mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
consolidate model manager parts into a single class
This commit is contained in:
Lincoln Stein
parent
a6508d1391
commit
1d724bca4a
6
invokeai/app/services/model_load/__init__.py
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6
invokeai/app/services/model_load/__init__.py
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@ -0,0 +1,6 @@
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"""Initialization file for model load service module."""
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from .model_load_base import ModelLoadServiceBase
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from .model_load_default import ModelLoadService
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__all__ = ["ModelLoadServiceBase", "ModelLoadService"]
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22
invokeai/app/services/model_load/model_load_base.py
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22
invokeai/app/services/model_load/model_load_base.py
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@ -0,0 +1,22 @@
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# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Team
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"""Base class for model loader."""
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from abc import ABC, abstractmethod
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from typing import Optional
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from invokeai.backend.model_manager import AnyModelConfig, SubModelType
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from invokeai.backend.model_manager.load import LoadedModel
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class ModelLoadServiceBase(ABC):
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"""Wrapper around AnyModelLoader."""
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@abstractmethod
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def load_model_by_key(self, key: str, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
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"""Given a model's key, load it and return the LoadedModel object."""
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pass
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@abstractmethod
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def load_model_by_config(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
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"""Given a model's configuration, load it and return the LoadedModel object."""
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pass
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54
invokeai/app/services/model_load/model_load_default.py
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54
invokeai/app/services/model_load/model_load_default.py
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# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Team
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"""Implementation of model loader service."""
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from typing import Optional
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from invokeai.app.services.config import InvokeAIAppConfig
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from invokeai.app.services.model_records import ModelRecordServiceBase
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from invokeai.backend.model_manager import AnyModelConfig, SubModelType
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from invokeai.backend.model_manager.load import AnyModelLoader, LoadedModel, ModelCache, ModelConvertCache
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from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
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from invokeai.backend.model_manager.load.ram_cache import ModelCacheBase
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from invokeai.backend.util.logging import InvokeAILogger
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from .model_load_base import ModelLoadServiceBase
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class ModelLoadService(ModelLoadServiceBase):
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"""Wrapper around AnyModelLoader."""
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def __init__(
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self,
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app_config: InvokeAIAppConfig,
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record_store: ModelRecordServiceBase,
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ram_cache: Optional[ModelCacheBase] = None,
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convert_cache: Optional[ModelConvertCacheBase] = None,
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):
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"""Initialize the model load service."""
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logger = InvokeAILogger.get_logger(self.__class__.__name__)
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logger.setLevel(app_config.log_level.upper())
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self._store = record_store
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self._any_loader = AnyModelLoader(
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app_config=app_config,
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logger=logger,
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ram_cache=ram_cache
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or ModelCache(
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max_cache_size=app_config.ram_cache_size,
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max_vram_cache_size=app_config.vram_cache_size,
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logger=logger,
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),
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convert_cache=convert_cache
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or ModelConvertCache(
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cache_path=app_config.models_convert_cache_path,
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max_size=app_config.convert_cache_size,
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),
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)
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def load_model_by_key(self, key: str, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
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"""Given a model's key, load it and return the LoadedModel object."""
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config = self._store.get_model(key)
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return self.load_model_by_config(config, submodel_type)
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def load_model_by_config(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
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"""Given a model's configuration, load it and return the LoadedModel object."""
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return self._any_loader.load_model(config, submodel_type)
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@ -1 +1,16 @@
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from .model_manager_default import ModelManagerService # noqa F401
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"""Initialization file for model manager service."""
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from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelType, SubModelType
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from invokeai.backend.model_manager.load import LoadedModel
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from .model_manager_default import ModelManagerService
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__all__ = [
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"ModelManagerService",
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"AnyModel",
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"AnyModelConfig",
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"BaseModelType",
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"ModelType",
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"SubModelType",
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"LoadedModel",
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]
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@ -1,286 +1,39 @@
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# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from logging import Logger
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from pathlib import Path
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from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
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from pydantic import Field
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from pydantic import BaseModel, Field
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from typing_extensions import Self
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from invokeai.app.services.config.config_default import InvokeAIAppConfig
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from invokeai.backend.model_management import (
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AddModelResult,
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BaseModelType,
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MergeInterpolationMethod,
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ModelInfo,
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ModelType,
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SchedulerPredictionType,
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SubModelType,
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)
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from invokeai.backend.model_management.model_cache import CacheStats
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if TYPE_CHECKING:
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from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext
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from ..config import InvokeAIAppConfig
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from ..events.events_base import EventServiceBase
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from ..download import DownloadQueueServiceBase
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from ..model_install import ModelInstallServiceBase
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from ..model_load import ModelLoadServiceBase
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from ..model_records import ModelRecordServiceBase
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from ..shared.sqlite.sqlite_database import SqliteDatabase
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class ModelManagerServiceBase(ABC):
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"""Responsible for managing models on disk and in memory"""
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class ModelManagerServiceBase(BaseModel, ABC):
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"""Abstract base class for the model manager service."""
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store: ModelRecordServiceBase = Field(description="An instance of the model record configuration service.")
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install: ModelInstallServiceBase = Field(description="An instance of the model install service.")
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load: ModelLoadServiceBase = Field(description="An instance of the model load service.")
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@classmethod
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@abstractmethod
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def __init__(
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self,
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config: InvokeAIAppConfig,
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logger: Logger,
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):
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def build_model_manager(
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cls,
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app_config: InvokeAIAppConfig,
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db: SqliteDatabase,
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download_queue: DownloadQueueServiceBase,
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events: EventServiceBase,
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) -> Self:
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"""
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Initialize with the path to the models.yaml config file.
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Optional parameters are the torch device type, precision, max_models,
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and sequential_offload boolean. Note that the default device
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type and precision are set up for a CUDA system running at half precision.
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"""
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pass
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@abstractmethod
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def get_model(
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self,
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model_name: str,
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base_model: BaseModelType,
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model_type: ModelType,
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submodel: Optional[SubModelType] = None,
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node: Optional[BaseInvocation] = None,
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context: Optional[InvocationContext] = None,
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) -> ModelInfo:
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"""Retrieve the indicated model with name and type.
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submodel can be used to get a part (such as the vae)
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of a diffusers pipeline."""
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pass
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@property
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@abstractmethod
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def logger(self):
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pass
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@abstractmethod
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def model_exists(
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self,
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model_name: str,
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base_model: BaseModelType,
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model_type: ModelType,
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) -> bool:
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pass
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@abstractmethod
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def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
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"""
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Given a model name returns a dict-like (OmegaConf) object describing it.
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Uses the exact format as the omegaconf stanza.
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"""
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pass
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@abstractmethod
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def list_models(self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None) -> dict:
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"""
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Return a dict of models in the format:
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{ model_type1:
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{ model_name1: {'status': 'active'|'cached'|'not loaded',
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'model_name' : name,
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'model_type' : SDModelType,
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'description': description,
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'format': 'folder'|'safetensors'|'ckpt'
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},
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model_name2: { etc }
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},
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model_type2:
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{ model_name_n: etc
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}
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"""
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pass
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@abstractmethod
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def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
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"""
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Return information about the model using the same format as list_models()
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"""
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pass
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@abstractmethod
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def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
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"""
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Returns a list of all the model names known.
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"""
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pass
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@abstractmethod
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def add_model(
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self,
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model_name: str,
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base_model: BaseModelType,
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model_type: ModelType,
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model_attributes: dict,
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clobber: bool = False,
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) -> AddModelResult:
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"""
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Update the named model with a dictionary of attributes. Will fail with an
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assertion error if the name already exists. Pass clobber=True to overwrite.
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On a successful update, the config will be changed in memory. Will fail
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with an assertion error if provided attributes are incorrect or
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the model name is missing. Call commit() to write changes to disk.
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"""
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pass
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@abstractmethod
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def update_model(
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self,
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model_name: str,
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base_model: BaseModelType,
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model_type: ModelType,
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model_attributes: dict,
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) -> AddModelResult:
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"""
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Update the named model with a dictionary of attributes. Will fail with a
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ModelNotFoundException if the name does not already exist.
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On a successful update, the config will be changed in memory. Will fail
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with an assertion error if provided attributes are incorrect or
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the model name is missing. Call commit() to write changes to disk.
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"""
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pass
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@abstractmethod
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def del_model(
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self,
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model_name: str,
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base_model: BaseModelType,
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model_type: ModelType,
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):
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"""
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Delete the named model from configuration. If delete_files is true,
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then the underlying weight file or diffusers directory will be deleted
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as well. Call commit() to write to disk.
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"""
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pass
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@abstractmethod
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def rename_model(
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self,
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model_name: str,
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base_model: BaseModelType,
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model_type: ModelType,
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new_name: str,
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):
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"""
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Rename the indicated model.
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"""
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pass
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@abstractmethod
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def list_checkpoint_configs(self) -> List[Path]:
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"""
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List the checkpoint config paths from ROOT/configs/stable-diffusion.
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"""
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pass
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@abstractmethod
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def convert_model(
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self,
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model_name: str,
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base_model: BaseModelType,
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model_type: Literal[ModelType.Main, ModelType.Vae],
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) -> AddModelResult:
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"""
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Convert a checkpoint file into a diffusers folder, deleting the cached
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version and deleting the original checkpoint file if it is in the models
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directory.
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:param model_name: Name of the model to convert
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:param base_model: Base model type
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:param model_type: Type of model ['vae' or 'main']
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This will raise a ValueError unless the model is not a checkpoint. It will
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also raise a ValueError in the event that there is a similarly-named diffusers
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directory already in place.
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"""
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pass
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@abstractmethod
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def heuristic_import(
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self,
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items_to_import: set[str],
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prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
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) -> dict[str, AddModelResult]:
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"""Import a list of paths, repo_ids or URLs. Returns the set of
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successfully imported items.
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:param items_to_import: Set of strings corresponding to models to be imported.
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:param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
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The prediction type helper is necessary to distinguish between
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models based on Stable Diffusion 2 Base (requiring
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SchedulerPredictionType.Epsilson) and Stable Diffusion 768
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(requiring SchedulerPredictionType.VPrediction). It is
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generally impossible to do this programmatically, so the
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prediction_type_helper usually asks the user to choose.
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The result is a set of successfully installed models. Each element
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of the set is a dict corresponding to the newly-created OmegaConf stanza for
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that model.
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"""
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pass
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@abstractmethod
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def merge_models(
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self,
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model_names: List[str] = Field(
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default=None, min_length=2, max_length=3, description="List of model names to merge"
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),
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base_model: Union[BaseModelType, str] = Field(
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default=None, description="Base model shared by all models to be merged"
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),
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merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
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alpha: Optional[float] = 0.5,
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interp: Optional[MergeInterpolationMethod] = None,
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force: Optional[bool] = False,
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merge_dest_directory: Optional[Path] = None,
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) -> AddModelResult:
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"""
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Merge two to three diffusrs pipeline models and save as a new model.
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:param model_names: List of 2-3 models to merge
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:param base_model: Base model to use for all models
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:param merged_model_name: Name of destination merged model
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:param alpha: Alpha strength to apply to 2d and 3d model
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:param interp: Interpolation method. None (default)
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:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
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"""
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pass
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@abstractmethod
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def search_for_models(self, directory: Path) -> List[Path]:
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"""
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Return list of all models found in the designated directory.
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"""
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pass
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@abstractmethod
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def sync_to_config(self):
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"""
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Re-read models.yaml, rescan the models directory, and reimport models
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in the autoimport directories. Call after making changes outside the
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model manager API.
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"""
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pass
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@abstractmethod
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def collect_cache_stats(self, cache_stats: CacheStats):
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"""
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Reset model cache statistics for graph with graph_id.
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"""
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pass
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@abstractmethod
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def commit(self, conf_file: Optional[Path] = None) -> None:
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"""
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Write current configuration out to the indicated file.
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If no conf_file is provided, then replaces the
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original file/database used to initialize the object.
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Construct the model manager service instance.
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Use it rather than the __init__ constructor. This class
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method simplifies the construction considerably.
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"""
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pass
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@ -1,413 +1,67 @@
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# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
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"""Implementation of ModelManagerServiceBase."""
|
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|
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from __future__ import annotations
|
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from typing_extensions import Self
|
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|
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from logging import Logger
|
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from pathlib import Path
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from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
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|
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import torch
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from pydantic import Field
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from invokeai.app.services.config.config_default import InvokeAIAppConfig
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from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException
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from invokeai.backend.model_management import (
|
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AddModelResult,
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BaseModelType,
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MergeInterpolationMethod,
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ModelInfo,
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ModelManager,
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ModelMerger,
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ModelNotFoundException,
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ModelType,
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SchedulerPredictionType,
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SubModelType,
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)
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from invokeai.backend.model_management.model_cache import CacheStats
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from invokeai.backend.model_management.model_search import FindModels
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from invokeai.backend.util import choose_precision, choose_torch_device
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from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache
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from invokeai.backend.model_manager.metadata import ModelMetadataStore
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from invokeai.backend.util.logging import InvokeAILogger
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|
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from ..config import InvokeAIAppConfig
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from ..download import DownloadQueueServiceBase
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from ..events.events_base import EventServiceBase
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from ..model_install import ModelInstallService
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from ..model_load import ModelLoadService
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from ..model_records import ModelRecordServiceSQL
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from ..shared.sqlite.sqlite_database import SqliteDatabase
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from .model_manager_base import ModelManagerServiceBase
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if TYPE_CHECKING:
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from invokeai.app.invocations.baseinvocation import InvocationContext
|
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|
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|
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# simple implementation
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class ModelManagerService(ModelManagerServiceBase):
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"""Responsible for managing models on disk and in memory"""
|
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|
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def __init__(
|
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self,
|
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config: InvokeAIAppConfig,
|
||||
logger: Logger,
|
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):
|
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"""
|
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Initialize with the path to the models.yaml config file.
|
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Optional parameters are the torch device type, precision, max_models,
|
||||
and sequential_offload boolean. Note that the default device
|
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type and precision are set up for a CUDA system running at half precision.
|
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The ModelManagerService handles various aspects of model installation, maintenance and loading.
|
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|
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It bundles three distinct services:
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model_manager.store -- Routines to manage the database of model configuration records.
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model_manager.install -- Routines to install, move and delete models.
|
||||
model_manager.load -- Routines to load models into memory.
|
||||
"""
|
||||
if config.model_conf_path and config.model_conf_path.exists():
|
||||
config_file = config.model_conf_path
|
||||
else:
|
||||
config_file = config.root_dir / "configs/models.yaml"
|
||||
|
||||
logger.debug(f"Config file={config_file}")
|
||||
@classmethod
|
||||
def build_model_manager(
|
||||
cls,
|
||||
app_config: InvokeAIAppConfig,
|
||||
db: SqliteDatabase,
|
||||
download_queue: DownloadQueueServiceBase,
|
||||
events: EventServiceBase,
|
||||
) -> Self:
|
||||
"""
|
||||
Construct the model manager service instance.
|
||||
|
||||
device = torch.device(choose_torch_device())
|
||||
device_name = torch.cuda.get_device_name() if device == torch.device("cuda") else ""
|
||||
logger.info(f"GPU device = {device} {device_name}")
|
||||
For simplicity, use this class method rather than the __init__ constructor.
|
||||
"""
|
||||
logger = InvokeAILogger.get_logger(cls.__name__)
|
||||
logger.setLevel(app_config.log_level.upper())
|
||||
|
||||
precision = config.precision
|
||||
if precision == "auto":
|
||||
precision = choose_precision(device)
|
||||
dtype = torch.float32 if precision == "float32" else torch.float16
|
||||
|
||||
# this is transitional backward compatibility
|
||||
# support for the deprecated `max_loaded_models`
|
||||
# configuration value. If present, then the
|
||||
# cache size is set to 2.5 GB times
|
||||
# the number of max_loaded_models. Otherwise
|
||||
# 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")
|
||||
|
||||
sequential_offload = config.sequential_guidance
|
||||
|
||||
self.mgr = ModelManager(
|
||||
config=config_file,
|
||||
device_type=device,
|
||||
precision=dtype,
|
||||
max_cache_size=max_cache_size,
|
||||
sequential_offload=sequential_offload,
|
||||
logger=logger,
|
||||
ram_cache = ModelCache(
|
||||
max_cache_size=app_config.ram_cache_size, max_vram_cache_size=app_config.vram_cache_size, logger=logger
|
||||
)
|
||||
logger.info("Model manager service initialized")
|
||||
|
||||
def get_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
submodel: Optional[SubModelType] = None,
|
||||
context: Optional[InvocationContext] = None,
|
||||
) -> ModelInfo:
|
||||
"""
|
||||
Retrieve the indicated model. submodel can be used to get a
|
||||
part (such as the vae) of a diffusers mode.
|
||||
"""
|
||||
|
||||
# we can emit model loading events if we are executing with access to the invocation context
|
||||
if context:
|
||||
self._emit_load_event(
|
||||
context=context,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=submodel,
|
||||
convert_cache = ModelConvertCache(
|
||||
cache_path=app_config.models_convert_cache_path, max_size=app_config.convert_cache_size
|
||||
)
|
||||
|
||||
model_info = self.mgr.get_model(
|
||||
model_name,
|
||||
base_model,
|
||||
model_type,
|
||||
submodel,
|
||||
record_store = ModelRecordServiceSQL(db=db)
|
||||
loader = ModelLoadService(
|
||||
app_config=app_config,
|
||||
record_store=record_store,
|
||||
ram_cache=ram_cache,
|
||||
convert_cache=convert_cache,
|
||||
)
|
||||
|
||||
if context:
|
||||
self._emit_load_event(
|
||||
context=context,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=submodel,
|
||||
model_info=model_info,
|
||||
)
|
||||
|
||||
return model_info
|
||||
|
||||
def model_exists(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
) -> bool:
|
||||
"""
|
||||
Given a model name, returns True if it is a valid
|
||||
identifier.
|
||||
"""
|
||||
return self.mgr.model_exists(
|
||||
model_name,
|
||||
base_model,
|
||||
model_type,
|
||||
)
|
||||
|
||||
def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]:
|
||||
"""
|
||||
Given a model name returns a dict-like (OmegaConf) object describing it.
|
||||
"""
|
||||
return self.mgr.model_info(model_name, base_model, model_type)
|
||||
|
||||
def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
|
||||
"""
|
||||
Returns a list of all the model names known.
|
||||
"""
|
||||
return self.mgr.model_names()
|
||||
|
||||
def list_models(
|
||||
self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None
|
||||
) -> list[dict]:
|
||||
"""
|
||||
Return a list of models.
|
||||
"""
|
||||
return self.mgr.list_models(base_model, model_type)
|
||||
|
||||
def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]:
|
||||
"""
|
||||
Return information about the model using the same format as list_models()
|
||||
"""
|
||||
return self.mgr.list_model(model_name=model_name, base_model=base_model, model_type=model_type)
|
||||
|
||||
def add_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
model_attributes: dict,
|
||||
clobber: bool = False,
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Update the named model with a dictionary of attributes. Will fail with an
|
||||
assertion error if the name already exists. Pass clobber=True to overwrite.
|
||||
On a successful update, the config will be changed in memory. Will fail
|
||||
with an assertion error if provided attributes are incorrect or
|
||||
the model name is missing. Call commit() to write changes to disk.
|
||||
"""
|
||||
self.logger.debug(f"add/update model {model_name}")
|
||||
return self.mgr.add_model(model_name, base_model, model_type, model_attributes, clobber)
|
||||
|
||||
def update_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
model_attributes: dict,
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Update the named model with a dictionary of attributes. Will fail with a
|
||||
ModelNotFoundException exception if the name does not already exist.
|
||||
On a successful update, the config will be changed in memory. Will fail
|
||||
with an assertion error if provided attributes are incorrect or
|
||||
the model name is missing. Call commit() to write changes to disk.
|
||||
"""
|
||||
self.logger.debug(f"update model {model_name}")
|
||||
if not self.model_exists(model_name, base_model, model_type):
|
||||
raise ModelNotFoundException(f"Unknown model {model_name}")
|
||||
return self.add_model(model_name, base_model, model_type, model_attributes, clobber=True)
|
||||
|
||||
def del_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
):
|
||||
"""
|
||||
Delete the named model from configuration. If delete_files is true,
|
||||
then the underlying weight file or diffusers directory will be deleted
|
||||
as well.
|
||||
"""
|
||||
self.logger.debug(f"delete model {model_name}")
|
||||
self.mgr.del_model(model_name, base_model, model_type)
|
||||
self.mgr.commit()
|
||||
|
||||
def convert_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: Literal[ModelType.Main, ModelType.Vae],
|
||||
convert_dest_directory: Optional[Path] = Field(
|
||||
default=None, description="Optional directory location for merged model"
|
||||
),
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Convert a checkpoint file into a diffusers folder, deleting the cached
|
||||
version and deleting the original checkpoint file if it is in the models
|
||||
directory.
|
||||
:param model_name: Name of the model to convert
|
||||
:param base_model: Base model type
|
||||
:param model_type: Type of model ['vae' or 'main']
|
||||
:param convert_dest_directory: Save the converted model to the designated directory (`models/etc/etc` by default)
|
||||
|
||||
This will raise a ValueError unless the model is not a checkpoint. It will
|
||||
also raise a ValueError in the event that there is a similarly-named diffusers
|
||||
directory already in place.
|
||||
"""
|
||||
self.logger.debug(f"convert model {model_name}")
|
||||
return self.mgr.convert_model(model_name, base_model, model_type, convert_dest_directory)
|
||||
|
||||
def collect_cache_stats(self, cache_stats: CacheStats):
|
||||
"""
|
||||
Reset model cache statistics for graph with graph_id.
|
||||
"""
|
||||
self.mgr.cache.stats = cache_stats
|
||||
|
||||
def commit(self, conf_file: Optional[Path] = None):
|
||||
"""
|
||||
Write current configuration out to the indicated file.
|
||||
If no conf_file is provided, then replaces the
|
||||
original file/database used to initialize the object.
|
||||
"""
|
||||
return self.mgr.commit(conf_file)
|
||||
|
||||
def _emit_load_event(
|
||||
self,
|
||||
context: InvocationContext,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
submodel: Optional[SubModelType] = None,
|
||||
model_info: Optional[ModelInfo] = None,
|
||||
):
|
||||
if context.services.queue.is_canceled(context.graph_execution_state_id):
|
||||
raise CanceledException()
|
||||
|
||||
if model_info:
|
||||
context.services.events.emit_model_load_completed(
|
||||
queue_id=context.queue_id,
|
||||
queue_item_id=context.queue_item_id,
|
||||
queue_batch_id=context.queue_batch_id,
|
||||
graph_execution_state_id=context.graph_execution_state_id,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=submodel,
|
||||
model_info=model_info,
|
||||
)
|
||||
else:
|
||||
context.services.events.emit_model_load_started(
|
||||
queue_id=context.queue_id,
|
||||
queue_item_id=context.queue_item_id,
|
||||
queue_batch_id=context.queue_batch_id,
|
||||
graph_execution_state_id=context.graph_execution_state_id,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=submodel,
|
||||
)
|
||||
|
||||
@property
|
||||
def logger(self):
|
||||
return self.mgr.logger
|
||||
|
||||
def heuristic_import(
|
||||
self,
|
||||
items_to_import: set[str],
|
||||
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
|
||||
) -> dict[str, AddModelResult]:
|
||||
"""Import a list of paths, repo_ids or URLs. Returns the set of
|
||||
successfully imported items.
|
||||
:param items_to_import: Set of strings corresponding to models to be imported.
|
||||
:param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
|
||||
|
||||
The prediction type helper is necessary to distinguish between
|
||||
models based on Stable Diffusion 2 Base (requiring
|
||||
SchedulerPredictionType.Epsilson) and Stable Diffusion 768
|
||||
(requiring SchedulerPredictionType.VPrediction). It is
|
||||
generally impossible to do this programmatically, so the
|
||||
prediction_type_helper usually asks the user to choose.
|
||||
|
||||
The result is a set of successfully installed models. Each element
|
||||
of the set is a dict corresponding to the newly-created OmegaConf stanza for
|
||||
that model.
|
||||
"""
|
||||
return self.mgr.heuristic_import(items_to_import, prediction_type_helper)
|
||||
|
||||
def merge_models(
|
||||
self,
|
||||
model_names: List[str] = Field(
|
||||
default=None, min_length=2, max_length=3, description="List of model names to merge"
|
||||
),
|
||||
base_model: Union[BaseModelType, str] = Field(
|
||||
default=None, description="Base model shared by all models to be merged"
|
||||
),
|
||||
merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
|
||||
alpha: float = 0.5,
|
||||
interp: Optional[MergeInterpolationMethod] = None,
|
||||
force: bool = False,
|
||||
merge_dest_directory: Optional[Path] = Field(
|
||||
default=None, description="Optional directory location for merged model"
|
||||
),
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Merge two to three diffusrs pipeline models and save as a new model.
|
||||
:param model_names: List of 2-3 models to merge
|
||||
:param base_model: Base model to use for all models
|
||||
:param merged_model_name: Name of destination merged model
|
||||
:param alpha: Alpha strength to apply to 2d and 3d model
|
||||
:param interp: Interpolation method. None (default)
|
||||
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
|
||||
"""
|
||||
merger = ModelMerger(self.mgr)
|
||||
try:
|
||||
result = merger.merge_diffusion_models_and_save(
|
||||
model_names=model_names,
|
||||
base_model=base_model,
|
||||
merged_model_name=merged_model_name,
|
||||
alpha=alpha,
|
||||
interp=interp,
|
||||
force=force,
|
||||
merge_dest_directory=merge_dest_directory,
|
||||
)
|
||||
except AssertionError as e:
|
||||
raise ValueError(e)
|
||||
return result
|
||||
|
||||
def search_for_models(self, directory: Path) -> List[Path]:
|
||||
"""
|
||||
Return list of all models found in the designated directory.
|
||||
"""
|
||||
search = FindModels([directory], self.logger)
|
||||
return search.list_models()
|
||||
|
||||
def sync_to_config(self):
|
||||
"""
|
||||
Re-read models.yaml, rescan the models directory, and reimport models
|
||||
in the autoimport directories. Call after making changes outside the
|
||||
model manager API.
|
||||
"""
|
||||
return self.mgr.sync_to_config()
|
||||
|
||||
def list_checkpoint_configs(self) -> List[Path]:
|
||||
"""
|
||||
List the checkpoint config paths from ROOT/configs/stable-diffusion.
|
||||
"""
|
||||
config = self.mgr.app_config
|
||||
conf_path = config.legacy_conf_path
|
||||
root_path = config.root_path
|
||||
return [(conf_path / x).relative_to(root_path) for x in conf_path.glob("**/*.yaml")]
|
||||
|
||||
def rename_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
new_name: Optional[str] = None,
|
||||
new_base: Optional[BaseModelType] = None,
|
||||
):
|
||||
"""
|
||||
Rename the indicated model. Can provide a new name and/or a new base.
|
||||
:param model_name: Current name of the model
|
||||
:param base_model: Current base of the model
|
||||
:param model_type: Model type (can't be changed)
|
||||
:param new_name: New name for the model
|
||||
:param new_base: New base for the model
|
||||
"""
|
||||
self.mgr.rename_model(
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
model_name=model_name,
|
||||
new_name=new_name,
|
||||
new_base=new_base,
|
||||
record_store._loader = loader # yeah, there is a circular reference here
|
||||
installer = ModelInstallService(
|
||||
app_config=app_config,
|
||||
record_store=record_store,
|
||||
download_queue=download_queue,
|
||||
metadata_store=ModelMetadataStore(db=db),
|
||||
event_bus=events,
|
||||
)
|
||||
return cls(store=record_store, install=installer, load=loader)
|
||||
|
||||
@ -1,5 +1,3 @@
|
||||
"""
|
||||
Initialization file for invokeai.backend
|
||||
"""
|
||||
from .model_management import BaseModelType, ModelCache, ModelInfo, ModelManager, ModelType, SubModelType # noqa: F401
|
||||
from .model_management.models import SilenceWarnings # noqa: F401
|
||||
|
||||
@ -21,7 +21,7 @@ Validation errors will raise an InvalidModelConfigException error.
|
||||
"""
|
||||
import time
|
||||
from enum import Enum
|
||||
from typing import Literal, Optional, Type, Union
|
||||
from typing import Literal, Optional, Type, Union, Class
|
||||
|
||||
import torch
|
||||
from diffusers import ModelMixin
|
||||
@ -333,9 +333,9 @@ class ModelConfigFactory(object):
|
||||
@classmethod
|
||||
def make_config(
|
||||
cls,
|
||||
model_data: Union[dict, AnyModelConfig],
|
||||
model_data: Union[Dict[str, Any], AnyModelConfig],
|
||||
key: Optional[str] = None,
|
||||
dest_class: Optional[Type] = None,
|
||||
dest_class: Optional[Type[Class]] = None,
|
||||
timestamp: Optional[float] = None,
|
||||
) -> AnyModelConfig:
|
||||
"""
|
||||
|
||||
@ -18,7 +18,7 @@ loaders = [x.stem for x in Path(Path(__file__).parent, "model_loaders").glob("*.
|
||||
for module in loaders:
|
||||
import_module(f"{__package__}.model_loaders.{module}")
|
||||
|
||||
__all__ = ["AnyModelLoader", "LoadedModel"]
|
||||
__all__ = ["AnyModelLoader", "LoadedModel", "ModelCache", "ModelConvertCache"]
|
||||
|
||||
|
||||
def get_standalone_loader(app_config: Optional[InvokeAIAppConfig]) -> AnyModelLoader:
|
||||
|
||||
@ -26,10 +26,10 @@ from pathlib import Path
|
||||
from typing import Callable, Optional, Set, Union
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from logging import Logger
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
|
||||
default_logger = InvokeAILogger.get_logger()
|
||||
default_logger: Logger = InvokeAILogger.get_logger()
|
||||
|
||||
|
||||
class SearchStats(BaseModel):
|
||||
@ -56,7 +56,7 @@ class ModelSearchBase(ABC, BaseModel):
|
||||
on_model_found : Optional[Callable[[Path], bool]] = Field(default=None, description="Called when a model is found.") # noqa E221
|
||||
on_search_completed : Optional[Callable[[Set[Path]], None]] = Field(default=None, description="Called when search is complete.") # noqa E221
|
||||
stats : SearchStats = Field(default_factory=SearchStats, description="Summary statistics after search") # noqa E221
|
||||
logger : InvokeAILogger = Field(default=default_logger, description="Logger instance.") # noqa E221
|
||||
logger : Logger = Field(default=default_logger, description="Logger instance.") # noqa E221
|
||||
# fmt: on
|
||||
|
||||
class Config:
|
||||
@ -128,13 +128,13 @@ class ModelSearch(ModelSearchBase):
|
||||
|
||||
def model_found(self, model: Path) -> None:
|
||||
self.stats.models_found += 1
|
||||
if not self.on_model_found or self.on_model_found(model):
|
||||
if self.on_model_found is None or self.on_model_found(model):
|
||||
self.stats.models_filtered += 1
|
||||
self.models_found.add(model)
|
||||
|
||||
def search_completed(self) -> None:
|
||||
if self.on_search_completed:
|
||||
self.on_search_completed(self._models_found)
|
||||
if self.on_search_completed is not None:
|
||||
self.on_search_completed(self.models_found)
|
||||
|
||||
def search(self, directory: Union[Path, str]) -> Set[Path]:
|
||||
self._directory = Path(directory)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user