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make choose_torch_dtype() usable outside an invocation context

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This commit is contained in:
Lincoln Stein 2024-04-16 19:19:19 -04:00
parent edac01d4fb
commit 84f5cbdd97
2 changed files with 8 additions and 2 deletions
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3
invokeai/app/services/model_install/model_install_default.py
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@ -43,6 +43,7 @@ from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMet
from invokeai.backend.model_manager.probe import ModelProbe from invokeai.backend.model_manager.probe import ModelProbe
from invokeai.backend.model_manager.search import ModelSearch from invokeai.backend.model_manager.search import ModelSearch
from invokeai.backend.util import InvokeAILogger from invokeai.backend.util import InvokeAILogger
from invokeai.backend.util.devices import TorchDevice
from .model_install_base import ( from .model_install_base import (
MODEL_SOURCE_TO_TYPE_MAP, MODEL_SOURCE_TO_TYPE_MAP,
@ -636,7 +637,7 @@ class ModelInstallService(ModelInstallServiceBase):
def _guess_variant(self) -> Optional[ModelRepoVariant]: def _guess_variant(self) -> Optional[ModelRepoVariant]:
"""Guess the best HuggingFace variant type to download.""" """Guess the best HuggingFace variant type to download."""
precision = torch.float16 if self._app_config.precision == "auto" else torch.dtype(self._app_config.precision) precision = TorchDevice.choose_torch_dtype()
return ModelRepoVariant.FP16 if precision == torch.float16 else None return ModelRepoVariant.FP16 if precision == torch.float16 else None
def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob: def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:

7
invokeai/backend/util/devices.py
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@ -60,6 +60,11 @@ class TorchDevice:
"""Return the torch.device to use for accelerated inference.""" """Return the torch.device to use for accelerated inference."""
if cls._model_cache: if cls._model_cache:
return cls._model_cache.get_execution_device() return cls._model_cache.get_execution_device()
else:
return cls._choose_device()
@classmethod
def _choose_device(cls) -> torch.device:
app_config = get_config() app_config = get_config()
if app_config.device != "auto": if app_config.device != "auto":
device = torch.device(app_config.device) device = torch.device(app_config.device)
@ -82,8 +87,8 @@ class TorchDevice:
@classmethod @classmethod
def choose_torch_dtype(cls, device: Optional[torch.device] = None) -> torch.dtype: def choose_torch_dtype(cls, device: Optional[torch.device] = None) -> torch.dtype:
"""Return the precision to use for accelerated inference.""" """Return the precision to use for accelerated inference."""
device = device or cls.choose_torch_device()
config = get_config() config = get_config()
device = device or cls._choose_device()
if device.type == "cuda" and torch.cuda.is_available(): if device.type == "cuda" and torch.cuda.is_available():
device_name = torch.cuda.get_device_name(device) device_name = torch.cuda.get_device_name(device)
if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name: if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name: