mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
configure/install basically working; needs edge case testing
This commit is contained in:
Lincoln Stein
@ -11,7 +11,6 @@ The work is actually done in backend code in model_install_backend.py.
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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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import textwrap
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import traceback
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@ -20,27 +19,21 @@ 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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from invokeai.backend.util.logging import InvokeAILogger
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from invokeai.backend.install.model_install_backend import (
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Dataset_path, # most of these should go!!
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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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ModelInstall
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InstallSelections,
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ModelInstall,
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SchedulerPredictionType,
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)
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from invokeai.backend.model_management import ModelManager, BaseModelType, ModelType
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from invokeai.backend.model_management import ModelManager, ModelType
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from invokeai.backend.util import choose_precision, choose_torch_device
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from invokeai.frontend.install.widgets import (
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CenteredTitleText,
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@ -133,7 +126,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
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bottom_of_table = self.nextrely
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self.nextrely = top_of_table
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self.pipeline_models = self.add_model_widgets(
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self.pipeline_models = self.add_pipeline_widgets(
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model_type=ModelType.Pipeline,
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window_width=window_width,
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exclude = self.starter_models
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@ -210,11 +203,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
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starters = self.starter_models
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starter_model_labels = self.model_labels
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recommended_models = set([
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x
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for x in starters
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if models[x].recommended
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])
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self.installed_models = sorted(
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[x for x in starters if models[x].installed]
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)
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@ -312,16 +300,18 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
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return widgets
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### Tab for arbitrary diffusers widgets ###
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def add_diffusers_widgets(self,
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model_type: ModelType=ModelType.Pipeline,
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window_width: int=120,
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)->dict[str,npyscreen.widget]:
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def add_pipeline_widgets(self,
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model_type: ModelType=ModelType.Pipeline,
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window_width: int=120,
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**kwargs,
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)->dict[str,npyscreen.widget]:
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'''Similar to add_model_widgets() but adds some additional widgets at the bottom
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to support the autoload directory'''
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widgets = self.add_model_widgets(
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model_type = model_type,
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window_width = window_width,
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install_prompt=f"Additional {model_type.value.title()} models already installed.",
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**kwargs,
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)
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label = "Directory to scan for models to automatically import (<tab> autocompletes):"
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@ -428,7 +418,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
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target = process_and_execute,
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kwargs=dict(
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opt = app.program_opts,
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selections = app.user_selections,
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selections = app.install_selections,
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conn_out = child_conn,
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)
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)
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@ -436,8 +426,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
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child_conn.close()
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self.subprocess_connection = parent_conn
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self.subprocess = p
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app.user_selections = UserSelections()
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# process_and_execute(app.opt, app.user_selections)
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app.install_selections = InstallSelections()
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# process_and_execute(app.opt, app.install_selections)
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def on_back(self):
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self.parentApp.switchFormPrevious()
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@ -453,7 +443,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
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self.parentApp.setNextForm(None)
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self.parentApp.user_cancelled = False
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self.editing = False
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########## This routine monitors the child process that is performing model installation and removal #####
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def while_waiting(self):
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'''Called during idle periods. Main task is to update the Log Messages box with messages
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@ -532,73 +522,24 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
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.autoscan_on_startup: True if invokeai should scan and import at startup time
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.import_model_paths: list of URLs, repo_ids and file paths to import
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"""
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# we're using a global here rather than storing the result in the parentapp
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# due to some bug in npyscreen that is causing attributes to be lost
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selections = self.parentApp.user_selections
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selections = self.parentApp.install_selections
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all_models = self.all_models
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# Starter models to install/remove
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# TO DO - turn these into a dict so we don't have to hard-code the attributes
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print(f'installed={[x for x in self.all_models if self.all_models[x].installed]}',file=f)
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for section in [self.starter_pipelines, self.pipeline_models,
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self.controlnet_models, self.lora_models, self.ti_models]:
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# Defined models (in INITIAL_CONFIG.yaml or models.yaml) to add/remove
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ui_sections = [self.starter_pipelines, self.pipeline_models,
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self.controlnet_models, self.lora_models, self.ti_models]
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for section in ui_sections:
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selected = set([section['models'][x] for x in section['models_selected'].value])
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models_to_install = [x for x in selected if not self.all_models[x].installed]
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models_to_remove = [x for x in section['models'] if x not in selected and self.all_models[x].installed]
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# "More" models
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selections.import_model_paths = self.pipeline_models['download_ids'].value.split()
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if diffusers_selected := self.pipeline_models.get('models_selected'):
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selections.remove_models.extend([x
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for x in diffusers_selected.values
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if self.installed_pipeline_models[x]
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and diffusers_selected.values.index(x) not in diffusers_selected.value
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]
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)
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# TODO: REFACTOR THIS REPETITIVE CODE
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if cn_models_selected := self.controlnet_models.get('models_selected'):
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selections.install_cn_models = [cn_models_selected.values[x]
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for x in cn_models_selected.value
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if not self.installed_cn_models[cn_models_selected.values[x]]
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]
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selections.remove_cn_models = [x
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for x in cn_models_selected.values
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if self.installed_cn_models[x]
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and cn_models_selected.values.index(x) not in cn_models_selected.value
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]
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if (additional_cns := self.controlnet_models['download_ids'].value.split()):
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valid_cns = [x for x in additional_cns if '/' in x]
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selections.install_cn_models.extend(valid_cns)
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selections.remove_models.extend(models_to_remove)
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selections.install_models.extend(all_models[x].path or all_models[x].repo_id \
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for x in models_to_install if all_models[x].path or all_models[x].repo_id)
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# same thing, for LoRAs
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if loras_selected := self.lora_models.get('models_selected'):
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selections.install_lora_models = [loras_selected.values[x]
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for x in loras_selected.value
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if not self.installed_lora_models[loras_selected.values[x]]
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]
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selections.remove_lora_models = [x
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for x in loras_selected.values
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if self.installed_lora_models[x]
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and loras_selected.values.index(x) not in loras_selected.value
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]
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if (additional_loras := self.lora_models['download_ids'].value.split()):
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selections.install_lora_models.extend(additional_loras)
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# same thing, for TIs
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# TODO: refactor
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if tis_selected := self.ti_models.get('models_selected'):
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selections.install_ti_models = [tis_selected.values[x]
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for x in tis_selected.value
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if not self.installed_ti_models[tis_selected.values[x]]
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]
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selections.remove_ti_models = [x
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for x in tis_selected.values
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if self.installed_ti_models[x]
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and tis_selected.values.index(x) not in tis_selected.value
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]
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if (additional_tis := self.ti_models['download_ids'].value.split()):
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selections.install_ti_models.extend(additional_tis)
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# models located in the 'download_ids" section
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for section in ui_sections:
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if downloads := section.get('download_ids'):
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selections.install_models.extend(downloads.value.split())
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# load directory and whether to scan on startup
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selections.scan_directory = self.pipeline_models['autoload_directory'].value
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@ -609,7 +550,7 @@ class AddModelApplication(npyscreen.NPSAppManaged):
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super().__init__()
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self.program_opts = opt
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self.user_cancelled = False
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self.user_selections = UserSelections()
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self.install_selections = InstallSelections()
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def onStart(self):
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npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
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@ -628,21 +569,17 @@ class StderrToMessage():
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pass
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# --------------------------------------------------------
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def ask_user_for_config_file(model_path: Path,
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tui_conn: Connection=None
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)->Path:
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def ask_user_for_prediction_type(model_path: Path,
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tui_conn: Connection=None
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)->Path:
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if tui_conn:
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logger.debug('Waiting for user response...')
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return _ask_user_for_cf_tui(model_path, tui_conn)
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return _ask_user_for_pt_tui(model_path, tui_conn)
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else:
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return _ask_user_for_cf_cmdline(model_path)
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return _ask_user_for_pt_cmdline(model_path)
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def _ask_user_for_cf_cmdline(model_path):
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choices = [
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config.legacy_conf_path / x
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for x in ['v2-inference.yaml','v2-inference-v.yaml']
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]
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choices.extend([None])
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def _ask_user_for_pt_cmdline(model_path):
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choices = [SchedulerPredictionType.Epsilon, SchedulerPredictionType.VPrediction, None]
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print(
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f"""
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Please select the type of the V2 checkpoint named {model_path.name}:
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@ -664,7 +601,7 @@ Please select the type of the V2 checkpoint named {model_path.name}:
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return
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return choice
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def _ask_user_for_cf_tui(model_path: Path, tui_conn: Connection)->Path:
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def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection)->Path:
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try:
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tui_conn.send_bytes(f'*need v2 config for:{model_path}'.encode('utf-8'))
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# note that we don't do any status checking here
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@ -672,20 +609,20 @@ def _ask_user_for_cf_tui(model_path: Path, tui_conn: Connection)->Path:
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if response is None:
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return None
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elif response == 'epsilon':
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return config.legacy_conf_path / 'v2-inference.yaml'
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return SchedulerPredictionType.epsilon
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elif response == 'v':
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return config.legacy_conf_path / 'v2-inference-v.yaml'
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return SchedulerPredictionType.VPrediction
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elif response == 'abort':
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logger.info('Conversion aborted')
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return None
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else:
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return Path(response)
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return response
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except:
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return None
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# --------------------------------------------------------
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def process_and_execute(opt: Namespace,
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selections: UserSelections,
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selections: InstallSelections,
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conn_out: Connection=None,
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):
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# set up so that stderr is sent to conn_out
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@ -696,34 +633,14 @@ def process_and_execute(opt: Namespace,
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logger = InvokeAILogger.getLogger()
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logger.handlers.clear()
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logger.addHandler(logging.StreamHandler(translator))
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models_to_install = selections.install_models
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models_to_remove = selections.remove_models
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directory_to_scan = selections.scan_directory
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scan_at_startup = selections.autoscan_on_startup
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potential_models_to_install = selections.import_model_paths
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name_map = selections.model_name_map
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install_requested_models(
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diffusers = ModelInstallList(models_to_install, [name_map[ModelType.Pipeline][x] for x in models_to_remove]),
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controlnet = ModelInstallList(selections.install_cn_models, [name_map[ModelType.ControlNet][x] for x in selections.remove_cn_models]),
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lora = ModelInstallList(selections.install_lora_models, [name_map[ModelType.Lora][x] for x in selections.remove_lora_models]),
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ti = ModelInstallList(selections.install_ti_models, [name_map[ModelType.TextualInversion][x] for x in selections.remove_ti_models]),
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scan_directory=Path(directory_to_scan) if directory_to_scan else None,
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external_models=potential_models_to_install,
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scan_at_startup=scan_at_startup,
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precision="float32"
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if opt.full_precision
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else choose_precision(torch.device(choose_torch_device())),
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config_file_path=Path(opt.config_file) if opt.config_file else config.model_conf_path,
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model_config_file_callback = lambda x: ask_user_for_config_file(x,conn_out)
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)
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installer = ModelInstall(config, prediction_type_helper=lambda x: ask_user_for_prediction_type(x,conn_out))
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installer.install(selections)
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if conn_out:
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conn_out.send_bytes('*done*'.encode('utf-8'))
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conn_out.close()
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def do_listings(opt)->bool:
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"""List installed models of various sorts, and return
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True if any were requested."""
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@ -754,38 +671,34 @@ def select_and_download_models(opt: Namespace):
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if opt.full_precision
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else choose_precision(torch.device(choose_torch_device()))
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)
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if do_listings(opt):
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pass
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# this processes command line additions/removals
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elif opt.diffusers or opt.controlnets or opt.textual_inversions or opt.loras:
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action = 'remove_models' if opt.delete else 'install_models'
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diffusers_args = {'diffusers':ModelInstallList(remove_models=opt.diffusers or [])} \
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if opt.delete \
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else {'external_models':opt.diffusers or []}
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install_requested_models(
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**diffusers_args,
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controlnet=ModelInstallList(**{action:opt.controlnets or []}),
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ti=ModelInstallList(**{action:opt.textual_inversions or []}),
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lora=ModelInstallList(**{action:opt.loras or []}),
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precision=precision,
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model_config_file_callback=lambda x: ask_user_for_config_file(x),
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config.precision = precision
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helper = lambda x: ask_user_for_prediction_type(x)
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# if do_listings(opt):
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# pass
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installer = ModelInstall(config, prediction_type_helper=helper)
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if opt.add or opt.delete:
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selections = InstallSelections(
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install_models = opt.add or [],
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remove_models = opt.delete or []
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)
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installer.install(selections)
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elif opt.default_only:
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install_requested_models(
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diffusers=ModelInstallList(install_models=default_dataset()),
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precision=precision,
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selections = InstallSelections(
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install_models = installer.default_model()
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)
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installer.install(selections)
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elif opt.yes_to_all:
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install_requested_models(
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diffusers=ModelInstallList(install_models=recommended_datasets()),
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precision=precision,
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selections = InstallSelections(
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install_models = installer.recommended_models()
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)
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installer.install(selections)
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# this is where the TUI is called
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else:
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# needed because the torch library is loaded, even though we don't use it
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torch.multiprocessing.set_start_method("spawn")
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# currently commented out because it has started generating errors (?)
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# torch.multiprocessing.set_start_method("spawn")
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# the third argument is needed in the Windows 11 environment in
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# order to launch and resize a console window running this program
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@ -801,35 +714,20 @@ def select_and_download_models(opt: Namespace):
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installApp.main_form.subprocess.terminate()
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installApp.main_form.subprocess = None
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raise e
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process_and_execute(opt, installApp.user_selections)
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process_and_execute(opt, installApp.install_selections)
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# -------------------------------------
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def main():
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parser = argparse.ArgumentParser(description="InvokeAI model downloader")
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parser.add_argument(
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"--diffusers",
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"--add",
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nargs="*",
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help="List of URLs or repo_ids of diffusers to install/delete",
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)
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parser.add_argument(
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"--loras",
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nargs="*",
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help="List of URLs or repo_ids of LoRA/LyCORIS models to install/delete",
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)
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parser.add_argument(
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"--controlnets",
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nargs="*",
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help="List of URLs or repo_ids of controlnet models to install/delete",
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)
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parser.add_argument(
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"--textual-inversions",
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nargs="*",
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help="List of URLs or repo_ids of textual inversion embeddings to install/delete",
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help="List of URLs, local paths or repo_ids of models to install",
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)
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parser.add_argument(
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"--delete",
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action="store_true",
|
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help="Delete models listed on command line rather than installing them",
|
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nargs="*",
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||||
help="List of names of models to idelete",
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||||
)
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parser.add_argument(
|
||||
"--full-precision",
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||||
@ -849,7 +747,7 @@ def main():
|
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parser.add_argument(
|
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"--default_only",
|
||||
action="store_true",
|
||||
help="only install the default model",
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help="Only install the default model",
|
||||
)
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parser.add_argument(
|
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"--list-models",
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||||
|
||||
@ -17,8 +17,8 @@ from shutil import get_terminal_size
|
||||
from curses import BUTTON2_CLICKED,BUTTON3_CLICKED
|
||||
|
||||
# minimum size for UIs
|
||||
MIN_COLS = 120
|
||||
MIN_LINES = 50
|
||||
MIN_COLS = 180
|
||||
MIN_LINES = 55
|
||||
|
||||
# -------------------------------------
|
||||
def set_terminal_size(columns: int, lines: int, launch_command: str=None):
|
||||
@ -384,7 +384,6 @@ def select_stable_diffusion_config_file(
|
||||
"An SD v2.x base model (512 pixels; no 'parameterization:' line in its yaml file)",
|
||||
"An SD v2.x v-predictive model (768 pixels; 'parameterization: \"v\"' line in its yaml file)",
|
||||
"Skip installation for now and come back later",
|
||||
"Enter config file path manually",
|
||||
]
|
||||
|
||||
F = ConfirmCancelPopup(
|
||||
@ -406,35 +405,17 @@ def select_stable_diffusion_config_file(
|
||||
mlw.values = message
|
||||
|
||||
choice = F.add(
|
||||
SingleSelectWithChanged,
|
||||
npyscreen.SelectOne,
|
||||
values = options,
|
||||
value = [0],
|
||||
max_height = len(options)+1,
|
||||
scroll_exit=True,
|
||||
)
|
||||
file = F.add(
|
||||
FileBox,
|
||||
name='Path to config file',
|
||||
max_height=3,
|
||||
hidden=True,
|
||||
must_exist=True,
|
||||
scroll_exit=True
|
||||
)
|
||||
|
||||
def toggle_visible(value):
|
||||
value = value[0]
|
||||
if value==3:
|
||||
file.hidden=False
|
||||
else:
|
||||
file.hidden=True
|
||||
F.display()
|
||||
|
||||
choice.on_changed = toggle_visible
|
||||
|
||||
F.editw = 1
|
||||
F.edit()
|
||||
if not F.value:
|
||||
return None
|
||||
assert choice.value[0] in range(0,4),'invalid choice'
|
||||
choices = ['epsilon','v','abort',file.value]
|
||||
assert choice.value[0] in range(0,3),'invalid choice'
|
||||
choices = ['epsilon','v','abort']
|
||||
return choices[choice.value[0]]
|
||||
|
||||
Reference in New Issue
Block a user