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 @@
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"""
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Init file for InvokeAI configure package
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"""
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from .invokeai_config import (
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InvokeAIAppConfig,
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get_invokeai_config,
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)
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239
invokeai/app/services/config/base.py
Normal file
239
invokeai/app/services/config/base.py
Normal file
@ -0,0 +1,239 @@
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# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
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"""
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Base class for the InvokeAI configuration system.
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It defines a type of pydantic BaseSettings object that
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is able to read and write from an omegaconf-based config file,
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with overriding of settings from environment variables and/or
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the command line.
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"""
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from __future__ import annotations
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import argparse
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import os
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import pydoc
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import sys
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from argparse import ArgumentParser
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from omegaconf import OmegaConf, DictConfig, ListConfig
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from pathlib import Path
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from pydantic import BaseSettings
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from typing import ClassVar, Dict, List, Literal, Union, get_origin, get_type_hints, get_args
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class PagingArgumentParser(argparse.ArgumentParser):
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"""
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A custom ArgumentParser that uses pydoc to page its output.
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It also supports reading defaults from an init file.
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"""
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def print_help(self, file=None):
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text = self.format_help()
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pydoc.pager(text)
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class InvokeAISettings(BaseSettings):
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"""
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Runtime configuration settings in which default values are
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read from an omegaconf .yaml file.
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"""
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initconf: ClassVar[DictConfig] = None
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argparse_groups: ClassVar[Dict] = {}
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def parse_args(self, argv: list = sys.argv[1:]):
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parser = self.get_parser()
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opt = parser.parse_args(argv)
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for name in self.__fields__:
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if name not in self._excluded():
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value = getattr(opt, name)
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if isinstance(value, ListConfig):
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value = list(value)
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elif isinstance(value, DictConfig):
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value = dict(value)
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setattr(self, name, value)
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def to_yaml(self) -> str:
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"""
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Return a YAML string representing our settings. This can be used
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as the contents of `invokeai.yaml` to restore settings later.
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"""
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cls = self.__class__
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type = get_args(get_type_hints(cls)["type"])[0]
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field_dict = dict({type: dict()})
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for name, field in self.__fields__.items():
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if name in cls._excluded_from_yaml():
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continue
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category = field.field_info.extra.get("category") or "Uncategorized"
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value = getattr(self, name)
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if category not in field_dict[type]:
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field_dict[type][category] = dict()
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# keep paths as strings to make it easier to read
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field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
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conf = OmegaConf.create(field_dict)
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return OmegaConf.to_yaml(conf)
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@classmethod
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def add_parser_arguments(cls, parser):
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if "type" in get_type_hints(cls):
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settings_stanza = get_args(get_type_hints(cls)["type"])[0]
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else:
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settings_stanza = "Uncategorized"
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env_prefix = cls.Config.env_prefix if hasattr(cls.Config, "env_prefix") else settings_stanza.upper()
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initconf = (
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cls.initconf.get(settings_stanza)
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if cls.initconf and settings_stanza in cls.initconf
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else OmegaConf.create()
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)
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# create an upcase version of the environment in
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# order to achieve case-insensitive environment
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# variables (the way Windows does)
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upcase_environ = dict()
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for key, value in os.environ.items():
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upcase_environ[key.upper()] = value
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fields = cls.__fields__
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cls.argparse_groups = {}
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for name, field in fields.items():
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if name not in cls._excluded():
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current_default = field.default
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category = field.field_info.extra.get("category", "Uncategorized")
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env_name = env_prefix + "_" + name
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if category in initconf and name in initconf.get(category):
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field.default = initconf.get(category).get(name)
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if env_name.upper() in upcase_environ:
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field.default = upcase_environ[env_name.upper()]
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cls.add_field_argument(parser, name, field)
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field.default = current_default
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@classmethod
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def cmd_name(self, command_field: str = "type") -> str:
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hints = get_type_hints(self)
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if command_field in hints:
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return get_args(hints[command_field])[0]
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else:
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return "Uncategorized"
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@classmethod
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def get_parser(cls) -> ArgumentParser:
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parser = PagingArgumentParser(
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prog=cls.cmd_name(),
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description=cls.__doc__,
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)
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cls.add_parser_arguments(parser)
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return parser
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@classmethod
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def add_subparser(cls, parser: argparse.ArgumentParser):
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parser.add_parser(cls.cmd_name(), help=cls.__doc__)
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@classmethod
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def _excluded(self) -> List[str]:
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# internal fields that shouldn't be exposed as command line options
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return ["type", "initconf"]
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@classmethod
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def _excluded_from_yaml(self) -> List[str]:
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# combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
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return [
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"type",
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"initconf",
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"version",
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"from_file",
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"model",
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"root",
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"max_cache_size",
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"max_vram_cache_size",
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"always_use_cpu",
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"free_gpu_mem",
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"xformers_enabled",
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"tiled_decode",
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]
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class Config:
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env_file_encoding = "utf-8"
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arbitrary_types_allowed = True
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case_sensitive = True
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@classmethod
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def add_field_argument(cls, command_parser, name: str, field, default_override=None):
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field_type = get_type_hints(cls).get(name)
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default = (
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default_override
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if default_override is not None
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else field.default
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if field.default_factory is None
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else field.default_factory()
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)
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if category := field.field_info.extra.get("category"):
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if category not in cls.argparse_groups:
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cls.argparse_groups[category] = command_parser.add_argument_group(category)
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argparse_group = cls.argparse_groups[category]
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else:
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argparse_group = command_parser
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if get_origin(field_type) == Literal:
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allowed_values = get_args(field.type_)
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allowed_types = set()
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for val in allowed_values:
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allowed_types.add(type(val))
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allowed_types_list = list(allowed_types)
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field_type = allowed_types_list[0] if len(allowed_types) == 1 else int_or_float_or_str
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argparse_group.add_argument(
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f"--{name}",
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dest=name,
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type=field_type,
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default=default,
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choices=allowed_values,
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help=field.field_info.description,
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)
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elif get_origin(field_type) == Union:
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argparse_group.add_argument(
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f"--{name}",
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dest=name,
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type=int_or_float_or_str,
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default=default,
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help=field.field_info.description,
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)
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elif get_origin(field_type) == list:
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argparse_group.add_argument(
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f"--{name}",
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dest=name,
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nargs="*",
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type=field.type_,
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default=default,
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action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
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help=field.field_info.description,
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)
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else:
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argparse_group.add_argument(
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f"--{name}",
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dest=name,
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type=field.type_,
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default=default,
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action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
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help=field.field_info.description,
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)
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def int_or_float_or_str(value: str) -> Union[int, float, str]:
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"""
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Workaround for argparse type checking.
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"""
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try:
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return int(value)
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except:
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pass
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try:
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return float(value)
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except:
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pass
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return str(value)
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@ -159,15 +159,13 @@ two configs are kept in separate sections of the config file:
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"""
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from __future__ import annotations
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import argparse
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import pydoc
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import os
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import sys
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from argparse import ArgumentParser
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from omegaconf import OmegaConf, DictConfig, ListConfig
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from omegaconf import OmegaConf, DictConfig
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from pathlib import Path
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from pydantic import BaseSettings, Field, parse_obj_as
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from typing import Any, ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
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from pydantic import Field, parse_obj_as
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from typing import ClassVar, Dict, List, Literal, Union, Optional, get_type_hints
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from .base import InvokeAISettings
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INIT_FILE = Path("invokeai.yaml")
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DB_FILE = Path("invokeai.db")
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@ -175,204 +173,6 @@ LEGACY_INIT_FILE = Path("invokeai.init")
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DEFAULT_MAX_VRAM = 0.5
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class InvokeAISettings(BaseSettings):
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"""
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Runtime configuration settings in which default values are
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read from an omegaconf .yaml file.
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"""
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|
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initconf: ClassVar[DictConfig] = None
|
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argparse_groups: ClassVar[Dict] = {}
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|
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def parse_args(self, argv: list = sys.argv[1:]):
|
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parser = self.get_parser()
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opt = parser.parse_args(argv)
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for name in self.__fields__:
|
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if name not in self._excluded():
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value = getattr(opt, name)
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if isinstance(value, ListConfig):
|
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value = list(value)
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elif isinstance(value, DictConfig):
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value = dict(value)
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setattr(self, name, value)
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|
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def to_yaml(self) -> str:
|
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"""
|
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Return a YAML string representing our settings. This can be used
|
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as the contents of `invokeai.yaml` to restore settings later.
|
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"""
|
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cls = self.__class__
|
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type = get_args(get_type_hints(cls)["type"])[0]
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field_dict = dict({type: dict()})
|
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for name, field in self.__fields__.items():
|
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if name in cls._excluded_from_yaml():
|
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continue
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category = field.field_info.extra.get("category") or "Uncategorized"
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value = getattr(self, name)
|
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if category not in field_dict[type]:
|
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field_dict[type][category] = dict()
|
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# keep paths as strings to make it easier to read
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field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
|
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conf = OmegaConf.create(field_dict)
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return OmegaConf.to_yaml(conf)
|
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|
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@classmethod
|
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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"
|
||||
|
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env_prefix = cls.Config.env_prefix if hasattr(cls.Config, "env_prefix") else settings_stanza.upper()
|
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|
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initconf = (
|
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cls.initconf.get(settings_stanza)
|
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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",
|
||||
]
|
||||
|
||||
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 _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
|
||||
|
||||
|
||||
class InvokeAIAppConfig(InvokeAISettings):
|
||||
"""
|
||||
Generate images using Stable Diffusion. Use "invokeai" to launch
|
||||
@ -387,6 +187,8 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
|
||||
# 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')
|
||||
@ -394,21 +196,15 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
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')
|
||||
|
||||
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=2.75, 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')
|
||||
precision : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Device')
|
||||
device : Literal[tuple(['cpu','cuda','mps','cuda','cuda:1','auto'])] = Field(default='auto',description='Generation device', category='Device')
|
||||
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')
|
||||
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Generation')
|
||||
|
||||
# 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')
|
||||
@ -419,16 +215,43 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
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')
|
||||
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
|
||||
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
|
||||
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
|
||||
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:
|
||||
@ -551,16 +374,6 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
"""Return true if precision set to float32"""
|
||||
return self.precision == "float32"
|
||||
|
||||
@property
|
||||
def xformers_enabled(self) -> bool:
|
||||
"""Return true if attention_type=='xformers'."""
|
||||
return self.attention_type=='xformers'
|
||||
|
||||
@property
|
||||
def disable_xformers(self) -> bool:
|
||||
"""Return true if xformers_enabled is false"""
|
||||
return not self.xformers_enabled
|
||||
|
||||
@property
|
||||
def try_patchmatch(self) -> bool:
|
||||
"""Return true if patchmatch true"""
|
||||
@ -577,15 +390,26 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
return True
|
||||
|
||||
@property
|
||||
def max_cache_size(self) -> Union[str, float]:
|
||||
"""return value of ram attribute."""
|
||||
return self.ram
|
||||
def ram_cache_size(self) -> float:
|
||||
return self.max_cache_size or self.ram
|
||||
|
||||
@property
|
||||
def max_vram_cache_size(self) -> Union[str, float]:
|
||||
"""return value of vram attribute."""
|
||||
return self.vram
|
||||
|
||||
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:
|
||||
"""
|
||||
@ -594,29 +418,6 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
"""
|
||||
return _find_root()
|
||||
|
||||
# @property
|
||||
# def attention_slice_size(self) -> Union[str, int]:
|
||||
# """
|
||||
# Return one of "auto", "max", or 1-8.
|
||||
# """
|
||||
# size = self.attention_slice
|
||||
# try:
|
||||
# size= int(size)
|
||||
# assert size > 0
|
||||
# except ValueError:
|
||||
# pass
|
||||
# return size
|
||||
|
||||
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)
|
||||
|
||||
|
||||
def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
|
||||
"""
|
||||
@ -624,17 +425,13 @@ def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
|
||||
"""
|
||||
return InvokeAIAppConfig.get_config(**kwargs)
|
||||
|
||||
def int_or_float_or_str(value:Any) -> Union[int, float, str]:
|
||||
"""
|
||||
Workaround for argparse type checking.
|
||||
"""
|
||||
try:
|
||||
return int(value)
|
||||
except:
|
||||
pass
|
||||
try:
|
||||
return float(value)
|
||||
except:
|
||||
pass
|
||||
return str(value)
|
||||
|
||||
|
||||
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
|
||||
@ -322,8 +322,8 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
# configuration value. If present, then the
|
||||
# cache size is set to 2.5 GB times
|
||||
# the number of max_loaded_models. Otherwise
|
||||
# use new `max_cache_size` config setting
|
||||
max_cache_size = config.max_cache_size if hasattr(config, "max_cache_size") else config.max_loaded_models * 2.5
|
||||
# use new `ram_cache_size` config setting
|
||||
max_cache_size = config.ram_cache_size
|
||||
|
||||
logger.debug(f"Maximum RAM cache size: {max_cache_size} GiB")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user