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
psychedelicious
parent
8db01ab1b3
commit
7956602b19
6
invokeai/app/services/model_load/__init__.py
Normal file
6
invokeai/app/services/model_load/__init__.py
Normal file
@ -0,0 +1,6 @@
|
||||
"""Initialization file for model load service module."""
|
||||
|
||||
from .model_load_base import ModelLoadServiceBase
|
||||
from .model_load_default import ModelLoadService
|
||||
|
||||
__all__ = ["ModelLoadServiceBase", "ModelLoadService"]
|
||||
22
invokeai/app/services/model_load/model_load_base.py
Normal file
22
invokeai/app/services/model_load/model_load_base.py
Normal file
@ -0,0 +1,22 @@
|
||||
# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Team
|
||||
"""Base class for model loader."""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Optional
|
||||
|
||||
from invokeai.backend.model_manager import AnyModelConfig, SubModelType
|
||||
from invokeai.backend.model_manager.load import LoadedModel
|
||||
|
||||
|
||||
class ModelLoadServiceBase(ABC):
|
||||
"""Wrapper around AnyModelLoader."""
|
||||
|
||||
@abstractmethod
|
||||
def load_model_by_key(self, key: str, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
|
||||
"""Given a model's key, load it and return the LoadedModel object."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def load_model_by_config(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
|
||||
"""Given a model's configuration, load it and return the LoadedModel object."""
|
||||
pass
|
||||
54
invokeai/app/services/model_load/model_load_default.py
Normal file
54
invokeai/app/services/model_load/model_load_default.py
Normal file
@ -0,0 +1,54 @@
|
||||
# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Team
|
||||
"""Implementation of model loader service."""
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from invokeai.app.services.model_records import ModelRecordServiceBase
|
||||
from invokeai.backend.model_manager import AnyModelConfig, SubModelType
|
||||
from invokeai.backend.model_manager.load import AnyModelLoader, LoadedModel, ModelCache, ModelConvertCache
|
||||
from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
|
||||
from invokeai.backend.model_manager.load.ram_cache import ModelCacheBase
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
|
||||
from .model_load_base import ModelLoadServiceBase
|
||||
|
||||
|
||||
class ModelLoadService(ModelLoadServiceBase):
|
||||
"""Wrapper around AnyModelLoader."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
app_config: InvokeAIAppConfig,
|
||||
record_store: ModelRecordServiceBase,
|
||||
ram_cache: Optional[ModelCacheBase] = None,
|
||||
convert_cache: Optional[ModelConvertCacheBase] = None,
|
||||
):
|
||||
"""Initialize the model load service."""
|
||||
logger = InvokeAILogger.get_logger(self.__class__.__name__)
|
||||
logger.setLevel(app_config.log_level.upper())
|
||||
self._store = record_store
|
||||
self._any_loader = AnyModelLoader(
|
||||
app_config=app_config,
|
||||
logger=logger,
|
||||
ram_cache=ram_cache
|
||||
or ModelCache(
|
||||
max_cache_size=app_config.ram_cache_size,
|
||||
max_vram_cache_size=app_config.vram_cache_size,
|
||||
logger=logger,
|
||||
),
|
||||
convert_cache=convert_cache
|
||||
or ModelConvertCache(
|
||||
cache_path=app_config.models_convert_cache_path,
|
||||
max_size=app_config.convert_cache_size,
|
||||
),
|
||||
)
|
||||
|
||||
def load_model_by_key(self, key: str, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
|
||||
"""Given a model's key, load it and return the LoadedModel object."""
|
||||
config = self._store.get_model(key)
|
||||
return self.load_model_by_config(config, submodel_type)
|
||||
|
||||
def load_model_by_config(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
|
||||
"""Given a model's configuration, load it and return the LoadedModel object."""
|
||||
return self._any_loader.load_model(config, submodel_type)
|
||||
@ -1 +1,16 @@
|
||||
from .model_manager_default import ModelManagerService # noqa F401
|
||||
"""Initialization file for model manager service."""
|
||||
|
||||
from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelType, SubModelType
|
||||
from invokeai.backend.model_manager.load import LoadedModel
|
||||
|
||||
from .model_manager_default import ModelManagerService
|
||||
|
||||
__all__ = [
|
||||
"ModelManagerService",
|
||||
"AnyModel",
|
||||
"AnyModelConfig",
|
||||
"BaseModelType",
|
||||
"ModelType",
|
||||
"SubModelType",
|
||||
"LoadedModel",
|
||||
]
|
||||
|
||||
@ -1,283 +1,39 @@
|
||||
# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from logging import Logger
|
||||
from pathlib import Path
|
||||
from typing import Callable, List, Literal, Optional, Tuple, Union
|
||||
|
||||
from pydantic import Field
|
||||
from pydantic import BaseModel, Field
|
||||
from typing_extensions import Self
|
||||
|
||||
from invokeai.app.services.config.config_default import InvokeAIAppConfig
|
||||
from invokeai.app.services.shared.invocation_context import InvocationContextData
|
||||
from invokeai.backend.model_management import (
|
||||
AddModelResult,
|
||||
BaseModelType,
|
||||
LoadedModelInfo,
|
||||
MergeInterpolationMethod,
|
||||
ModelType,
|
||||
SchedulerPredictionType,
|
||||
SubModelType,
|
||||
)
|
||||
from invokeai.backend.model_management.model_cache import CacheStats
|
||||
from ..config import InvokeAIAppConfig
|
||||
from ..download import DownloadQueueServiceBase
|
||||
from ..events.events_base import EventServiceBase
|
||||
from ..model_install import ModelInstallServiceBase
|
||||
from ..model_load import ModelLoadServiceBase
|
||||
from ..model_records import ModelRecordServiceBase
|
||||
from ..shared.sqlite.sqlite_database import SqliteDatabase
|
||||
|
||||
|
||||
class ModelManagerServiceBase(ABC):
|
||||
"""Responsible for managing models on disk and in memory"""
|
||||
class ModelManagerServiceBase(BaseModel, ABC):
|
||||
"""Abstract base class for the model manager service."""
|
||||
|
||||
store: ModelRecordServiceBase = Field(description="An instance of the model record configuration service.")
|
||||
install: ModelInstallServiceBase = Field(description="An instance of the model install service.")
|
||||
load: ModelLoadServiceBase = Field(description="An instance of the model load service.")
|
||||
|
||||
@classmethod
|
||||
@abstractmethod
|
||||
def __init__(
|
||||
self,
|
||||
config: InvokeAIAppConfig,
|
||||
logger: Logger,
|
||||
):
|
||||
def build_model_manager(
|
||||
cls,
|
||||
app_config: InvokeAIAppConfig,
|
||||
db: SqliteDatabase,
|
||||
download_queue: DownloadQueueServiceBase,
|
||||
events: EventServiceBase,
|
||||
) -> Self:
|
||||
"""
|
||||
Initialize with the path to the models.yaml config file.
|
||||
Optional parameters are the torch device type, precision, max_models,
|
||||
and sequential_offload boolean. Note that the default device
|
||||
type and precision are set up for a CUDA system running at half precision.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
submodel: Optional[SubModelType] = None,
|
||||
context_data: Optional[InvocationContextData] = None,
|
||||
) -> LoadedModelInfo:
|
||||
"""Retrieve the indicated model with name and type.
|
||||
submodel can be used to get a part (such as the vae)
|
||||
of a diffusers pipeline."""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def logger(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def model_exists(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
) -> bool:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
|
||||
"""
|
||||
Given a model name returns a dict-like (OmegaConf) object describing it.
|
||||
Uses the exact format as the omegaconf stanza.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def list_models(self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None) -> dict:
|
||||
"""
|
||||
Return a dict of models in the format:
|
||||
{ model_type1:
|
||||
{ model_name1: {'status': 'active'|'cached'|'not loaded',
|
||||
'model_name' : name,
|
||||
'model_type' : SDModelType,
|
||||
'description': description,
|
||||
'format': 'folder'|'safetensors'|'ckpt'
|
||||
},
|
||||
model_name2: { etc }
|
||||
},
|
||||
model_type2:
|
||||
{ model_name_n: etc
|
||||
}
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
|
||||
"""
|
||||
Return information about the model using the same format as list_models()
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
|
||||
"""
|
||||
Returns a list of all the model names known.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
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.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
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 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.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
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. Call commit() to write to disk.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def rename_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
new_name: str,
|
||||
):
|
||||
"""
|
||||
Rename the indicated model.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def list_checkpoint_configs(self) -> List[Path]:
|
||||
"""
|
||||
List the checkpoint config paths from ROOT/configs/stable-diffusion.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def convert_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: Literal[ModelType.Main, ModelType.Vae],
|
||||
) -> 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']
|
||||
|
||||
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.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
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.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
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: Optional[float] = 0.5,
|
||||
interp: Optional[MergeInterpolationMethod] = None,
|
||||
force: Optional[bool] = False,
|
||||
merge_dest_directory: Optional[Path] = None,
|
||||
) -> 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)
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def search_for_models(self, directory: Path) -> List[Path]:
|
||||
"""
|
||||
Return list of all models found in the designated directory.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
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.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def collect_cache_stats(self, cache_stats: CacheStats):
|
||||
"""
|
||||
Reset model cache statistics for graph with graph_id.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def commit(self, conf_file: Optional[Path] = None) -> 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.
|
||||
Construct the model manager service instance.
|
||||
|
||||
Use it rather than the __init__ constructor. This class
|
||||
method simplifies the construction considerably.
|
||||
"""
|
||||
pass
|
||||
|
||||
@ -1,421 +1,67 @@
|
||||
# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
|
||||
"""Implementation of ModelManagerServiceBase."""
|
||||
|
||||
from __future__ import annotations
|
||||
from typing_extensions import Self
|
||||
|
||||
from logging import Logger
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from pydantic import Field
|
||||
|
||||
from invokeai.app.services.config.config_default import InvokeAIAppConfig
|
||||
from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException
|
||||
from invokeai.app.services.invoker import Invoker
|
||||
from invokeai.app.services.shared.invocation_context import InvocationContextData
|
||||
from invokeai.backend.model_management import (
|
||||
AddModelResult,
|
||||
BaseModelType,
|
||||
LoadedModelInfo,
|
||||
MergeInterpolationMethod,
|
||||
ModelManager,
|
||||
ModelMerger,
|
||||
ModelNotFoundException,
|
||||
ModelType,
|
||||
SchedulerPredictionType,
|
||||
SubModelType,
|
||||
)
|
||||
from invokeai.backend.model_management.model_cache import CacheStats
|
||||
from invokeai.backend.model_management.model_search import FindModels
|
||||
from invokeai.backend.util import choose_precision, choose_torch_device
|
||||
from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache
|
||||
from invokeai.backend.model_manager.metadata import ModelMetadataStore
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
|
||||
from ..config import InvokeAIAppConfig
|
||||
from ..download import DownloadQueueServiceBase
|
||||
from ..events.events_base import EventServiceBase
|
||||
from ..model_install import ModelInstallService
|
||||
from ..model_load import ModelLoadService
|
||||
from ..model_records import ModelRecordServiceSQL
|
||||
from ..shared.sqlite.sqlite_database import SqliteDatabase
|
||||
from .model_manager_base import ModelManagerServiceBase
|
||||
|
||||
if TYPE_CHECKING:
|
||||
pass
|
||||
|
||||
|
||||
# simple implementation
|
||||
class ModelManagerService(ModelManagerServiceBase):
|
||||
"""Responsible for managing models on disk and in memory"""
|
||||
"""
|
||||
The ModelManagerService handles various aspects of model installation, maintenance and loading.
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: InvokeAIAppConfig,
|
||||
logger: Logger,
|
||||
):
|
||||
It bundles three distinct services:
|
||||
model_manager.store -- Routines to manage the database of model configuration records.
|
||||
model_manager.install -- Routines to install, move and delete models.
|
||||
model_manager.load -- Routines to load models into memory.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def build_model_manager(
|
||||
cls,
|
||||
app_config: InvokeAIAppConfig,
|
||||
db: SqliteDatabase,
|
||||
download_queue: DownloadQueueServiceBase,
|
||||
events: EventServiceBase,
|
||||
) -> Self:
|
||||
"""
|
||||
Initialize with the path to the models.yaml config file.
|
||||
Optional parameters are the torch device type, precision, max_models,
|
||||
and sequential_offload boolean. Note that the default device
|
||||
type and precision are set up for a CUDA system running at half precision.
|
||||
Construct the model manager service instance.
|
||||
|
||||
For simplicity, use this class method rather than the __init__ constructor.
|
||||
"""
|
||||
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 = InvokeAILogger.get_logger(cls.__name__)
|
||||
logger.setLevel(app_config.log_level.upper())
|
||||
|
||||
logger.debug(f"Config file={config_file}")
|
||||
|
||||
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}")
|
||||
|
||||
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 start(self, invoker: Invoker) -> None:
|
||||
self._invoker: Optional[Invoker] = invoker
|
||||
|
||||
def get_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
submodel: Optional[SubModelType] = None,
|
||||
context_data: Optional[InvocationContextData] = None,
|
||||
) -> LoadedModelInfo:
|
||||
"""
|
||||
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_data is not None:
|
||||
self._emit_load_event(
|
||||
context_data=context_data,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=submodel,
|
||||
)
|
||||
|
||||
loaded_model_info = self.mgr.get_model(
|
||||
model_name,
|
||||
base_model,
|
||||
model_type,
|
||||
submodel,
|
||||
convert_cache = ModelConvertCache(
|
||||
cache_path=app_config.models_convert_cache_path, max_size=app_config.convert_cache_size
|
||||
)
|
||||
|
||||
if context_data is not None:
|
||||
self._emit_load_event(
|
||||
context_data=context_data,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=submodel,
|
||||
loaded_model_info=loaded_model_info,
|
||||
)
|
||||
|
||||
return loaded_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,
|
||||
record_store = ModelRecordServiceSQL(db=db)
|
||||
loader = ModelLoadService(
|
||||
app_config=app_config,
|
||||
record_store=record_store,
|
||||
ram_cache=ram_cache,
|
||||
convert_cache=convert_cache,
|
||||
)
|
||||
|
||||
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_data: InvocationContextData,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
submodel: Optional[SubModelType] = None,
|
||||
loaded_model_info: Optional[LoadedModelInfo] = None,
|
||||
):
|
||||
if self._invoker is None:
|
||||
return
|
||||
|
||||
if self._invoker.services.queue.is_canceled(context_data.session_id):
|
||||
raise CanceledException()
|
||||
|
||||
if loaded_model_info:
|
||||
self._invoker.services.events.emit_model_load_completed(
|
||||
queue_id=context_data.queue_id,
|
||||
queue_item_id=context_data.queue_item_id,
|
||||
queue_batch_id=context_data.batch_id,
|
||||
graph_execution_state_id=context_data.session_id,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=submodel,
|
||||
loaded_model_info=loaded_model_info,
|
||||
)
|
||||
else:
|
||||
self._invoker.services.events.emit_model_load_started(
|
||||
queue_id=context_data.queue_id,
|
||||
queue_item_id=context_data.queue_item_id,
|
||||
queue_batch_id=context_data.batch_id,
|
||||
graph_execution_state_id=context_data.session_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)
|
||||
|
||||
@ -1,12 +1,3 @@
|
||||
"""
|
||||
Initialization file for invokeai.backend
|
||||
"""
|
||||
from .model_management import ( # noqa: F401
|
||||
BaseModelType,
|
||||
LoadedModelInfo,
|
||||
ModelCache,
|
||||
ModelManager,
|
||||
ModelType,
|
||||
SubModelType,
|
||||
)
|
||||
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