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https://github.com/invoke-ai/InvokeAI
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make choose_torch_dtype() usable outside an invocation context
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@ -43,6 +43,7 @@ from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMet
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from invokeai.backend.model_manager.probe import ModelProbe
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from invokeai.backend.model_manager.search import ModelSearch
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from invokeai.backend.util import InvokeAILogger
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from invokeai.backend.util.devices import TorchDevice
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from .model_install_base import (
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MODEL_SOURCE_TO_TYPE_MAP,
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@ -636,7 +637,7 @@ class ModelInstallService(ModelInstallServiceBase):
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def _guess_variant(self) -> Optional[ModelRepoVariant]:
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"""Guess the best HuggingFace variant type to download."""
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precision = torch.float16 if self._app_config.precision == "auto" else torch.dtype(self._app_config.precision)
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precision = TorchDevice.choose_torch_dtype()
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return ModelRepoVariant.FP16 if precision == torch.float16 else None
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def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
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@ -60,6 +60,11 @@ class TorchDevice:
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"""Return the torch.device to use for accelerated inference."""
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if cls._model_cache:
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return cls._model_cache.get_execution_device()
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else:
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return cls._choose_device()
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@classmethod
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def _choose_device(cls) -> torch.device:
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app_config = get_config()
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if app_config.device != "auto":
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device = torch.device(app_config.device)
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@ -82,8 +87,8 @@ class TorchDevice:
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@classmethod
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def choose_torch_dtype(cls, device: Optional[torch.device] = None) -> torch.dtype:
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"""Return the precision to use for accelerated inference."""
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device = device or cls.choose_torch_device()
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config = get_config()
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device = device or cls._choose_device()
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if device.type == "cuda" and torch.cuda.is_available():
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device_name = torch.cuda.get_device_name(device)
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if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name:
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