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fix relative model paths to be against config.models_path, not root

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This commit is contained in:
Lincoln Stein 2023-07-29 10:30:27 -04:00
parent 974175be45
commit 9968ff2893
7 changed files with 39 additions and 42 deletions
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3
invokeai/app/services/config.py
View File

@ -171,7 +171,6 @@ from pydantic import BaseSettings, Field, parse_obj_as
from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
INIT_FILE = Path("invokeai.yaml")
MODEL_CORE = Path("models/core")
DB_FILE = Path("invokeai.db")
LEGACY_INIT_FILE = Path("invokeai.init")
@ -357,7 +356,7 @@ def _find_root() -> Path:
venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
if os.environ.get("INVOKEAI_ROOT"):
root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE, MODEL_CORE]]):
elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
root = (venv.parent).resolve()
else:
root = Path("~/invokeai").expanduser().resolve()

2
invokeai/backend/install/invokeai_configure.py
View File

@ -181,7 +181,7 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
def download_conversion_models():
target_dir = config.root_path / "models/core/convert"
target_dir = config.models_path / "core/convert"
kwargs = dict() # for future use
try:
logger.info("Downloading core tokenizers and text encoders")

8
invokeai/backend/install/model_install_backend.py
View File

@ -103,6 +103,7 @@ class ModelInstall(object):
access_token: str = None,
):
self.config = config
# force model manager to be a singleton
self.mgr = model_manager or ModelManager(config.model_conf_path)
self.datasets = OmegaConf.load(Dataset_path)
self.prediction_helper = prediction_type_helper
@ -273,6 +274,7 @@ class ModelInstall(object):
logger.error(f"Unable to download {url}. Skipping.")
info = ModelProbe().heuristic_probe(location)
dest = self.config.models_path / info.base_type.value / info.model_type.value / location.name
dest.parent.mkdir(parents=True, exist_ok=True)
models_path = shutil.move(location, dest)
# staged version will be garbage-collected at this time
@ -346,7 +348,7 @@ class ModelInstall(object):
if key in self.datasets:
description = self.datasets[key].get("description") or description
rel_path = self.relative_to_root(path)
rel_path = self.relative_to_root(path,self.config.models_path)
attributes = dict(
path=str(rel_path),
@ -386,8 +388,8 @@ class ModelInstall(object):
attributes.update(dict(config=str(legacy_conf)))
return attributes
def relative_to_root(self, path: Path) -> Path:
root = self.config.root_path
def relative_to_root(self, path: Path, root: None) -> Path:
root = root or self.config.root_path
if path.is_relative_to(root):
return path.relative_to(root)
else:

6
invokeai/backend/model_management/convert_ckpt_to_diffusers.py
View File

@ -63,7 +63,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionS
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.app.services.config import InvokeAIAppConfig, MODEL_CORE
from invokeai.app.services.config import InvokeAIAppConfig
from picklescan.scanner import scan_file_path
from .models import BaseModelType, ModelVariantType
@ -81,7 +81,7 @@ if is_accelerate_available():
from accelerate.utils import set_module_tensor_to_device
logger = InvokeAILogger.getLogger(__name__)
CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / MODEL_CORE / "convert"
CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().models_path / "core/convert"
def shave_segments(path, n_shave_prefix_segments=1):
@ -1281,7 +1281,7 @@ def download_from_original_stable_diffusion_ckpt(
original_config = OmegaConf.load(original_config_file)
if (
model_version == BaseModelType.StableDiffusion2
and original_config["model"]["params"]["parameterization"] == "v"
and original_config["model"]["params"].get("parameterization") == "v"
):
prediction_type = "v_prediction"
upcast_attention = True

29
invokeai/backend/model_management/model_manager.py
View File

@ -456,7 +456,7 @@ class ModelManager(object):
raise ModelNotFoundException(f"Model not found - {model_key}")
model_config = self.models[model_key]
model_path = self.app_config.root_path / model_config.path
model_path = self.app_config.models_path / model_config.path
if not model_path.exists():
if model_class.save_to_config:
@ -623,7 +623,7 @@ class ModelManager(object):
self.cache.uncache_model(cache_id)
# if model inside invoke models folder - delete files
model_path = self.app_config.root_path / model_cfg.path
model_path = self.app_config.models_path / model_cfg.path
cache_path = self._get_model_cache_path(model_path)
if cache_path.exists():
rmtree(str(cache_path))
@ -656,8 +656,8 @@ class ModelManager(object):
"""
# relativize paths as they go in - this makes it easier to move the root directory around
if path := model_attributes.get("path"):
if Path(path).is_relative_to(self.app_config.root_path):
model_attributes["path"] = str(Path(path).relative_to(self.app_config.root_path))
if Path(path).is_relative_to(self.app_config.models_path):
model_attributes["path"] = str(Path(path).relative_to(self.app_config.models_path))
model_class = MODEL_CLASSES[base_model][model_type]
model_config = model_class.create_config(**model_attributes)
@ -732,7 +732,7 @@ class ModelManager(object):
/ new_name
)
move(old_path, new_path)
model_cfg.path = str(new_path.relative_to(self.app_config.root_path))
model_cfg.path = str(new_path.relative_to(self.app_config.models_path))
# clean up caches
old_model_cache = self._get_model_cache_path(old_path)
@ -795,7 +795,7 @@ class ModelManager(object):
info["path"] = (
str(new_diffusers_path)
if dest_directory
else str(new_diffusers_path.relative_to(self.app_config.root_path))
else str(new_diffusers_path.relative_to(self.app_config.models_path))
)
info.pop("config")
@ -883,10 +883,17 @@ class ModelManager(object):
new_models_found = False
self.logger.info(f"Scanning {self.app_config.models_path} for new models")
with Chdir(self.app_config.root_path):
with Chdir(self.app_config.models_path):
for model_key, model_config in list(self.models.items()):
model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
model_path = self.app_config.root_path.absolute() / model_config.path
# Patch for relative path bug in older models.yaml - paths should not
# be starting with a hard-coded 'models'. This will also fix up
# models.yaml when committed.
if model_config.path.startswith('models'):
model_config.path = str(Path(*Path(model_config.path).parts[1:]))
model_path = self.app_config.models_path.absolute() / model_config.path
if not model_path.exists():
model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
if model_class.save_to_config:
@ -919,8 +926,8 @@ class ModelManager(object):
if model_key in self.models:
raise DuplicateModelException(f"Model with key {model_key} added twice")
if model_path.is_relative_to(self.app_config.root_path):
model_path = model_path.relative_to(self.app_config.root_path)
if model_path.is_relative_to(self.app_config.models_path):
model_path = model_path.relative_to(self.app_config.models_path)
model_config: ModelConfigBase = model_class.probe_config(str(model_path))
self.models[model_key] = model_config
@ -971,7 +978,7 @@ class ModelManager(object):
# LS: hacky
# Patch in the SD VAE from core so that it is available for use by the UI
try:
self.heuristic_import({config.root_path / "models/core/convert/sd-vae-ft-mse"})
self.heuristic_import({config.models_path / "core/convert/sd-vae-ft-mse"})
except:
pass

2
invokeai/backend/model_management/models/stable_diffusion.py
View File

@ -259,7 +259,7 @@ def _convert_ckpt_and_cache(
"""
app_config = InvokeAIAppConfig.get_config()
weights = app_config.root_path / model_config.path
weights = app_config.models_path / model_config.path
config_file = app_config.root_path / model_config.config
output_path = Path(output_path)

23
invokeai/frontend/install/model_install.py
View File

@ -153,7 +153,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
BufferBox,
name="Log Messages",
editable=False,
max_height=8,
max_height=15,
)
self.nextrely += 1
@ -399,7 +399,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self.ok_button.hidden = True
self.display()
# for communication with the subprocess
# TO DO: Spawn a worker thread, not a subprocess
parent_conn, child_conn = Pipe()
p = Process(
target=process_and_execute,
@ -414,7 +414,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
self.subprocess_connection = parent_conn
self.subprocess = p
app.install_selections = InstallSelections()
# process_and_execute(app.opt, app.install_selections)
def on_back(self):
self.parentApp.switchFormPrevious()
@ -489,8 +488,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
# rebuild the form, saving and restoring some of the fields that need to be preserved.
saved_messages = self.monitor.entry_widget.values
# autoload_dir = str(config.root_path / self.pipeline_models['autoload_directory'].value)
# autoscan = self.pipeline_models['autoscan_on_startup'].value
app.main_form = app.addForm(
"MAIN",
@ -544,13 +541,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
if downloads := section.get("download_ids"):
selections.install_models.extend(downloads.value.split())
# load directory and whether to scan on startup
# if self.parentApp.autoload_pending:
# selections.scan_directory = str(config.root_path / self.pipeline_models['autoload_directory'].value)
# self.parentApp.autoload_pending = False
# selections.autoscan_on_startup = self.pipeline_models['autoscan_on_startup'].value
class AddModelApplication(npyscreen.NPSAppManaged):
def __init__(self, opt):
super().__init__()
@ -639,6 +629,10 @@ def process_and_execute(
selections: InstallSelections,
conn_out: Connection = None,
):
# need to reinitialize config in subprocess
config = InvokeAIAppConfig.get_config()
config.parse_args()
# set up so that stderr is sent to conn_out
if conn_out:
translator = StderrToMessage(conn_out)
@ -685,9 +679,6 @@ def select_and_download_models(opt: Namespace):
precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
config.precision = precision
helper = lambda x: ask_user_for_prediction_type(x)
# if do_listings(opt):
# pass
installer = ModelInstall(config, prediction_type_helper=helper)
if opt.list_models:
installer.list_models(opt.list_models)
@ -706,8 +697,6 @@ def select_and_download_models(opt: Namespace):
# needed to support the probe() method running under a subprocess
torch.multiprocessing.set_start_method("spawn")
# the third argument is needed in the Windows 11 environment in
# order to launch and resize a console window running this program
set_min_terminal_size(MIN_COLS, MIN_LINES)
installApp = AddModelApplication(opt)
try: