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listing, downloading and deleting LoRAs working; TI support pending

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This commit is contained in:
Lincoln Stein 2023-06-02 00:40:15 -04:00
parent ff9240b51d
commit 41f7758977
4 changed files with 387 additions and 249 deletions
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3
invokeai/backend/install/model_install_backend.py
View File

@ -95,6 +95,9 @@ def install_requested_models(
model_manager.install_controlnet_models(controlnet.install_models, access_token=access_token)
model_manager.delete_controlnet_models(controlnet.remove_models)
model_manager.install_lora_models(lora.install_models)
model_manager.delete_lora_models(lora.remove_models)
# TODO: Replace next three paragraphs with calls into new model manager
if diffusers.remove_models and len(diffusers.remove_models) > 0:
logger.info("DELETING UNCHECKED STARTER MODELS")

60
invokeai/backend/model_management/model_manager.py
View File

@ -20,7 +20,7 @@ import warnings
from enum import Enum, auto
from pathlib import Path
from shutil import move, rmtree
from typing import Any, Optional, Union, Callable, types
from typing import Any, Optional, Union, Callable, Dict, List, types
import safetensors
import safetensors.torch
@ -1322,15 +1322,69 @@ class ModelManager(object):
os.getenv("HF_HOME") is not None or os.getenv("XDG_CACHE_HOME") is not None
)
def list_lora_models(self)->Dict[str,bool]:
'''Return a dict of installed lora models; key is either the shortname
defined in INITIAL_MODELS, or the basename of the file in the LoRA
directory. Value is True if installed'''
models = OmegaConf.load(Dataset_path).get('lora') or {}
installed_models = {x: False for x in models.keys()}
dir = self.globals.lora_path
installed_models = dict()
for root, dirs, files in os.walk(dir):
for name in files:
if Path(name).suffix in ['.safetensors','.ckpt','.pt']:
installed_models.update({name: True})
return installed_models
def install_lora_models(self, model_names: list[str]):
'''Download list of LoRA/LyCORIS models'''
short_names = OmegaConf.load(Dataset_path).get('lora') or {}
for name in model_names:
url = short_names.get(name) or name
download_with_resume(url, self.globals.lora_path)
def delete_lora_models(self, model_names: List[str]):
'''Remove the list of lora models'''
for name in model_names:
path = self.globals.lora_path / name
if path.exists():
self.logger.info(f'Purging lora model {name}')
path.unlink()
def list_ti_models(self)->Dict[str,bool]:
'''Return a dict of installed textual models; key is either the shortname
defined in INITIAL_MODELS, or the basename of the file in the LoRA
directory. Value is True if installed'''
models = OmegaConf.load(Dataset_path).get('textual_inversion') or {}
installed_models = {x: False for x in models.keys()}
dir = self.globals.embedding_path
installed_models = dict()
for root, dirs, files in os.walk(dir):
for name in files:
if name == 'learned_embeds.bin':
name = str(Path(root,name).parent)
installed_models.update({name: True})
return installed_models
def install_ti_models(self, model_names: list[str]):
'''Download list of textual inversion embeddings'''
short_names = OmegaConf.load(Dataset_path).get('textual_inversion') or {}
for name in model_names:
url = short_names.get(name) or name
download_with_resume(url, self.globals.embedding_path)
def list_controlnet_models(self)->Dict[str,bool]:
'''Return a dict of installed controlnet models; key is repo_id or short name
of model (defined in INITIAL_MODELS), and valule is True if installed'''
of model (defined in INITIAL_MODELS), and value is True if installed'''
cn_models = OmegaConf.load(Dataset_path).get('controlnet') or {}
installed_models = {x: False for x in cn_models.keys()}
cn_dir = self.globals.controlnet_path
installed_cn_models = dict()
for root, dirs, files in os.walk(cn_dir):
for name in dirs:
if Path(root, name, '.download_complete').exists():

5
invokeai/configs/INITIAL_MODELS.yaml
View File

@ -96,3 +96,8 @@ controlnet:
shuffle: lllyasviel/control_v11e_sd15_shuffle
tile: lllyasviel/control_v11f1e_sd15_tile
ip2p: lllyasviel/control_v11e_sd15_ip2p
textual_inversion:
'EasyNegative.safetensors': https://huggingface.co/embed/EasyNegative/blob/main/EasyNegative.safetensors
lora:
'LowRA.safetensors': https://civitai.com/api/download/models/63006
'Ink scenery.safetensors': https://civitai.com/api/download/models/83390

568
invokeai/frontend/install/model_install.py
View File

@ -62,6 +62,8 @@ class addModelsForm(npyscreen.FormMultiPage):
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())
@ -73,18 +75,7 @@ class addModelsForm(npyscreen.FormMultiPage):
def create(self):
window_width, window_height = get_terminal_size()
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]
)
cn_model_list = sorted(self.installed_cn_models.keys())
self.nextrely -= 1
self.add_widget_intelligent(
npyscreen.FixedText,
@ -98,206 +89,47 @@ class addModelsForm(npyscreen.FormMultiPage):
editable=False,
color="CAUTION",
)
self.nextrely += 1
if len(self.starter_model_list) > 0:
self.add_widget_intelligent(
CenteredTitleText,
name="== DIFFUSERS MODEL STARTER PACK ==",
editable=False,
color="CONTROL",
)
self.nextrely -= 1
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
self.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,
)
self.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
self.add_widget_intelligent(
CenteredTitleText,
name="== IMPORT MORE DIFFUSERS MODELS FROM YOUR LOCAL DISK OR THE INTERNET ==",
editable=False,
color="CONTROL",
)
self.nextrely -= 1
for line in [
"Enter URLs, file paths, or HuggingFace repository IDs, separated by spaces. Use shift-control-V to paste:",
]:
self.add_widget_intelligent(
CenteredTitleText,
name=line,
editable=False,
labelColor="CONTROL",
relx=4,
)
self.nextrely -= 1
self.import_model_paths = self.add_widget_intelligent(
TextBox, max_height=4, scroll_exit=True, editable=True, relx=4
)
self.nextrely += 1
self.show_directory_fields = self.add_widget_intelligent(
npyscreen.FormControlCheckbox,
name="Select a directory for models to import automatically at startup",
value=False,
)
self.autoload_directory = self.add_widget_intelligent(
npyscreen.TitleFilename,
name="Directory (<tab> autocompletes):",
select_dir=True,
must_exist=True,
use_two_lines=False,
labelColor="DANGER",
begin_entry_at=34,
scroll_exit=True,
)
self.autoscan_on_startup = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Scan this directory each time InvokeAI starts for new models to import",
value=False,
relx=4,
scroll_exit=True,
)
self.add_widget_intelligent(
CenteredTitleText,
name='_' * (window_width-5),
editable=False,
labelColor='CAUTION'
)
self.nextrely += 1
self.tabs = self.add_widget_intelligent(
SingleSelectColumns,
values=['ADD CONTROLNET MODELS','ADD LORA/LYCORIS MODELS', 'ADD TEXTUAL INVERSION MODELS'],
values=[
'DIFFUSERS MODELS',
'CONTROLNET MODELS',
'LORA/LYCORIS MODELS',
'TEXTUAL INVERSION MODELS'
],
value=0,
columns = 4,
max_height = 2,
relx=8,
scroll_exit = True,
)
# self.add_widget_intelligent(
# CenteredTitleText,
# name="== CONTROLNET MODELS ==",
# editable=False,
# color="CONTROL",
# )
self.tabs.on_changed = self._toggle_tables
top_of_table = self.nextrely
self.cn_label_1 = self.add_widget_intelligent(
CenteredTitleText,
name="Select the desired models to install. Unchecked models will be purged from disk.",
editable=False,
labelColor="CAUTION",
)
columns=6
self.cn_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
self.cn_label_2 = 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
self.additional_controlnet_ids = self.add_widget_intelligent(
TextBox, max_height=2, scroll_exit=True, editable=True, relx=4
)
self.diffusers_models = self.add_diffusers()
bottom_of_table = self.nextrely
self.nextrely = top_of_table
self.lora_label_1 = self.add_widget_intelligent(
npyscreen.TitleFixedText,
name='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
self.loras = self.add_widget_intelligent(
TextBox,
max_height=2,
scroll_exit=True,
editable=True,
relx=4,
hidden=True,
)
self.nextrely = top_of_table
self.ti_label_1 = self.add_widget_intelligent(
npyscreen.TitleFixedText,
name='Textual Inversion 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
self.tis = self.add_widget_intelligent(
TextBox,
max_height=2,
scroll_exit=True,
editable=True,
relx=4,
hidden=True,
)
self.nextrely = bottom_of_table
self.nextrely += 1
self.add_widget_intelligent(
CenteredTitleText,
name='_' * (window_width-5),
editable=False,
labelColor='CAUTION'
)
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
self.cancel = self.add_widget_intelligent(
npyscreen.ButtonPress,
name="CANCEL",
rely=-3,
when_pressed_function=self.on_cancel,
)
done_label = "DONE"
back_label = "BACK"
button_length = len(done_label)
@ -322,37 +154,285 @@ class addModelsForm(npyscreen.FormMultiPage):
when_pressed_function=self.on_ok,
)
for i in [self.autoload_directory, self.autoscan_on_startup]:
self.show_directory_fields.addVisibleWhenSelected(i)
self._toggle_tables([0])
self.tabs.on_changed = self._toggle_tables
def add_diffusers(self)->dict[str, npyscreen.widget]:
'''Add widgets responsible for selecting diffusers models'''
widgets = dict()
self.show_directory_fields.when_value_edited = self._clear_scan_directory
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="== DIFFUSERS MODEL STARTER PACK ==",
editable=False,
color="CONTROL",
)
)
self.nextrely -= 1
widgets.update(
label2 = 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 (<tab> 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 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=2, 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)
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=2,
scroll_exit=True,
editable=True,
relx=4,
hidden=True,
)
)
return widgets
def add_tis(self)->dict[str, npyscreen.widget]:
widgets = dict()
widgets.update(
label1 = self.add_widget_intelligent(
npyscreen.TitleFixedText,
name='Textual Inversion 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=2,
scroll_exit=True,
editable=True,
relx=4,
hidden=True,
)
)
return widgets
def resize(self):
super().resize()
if hasattr(self, "models_selected"):
self.models_selected.values = self._get_starter_model_labels()
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.cn_label_1, self.cn_models_selected, self.cn_label_2, self.additional_controlnet_ids],
[self.lora_label_1,self.loras],
[self.ti_label_1,self.tis],
self.diffusers_models,
self.controlnet_models,
self.lora_models,
self.ti_models,
]
for group in widgets:
for w in group:
w.hidden = True
for w in widgets[selected_tab]:
w.hidden = False
for k,v in group.items():
v.hidden = True
for k,v in widgets[selected_tab].items():
v.hidden = False
self.display()
def _clear_scan_directory(self):
if not self.show_directory_fields.value:
self.autoload_directory.value = ""
def _get_starter_model_labels(self) -> List[str]:
window_width, window_height = get_terminal_size()
label_width = 25
@ -372,22 +452,6 @@ class addModelsForm(npyscreen.FormMultiPage):
for x in range(0, len(names))
]
def _get_installed_cn_models(self)->list[str]:
cn_dir = config.controlnet_path
installed_cn_models = set()
for root, dirs, files in os.walk(cn_dir):
for name in dirs:
if Path(root, name, '.download_complete').exists():
installed_cn_models.add(name.replace('--','/'))
return installed_cn_models
def _add_additional_cn_models(self, known_models: dict, installed_models: set):
for i in installed_models:
if i in known_models:
continue
# translate from name to repo_id
repo_id = i.replace('--','/')
known_models.update({i: repo_id})
def _get_columns(self) -> int:
window_width, window_height = get_terminal_size()
@ -435,43 +499,54 @@ class addModelsForm(npyscreen.FormMultiPage):
selections = self.parentApp.user_selections
# starter models to install/remove
if hasattr(self, "models_selected"):
starter_models = dict(
map(
lambda x: (self.starter_model_list[x], True),
self.models_selected.value,
)
starter_models = dict(
map(
lambda x: (self.starter_model_list[x], True),
self.diffusers_models['models_selected'].value,
)
else:
starter_models = dict()
selections.purge_deleted_models = self.purge_deleted.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]
selections.install_cn_models = [self.cn_models_selected.values[x]
for x in self.cn_models_selected.value
if not self.installed_cn_models[self.cn_models_selected.values[x]]
# TODO: REFACTOR CUT AND PASTE 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 self.cn_models_selected.values
for x in cn_models_selected.values
if self.installed_cn_models[x]
and self.cn_models_selected.values.index(x) not in self.cn_models_selected.value
and cn_models_selected.values.index(x) not in cn_models_selected.value
]
if (additional_cns := self.additional_controlnet_ids.value.split()):
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()):
valid_loras = [x for x in additional_loras if x.startswith(('http:','https:','ftp:'))]
selections.install_lora_models.extend(valid_loras)
# load directory and whether to scan on startup
if self.show_directory_fields.value:
selections.scan_directory = self.autoload_directory.value
selections.autoscan_on_startup = self.autoscan_on_startup.value
else:
selections.scan_directory = None
selections.autoscan_on_startup = False
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.import_model_paths.value.split()
selections.import_model_paths = self.diffusers_models['download_ids'].value.split()
@dataclass
class UserSelections():
@ -507,6 +582,7 @@ def process_and_execute(opt: Namespace, selections: Namespace):
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),
scan_directory=Path(directory_to_scan) if directory_to_scan else None,
external_models=potential_models_to_install,
scan_at_startup=scan_at_startup,