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Port the command-line tools to use model_manager2 (#5546)

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* Port the command-line tools to use model_manager2

1.Reimplement the following:

  - invokeai-model-install
  - invokeai-merge
  - invokeai-ti

  To avoid breaking the original modeal manager, the udpated tools
  have been renamed invokeai-model-install2 and invokeai-merge2. The
  textual inversion training script should continue to work with
  existing installations. The "starter" models now live in
  `invokeai/configs/INITIAL_MODELS2.yaml`.

  When the full model manager 2 is in place and working, I'll rename
  these files and commands.

2. Add the `merge` route to the web API. This will merge two or three models,
   resulting a new one.

   - Note that because the model installer selectively installs the `fp16` variant
     of models (rather than both 16- and 32-bit versions as previous),
     the diffusers merge script will choke on any huggingface diffuserse models
     that were downloaded with the new installer. Previously-downloaded models
     should continue to merge correctly. I have a PR
     upstream https://github.com/huggingface/diffusers/pull/6670 to fix
     this.

3. (more important!)
  During implementation of the CLI tools, found and fixed a number of small
  runtime bugs in the model_manager2 implementation:

  - During model database migration, if a registered models file was
    not found on disk, the migration would be aborted. Now the
    offending model is skipped with a log warning.

  - Caught and fixed a condition in which the installer would download the
    entire diffusers repo when the user provided a single `.safetensors`
    file URL.

  - Caught and fixed a condition in which the installer would raise an
    exception and stop the app when a request for an unknown model's metadata
    was passed to Civitai. Now an error is logged and the installer continues.

  - Replaced the LoWRA starter LoRA with FlatColor. The former has been removed
    from Civitai.

* fix ruff issue

---------

Co-authored-by: Lincoln Stein <lstein@gmail.com>
This commit is contained in:
Lincoln Stein
2024-02-02 12:18:47 -05:00
committed by GitHub
parent d3320dc4ee
commit f2777f5096
17 changed files with 2297 additions and 78 deletions
Download Patch File Download Diff File Expand all files Collapse all files

68
invokeai/frontend/training/textual_inversion.py
View File

@ -3,7 +3,7 @@
"""
This is the frontend to "textual_inversion_training.py".
Copyright (c) 2023 Lincoln Stein and the InvokeAI Development Team
Copyright (c) 2023-24 Lincoln Stein and the InvokeAI Development Team
"""
@ -14,7 +14,7 @@ import sys
import traceback
from argparse import Namespace
from pathlib import Path
from typing import List, Tuple
from typing import Dict, List, Optional, Tuple
import npyscreen
from npyscreen import widget
@ -22,8 +22,9 @@ from omegaconf import OmegaConf
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig
from ...backend.training import do_textual_inversion_training, parse_args
from invokeai.backend.install.install_helper import initialize_installer
from invokeai.backend.model_manager import ModelType
from invokeai.backend.training import do_textual_inversion_training, parse_args
TRAINING_DATA = "text-inversion-training-data"
TRAINING_DIR = "text-inversion-output"
@ -44,19 +45,21 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
precisions = ["no", "fp16", "bf16"]
learnable_properties = ["object", "style"]
def __init__(self, parentApp, name, saved_args=None):
def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, saved_args: Optional[Dict[str, str]] = None):
self.saved_args = saved_args or {}
super().__init__(parentApp, name)
def afterEditing(self):
def afterEditing(self) -> None:
self.parentApp.setNextForm(None)
def create(self):
def create(self) -> None:
self.model_names, default = self.get_model_names()
default_initializer_token = "★"
default_placeholder_token = ""
saved_args = self.saved_args
assert config is not None
try:
default = self.model_names.index(saved_args["model"])
except Exception:
@ -71,7 +74,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
self.model = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Model Name:",
values=self.model_names,
values=sorted(self.model_names),
value=default,
max_height=len(self.model_names) + 1,
scroll_exit=True,
@ -236,7 +239,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
)
self.model.editing = True
def initializer_changed(self):
def initializer_changed(self) -> None:
placeholder = self.placeholder_token.value
self.prompt_token.value = f"(Trigger by using <{placeholder}> in your prompts)"
self.train_data_dir.value = str(config.root_dir / TRAINING_DATA / placeholder)
@ -275,10 +278,13 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
return True
def get_model_names(self) -> Tuple[List[str], int]:
conf = OmegaConf.load(config.root_dir / "configs/models.yaml")
model_names = [idx for idx in sorted(conf.keys()) if conf[idx].get("format", None) == "diffusers"]
defaults = [idx for idx in range(len(model_names)) if "default" in conf[model_names[idx]]]
default = defaults[0] if len(defaults) > 0 else 0
global config
assert config is not None
installer = initialize_installer(config)
store = installer.record_store
main_models = store.search_by_attr(model_type=ModelType.Main)
model_names = [f"{x.base.value}/{x.type.value}/{x.name}" for x in main_models if x.format == "diffusers"]
default = 0
return (model_names, default)
def marshall_arguments(self) -> dict:
@ -326,7 +332,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
class MyApplication(npyscreen.NPSAppManaged):
def __init__(self, saved_args=None):
def __init__(self, saved_args: Optional[Dict[str, str]] = None):
super().__init__()
self.ti_arguments = None
self.saved_args = saved_args
@ -341,11 +347,12 @@ class MyApplication(npyscreen.NPSAppManaged):
)
def copy_to_embeddings_folder(args: dict):
def copy_to_embeddings_folder(args: Dict[str, str]) -> None:
"""
Copy learned_embeds.bin into the embeddings folder, and offer to
delete the full model and checkpoints.
"""
assert config is not None
source = Path(args["output_dir"], "learned_embeds.bin")
dest_dir_name = args["placeholder_token"].strip("<>")
destination = config.root_dir / "embeddings" / dest_dir_name
@ -358,10 +365,11 @@ def copy_to_embeddings_folder(args: dict):
logger.info(f'Keeping {args["output_dir"]}')
def save_args(args: dict):
def save_args(args: dict) -> None:
"""
Save the current argument values to an omegaconf file
"""
assert config is not None
dest_dir = config.root_dir / TRAINING_DIR
os.makedirs(dest_dir, exist_ok=True)
conf_file = dest_dir / CONF_FILE
@ -373,6 +381,7 @@ def previous_args() -> dict:
"""
Get the previous arguments used.
"""
assert config is not None
conf_file = config.root_dir / TRAINING_DIR / CONF_FILE
try:
conf = OmegaConf.load(conf_file)
@ -383,24 +392,26 @@ def previous_args() -> dict:
return conf
def do_front_end(args: Namespace):
def do_front_end() -> None:
global config
saved_args = previous_args()
myapplication = MyApplication(saved_args=saved_args)
myapplication.run()
if args := myapplication.ti_arguments:
os.makedirs(args["output_dir"], exist_ok=True)
if my_args := myapplication.ti_arguments:
os.makedirs(my_args["output_dir"], exist_ok=True)
# Automatically add angle brackets around the trigger
if not re.match("^<.+>$", args["placeholder_token"]):
args["placeholder_token"] = f"<{args['placeholder_token']}>"
if not re.match("^<.+>$", my_args["placeholder_token"]):
my_args["placeholder_token"] = f"<{my_args['placeholder_token']}>"
args["only_save_embeds"] = True
save_args(args)
my_args["only_save_embeds"] = True
save_args(my_args)
try:
do_textual_inversion_training(InvokeAIAppConfig.get_config(), **args)
copy_to_embeddings_folder(args)
print(my_args)
do_textual_inversion_training(config, **my_args)
copy_to_embeddings_folder(my_args)
except Exception as e:
logger.error("An exception occurred during training. The exception was:")
logger.error(str(e))
@ -408,11 +419,12 @@ def do_front_end(args: Namespace):
logger.error(traceback.format_exc())
def main():
def main() -> None:
global config
args = parse_args()
args: Namespace = parse_args()
config = InvokeAIAppConfig.get_config()
config.parse_args([])
# change root if needed
if args.root_dir:
@ -420,7 +432,7 @@ def main():
try:
if args.front_end:
do_front_end(args)
do_front_end()
else:
do_textual_inversion_training(config, **vars(args))
except AssertionError as e:

454
invokeai/frontend/training/textual_inversion2.py Normal file
View File

@ -0,0 +1,454 @@
#!/usr/bin/env python
"""
This is the frontend to "textual_inversion_training.py".
Copyright (c) 2023-24 Lincoln Stein and the InvokeAI Development Team
"""
import os
import re
import shutil
import sys
import traceback
from argparse import Namespace
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import npyscreen
from npyscreen import widget
from omegaconf import OmegaConf
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend.install.install_helper import initialize_installer
from invokeai.backend.model_manager import ModelType
from invokeai.backend.training import do_textual_inversion_training, parse_args
TRAINING_DATA = "text-inversion-training-data"
TRAINING_DIR = "text-inversion-output"
CONF_FILE = "preferences.conf"
config = None
class textualInversionForm(npyscreen.FormMultiPageAction):
resolutions = [512, 768, 1024]
lr_schedulers = [
"linear",
"cosine",
"cosine_with_restarts",
"polynomial",
"constant",
"constant_with_warmup",
]
precisions = ["no", "fp16", "bf16"]
learnable_properties = ["object", "style"]
def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, saved_args: Optional[Dict[str, str]] = None):
self.saved_args = saved_args or {}
super().__init__(parentApp, name)
def afterEditing(self) -> None:
self.parentApp.setNextForm(None)
def create(self) -> None:
self.model_names, default = self.get_model_names()
default_initializer_token = "★"
default_placeholder_token = ""
saved_args = self.saved_args
assert config is not None
try:
default = self.model_names.index(saved_args["model"])
except Exception:
pass
self.add_widget_intelligent(
npyscreen.FixedText,
value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields, cursor arrows to make a selection, and space to toggle checkboxes.",
editable=False,
)
self.model = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Model Name:",
values=sorted(self.model_names),
value=default,
max_height=len(self.model_names) + 1,
scroll_exit=True,
)
self.placeholder_token = self.add_widget_intelligent(
npyscreen.TitleText,
name="Trigger Term:",
value="", # saved_args.get('placeholder_token',''), # to restore previous term
scroll_exit=True,
)
self.placeholder_token.when_value_edited = self.initializer_changed
self.nextrely -= 1
self.nextrelx += 30
self.prompt_token = self.add_widget_intelligent(
npyscreen.FixedText,
name="Trigger term for use in prompt",
value="",
editable=False,
scroll_exit=True,
)
self.nextrelx -= 30
self.initializer_token = self.add_widget_intelligent(
npyscreen.TitleText,
name="Initializer:",
value=saved_args.get("initializer_token", default_initializer_token),
scroll_exit=True,
)
self.resume_from_checkpoint = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Resume from last saved checkpoint",
value=False,
scroll_exit=True,
)
self.learnable_property = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Learnable property:",
values=self.learnable_properties,
value=self.learnable_properties.index(saved_args.get("learnable_property", "object")),
max_height=4,
scroll_exit=True,
)
self.train_data_dir = self.add_widget_intelligent(
npyscreen.TitleFilename,
name="Data Training Directory:",
select_dir=True,
must_exist=False,
value=str(
saved_args.get(
"train_data_dir",
config.root_dir / TRAINING_DATA / default_placeholder_token,
)
),
scroll_exit=True,
)
self.output_dir = self.add_widget_intelligent(
npyscreen.TitleFilename,
name="Output Destination Directory:",
select_dir=True,
must_exist=False,
value=str(
saved_args.get(
"output_dir",
config.root_dir / TRAINING_DIR / default_placeholder_token,
)
),
scroll_exit=True,
)
self.resolution = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Image resolution (pixels):",
values=self.resolutions,
value=self.resolutions.index(saved_args.get("resolution", 512)),
max_height=4,
scroll_exit=True,
)
self.center_crop = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Center crop images before resizing to resolution",
value=saved_args.get("center_crop", False),
scroll_exit=True,
)
self.mixed_precision = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Mixed Precision:",
values=self.precisions,
value=self.precisions.index(saved_args.get("mixed_precision", "fp16")),
max_height=4,
scroll_exit=True,
)
self.num_train_epochs = self.add_widget_intelligent(
npyscreen.TitleSlider,
name="Number of training epochs:",
out_of=1000,
step=50,
lowest=1,
value=saved_args.get("num_train_epochs", 100),
scroll_exit=True,
)
self.max_train_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name="Max Training Steps:",
out_of=10000,
step=500,
lowest=1,
value=saved_args.get("max_train_steps", 3000),
scroll_exit=True,
)
self.train_batch_size = self.add_widget_intelligent(
npyscreen.TitleSlider,
name="Batch Size (reduce if you run out of memory):",
out_of=50,
step=1,
lowest=1,
value=saved_args.get("train_batch_size", 8),
scroll_exit=True,
)
self.gradient_accumulation_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name="Gradient Accumulation Steps (may need to decrease this to resume from a checkpoint):",
out_of=10,
step=1,
lowest=1,
value=saved_args.get("gradient_accumulation_steps", 4),
scroll_exit=True,
)
self.lr_warmup_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name="Warmup Steps:",
out_of=100,
step=1,
lowest=0,
value=saved_args.get("lr_warmup_steps", 0),
scroll_exit=True,
)
self.learning_rate = self.add_widget_intelligent(
npyscreen.TitleText,
name="Learning Rate:",
value=str(
saved_args.get("learning_rate", "5.0e-04"),
),
scroll_exit=True,
)
self.scale_lr = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Scale learning rate by number GPUs, steps and batch size",
value=saved_args.get("scale_lr", True),
scroll_exit=True,
)
self.enable_xformers_memory_efficient_attention = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Use xformers acceleration",
value=saved_args.get("enable_xformers_memory_efficient_attention", False),
scroll_exit=True,
)
self.lr_scheduler = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Learning rate scheduler:",
values=self.lr_schedulers,
max_height=7,
value=self.lr_schedulers.index(saved_args.get("lr_scheduler", "constant")),
scroll_exit=True,
)
self.model.editing = True
def initializer_changed(self) -> None:
placeholder = self.placeholder_token.value
self.prompt_token.value = f"(Trigger by using <{placeholder}> in your prompts)"
self.train_data_dir.value = str(config.root_dir / TRAINING_DATA / placeholder)
self.output_dir.value = str(config.root_dir / TRAINING_DIR / placeholder)
self.resume_from_checkpoint.value = Path(self.output_dir.value).exists()
def on_ok(self):
if self.validate_field_values():
self.parentApp.setNextForm(None)
self.editing = False
self.parentApp.ti_arguments = self.marshall_arguments()
npyscreen.notify("Launching textual inversion training. This will take a while...")
else:
self.editing = True
def ok_cancel(self):
sys.exit(0)
def validate_field_values(self) -> bool:
bad_fields = []
if self.model.value is None:
bad_fields.append("Model Name must correspond to a known model in models.yaml")
if not re.match("^[a-zA-Z0-9.-]+$", self.placeholder_token.value):
bad_fields.append("Trigger term must only contain alphanumeric characters, the dot and hyphen")
if self.train_data_dir.value is None:
bad_fields.append("Data Training Directory cannot be empty")
if self.output_dir.value is None:
bad_fields.append("The Output Destination Directory cannot be empty")
if len(bad_fields) > 0:
message = "The following problems were detected and must be corrected:"
for problem in bad_fields:
message += f"\n* {problem}"
npyscreen.notify_confirm(message)
return False
else:
return True
def get_model_names(self) -> Tuple[List[str], int]:
global config
assert config is not None
installer = initialize_installer(config)
store = installer.record_store
main_models = store.search_by_attr(model_type=ModelType.Main)
model_names = [f"{x.base.value}/{x.type.value}/{x.name}" for x in main_models if x.format == "diffusers"]
default = 0
return (model_names, default)
def marshall_arguments(self) -> dict:
args = {}
# the choices
args.update(
model=self.model_names[self.model.value[0]],
resolution=self.resolutions[self.resolution.value[0]],
lr_scheduler=self.lr_schedulers[self.lr_scheduler.value[0]],
mixed_precision=self.precisions[self.mixed_precision.value[0]],
learnable_property=self.learnable_properties[self.learnable_property.value[0]],
)
# all the strings and booleans
for attr in (
"initializer_token",
"placeholder_token",
"train_data_dir",
"output_dir",
"scale_lr",
"center_crop",
"enable_xformers_memory_efficient_attention",
):
args[attr] = getattr(self, attr).value
# all the integers
for attr in (
"train_batch_size",
"gradient_accumulation_steps",
"num_train_epochs",
"max_train_steps",
"lr_warmup_steps",
):
args[attr] = int(getattr(self, attr).value)
# the floats (just one)
args.update(learning_rate=float(self.learning_rate.value))
# a special case
if self.resume_from_checkpoint.value and Path(self.output_dir.value).exists():
args["resume_from_checkpoint"] = "latest"
return args
class MyApplication(npyscreen.NPSAppManaged):
def __init__(self, saved_args: Optional[Dict[str, str]] = None):
super().__init__()
self.ti_arguments = None
self.saved_args = saved_args
def onStart(self):
npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
self.main = self.addForm(
"MAIN",
textualInversionForm,
name="Textual Inversion Settings",
saved_args=self.saved_args,
)
def copy_to_embeddings_folder(args: Dict[str, str]) -> None:
"""
Copy learned_embeds.bin into the embeddings folder, and offer to
delete the full model and checkpoints.
"""
assert config is not None
source = Path(args["output_dir"], "learned_embeds.bin")
dest_dir_name = args["placeholder_token"].strip("<>")
destination = config.root_dir / "embeddings" / dest_dir_name
os.makedirs(destination, exist_ok=True)
logger.info(f"Training completed. Copying learned_embeds.bin into {str(destination)}")
shutil.copy(source, destination)
if (input("Delete training logs and intermediate checkpoints? [y] ") or "y").startswith(("y", "Y")):
shutil.rmtree(Path(args["output_dir"]))
else:
logger.info(f'Keeping {args["output_dir"]}')
def save_args(args: dict) -> None:
"""
Save the current argument values to an omegaconf file
"""
assert config is not None
dest_dir = config.root_dir / TRAINING_DIR
os.makedirs(dest_dir, exist_ok=True)
conf_file = dest_dir / CONF_FILE
conf = OmegaConf.create(args)
OmegaConf.save(config=conf, f=conf_file)
def previous_args() -> dict:
"""
Get the previous arguments used.
"""
assert config is not None
conf_file = config.root_dir / TRAINING_DIR / CONF_FILE
try:
conf = OmegaConf.load(conf_file)
conf["placeholder_token"] = conf["placeholder_token"].strip("<>")
except Exception:
conf = None
return conf
def do_front_end() -> None:
global config
saved_args = previous_args()
myapplication = MyApplication(saved_args=saved_args)
myapplication.run()
if my_args := myapplication.ti_arguments:
os.makedirs(my_args["output_dir"], exist_ok=True)
# Automatically add angle brackets around the trigger
if not re.match("^<.+>$", my_args["placeholder_token"]):
my_args["placeholder_token"] = f"<{my_args['placeholder_token']}>"
my_args["only_save_embeds"] = True
save_args(my_args)
try:
print(my_args)
do_textual_inversion_training(config, **my_args)
copy_to_embeddings_folder(my_args)
except Exception as e:
logger.error("An exception occurred during training. The exception was:")
logger.error(str(e))
logger.error("DETAILS:")
logger.error(traceback.format_exc())
def main() -> None:
global config
args: Namespace = parse_args()
config = InvokeAIAppConfig.get_config()
config.parse_args([])
# change root if needed
if args.root_dir:
config.root = args.root_dir
try:
if args.front_end:
do_front_end()
else:
do_textual_inversion_training(config, **vars(args))
except AssertionError as e:
logger.error(e)
sys.exit(-1)
except KeyboardInterrupt:
pass
except (widget.NotEnoughSpaceForWidget, Exception) as e:
if str(e).startswith("Height of 1 allocated"):
logger.error("You need to have at least one diffusers models defined in models.yaml in order to train")
elif str(e).startswith("addwstr"):
logger.error("Not enough window space for the interface. Please make your window larger and try again.")
else:
logger.error(e)
sys.exit(-1)
if __name__ == "__main__":
main()