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expose max_cache_size in config

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This commit is contained in:
Eugene Brodsky 2023-07-04 17:05:35 -04:00
parent 66c10cc2f7
commit 7170e82f73
2 changed files with 28 additions and 25 deletions
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19
invokeai/app/services/config.py
View File

@ -228,10 +228,10 @@ class InvokeAISettings(BaseSettings):
upcase_environ = dict() upcase_environ = dict()
for key,value in os.environ.items(): for key,value in os.environ.items():
upcase_environ[key.upper()] = value upcase_environ[key.upper()] = value
fields = cls.__fields__ fields = cls.__fields__
cls.argparse_groups = {} cls.argparse_groups = {}
for name, field in fields.items(): for name, field in fields.items():
if name not in cls._excluded(): if name not in cls._excluded():
current_default = field.default current_default = field.default
@ -348,7 +348,7 @@ setting environment variables INVOKEAI_<setting>.
''' '''
singleton_config: ClassVar[InvokeAIAppConfig] = None singleton_config: ClassVar[InvokeAIAppConfig] = None
singleton_init: ClassVar[Dict] = None singleton_init: ClassVar[Dict] = None
#fmt: off #fmt: off
type: Literal["InvokeAI"] = "InvokeAI" type: Literal["InvokeAI"] = "InvokeAI"
host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server') host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
@ -367,7 +367,8 @@ setting environment variables INVOKEAI_<setting>.
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance') always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
free_gpu_mem : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance') free_gpu_mem : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
max_loaded_models : int = Field(default=3, gt=0, description="Maximum number of models to keep in memory for rapid switching", category='Memory/Performance') max_loaded_models : int = Field(default=3, gt=0, description="(DEPRECATED: use max_cache_size) Maximum number of models to keep in memory for rapid switching", category='Memory/Performance')
max_cache_size : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
precision : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='float16',description='Floating point precision', category='Memory/Performance') precision : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='float16',description='Floating point precision', category='Memory/Performance')
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance') sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance') xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
@ -385,9 +386,9 @@ setting environment variables INVOKEAI_<setting>.
outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths') outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths') from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths') use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
model : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models') model : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models')
log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging") log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
log_format : Literal[tuple(['plain','color','syslog','legacy'])] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging") log_format : Literal[tuple(['plain','color','syslog','legacy'])] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
@ -396,7 +397,7 @@ setting environment variables INVOKEAI_<setting>.
def parse_args(self, argv: List[str]=None, conf: DictConfig = None, clobber=False): def parse_args(self, argv: List[str]=None, conf: DictConfig = None, clobber=False):
''' '''
Update settings with contents of init file, environment, and Update settings with contents of init file, environment, and
command-line settings. command-line settings.
:param conf: alternate Omegaconf dictionary object :param conf: alternate Omegaconf dictionary object
:param argv: aternate sys.argv list :param argv: aternate sys.argv list
@ -411,7 +412,7 @@ setting environment variables INVOKEAI_<setting>.
except: except:
pass pass
InvokeAISettings.initconf = conf InvokeAISettings.initconf = conf
# parse args again in order to pick up settings in configuration file # parse args again in order to pick up settings in configuration file
super().parse_args(argv) super().parse_args(argv)
@ -431,7 +432,7 @@ setting environment variables INVOKEAI_<setting>.
cls.singleton_config = cls(**kwargs) cls.singleton_config = cls(**kwargs)
cls.singleton_init = kwargs cls.singleton_init = kwargs
return cls.singleton_config return cls.singleton_config
@property @property
def root_path(self)->Path: def root_path(self)->Path:
''' '''

34
invokeai/app/services/model_manager_service.py
View File

@ -33,13 +33,13 @@ class ModelManagerServiceBase(ABC):
logger: types.ModuleType, logger: types.ModuleType,
): ):
""" """
Initialize with the path to the models.yaml config file. Initialize with the path to the models.yaml config file.
Optional parameters are the torch device type, precision, max_models, Optional parameters are the torch device type, precision, max_models,
and sequential_offload boolean. Note that the default device and sequential_offload boolean. Note that the default device
type and precision are set up for a CUDA system running at half precision. type and precision are set up for a CUDA system running at half precision.
""" """
pass pass
@abstractmethod @abstractmethod
def get_model( def get_model(
self, self,
@ -50,8 +50,8 @@ class ModelManagerServiceBase(ABC):
node: Optional[BaseInvocation] = None, node: Optional[BaseInvocation] = None,
context: Optional[InvocationContext] = None, context: Optional[InvocationContext] = None,
) -> ModelInfo: ) -> ModelInfo:
"""Retrieve the indicated model with name and type. """Retrieve the indicated model with name and type.
submodel can be used to get a part (such as the vae) submodel can be used to get a part (such as the vae)
of a diffusers pipeline.""" of a diffusers pipeline."""
pass pass
@ -115,8 +115,8 @@ class ModelManagerServiceBase(ABC):
""" """
Update the named model with a dictionary of attributes. Will fail with an 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. 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 On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk. the model name is missing. Call commit() to write changes to disk.
""" """
pass pass
@ -129,8 +129,8 @@ class ModelManagerServiceBase(ABC):
model_type: ModelType, model_type: ModelType,
): ):
""" """
Delete the named model from configuration. If delete_files is true, Delete the named model from configuration. If delete_files is true,
then the underlying weight file or diffusers directory will be deleted then the underlying weight file or diffusers directory will be deleted
as well. Call commit() to write to disk. as well. Call commit() to write to disk.
""" """
pass pass
@ -176,7 +176,7 @@ class ModelManagerService(ModelManagerServiceBase):
logger: types.ModuleType, logger: types.ModuleType,
): ):
""" """
Initialize with the path to the models.yaml config file. Initialize with the path to the models.yaml config file.
Optional parameters are the torch device type, precision, max_models, Optional parameters are the torch device type, precision, max_models,
and sequential_offload boolean. Note that the default device and sequential_offload boolean. Note that the default device
type and precision are set up for a CUDA system running at half precision. type and precision are set up for a CUDA system running at half precision.
@ -206,6 +206,8 @@ class ModelManagerService(ModelManagerServiceBase):
if hasattr(config,'max_cache_size') \ if hasattr(config,'max_cache_size') \
else config.max_loaded_models * 2.5 else config.max_loaded_models * 2.5
logger.debug(f"Maximum RAM cache size: {max_cache_size} GiB")
sequential_offload = config.sequential_guidance sequential_offload = config.sequential_guidance
self.mgr = ModelManager( self.mgr = ModelManager(
@ -261,7 +263,7 @@ class ModelManagerService(ModelManagerServiceBase):
submodel=submodel, submodel=submodel,
model_info=model_info model_info=model_info
) )
return model_info return model_info
def model_exists( def model_exists(
@ -314,8 +316,8 @@ class ModelManagerService(ModelManagerServiceBase):
""" """
Update the named model with a dictionary of attributes. Will fail with an 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. 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 On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk. the model name is missing. Call commit() to write changes to disk.
""" """
return self.mgr.add_model(model_name, base_model, model_type, model_attributes, clobber) return self.mgr.add_model(model_name, base_model, model_type, model_attributes, clobber)
@ -328,8 +330,8 @@ class ModelManagerService(ModelManagerServiceBase):
model_type: ModelType, model_type: ModelType,
): ):
""" """
Delete the named model from configuration. If delete_files is true, Delete the named model from configuration. If delete_files is true,
then the underlying weight file or diffusers directory will be deleted then the underlying weight file or diffusers directory will be deleted
as well. Call commit() to write to disk. as well. Call commit() to write to disk.
""" """
self.mgr.del_model(model_name, base_model, model_type) self.mgr.del_model(model_name, base_model, model_type)
@ -383,7 +385,7 @@ class ModelManagerService(ModelManagerServiceBase):
@property @property
def logger(self): def logger(self):
return self.mgr.logger return self.mgr.logger
def heuristic_import(self, def heuristic_import(self,
items_to_import: Set[str], items_to_import: Set[str],
prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None, prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
@ -404,4 +406,4 @@ class ModelManagerService(ModelManagerServiceBase):
of the set is a dict corresponding to the newly-created OmegaConf stanza for of the set is a dict corresponding to the newly-created OmegaConf stanza for
that model. that model.
''' '''
return self.mgr.heuristic_import(items_to_import, prediction_type_helper) return self.mgr.heuristic_import(items_to_import, prediction_type_helper)