mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
refactor InvokeAIAppConfig
This commit is contained in:
Lincoln Stein
8
invokeai/app/services/config/__init__.py
Normal file
8
invokeai/app/services/config/__init__.py
Normal file
@ -0,0 +1,8 @@
|
||||
"""
|
||||
Init file for InvokeAI configure package
|
||||
"""
|
||||
|
||||
from .invokeai_config import (
|
||||
InvokeAIAppConfig,
|
||||
get_invokeai_config,
|
||||
)
|
||||
239
invokeai/app/services/config/base.py
Normal file
239
invokeai/app/services/config/base.py
Normal file
@ -0,0 +1,239 @@
|
||||
# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
|
||||
|
||||
"""
|
||||
Base class for the InvokeAI configuration system.
|
||||
It defines a type of pydantic BaseSettings object that
|
||||
is able to read and write from an omegaconf-based config file,
|
||||
with overriding of settings from environment variables and/or
|
||||
the command line.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
import argparse
|
||||
import os
|
||||
import pydoc
|
||||
import sys
|
||||
from argparse import ArgumentParser
|
||||
from omegaconf import OmegaConf, DictConfig, ListConfig
|
||||
from pathlib import Path
|
||||
from pydantic import BaseSettings
|
||||
from typing import ClassVar, Dict, List, Literal, Union, get_origin, get_type_hints, get_args
|
||||
|
||||
|
||||
class PagingArgumentParser(argparse.ArgumentParser):
|
||||
"""
|
||||
A custom ArgumentParser that uses pydoc to page its output.
|
||||
It also supports reading defaults from an init file.
|
||||
"""
|
||||
|
||||
def print_help(self, file=None):
|
||||
text = self.format_help()
|
||||
pydoc.pager(text)
|
||||
|
||||
|
||||
class InvokeAISettings(BaseSettings):
|
||||
"""
|
||||
Runtime configuration settings in which default values are
|
||||
read from an omegaconf .yaml file.
|
||||
"""
|
||||
|
||||
initconf: ClassVar[DictConfig] = None
|
||||
argparse_groups: ClassVar[Dict] = {}
|
||||
|
||||
def parse_args(self, argv: list = sys.argv[1:]):
|
||||
parser = self.get_parser()
|
||||
opt = parser.parse_args(argv)
|
||||
for name in self.__fields__:
|
||||
if name not in self._excluded():
|
||||
value = getattr(opt, name)
|
||||
if isinstance(value, ListConfig):
|
||||
value = list(value)
|
||||
elif isinstance(value, DictConfig):
|
||||
value = dict(value)
|
||||
setattr(self, name, value)
|
||||
|
||||
def to_yaml(self) -> str:
|
||||
"""
|
||||
Return a YAML string representing our settings. This can be used
|
||||
as the contents of `invokeai.yaml` to restore settings later.
|
||||
"""
|
||||
cls = self.__class__
|
||||
type = get_args(get_type_hints(cls)["type"])[0]
|
||||
field_dict = dict({type: dict()})
|
||||
for name, field in self.__fields__.items():
|
||||
if name in cls._excluded_from_yaml():
|
||||
continue
|
||||
category = field.field_info.extra.get("category") or "Uncategorized"
|
||||
value = getattr(self, name)
|
||||
if category not in field_dict[type]:
|
||||
field_dict[type][category] = dict()
|
||||
# keep paths as strings to make it easier to read
|
||||
field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
|
||||
conf = OmegaConf.create(field_dict)
|
||||
return OmegaConf.to_yaml(conf)
|
||||
|
||||
@classmethod
|
||||
def add_parser_arguments(cls, parser):
|
||||
if "type" in get_type_hints(cls):
|
||||
settings_stanza = get_args(get_type_hints(cls)["type"])[0]
|
||||
else:
|
||||
settings_stanza = "Uncategorized"
|
||||
|
||||
env_prefix = cls.Config.env_prefix if hasattr(cls.Config, "env_prefix") else settings_stanza.upper()
|
||||
|
||||
initconf = (
|
||||
cls.initconf.get(settings_stanza)
|
||||
if cls.initconf and settings_stanza in cls.initconf
|
||||
else OmegaConf.create()
|
||||
)
|
||||
|
||||
# create an upcase version of the environment in
|
||||
# order to achieve case-insensitive environment
|
||||
# variables (the way Windows does)
|
||||
upcase_environ = dict()
|
||||
for key, value in os.environ.items():
|
||||
upcase_environ[key.upper()] = value
|
||||
|
||||
fields = cls.__fields__
|
||||
cls.argparse_groups = {}
|
||||
|
||||
for name, field in fields.items():
|
||||
if name not in cls._excluded():
|
||||
current_default = field.default
|
||||
|
||||
category = field.field_info.extra.get("category", "Uncategorized")
|
||||
env_name = env_prefix + "_" + name
|
||||
if category in initconf and name in initconf.get(category):
|
||||
field.default = initconf.get(category).get(name)
|
||||
if env_name.upper() in upcase_environ:
|
||||
field.default = upcase_environ[env_name.upper()]
|
||||
cls.add_field_argument(parser, name, field)
|
||||
|
||||
field.default = current_default
|
||||
|
||||
@classmethod
|
||||
def cmd_name(self, command_field: str = "type") -> str:
|
||||
hints = get_type_hints(self)
|
||||
if command_field in hints:
|
||||
return get_args(hints[command_field])[0]
|
||||
else:
|
||||
return "Uncategorized"
|
||||
|
||||
@classmethod
|
||||
def get_parser(cls) -> ArgumentParser:
|
||||
parser = PagingArgumentParser(
|
||||
prog=cls.cmd_name(),
|
||||
description=cls.__doc__,
|
||||
)
|
||||
cls.add_parser_arguments(parser)
|
||||
return parser
|
||||
|
||||
@classmethod
|
||||
def add_subparser(cls, parser: argparse.ArgumentParser):
|
||||
parser.add_parser(cls.cmd_name(), help=cls.__doc__)
|
||||
|
||||
@classmethod
|
||||
def _excluded(self) -> List[str]:
|
||||
# internal fields that shouldn't be exposed as command line options
|
||||
return ["type", "initconf"]
|
||||
|
||||
@classmethod
|
||||
def _excluded_from_yaml(self) -> List[str]:
|
||||
# combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
|
||||
return [
|
||||
"type",
|
||||
"initconf",
|
||||
"version",
|
||||
"from_file",
|
||||
"model",
|
||||
"root",
|
||||
"max_cache_size",
|
||||
"max_vram_cache_size",
|
||||
"always_use_cpu",
|
||||
"free_gpu_mem",
|
||||
"xformers_enabled",
|
||||
"tiled_decode",
|
||||
]
|
||||
|
||||
class Config:
|
||||
env_file_encoding = "utf-8"
|
||||
arbitrary_types_allowed = True
|
||||
case_sensitive = True
|
||||
|
||||
@classmethod
|
||||
def add_field_argument(cls, command_parser, name: str, field, default_override=None):
|
||||
field_type = get_type_hints(cls).get(name)
|
||||
default = (
|
||||
default_override
|
||||
if default_override is not None
|
||||
else field.default
|
||||
if field.default_factory is None
|
||||
else field.default_factory()
|
||||
)
|
||||
if category := field.field_info.extra.get("category"):
|
||||
if category not in cls.argparse_groups:
|
||||
cls.argparse_groups[category] = command_parser.add_argument_group(category)
|
||||
argparse_group = cls.argparse_groups[category]
|
||||
else:
|
||||
argparse_group = command_parser
|
||||
|
||||
if get_origin(field_type) == Literal:
|
||||
allowed_values = get_args(field.type_)
|
||||
allowed_types = set()
|
||||
for val in allowed_values:
|
||||
allowed_types.add(type(val))
|
||||
allowed_types_list = list(allowed_types)
|
||||
field_type = allowed_types_list[0] if len(allowed_types) == 1 else int_or_float_or_str
|
||||
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=field_type,
|
||||
default=default,
|
||||
choices=allowed_values,
|
||||
help=field.field_info.description,
|
||||
)
|
||||
|
||||
elif get_origin(field_type) == Union:
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=int_or_float_or_str,
|
||||
default=default,
|
||||
help=field.field_info.description,
|
||||
)
|
||||
|
||||
elif get_origin(field_type) == list:
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
nargs="*",
|
||||
type=field.type_,
|
||||
default=default,
|
||||
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
|
||||
help=field.field_info.description,
|
||||
)
|
||||
else:
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=field.type_,
|
||||
default=default,
|
||||
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
|
||||
help=field.field_info.description,
|
||||
)
|
||||
|
||||
|
||||
def int_or_float_or_str(value: str) -> Union[int, float, str]:
|
||||
"""
|
||||
Workaround for argparse type checking.
|
||||
"""
|
||||
try:
|
||||
return int(value)
|
||||
except:
|
||||
pass
|
||||
try:
|
||||
return float(value)
|
||||
except:
|
||||
pass
|
||||
return str(value)
|
||||
437
invokeai/app/services/config/invokeai_config.py
Normal file
437
invokeai/app/services/config/invokeai_config.py
Normal file
@ -0,0 +1,437 @@
|
||||
# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
|
||||
|
||||
"""Invokeai configuration system.
|
||||
|
||||
Arguments and fields are taken from the pydantic definition of the
|
||||
model. Defaults can be set by creating a yaml configuration file that
|
||||
has a top-level key of "InvokeAI" and subheadings for each of the
|
||||
categories returned by `invokeai --help`. The file looks like this:
|
||||
|
||||
[file: invokeai.yaml]
|
||||
|
||||
InvokeAI:
|
||||
Paths:
|
||||
root: /home/lstein/invokeai-main
|
||||
conf_path: configs/models.yaml
|
||||
legacy_conf_dir: configs/stable-diffusion
|
||||
outdir: outputs
|
||||
autoimport_dir: null
|
||||
Models:
|
||||
model: stable-diffusion-1.5
|
||||
embeddings: true
|
||||
Memory/Performance:
|
||||
xformers_enabled: false
|
||||
sequential_guidance: false
|
||||
precision: float16
|
||||
max_cache_size: 6
|
||||
max_vram_cache_size: 0.5
|
||||
always_use_cpu: false
|
||||
free_gpu_mem: false
|
||||
Features:
|
||||
esrgan: true
|
||||
patchmatch: true
|
||||
internet_available: true
|
||||
log_tokenization: false
|
||||
Web Server:
|
||||
host: 127.0.0.1
|
||||
port: 8081
|
||||
allow_origins: []
|
||||
allow_credentials: true
|
||||
allow_methods:
|
||||
- '*'
|
||||
allow_headers:
|
||||
- '*'
|
||||
|
||||
The default name of the configuration file is `invokeai.yaml`, located
|
||||
in INVOKEAI_ROOT. You can replace supersede this by providing any
|
||||
OmegaConf dictionary object initialization time:
|
||||
|
||||
omegaconf = OmegaConf.load('/tmp/init.yaml')
|
||||
conf = InvokeAIAppConfig()
|
||||
conf.parse_args(conf=omegaconf)
|
||||
|
||||
InvokeAIAppConfig.parse_args() will parse the contents of `sys.argv`
|
||||
at initialization time. You may pass a list of strings in the optional
|
||||
`argv` argument to use instead of the system argv:
|
||||
|
||||
conf.parse_args(argv=['--xformers_enabled'])
|
||||
|
||||
It is also possible to set a value at initialization time. However, if
|
||||
you call parse_args() it may be overwritten.
|
||||
|
||||
conf = InvokeAIAppConfig(xformers_enabled=True)
|
||||
conf.parse_args(argv=['--no-xformers'])
|
||||
conf.xformers_enabled
|
||||
# False
|
||||
|
||||
|
||||
To avoid this, use `get_config()` to retrieve the application-wide
|
||||
configuration object. This will retain any properties set at object
|
||||
creation time:
|
||||
|
||||
conf = InvokeAIAppConfig.get_config(xformers_enabled=True)
|
||||
conf.parse_args(argv=['--no-xformers'])
|
||||
conf.xformers_enabled
|
||||
# True
|
||||
|
||||
Any setting can be overwritten by setting an environment variable of
|
||||
form: "INVOKEAI_<setting>", as in:
|
||||
|
||||
export INVOKEAI_port=8080
|
||||
|
||||
Order of precedence (from highest):
|
||||
1) initialization options
|
||||
2) command line options
|
||||
3) environment variable options
|
||||
4) config file options
|
||||
5) pydantic defaults
|
||||
|
||||
Typical usage at the top level file:
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
# get global configuration and print its cache size
|
||||
conf = InvokeAIAppConfig.get_config()
|
||||
conf.parse_args()
|
||||
print(conf.max_cache_size)
|
||||
|
||||
Typical usage in a backend module:
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
# get global configuration and print its cache size value
|
||||
conf = InvokeAIAppConfig.get_config()
|
||||
print(conf.max_cache_size)
|
||||
|
||||
|
||||
Computed properties:
|
||||
|
||||
The InvokeAIAppConfig object has a series of properties that
|
||||
resolve paths relative to the runtime root directory. They each return
|
||||
a Path object:
|
||||
|
||||
root_path - path to InvokeAI root
|
||||
output_path - path to default outputs directory
|
||||
model_conf_path - path to models.yaml
|
||||
conf - alias for the above
|
||||
embedding_path - path to the embeddings directory
|
||||
lora_path - path to the LoRA directory
|
||||
|
||||
In most cases, you will want to create a single InvokeAIAppConfig
|
||||
object for the entire application. The InvokeAIAppConfig.get_config() function
|
||||
does this:
|
||||
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
config.parse_args() # read values from the command line/config file
|
||||
print(config.root)
|
||||
|
||||
# Subclassing
|
||||
|
||||
If you wish to create a similar class, please subclass the
|
||||
`InvokeAISettings` class and define a Literal field named "type",
|
||||
which is set to the desired top-level name. For example, to create a
|
||||
"InvokeBatch" configuration, define like this:
|
||||
|
||||
class InvokeBatch(InvokeAISettings):
|
||||
type: Literal["InvokeBatch"] = "InvokeBatch"
|
||||
node_count : int = Field(default=1, description="Number of nodes to run on", category='Resources')
|
||||
cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", category='Resources')
|
||||
|
||||
This will now read and write from the "InvokeBatch" section of the
|
||||
config file, look for environment variables named INVOKEBATCH_*, and
|
||||
accept the command-line arguments `--node_count` and `--cpu_count`. The
|
||||
two configs are kept in separate sections of the config file:
|
||||
|
||||
# invokeai.yaml
|
||||
|
||||
InvokeBatch:
|
||||
Resources:
|
||||
node_count: 1
|
||||
cpu_count: 8
|
||||
|
||||
InvokeAI:
|
||||
Paths:
|
||||
root: /home/lstein/invokeai-main
|
||||
conf_path: configs/models.yaml
|
||||
legacy_conf_dir: configs/stable-diffusion
|
||||
outdir: outputs
|
||||
...
|
||||
|
||||
"""
|
||||
from __future__ import annotations
|
||||
import os
|
||||
from omegaconf import OmegaConf, DictConfig
|
||||
from pathlib import Path
|
||||
from pydantic import Field, parse_obj_as
|
||||
from typing import ClassVar, Dict, List, Literal, Union, Optional, get_type_hints
|
||||
|
||||
from .base import InvokeAISettings
|
||||
|
||||
INIT_FILE = Path("invokeai.yaml")
|
||||
DB_FILE = Path("invokeai.db")
|
||||
LEGACY_INIT_FILE = Path("invokeai.init")
|
||||
DEFAULT_MAX_VRAM = 0.5
|
||||
|
||||
|
||||
class InvokeAIAppConfig(InvokeAISettings):
|
||||
"""
|
||||
Generate images using Stable Diffusion. Use "invokeai" to launch
|
||||
the command-line client (recommended for experts only), or
|
||||
"invokeai-web" to launch the web server. Global options
|
||||
can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
|
||||
setting environment variables INVOKEAI_<setting>.
|
||||
"""
|
||||
|
||||
singleton_config: ClassVar[InvokeAIAppConfig] = None
|
||||
singleton_init: ClassVar[Dict] = None
|
||||
|
||||
# fmt: off
|
||||
type: Literal["InvokeAI"] = "InvokeAI"
|
||||
|
||||
# WEB
|
||||
host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
|
||||
port : int = Field(default=9090, description="Port to bind to", category='Web Server')
|
||||
allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", category='Web Server')
|
||||
allow_credentials : bool = Field(default=True, description="Allow CORS credentials", category='Web Server')
|
||||
allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", category='Web Server')
|
||||
allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", category='Web Server')
|
||||
|
||||
# FEATURES
|
||||
esrgan : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
|
||||
internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
|
||||
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
|
||||
patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
|
||||
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
|
||||
|
||||
|
||||
# PATHS
|
||||
root : Path = Field(default=None, description='InvokeAI runtime root directory', category='Paths')
|
||||
autoimport_dir : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
|
||||
lora_dir : Path = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='Paths')
|
||||
embedding_dir : Path = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', category='Paths')
|
||||
controlnet_dir : Path = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', category='Paths')
|
||||
conf_path : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
|
||||
models_dir : Path = Field(default='models', description='Path to the models directory', category='Paths')
|
||||
legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
|
||||
db_dir : Path = Field(default='databases', description='Path to InvokeAI databases directory', category='Paths')
|
||||
outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
|
||||
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
|
||||
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
|
||||
|
||||
# LOGGING
|
||||
log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
|
||||
# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
|
||||
log_format : Literal[tuple(['plain','color','syslog','legacy'])] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
|
||||
log_level : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="info", description="Emit logging messages at this level or higher", category="Logging")
|
||||
|
||||
version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
|
||||
|
||||
# CACHE
|
||||
ram : Union[float, Literal["auto"]] = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number or 'auto')", category="Cache", )
|
||||
vram : Union[float, Literal["auto"]] = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number or 'auto')", category="Cache", )
|
||||
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", category="Cache", )
|
||||
|
||||
# DEVICE
|
||||
device : Literal[tuple(["auto", "cpu", "cuda", "cuda:1", "mps"])] = Field(default="auto", description="Generation device", category="Device", )
|
||||
precision: Literal[tuple(["auto", "float16", "float32", "autocast"])] = Field(default="auto", description="Floating point precision", category="Device", )
|
||||
|
||||
# GENERATION
|
||||
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category="Generation", )
|
||||
attention_type : Literal[tuple(["auto", "normal", "xformers", "sliced", "torch-sdp"])] = Field(default="auto", description="Attention type", category="Generation", )
|
||||
attention_slice_size: Literal[tuple(["auto", "max", 1, 2, 3, 4, 5, 6, 7, 8])] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
|
||||
force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
|
||||
|
||||
# DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
|
||||
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
|
||||
free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
|
||||
max_cache_size : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
|
||||
max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
|
||||
precision : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Memory/Performance')
|
||||
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
|
||||
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
|
||||
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
|
||||
|
||||
# See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
|
||||
# fmt: on
|
||||
|
||||
class Config:
|
||||
validate_assignment = True
|
||||
|
||||
def parse_args(self, argv: List[str] = None, conf: DictConfig = None, clobber=False):
|
||||
"""
|
||||
Update settings with contents of init file, environment, and
|
||||
command-line settings.
|
||||
:param conf: alternate Omegaconf dictionary object
|
||||
:param argv: aternate sys.argv list
|
||||
:param clobber: ovewrite any initialization parameters passed during initialization
|
||||
"""
|
||||
# Set the runtime root directory. We parse command-line switches here
|
||||
# in order to pick up the --root_dir option.
|
||||
super().parse_args(argv)
|
||||
if conf is None:
|
||||
try:
|
||||
conf = OmegaConf.load(self.root_dir / INIT_FILE)
|
||||
except:
|
||||
pass
|
||||
InvokeAISettings.initconf = conf
|
||||
|
||||
# parse args again in order to pick up settings in configuration file
|
||||
super().parse_args(argv)
|
||||
|
||||
if self.singleton_init and not clobber:
|
||||
hints = get_type_hints(self.__class__)
|
||||
for k in self.singleton_init:
|
||||
setattr(self, k, parse_obj_as(hints[k], self.singleton_init[k]))
|
||||
|
||||
@classmethod
|
||||
def get_config(cls, **kwargs) -> InvokeAIAppConfig:
|
||||
"""
|
||||
This returns a singleton InvokeAIAppConfig configuration object.
|
||||
"""
|
||||
if (
|
||||
cls.singleton_config is None
|
||||
or type(cls.singleton_config) != cls
|
||||
or (kwargs and cls.singleton_init != kwargs)
|
||||
):
|
||||
cls.singleton_config = cls(**kwargs)
|
||||
cls.singleton_init = kwargs
|
||||
return cls.singleton_config
|
||||
|
||||
@property
|
||||
def root_path(self) -> Path:
|
||||
"""
|
||||
Path to the runtime root directory
|
||||
"""
|
||||
if self.root:
|
||||
root = Path(self.root).expanduser().absolute()
|
||||
else:
|
||||
root = self.find_root().expanduser().absolute()
|
||||
self.root = root # insulate ourselves from relative paths that may change
|
||||
return root
|
||||
|
||||
@property
|
||||
def root_dir(self) -> Path:
|
||||
"""
|
||||
Alias for above.
|
||||
"""
|
||||
return self.root_path
|
||||
|
||||
def _resolve(self, partial_path: Path) -> Path:
|
||||
return (self.root_path / partial_path).resolve()
|
||||
|
||||
@property
|
||||
def init_file_path(self) -> Path:
|
||||
"""
|
||||
Path to invokeai.yaml
|
||||
"""
|
||||
return self._resolve(INIT_FILE)
|
||||
|
||||
@property
|
||||
def output_path(self) -> Path:
|
||||
"""
|
||||
Path to defaults outputs directory.
|
||||
"""
|
||||
return self._resolve(self.outdir)
|
||||
|
||||
@property
|
||||
def db_path(self) -> Path:
|
||||
"""
|
||||
Path to the invokeai.db file.
|
||||
"""
|
||||
return self._resolve(self.db_dir) / DB_FILE
|
||||
|
||||
@property
|
||||
def model_conf_path(self) -> Path:
|
||||
"""
|
||||
Path to models configuration file.
|
||||
"""
|
||||
return self._resolve(self.conf_path)
|
||||
|
||||
@property
|
||||
def legacy_conf_path(self) -> Path:
|
||||
"""
|
||||
Path to directory of legacy configuration files (e.g. v1-inference.yaml)
|
||||
"""
|
||||
return self._resolve(self.legacy_conf_dir)
|
||||
|
||||
@property
|
||||
def models_path(self) -> Path:
|
||||
"""
|
||||
Path to the models directory
|
||||
"""
|
||||
return self._resolve(self.models_dir)
|
||||
|
||||
@property
|
||||
def autoconvert_path(self) -> Path:
|
||||
"""
|
||||
Path to the directory containing models to be imported automatically at startup.
|
||||
"""
|
||||
return self._resolve(self.autoconvert_dir) if self.autoconvert_dir else None
|
||||
|
||||
# the following methods support legacy calls leftover from the Globals era
|
||||
@property
|
||||
def full_precision(self) -> bool:
|
||||
"""Return true if precision set to float32"""
|
||||
return self.precision == "float32"
|
||||
|
||||
@property
|
||||
def try_patchmatch(self) -> bool:
|
||||
"""Return true if patchmatch true"""
|
||||
return self.patchmatch
|
||||
|
||||
@property
|
||||
def nsfw_checker(self) -> bool:
|
||||
"""NSFW node is always active and disabled from Web UIe"""
|
||||
return True
|
||||
|
||||
@property
|
||||
def invisible_watermark(self) -> bool:
|
||||
"""invisible watermark node is always active and disabled from Web UIe"""
|
||||
return True
|
||||
|
||||
@property
|
||||
def ram_cache_size(self) -> float:
|
||||
return self.max_cache_size or self.ram
|
||||
|
||||
@property
|
||||
def vram_cache_size(self) -> float:
|
||||
return self.max_vram_cache_size or self.vram
|
||||
|
||||
@property
|
||||
def use_cpu(self) -> bool:
|
||||
return self.always_use_cpu or self.device == "cpu"
|
||||
|
||||
@property
|
||||
def disable_xformers(self) -> bool:
|
||||
"""
|
||||
Return true if enable_xformers is false (reversed logic)
|
||||
and attention type is not set to xformers.
|
||||
"""
|
||||
disabled_in_config = not self.xformers_enabled
|
||||
return disabled_in_config and self.attention_type != "xformers"
|
||||
|
||||
@staticmethod
|
||||
def find_root() -> Path:
|
||||
"""
|
||||
Choose the runtime root directory when not specified on command line or
|
||||
init file.
|
||||
"""
|
||||
return _find_root()
|
||||
|
||||
|
||||
def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
|
||||
"""
|
||||
Legacy function which returns InvokeAIAppConfig.get_config()
|
||||
"""
|
||||
return InvokeAIAppConfig.get_config(**kwargs)
|
||||
|
||||
|
||||
def _find_root() -> Path:
|
||||
venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
|
||||
if os.environ.get("INVOKEAI_ROOT"):
|
||||
root = Path(os.environ["INVOKEAI_ROOT"])
|
||||
elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
|
||||
root = (venv.parent).resolve()
|
||||
else:
|
||||
root = Path("~/invokeai").expanduser().resolve()
|
||||
return root
|
||||
Reference in New Issue
Block a user