mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
refactor(config): simplified config
- Remove OmegaConf. It functioned as an intermediary data format, between YAML/argparse and pydantic. It's not necessary - we can parse YAML or CLI args directly with pydantic. - Remove dynamic CLI args. Only `root` is explicitly supported. This greatly simplifies config handling. Configuration is done by editing the YAML file. Frequently-used args can be added if there is a demand. - A separate arg parser is created to handle the slimmed-down CLI args. It's run immediately in the `invokeai-web` script to handle `--version` and `--help`. It is also used inside the singleton config getter (see below). - Remove categories from the config. Our settings model is mostly flat. Handling categories adds complexity for both us and users - we have to handle transforming a flat config to categorized config (and vice-versa), while users have to be careful with indentation in their YAML file. - Add a `meta` key to the config file. Currently, this holds the config schema version only. It is not a part of the config object itself. - Remove legacy settings that are no longer referenced, or were effectively no-op settings when referenced in code. - Implement simple migration logic to for v3 configs. If migration is successful, the v3 config file is backed up to `invokeai.yaml.bak` and the new config written to `invokeai.yaml`. - Previously, the singleton config was accessed by calling `InvokeAIAppConfig.get_config()`. This returned an instance of `InvokeAIAppConfig`, which _also_ has the `get_config` function. This created to a confusing situation where you weren't sure if you needed to call `get_config` or just use the config object. This method is replaced by a standalone `get_config` function which returns a singleton config object. - Wrap CLI arg parsing (for `root`) and loading/migrating `invokeai.yaml` into the new `get_config()` function. - Move `generate_config_docstrings` into standalone utility function. - Make `root` a private attr (`_root`). This reduces the temptation to directly modify and or use this sensitive field and ensures it is neither serialized nor read from input data. Use `root_path` to access the resolved root path, or `set_root` to set the root to something.
This commit is contained in:
psychedelicious
parent
7387b0bdc9
commit
3fb116155b
@ -2,6 +2,6 @@
|
||||
|
||||
from invokeai.app.services.config.config_common import PagingArgumentParser
|
||||
|
||||
from .config_default import InvokeAIAppConfig, get_invokeai_config
|
||||
from .config_default import InvokeAIAppConfig, get_config
|
||||
|
||||
__all__ = ["InvokeAIAppConfig", "get_invokeai_config", "PagingArgumentParser"]
|
||||
__all__ = ["InvokeAIAppConfig", "get_config", "PagingArgumentParser"]
|
||||
|
||||
@ -1,241 +0,0 @@
|
||||
# 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 sys
|
||||
from argparse import ArgumentParser
|
||||
from pathlib import Path
|
||||
from typing import Any, ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
|
||||
|
||||
from omegaconf import DictConfig, DictKeyType, ListConfig, OmegaConf
|
||||
from pydantic import BaseModel
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
|
||||
from invokeai.app.services.config.config_common import PagingArgumentParser, int_or_float_or_str
|
||||
|
||||
|
||||
class InvokeAISettings(BaseSettings):
|
||||
"""Runtime configuration settings in which default values are read from an omegaconf .yaml file."""
|
||||
|
||||
initconf: ClassVar[Optional[DictConfig]] = None
|
||||
argparse_groups: ClassVar[Dict[str, Any]] = {}
|
||||
|
||||
model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
|
||||
|
||||
def parse_args(self, argv: Optional[List[str]] = sys.argv[1:]) -> None:
|
||||
"""Call to parse command-line arguments."""
|
||||
parser = self.get_parser()
|
||||
opt, unknown_opts = parser.parse_known_args(argv)
|
||||
if len(unknown_opts) > 0:
|
||||
print("Unknown args:", unknown_opts)
|
||||
for name in self.model_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[str, Dict[str, Any]] = {type: {}}
|
||||
for name, field in self.model_fields.items():
|
||||
if name in cls._excluded_from_yaml():
|
||||
continue
|
||||
assert isinstance(field.json_schema_extra, dict)
|
||||
category = (
|
||||
field.json_schema_extra.get("category", "Uncategorized") if field.json_schema_extra else "Uncategorized"
|
||||
)
|
||||
value = getattr(self, name)
|
||||
assert isinstance(category, str)
|
||||
if category not in field_dict[type]:
|
||||
field_dict[type][category] = {}
|
||||
if isinstance(value, BaseModel):
|
||||
dump = value.model_dump(exclude_defaults=True, exclude_unset=True, exclude_none=True)
|
||||
field_dict[type][category][name] = dump
|
||||
continue
|
||||
if isinstance(value, list):
|
||||
if not value or len(value) == 0:
|
||||
continue
|
||||
primitive = isinstance(value[0], get_args(DictKeyType))
|
||||
if not primitive:
|
||||
val_list: List[Dict[str, Any]] = []
|
||||
for list_val in value:
|
||||
if isinstance(list_val, BaseModel):
|
||||
dump = list_val.model_dump(exclude_defaults=True, exclude_unset=True, exclude_none=True)
|
||||
val_list.append(dump)
|
||||
field_dict[type][category][name] = val_list
|
||||
continue
|
||||
# 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: ArgumentParser) -> None:
|
||||
"""Dynamically create arguments for a settings parser."""
|
||||
if "type" in get_type_hints(cls):
|
||||
settings_stanza = get_args(get_type_hints(cls)["type"])[0]
|
||||
else:
|
||||
settings_stanza = "Uncategorized"
|
||||
|
||||
env_prefix = getattr(cls.model_config, "env_prefix", None)
|
||||
env_prefix = env_prefix if env_prefix is not None 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 = {}
|
||||
for key, value in os.environ.items():
|
||||
upcase_environ[key.upper()] = value
|
||||
|
||||
fields = cls.model_fields
|
||||
cls.argparse_groups = {}
|
||||
|
||||
for name, field in fields.items():
|
||||
if name not in cls._excluded():
|
||||
current_default = field.default
|
||||
|
||||
category = (
|
||||
field.json_schema_extra.get("category", "Uncategorized")
|
||||
if field.json_schema_extra
|
||||
else "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(cls, command_field: str = "type") -> str:
|
||||
"""Return the category of a setting."""
|
||||
hints = get_type_hints(cls)
|
||||
if command_field in hints:
|
||||
result: str = get_args(hints[command_field])[0]
|
||||
return result
|
||||
else:
|
||||
return "Uncategorized"
|
||||
|
||||
@classmethod
|
||||
def get_parser(cls) -> ArgumentParser:
|
||||
"""Get the command-line parser for a setting."""
|
||||
parser = PagingArgumentParser(
|
||||
prog=cls.cmd_name(),
|
||||
description=cls.__doc__,
|
||||
)
|
||||
cls.add_parser_arguments(parser)
|
||||
return parser
|
||||
|
||||
@classmethod
|
||||
def _excluded(cls) -> List[str]:
|
||||
# internal fields that shouldn't be exposed as command line options
|
||||
return ["type", "initconf"]
|
||||
|
||||
@classmethod
|
||||
def _excluded_from_yaml(cls) -> 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",
|
||||
"lora_dir",
|
||||
"embedding_dir",
|
||||
"controlnet_dir",
|
||||
"conf_path",
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def add_field_argument(cls, command_parser, name: str, field, default_override=None) -> None:
|
||||
"""Add the argparse arguments for a setting parser."""
|
||||
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.json_schema_extra.get("category", None) if field.json_schema_extra else None):
|
||||
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.annotation)
|
||||
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.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.description,
|
||||
)
|
||||
|
||||
elif get_origin(field_type) == list:
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
nargs="*",
|
||||
type=field.annotation,
|
||||
default=default,
|
||||
action=argparse.BooleanOptionalAction if field.annotation == bool else "store",
|
||||
help=field.description,
|
||||
)
|
||||
else:
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=field.annotation,
|
||||
default=default,
|
||||
action=argparse.BooleanOptionalAction if field.annotation == bool else "store",
|
||||
help=field.description,
|
||||
)
|
||||
@ -12,7 +12,6 @@ from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import pydoc
|
||||
from typing import Union
|
||||
|
||||
|
||||
class PagingArgumentParser(argparse.ArgumentParser):
|
||||
@ -24,18 +23,3 @@ class PagingArgumentParser(argparse.ArgumentParser):
|
||||
def print_help(self, file=None) -> None:
|
||||
text = self.format_help()
|
||||
pydoc.pager(text)
|
||||
|
||||
|
||||
def int_or_float_or_str(value: str) -> Union[int, float, str]:
|
||||
"""
|
||||
Workaround for argparse type checking.
|
||||
"""
|
||||
try:
|
||||
return int(value)
|
||||
except Exception as e: # noqa F841
|
||||
pass
|
||||
try:
|
||||
return float(value)
|
||||
except Exception as e: # noqa F841
|
||||
pass
|
||||
return str(value)
|
||||
|
||||
@ -1,187 +1,17 @@
|
||||
# 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:
|
||||
Web Server:
|
||||
host: 127.0.0.1
|
||||
port: 9090
|
||||
allow_origins: []
|
||||
allow_credentials: true
|
||||
allow_methods:
|
||||
- '*'
|
||||
allow_headers:
|
||||
- '*'
|
||||
Features:
|
||||
esrgan: true
|
||||
internet_available: true
|
||||
log_tokenization: false
|
||||
patchmatch: true
|
||||
ignore_missing_core_models: false
|
||||
Paths:
|
||||
autoimport_dir: autoimport
|
||||
lora_dir: null
|
||||
embedding_dir: null
|
||||
controlnet_dir: null
|
||||
models_dir: models
|
||||
legacy_conf_dir: configs/stable-diffusion
|
||||
db_dir: databases
|
||||
outdir: /home/lstein/invokeai-main/outputs
|
||||
use_memory_db: false
|
||||
Logging:
|
||||
log_handlers:
|
||||
- console
|
||||
log_format: plain
|
||||
log_level: info
|
||||
Model Cache:
|
||||
ram: 13.5
|
||||
vram: 0.25
|
||||
lazy_offload: true
|
||||
log_memory_usage: false
|
||||
Device:
|
||||
device: auto
|
||||
precision: auto
|
||||
Generation:
|
||||
sequential_guidance: false
|
||||
attention_type: xformers
|
||||
attention_slice_size: auto
|
||||
force_tiled_decode: false
|
||||
|
||||
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=['--log_tokenization'])
|
||||
|
||||
It is also possible to set a value at initialization time. However, if
|
||||
you call parse_args() it may be overwritten.
|
||||
|
||||
conf = InvokeAIAppConfig(log_tokenization=True)
|
||||
conf.parse_args(argv=['--no-log_tokenization'])
|
||||
conf.log_tokenization
|
||||
# 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(log_tokenization=True)
|
||||
conf.parse_args(argv=['--no-log_tokenization'])
|
||||
conf.log_tokenization
|
||||
# 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.ram_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.ram_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
|
||||
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", json_schema_extra=dict(category='Resources'))
|
||||
cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", json_schema_extra=dict(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
|
||||
legacy_conf_dir: configs/stable-diffusion
|
||||
outdir: outputs
|
||||
...
|
||||
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import re
|
||||
from functools import lru_cache
|
||||
from pathlib import Path
|
||||
from typing import Any, ClassVar, Dict, List, Literal, Optional
|
||||
from typing import Any, Literal, Optional
|
||||
|
||||
from omegaconf import DictConfig, OmegaConf
|
||||
from pydantic import BaseModel, Field, field_validator
|
||||
from pydantic.config import JsonDict
|
||||
from pydantic_settings import SettingsConfigDict
|
||||
import yaml
|
||||
from pydantic import BaseModel, Field, PrivateAttr, field_validator
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
|
||||
from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS
|
||||
|
||||
from .config_base import InvokeAISettings
|
||||
from invokeai.frontend.cli.app_arg_parser import app_arg_parser
|
||||
|
||||
INIT_FILE = Path("invokeai.yaml")
|
||||
DB_FILE = Path("invokeai.db")
|
||||
@ -189,28 +19,16 @@ LEGACY_INIT_FILE = Path("invokeai.init")
|
||||
DEFAULT_RAM_CACHE = 10.0
|
||||
DEFAULT_VRAM_CACHE = 0.25
|
||||
DEFAULT_CONVERT_CACHE = 20.0
|
||||
DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
|
||||
PRECISION = Literal["auto", "float16", "bfloat16", "float32", "autocast"]
|
||||
ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
|
||||
ATTENTION_SLICE_SIZE = Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8]
|
||||
LOG_FORMAT = Literal["plain", "color", "syslog", "legacy"]
|
||||
LOG_LEVEL = Literal["debug", "info", "warning", "error", "critical"]
|
||||
CONFIG_SCHEMA_VERSION = 4
|
||||
|
||||
|
||||
class Categories(object):
|
||||
"""Category headers for configuration variable groups."""
|
||||
|
||||
WebServer: JsonDict = {"category": "Web Server"}
|
||||
Features: JsonDict = {"category": "Features"}
|
||||
Paths: JsonDict = {"category": "Paths"}
|
||||
Logging: JsonDict = {"category": "Logging"}
|
||||
Development: JsonDict = {"category": "Development"}
|
||||
CLIArgs: JsonDict = {"category": "CLIArgs"}
|
||||
ModelInstall: JsonDict = {"category": "Model Install"}
|
||||
ModelCache: JsonDict = {"category": "Model Cache"}
|
||||
Device: JsonDict = {"category": "Device"}
|
||||
Generation: JsonDict = {"category": "Generation"}
|
||||
Queue: JsonDict = {"category": "Queue"}
|
||||
Nodes: JsonDict = {"category": "Nodes"}
|
||||
MemoryPerformance: JsonDict = {"category": "Memory/Performance"}
|
||||
Deprecated: JsonDict = {"category": "Deprecated"}
|
||||
|
||||
|
||||
class URLRegexToken(BaseModel):
|
||||
class URLRegexTokenPair(BaseModel):
|
||||
url_regex: str = Field(description="Regular expression to match against the URL")
|
||||
token: str = Field(description="Token to use when the URL matches the regex")
|
||||
|
||||
@ -225,397 +43,364 @@ class URLRegexToken(BaseModel):
|
||||
return v
|
||||
|
||||
|
||||
class InvokeAIAppConfig(InvokeAISettings):
|
||||
"""Invoke App Configuration
|
||||
class ConfigMeta(BaseModel):
|
||||
"""Metadata for the config file. This is not stored in the config object."""
|
||||
|
||||
schema_version: int = CONFIG_SCHEMA_VERSION
|
||||
|
||||
|
||||
class InvokeAIAppConfig(BaseSettings):
|
||||
"""Invoke's global app configuration.
|
||||
|
||||
Typically, you won't need to interact with this class directly. Instead, use the `get_config` function from `invokeai.app.services.config` to get a singleton config object.
|
||||
|
||||
Attributes:
|
||||
host: **Web Server**: IP address to bind to. Use `0.0.0.0` to serve to your local network.
|
||||
port: **Web Server**: Port to bind to.
|
||||
allow_origins: **Web Server**: Allowed CORS origins.
|
||||
allow_credentials: **Web Server**: Allow CORS credentials.
|
||||
allow_methods: **Web Server**: Methods allowed for CORS.
|
||||
allow_headers: **Web Server**: Headers allowed for CORS.
|
||||
ssl_certfile: **Web Server**: SSL certificate file for HTTPS.
|
||||
ssl_keyfile: **Web Server**: SSL key file for HTTPS.
|
||||
esrgan: **Features**: Enables or disables the upscaling code.
|
||||
internet_available: **Features**: If true, attempt to download models on the fly; otherwise only use local models.
|
||||
log_tokenization: **Features**: Enable logging of parsed prompt tokens.
|
||||
patchmatch: **Features**: Enable patchmatch inpaint code.
|
||||
ignore_missing_core_models: **Features**: Ignore missing core models on startup. If `True`, the app will attempt to download missing models on startup.
|
||||
root: **Paths**: The InvokeAI runtime root directory.
|
||||
autoimport_dir: **Paths**: Path to a directory of models files to be imported on startup.
|
||||
models_dir: **Paths**: Path to the models directory.
|
||||
convert_cache_dir: **Paths**: Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.
|
||||
legacy_conf_dir: **Paths**: Path to directory of legacy checkpoint config files.
|
||||
db_dir: **Paths**: Path to InvokeAI databases directory.
|
||||
outdir: **Paths**: Path to directory for outputs.
|
||||
custom_nodes_dir: **Paths**: Path to directory for custom nodes.
|
||||
from_file: **Paths**: Take command input from the indicated file (command-line client only).
|
||||
log_handlers: **Logging**: Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".
|
||||
log_format: **Logging**: Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.
|
||||
log_level: **Logging**: Emit logging messages at this level or higher.
|
||||
log_sql: **Logging**: Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.
|
||||
use_memory_db: **Development**: Use in-memory database. Useful for development.
|
||||
dev_reload: **Development**: Automatically reload when Python sources are changed. Does not reload node definitions.
|
||||
profile_graphs: **Development**: Enable graph profiling using `cProfile`.
|
||||
profile_prefix: **Development**: An optional prefix for profile output files.
|
||||
profiles_dir: **Development**: Path to profiles output directory.
|
||||
version: **CLIArgs**: CLI arg - show InvokeAI version and exit.
|
||||
hashing_algorithm: **Model Install**: Model hashing algorthim for model installs. 'blake3' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.
|
||||
remote_api_tokens: **Model Install**: List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.
|
||||
ram: **Model Cache**: Maximum memory amount used by memory model cache for rapid switching (GB).
|
||||
vram: **Model Cache**: Amount of VRAM reserved for model storage (GB)
|
||||
convert_cache: **Model Cache**: Maximum size of on-disk converted models cache (GB)
|
||||
lazy_offload: **Model Cache**: Keep models in VRAM until their space is needed.
|
||||
log_memory_usage: **Model Cache**: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
|
||||
device: **Device**: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.
|
||||
precision: **Device**: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.
|
||||
sequential_guidance: **Generation**: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
|
||||
attention_type: **Generation**: Attention type.
|
||||
attention_slice_size: **Generation**: Slice size, valid when attention_type=="sliced".
|
||||
force_tiled_decode: **Generation**: Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).
|
||||
png_compress_level: **Generation**: The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.
|
||||
max_queue_size: **Queue**: Maximum number of items in the session queue.
|
||||
allow_nodes: **Nodes**: List of nodes to allow. Omit to allow all.
|
||||
deny_nodes: **Nodes**: List of nodes to deny. Omit to deny none.
|
||||
node_cache_size: **Nodes**: How many cached nodes to keep in memory.
|
||||
host: IP address to bind to. Use `0.0.0.0` to serve to your local network.
|
||||
port: Port to bind to.
|
||||
allow_origins: Allowed CORS origins.
|
||||
allow_credentials: Allow CORS credentials.
|
||||
allow_methods: Methods allowed for CORS.
|
||||
allow_headers: Headers allowed for CORS.
|
||||
ssl_certfile: SSL certificate file for HTTPS. See https://www.uvicorn.org/settings/#https.
|
||||
ssl_keyfile: SSL key file for HTTPS. See https://www.uvicorn.org/settings/#https.
|
||||
log_tokenization: Enable logging of parsed prompt tokens.
|
||||
patchmatch: Enable patchmatch inpaint code.
|
||||
ignore_missing_core_models: Ignore missing core models on startup. If `True`, the app will attempt to download missing models on startup.
|
||||
autoimport_dir: Path to a directory of models files to be imported on startup. WARNING: This may be a relative path. Use `autoimport_path` to get the resolved absolute path.
|
||||
models_dir: Path to the models directory. WARNING: This may be a relative path. Use `models_path` to get the resolved absolute path.
|
||||
convert_cache_dir: Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location. WARNING: This may be a relative path. Use `convert_cache_path` to get the resolved absolute path.
|
||||
legacy_conf_dir: Path to directory of legacy checkpoint config files. WARNING: This may be a relative path. Use `legacy_conf_path` to get the resolved absolute path.
|
||||
db_dir: Path to InvokeAI databases directory. WARNING: This may be a relative path. Use `db_path` to get the resolved absolute path.
|
||||
outputs_dir: Path to directory for outputs. WARNING: This may be a relative path. Use `outputs_path` to get the resolved absolute path.
|
||||
custom_nodes_dir: Path to directory for custom nodes. WARNING: This may be a relative path. Use `custom_nodes_path` to get the resolved absolute path.
|
||||
log_handlers: Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".
|
||||
log_format: Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.
|
||||
log_level: Emit logging messages at this level or higher.
|
||||
log_sql: Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.
|
||||
use_memory_db: Use in-memory database. Useful for development.
|
||||
dev_reload: Automatically reload when Python sources are changed. Does not reload node definitions.
|
||||
profile_graphs: Enable graph profiling using `cProfile`.
|
||||
profile_prefix: An optional prefix for profile output files.
|
||||
profiles_dir: Path to profiles output directory. WARNING: This may be a relative path. Use `profiles_path` to get the resolved absolute path.
|
||||
ram: Maximum memory amount used by memory model cache for rapid switching (GB).
|
||||
vram: Amount of VRAM reserved for model storage (GB)
|
||||
convert_cache: Maximum size of on-disk converted models cache (GB)
|
||||
lazy_offload: Keep models in VRAM until their space is needed.
|
||||
log_memory_usage: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
|
||||
device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.
|
||||
precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.
|
||||
sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
|
||||
attention_type: Attention type.
|
||||
attention_slice_size: Slice size, valid when attention_type=="sliced".
|
||||
force_tiled_decode: Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).
|
||||
png_compress_level: The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.
|
||||
max_queue_size: Maximum number of items in the session queue.
|
||||
allow_nodes: List of nodes to allow. Omit to allow all.
|
||||
deny_nodes: List of nodes to deny. Omit to deny none.
|
||||
node_cache_size: How many cached nodes to keep in memory.
|
||||
hashing_algorithm: Model hashing algorthim for model installs. 'blake3' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.
|
||||
remote_api_tokens: List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.
|
||||
"""
|
||||
|
||||
singleton_config: ClassVar[Optional[InvokeAIAppConfig]] = None
|
||||
singleton_init: ClassVar[Optional[Dict[str, Any]]] = None
|
||||
_root: Optional[Path] = PrivateAttr(default=None)
|
||||
|
||||
# fmt: off
|
||||
type: Literal["InvokeAI"] = "InvokeAI"
|
||||
|
||||
# WEB
|
||||
host : str = Field(default="127.0.0.1", description="IP address to bind to. Use `0.0.0.0` to serve to your local network.", json_schema_extra=Categories.WebServer)
|
||||
port : int = Field(default=9090, description="Port to bind to.", json_schema_extra=Categories.WebServer)
|
||||
allow_origins : List[str] = Field(default=[], description="Allowed CORS origins.", json_schema_extra=Categories.WebServer)
|
||||
allow_credentials : bool = Field(default=True, description="Allow CORS credentials.", json_schema_extra=Categories.WebServer)
|
||||
allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS.", json_schema_extra=Categories.WebServer)
|
||||
allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS.", json_schema_extra=Categories.WebServer)
|
||||
# SSL options correspond to https://www.uvicorn.org/settings/#https
|
||||
ssl_certfile : Optional[Path] = Field(default=None, description="SSL certificate file for HTTPS.", json_schema_extra=Categories.WebServer)
|
||||
ssl_keyfile : Optional[Path] = Field(default=None, description="SSL key file for HTTPS.", json_schema_extra=Categories.WebServer)
|
||||
host: str = Field(default="127.0.0.1", description="IP address to bind to. Use `0.0.0.0` to serve to your local network.")
|
||||
port: int = Field(default=9090, description="Port to bind to.")
|
||||
allow_origins: list[str] = Field(default=[], description="Allowed CORS origins.")
|
||||
allow_credentials: bool = Field(default=True, description="Allow CORS credentials.")
|
||||
allow_methods: list[str] = Field(default=["*"], description="Methods allowed for CORS.")
|
||||
allow_headers: list[str] = Field(default=["*"], description="Headers allowed for CORS.")
|
||||
ssl_certfile: Optional[Path] = Field(default=None, description="SSL certificate file for HTTPS. See https://www.uvicorn.org/settings/#https.")
|
||||
ssl_keyfile: Optional[Path] = Field(default=None, description="SSL key file for HTTPS. See https://www.uvicorn.org/settings/#https.")
|
||||
|
||||
# FEATURES
|
||||
esrgan : bool = Field(default=True, description="Enables or disables the upscaling code.", json_schema_extra=Categories.Features)
|
||||
# TODO(psyche): This is not used anywhere.
|
||||
internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models.", json_schema_extra=Categories.Features)
|
||||
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", json_schema_extra=Categories.Features)
|
||||
patchmatch : bool = Field(default=True, description="Enable patchmatch inpaint code.", json_schema_extra=Categories.Features)
|
||||
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing core models on startup. If `True`, the app will attempt to download missing models on startup.', json_schema_extra=Categories.Features)
|
||||
# MISC FEATURES
|
||||
log_tokenization: bool = Field(default=False, description="Enable logging of parsed prompt tokens.")
|
||||
patchmatch: bool = Field(default=True, description="Enable patchmatch inpaint code.")
|
||||
ignore_missing_core_models: bool = Field(default=False, description="Ignore missing core models on startup. If `True`, the app will attempt to download missing models on startup.")
|
||||
|
||||
# PATHS
|
||||
root : Optional[Path] = Field(default=None, description='The InvokeAI runtime root directory.', json_schema_extra=Categories.Paths)
|
||||
autoimport_dir : Path = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
|
||||
models_dir : Path = Field(default=Path('models'), description='Path to the models directory.', json_schema_extra=Categories.Paths)
|
||||
convert_cache_dir : Path = Field(default=Path('models/.cache'), description='Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.', json_schema_extra=Categories.Paths)
|
||||
legacy_conf_dir : Path = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files.', json_schema_extra=Categories.Paths)
|
||||
db_dir : Path = Field(default=Path('databases'), description='Path to InvokeAI databases directory.', json_schema_extra=Categories.Paths)
|
||||
outdir : Path = Field(default=Path('outputs'), description='Path to directory for outputs.', json_schema_extra=Categories.Paths)
|
||||
custom_nodes_dir : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes.', json_schema_extra=Categories.Paths)
|
||||
# TODO(psyche): This is not used anywhere.
|
||||
from_file : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only).', json_schema_extra=Categories.Paths)
|
||||
autoimport_dir: Path = Field(default=Path("autoimport"), description="Path to a directory of models files to be imported on startup. WARNING: This may be a relative path. Use `autoimport_path` to get the resolved absolute path.")
|
||||
models_dir: Path = Field(default=Path("models"), description="Path to the models directory. WARNING: This may be a relative path. Use `models_path` to get the resolved absolute path.")
|
||||
convert_cache_dir: Path = Field(default=Path("models/.cache"), description="Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location. WARNING: This may be a relative path. Use `convert_cache_path` to get the resolved absolute path.")
|
||||
legacy_conf_dir: Path = Field(default=Path("configs/stable-diffusion"), description="Path to directory of legacy checkpoint config files. WARNING: This may be a relative path. Use `legacy_conf_path` to get the resolved absolute path.")
|
||||
db_dir: Path = Field(default=Path("databases"), description="Path to InvokeAI databases directory. WARNING: This may be a relative path. Use `db_path` to get the resolved absolute path.")
|
||||
outputs_dir: Path = Field(default=Path("outputs"), description="Path to directory for outputs. WARNING: This may be a relative path. Use `outputs_path` to get the resolved absolute path.")
|
||||
custom_nodes_dir: Path = Field(default=Path("nodes"), description="Path to directory for custom nodes. WARNING: This may be a relative path. Use `custom_nodes_path` to get the resolved absolute path.")
|
||||
|
||||
# LOGGING
|
||||
log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".', json_schema_extra=Categories.Logging)
|
||||
log_handlers: list[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".')
|
||||
# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
|
||||
log_format : Literal['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.', json_schema_extra=Categories.Logging)
|
||||
log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher.", json_schema_extra=Categories.Logging)
|
||||
log_sql : bool = Field(default=False, description="Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.", json_schema_extra=Categories.Logging)
|
||||
log_format: LOG_FORMAT = 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.')
|
||||
log_level: LOG_LEVEL = Field(default="info", description="Emit logging messages at this level or higher.")
|
||||
log_sql: bool = Field(default=False, description="Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.")
|
||||
|
||||
# Development
|
||||
use_memory_db : bool = Field(default=False, description='Use in-memory database. Useful for development.', json_schema_extra=Categories.Development)
|
||||
dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed. Does not reload node definitions.", json_schema_extra=Categories.Development)
|
||||
profile_graphs : bool = Field(default=False, description="Enable graph profiling using `cProfile`.", json_schema_extra=Categories.Development)
|
||||
profile_prefix : Optional[str] = Field(default=None, description="An optional prefix for profile output files.", json_schema_extra=Categories.Development)
|
||||
profiles_dir : Path = Field(default=Path('profiles'), description="Path to profiles output directory.", json_schema_extra=Categories.Development)
|
||||
|
||||
version : bool = Field(default=False, description="CLI arg - show InvokeAI version and exit.", json_schema_extra=Categories.CLIArgs)
|
||||
use_memory_db: bool = Field(default=False, description="Use in-memory database. Useful for development.")
|
||||
dev_reload: bool = Field(default=False, description="Automatically reload when Python sources are changed. Does not reload node definitions.")
|
||||
profile_graphs: bool = Field(default=False, description="Enable graph profiling using `cProfile`.")
|
||||
profile_prefix: Optional[str] = Field(default=None, description="An optional prefix for profile output files.")
|
||||
profiles_dir: Path = Field(default=Path("profiles"), description="Path to profiles output directory. WARNING: This may be a relative path. Use `profiles_path` to get the resolved absolute path.")
|
||||
|
||||
# CACHE
|
||||
ram : float = Field(default=DEFAULT_RAM_CACHE, gt=0, description="Maximum memory amount used by memory model cache for rapid switching (GB).", json_schema_extra=Categories.ModelCache, )
|
||||
vram : float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (GB)", json_schema_extra=Categories.ModelCache, )
|
||||
convert_cache : float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB)", json_schema_extra=Categories.ModelCache)
|
||||
|
||||
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed.", json_schema_extra=Categories.ModelCache, )
|
||||
log_memory_usage : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
|
||||
ram: float = Field(default=DEFAULT_RAM_CACHE, gt=0, description="Maximum memory amount used by memory model cache for rapid switching (GB).")
|
||||
vram: float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (GB)")
|
||||
convert_cache: float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB)")
|
||||
lazy_offload: bool = Field(default=True, description="Keep models in VRAM until their space is needed.")
|
||||
log_memory_usage: bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.")
|
||||
|
||||
# DEVICE
|
||||
device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.", json_schema_extra=Categories.Device)
|
||||
precision : Literal["auto", "float16", "bfloat16", "float32", "autocast"] = Field(default="auto", description="Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.", json_schema_extra=Categories.Device)
|
||||
device: DEVICE = Field(default="auto", description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.")
|
||||
precision: PRECISION = Field(default="auto", description="Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.")
|
||||
|
||||
# GENERATION
|
||||
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.", json_schema_extra=Categories.Generation)
|
||||
attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type.", json_schema_extra=Categories.Generation)
|
||||
attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced".', json_schema_extra=Categories.Generation)
|
||||
force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).", json_schema_extra=Categories.Generation)
|
||||
png_compress_level : int = Field(default=1, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.", json_schema_extra=Categories.Generation)
|
||||
|
||||
# QUEUE
|
||||
max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue.", json_schema_extra=Categories.Queue)
|
||||
sequential_guidance: bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.")
|
||||
attention_type: ATTENTION_TYPE = Field(default="auto", description="Attention type.")
|
||||
attention_slice_size: ATTENTION_SLICE_SIZE = Field(default="auto", description='Slice size, valid when attention_type=="sliced".')
|
||||
force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).")
|
||||
png_compress_level: int = Field(default=1, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.")
|
||||
max_queue_size: int = Field(default=10000, gt=0, description="Maximum number of items in the session queue.")
|
||||
|
||||
# NODES
|
||||
allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", json_schema_extra=Categories.Nodes)
|
||||
deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", json_schema_extra=Categories.Nodes)
|
||||
node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory.", json_schema_extra=Categories.Nodes)
|
||||
allow_nodes: Optional[list[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.")
|
||||
deny_nodes: Optional[list[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.")
|
||||
node_cache_size: int = Field(default=512, description="How many cached nodes to keep in memory.")
|
||||
|
||||
# MODEL INSTALL
|
||||
hashing_algorithm : HASHING_ALGORITHMS = Field(default="blake3", description="Model hashing algorthim for model installs. 'blake3' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.", json_schema_extra=Categories.ModelInstall)
|
||||
remote_api_tokens : Optional[list[URLRegexToken]] = Field(
|
||||
default=None,
|
||||
description="List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.",
|
||||
json_schema_extra=Categories.ModelInstall
|
||||
)
|
||||
hashing_algorithm: HASHING_ALGORITHMS = Field(default="blake3", description="Model hashing algorthim for model installs. 'blake3' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.")
|
||||
remote_api_tokens: Optional[list[URLRegexTokenPair]] = Field(default=None, description="List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.")
|
||||
|
||||
# TODO(psyche): Can we just remove these then?
|
||||
# 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.", json_schema_extra=Categories.Deprecated)
|
||||
max_cache_size : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", json_schema_extra=Categories.Deprecated)
|
||||
max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", json_schema_extra=Categories.Deprecated)
|
||||
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", json_schema_extra=Categories.Deprecated)
|
||||
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.Deprecated)
|
||||
lora_dir : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Deprecated)
|
||||
embedding_dir : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Deprecated)
|
||||
controlnet_dir : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Deprecated)
|
||||
conf_path : Path = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Deprecated)
|
||||
|
||||
# this is not referred to in the source code and can be removed entirely
|
||||
#free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
|
||||
|
||||
# See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
|
||||
# fmt: on
|
||||
|
||||
model_config = SettingsConfigDict(validate_assignment=True, env_prefix="INVOKEAI")
|
||||
model_config = SettingsConfigDict(env_file_encoding="utf-8", case_sensitive=True, env_prefix="INVOKEAI")
|
||||
|
||||
def parse_args(
|
||||
self,
|
||||
argv: Optional[list[str]] = None,
|
||||
conf: Optional[DictConfig] = None,
|
||||
clobber: Optional[bool] = False,
|
||||
) -> None:
|
||||
def update_config(self, config: dict[str, Any] | InvokeAIAppConfig) -> None:
|
||||
"""Updates the config, overwriting existing values.
|
||||
|
||||
Args:
|
||||
config: A dictionary of config settings, or instance of `InvokeAIAppConfig`. If an instance of \
|
||||
`InvokeAIAppConfig`, only the explicitly set fields will be merged into the singleton config.
|
||||
"""
|
||||
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
|
||||
if isinstance(config, dict):
|
||||
new_config = self.model_validate(config)
|
||||
else:
|
||||
new_config = config
|
||||
|
||||
for field_name in new_config.model_fields_set:
|
||||
setattr(self, field_name, getattr(new_config, field_name))
|
||||
|
||||
def write_file(self, exclude_defaults: bool) -> None:
|
||||
"""Write the current configuration to the `invokeai.yaml` file. This will overwrite the existing file.
|
||||
|
||||
A `meta` stanza is added to the top of the file, containing metadata about the config file. This is not stored in the config object.
|
||||
|
||||
Args:
|
||||
exclude_defaults: If `True`, only include settings that were explicitly set. If `False`, include all settings, including defaults.
|
||||
"""
|
||||
# Set the runtime root directory. We parse command-line switches here
|
||||
# in order to pick up the --root_dir option.
|
||||
super().parse_args(argv)
|
||||
loaded_conf = None
|
||||
if conf is None:
|
||||
try:
|
||||
loaded_conf = OmegaConf.load(self.root_dir / INIT_FILE)
|
||||
except Exception:
|
||||
pass
|
||||
if isinstance(loaded_conf, DictConfig):
|
||||
InvokeAISettings.initconf = loaded_conf
|
||||
else:
|
||||
InvokeAISettings.initconf = conf
|
||||
with open(self.init_file_path, "w") as file:
|
||||
meta_dict = {"meta": ConfigMeta().model_dump()}
|
||||
config_dict = self.model_dump(mode="json", exclude_unset=True, exclude_defaults=exclude_defaults)
|
||||
file.write("# Internal metadata\n")
|
||||
file.write(yaml.dump(meta_dict, sort_keys=False))
|
||||
file.write("\n")
|
||||
file.write("# User settings\n")
|
||||
file.write(yaml.dump(config_dict, sort_keys=False))
|
||||
|
||||
# parse args again in order to pick up settings in configuration file
|
||||
super().parse_args(argv)
|
||||
|
||||
if self.singleton_init and not clobber:
|
||||
# When setting values in this way, set validate_assignment to true if you want to validate the value.
|
||||
for k, v in self.singleton_init.items():
|
||||
setattr(self, k, v)
|
||||
|
||||
@classmethod
|
||||
def get_config(cls, **kwargs: Any) -> InvokeAIAppConfig:
|
||||
"""Return a singleton InvokeAIAppConfig configuration object."""
|
||||
if (
|
||||
cls.singleton_config is None
|
||||
or type(cls.singleton_config) is not 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.resolve()
|
||||
|
||||
@property
|
||||
def root_dir(self) -> Path:
|
||||
"""Alias for above."""
|
||||
return self.root_path
|
||||
def set_root(self, root: Path) -> None:
|
||||
"""Set the runtime root directory. This is typically set using a CLI arg."""
|
||||
self._root = root
|
||||
|
||||
def _resolve(self, partial_path: Path) -> Path:
|
||||
return (self.root_path / partial_path).resolve()
|
||||
|
||||
@property
|
||||
def root_path(self) -> Path:
|
||||
"""Path to the runtime root directory, resolved to an absolute path."""
|
||||
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.resolve()
|
||||
|
||||
@property
|
||||
def init_file_path(self) -> Path:
|
||||
"""Path to invokeai.yaml."""
|
||||
"""Path to invokeai.yaml, resolved to an absolute path.."""
|
||||
resolved_path = self._resolve(INIT_FILE)
|
||||
assert resolved_path is not None
|
||||
return resolved_path
|
||||
|
||||
@property
|
||||
def output_path(self) -> Optional[Path]:
|
||||
"""Path to defaults outputs directory."""
|
||||
return self._resolve(self.outdir)
|
||||
def autoimport_path(self) -> Path:
|
||||
"""Path to the autoimports directory, resolved to an absolute path.."""
|
||||
return self._resolve(self.autoimport_dir)
|
||||
|
||||
@property
|
||||
def outputs_path(self) -> Optional[Path]:
|
||||
"""Path to the outputs directory, resolved to an absolute path.."""
|
||||
return self._resolve(self.outputs_dir)
|
||||
|
||||
@property
|
||||
def db_path(self) -> Path:
|
||||
"""Path to the invokeai.db file."""
|
||||
"""Path to the invokeai.db file, resolved to an absolute path.."""
|
||||
db_dir = self._resolve(self.db_dir)
|
||||
assert db_dir is not None
|
||||
return 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)."""
|
||||
"""Path to directory of legacy configuration files (e.g. v1-inference.yaml), resolved to an absolute path.."""
|
||||
return self._resolve(self.legacy_conf_dir)
|
||||
|
||||
@property
|
||||
def models_path(self) -> Path:
|
||||
"""Path to the models directory."""
|
||||
"""Path to the models directory, resolved to an absolute path.."""
|
||||
return self._resolve(self.models_dir)
|
||||
|
||||
@property
|
||||
def models_convert_cache_path(self) -> Path:
|
||||
"""Path to the converted cache models directory."""
|
||||
def convert_cache_path(self) -> Path:
|
||||
"""Path to the converted cache models directory, resolved to an absolute path.."""
|
||||
return self._resolve(self.convert_cache_dir)
|
||||
|
||||
@property
|
||||
def custom_nodes_path(self) -> Path:
|
||||
"""Path to the custom nodes directory."""
|
||||
"""Path to the custom nodes directory, resolved to an absolute path.."""
|
||||
custom_nodes_path = self._resolve(self.custom_nodes_dir)
|
||||
assert custom_nodes_path is not None
|
||||
return custom_nodes_path
|
||||
|
||||
# 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:
|
||||
"""Return value for NSFW checker. The NSFW node is always active and disabled from Web UI."""
|
||||
return True
|
||||
|
||||
@property
|
||||
def invisible_watermark(self) -> bool:
|
||||
"""Return value of invisible watermark. It is always active and disabled from Web UI."""
|
||||
return True
|
||||
|
||||
@property
|
||||
def ram_cache_size(self) -> float:
|
||||
"""Return the ram cache size using the legacy or modern setting (GB)."""
|
||||
return self.max_cache_size or self.ram
|
||||
|
||||
@property
|
||||
def vram_cache_size(self) -> float:
|
||||
"""Return the vram cache size using the legacy or modern setting (GB)."""
|
||||
return self.max_vram_cache_size or self.vram
|
||||
|
||||
@property
|
||||
def convert_cache_size(self) -> float:
|
||||
"""Return the convert cache size on disk (GB)."""
|
||||
return self.convert_cache
|
||||
|
||||
@property
|
||||
def use_cpu(self) -> bool:
|
||||
"""Return true if the device is set to CPU or the always_use_cpu flag is set."""
|
||||
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"
|
||||
|
||||
@property
|
||||
def profiles_path(self) -> Path:
|
||||
"""Path to the graph profiles directory."""
|
||||
"""Path to the graph profiles directory, resolved to an absolute path.."""
|
||||
return self._resolve(self.profiles_dir)
|
||||
|
||||
@staticmethod
|
||||
def find_root() -> Path:
|
||||
"""Choose the runtime root directory when not specified on command line or init file."""
|
||||
return _find_root()
|
||||
|
||||
@staticmethod
|
||||
def generate_docstrings() -> str:
|
||||
"""Helper function for mkdocs. Generates a docstring for the InvokeAIAppConfig class.
|
||||
|
||||
You shouldn't run this manually. Instead, run `scripts/update-config-docstring.py` to update the docstring.
|
||||
A makefile target is also available: `make update-config-docstring`.
|
||||
|
||||
See that script for more information about why this is necessary.
|
||||
"""
|
||||
docstring = ' """Invoke App Configuration\n\n'
|
||||
docstring += " Attributes:"
|
||||
|
||||
field_descriptions: dict[str, list[str]] = {}
|
||||
|
||||
for k, v in InvokeAIAppConfig.model_fields.items():
|
||||
if not isinstance(v.json_schema_extra, dict):
|
||||
# Should never happen
|
||||
continue
|
||||
|
||||
category = v.json_schema_extra.get("category", None)
|
||||
if not isinstance(category, str) or category == "Deprecated":
|
||||
continue
|
||||
if not field_descriptions.get(category):
|
||||
field_descriptions[category] = []
|
||||
field_descriptions[category].append(f" {k}: **{category}**: {v.description}")
|
||||
|
||||
for c in [
|
||||
"Web Server",
|
||||
"Features",
|
||||
"Paths",
|
||||
"Logging",
|
||||
"Development",
|
||||
"CLIArgs",
|
||||
"Model Install",
|
||||
"Model Cache",
|
||||
"Device",
|
||||
"Generation",
|
||||
"Queue",
|
||||
"Nodes",
|
||||
]:
|
||||
docstring += "\n"
|
||||
docstring += "\n".join(field_descriptions[c])
|
||||
|
||||
docstring += '\n """'
|
||||
|
||||
return docstring
|
||||
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
|
||||
|
||||
|
||||
def get_invokeai_config(**kwargs: Any) -> InvokeAIAppConfig:
|
||||
"""Legacy function which returns InvokeAIAppConfig.get_config()."""
|
||||
return InvokeAIAppConfig.get_config(**kwargs)
|
||||
def generate_config_docstrings() -> str:
|
||||
"""Helper function for mkdocs. Generates a docstring for the InvokeAIAppConfig class.
|
||||
|
||||
You shouldn't run this manually. Instead, run `scripts/update-config-docstring.py` to update the docstring.
|
||||
A makefile target is also available: `make update-config-docstring`.
|
||||
|
||||
See that script for more information about why this is necessary.
|
||||
"""
|
||||
docstring = ' """Invoke\'s global app configuration.\n\n'
|
||||
docstring += " Typically, you won't need to interact with this class directly. Instead, use the `get_config` function from `invokeai.app.services.config` to get a singleton config object.\n\n"
|
||||
docstring += " Attributes:\n"
|
||||
|
||||
field_descriptions: list[str] = []
|
||||
|
||||
for k, v in InvokeAIAppConfig.model_fields.items():
|
||||
if v.exclude:
|
||||
continue
|
||||
field_descriptions.append(f" {k}: {v.description}")
|
||||
|
||||
docstring += "\n".join(field_descriptions)
|
||||
docstring += '\n """'
|
||||
|
||||
return docstring
|
||||
|
||||
|
||||
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()
|
||||
def load_config_from_file(config_path: Path) -> InvokeAIAppConfig:
|
||||
"""Parse a config file into an InvokeAIAppConfig object. The file should be in YAML format."""
|
||||
assert config_path.suffix == ".yaml"
|
||||
with open(config_path) as file:
|
||||
loaded_config = InvokeAIAppConfig.model_validate(yaml.safe_load(file))
|
||||
return loaded_config
|
||||
|
||||
|
||||
def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
|
||||
"""Migrate a v3 config dictionary to the latest version.
|
||||
|
||||
Args:
|
||||
config_dict: A dictionary of settings from a v3 config file.
|
||||
|
||||
Returns:
|
||||
An instance of `InvokeAIAppConfig` with the migrated settings.
|
||||
|
||||
"""
|
||||
parsed_config_dict: dict[str, Any] = {}
|
||||
for _category_name, category_dict in config_dict["InvokeAI"].items():
|
||||
for k, v in category_dict.items():
|
||||
if k == "outdir":
|
||||
# `outdir` was renamed to `outputs_dir`
|
||||
parsed_config_dict["outputs_dir"] = v
|
||||
elif k in InvokeAIAppConfig.model_fields:
|
||||
# skip unknown fields
|
||||
parsed_config_dict[k] = v
|
||||
return InvokeAIAppConfig.model_validate(parsed_config_dict)
|
||||
|
||||
|
||||
def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
|
||||
"""Load and migrate a config file to the latest version.
|
||||
|
||||
Args:
|
||||
config_path: Path to the config file.
|
||||
|
||||
Returns:
|
||||
An instance of `InvokeAIAppConfig` with the loaded and migrated settings.
|
||||
"""
|
||||
assert config_path.suffix == ".yaml"
|
||||
with open(config_path) as file:
|
||||
loaded_config_dict = yaml.safe_load(file)
|
||||
|
||||
assert isinstance(loaded_config_dict, dict)
|
||||
|
||||
if "InvokeAI" in loaded_config_dict:
|
||||
# This is a v3 config file, attempt to migrate it
|
||||
try:
|
||||
config = migrate_v3_config_dict(loaded_config_dict)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load and migrate v3 config file {config_path}: {e}") from e
|
||||
config_path.rename(config_path.with_suffix(".yaml.bak"))
|
||||
# By excluding defaults, we ensure that the new config file only contains the settings that were explicitly set
|
||||
config.write_file(exclude_defaults=True)
|
||||
return config
|
||||
else:
|
||||
root = Path("~/invokeai").expanduser().resolve()
|
||||
return root
|
||||
# This is a v4 config file, attempt to load it
|
||||
try:
|
||||
# Meta is not included in the model fields, so we need to validate it separately
|
||||
config_meta = ConfigMeta.model_validate(loaded_config_dict.pop("meta"))
|
||||
assert (
|
||||
config_meta.schema_version == CONFIG_SCHEMA_VERSION
|
||||
), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config_meta.schema_version}"
|
||||
return InvokeAIAppConfig.model_validate(loaded_config_dict)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e
|
||||
|
||||
|
||||
@lru_cache(maxsize=1)
|
||||
def get_config() -> InvokeAIAppConfig:
|
||||
"""Return the global singleton app config.
|
||||
|
||||
On first call, CLI args are parsed and the config file is read and merged in to the singleton config.
|
||||
|
||||
On subsequent calls, the singleton config is returned.
|
||||
"""
|
||||
# Singleton app config
|
||||
config = InvokeAIAppConfig()
|
||||
# User may have specified a `root` as a CLI arg
|
||||
opt = app_arg_parser.parse_args()
|
||||
if root := getattr(opt, "root", None):
|
||||
config.set_root(Path(root))
|
||||
else:
|
||||
config.set_root(config.find_root())
|
||||
|
||||
# Load the config file and merge it in to the app config
|
||||
config_from_file = load_and_migrate_config(config.init_file_path)
|
||||
config.update_config(config_from_file)
|
||||
return config
|
||||
|
||||
@ -1,5 +0,0 @@
|
||||
"""
|
||||
Initialization file for invokeai.frontend.CLI
|
||||
"""
|
||||
|
||||
from .CLI import main as invokeai_command_line_interface # noqa: F401
|
||||
0
invokeai/frontend/cli/__init__.py
Normal file
0
invokeai/frontend/cli/__init__.py
Normal file
14
invokeai/frontend/cli/app_arg_parser.py
Normal file
14
invokeai/frontend/cli/app_arg_parser.py
Normal file
@ -0,0 +1,14 @@
|
||||
from argparse import ArgumentParser, RawTextHelpFormatter
|
||||
|
||||
from invokeai.version import __version__
|
||||
|
||||
root_help = r"""Sets a root directory for the app. If omitted, the app will search for the root directory in the following order:
|
||||
- The `$INVOKEAI_ROOT` environment variable
|
||||
- The currently active virtual environment's parent directory
|
||||
- `$HOME/invokeai`"""
|
||||
|
||||
app_arg_parser = ArgumentParser(description="Invoke Studio", formatter_class=RawTextHelpFormatter)
|
||||
app_arg_parser.add_argument("--root", type=str, help=root_help)
|
||||
app_arg_parser.add_argument(
|
||||
"--version", action="version", version=__version__, help="Displays the version and exits."
|
||||
)
|
||||
@ -5,10 +5,16 @@
|
||||
import logging
|
||||
import os
|
||||
|
||||
from invokeai.frontend.cli.app_arg_parser import app_arg_parser
|
||||
|
||||
logging.getLogger("xformers").addFilter(lambda record: "A matching Triton is not available" not in record.getMessage())
|
||||
|
||||
|
||||
def main():
|
||||
# Parse CLI args immediately to handle `version` and `help` commands. Once the app starts up, we will parse the
|
||||
# args again to get configuration args.
|
||||
app_arg_parser.parse_args()
|
||||
|
||||
# Change working directory to the repo root
|
||||
os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
|
||||
|
||||
|
||||
@ -14,9 +14,9 @@ def main():
|
||||
# Change working directory to the repo root
|
||||
os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
|
||||
|
||||
from invokeai.app.services.config.config_default import InvokeAIAppConfig
|
||||
from invokeai.app.services.config.config_default import generate_config_docstrings
|
||||
|
||||
docstring = InvokeAIAppConfig.generate_docstrings()
|
||||
docstring = generate_config_docstrings()
|
||||
|
||||
# Replace the docstring in the file
|
||||
with open("invokeai/app/services/config/config_default.py", "r") as f:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user