InvokeAI/invokeai/app/services/config/config_default.py

# TODO(psyche): pydantic-settings supports YAML settings sources. If we can figure out a way to integrate the YAML
# migration logic, we could use that for simpler config loading.

from __future__ import annotations

import os
import re
import shutil
from functools import lru_cache
from pathlib import Path
from typing import Any, Literal, Optional

import psutil
import yaml
from pydantic import BaseModel, Field, PrivateAttr, field_validator
from pydantic_settings import BaseSettings, SettingsConfigDict

import invokeai.configs as model_configs
from invokeai.app.util.hf_login import hf_login
from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS
from invokeai.frontend.cli.arg_parser import InvokeAIArgs

INIT_FILE = Path("invokeai.yaml")
DB_FILE = Path("invokeai.db")
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


def get_default_ram_cache_size() -> float:
    """Run a heuristic for the default RAM cache based on installed RAM."""

    # On some machines, psutil.virtual_memory().total gives a value that is slightly less than the actual RAM, so the
    # limits are set slightly lower than than what we expect the actual RAM to be.

    GB = 1024**3
    max_ram = psutil.virtual_memory().total / GB

    if max_ram >= 60:
        return 15.0
    if max_ram >= 30:
        return 7.5
    if max_ram >= 14:
        return 4.0
    return 2.1  # 2.1 is just large enough for sd 1.5 ;-)


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")

    @field_validator("url_regex")
    @classmethod
    def validate_url_regex(cls, v: str) -> str:
        """Validate that the value is a valid regex."""
        try:
            re.compile(v)
        except re.error as e:
            raise ValueError(f"Invalid regex: {e}")
        return v


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: 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.
        autoimport_dir: Path to a directory of models files to be imported on startup.
        models_dir: Path to the models directory.
        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.
        legacy_conf_dir: Path to directory of legacy checkpoint config files.
        db_dir: Path to InvokeAI databases directory.
        outputs_dir: Path to directory for outputs.
        custom_nodes_dir: Path to directory for custom nodes.
        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.<br>Valid values: `plain`, `color`, `syslog`, `legacy`
        log_level: Emit logging messages at this level or higher.<br>Valid values: `debug`, `info`, `warning`, `error`, `critical`
        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.
        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.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
        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.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`, `autocast`
        sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
        attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
        attention_slice_size: Slice size, valid when attention_type=="sliced".<br>Valid values: `auto`, `balanced`, `max`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`
        force_tiled_decode: Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).
        pil_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.<br>Valid values: `md5`, `sha1`, `sha224`, `sha256`, `sha384`, `sha512`, `blake2b`, `blake2s`, `sha3_224`, `sha3_256`, `sha3_384`, `sha3_512`, `shake_128`, `shake_256`, `blake3`, `blake3_single`, `random`
        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.
    """

    _root: Optional[Path] = PrivateAttr(default=None)
    _config_file: Optional[Path] = PrivateAttr(default=None)

    # fmt: off

    # INTERNAL
    schema_version:                 int = Field(default=CONFIG_SCHEMA_VERSION, description="Schema version of the config file. This is not a user-configurable setting.")
    legacy_models_yaml_path: Optional[Path] = Field(default=None,           description="Path to the legacy models.yaml file. This is not a user-configurable setting.")

    # 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.")
    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.")

    # 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.")

    # PATHS
    autoimport_dir:                Path = Field(default=Path("autoimport"), description="Path to a directory of models files to be imported on startup.")
    models_dir:                    Path = Field(default=Path("models"),     description="Path to the models directory.")
    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.")
    legacy_conf_dir:               Path = Field(default=Path("configs"), description="Path to directory of legacy checkpoint config files.")
    db_dir:                        Path = Field(default=Path("databases"),  description="Path to InvokeAI databases directory.")
    outputs_dir:                   Path = Field(default=Path("outputs"),    description="Path to directory for outputs.")
    custom_nodes_dir:              Path = Field(default=Path("nodes"),      description="Path to directory for custom nodes.")

    # 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:              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.")
    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.")

    # CACHE
    ram:                          float = Field(default_factory=get_default_ram_cache_size, 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:                      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.")
    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).")
    pil_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.")
    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.")
    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.")

    # fmt: on

    model_config = SettingsConfigDict(env_prefix="INVOKEAI_", env_ignore_empty=True)

    def update_config(self, config: dict[str, Any] | InvokeAIAppConfig, clobber: bool = True) -> 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.
            clobber: If `True`, overwrite existing values. If `False`, only update fields that are not already set.
        """

        if isinstance(config, dict):
            new_config = self.model_validate(config)
        else:
            new_config = config

        for field_name in new_config.model_fields_set:
            new_value = getattr(new_config, field_name)
            current_value = getattr(self, field_name)

            if field_name in self.model_fields_set and not clobber:
                continue

            if new_value != current_value:
                setattr(self, field_name, new_value)

    def write_file(self, dest_path: Path) -> None:
        """Write the current configuration to 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:
            dest_path: Path to write the config to.
        """
        with open(dest_path, "w") as file:
            # Meta fields should be written in a separate stanza
            meta_dict = self.model_dump(mode="json", include={"schema_version"})
            # Only include the legacy_models_yaml_path if it's set
            if self.legacy_models_yaml_path:
                meta_dict.update(self.model_dump(mode="json", include={"legacy_models_yaml_path"}))

            # User settings
            config_dict = self.model_dump(
                mode="json",
                exclude_unset=True,
                exclude_defaults=True,
                exclude={"schema_version", "legacy_models_yaml_path"},
            )

            file.write("# Internal metadata - do not edit:\n")
            file.write(yaml.dump(meta_dict, sort_keys=False))
            file.write("\n")
            file.write("# Put user settings here - see https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/:\n")
            if len(config_dict) > 0:
                file.write(yaml.dump(config_dict, sort_keys=False))

    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 config_file_path(self) -> Path:
        """Path to invokeai.yaml, resolved to an absolute path.."""
        resolved_path = self._resolve(self._config_file or INIT_FILE)
        assert resolved_path is not None
        return resolved_path

    @property
    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, 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 legacy_conf_path(self) -> Path:
        """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, resolved to an absolute path.."""
        return self._resolve(self.models_dir)

    @property
    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, 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

    @property
    def profiles_path(self) -> Path:
        """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."""
        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 migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
    """Migrate a v3 config dictionary to a current config object.

    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():
            # `outdir` was renamed to `outputs_dir` in v4
            if k == "outdir":
                parsed_config_dict["outputs_dir"] = v
            # `max_cache_size` was renamed to `ram` some time in v3, but both names were used
            if k == "max_cache_size" and "ram" not in category_dict:
                parsed_config_dict["ram"] = v
            # `max_vram_cache_size` was renamed to `vram` some time in v3, but both names were used
            if k == "max_vram_cache_size" and "vram" not in category_dict:
                parsed_config_dict["vram"] = v
            if k == "conf_path":
                parsed_config_dict["legacy_models_yaml_path"] = v
            elif k in InvokeAIAppConfig.model_fields:
                # skip unknown fields
                parsed_config_dict[k] = v
    config = InvokeAIAppConfig.model_validate(parsed_config_dict)

    return config


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
        shutil.copy(config_path, config_path.with_suffix(".yaml.bak"))
        try:
            # This could be the wrong shape, but we will catch all exceptions below
            config = migrate_v3_config_dict(loaded_config_dict)  # pyright: ignore [reportUnknownArgumentType]
        except Exception as e:
            shutil.copy(config_path.with_suffix(".yaml.bak"), config_path)
            raise RuntimeError(f"Failed to load and migrate v3 config file {config_path}: {e}") from e
        # By excluding defaults, we ensure that the new config file only contains the settings that were explicitly set
        config.write_file(config_path)
        return config
    else:
        # Attempt to load as a v4 config file
        try:
            # Meta is not included in the model fields, so we need to validate it separately
            config = InvokeAIAppConfig.model_validate(loaded_config_dict)
            assert (
                config.schema_version == CONFIG_SCHEMA_VERSION
            ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
            return config
        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:
    """Get the global singleton app config.

    When first called, this function:
    - Creates a config object. `pydantic-settings` handles merging of settings from environment variables, but not the init file.
    - Retrieves any provided CLI args from the InvokeAIArgs class. It does not _parse_ the CLI args; that is done in the main entrypoint.
    - Sets the root dir, if provided via CLI args.
    - Logs in to HF if there is no valid token already.
    - Copies all legacy configs to the legacy conf dir (needed for conversion from ckpt to diffusers).
    - Reads and merges in settings from the config file if it exists, else writes out a default config file.

    On subsequent calls, the object is returned from the cache.
    """
    config = InvokeAIAppConfig()

    args = InvokeAIArgs.args

    # This flag serves as a proxy for whether the config was retrieved in the context of the full application or not.
    # If it is False, we should just return a default config and not set the root, log in to HF, etc.
    if not InvokeAIArgs.did_parse:
        return config

    # Set CLI args
    if root := getattr(args, "root", None):
        config._root = Path(root)
    if config_file := getattr(args, "config_file", None):
        config._config_file = Path(config_file)

    # Log in to HF
    hf_login()

    # Copy all legacy configs - We know `__path__[0]` is correct here
    configs_src = Path(model_configs.__path__[0])  # pyright: ignore [reportUnknownMemberType, reportUnknownArgumentType, reportAttributeAccessIssue]
    shutil.copytree(configs_src, config.legacy_conf_path, dirs_exist_ok=True)

    if config.config_file_path.exists():
        incoming_config = load_and_migrate_config(config.config_file_path)
        # Clobbering here will overwrite any settings that were set via environment variables
        config.update_config(incoming_config, clobber=False)
    else:
        config.write_file(config.config_file_path)

    return config