mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
532 lines
20 KiB
Python
532 lines
20 KiB
Python
# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
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'''Invokeai configuration system.
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Arguments and fields are taken from the pydantic definition of the
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model. Defaults can be set by creating a yaml configuration file that
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has a top-level key of "InvokeAI" and subheadings for each of the
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categories returned by `invokeai --help`. The file looks like this:
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[file: invokeai.yaml]
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InvokeAI:
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Paths:
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root: /home/lstein/invokeai-main
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conf_path: configs/models.yaml
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legacy_conf_dir: configs/stable-diffusion
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outdir: outputs
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embedding_dir: embeddings
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lora_dir: loras
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autoconvert_dir: null
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gfpgan_model_dir: models/gfpgan/GFPGANv1.4.pth
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Models:
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model: stable-diffusion-1.5
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embeddings: true
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Memory/Performance:
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xformers_enabled: false
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sequential_guidance: false
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precision: float16
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max_loaded_models: 4
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always_use_cpu: false
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free_gpu_mem: false
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Features:
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nsfw_checker: true
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restore: true
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esrgan: true
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patchmatch: true
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internet_available: true
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log_tokenization: false
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Web Server:
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host: 127.0.0.1
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port: 8081
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allow_origins: []
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allow_credentials: true
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allow_methods:
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- '*'
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allow_headers:
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- '*'
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The default name of the configuration file is `invokeai.yaml`, located
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in INVOKEAI_ROOT. You can replace supersede this by providing any
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OmegaConf dictionary object initialization time:
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omegaconf = OmegaConf.load('/tmp/init.yaml')
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conf = InvokeAIAppConfig(conf=omegaconf)
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By default, InvokeAIAppConfig will parse the contents of `sys.argv` at
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initialization time. You may pass a list of strings in the optional
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`argv` argument to use instead of the system argv:
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conf = InvokeAIAppConfig(arg=['--xformers_enabled'])
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It is also possible to set a value at initialization time. This value
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has highest priority.
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conf = InvokeAIAppConfig(xformers_enabled=True)
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Any setting can be overwritten by setting an environment variable of
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form: "INVOKEAI_<setting>", as in:
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export INVOKEAI_port=8080
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Order of precedence (from highest):
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1) initialization options
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2) command line options
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3) environment variable options
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4) config file options
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5) pydantic defaults
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Typical usage:
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from invokeai.app.services.config import InvokeAIAppConfig
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from invokeai.invocations.generate import TextToImageInvocation
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# get global configuration and print its nsfw_checker value
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conf = InvokeAIAppConfig()
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print(conf.nsfw_checker)
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# get the text2image invocation and print its step value
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text2image = TextToImageInvocation()
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print(text2image.steps)
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Computed properties:
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The InvokeAIAppConfig object has a series of properties that
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resolve paths relative to the runtime root directory. They each return
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a Path object:
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root_path - path to InvokeAI root
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output_path - path to default outputs directory
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model_conf_path - path to models.yaml
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conf - alias for the above
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embedding_path - path to the embeddings directory
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lora_path - path to the LoRA directory
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In most cases, you will want to create a single InvokeAIAppConfig
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object for the entire application. The get_invokeai_config() function
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does this:
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config = get_invokeai_config()
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print(config.root)
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# Subclassing
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If you wish to create a similar class, please subclass the
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`InvokeAISettings` class and define a Literal field named "type",
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which is set to the desired top-level name. For example, to create a
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"InvokeBatch" configuration, define like this:
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class InvokeBatch(InvokeAISettings):
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type: Literal["InvokeBatch"] = "InvokeBatch"
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node_count : int = Field(default=1, description="Number of nodes to run on", category='Resources')
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cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", category='Resources')
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This will now read and write from the "InvokeBatch" section of the
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config file, look for environment variables named INVOKEBATCH_*, and
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accept the command-line arguments `--node_count` and `--cpu_count`. The
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two configs are kept in separate sections of the config file:
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# invokeai.yaml
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InvokeBatch:
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Resources:
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node_count: 1
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cpu_count: 8
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InvokeAI:
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Paths:
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root: /home/lstein/invokeai-main
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conf_path: configs/models.yaml
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legacy_conf_dir: configs/stable-diffusion
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outdir: outputs
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...
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'''
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import argparse
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import pydoc
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import typing
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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
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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, Literal, Type, Union, get_origin, get_type_hints, get_args
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INIT_FILE = Path('invokeai.yaml')
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LEGACY_INIT_FILE = Path('invokeai.init')
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# This global stores a singleton InvokeAIAppConfig configuration object
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global_config = None
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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_known_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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setattr(self, name, getattr(opt,name))
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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():
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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 = 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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# 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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return ['type','initconf']
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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 = default_override if default_override is not None else field.default if field.default_factory is None else field.default_factory()
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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 Union[allowed_types_list] # type: ignore
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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) == 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 _find_root()->Path:
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if os.environ.get("INVOKEAI_ROOT"):
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root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
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elif (
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os.environ.get("VIRTUAL_ENV")
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and (Path(os.environ.get("VIRTUAL_ENV"), "..", INIT_FILE).exists()
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or
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Path(os.environ.get("VIRTUAL_ENV"), "..", LEGACY_INIT_FILE).exists()
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)
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):
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root = Path(os.environ.get("VIRTUAL_ENV"), "..").resolve()
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else:
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root = Path("~/invokeai").expanduser().resolve()
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return root
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class InvokeAIAppConfig(InvokeAISettings):
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'''
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Generate images using Stable Diffusion. Use "invokeai" to launch
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the command-line client (recommended for experts only), or
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"invokeai-web" to launch the web server. Global options
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can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
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setting environment variables INVOKEAI_<setting>.
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'''
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#fmt: off
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type: Literal["InvokeAI"] = "InvokeAI"
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host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
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port : int = Field(default=9090, description="Port to bind to", category='Web Server')
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allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", category='Web Server')
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allow_credentials : bool = Field(default=True, description="Allow CORS credentials", category='Web Server')
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allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", category='Web Server')
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allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", category='Web Server')
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esrgan : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
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internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
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log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
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nsfw_checker : bool = Field(default=True, description="Enable/disable the NSFW checker", category='Features')
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patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
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restore : bool = Field(default=True, description="Enable/disable face restoration code", category='Features')
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always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
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free_gpu_mem : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
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max_loaded_models : int = Field(default=2, gt=0, description="Maximum number of models to keep in memory for rapid switching", category='Memory/Performance')
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precision : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='float16',description='Floating point precision', category='Memory/Performance')
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sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
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xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
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root : Path = Field(default=_find_root(), description='InvokeAI runtime root directory', category='Paths')
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autoconvert_dir : Path = Field(default=None, description='Path to a directory of ckpt files to be converted into diffusers and imported on startup.', category='Paths')
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conf_path : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
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embedding_dir : Path = Field(default='embeddings', description='Path to InvokeAI textual inversion aembeddings directory', category='Paths')
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gfpgan_model_dir : Path = Field(default="./models/gfpgan/GFPGANv1.4.pth", description='Path to GFPGAN models directory.', category='Paths')
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controlnet_dir : Path = Field(default="controlnet", description='Path to directory of ControlNet models.', category='Paths')
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legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
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lora_dir : Path = Field(default='loras', description='Path to InvokeAI LoRA model directory', category='Paths')
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outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
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from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
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use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
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model : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models')
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embeddings : bool = Field(default=True, description='Load contents of embeddings directory', category='Models')
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#fmt: on
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def __init__(self, conf: DictConfig = None, argv: List[str]=None, **kwargs):
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'''
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Initialize InvokeAIAppconfig.
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:param conf: alternate Omegaconf dictionary object
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:param argv: aternate sys.argv list
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:param **kwargs: attributes to initialize with
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'''
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super().__init__(**kwargs)
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# Set the runtime root directory. We parse command-line switches here
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# in order to pick up the --root_dir option.
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self.parse_args(argv)
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if conf is None:
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try:
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conf = OmegaConf.load(self.root_dir / INIT_FILE)
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except:
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pass
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InvokeAISettings.initconf = conf
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# parse args again in order to pick up settings in configuration file
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self.parse_args(argv)
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# restore initialization values
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hints = get_type_hints(self)
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for k in kwargs:
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setattr(self,k,parse_obj_as(hints[k],kwargs[k]))
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@property
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def root_path(self)->Path:
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'''
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Path to the runtime root directory
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'''
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if self.root:
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return Path(self.root).expanduser()
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else:
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return self.find_root()
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@property
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def root_dir(self)->Path:
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'''
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Alias for above.
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'''
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return self.root_path
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def _resolve(self,partial_path:Path)->Path:
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return (self.root_path / partial_path).resolve()
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@property
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def output_path(self)->Path:
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'''
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Path to defaults outputs directory.
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'''
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return self._resolve(self.outdir)
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@property
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def model_conf_path(self)->Path:
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'''
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Path to models configuration file.
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'''
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return self._resolve(self.conf_path)
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@property
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def legacy_conf_path(self)->Path:
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'''
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Path to directory of legacy configuration files (e.g. v1-inference.yaml)
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'''
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return self._resolve(self.legacy_conf_dir)
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@property
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def cache_dir(self)->Path:
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'''
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Path to the global cache directory for HuggingFace hub-managed models
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'''
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return self.models_dir / "hub"
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@property
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def models_dir(self)->Path:
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'''
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Path to the models directory
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'''
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return self._resolve("models")
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@property
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def embedding_path(self)->Path:
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'''
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Path to the textual inversion embeddings directory.
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'''
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return self._resolve(self.embedding_dir) if self.embedding_dir else None
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@property
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def lora_path(self)->Path:
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'''
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Path to the LoRA models directory.
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'''
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return self._resolve(self.lora_dir) if self.lora_dir else None
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@property
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def controlnet_path(self)->Path:
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'''
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Path to the controlnet models directory.
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'''
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return self._resolve(self.controlnet_dir) if self.controlnet_dir else None
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@property
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def autoconvert_path(self)->Path:
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'''
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Path to the directory containing models to be imported automatically at startup.
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'''
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return self._resolve(self.autoconvert_dir) if self.autoconvert_dir else None
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@property
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def gfpgan_model_path(self)->Path:
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'''
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Path to the GFPGAN model.
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'''
|
|
return self._resolve(self.gfpgan_model_dir) if self.gfpgan_model_dir else None
|
|
|
|
# the following methods support legacy calls leftover from the Globals era
|
|
@property
|
|
def full_precision(self)->bool:
|
|
"""Return true if precision set to float32"""
|
|
return self.precision=='float32'
|
|
|
|
@property
|
|
def 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"""
|
|
return self.patchmatch
|
|
|
|
@staticmethod
|
|
def find_root()->Path:
|
|
'''
|
|
Choose the runtime root directory when not specified on command line or
|
|
init file.
|
|
'''
|
|
return _find_root()
|
|
|
|
|
|
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(cls:Type[InvokeAISettings]=InvokeAIAppConfig,**kwargs)->InvokeAIAppConfig:
|
|
'''
|
|
This returns a singleton InvokeAIAppConfig configuration object.
|
|
'''
|
|
global global_config
|
|
if global_config is None or type(global_config)!=cls:
|
|
global_config = cls(**kwargs)
|
|
return global_config
|