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class addModelsForm(npyscreen.FormMultiPage):
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# for responsive resizing - disabled
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# FIX_MINIMUM_SIZE_WHEN_CREATED = False
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# for persistence
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current_tab = 0
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def __init__(self, parentApp, name, multipage=False, *args, **keywords):
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self.multipage = multipage
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super().__init__(parentApp=parentApp, name=name, *args, **keywords)
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def create(self):
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self.keypress_timeout = 10
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self.counter = 0
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self.subprocess_connection = None
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model_manager = ModelManager(config.model_conf_path)
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self.initial_models = OmegaConf.load(Dataset_path)['diffusers']
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self.installed_cn_models = model_manager.list_controlnet_models()
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self.installed_lora_models = model_manager.list_lora_models()
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self.installed_ti_models = model_manager.list_ti_models()
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try:
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self.existing_models = OmegaConf.load(default_config_file())
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except:
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self.existing_models = dict()
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self.starter_model_list = list(self.initial_models.keys())
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self.installed_models = dict()
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window_width, window_height = get_terminal_size()
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self.nextrely -= 1
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self.add_widget_intelligent(
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npyscreen.FixedText,
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value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields,",
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editable=False,
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color="CAUTION",
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)
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self.add_widget_intelligent(
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npyscreen.FixedText,
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value="Use cursor arrows to make a selection, and space to toggle checkboxes.",
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editable=False,
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color="CAUTION",
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)
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self.nextrely += 1
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self.tabs = self.add_widget_intelligent(
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SingleSelectColumns,
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values=[
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'DIFFUSERS MODELS',
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'CONTROLNET MODELS',
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'LORA/LYCORIS MODELS',
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'TEXTUAL INVERSION MODELS'
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],
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value=[self.current_tab],
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columns = 4,
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max_height = 2,
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relx=8,
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scroll_exit = True,
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)
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self.tabs.on_changed = self._toggle_tables
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top_of_table = self.nextrely
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self.diffusers_models = self.add_diffusers()
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bottom_of_table = self.nextrely
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self.nextrely = top_of_table
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self.controlnet_models = self.add_controlnets()
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self.nextrely = top_of_table
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self.lora_models = self.add_loras()
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self.nextrely = top_of_table
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self.ti_models = self.add_tis()
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self.nextrely = bottom_of_table+1
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self.monitor = self.add_widget_intelligent(
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BufferBox,
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name='Log Messages',
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editable=False,
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max_height = 15,
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)
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self.nextrely += 1
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done_label = "INSTALL/REMOVE"
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back_label = "BACK"
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button_length = len(done_label)
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button_offset = 0
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if self.multipage:
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button_length += len(back_label) + 1
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button_offset += len(back_label) + 1
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self.back_button = self.add_widget_intelligent(
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OffsetButtonPress,
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name=back_label,
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relx=(window_width - button_length) // 2,
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offset=-3,
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rely=-3,
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when_pressed_function=self.on_back,
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)
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self.ok_button = self.add_widget_intelligent(
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OffsetButtonPress,
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name=done_label,
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offset=+3,
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relx=button_offset + 1 + (window_width - button_length) // 2,
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rely=-3,
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when_pressed_function=self.on_execute
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)
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self.cancel = self.add_widget_intelligent(
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npyscreen.ButtonPress,
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name="QUIT",
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rely=-3,
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relx=window_width-20,
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when_pressed_function=self.on_cancel,
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)
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# This restores the selected page on return from an installation
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for i in range(1,self.current_tab+1):
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self.tabs.h_cursor_line_down(1)
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self._toggle_tables([self.current_tab])
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############# diffusers tab ##########
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def add_diffusers(self)->dict[str, npyscreen.widget]:
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'''Add widgets responsible for selecting diffusers models'''
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widgets = dict()
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starter_model_labels = self._get_starter_model_labels()
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recommended_models = [
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x
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for x in self.starter_model_list
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if self.initial_models[x].get("recommended", False)
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]
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self.installed_models = sorted(
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[x for x in list(self.initial_models.keys()) if x in self.existing_models]
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)
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widgets.update(
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label1 = self.add_widget_intelligent(
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CenteredTitleText,
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name="Select from a starter set of Stable Diffusion models from HuggingFace.",
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editable=False,
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labelColor="CAUTION",
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)
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)
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self.nextrely -= 1
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# if user has already installed some initial models, then don't patronize them
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# by showing more recommendations
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show_recommended = not self.existing_models
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widgets.update(
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models_selected = self.add_widget_intelligent(
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npyscreen.MultiSelect,
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name="Install Starter Models",
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values=starter_model_labels,
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value=[
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self.starter_model_list.index(x)
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for x in self.starter_model_list
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if (show_recommended and x in recommended_models)\
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or (x in self.existing_models)
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],
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max_height=len(starter_model_labels) + 1,
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relx=4,
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scroll_exit=True,
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)
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)
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widgets.update(
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purge_deleted = self.add_widget_intelligent(
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npyscreen.Checkbox,
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name="Purge unchecked diffusers models from disk",
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value=False,
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scroll_exit=True,
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relx=4,
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)
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)
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self.nextrely += 1
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widgets.update(
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label3 = self.add_widget_intelligent(
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CenteredTitleText,
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name="== IMPORT MORE DIFFUSERS MODELS FROM YOUR LOCAL DISK OR THE INTERNET ==",
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editable=False,
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color="CONTROL",
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)
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)
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self.nextrely -= 1
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widgets.update(
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label4 = self.add_widget_intelligent(
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CenteredTitleText,
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name="Enter URLs, file paths, or HuggingFace repository IDs, separated by spaces. Use shift-control-V to paste:",
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editable=False,
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labelColor="CONTROL",
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relx=4,
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)
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)
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self.nextrely -= 1
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widgets.update(
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download_ids = self.add_widget_intelligent(
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TextBox, max_height=4, scroll_exit=True, editable=True, relx=4
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)
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)
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self.nextrely += 1
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widgets.update(
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autoload_directory = self.add_widget_intelligent(
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npyscreen.TitleFilename,
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name="Directory to scan for models to import (<tab> autocompletes):",
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select_dir=True,
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must_exist=True,
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use_two_lines=False,
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labelColor="DANGER",
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begin_entry_at=65,
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scroll_exit=True,
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)
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)
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widgets.update(
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autoscan_on_startup = self.add_widget_intelligent(
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npyscreen.Checkbox,
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name="Scan and import from this directory each time InvokeAI starts",
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value=False,
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relx=4,
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scroll_exit=True,
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)
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)
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return widgets
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############# controlnet tab ##########
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def add_controlnets(self)->dict[str, npyscreen.widget]:
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widgets = dict()
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cn_model_list = sorted(self.installed_cn_models.keys())
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widgets.update(
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label1 = self.add_widget_intelligent(
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CenteredTitleText,
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name="Select the desired ControlNet models to install. Unchecked models will be purged from disk.",
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editable=False,
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labelColor="CAUTION",
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)
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)
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columns=6
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widgets.update(
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models_selected = self.add_widget_intelligent(
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MultiSelectColumns,
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columns=columns,
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name="Install ControlNet Models",
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values=cn_model_list,
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value=[
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cn_model_list.index(x)
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for x in cn_model_list
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if self.installed_cn_models[x]
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],
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max_height=len(cn_model_list)//columns + 1,
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relx=4,
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scroll_exit=True,
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)
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)
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self.nextrely += 1
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widgets.update(
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label2 = self.add_widget_intelligent(
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npyscreen.TitleFixedText,
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name='Additional ControlNet HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):',
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relx=4,
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color='CONTROL',
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editable=False,
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scroll_exit=True
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)
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)
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self.nextrely -= 1
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widgets.update(
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download_ids = self.add_widget_intelligent(
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TextBox,
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max_height=4,
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scroll_exit=True,
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editable=True,
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relx=4
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)
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)
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return widgets
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############# LoRA tab ############
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# TO DO - create generic function for loras and textual inversions
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def add_loras(self)->dict[str,npyscreen.widget]:
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widgets = dict()
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model_list = sorted(self.installed_lora_models.keys())
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widgets.update(
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label1 = self.add_widget_intelligent(
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CenteredTitleText,
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name="Select the desired LoRA/LyCORIS models to install. Unchecked models will be purged from disk.",
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editable=False,
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labelColor="CAUTION",
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)
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)
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columns=min(len(model_list),3) or 1
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widgets.update(
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models_selected = self.add_widget_intelligent(
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MultiSelectColumns,
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columns=columns,
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name="Install ControlNet Models",
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values=model_list,
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value=[
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model_list.index(x)
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for x in model_list
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if self.installed_lora_models[x]
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],
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max_height=len(model_list)//columns + 1,
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relx=4,
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scroll_exit=True,
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)
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)
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self.nextrely += 1
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widgets.update(
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label2 = self.add_widget_intelligent(
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npyscreen.TitleFixedText,
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name='URLs for new LoRA/LYCORIS models to download and install (Space separated. Use shift-control-V to paste):',
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relx=4,
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color='CONTROL',
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editable=False,
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hidden=True,
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scroll_exit=True
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)
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)
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self.nextrely -= 1
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widgets.update(
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download_ids = self.add_widget_intelligent(
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TextBox,
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max_height=4,
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scroll_exit=True,
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editable=True,
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relx=4,
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hidden=True,
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)
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)
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return widgets
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############# Textual Inversion tab ############
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def add_tis(self)->dict[str, npyscreen.widget]:
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widgets = dict()
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model_list = sorted(self.installed_ti_models.keys())
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widgets.update(
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label1 = self.add_widget_intelligent(
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CenteredTitleText,
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name="Select the desired models to install. Unchecked models will be purged from disk.",
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editable=False,
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labelColor="CAUTION",
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)
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)
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columns=min(len(model_list),6) or 1
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widgets.update(
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models_selected = self.add_widget_intelligent(
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MultiSelectColumns,
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columns=columns,
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name="Install Textual Inversion Embeddings",
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values=model_list,
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value=[
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model_list.index(x)
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for x in model_list
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if self.installed_ti_models[x]
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],
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max_height=len(model_list)//columns + 1,
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relx=4,
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scroll_exit=True,
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)
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)
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widgets.update(
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label2 = self.add_widget_intelligent(
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npyscreen.TitleFixedText,
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name='Textual Inversion models to download, use URLs or HugggingFace repo_ids (Space separated. Use shift-control-V to paste):',
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relx=4,
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color='CONTROL',
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editable=False,
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hidden=True,
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scroll_exit=True
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)
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)
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self.nextrely -= 1
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widgets.update(
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download_ids = self.add_widget_intelligent(
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TextBox,
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max_height=4,
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scroll_exit=True,
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editable=True,
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relx=4,
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hidden=True,
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)
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)
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return widgets
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def resize(self):
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super().resize()
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if (s := self.diffusers_models.get("models_selected")):
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s.values = self._get_starter_model_labels()
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def _toggle_tables(self, value=None):
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selected_tab = value[0]
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widgets = [
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self.diffusers_models,
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self.controlnet_models,
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self.lora_models,
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self.ti_models,
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]
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for group in widgets:
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for k,v in group.items():
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v.hidden = True
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for k,v in widgets[selected_tab].items():
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v.hidden = False
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self.__class__.current_tab = selected_tab # for persistence
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self.display()
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def _get_starter_model_labels(self) -> List[str]:
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window_width, window_height = get_terminal_size()
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label_width = 25
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checkbox_width = 4
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spacing_width = 2
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description_width = window_width - label_width - checkbox_width - spacing_width
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im = self.initial_models
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names = self.starter_model_list
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descriptions = [
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im[x].description[0 : description_width - 3] + "..."
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if len(im[x].description) > description_width
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else im[x].description
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for x in names
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]
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return [
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f"%-{label_width}s %s" % (names[x], descriptions[x])
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for x in range(0, len(names))
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]
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def _get_columns(self) -> int:
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window_width, window_height = get_terminal_size()
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cols = (
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4
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if window_width > 240
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else 3
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if window_width > 160
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else 2
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if window_width > 80
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else 1
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)
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return min(cols, len(self.installed_models))
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def on_execute(self):
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self.monitor.entry_widget.buffer(['Installing...'],scroll_end=True)
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self.marshall_arguments()
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app = self.parentApp
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self.display()
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# for communication with the subprocess
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parent_conn, child_conn = Pipe()
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p = Process(
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target = process_and_execute,
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kwargs=dict(
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opt = app.opt,
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selections = app.user_selections,
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conn_out = child_conn,
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)
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)
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p.start()
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child_conn.close()
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self.subprocess_connection = parent_conn
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# process_and_execute(app.opt, app.user_selections)
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def on_ok(self):
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self.parentApp.setNextForm(None)
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self.editing = False
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self.parentApp.user_cancelled = False
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self.marshall_arguments()
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def on_back(self):
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self.parentApp.switchFormPrevious()
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self.editing = False
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def on_cancel(self):
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self.parentApp.setNextForm(None)
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self.parentApp.user_cancelled = True
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self.editing = False
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|
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def while_waiting(self):
|
|
if c := self.subprocess_connection:
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|
while c.poll():
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|
try:
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|
data = c.recv_bytes().decode('utf-8')
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|
data.strip('\n')
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|
if data=='*done*':
|
|
self.subprocess_connection = None
|
|
self.monitor.entry_widget.buffer(['** Action Complete **'])
|
|
self.display()
|
|
# rebuild the form, saving log messages
|
|
saved_messages = self.monitor.entry_widget.values
|
|
self.parentApp.main_form = self.parentApp.addForm(
|
|
"MAIN", addModelsForm, name="Install Stable Diffusion Models"
|
|
)
|
|
self.parentApp.switchForm('MAIN')
|
|
self.parentApp.main_form.monitor.entry_widget.values = saved_messages
|
|
return
|
|
self.monitor.entry_widget.buffer([data])
|
|
self.display()
|
|
except (EOFError,OSError):
|
|
self.subprocess_connection = None
|
|
|
|
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()
|
|
|
|
class AddModelApplication(npyscreen.NPSAppManaged):
|
|
def __init__(self,opt):
|
|
super().__init__()
|
|
self.opt = opt
|
|
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"
|
|
)
|
|
|
|
class StderrToMessage():
|
|
def __init__(self, connection: Connection):
|
|
self.connection = connection
|
|
|
|
def write(self, data:str):
|
|
self.connection.send_bytes(data.encode('utf-8'))
|
|
|
|
def flush(self):
|
|
pass
|
|
|
|
# --------------------------------------------------------
|
|
def process_and_execute(opt: Namespace,
|
|
selections: Namespace,
|
|
conn_out: Connection=None,
|
|
):
|
|
# set up so that stderr is sent to conn_out
|
|
if conn_out:
|
|
translator = StderrToMessage(conn_out)
|
|
sys.stderr = translator
|
|
sys.stdout = translator
|
|
InvokeAILogger.getLogger().handlers[0]=logging.StreamHandler(translator)
|
|
|
|
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,
|
|
)
|
|
|
|
if conn_out:
|
|
conn_out.send_bytes('*done*'.encode('utf-8'))
|
|
conn_out.close()
|
|
|
|
|
|
# --------------------------------------------------------
|
|
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:
|
|
# needed because the torch library is loaded, even though we don't use it
|
|
torch.multiprocessing.set_start_method("spawn")
|
|
|
|
set_min_terminal_size(MIN_COLS, MIN_LINES)
|
|
installApp = AddModelApplication(opt)
|
|
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()
|